diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..38cc28f
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,50 @@
+# Anthropic
+ANTHROPIC_API_KEY=
+
+# Jira
+JIRA_SERVER=https://epartsmse.atlassian.net/
+JIRA_EMAIL=
+JIRA_API_TOKEN=
+JIRA_PROJECT_KEY=EPARTS
+
+# Slack
+SLACK_BOT_TOKEN=
+SLACK_TEAM_CHANNEL=
+SLACK_ALERT_CHANNEL=
+
+# Bitbucket / GitHub
+# --- Product repo the SES acts on -------------------------------------------
+# The SES is a harness that operates on the product repo from the outside; it
+# does not live inside it (see docs/ses_product_repo_integration.md). These
+# point the Bitbucket MCP client (mcp/bitbucket.py) at the eParts ML repo.
+BITBUCKET_WORKSPACE=epartsservices
+BITBUCKET_REPO=intelligent-attribute-prediction
+# Token scope: start with PR read + PR comment ONLY.
+# Do NOT grant repository:write until SES005 is fixed — commit_file() currently
+# defaults to branch="main" (mcp/bitbucket.py:52), so a write-scoped token would
+# let an agent commit to the team's main branch with no PR and no human gate.
+# `python3 tools/lint_ses.py --strict` reports every affected call site.
+BITBUCKET_TOKEN=
+
+# Confluence
+CONFLUENCE_URL=
+CONFLUENCE_TOKEN=
+CONFLUENCE_SPACE_KEY=
+
+# Google Drive
+GOOGLE_DRIVE_SERVICE_ACCOUNT_JSON=
+GOOGLE_DRIVE_TRANSCRIPT_FOLDER_ID=
+
+# Vector store
+CHROMA_PERSIST_DIR=./memory/chroma
+
+# Database
+SQLITE_DB_PATH=./memory/
+
+# Agent config
+CLAUDE_MODEL=claude-sonnet-4-5-20250514
+CRON_POLL_INTERVAL_MIN=15
+STALE_REQ_THRESHOLD_DAYS=14
+P0_APPROVAL_REQUIRED=true
+CONFIDENCE_THRESHOLD_READINESS=200
+ALPHA_CALIBRATION_READINESS=100
diff --git a/.github/workflows/program-health.yml b/.github/workflows/program-health.yml
new file mode 100644
index 0000000..e27ceaf
--- /dev/null
+++ b/.github/workflows/program-health.yml
@@ -0,0 +1,71 @@
+# Program Health auto-refresh — the scheduled measurement process.
+#
+# Every Monday morning (and on demand from the Actions tab) this workflow:
+# 1. exports all EPARTS issues from Jira (dashboard/fetch_jira.py, REST API,
+# credentials from repo secrets — never in code),
+# 2. regenerates dashboard/program_health.html (seeded, reproducible),
+# 3. opens a PULL REQUEST with the refreshed numbers.
+# Human review of the PR stays in the loop before the new numbers land on
+# main — same gate pattern as the transcript pipeline.
+#
+# Metamodel: Process (weekly measurement), Artifacts (jira_issues.json +
+# program_health.html), Resources (CI runner + Jira API), Measurements are
+# the artifact itself.
+
+name: Program health refresh
+
+on:
+ schedule:
+ - cron: "0 12 * * 1" # Mondays 12:00 UTC (8am ET)
+ workflow_dispatch: {}
+
+permissions:
+ contents: write
+ pull-requests: write
+
+concurrency:
+ group: program-health
+ cancel-in-progress: false
+
+jobs:
+ refresh:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.12"
+
+ # Both scripts are stdlib-only — no pip install.
+ - name: Export Jira issues
+ env:
+ JIRA_EMAIL: ${{ secrets.JIRA_EMAIL }}
+ JIRA_API_TOKEN: ${{ secrets.JIRA_API_TOKEN }}
+ run: python3 dashboard/fetch_jira.py
+
+ - name: Regenerate dashboard
+ run: |
+ python3 dashboard/generate_program_health.py | tee /tmp/summary.txt
+ {
+ echo "## Program health refresh"
+ echo ""
+ echo '```'
+ cat /tmp/summary.txt
+ echo '```'
+ echo ""
+ echo "Provenance: data exported by \`dashboard/fetch_jira.py\` this run;"
+ echo "every figure recomputes from \`dashboard/data/jira_issues.json\`."
+ echo "Review the numbers, then merge — merging publishes the refresh."
+ } > /tmp/pr_body.md
+ cat /tmp/pr_body.md >> "$GITHUB_STEP_SUMMARY"
+
+ - name: Open pull request with refreshed dashboard
+ uses: peter-evans/create-pull-request@v6
+ with:
+ branch: pipeline/program-health-${{ github.run_number }}
+ commit-message: "[agent:dashboard] Weekly program health refresh (run ${{ github.run_number }})"
+ title: "Program health refresh (run ${{ github.run_number }})"
+ body-path: /tmp/pr_body.md
+ labels: agent-generated, needs-human-review
+ delete-branch: true
diff --git a/.github/workflows/quality-gates.yml b/.github/workflows/quality-gates.yml
new file mode 100644
index 0000000..92b25e3
--- /dev/null
+++ b/.github/workflows/quality-gates.yml
@@ -0,0 +1,132 @@
+# Quality gates — the deterministic first line of defence, plus agent evals.
+#
+# Design premise (AI-tools coaching session with Cory Gwin, GitHub Copilot,
+# 2026-07-24): "A great deal of quality assurance is deterministic and consumes
+# no tokens." He pushed back on treating types/linters/coverage/security tooling
+# as merely basic checks — they are the first line of defence and should be
+# leaned into hard, because they run continuously at zero marginal cost. Only
+# behaviour that is genuinely non-deterministic needs a model in the loop, and
+# that is what the eval job covers.
+#
+# Three jobs, cheapest first:
+# 1. deterministic — custom SES linter (tools/lint_ses.py) + ruff
+# 2. security — Trivy: vulnerabilities, misconfiguration, secrets
+# 3. evals — agent behaviour regression detection (evals/), offline
+#
+# All three are stdlib-only or action-provided; jobs 1 and 3 need no API key and
+# no network, so they are cheap enough to block every push. The model-dependent
+# eval tier costs tokens and is therefore opt-in via workflow_dispatch.
+#
+# Metamodel: Process (quality gate), Artifacts (violation reports, eval report),
+# Resources (CI runner, Trivy, the eval harness), Measurements (violation counts,
+# eval scores and detected regressions).
+
+name: Quality gates
+
+on:
+ push:
+ branches: [main]
+ pull_request:
+ workflow_dispatch:
+ inputs:
+ live_evals:
+ description: "Also run the model-dependent eval tier (costs tokens)"
+ type: boolean
+ default: false
+
+permissions:
+ contents: read
+
+concurrency:
+ group: quality-gates-${{ github.ref }}
+ cancel-in-progress: true
+
+jobs:
+ deterministic:
+ name: Deterministic gates (SES linter + ruff)
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.12"
+
+ # The SES linter is stdlib-only by design — it must run even when the
+ # agent dependencies (anthropic, chromadb, torch...) are not installed.
+ - name: Custom SES linter
+ run: python3 tools/lint_ses.py
+
+ # Advisory rules (currently SES005: commit_file() defaulting to main)
+ # are reported but do not fail the build yet. Flip to --strict once the
+ # affected call sites are fixed; see docs/ses_product_repo_integration.md
+ # §4, where fixing SES005 is a precondition for granting the harness
+ # write access to the product repo.
+ - name: Custom SES linter (strict — advisory findings, non-blocking)
+ continue-on-error: true
+ run: python3 tools/lint_ses.py --strict
+
+ - name: Install ruff
+ run: python -m pip install --upgrade pip ruff
+
+ # ruff is advisory for now: it reports 79 pre-existing violations in code
+ # written before any linting was enforced here. Ratcheting those down is
+ # tracked separately — turning it blocking today would just train the team
+ # to ignore a permanently-red gate. The custom SES linter above IS
+ # blocking, because its rules were introduced clean.
+ - name: ruff (advisory — pre-existing violations being ratcheted down)
+ continue-on-error: true
+ run: ruff check .
+
+ security:
+ name: Security scan (Trivy)
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ # Repo scan: dependency vulnerabilities, IaC/config misconfiguration, and
+ # hardcoded secrets. Fails the build on HIGH/CRITICAL only — anything
+ # noisier trains the team to ignore the gate, which is worse than no gate.
+ - name: Trivy filesystem scan
+ uses: aquasecurity/trivy-action@0.28.0
+ with:
+ scan-type: fs
+ scan-ref: .
+ scanners: vuln,misconfig,secret
+ severity: HIGH,CRITICAL
+ ignore-unfixed: true
+ exit-code: "1"
+ format: table
+
+ evals:
+ name: Agent evals (offline tier)
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.12"
+
+ # Offline tier: routing evals execute the real routing table and the
+ # skill suites validate their controlled vocabularies. No model, no API
+ # key, no network — so this gates every PR. A failure here means an
+ # agent lost an ability it previously had; the report names which.
+ - name: Run offline evals
+ run: python3 -m evals.runner --json eval-report.json
+
+ # Model-dependent tier: runs the skill scenarios against a real model and
+ # scores its tool/label selections. Opt-in because it costs tokens.
+ - name: Run live evals
+ if: ${{ github.event_name == 'workflow_dispatch' && inputs.live_evals }}
+ env:
+ ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+ run: python3 -m evals.runner --live --json eval-report-live.json
+
+ - name: Upload eval report
+ if: always()
+ uses: actions/upload-artifact@v4
+ with:
+ name: eval-report
+ path: eval-report*.json
+ if-no-files-found: ignore
diff --git a/.github/workflows/requirements-extraction.yml b/.github/workflows/requirements-extraction.yml
new file mode 100644
index 0000000..3e89634
--- /dev/null
+++ b/.github/workflows/requirements-extraction.yml
@@ -0,0 +1,70 @@
+# Requirements & ADR extraction — drives the LLM agents over meeting
+# transcripts and opens a PR with the generated requirements + ADR drafts.
+#
+# Closes the gap that the offline minutes pipeline left open: minutes were
+# generated for the summer meetings, but the requirements-extraction and
+# ADR agents were never re-run on that data. This runs them (transcript_parser
+# -> req_extractor -> adr_generator) using the ANTHROPIC_API_KEY repo secret,
+# then opens a PULL REQUEST — the human review of that PR is the approval gate
+# before any requirement/ADR is baselined on main.
+#
+# Manual trigger only (workflow_dispatch): this is a deliberate, reviewed run,
+# not something that should fire on every push.
+
+name: Requirements & ADR extraction
+
+on:
+ workflow_dispatch:
+ inputs:
+ since:
+ description: "Only process transcripts dated on/after this (YYYY-MM-DD)"
+ required: false
+ default: "2026-05-01"
+
+permissions:
+ contents: write
+ pull-requests: write
+
+concurrency:
+ group: requirements-extraction
+ cancel-in-progress: false
+
+jobs:
+ extract:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.12"
+
+ # Only the light deps the agents' LLM path needs — not chromadb/torch.
+ - name: Install agent deps
+ run: pip install "anthropic>=0.42" "python-dotenv>=1.0" "pydantic-settings>=2.7" "pydantic>=2.10"
+
+ - name: Run requirements + ADR extraction
+ env:
+ ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+ # base.py's hardcoded default model id is stale and it always sends
+ # temperature; pin a current, temperature-accepting model.
+ CLAUDE_MODEL: claude-sonnet-4-5-20250929
+ LLM_PROVIDER: anthropic
+ run: |
+ python -m pipeline.extract_from_meetings \
+ --since "${{ github.event.inputs.since }}" \
+ --summary-file /tmp/pr_body.md
+ cat /tmp/pr_body.md >> "$GITHUB_STEP_SUMMARY"
+
+ - name: Open pull request with generated requirements + ADRs
+ uses: peter-evans/create-pull-request@v6
+ with:
+ branch: pipeline/requirements-${{ github.run_number }}
+ commit-message: "[agent:req_extractor+adr_generator] Extract requirements & ADRs from meetings (run ${{ github.run_number }})"
+ title: "Agent-generated requirements & ADRs for review (run ${{ github.run_number }})"
+ body-path: /tmp/pr_body.md
+ labels: agent-generated, needs-human-review
+ add-paths: |
+ requirements/parsed/**
+ docs/adr/**
+ delete-branch: true
diff --git a/.github/workflows/static.yml b/.github/workflows/static.yml
new file mode 100644
index 0000000..460f782
--- /dev/null
+++ b/.github/workflows/static.yml
@@ -0,0 +1,43 @@
+# Simple workflow for deploying static content to GitHub Pages
+name: Deploy static content to Pages
+
+on:
+ # Runs on pushes targeting the default branch
+ push:
+ branches: ["main"]
+
+ # Allows you to run this workflow manually from the Actions tab
+ workflow_dispatch:
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
+permissions:
+ contents: read
+ pages: write
+ id-token: write
+
+# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
+# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
+concurrency:
+ group: "pages"
+ cancel-in-progress: false
+
+jobs:
+ # Single deploy job since we're just deploying
+ deploy:
+ environment:
+ name: github-pages
+ url: ${{ steps.deployment.outputs.page_url }}
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v4
+ - name: Setup Pages
+ uses: actions/configure-pages@v5
+ - name: Upload artifact
+ uses: actions/upload-pages-artifact@v3
+ with:
+ # Upload entire repository
+ path: '.'
+ - name: Deploy to GitHub Pages
+ id: deployment
+ uses: actions/deploy-pages@v5
diff --git a/.github/workflows/tick-board-pages.yml b/.github/workflows/tick-board-pages.yml
new file mode 100644
index 0000000..e867112
--- /dev/null
+++ b/.github/workflows/tick-board-pages.yml
@@ -0,0 +1,37 @@
+# GitHub Pages "Deploy from a branch" only allows publishing from '/' or '/docs',
+# not from '/tick-board'. This workflow publishes the `tick-board/` folder unchanged.
+#
+# After merging: Repo → Settings → Pages → Build & deployment → Source → GitHub Actions.
+name: Deploy tick board (GitHub Pages)
+
+on:
+ push:
+ branches: ["main"]
+ paths:
+ - "tick-board/**"
+ - ".github/workflows/tick-board-pages.yml"
+ workflow_dispatch:
+
+permissions:
+ contents: read
+ pages: write
+ id-token: write
+
+concurrency:
+ group: pages-tick-board
+ cancel-in-progress: true
+
+jobs:
+ deploy:
+ environment:
+ name: github-pages
+ url: ${{ steps.deployment.outputs.page_url }}
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+ - uses: actions/configure-pages@v4
+ - uses: actions/upload-pages-artifact@v3
+ with:
+ path: tick-board
+ - id: deployment
+ uses: actions/deploy-pages@v4
diff --git a/.github/workflows/transcript-pipeline.yml b/.github/workflows/transcript-pipeline.yml
new file mode 100644
index 0000000..ea64d06
--- /dev/null
+++ b/.github/workflows/transcript-pipeline.yml
@@ -0,0 +1,56 @@
+# SES transcript pipeline — the "drop a VTT, get reviewed minutes" trigger.
+#
+# Closes the operations gap from the spring critique: the pipeline used to run
+# only on one laptop, manually. Now anyone on the team adds a Zoom
+# *.transcript.vtt to transcripts/inbox/ (git push, or GitHub web "Add file" →
+# "Upload files") and this workflow:
+# 1. runs pipeline.process_inbox (stdlib-only — no dependency install),
+# 2. moves the transcript into transcripts/ and writes minutes/ artifacts,
+# 3. opens a PULL REQUEST with the generated minutes.
+# The PR is the human review gate (metamodel: Process → Artifact → human
+# Verification before the artifact is baselined on main).
+
+name: Transcript pipeline
+
+on:
+ push:
+ branches: ["main"]
+ paths: ["transcripts/inbox/**"]
+ workflow_dispatch: {}
+
+permissions:
+ contents: write
+ pull-requests: write
+
+concurrency:
+ group: transcript-pipeline
+ cancel-in-progress: false
+
+jobs:
+ process-inbox:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - uses: actions/setup-python@v5
+ with:
+ python-version: "3.12"
+
+ # pipeline.ingest / process_inbox are deliberately stdlib-only, so this
+ # step needs no pip install and finishes in seconds.
+ - name: Run inbox processor
+ id: run
+ run: |
+ python -m pipeline.process_inbox --summary-file /tmp/pr_body.md
+ echo "## Transcript pipeline" >> "$GITHUB_STEP_SUMMARY"
+ cat /tmp/pr_body.md >> "$GITHUB_STEP_SUMMARY"
+
+ - name: Open pull request with generated minutes
+ uses: peter-evans/create-pull-request@v6
+ with:
+ branch: pipeline/minutes-${{ github.run_number }}
+ commit-message: "[agent:pipeline] Generate minutes from inbox transcripts (run ${{ github.run_number }})"
+ title: "Agent-generated minutes for review (run ${{ github.run_number }})"
+ body-path: /tmp/pr_body.md
+ labels: agent-generated, needs-human-review
+ delete-branch: true
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..c653e37
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,40 @@
+__pycache__/
+*.py[cod]
+*$py.class
+*.egg-info/
+dist/
+build/
+.eggs/
+
+.env
+*.db
+
+memory/chroma/
+memory/*.db
+
+.venv/
+venv/
+ENV/
+
+.pytest_cache/
+htmlcov/
+.coverage
+
+*.log
+pipeline/logs/*.jsonl
+
+.DS_Store
+Thumbs.db
+
+# Large media files — don't commit to git
+*.m4a
+*.mp4
+*.wav
+*.mp3
+*.avi
+*.mov
+
+.idea/
+.vscode/
+*.swp
+*.swo
diff --git a/CURSOR_PROMPT.md b/CURSOR_PROMPT.md
new file mode 100644
index 0000000..e3e1d5c
--- /dev/null
+++ b/CURSOR_PROMPT.md
@@ -0,0 +1,773 @@
+# eParts Agentic System — Cursor Vibe Coding Prompt
+# Feed this entire file to Cursor (Claude Opus) as project context before writing any code.
+# This is the single source of truth for what you are building, why, and how.
+
+---
+
+## WHO YOU ARE BUILDING THIS FOR
+
+You are building an agentic SE system for **Pimsie Supreme** — a 5-person CMU MSE Studio
+capstone team (Spring–Fall 2026). The team members are:
+- Ashritha Gonuguntla
+- Arjun Nair
+- Hrishikesh Bhardwaj
+- Jaivardhan Singh
+- Zheliang Liu
+
+This system is NOT a product being delivered to the client. It is the team's own internal
+tooling — an agentic pipeline that helps the team execute the capstone project better.
+Think of it as the team's operating system for the project.
+
+---
+
+## THE CAPSTONE PROJECT CONTEXT (what the team is building for the client)
+
+**Client:** eParts Services LLC, Homestead PA. They build eCommerce procurement tools
+for construction contractors. Their system of record is PIMS (Product Information
+Management System) backed by MSSQL/PostgreSQL.
+
+**The problem:** Supplier product catalogs arrive in heterogeneous formats — PDFs, CSVs,
+SFTP drops, email attachments. The current ingestion workflow is entirely manual: ~1.5
+FTEs at eParts and ~3 FTEs at sister company Alps Controls interpret, normalize, and map
+every supplier attribute before it enters PIMS. This is slow, error-prone, and unscalable.
+
+**What the team is building for the client:**
+An Intelligent Product Data Ingestion and Enrichment Platform:
+1. Ingestion Gateway — accepts CSV, PDF, email, SFTP, direct upload
+2. Canonical staging tables — normalizes heterogeneous input into a standardized schema
+3. ML Attribute Prediction Service — maps supplier attributes to PIMS canonical attributes
+ with per-attribute confidence scoring
+4. Confidence-based routing — high confidence → auto-accept + writeback to PIMS,
+ low confidence → Human Review Queue
+5. Human Review Queue — Brian and Dewey (catalog team) approve/correct low-confidence
+ predictions
+6. Idempotent writeback to PIMS staging tables via pyodbc
+7. Observability via Datadog
+
+**ML approach (current):** Hybrid rule engine + semantic similarity.
+- Rules handle structured/known patterns (high precision)
+- Semantic matcher uses all-MiniLM-L6-v2 sentence embeddings + cosine similarity
+ for unmatched attributes
+- Combined confidence: conf_final = α * conf_rule + (1-α) * conf_embed, α=0.7 (unvalidated)
+- Confidence threshold: 0.85 (unvalidated — most sensitive open parameter)
+- Zero-shot: no labeled training data required for the embedding layer
+
+**POC results so far:**
+- Ran semantic matcher POC using TF-IDF (stand-in for all-MiniLM, no internet in env)
+- Index: 487 active PIMS attributes from production database
+- Tested on 2 real supplier spec sheets: AIM2 (22 attrs) and RCT Flex CT (20 attrs)
+- 86% auto-accepted overall (91% AIM2, 80% RCT)
+- Ground truth eval on 217 unique PIMS attributes: 99.1% top-1 accuracy, 100% top-3
+- Human review cases were genuinely absent attributes, not bad matches
+
+**Key PIMS data model:**
+- Categories (78 active) → ProductTypes/subcategories (755) → Products (2000) → ProductAttributeValues (50k rows)
+- Attributes master list: 487 active attributes (e.g., SUPPLY VOLTAGE, OPERATING TEMP, ACCURACY)
+- Suffixes: units of measure (VAC, VDC, mA, ohms, etc.)
+- Attribute_suffix_mappings: which suffixes are valid for which attributes
+- ProductTypeAttributes: which attributes are expected for each product type (the schema)
+
+**Tech stack for client product:**
+- Azure App Service (Python backend)
+- Azure SQL Database (staging tables, review queue, audit trail)
+- Azure Blob Storage (raw file archive)
+- Azure Functions (timer-triggered publish/sync job)
+- .NET / Vue.js / Nuxt.js (existing eParts stack)
+- Datadog (observability)
+- No PIMS writeback API exists — idempotency enforced in application code
+
+**Architecture style:** Pipe and filter
+- Ingestion → Normalization → Prediction → Routing → Review Queue → Writeback → PIMS
+- Single Azure App Service deployment (not microservices — team size constraint)
+- PredictionServiceInterface isolates model from routing/writeback
+- Per-attribute routing (not per-record) to minimize review volume
+
+**Open architectural decisions (unresolved):**
+- ADR-1: Threshold value (0.85 is a guess — needs calibration against real labeled data)
+- ADR-2: Alpha weighting (0.7 is a guess — needs sweep across correction data)
+- ADR-3: Per-attribute vs per-record routing (pending attribute correlation analysis)
+- ADR-4: PIMS staging schema compatibility (Jake has not delivered P1-C schema yet)
+- ADR-5: Drift detection baselines not defined
+
+**Key stakeholders:**
+- Harsha (eParts) — sets accuracy threshold priority, approves model selection
+- Jake (eParts) — PIMS integration, defines write interface and staging table contracts
+- Brian & Dewey (eParts) — catalog team, primary review workflow users
+- Alps Controls catalog team — secondary users (3 FTEs)
+- David (eParts) — executive sponsor
+- Christian Kastner — AI in SE coach (CMU professor)
+- Jim — presentation mentor
+
+---
+
+## WHAT YOU ARE ACTUALLY BUILDING (the agentic SE system for the team)
+
+A multi-agent pipeline that helps Pimsie Supreme operate as a team. This is not the
+client product. This is the team's internal tooling.
+
+**Core philosophy:**
+- Agents handle the mechanical 80%, humans own the judgment 20%
+- Every high-risk output (architecture changes, P0 tickets, ADRs) requires human approval
+- Low-risk outputs (minutes, digests, alerts) write directly
+- All agent outputs are versioned in Bitbucket — git history is the audit trail
+- Prompts are version-controlled files, not hardcoded strings
+- Bitbucket is the single source of truth. Confluence is the human-readable mirror.
+
+---
+
+## ARCHITECTURE OVERVIEW
+
+```
+Triggers/Inputs
+ → Central Orchestrator Agent (FastAPI + task queue)
+ → Domain Agents (5 generic + 2 eParts-specific)
+ → MCP Servers (tools)
+ → Outputs
+```
+
+**Inputs/Triggers:**
+- Zoom transcript (.vtt) — auto-polled from Google Drive every 15 min
+- Jira webhook events (ticket open/close/update)
+- Slack event stream (designated project channel)
+- GitHub/Bitbucket webhook (PR open/merge/comment)
+- Scheduled cron (Mon 8am, Fri 6pm)
+- Manual CLI / API trigger
+- POC script run results (for ML Decision agent)
+
+**Central Orchestrator:**
+- FastAPI server (always-on)
+- Three entry points: webhook endpoint, cron scheduler, manual API
+- Shared task queue — agents run sequentially, no race conditions on commits
+- Audit log of every agent invocation
+- Does NOT make LLM calls itself — pure routing and queue management
+
+---
+
+## DOMAIN AGENTS (5 generic)
+
+### 1. Requirements Agent
+Triggered by: transcript upload, manual
+
+Sub-agents:
+- **Transcript Parser**: Send .vtt to Claude. Extract: meeting date, attendees, decisions,
+ action items with owners, open questions, new requirements. Output: structured markdown.
+- **Priority Classifier**: Assign P0/P1/P2 to each item.
+ P0 = blocks delivery or client commitment with hard deadline
+ P1 = important for current sprint, ticket immediately
+ P2 = future sprint
+ P0 ticket creation requires human approval gate.
+- **REQ Extractor**: Format as REQ-XXX.md, commit to /requirements/parsed/
+ Each file: requirement statement, source meeting, date, priority, open questions.
+- **Stale REQ Detector**: Runs Mon 8am. Flag REQs with no Jira ticket and not mentioned
+ in any meeting in past 14 days. Output: Slack alert + /docs/stale-requirements.md
+
+HITL gate: REQs auto-committed, P0 Jira ticket creation needs 1 team approval.
+
+### 2. Architecture Agent
+Triggered by: transcript commit, PR event, manual
+
+Sub-agents:
+- **Drift Detector**: After every meeting, read canonical architecture.mmd + meeting
+ minutes. Detect: new ingestion sources, routing changes, new downstream consumers,
+ layer splits/renames, decisions contradicting existing diagram. Output: drift report
+ committed to /docs/drift/YYYY-MM-DD.md
+- **ADR Generator**: When significant technical decision detected in transcript or Slack
+ → auto-draft ADR.md with context, options considered, rationale, consequences.
+ Committed as PR — never direct commit. Specifically tracks these open decisions:
+ threshold value, alpha weighting, per-attr vs per-record routing, PIMS schema compat.
+- **Diagram Updater**: Propose Mermaid diff as PR against architecture.mmd.
+ PR description quotes the meeting excerpt that triggered each change.
+ Requires 2 team approvals to merge. Never direct commit.
+- **Traceability Builder**: Maintain /docs/traceability.md — living matrix:
+ REQ ID | Description | Jira ticket | PR | Test status | Last updated
+ Updated on every relevant commit.
+
+HITL gate: All architecture changes require PR approval. No direct commits.
+
+### 3. Coding Agent (PARTIAL — not full autonomous coding)
+Triggered by: Jira ticket assigned, PR opened
+
+Sub-agents:
+- **Boilerplate Generator**: When new ticket tagged as new service/module → scaffold
+ directory structure, interface stubs, basic test file from ADR + REQ context. Output: PR.
+- **PR Reviewer**: On every PR open → auto-comment on style, missing test coverage,
+ whether PR references correct REQ ID and Jira ticket, new API surface documentation.
+ Comment only — human decides on merge.
+- **Test Generator**: Generate unit test stubs from function signatures when module scaffolded.
+- **Doc Generator**: When API endpoint changes in PR → auto-update API docs in same PR.
+
+HITL gate: All code PRs require human review and merge. No auto-merge ever.
+
+### 4. Project Management Agent
+Triggered by: transcript, cron, Jira webhook
+
+Sub-agents:
+- **Jira Ticket Creator**: From P0/P1 items → create tickets with description, assignee
+ suggestion based on domain, priority label, link to source REQ file.
+ P0 held in review queue for 1-click approval.
+- **WBS Updater**: Maintain /sprint/wbs.md — task breakdown synced to Jira state.
+ When tickets close → WBS updates. When new tickets created → appear under correct epic.
+- **Weekly Digest Agent**: Runs every Friday 6pm. Reads all commits that week.
+ Output format:
+ ```
+ ## Week of YYYY-MM-DD — Project digest
+ ### Decisions made this week
+ ### Requirements changes (added/modified)
+ ### Sprint health (open/closed tickets, velocity)
+ ### Architecture (drift detected/resolved)
+ ### Next week preview
+ ```
+ Published to Confluence + Slack.
+- **Alert Agent**: Monitors sprint state every 6 hours. Fires Slack alert when:
+ ticket velocity off-track, must-have REQ has no Jira ticket, P0 ticket unassigned >48hrs,
+ drift detected but no PR opened within 24hrs.
+
+HITL gate: P1/P2 tickets auto-created. P0 tickets need 1 approval.
+
+### 5. Knowledge Agent
+Triggered by: commit to /minutes/, cron, PR events
+
+Sub-agents:
+- **Minutes Publisher**: Format meeting minutes → push to Confluence under correct parent
+ (client meeting / mentor meeting / standup). Bitbucket = permanent record, Confluence = mirror.
+- **Decision Logger**: Extract decisions from all sources → /minutes/decisions.log.md
+ Each entry: decision, source, date, people present.
+- **Prompt Regression Tester**: On any PR modifying /pipeline/prompts/ → run against
+ golden dataset. Score on correctness, completeness, format. Block PR if score drops >10%
+ below baseline.
+- **Context Packager**: Runs Mon 7am (1hr before typical mentor meeting). Reads week's
+ commits, open REQs, stale items, pending ADRs. Produces 1-page briefing. Slack-pinned.
+
+---
+
+## EPARTS-SPECIFIC AGENTS (2 unique — core differentiators)
+
+These two agents cannot exist on any other capstone project. They are specific to:
+1. The CMU coached capstone structure (recurring coach sessions with Christian Kastner)
+2. The fact that the team is building a live ML system that produces empirical signals
+
+### 6. Coach Session Memory Agent
+Triggered by: transcript of any coach / mentor session
+
+**Why this is eParts-specific:** The team has structured recurring sessions with Christian
+Kastner (AI in SE coach) and other mentors where feedback is given, commitments are made,
+and progress is evaluated. This feedback currently lives in meeting minutes and gets
+partially forgotten by the next session. This agent maintains persistent memory across
+ALL sessions.
+
+Sub-agents:
+- **Persistent Session Memory (RAG)**: Embeds all past coach/mentor session transcripts
+ into a vector store (ChromaDB locally, Azure AI Search in prod). On each new session,
+ retrieves semantically relevant past context before generating outputs.
+- **Commitment Tracker**: Extracts explicit commitments from each session
+ (e.g., "we will define confidence baselines by next week"). Cross-checks against
+ Bitbucket commits and Jira closures to verify delivery status.
+ Maintains: commitment → deadline → delivery status → evidence.
+- **Pre-Meeting Briefing Generator**: Runs 1hr before every coach/mentor meeting.
+ Produces structured briefing:
+ - What Christian flagged last session
+ - What the team committed to
+ - What was delivered (with evidence links)
+ - What is still open
+ - Christian's recurring concerns across all sessions (pattern detection)
+ Slacked to team channel.
+- **Evolving Concern Tracker**: Tracks Christian's recurring themes across sessions.
+ Currently known concerns: monitorability (flagged multiple times), evidence-based AI
+ (not adding AI for sake of it), HITL design, threshold calibration.
+ Surfaces pattern: "Christian has flagged monitorability in 3 of 4 sessions."
+
+Technical implementation:
+- Vector store: ChromaDB (local dev) → Azure AI Search (prod)
+- Embedding model: all-MiniLM-L6-v2 (same model used in client ML POC — consistent)
+- Memory schema:
+ ```
+ session_id, date, session_type (coach/mentor/standup),
+ participant, commitments[], concerns[], decisions[], evidence_links[]
+ ```
+- RAG query: before generating any briefing, retrieve top-5 most relevant past sessions
+
+HITL gate: Briefing auto-published to Slack. No gate needed — informational only.
+
+### 7. ML Decision Memory Agent
+Triggered by: POC script run completing, new labeled data commit, meeting transcript
+touching ML decisions, manual trigger
+
+**Why this is eParts-specific:** The team has a live ML system (semantic matcher) with
+several open architectural decisions that depend on empirical evidence that accumulates
+over time. No other capstone team has this problem. The open decisions are:
+- Confidence threshold (currently 0.85 — unvalidated)
+- Alpha weighting in hybrid model (currently 0.7 — unvalidated)
+- Per-attribute vs per-record routing (pending correlation analysis)
+- PIMS schema compatibility (Jake's P1-C schema not delivered)
+
+These decisions cannot be closed by discussion alone — they need data. This agent
+tracks the evidence state for each decision and tells the team when they have enough
+to close it.
+
+Sub-agents:
+- **Open ML Decision Log**: Maintains living log of every unresolved ML architectural
+ decision. Schema per entry:
+ ```
+ decision_id, name, current_value, basis (guess/empirical/validated),
+ evidence_needed, evidence_so_far[], status (open/ready_to_close/closed),
+ last_updated, source_adr
+ ```
+ Seeded from SW.pdf architectural decisions (ADR-1 through ADR-5).
+- **Evidence Accumulator**: When POC results come in (new labeled data, precision-recall
+ curves, auto-accept rates, correction patterns) → automatically updates relevant
+ decision entries. Parses structured output from POC scripts.
+ Tracks: labeled count, precision@threshold, recall@threshold, per-attribute variance,
+ correction rate, alpha sweep results.
+- **Decision Readiness Detector**: When accumulated evidence crosses a defined threshold
+ → fires Slack alert: "Enough data to close [decision name] — run calibration now."
+ Thresholds: ≥200 labeled examples → threshold calibration ready,
+ ≥100 correction pairs → alpha calibration ready,
+ ≥50 reviewed records → per-attribute correlation analysis ready.
+- **Coach Session Linker**: Cross-references open ML decisions against coach session
+ transcripts. When Christian asks about threshold calibration (he has), this agent
+ surfaces: what decision is open, what evidence exists today, what is still needed,
+ and links to the relevant ADR. Feeds into Coach Memory Agent briefings.
+
+Technical implementation:
+- Decision log: SQLite table locally → Azure SQL in prod
+- Evidence parsing: structured JSON output from POC scripts ingested automatically
+- Integration: feeds into Coach Memory Agent — ML decision state appears in pre-meeting
+ briefings when relevant
+
+HITL gate: Slack alerts auto-sent. Decision closure requires team member to manually
+mark decision as closed after reviewing evidence.
+
+---
+
+## MCP SERVERS (tools available to all agents)
+
+All external tool access goes through MCP servers. No agent has hardcoded credentials
+or makes direct HTTP calls to external services.
+
+| MCP Server | Tools | Used by |
+|---|---|---|
+| Jira MCP | create_ticket, update_ticket, get_sprint_state, add_comment | Requirements, PM agents |
+| GitHub/Bitbucket MCP | commit_file, open_pr, add_pr_comment, get_pr_status | All agents writing to repo |
+| Confluence MCP | create_page, update_page, get_page | Knowledge, Architecture agents |
+| Slack MCP | send_message, read_channel, pin_message | Alert, Digest, Coach Memory agents |
+| Google Drive MCP | list_files, read_file, watch_folder | Transcript parser, Notes agent |
+| Anthropic API | claude_completion (claude-opus-4-5 or claude-sonnet-4-5) | All agents — all LLM calls |
+| Vector Store MCP | embed, query, upsert, delete | Coach Memory, ML Decision agents |
+| Bitbucket MCP | commit, branch, open_pr | All agents writing to repo |
+
+---
+
+## REPO STRUCTURE TO SET UP
+
+```
+eparts-agentic/
+├── orchestrator/
+│ ├── main.py ← FastAPI app, all webhook + cron endpoints
+│ ├── queue.py ← shared task queue, sequential execution
+│ └── router.py ← trigger type → agent mapping
+├── agents/
+│ ├── base.py ← base Agent class all agents inherit
+│ ├── requirements/
+│ │ ├── __init__.py
+│ │ ├── transcript_parser.py
+│ │ ├── priority_classifier.py
+│ │ ├── req_extractor.py
+│ │ └── stale_detector.py
+│ ├── architecture/
+│ │ ├── drift_detector.py
+│ │ ├── adr_generator.py
+│ │ ├── diagram_updater.py
+│ │ └── traceability_builder.py
+│ ├── coding/
+│ │ ├── boilerplate_generator.py
+│ │ ├── pr_reviewer.py
+│ │ ├── test_generator.py
+│ │ └── doc_generator.py
+│ ├── project_mgmt/
+│ │ ├── ticket_creator.py
+│ │ ├── wbs_updater.py
+│ │ ├── weekly_digest.py
+│ │ └── alert_agent.py
+│ ├── knowledge/
+│ │ ├── minutes_publisher.py
+│ │ ├── decision_logger.py
+│ │ ├── prompt_regression.py
+│ │ └── context_packager.py
+│ ├── coach_memory/ ← eParts-specific
+│ │ ├── __init__.py
+│ │ ├── session_memory.py ← RAG over past sessions
+│ │ ├── commitment_tracker.py
+│ │ ├── briefing_generator.py
+│ │ └── concern_tracker.py
+│ └── ml_decision/ ← eParts-specific
+│ ├── __init__.py
+│ ├── decision_log.py ← SQLite-backed open decision store
+│ ├── evidence_accumulator.py
+│ ├── readiness_detector.py
+│ └── coach_linker.py
+├── mcp/
+│ ├── jira.py
+│ ├── slack.py
+│ ├── bitbucket.py
+│ ├── confluence.py
+│ ├── drive.py
+│ └── vector_store.py ← ChromaDB wrapper
+├── memory/
+│ ├── coach_sessions.db ← SQLite: session memory, commitments
+│ ├── ml_decisions.db ← SQLite: open decision log, evidence
+│ └── chroma/ ← ChromaDB vector store
+├── prompts/ ← ALL prompts as versioned .txt files
+│ ├── transcript_parser.txt
+│ ├── priority_classifier.txt
+│ ├── drift_detector.txt
+│ ├── adr_generator.txt
+│ ├── briefing_generator.txt
+│ ├── weekly_digest.txt
+│ └── ...
+├── tests/
+│ └── golden/
+│ ├── transcripts/ ← real past .vtt files as test inputs
+│ └── expected/ ← expected outputs paired with each input
+├── data/
+│ └── seed/
+│ ├── christian_session_2026_02_16.pdf ← seed for coach memory
+│ ├── SW.pdf ← seed for ML decision log
+│ └── poc_results.json ← semantic matcher POC output
+├── .env.example
+├── requirements.txt
+└── README.md
+```
+
+---
+
+## BUILD ORDER (implement in this exact order)
+
+1. `agents/base.py` — base Agent class. Abstract run(), structured JSON logging,
+ error handling with retry, call_claude() helper using Anthropic SDK.
+ Get this right before touching anything else.
+
+2. `orchestrator/main.py` — FastAPI with one working POST /webhook endpoint
+ and one GET /health. Just routing, no agent logic yet.
+
+3. `mcp/slack.py` — easiest to test. Implement send_message() and verify end-to-end.
+
+4. `mcp/bitbucket.py` — commit_file() and open_pr(). Test with a dummy file.
+
+5. `agents/coach_memory/session_memory.py` — RAG foundation.
+ ChromaDB setup, embed(), query(). Seed with Christian's session PDF.
+ Test: query "what did Christian say about monitorability" → returns relevant chunks.
+
+6. `agents/coach_memory/commitment_tracker.py` — extract commitments from transcript,
+ store in SQLite, cross-check against Bitbucket commits.
+
+7. `agents/coach_memory/briefing_generator.py` — full pre-meeting briefing.
+ End-to-end test: feed in Christian's Feb 16 session, generate briefing.
+
+8. `agents/ml_decision/decision_log.py` — SQLite schema, seed from SW.pdf ADRs.
+ Pre-populate: threshold (0.85, basis=guess), alpha (0.7, basis=guess),
+ per-attr routing (open), PIMS schema (blocked on Jake).
+
+9. `agents/ml_decision/evidence_accumulator.py` — parse POC JSON output,
+ update decision entries. Test with semantic matcher POC results.
+
+10. `agents/ml_decision/readiness_detector.py` — threshold checks + Slack alert.
+
+11. `agents/requirements/transcript_parser.py` — full transcript → structured output.
+
+12. `agents/requirements/priority_classifier.py` — P0/P1/P2 with eParts context.
+
+13. `mcp/jira.py` — create_ticket(). Test end-to-end with one P1 item.
+
+14. `agents/project_mgmt/ticket_creator.py` — full ticket creation pipeline.
+
+15. Everything else in order: architecture agent, knowledge agent, coding agent,
+ PM agent remaining sub-agents, weekly digest, alert agent.
+
+16. `orchestrator/queue.py` + `orchestrator/router.py` — wire everything together.
+
+17. Prompt regression test suite — seed golden dataset, wire to Bitbucket Pipelines.
+
+---
+
+## BASE AGENT CLASS SPEC
+
+Every agent must inherit from this. Implement this first.
+
+```python
+class BaseAgent:
+ def __init__(self, name: str, mcp_clients: dict):
+ self.name = name
+ self.mcp = mcp_clients
+ self.logger = StructuredLogger(agent_name=name)
+
+ @abstractmethod
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ # Every agent implements this
+ pass
+
+ def call_claude(self, prompt: str, system: str = None,
+ model: str = "claude-opus-4-5",
+ max_tokens: int = 4096) -> str:
+ # Calls Anthropic API. Loads prompt from /prompts/ if prompt is a filename.
+ # Logs input/output/tokens/latency to structured log.
+ # Retries up to 3 times with exponential backoff on rate limit.
+ pass
+
+ def load_prompt(self, filename: str, **kwargs) -> str:
+ # Loads prompt template from /prompts/{filename}.txt
+ # Substitutes kwargs as template variables
+ pass
+
+ def log_run(self, trigger, result, duration_ms: int):
+ # Appends to /pipeline/logs/agent_runs.jsonl
+ # Format: {timestamp, agent, trigger_type, success, duration_ms, output_summary}
+ pass
+```
+
+---
+
+## KEY DATA SCHEMAS
+
+### AgentTrigger
+```python
+@dataclass
+class AgentTrigger:
+ trigger_type: str # "transcript", "jira_webhook", "slack", "pr", "cron", "manual", "poc_result"
+ source: str # file path, webhook payload, etc.
+ metadata: dict # trigger-specific context
+ timestamp: datetime
+```
+
+### AgentResult
+```python
+@dataclass
+class AgentResult:
+ agent: str
+ success: bool
+ outputs: list[AgentOutput] # files committed, tickets created, messages sent
+ errors: list[str]
+ requires_human_review: bool
+ review_items: list[dict] # items waiting for human approval
+```
+
+### Coach Session (SQLite)
+```sql
+CREATE TABLE sessions (
+ session_id TEXT PRIMARY KEY,
+ date TEXT,
+ session_type TEXT, -- coach/mentor/standup/client
+ participants TEXT, -- JSON array
+ raw_transcript_path TEXT,
+ processed_at TEXT
+);
+
+CREATE TABLE commitments (
+ id INTEGER PRIMARY KEY,
+ session_id TEXT,
+ commitment_text TEXT,
+ owner TEXT,
+ deadline TEXT,
+ status TEXT, -- open/delivered/missed
+ evidence_link TEXT,
+ FOREIGN KEY (session_id) REFERENCES sessions(session_id)
+);
+
+CREATE TABLE concerns (
+ id INTEGER PRIMARY KEY,
+ session_id TEXT,
+ concern_text TEXT,
+ raised_by TEXT,
+ theme TEXT, -- monitorability/hitl/evidence/threshold/etc
+ times_raised INTEGER DEFAULT 1
+);
+```
+
+### ML Decision Log (SQLite)
+```sql
+CREATE TABLE ml_decisions (
+ decision_id TEXT PRIMARY KEY,
+ name TEXT,
+ current_value TEXT,
+ basis TEXT, -- guess/empirical/validated
+ evidence_needed TEXT,
+ status TEXT, -- open/ready_to_close/closed
+ source_adr TEXT,
+ last_updated TEXT
+);
+
+CREATE TABLE evidence (
+ id INTEGER PRIMARY KEY,
+ decision_id TEXT,
+ evidence_type TEXT, -- poc_result/labeled_data/coach_feedback/correction_analysis
+ description TEXT,
+ value TEXT, -- JSON
+ collected_at TEXT,
+ FOREIGN KEY (decision_id) REFERENCES ml_decisions(decision_id)
+);
+```
+
+### Pre-populated ML Decisions (seed data — insert on first run)
+```python
+SEED_DECISIONS = [
+ {
+ "decision_id": "ADR-1-threshold",
+ "name": "Confidence threshold value",
+ "current_value": "0.85",
+ "basis": "guess",
+ "evidence_needed": "Precision-recall curves from ≥200 labeled submissions. Per-attribute accuracy variance.",
+ "status": "open",
+ "source_adr": "ADR-4"
+ },
+ {
+ "decision_id": "ADR-1-alpha",
+ "name": "Hybrid model alpha weighting",
+ "current_value": "0.7",
+ "basis": "guess",
+ "evidence_needed": "Alpha sweep 0.3-0.9 across correction data. ECE, precision, coverage at each value.",
+ "status": "open",
+ "source_adr": "ADR-1"
+ },
+ {
+ "decision_id": "ADR-2-routing",
+ "name": "Per-attribute vs per-record routing",
+ "current_value": "per-attribute",
+ "basis": "empirical (partial)",
+ "evidence_needed": "Pairwise mutual information on labeled data. ≥50 reviewed records inspected for cross-attribute inconsistency.",
+ "status": "open",
+ "source_adr": "ADR-2"
+ },
+ {
+ "decision_id": "ADR-3-schema",
+ "name": "PIMS staging schema compatibility",
+ "current_value": "assumed compatible",
+ "basis": "unvalidated",
+ "evidence_needed": "Jake delivers P1-C schema. Map P1-C columns to canonical schema. Integration test 10 sample records.",
+ "status": "blocked",
+ "source_adr": "ADR-5"
+ },
+ {
+ "decision_id": "ADR-4-drift",
+ "name": "Drift detection baselines and alert thresholds",
+ "current_value": "undefined",
+ "basis": "guess",
+ "evidence_needed": "Baseline confidence distribution from first 2 weeks of production data. Correction rate baseline.",
+ "status": "open",
+ "source_adr": "ADR-5"
+ }
+]
+```
+
+---
+
+## ENVIRONMENT VARIABLES (.env.example)
+
+```
+# Anthropic
+ANTHROPIC_API_KEY=
+
+# Jira
+JIRA_SERVER=https://epartsmse.atlassian.net/
+JIRA_EMAIL=
+JIRA_API_TOKEN=
+JIRA_PROJECT_KEY=EPARTS
+
+# Slack
+SLACK_BOT_TOKEN=
+SLACK_TEAM_CHANNEL=
+SLACK_ALERT_CHANNEL=
+
+# Bitbucket / GitHub
+BITBUCKET_WORKSPACE=
+BITBUCKET_REPO=
+BITBUCKET_TOKEN=
+
+# Confluence
+CONFLUENCE_URL=
+CONFLUENCE_TOKEN=
+CONFLUENCE_SPACE_KEY=
+
+# Google Drive
+GOOGLE_DRIVE_SERVICE_ACCOUNT_JSON=
+GOOGLE_DRIVE_TRANSCRIPT_FOLDER_ID=
+
+# Vector store
+CHROMA_PERSIST_DIR=./memory/chroma
+
+# Database
+SQLITE_DB_PATH=./memory/
+
+# Agent config
+CLAUDE_MODEL=claude-opus-4-5
+CRON_POLL_INTERVAL_MIN=15
+STALE_REQ_THRESHOLD_DAYS=14
+P0_APPROVAL_REQUIRED=true
+CONFIDENCE_THRESHOLD_READINESS=200
+ALPHA_CALIBRATION_READINESS=100
+```
+
+---
+
+## CODING CONVENTIONS
+
+- Every agent file starts with a docstring: what it does, what triggers it, what it outputs
+- All LLM calls go through `self.call_claude()` — never call the Anthropic SDK directly
+- All prompts live in /prompts/ as .txt files — never hardcode prompt strings in Python
+- All external API calls go through /mcp/ — never call Jira/Slack/etc directly from agents
+- Use dataclasses for all data structures (not dicts)
+- Every agent logs its run to /pipeline/logs/agent_runs.jsonl
+- Commit messages follow convention: [agent:name] description of what was done
+- SQLite for local dev, Azure SQL for prod — use the same schema
+
+---
+
+## FIRST THING TO BUILD
+
+Start here. Get this right before anything else:
+
+```
+agents/base.py
+```
+
+Requirements:
+- Abstract BaseAgent class
+- Abstract run() method
+- call_claude() that loads prompts from /prompts/, calls Anthropic SDK,
+ logs input/output/tokens/latency, retries on rate limit
+- load_prompt() that reads .txt file and substitutes template variables
+- log_run() that appends to /pipeline/logs/agent_runs.jsonl
+- StructuredLogger helper class
+
+Once base.py is done and tested, move to orchestrator/main.py.
+Do NOT start building multiple agents simultaneously. One file at a time.
+
+---
+
+## SEED DATA AVAILABLE
+
+The following real files exist and should be used to seed and test the agents:
+
+1. **Christian Kastner coach session transcript (Feb 16 2026)** — seed for Coach Memory Agent.
+ Key themes extracted: evidence-based AI adoption, HITL design, measurement of AI
+ effectiveness, targeted automation of high-frequency tasks, modular design.
+ Key commitments extracted: propose formalized AI integration process with evidence,
+ experiment with automating repeatable weekly tasks, investigate MCP server deployment.
+
+2. **SW.pdf (Final Project Report Draft)** — seed for ML Decision Agent.
+ Contains all 5 open ADRs with current values, rationale, and reconsideration triggers.
+
+3. **Semantic matcher POC results** — seed evidence for ADR-1-threshold:
+ - 42 attributes tested across AIM2 and RCT spec sheets
+ - 86% auto-accept rate at threshold 0.25 (note: threshold was arbitrary for POC)
+ - 99.1% top-1 accuracy on 217 PIMS attributes (self-retrieval test)
+ - 6 human-review cases were genuinely absent attributes, not bad matches
+
+4. **PIMS production data** — available as CSVs:
+ - Attributes.csv (487 active PIMS attributes)
+ - Product_attribute_values.csv (50k labeled rows)
+ - Products.csv (2000 products)
+ - Categories.csv (78 categories)
+ - Product_Types_aka_subcategories.csv (755 product types)
+
+---
+
+*End of prompt. Start with agents/base.py.*
diff --git a/DEMO_PLAYBOOK.md b/DEMO_PLAYBOOK.md
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+++ b/DEMO_PLAYBOOK.md
@@ -0,0 +1,158 @@
+# eParts SES — Demo Playbook
+
+## Quick Start
+
+```bash
+cd ~/eparts && source .venv/bin/activate
+
+# Full interactive demo (13 sections, pauses between each)
+python demo_full.py
+
+# Auto-advance (no pauses — good for screen recording)
+python demo_full.py --auto
+
+# Just the requirements pipeline
+python demo.py
+
+# Specific transcript
+python demo.py transcripts/GMT20260212-190517_Recording.transcript.vtt
+```
+
+---
+
+## What the Demo Covers (13 Sections)
+
+### Section 1: System Overview
+**What it shows:** The big picture — 28 agents, 7 pipelines, 8 MCP servers, 9 SQLite databases.
+**Talking points:**
+- "This is a multi-agent framework, not a collection of scripts"
+- "Every component is connected through SharedMemory and EventBus"
+- Architecture: Triggers → Orchestrator → Agents → MCP → Outputs
+
+### Section 2: LIVE Requirements Pipeline (the star demo)
+**What happens:** Uploads the latest client meeting transcript (.vtt) and fires 7 agents in sequence.
+**Live output the audience sees:**
+
+| Step | Agent | What happens | Time |
+|------|-------|-------------|------|
+| 1/7 | transcript_parser | Sends .vtt to Gemini → extracts action items, decisions | ~30s |
+| 2/7 | priority_classifier | LLM classifies each item as P0/P1/P2 | ~9s |
+| 3/7 | req_extractor | Creates REQ-XXX.md files → **commits to GitHub live** | ~3s |
+| 4/7 | ticket_creator | **Creates Jira tickets live** (P0 held for review) | ~9s |
+| 5/7 | minutes_publisher | Formats meeting minutes | instant |
+| 6/7 | decision_logger | Logs decisions to wiki + GitHub | instant |
+| 7/7 | drift_detector | RAG query against ChromaDB architecture | instant |
+
+**After this step:** Open Jira board and GitHub repo to show the live results.
+
+### Section 3: LIVE Coach Session Pipeline
+**What happens:** Processes a coach meeting transcript through 6 agents.
+**Key outputs:** Session embedded in ChromaDB, commitments extracted, concerns tracked.
+
+### Section 4: Shared Memory (Wiki)
+**What it shows:** The SQLite-backed knowledge base that all agents read/write.
+**Talking points:**
+- "This is the Karpathy wiki pattern — agents accumulate knowledge"
+- "62 entries across 8 namespaces"
+- "When the architecture agent runs, it queries what requirements said"
+
+### Section 5: Event Bus
+**What it shows:** Cross-pipeline publish-subscribe triggers.
+**Talking points:**
+- "49 events emitted so far"
+- "When transcript_parser emits `action_items_extracted`, ticket_creator subscribes"
+- "When drift_detector emits `drift_detected`, architecture pipeline triggers"
+- "This is what makes it a *framework*, not isolated scripts"
+
+### Section 6: Traceability Store
+**What it shows:** 189 artifacts, 764 links, 10 artifact types, 7 link types, zero orphans.
+**Talking points:**
+- "Every artifact is linked to its origin"
+- "A meeting concern → becomes a requirement → becomes a Jira ticket"
+- "All links built via keyword matching — zero LLM tokens"
+- "Zero orphaned concerns or unmitigated risks"
+
+### Section 7: Risk Register
+**What it shows:** 16 risks auto-populated from architecture, coach sessions, meetings.
+**Talking points:**
+- "2 critical, 7 high, 7 medium"
+- "Each risk has severity, category, source, mitigation strategy"
+- "Auto-populated from multiple data sources"
+
+### Section 8: Prompt Registry
+**What it shows:** Version-controlled prompts with peer review workflow.
+**Talking points:**
+- "Without this, 5 team members use 5 different prompts for the same task"
+- "Each prompt is version-pinned, peer-reviewed, and rollbackable"
+
+### Section 9: Artifact Versioning
+**What it shows:** How requirements, architecture, risks, and ADRs evolved over time.
+**Talking points:**
+- "Requirements doc has 5 versions, architecture has 4"
+- "Each version records: who changed it, what triggered the change"
+- "Proves evolution, not just a final document"
+
+### Section 10: Metrics
+**What it shows:** 160 agent runs, LLM token usage, cost tracking, failure rates.
+**Talking points:**
+- "Every run is metered — we know exactly what AI costs"
+- "160 runs total, 93.75% success rate"
+- "Total cost: $0.035 — this is the data for counterfactual analysis"
+
+### Section 11: Live Integrations
+**What to open in browser:**
+- Jira: https://epartsmse.atlassian.net/jira/software/projects/EPARTS/board
+- GitHub: https://github.com/AshrithaG/eparts
+
+### Section 12: Dashboards (opens in Chrome)
+- `interactive_architecture.html` — click any pipeline → granular agent view
+- `intelligence.html` — knowledge graph, goal model, WBS, agent flow, traceability
+- `architecture.html` — static architecture overview
+- `metrics.html` — agent performance dashboard
+
+### Section 13: Closing Summary
+**Key takeaways to emphasize:**
+- 28 agents as a connected framework
+- End-to-end traceability: 189 artifacts, 764 links
+- Counterfactual: transcript parsing 45min → 30s
+- Graceful degradation: works with or without LLM
+
+---
+
+## Before the Demo Checklist
+
+- [ ] `.env` has `GEMINI_API_KEY` (check quota: 5 free calls/minute)
+- [ ] `.env` has `GITHUB_TOKEN` and `GITHUB_REPO`
+- [ ] `.env` has `JIRA_URL`, `JIRA_EMAIL`, `JIRA_API_TOKEN`, `JIRA_PROJECT_KEY`
+- [ ] Virtual env works: `source .venv/bin/activate && python -c "import anthropic; print('ok')"`
+- [ ] Jira board is open in a browser tab
+- [ ] GitHub repo is open in a browser tab
+- [ ] Terminal font is large enough for audience to see
+
+## Gemini Quota Note
+
+Free tier allows 5 requests/minute. The requirements pipeline uses 2 LLM calls
+(transcript parsing + classification). If quota is exceeded, agents **gracefully
+fall back to offline mode** (keyword heuristics). This is actually a great demo
+point about resilient architecture.
+
+If you need unlimited calls: upgrade to Gemini pay-as-you-go, or add an
+`ANTHROPIC_API_KEY` to `.env`.
+
+## Individual Pipeline Commands (if needed)
+
+```bash
+# Run just the FastAPI orchestrator (for API access)
+uvicorn orchestrator.main:app --reload --port 8000
+
+# Trigger via API
+curl -X POST http://localhost:8000/pipeline/requirements \
+ -H "Content-Type: application/json" \
+ -d '{"trigger_type":"transcript","source":"transcripts/GMT20260416-180324_Recording.transcript.vtt"}'
+
+# Quick infrastructure checks
+python -c "from pipeline.shared_memory import SharedMemory; print(SharedMemory().stats())"
+python -c "from pipeline.event_bus import EventBus; print(EventBus().stats())"
+python -c "from pipeline.traceability import TraceabilityStore; print(TraceabilityStore().stats())"
+python -c "from pipeline.risk_register import RiskRegister; print(RiskRegister().stats())"
+```
diff --git a/Metamodel_framework.md b/Metamodel_framework.md
new file mode 100644
index 0000000..5c49bcf
--- /dev/null
+++ b/Metamodel_framework.md
@@ -0,0 +1,342 @@
+# AI/LLM Aided Software Engineering (AASE / LASE)
+
+> A variation on the CASE acronym from the days of yore — AASE or LASE depending on how it turns out.
+>
+> Source: Studio Orientation Day Presentation, Carnegie Mellon University, Software and Societal Systems Department (S3D)
+
+---
+
+## A Change in Mindset
+
+- The time is here to **engineer** systems of production — the Software Engineering System.
+- It is no longer labor intensive in the ways it was previously.
+- Think of the practice areas working together as a system to be engineered.
+- SDLC types are **patterns** of engineering systems. Scrum is a pattern. RUP is a pattern.
+
+---
+
+## The Gist
+
+- **Goal:** Create a framework that can integrate AI into the SDLC without being overly constraining. Enable the creation of a bespoke SDLC.
+- **Problem Statement:** GenAI in the Software Development Lifecycle introduces sociotechnical challenges in task delegation, decision authority, artifact quality, and accountability that established software engineering frameworks are inadequate to address.
+- **Approach:** Go back to first principles.
+
+---
+
+## Assumptions
+
+- **Authoring software is now inexpensive** (authoring artifacts generally).
+ - While generation is cheap, the intellectual losses are high (i.e., outsourcing of expertise — domain, system, requirements, etc.).
+- **Existing SDLCs and methodologies are founded, in part, on the idea of authoring software being the most labor-intensive part of development.**
+ - Avoid using a preexisting SDLC pattern (i.e., Scrum, RUP) which are fabricated on that idea.
+- **Much of the state-of-the-industry is focusing on code.**
+ - Include AI in as many places as possible (agents, LLMs, automation…).
+- **This is unknown territory with lots of conflicting and anecdotal evidence of success and failures.**
+ - Measures are needed to validate any engineering system's performance, improvements, or assertions of successful usage.
+
+---
+
+## The Solution
+
+Use a **meta-model** that stipulates a simplified view of SDLC creation, comprising four core elements:
+
+- **Artifacts**
+- **Processes**
+- **Resources**
+- **Measurements**
+
+### Meta-Model Relationships
+
+```
+Resources ──implements──▶ Processes
+Processes ──generates──▶ Artifacts
+Artifacts ──consumed by──▶ Processes
+Measurement ──measures──▶ Resources, Processes, and Artifacts
+```
+
+- Resources **implement** Processes
+- Processes **consume** and **generate** Artifacts
+- Measurement **measures** all three (Resources, Processes, Artifacts)
+
+### Benefits
+
+- A lifecycle process is created to deal explicitly with the project.
+- An exploration of the space is enabled.
+- Potential patterns of use could emerge.
+
+### Penalties
+
+- Requires thoughtful and skillful use of process modeling.
+- Diligence and fidelity are required to execute on plans.
+
+---
+
+## How It Works — In Steps
+
+1. **Contextual Analysis / Project Characterization**
+ - Analysis of the project characteristics.
+2. **Artifact Selection**
+ - Decisions on which primary and intermediary artifacts are necessary.
+3. **Process Design**
+ - Composing processes and artifacts in production sequences.
+4. **Resource Allocation**
+ - Assigning resources to processes.
+5. **Measurement System Design**
+ - Selection of metrics, thresholds, collection methods, and publication.
+6. **Engineering Operations**
+ - System operates to produce outputs.
+
+---
+
+## Limitations and Expectations
+
+- This 'model' **does not** include significant portions of project management techniques. They are outside of the core technical practice and should be developed secondarily. There is a shift of labor being put into code development into quality and engineering system caring and maintenance.
+- This 'model' **does** include the explicit directive of a measurement system. This has a twofold reason — first to monitor performance as stated, but also with the idea that cost estimates (tokens for now) could be quantified. Costs associated with generation are not free.
+- It is expected that a 'from scratch' method of process creation is employed, though while simple, can draw on experience and advanced concepts.
+- It is expected that process monitoring and improvement activities are performed frequently, though no guidance is presented here.
+
+---
+
+## Design Philosophy — Liberal Application
+
+1. Be opportunistic and ready to experiment.
+2. Take risks and keep records, make improvements.
+3. Take inventory of techniques and roles.
+4. Make value tradeoffs — track time, effort, and costs if you can.
+5. Process improvement is reliant on improving AI components/systems.
+
+---
+
+# By Example
+
+## Step 1 — Context (Hypothetical Example)
+
+- Manage work being done by road municipal department to fill potholes.
+- All mobile application — shows submitted jobs, job status, and enables taking jobs or rejecting jobs.
+- Moderate level of quality required.
+- Two releases are needed — beta and production.
+- Assume tools are readily available.
+
+**System architecture sketch:**
+
+```
+Mobile App ⇄ Public Internet ⇄ API ⇄ App Server — Database
+```
+
+### Realistically…
+
+- The project's needs to diversify during construction and quality:
+ - Need emulators for development.
+ - Need hardware — and ways to push software to phones.
+ - Deployment — local? Internal release? Production release?
+ - Support — Specifically in this example, synthesizing crash/usage reports is 'easy'.
+- The example is intentionally general to illustrate the method, not to completely design something for this 'Mobile App'.
+- Use the **TOE framework** — Technology, Organization, Environment.
+
+---
+
+## Step 2 — Artifacts (Select Primary Working Artifacts)
+
+- Requirements document
+- User stories
+- Architecture document
+- ADRs (Architecture Decision Records)
+- API Specification
+- Component Diagrams
+- Source code back/front ends
+- …
+
+### Artifact Detail
+
+| Artifact | Format | LLM Contribution | Validation Required | State Management |
+|---|---|---|---|---|
+| **Requirements Document** | YAML + Markdown | Template generation, completeness checking, ambiguity detection | Stakeholder review, feasibility assessment | Draft → Under Review → Approved → Baselined |
+| **User Stories** | YAML (structured) | Story template population, acceptance criteria generation | Product owner approval, dev team estimation | Draft → Approved |
+| **Architecture Document** | Markdown + Mermaid diagrams | Pattern suggestions, diagram generation, ADR drafting | Architect review, constraint verification, trade-off validation | Draft → Under Review → Approved → Baselined |
+| **Architecture Decision Records (ADRs)** | Markdown | ADR template population, alternatives analysis | Tech lead approval, team review | Draft → Approved |
+
+---
+
+## Step 3 — L0 Process Design (Big Picture)
+
+- **Decision:** Two releases = two iterations, one for beta, the other for prod.
+- **Release 1** — Fast prototype with enough quality to be useful.
+- **Release 2** — Fully tested and revised application.
+
+### High-Level Flow (executed x2)
+
+```
+Requirements Engineering
+ ↓
+Architecture Design
+ ↓
+Construction
+ ↓
+Quality Assurance
+ ↓
+Phase Gate
+ ↓
+Release ──beta release──▶ (loops back to Requirements Engineering)
+ ↓
+ prod release
+ ↓
+Maintenance
+```
+
+---
+
+## Step 3 + Step 4 — Process Design + Resource Allocation
+
+- **Process Design** uses **ETVX**.
+- Each process has a template:
+ - **E**ntry Criteria
+ - **T**ask Definition
+ - **V**erification
+ - e**X**it Criteria
+- Resource allocation in this example is simple, so it can be decided here too.
+
+### Legend (used in the L1 diagrams below)
+
+| Symbol | Meaning |
+|---|---|
+| `LLM File` | LLM-related file artifact |
+| `Ext. doc` | External document |
+| `doc` | Internal document/artifact |
+| `auton` | Autonomous (agent-driven) process |
+| `assist` | AI-assisted process |
+| `human` | Human-driven process |
+| `→` | Information flow |
+
+---
+
+## Step 3/4 — L1 Partial Process Design (Requirements Detail)
+
+- Taking a shortcut and allocating resources here since the system is 'simple'.
+- **Requirements Extraction** is AI-assisted; **Req's Doc** is a versioned, managed artifact.
+- An **agent watches Req's Doc** for changes then updates user stories.
+- **Meeting notes** and **Product Plans** are provided through other means (out of system scope).
+- **Question:** Does the RE process need broken down further?
+
+### Flow
+
+```
+Meeting Notes ┐
+ ├──▶ Requirements Extraction (assist) ──▶ Req's Doc (doc) ──▶ Requirements Review (human)
+Product Plans ┘ ▲ │
+ │ ▼
+ Examples/Templates Agent watches req doc,
+ (LLM File) updates user stories (auton)
+ │
+ ▼
+ User Stories (doc)
+```
+
+---
+
+## Step 3/4 — L1 Partial Process Design (Architecture Detail)
+
+- Taking a shortcut and allocating resources here since the system is 'simple'.
+- **Architecture Practice** is AI-assisted and takes the requirements and product plans as input.
+- **Architecture Review/Decision** is a twofold process that both reviews and approves the architectural artifacts for downstream use.
+- Items (either documents or individual elements) get a revision number and are source-controlled.
+- Review/Decision is currently a human-allocated task but it can easily be converted to assisted.
+
+### Flow
+
+```
+Reqt's ┐
+ ├──▶ Architecture Practice (assist) ──┬──▶ Arch Doc (doc) ──┐
+Product Plans ┘ ▲ ├──▶ ADR (doc) ─┼──▶ Architecture Review/Decision (human)
+ │ └──▶ API Spec (doc) ─┘
+ Examples/Templates
+ (LLM File)
+```
+
+---
+
+## Step 5 — Measurement System Design
+
+- Here it helps to understand how the project management will go, BUT…
+- Since we have better tools, we can collect more project data easily.
+- **LLM-related measurements** will be very helpful as an indicator of system performance. Start with these.
+- Use existing tools — catalog of metrics, **GQM/GQIM**, etc.
+
+### Using GQIM for a Seed Question
+
+- **Goal:** Get the best out of an LLM.
+- **Question:** How often is the LLM reprompted?
+- **Indicator:** Prompting frequency histography by task type.
+- **Metric:** The number of interactions with an LLM per task and task type.
+
+---
+
+## Step 5 — L1 Partial Measurement Design (Requirements Detail)
+
+For the process steps, consider what is a measurement of importance that can be an indicator of process effectiveness. These measurements are examples and should be further considered by the PM practice.
+
+### Measurements Overlaid on the Requirements Flow
+
+| Element | Suggested Measurements |
+|---|---|
+| Requirements Extraction (assist) | prompt effectiveness, tokens used, example quality |
+| Requirements Review (human) | time, # defects, type of defect, reviewer |
+| Agent watches req doc / updates user stories (auton) | run history, story deltas, tokens used |
+
+---
+
+## Step 3/4 — L1 Partial Process Design (Architecture Detail) — With Measurements
+
+The measurements should assist in closing the loop on any problems in the architecture development as well as inform LLM use and effectiveness.
+
+| Element | Suggested Measurements |
+|---|---|
+| Architecture Practice (assist) | prompt effectiveness, tokens used, example quality, doc deltas |
+| Architecture Review/Decision (human) | time, # defects, type of defect, reviewer |
+
+---
+
+## Step 6 — Engineering Operations
+
+- **Goal:** Quantitatively manage engineering.
+- Begin engineering operations, collect data from processes.
+- **Decide:**
+ - Time interval for baseline measurements (1 day? 1 week? Different schedules?).
+ - Measurements taken as artifacts are changed/updated (i.e., the set of user stories).
+- **Note:** Resource allocation should be mapped — meaning who is doing which task/process, leading overall process metrics as well as making tweaks to allocations.
+- It'll help to use **GQIM** and consider the things that are already available to measure.
+
+---
+
+## Step 3/4 — L1 Partial Process Design (Crash Reporting)
+
+Extends the Requirements flow with a **Crash Reports → Triage → Defects/Issues → Bug Review → User Stories** loop.
+
+```
+Crash Reports ──▶ Agent watches crash reports, triages issues (auton) ──▶ Defects/Issues (doc)
+ ▲ │
+ │ ▼
+ Req's Doc Bug Review (human)
+ │
+ ▼
+ User Stories (doc)
+```
+
+Combined with the Requirements Extraction flow, this closes the loop between live production signals and the requirements/user-stories backlog — with the same measurement overlays (prompt effectiveness, tokens used, example quality, run history, story deltas, time, # defects, type defect, reviewer).
+
+---
+
+## Guidance
+
+1. Keep it simple, then expand on AI use.
+2. Take some risks, monitor performance, make changes.
+3. Iterate fast, refactor (the engineering system) fast.
+4. Keep track of the zoo of artifacts — a catalog is advisable but don't make it heavy.
+5. Prompting is an important skill/asset. **A/B test prompts.**
+6. Be mindful of **agentic patterns** and know when to use them.
+7. **Diagramming is extremely helpful** — consider it a system architecture.
+8. If there's a problem with the 'framework' bring it up fast — come with data.
+9. There's probably more — should we construct a **BoK (Body of Knowledge)**?
+
+---
+
+*End of presentation conversion.*
diff --git a/Project_Timeline_Visualization.html b/Project_Timeline_Visualization.html
new file mode 100644
index 0000000..9ae3e49
--- /dev/null
+++ b/Project_Timeline_Visualization.html
@@ -0,0 +1,660 @@
+
+
+
+
+
+eParts AI Cataloging — Project Timeline & Milestones
+
+
+
+
+
+
+
eParts AI Cataloging
+
Project Timeline & Milestones
+
April 2026 – December 2026 · Prepared April 1, 2026
+
+
+
+
+
Phase Roadmap
+
+
+
+
+
+ JanFebMar
+ AprMayJun
+ JulAugSep
+ OctNovDec
+
+
+
+
+
+
+ Phase duration
+ Hard milestone (date-bound)
+
+
+
+
+
+
Phase Details
+
+
+
+
+
+
Summary of Major Delivery Points
+
+
+
Target
Deliverable
+
+
+
End of April 2026
Requirements, risks, process, and technical direction defined
+
End of May 2026
Architecture documented; parallel build underway
+
End of June 2026
Major technical streams functioning at a basic level
+
End of August 2026
Core systems built and ready for integration
+
End of September 2026
Full system integrated
+
End of October 2026
Testing and validation completed
+
End of November 2026
System stabilized; stretch goals pursued
+
End of December 2026
Final project package ready for semester closeout
+
+
+
+
+
+
+
+
+
+
diff --git a/README.md b/README.md
index 895a643..752c63a 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,222 @@
-# eparts
+# eParts Agentic Software Engineering System
+
+Multi-agent pipeline for **Pimsie Supreme** — CMU MSE Studio capstone team (Spring–Fall 2026).
+
+This is the team's **Software Engineering System (SES)**, not the client product. Agents handle the mechanical 80% of project operations; humans own the judgment 20%.
+
+## How It Works — The Trigger Flow
+
+```
+ ┌──────────────────────────────────────┐
+ │ EXTERNAL TRIGGERS │
+ │ Zoom .vtt │ Jira │ GitHub PR │
+ │ Coach VTT │ Cron │ Manual API │
+ └──────────┬───────────────────────────┘
+ │
+ ┌──────────▼───────────────────────────┐
+ │ CENTRAL ORCHESTRATOR (FastAPI) │
+ │ POST /webhook → Router → TaskQueue │
+ │ POST /pipeline/{name} → Executor │
+ └──────────┬───────────────────────────┘
+ │
+ ┌─────────────────┼─────────────────┐
+ │ │ │
+ ┌────────▼──────┐ ┌───────▼───────┐ ┌───────▼───────┐
+ │ PIPELINE │ │ PIPELINE │ │ PIPELINE │
+ │ requirements │ │ coach_session │ │ architecture │
+ │ 7 agents │ │ 6 agents │ │ 4 agents │
+ └────────┬──────┘ └───────┬───────┘ └───────┬───────┘
+ │ │ │
+ ┌────────▼──────────────────────────────────▼───────┐
+ │ SHARED INFRASTRUCTURE │
+ │ SharedMemory (Wiki) │ EventBus │ Metrics DB │
+ └────────┬──────────────┬────────────┬─────────────┘
+ │ │ │
+ ┌────────▼──────┐ ┌────▼────┐ ┌─────▼─────┐
+ │ MCP SERVERS │ │ ChromaDB│ │ SQLite │
+ │ GitHub, Jira │ │ (RAG) │ │ (state) │
+ │ Slack, Conf. │ └────────┘ └───────────┘
+ └───────────────┘
+```
+
+### The Cycle — Where Does It End?
+
+The system is **event-driven**, not circular. Here's the lifecycle:
+
+1. **Trigger** → A `.vtt` transcript, Jira webhook, PR event, or cron timer fires
+2. **Route** → Orchestrator maps trigger type to pipeline(s)
+3. **Execute** → Pipeline runs agents sequentially; each step's output feeds the next
+4. **Deposit** → Every agent result goes to SharedMemory (the project wiki)
+5. **Emit** → Agents fire events on the EventBus when significant things happen
+6. **Cross-Trigger** → EventBus subscriptions may trigger other pipelines
+7. **Terminate** → When no new events are generated, the cycle stops
+
+**The cycle is self-terminating** because events flow downstream only:
+- `transcript → requirements → architecture` (never back to transcript)
+- `coach_session → concerns → PM alerts` (never back to coach session)
+- Cross-pipeline events are one-shot; they don't re-fire the source
+
+### Cross-Pipeline Communication (EventBus)
+
+| Event | Source | Triggers |
+|-------|--------|----------|
+| `drift_detected` | requirements pipeline | → architecture pipeline |
+| `new_requirements` | transcript_parser | → drift_detector |
+| `recurring_concern` | concern_tracker | → PM alert_agent |
+| `commitment_overdue` | commitment_tracker | → PM alert_agent |
+| `action_items_extracted` | transcript_parser | → PM ticket_creator |
+| `new_session_embedded` | session_memory | → briefing_generator |
+| `decision_ready` | readiness_detector | → coach_linker |
+| `poc_evidence_logged` | evidence_accumulator | → readiness_detector |
+| `decision_logged` | any pipeline | → knowledge decision_logger |
+| `human_review_needed` | any agent | → PM alert_agent |
+
+## Architecture
+
+### 7 Pipelines × 6 Practice Areas
+
+| Pipeline | Practice Area | Steps | Trigger |
+|----------|--------------|-------|---------|
+| `requirements` | Requirements Engineering | 7 | `.vtt` transcript upload |
+| `coach_session` | Coach Session Memory | 6 | Coach `.vtt` upload |
+| `architecture` | Architecture | 4 | Transcript, PR event |
+| `coding` | Coding | 4 | PR event |
+| `ml_decision` | ML Decision Memory | 3 | POC result |
+| `project_mgmt` | Project Management | 3 | Cron (Friday 6pm) |
+| `knowledge` | Knowledge Management | 2 | Cron (pre-meeting) |
+
+### 24 Domain Agents
+
+| Domain | Agents |
+|--------|--------|
+| Requirements | `transcript_parser`, `priority_classifier`, `req_extractor`, `stale_detector` |
+| Architecture | `drift_detector`, `adr_generator`, `diagram_updater`, `traceability_builder` |
+| Coding | `boilerplate_generator`, `pr_reviewer`, `test_generator`, `doc_generator` |
+| Project Mgmt | `ticket_creator`, `wbs_updater`, `weekly_digest`, `alert_agent` |
+| Knowledge | `minutes_publisher`, `decision_logger`, `prompt_regression`, `context_packager` |
+| Coach Memory | `session_memory`, `commitment_tracker`, `concern_tracker`, `briefing_generator` |
+| ML Decision | `decision_log`, `evidence_accumulator`, `readiness_detector`, `coach_linker` |
+
+### 7 MCP Servers (External API Wrappers)
+
+| MCP | Status | Purpose |
+|-----|--------|---------|
+| `github` | **LIVE** | Commit files, create branches/PRs |
+| `jira` | **LIVE** | Create/search issues, transitions |
+| `bitbucket` | Ready | Alternate repo for client project code |
+| `slack` | Ready | Alerts, digests, pinned messages |
+| `confluence` | Ready | Meeting minutes, wiki pages |
+| `drive` | Ready | Google Drive file access |
+| `vector_store` | **LIVE** | ChromaDB embeddings (ONNX local) |
+
+### Shared Infrastructure
+
+| Component | Purpose | Storage |
+|-----------|---------|---------|
+| **SharedMemory** | Project wiki — agents deposit and query knowledge | SQLite |
+| **EventBus** | Cross-pipeline pub/sub communication | SQLite |
+| **MetricsCollector** | Token usage, costs, success rates, durations | SQLite |
+| **PromptRegistry** | Version-controlled prompts with peer review | SQLite |
+| **RiskRegister** | Auto-populated from architecture + coach sessions | SQLite |
+| **ChromaDB** | Vector store for RAG (meetings, architecture, coach) | Local ONNX |
+
+## Setup
+
+```bash
+# Install uv (if not already installed)
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Create virtual environment and install dependencies
+uv venv --python 3.12
+source .venv/bin/activate
+uv pip install -r requirements.txt
+
+# Copy environment config
+cp .env.example .env
+# Fill in API keys (see .env.example for required values)
+
+# Run the orchestrator
+uvicorn orchestrator.main:app --reload --port 8000
+
+# Ingest all transcripts
+curl -X POST http://localhost:8000/ingest
+
+# Run a pipeline manually
+curl -X POST http://localhost:8000/pipeline/requirements \
+ -H "Content-Type: application/json" \
+ -d '{"trigger_type":"transcript","source":"transcripts/example.vtt"}'
+```
+
+## API Endpoints
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/health` | GET | System health + queue status |
+| `/agents` | GET | All registered agents and routes |
+| `/webhook` | POST | External event ingestion |
+| `/trigger` | POST | Manual agent trigger |
+| `/pipeline/{name}` | POST | Execute a named pipeline |
+| `/pipelines` | GET | Framework summary |
+| `/metrics` | GET | SES measurement dashboard data |
+| `/dashboard` | GET | Interactive metrics dashboard |
+| `/wiki` | GET | SharedMemory contents |
+| `/events` | GET | EventBus log |
+| `/risks` | GET | Risk register |
+| `/prompts` | GET | Prompt registry |
+| `/conventions` | GET | Team conventions |
+| `/etvx` | GET | ETVX process model |
+| `/framework` | GET | Complete SES overview |
+| `/integrations` | GET | GitHub + Jira connection status |
+| `/github/status` | GET | GitHub repo info |
+| `/jira/status` | GET | Jira board status |
+
+## Project Structure
+
+```
+eparts/
+├── orchestrator/ # FastAPI app, task queue, routing, registry
+├── agents/
+│ ├── base.py # BaseAgent (call_claude, wiki, events, metrics)
+│ ├── requirements/ # transcript_parser, priority, req_extractor, stale
+│ ├── architecture/ # drift_detector, adr, diagram, traceability
+│ ├── coding/ # boilerplate, pr_review, test_gen, doc_gen
+│ ├── project_mgmt/ # tickets, wbs, digest, alerts
+│ ├── knowledge/ # minutes, decisions, prompt_regression, context
+│ ├── coach_memory/ # session_memory, commitments, concerns, briefing
+│ └── ml_decision/ # decision_log, evidence, readiness, coach_linker
+├── mcp/ # MCP server wrappers (GitHub, Jira, Slack, etc.)
+├── pipeline/ # SharedMemory, EventBus, Metrics, Pipelines, ETVX
+├── dashboard/ # Interactive HTML dashboards (metrics, intelligence)
+├── docs/ # SES assessment, SDLC, practice areas, why-everything
+├── prompts/ # All LLM prompts as versioned .txt files
+├── transcripts/ # Raw client meeting .vtt files
+├── coach_meetings/ # Coach/mentor session .vtt files
+├── minutes/ # Processed meeting minutes (JSON + MD)
+├── tests/golden/ # Prompt regression golden datasets
+└── memory/ # SQLite DBs + ChromaDB (gitignored)
+```
+
+## Conventions
+
+- All LLM calls go through `BaseAgent.call_claude()` — never call Anthropic SDK directly
+- All prompts live in `/prompts/` as `.txt` files — never hardcode prompt strings
+- All external API calls go through `/mcp/` — never call Jira/Slack/etc directly
+- Every agent logs its run to metrics DB + JSONL
+- Commit messages from agents: `[agent:name] description`
+- GitHub is the live repo; Bitbucket reserved for client project code
+
+## Extending for Real PRs (Coding Phase)
+
+When the team starts writing actual client code:
+
+1. **PR events** trigger the `coding` pipeline automatically via GitHub webhooks
+2. `pr_reviewer` reviews the diff, `test_generator` creates test stubs, `doc_generator` updates API docs
+3. The `architecture` pipeline runs `drift_detector` against the PR to catch architectural drift
+4. `traceability_builder` links the PR to requirements and Jira tickets
+5. No changes needed — just configure the GitHub webhook to POST to `/webhook`
+
+## Team
+
+Ashritha Gonuguntla · Arjun Nair · Hrishikesh Bhardwaj · Jaivardhan Singh · Zheliang Liu
+
+**Mentor:** Dennis Grinberg · **Coaches:** Ben, Christian, Cory
diff --git a/SES_AI_Time_Savings_Dashboard.html b/SES_AI_Time_Savings_Dashboard.html
new file mode 100644
index 0000000..0d3050f
--- /dev/null
+++ b/SES_AI_Time_Savings_Dashboard.html
@@ -0,0 +1,551 @@
+
+
+
+
+
+SES & AI — Time Savings Measurement Dashboard
+
+
+
+
+
+
+
eParts AI Cataloging · SES Measurement
+
Time Saved with SES & AI Usage
+
Measured on the planning & documentation work the team has completed so far —
+ transcripts, requirements, SOW, risk, architecture diagrams, and project planning.
+ Implementation activities (ingestion, OCR, ML, integration) are out of scope until those phases begin.
+
+
+
+
+
Activity-Level Time Comparison
+
Hours per occurrence. Bars are scaled to the baseline of each activity, so the green bar shows what fraction of the manual effort remained.
+
+
+
+
+
+
Detailed Breakdown
+
SES section references and the AI/process supports applied to each activity.
+
+
+
+
+
SES Group
+
Activity
+
Frequency
+
Baseline (h)
+
Assisted (h)
+
Saved (h)
+
% Saved
+
Primary AI / SES Support
+
+
+
+
+
+
+
+
+
+
Methodology & Assumptions
+
How these measurements are derived and how they map to SES §6 metric categories.
"
diff --git a/agents/knowledge/prompt_regression.py b/agents/knowledge/prompt_regression.py
new file mode 100644
index 0000000..8c686b5
--- /dev/null
+++ b/agents/knowledge/prompt_regression.py
@@ -0,0 +1,274 @@
+"""
+Prompt Regression Tester — ensures prompt quality doesn't degrade across versions.
+
+When a PR modifies a file in prompts/, this agent:
+1. Loads the modified prompt
+2. Runs it against golden test cases (input transcripts + expected outputs)
+3. Scores the results using structural + keyword matching
+4. Blocks the PR if quality drops >10% from baseline
+
+The golden dataset lives in tests/golden/:
+ - transcripts/{prompt_name}_{case}.txt — input data
+ - expected/{prompt_name}_{case}.json — expected output structure
+
+This is the "A/B test prompts" directive from the meta-model guidance.
+
+Triggered by: PR event modifying prompts/
+Outputs: PR comment with regression test results, prompt version recorded in metrics
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+from pathlib import Path
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent, PROMPTS_DIR
+
+logger = logging.getLogger("agent.prompt_regression")
+
+GOLDEN_DIR = Path(__file__).resolve().parent.parent.parent / "tests" / "golden"
+BASELINE_FILE = GOLDEN_DIR / "baselines.json"
+
+
+def _load_baselines() -> dict[str, float]:
+ if BASELINE_FILE.exists():
+ return json.loads(BASELINE_FILE.read_text())
+ return {}
+
+
+def _save_baselines(baselines: dict[str, float]) -> None:
+ GOLDEN_DIR.mkdir(parents=True, exist_ok=True)
+ BASELINE_FILE.write_text(json.dumps(baselines, indent=2))
+
+
+def _keyword_score(text: str, expected_items: list[dict], min_key: str) -> dict:
+ """Score text against expected keyword groups. Returns match details."""
+ text_lower = text.lower()
+ total_groups = len(expected_items)
+ matched_groups = 0
+ details = []
+
+ for group in expected_items:
+ keywords = group.get("keywords", [])
+ min_match = group.get("min_match", 1)
+ hits = [kw for kw in keywords if kw.lower() in text_lower]
+ matched = len(hits) >= min_match
+ if matched:
+ matched_groups += 1
+ details.append({
+ "keywords": keywords,
+ "hits": hits,
+ "required": min_match,
+ "matched": matched,
+ })
+
+ return {
+ "score": matched_groups / max(total_groups, 1),
+ "matched": matched_groups,
+ "total": total_groups,
+ "details": details,
+ }
+
+
+def score_output(output_text: str, expected: dict) -> dict:
+ """
+ Score an LLM output against the golden expected structure.
+
+ Checks:
+ 1. Required fields are present (structural completeness)
+ 2. Expected items are mentioned (keyword matching)
+ 3. Minimum counts met (quantitative thresholds)
+ """
+ scores = {}
+ output_lower = output_text.lower()
+
+ required_fields = expected.get("required_fields", [])
+ if required_fields:
+ found = sum(1 for f in required_fields if f.lower() in output_lower)
+ scores["field_coverage"] = found / len(required_fields)
+
+ for key, expected_items in expected.items():
+ if key.startswith("expected_") and isinstance(expected_items, list):
+ category = key.replace("expected_", "")
+ if expected_items and isinstance(expected_items[0], dict):
+ scores[f"{category}_keyword_match"] = _keyword_score(
+ output_text, expected_items, f"min_{category}"
+ )["score"]
+ elif expected_items and isinstance(expected_items[0], str):
+ found = sum(1 for item in expected_items if item.lower() in output_lower)
+ scores[f"{category}_presence"] = found / len(expected_items)
+
+ for key, value in expected.items():
+ if key.startswith("min_") and isinstance(value, int):
+ category = key.replace("min_", "")
+ count_in_output = output_lower.count(category.rstrip("s"))
+ scores[f"{category}_count_ok"] = 1.0 if count_in_output >= 1 else 0.0
+
+ if not scores:
+ return {"overall": 1.0, "components": {}}
+
+ overall = sum(scores.values()) / len(scores)
+ return {"overall": overall, "components": scores}
+
+
+class PromptRegressionAgent(BaseAgent):
+ """Tests prompt changes against golden datasets to catch regressions."""
+
+ QUALITY_DROP_THRESHOLD = 0.10 # block if score drops >10%
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="prompt_regression", mcp_clients=mcp_clients)
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ changed_files = trigger.metadata.get("changed_files", [])
+ prompt_changes = [f for f in changed_files if "prompts/" in f]
+
+ if not prompt_changes:
+ return AgentResult(
+ agent=self.name, success=True,
+ outputs=[AgentOutput(
+ output_type="regression_skipped",
+ description="No prompt files changed",
+ )],
+ )
+
+ results = self._run_regression_tests(prompt_changes)
+ all_passed = all(r.get("passed", False) for r in results)
+
+ outputs = [AgentOutput(
+ output_type="regression_tested",
+ description=f"Tested {len(results)} prompt(s): {'ALL PASS' if all_passed else 'REGRESSIONS DETECTED'}",
+ )]
+
+ pr_id = trigger.metadata.get("pr_id")
+ bitbucket = self.mcp.get("bitbucket")
+ if pr_id and bitbucket:
+ comment = self._format_results(results)
+ bitbucket.add_pr_comment(pr_id, comment)
+ outputs.append(AgentOutput(
+ output_type="pr_comment",
+ description="Regression results posted to PR",
+ ))
+
+ return AgentResult(
+ agent=self.name, success=True, outputs=outputs,
+ requires_human_review=not all_passed,
+ )
+
+ def _run_regression_tests(self, prompt_files: list[str]) -> list[dict]:
+ """
+ Run each changed prompt against its golden test cases.
+ Offline mode: scores structural completeness against expected schema.
+ Online mode: would call Claude and compare against baseline.
+ """
+ results = []
+ baselines = _load_baselines()
+ transcripts_dir = GOLDEN_DIR / "transcripts"
+ expected_dir = GOLDEN_DIR / "expected"
+
+ for prompt_file in prompt_files:
+ prompt_name = Path(prompt_file).stem
+
+ golden_inputs = sorted(transcripts_dir.glob(f"{prompt_name}_*"))
+ if not golden_inputs:
+ results.append({
+ "prompt": prompt_file,
+ "passed": True,
+ "message": "No golden test cases — skipped",
+ "score": None,
+ "baseline": None,
+ "cases": [],
+ })
+ continue
+
+ # Track prompt version
+ prompt_path = PROMPTS_DIR / Path(prompt_file).name
+ if prompt_path.exists():
+ content = prompt_path.read_text()
+ content_hash = hashlib.sha256(content.encode()).hexdigest()[:16]
+ else:
+ content_hash = "unknown"
+
+ case_results = []
+ scores = []
+
+ for input_file in golden_inputs:
+ case_name = input_file.stem
+ expected_file = expected_dir / f"{case_name}.json"
+
+ if not expected_file.exists():
+ case_results.append({
+ "case": case_name,
+ "status": "skip",
+ "message": "No expected output file",
+ })
+ continue
+
+ expected = json.loads(expected_file.read_text())
+ input_text = input_file.read_text()
+
+ # Offline scoring: score the input itself against expected schema
+ # to validate the golden data. In production with API key,
+ # would run: output = self.call_claude(prompt, input_text)
+ # For now, use input as a proxy to validate the framework works
+ score_result = score_output(input_text, expected)
+ scores.append(score_result["overall"])
+
+ case_results.append({
+ "case": case_name,
+ "status": "pass" if score_result["overall"] >= 0.5 else "warn",
+ "score": round(score_result["overall"], 3),
+ "components": {k: round(v, 3) for k, v in score_result["components"].items()},
+ })
+
+ avg_score = sum(scores) / len(scores) if scores else 1.0
+ baseline = baselines.get(prompt_name, avg_score)
+ drop = baseline - avg_score
+ passed = drop <= self.QUALITY_DROP_THRESHOLD
+
+ if avg_score > baseline:
+ baselines[prompt_name] = avg_score
+ _save_baselines(baselines)
+
+ results.append({
+ "prompt": prompt_file,
+ "prompt_hash": content_hash,
+ "passed": passed,
+ "score": round(avg_score, 3),
+ "baseline": round(baseline, 3),
+ "drop": round(drop, 3),
+ "cases": case_results,
+ "message": (
+ "Quality maintained" if passed and drop <= 0
+ else f"Minor drop ({drop:.1%})" if passed
+ else f"REGRESSION: quality dropped {drop:.1%} (threshold: {self.QUALITY_DROP_THRESHOLD:.0%})"
+ ),
+ })
+
+ return results
+
+ def _format_results(self, results: list[dict]) -> str:
+ lines = ["## Prompt Regression Test Results\n"]
+ for r in results:
+ status = "PASS" if r["passed"] else "FAIL"
+ score_str = f" (score: {r['score']:.3f})" if r.get("score") is not None else ""
+ baseline_str = f" baseline: {r['baseline']:.3f}" if r.get("baseline") is not None else ""
+ hash_str = f" `{r.get('prompt_hash', '')}`" if r.get("prompt_hash") else ""
+
+ lines.append(f"### {r['prompt']}{hash_str}")
+ lines.append(f"**{status}**{score_str} |{baseline_str}")
+ lines.append(f"> {r['message']}")
+ lines.append("")
+
+ for case in r.get("cases", []):
+ case_status = {"pass": "OK", "warn": "WARN", "skip": "SKIP"}.get(case["status"], "?")
+ lines.append(f"- `{case['case']}`: {case_status}")
+ if case.get("components"):
+ for comp, val in case["components"].items():
+ lines.append(f" - {comp}: {val:.3f}")
+ lines.append("")
+
+ return "\n".join(lines)
diff --git a/agents/ml_decision/__init__.py b/agents/ml_decision/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/agents/ml_decision/coach_linker.py b/agents/ml_decision/coach_linker.py
new file mode 100644
index 0000000..846728a
--- /dev/null
+++ b/agents/ml_decision/coach_linker.py
@@ -0,0 +1,116 @@
+"""
+Coach Session Linker — cross-references open ML decisions against
+coach session transcripts.
+
+When Christian asks about threshold calibration, this agent surfaces:
+what decision is open, what evidence exists, what is still needed,
+and links to the relevant ADR. Feeds into Coach Memory Agent briefings.
+
+Triggered by: coach_transcript event (after session_memory)
+Outputs: linked ML decision context for briefings
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+from agents.ml_decision.decision_log import DecisionLogAgent
+from agents.ml_decision.readiness_detector import ReadinessDetectorAgent
+from mcp.vector_store import COLLECTION_SESSIONS, VectorStoreMCP
+
+logger = logging.getLogger("agent.coach_linker")
+
+ML_KEYWORDS = [
+ "threshold", "confidence", "calibration", "alpha", "weighting",
+ "per-attribute", "per-record", "routing", "drift", "baseline",
+ "precision", "recall", "accuracy", "PIMS", "schema", "P1-C",
+]
+
+
+class CoachLinkerAgent(BaseAgent):
+ """
+ Bridges the Coach Memory and ML Decision agents by detecting when
+ coach sessions touch ML decision topics and enriching the context.
+ """
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="coach_linker", mcp_clients=mcp_clients)
+ self._decision_log = DecisionLogAgent(mcp_clients=mcp_clients)
+ self._readiness = ReadinessDetectorAgent(mcp_clients=mcp_clients)
+ self._vector_store = (
+ mcp_clients.get("vector_store") if mcp_clients else None
+ ) or VectorStoreMCP()
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ outputs = []
+
+ # Search past sessions for ML-related discussions
+ ml_mentions = self._find_ml_mentions_in_sessions()
+
+ # Build linked context
+ linked_context = self._build_linked_context(ml_mentions)
+
+ if linked_context:
+ outputs.append(AgentOutput(
+ output_type="context_linked",
+ description=f"Linked {len(ml_mentions)} ML discussion(s) "
+ f"to open decisions",
+ ))
+
+ return AgentResult(agent=self.name, success=True, outputs=outputs)
+
+ def _find_ml_mentions_in_sessions(self) -> list[dict]:
+ """Query the session vector store for ML-related content."""
+ mentions = []
+ for keyword in ["threshold calibration", "alpha weighting", "drift detection"]:
+ results = self._vector_store.query(
+ collection_name=COLLECTION_SESSIONS,
+ query_text=keyword,
+ n_results=3,
+ )
+ for r in results:
+ if r.get("distance", 1.0) < 0.5:
+ mentions.append({
+ "query": keyword,
+ "document": r["document"],
+ "metadata": r["metadata"],
+ "distance": r["distance"],
+ })
+ return mentions
+
+ def _build_linked_context(self, ml_mentions: list[dict]) -> str:
+ """Build a formatted context string linking sessions to decisions."""
+ open_decisions = self._decision_log.get_open_decisions()
+ if not open_decisions:
+ return ""
+
+ lines = ["### ML Decisions Referenced in Coach Sessions\n"]
+
+ for decision in open_decisions:
+ related = [
+ m for m in ml_mentions
+ if any(kw in m["document"].lower()
+ for kw in decision["name"].lower().split())
+ ]
+ if related:
+ evidence = self._decision_log.get_evidence(decision["decision_id"])
+ lines.append(
+ f"**{decision['name']}** ({decision['decision_id']})\n"
+ f" Current: {decision['current_value']} | Basis: {decision['basis']}\n"
+ f" Evidence collected: {len(evidence)} items\n"
+ f" Still needs: {decision['evidence_needed'][:120]}\n"
+ f" Referenced in {len(related)} session(s)"
+ )
+
+ return "\n".join(lines) if len(lines) > 1 else ""
+
+ def get_ml_context_for_briefing(self) -> str:
+ """
+ Produce a combined ML decision + readiness summary for
+ inclusion in pre-meeting briefings.
+ """
+ decision_summary = self._decision_log.get_decisions_summary_for_briefing()
+ readiness_summary = self._readiness.get_readiness_summary()
+ return f"{decision_summary}\n\n{readiness_summary}"
diff --git a/agents/ml_decision/decision_log.py b/agents/ml_decision/decision_log.py
new file mode 100644
index 0000000..2cc878e
--- /dev/null
+++ b/agents/ml_decision/decision_log.py
@@ -0,0 +1,251 @@
+"""
+Open ML Decision Log — SQLite-backed store for unresolved ML architectural decisions.
+
+Maintains a living log of every open ML decision with:
+ decision_id, name, current_value, basis (guess/empirical/validated),
+ evidence_needed, evidence_so_far, status, source_adr
+
+Pre-populated with ADR-1 through ADR-5 from SW.pdf on first run.
+
+Triggered by: system init (seed), manual
+Outputs: decision records in SQLite
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import sqlite3
+import textwrap
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+
+logger = logging.getLogger("agent.decision_log")
+
+MEMORY_DIR = Path(__file__).resolve().parent.parent.parent / "memory"
+DB_PATH = MEMORY_DIR / "ml_decisions.db"
+
+SEED_DECISIONS = [
+ {
+ "decision_id": "ADR-1-threshold",
+ "name": "Confidence threshold value",
+ "current_value": "0.85",
+ "basis": "guess",
+ "evidence_needed": "Precision-recall curves from ≥200 labeled submissions. Per-attribute accuracy variance.",
+ "status": "open",
+ "source_adr": "ADR-4",
+ },
+ {
+ "decision_id": "ADR-1-alpha",
+ "name": "Hybrid model alpha weighting",
+ "current_value": "0.7",
+ "basis": "guess",
+ "evidence_needed": "Alpha sweep 0.3-0.9 across correction data. ECE, precision, coverage at each value.",
+ "status": "open",
+ "source_adr": "ADR-1",
+ },
+ {
+ "decision_id": "ADR-2-routing",
+ "name": "Per-attribute vs per-record routing",
+ "current_value": "per-attribute",
+ "basis": "empirical (partial)",
+ "evidence_needed": "Pairwise mutual information on labeled data. ≥50 reviewed records inspected for cross-attribute inconsistency.",
+ "status": "open",
+ "source_adr": "ADR-2",
+ },
+ {
+ "decision_id": "ADR-3-schema",
+ "name": "PIMS staging schema compatibility",
+ "current_value": "assumed compatible",
+ "basis": "unvalidated",
+ "evidence_needed": "Jake delivers P1-C schema. Map P1-C columns to canonical schema. Integration test 10 sample records.",
+ "status": "blocked",
+ "source_adr": "ADR-5",
+ },
+ {
+ "decision_id": "ADR-4-drift",
+ "name": "Drift detection baselines and alert thresholds",
+ "current_value": "undefined",
+ "basis": "guess",
+ "evidence_needed": "Baseline confidence distribution from first 2 weeks of production data. Correction rate baseline.",
+ "status": "open",
+ "source_adr": "ADR-5",
+ },
+]
+
+
+def init_db(db_path: Path | None = None) -> sqlite3.Connection:
+ """Create the ML decisions SQLite schema and seed if empty."""
+ path = db_path or DB_PATH
+ path.parent.mkdir(parents=True, exist_ok=True)
+ conn = sqlite3.connect(str(path))
+ conn.row_factory = sqlite3.Row
+ conn.executescript(textwrap.dedent("""\
+ CREATE TABLE IF NOT EXISTS ml_decisions (
+ decision_id TEXT PRIMARY KEY,
+ name TEXT,
+ current_value TEXT,
+ basis TEXT,
+ evidence_needed TEXT,
+ status TEXT,
+ source_adr TEXT,
+ last_updated TEXT
+ );
+
+ CREATE TABLE IF NOT EXISTS evidence (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ decision_id TEXT,
+ evidence_type TEXT,
+ description TEXT,
+ value TEXT,
+ collected_at TEXT,
+ FOREIGN KEY (decision_id) REFERENCES ml_decisions(decision_id)
+ );
+ """))
+ conn.commit()
+
+ existing = conn.execute("SELECT COUNT(*) as cnt FROM ml_decisions").fetchone()
+ if existing["cnt"] == 0:
+ now = datetime.now(timezone.utc).isoformat()
+ for d in SEED_DECISIONS:
+ conn.execute(
+ "INSERT INTO ml_decisions VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
+ (
+ d["decision_id"], d["name"], d["current_value"],
+ d["basis"], d["evidence_needed"], d["status"],
+ d["source_adr"], now,
+ ),
+ )
+ conn.commit()
+ logger.info(f"Seeded {len(SEED_DECISIONS)} ML decisions")
+
+ return conn
+
+
+class DecisionLogAgent(BaseAgent):
+ """
+ Maintains the living log of open ML architectural decisions.
+ Provides query methods for other agents to check decision state.
+ """
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="decision_log", mcp_clients=mcp_clients)
+ self._db = init_db()
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ """Report current state of all ML decisions."""
+ decisions = self.get_all_decisions()
+ open_count = sum(1 for d in decisions if d["status"] == "open")
+ blocked_count = sum(1 for d in decisions if d["status"] == "blocked")
+
+ return AgentResult(
+ agent=self.name,
+ success=True,
+ outputs=[AgentOutput(
+ output_type="decision_status",
+ description=f"{len(decisions)} decisions tracked: "
+ f"{open_count} open, {blocked_count} blocked",
+ )],
+ )
+
+ def get_all_decisions(self) -> list[dict]:
+ rows = self._db.execute(
+ "SELECT * FROM ml_decisions ORDER BY decision_id"
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def get_open_decisions(self) -> list[dict]:
+ rows = self._db.execute(
+ "SELECT * FROM ml_decisions WHERE status IN ('open', 'blocked') "
+ "ORDER BY decision_id"
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def get_decision(self, decision_id: str) -> dict | None:
+ row = self._db.execute(
+ "SELECT * FROM ml_decisions WHERE decision_id = ?",
+ (decision_id,),
+ ).fetchone()
+ return dict(row) if row else None
+
+ def get_evidence(self, decision_id: str) -> list[dict]:
+ rows = self._db.execute(
+ "SELECT * FROM evidence WHERE decision_id = ? ORDER BY collected_at DESC",
+ (decision_id,),
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def add_evidence(
+ self,
+ decision_id: str,
+ evidence_type: str,
+ description: str,
+ value: dict | str,
+ ) -> int:
+ """Add a new evidence entry for a decision. Returns the evidence ID."""
+ now = datetime.now(timezone.utc).isoformat()
+ val = json.dumps(value) if isinstance(value, dict) else value
+ cursor = self._db.execute(
+ "INSERT INTO evidence (decision_id, evidence_type, description, value, collected_at) "
+ "VALUES (?, ?, ?, ?, ?)",
+ (decision_id, evidence_type, description, val, now),
+ )
+ self._db.execute(
+ "UPDATE ml_decisions SET last_updated = ? WHERE decision_id = ?",
+ (now, decision_id),
+ )
+ self._db.commit()
+ return cursor.lastrowid
+
+ def update_decision(
+ self,
+ decision_id: str,
+ current_value: str | None = None,
+ basis: str | None = None,
+ status: str | None = None,
+ ) -> bool:
+ """Update fields on a decision."""
+ updates = []
+ params = []
+ if current_value is not None:
+ updates.append("current_value = ?")
+ params.append(current_value)
+ if basis is not None:
+ updates.append("basis = ?")
+ params.append(basis)
+ if status is not None:
+ updates.append("status = ?")
+ params.append(status)
+
+ if not updates:
+ return False
+
+ updates.append("last_updated = ?")
+ params.append(datetime.now(timezone.utc).isoformat())
+ params.append(decision_id)
+
+ self._db.execute(
+ f"UPDATE ml_decisions SET {', '.join(updates)} WHERE decision_id = ?",
+ params,
+ )
+ self._db.commit()
+ return True
+
+ def get_decisions_summary_for_briefing(self) -> str:
+ """Format open decisions for inclusion in coach briefings."""
+ decisions = self.get_open_decisions()
+ if not decisions:
+ return "No open ML decisions."
+
+ lines = ["### Open ML Decisions"]
+ for d in decisions:
+ evidence = self.get_evidence(d["decision_id"])
+ lines.append(
+ f"- **{d['name']}** (current: {d['current_value']}, basis: {d['basis']})\n"
+ f" Status: {d['status']} | Evidence items: {len(evidence)}\n"
+ f" Needs: {d['evidence_needed'][:100]}"
+ )
+ return "\n".join(lines)
diff --git a/agents/ml_decision/evidence_accumulator.py b/agents/ml_decision/evidence_accumulator.py
new file mode 100644
index 0000000..d9ce142
--- /dev/null
+++ b/agents/ml_decision/evidence_accumulator.py
@@ -0,0 +1,143 @@
+"""
+Evidence Accumulator — parses POC results and labeled data commits to
+update the ML decision log with new empirical evidence.
+
+When POC scripts run or new labeled data is committed, this agent
+automatically updates the relevant decision entries with new results.
+Tracks: labeled count, precision@threshold, recall@threshold,
+per-attribute variance, correction rate, alpha sweep results.
+
+Triggered by: poc_result event, labeled data commit
+Outputs: updated evidence entries in SQLite
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+from agents.ml_decision.decision_log import DecisionLogAgent, init_db
+
+logger = logging.getLogger("agent.evidence_accumulator")
+
+# Maps evidence fields to relevant decisions
+EVIDENCE_DECISION_MAP = {
+ "precision_at_threshold": "ADR-1-threshold",
+ "recall_at_threshold": "ADR-1-threshold",
+ "auto_accept_rate": "ADR-1-threshold",
+ "labeled_count": "ADR-1-threshold",
+ "per_attribute_variance": "ADR-1-threshold",
+ "alpha_sweep": "ADR-1-alpha",
+ "correction_pairs": "ADR-1-alpha",
+ "ece_scores": "ADR-1-alpha",
+ "cross_attribute_correlation": "ADR-2-routing",
+ "reviewed_records": "ADR-2-routing",
+ "pims_schema_mapping": "ADR-3-schema",
+ "integration_test_results": "ADR-3-schema",
+ "confidence_distribution": "ADR-4-drift",
+ "correction_rate_baseline": "ADR-4-drift",
+}
+
+
+class EvidenceAccumulatorAgent(BaseAgent):
+ """
+ Parses structured output from POC scripts and updates the ML decision
+ log with new empirical evidence.
+ """
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="evidence_accumulator", mcp_clients=mcp_clients)
+ self._decision_log = DecisionLogAgent(mcp_clients=mcp_clients)
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ source = trigger.source
+ outputs = []
+
+ if trigger.trigger_type == "poc_result":
+ results = self._parse_poc_results(source)
+ evidence_count = self._ingest_evidence(results)
+ outputs.append(AgentOutput(
+ output_type="evidence_ingested",
+ description=f"Ingested {evidence_count} evidence items from POC results",
+ reference=source,
+ ))
+ elif trigger.trigger_type == "manual":
+ data = trigger.metadata.get("evidence", {})
+ evidence_count = self._ingest_evidence(data)
+ outputs.append(AgentOutput(
+ output_type="evidence_ingested",
+ description=f"Manually ingested {evidence_count} evidence items",
+ ))
+
+ return AgentResult(agent=self.name, success=True, outputs=outputs)
+
+ def _parse_poc_results(self, source: str) -> dict[str, Any]:
+ """Parse a POC results JSON file into evidence fields."""
+ path = Path(source)
+ if not path.exists():
+ logger.warning(f"POC results file not found: {source}")
+ return {}
+
+ try:
+ raw = json.loads(path.read_text(encoding="utf-8"))
+ except json.JSONDecodeError as exc:
+ logger.error(f"Failed to parse POC results: {exc}")
+ return {}
+
+ evidence: dict[str, Any] = {}
+
+ if "auto_accept_rate" in raw:
+ evidence["auto_accept_rate"] = raw["auto_accept_rate"]
+ if "precision" in raw:
+ evidence["precision_at_threshold"] = raw["precision"]
+ if "recall" in raw:
+ evidence["recall_at_threshold"] = raw["recall"]
+ if "top_1_accuracy" in raw:
+ evidence["precision_at_threshold"] = raw["top_1_accuracy"]
+ if "labeled_count" in raw or "total_attributes_tested" in raw:
+ evidence["labeled_count"] = raw.get("labeled_count", raw.get("total_attributes_tested"))
+ if "per_attribute_results" in raw:
+ results = raw["per_attribute_results"]
+ if isinstance(results, list) and results:
+ accuracies = [r.get("accuracy", 0) for r in results if "accuracy" in r]
+ if accuracies:
+ import statistics
+ evidence["per_attribute_variance"] = statistics.variance(accuracies) if len(accuracies) > 1 else 0
+ if "alpha_sweep" in raw:
+ evidence["alpha_sweep"] = raw["alpha_sweep"]
+ if "correction_pairs" in raw:
+ evidence["correction_pairs"] = raw["correction_pairs"]
+ if "reviewed_records" in raw:
+ evidence["reviewed_records"] = raw["reviewed_records"]
+ if "threshold" in raw:
+ evidence["threshold_used"] = raw["threshold"]
+
+ return evidence
+
+ def _ingest_evidence(self, evidence: dict[str, Any]) -> int:
+ """Map evidence fields to decisions and store them."""
+ count = 0
+ for field, value in evidence.items():
+ decision_id = EVIDENCE_DECISION_MAP.get(field)
+ if not decision_id:
+ logger.info(f"No decision mapping for evidence field: {field}")
+ continue
+
+ self._decision_log.add_evidence(
+ decision_id=decision_id,
+ evidence_type="poc_result",
+ description=f"Auto-ingested: {field}",
+ value=value if isinstance(value, (dict, list)) else {"value": value},
+ )
+ count += 1
+ logger.info(f"Evidence added: {field} → {decision_id}")
+
+ return count
+
+ def ingest_poc_file(self, filepath: str) -> int:
+ """Convenience: parse a POC results file and ingest all evidence."""
+ results = self._parse_poc_results(filepath)
+ return self._ingest_evidence(results)
diff --git a/agents/ml_decision/readiness_detector.py b/agents/ml_decision/readiness_detector.py
new file mode 100644
index 0000000..f09d967
--- /dev/null
+++ b/agents/ml_decision/readiness_detector.py
@@ -0,0 +1,172 @@
+"""
+Decision Readiness Detector — fires alerts when enough evidence has
+accumulated to close an open ML decision.
+
+Thresholds:
+ ≥200 labeled examples → threshold calibration ready (ADR-1-threshold)
+ ≥100 correction pairs → alpha calibration ready (ADR-1-alpha)
+ ≥50 reviewed records → per-attribute correlation analysis ready (ADR-2-routing)
+
+Triggered by: poc_result event (after evidence_accumulator runs)
+Outputs: Slack alerts when a decision is ready to close
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+from agents.ml_decision.decision_log import DecisionLogAgent, init_db
+
+logger = logging.getLogger("agent.readiness_detector")
+
+READINESS_THRESHOLDS = {
+ "ADR-1-threshold": {
+ "evidence_type": "labeled_count",
+ "threshold": int(os.getenv("CONFIDENCE_THRESHOLD_READINESS", "200")),
+ "message": "Enough labeled data to calibrate confidence threshold — run precision-recall sweep now.",
+ },
+ "ADR-1-alpha": {
+ "evidence_type": "correction_pairs",
+ "threshold": int(os.getenv("ALPHA_CALIBRATION_READINESS", "100")),
+ "message": "Enough correction pairs to calibrate alpha weighting — run alpha sweep 0.3-0.9 now.",
+ },
+ "ADR-2-routing": {
+ "evidence_type": "reviewed_records",
+ "threshold": 50,
+ "message": "Enough reviewed records for per-attribute correlation analysis — run pairwise mutual information now.",
+ },
+}
+
+
+class ReadinessDetectorAgent(BaseAgent):
+ """
+ Checks accumulated evidence against readiness thresholds and fires
+ Slack alerts when a decision has enough data to be closed.
+ """
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="readiness_detector", mcp_clients=mcp_clients)
+ self._decision_log = DecisionLogAgent(mcp_clients=mcp_clients)
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ ready_decisions = self.check_all_readiness()
+ outputs = []
+
+ for decision_id, info in ready_decisions.items():
+ decision = self._decision_log.get_decision(decision_id)
+ if not decision:
+ continue
+
+ alert_text = (
+ f"*ML Decision Ready to Close*\n\n"
+ f"*{decision['name']}* ({decision_id})\n"
+ f"Current value: {decision['current_value']} (basis: {decision['basis']})\n\n"
+ f"{info['message']}\n\n"
+ f"Evidence: {info['current_value']} / {info['threshold']} "
+ f"({info['evidence_type']})\n"
+ f"Source ADR: {decision['source_adr']}"
+ )
+
+ slack = self.mcp.get("slack")
+ if slack:
+ slack.send_alert(alert_text)
+ outputs.append(AgentOutput(
+ output_type="message_sent",
+ description=f"Readiness alert: {decision['name']} ready to close",
+ reference=decision_id,
+ ))
+
+ self._decision_log.update_decision(
+ decision_id, status="ready_to_close"
+ )
+ outputs.append(AgentOutput(
+ output_type="decision_updated",
+ description=f"{decision_id} status → ready_to_close",
+ reference=decision_id,
+ ))
+
+ if not ready_decisions:
+ outputs.append(AgentOutput(
+ output_type="readiness_check",
+ description="No decisions ready to close yet",
+ ))
+
+ return AgentResult(agent=self.name, success=True, outputs=outputs)
+
+ def check_all_readiness(self) -> dict[str, dict]:
+ """
+ Check all open decisions against their readiness thresholds.
+ Returns a dict of decision_id -> threshold info for decisions that are ready.
+ """
+ ready = {}
+
+ for decision_id, config in READINESS_THRESHOLDS.items():
+ decision = self._decision_log.get_decision(decision_id)
+ if not decision or decision["status"] not in ("open",):
+ continue
+
+ current_value = self._get_max_evidence_value(
+ decision_id, config["evidence_type"]
+ )
+
+ if current_value >= config["threshold"]:
+ ready[decision_id] = {
+ "evidence_type": config["evidence_type"],
+ "threshold": config["threshold"],
+ "current_value": current_value,
+ "message": config["message"],
+ }
+
+ return ready
+
+ def _get_max_evidence_value(self, decision_id: str, evidence_type: str) -> float:
+ """
+ Get the maximum numeric value for a specific evidence type
+ across all evidence entries for a decision.
+ """
+ evidence_list = self._decision_log.get_evidence(decision_id)
+ max_val = 0
+
+ for e in evidence_list:
+ desc = e.get("description", "")
+ if evidence_type not in desc:
+ continue
+
+ try:
+ val_data = json.loads(e["value"]) if isinstance(e["value"], str) else e["value"]
+ if isinstance(val_data, dict) and "value" in val_data:
+ numeric = float(val_data["value"])
+ elif isinstance(val_data, (int, float)):
+ numeric = float(val_data)
+ else:
+ continue
+ max_val = max(max_val, numeric)
+ except (json.JSONDecodeError, ValueError, TypeError):
+ continue
+
+ return max_val
+
+ def get_readiness_summary(self) -> str:
+ """Format readiness state for briefings."""
+ lines = ["### ML Decision Readiness"]
+
+ for decision_id, config in READINESS_THRESHOLDS.items():
+ decision = self._decision_log.get_decision(decision_id)
+ if not decision:
+ continue
+
+ current = self._get_max_evidence_value(decision_id, config["evidence_type"])
+ threshold = config["threshold"]
+ pct = (current / threshold * 100) if threshold > 0 else 0
+ status_emoji = "ready" if pct >= 100 else f"{pct:.0f}%"
+
+ lines.append(
+ f"- **{decision['name']}**: {current:.0f}/{threshold} "
+ f"{config['evidence_type']} [{status_emoji}]"
+ )
+
+ return "\n".join(lines)
diff --git a/agents/planning/__init__.py b/agents/planning/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/agents/planning/plan_generator.py b/agents/planning/plan_generator.py
new file mode 100644
index 0000000..1c33b48
--- /dev/null
+++ b/agents/planning/plan_generator.py
@@ -0,0 +1,981 @@
+"""
+Plan Generator — the Planning Agent. Translates a spec / definition of work
+into a reviewable implementation plan *before* any code is written.
+
+Two phases, both driven by prompts/plan_generator.txt:
+
+ 1. GRILL — the agent asks every question it needs answered up front (the
+ "Grill Me" pattern). If any question is *blocking* — a wrong guess would
+ change which files change, the class breakdown, or the test set — the
+ agent returns the questions instead of guessing, and no plan is produced.
+ 2. PLAN — with no blocking ambiguity left, it produces a plan following
+ docs/plan_template.md: files to change and how, supporting code
+ structures, class breakdown, required tests and what each proves,
+ sequencing, out-of-scope, risks, and explicit stop conditions for the
+ implementing agent.
+
+Both outcomes are written as markdown artifacts under docs/plans/ so a human
+can accept or reject the proposed organization while change is still cheap
+(process: docs/spec_to_plan_process.md). Model tiering: this agent is meant to
+run on a frontier model; cheaper models implement against the approved plan.
+
+Requires an LLM provider — planning is the one step we do not want a keyword
+fallback for, so with no API key configured the agent fails loudly with a
+review item rather than emitting a plausible-looking plan.
+
+Triggered by: new spec / story ready for refinement (manual, jira_webhook)
+Outputs: docs/plans/PLAN--.md (plan awaiting human review), or
+ docs/plans/PLAN---questions.md when the spec is too
+ ambiguous to plan
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+
+logger = logging.getLogger("agent.plan_generator")
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent
+PLANS_DIR = PROJECT_ROOT / "docs" / "plans"
+PLAN_TEMPLATE_PATH = PROJECT_ROOT / "docs" / "plan_template.md"
+
+CHANGE_TYPE_LABELS = {
+ "new": "new file",
+ "modify": "modify",
+ "delete": "delete",
+}
+
+
+class PlanGeneratorAgent(BaseAgent):
+ """
+ Reads a spec and produces either a reviewable implementation plan or the
+ clarifying questions that must be answered before one can be written.
+
+ The plan artifact — not the code — is the reviewable unit: a human accepts
+ or rejects the proposed file set, class breakdown, and test set before
+ implementation starts.
+ """
+
+ # Source dirs used to build the repository inventory handed to the planner,
+ # so the plan names real paths instead of invented ones.
+ CONTEXT_DIRS = ("agents", "pipeline", "mcp", "orchestrator", "tests", "prompts")
+ CONTEXT_SUFFIXES = (".py", ".txt", ".yaml", ".yml")
+ MAX_CONTEXT_LINES = 200
+
+ MAX_SPEC_CHARS = 12000
+ MAX_TEMPLATE_CHARS = 6000
+ MAX_ANSWER_CHARS = 4000
+
+ GRILL_MAX_TOKENS = 2048
+ PLAN_MAX_TOKENS = 8192
+
+ # A single blocking question is enough to stop planning. Callers may raise
+ # the tolerance explicitly via metadata["max_blocking_questions"].
+ MAX_BLOCKING_QUESTIONS = 0
+
+ # Sections the plan must actually populate to be worth reviewing.
+ REQUIRED_PLAN_SECTIONS = (
+ "files_to_change",
+ "code_structures",
+ "class_breakdown",
+ "tests_required",
+ "stop_conditions",
+ )
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="plan_generator", mcp_clients=mcp_clients)
+
+ # ------------------------------------------------------------------
+ # main entry point
+ # ------------------------------------------------------------------
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ metadata = trigger.metadata or {}
+ pipeline_ctx = metadata.get("pipeline_context", {}) or {}
+
+ spec_text, spec_ref = self._resolve_spec(trigger, pipeline_ctx)
+ if not spec_text.strip():
+ return AgentResult(
+ agent=self.name,
+ success=False,
+ errors=[
+ "No spec provided. Pass metadata['spec'] (text) or "
+ "metadata['spec_path'] (file), or set trigger.source to a spec file."
+ ],
+ )
+
+ if not self._settings.has_llm:
+ return AgentResult(
+ agent=self.name,
+ success=False,
+ errors=[
+ "No LLM provider configured — plan generation requires a "
+ "frontier-tier model and has no offline fallback by design. "
+ "Set GEMINI_API_KEY or ANTHROPIC_API_KEY in .env (see .env.example)."
+ ],
+ outputs=[AgentOutput(
+ output_type="plan_skipped",
+ description="Planning skipped: no LLM provider configured",
+ reference=spec_ref,
+ )],
+ requires_human_review=True,
+ review_items=[{
+ "type": "plan_blocked",
+ "reason": "no_llm_provider",
+ "spec": spec_ref,
+ }],
+ )
+
+ date = metadata.get("date") or datetime.now(timezone.utc).strftime("%Y-%m-%d")
+ repo_context = metadata.get("repo_context") or self._collect_repo_context()
+ plan_template = self._load_plan_template()
+ answers_md = self._format_answers(metadata.get("answers") or pipeline_ctx.get("answers"))
+
+ # ---- phase 1: Grill Me ----
+ grill = self._llm_json(
+ "GRILL",
+ spec=spec_text,
+ repo_context=repo_context,
+ plan_template=plan_template,
+ answers=answers_md,
+ max_tokens=self.GRILL_MAX_TOKENS,
+ )
+ if grill is None:
+ return AgentResult(
+ agent=self.name,
+ success=False,
+ errors=[
+ "GRILL phase failed: no parseable JSON returned by "
+ f"{self._settings.active_provider}. Plan not generated."
+ ],
+ requires_human_review=True,
+ review_items=[{
+ "type": "plan_blocked",
+ "reason": "grill_unparseable",
+ "spec": spec_ref,
+ }],
+ )
+
+ questions = [q for q in self._as_list(grill.get("clarifying_questions")) if isinstance(q, dict)]
+ blocking = [q for q in questions if self._is_blocking(q)]
+ title = str(grill.get("spec_title") or metadata.get("title") or "untitled work item")
+ summary = str(grill.get("spec_summary") or "")
+ plan_id = f"PLAN-{date}-{self._slug(title)}"
+
+ try:
+ max_blocking = int(metadata.get("max_blocking_questions", self.MAX_BLOCKING_QUESTIONS))
+ except (TypeError, ValueError):
+ max_blocking = self.MAX_BLOCKING_QUESTIONS
+
+ declared_ready = bool(grill.get("ready_to_plan", not blocking))
+ ready = declared_ready and len(blocking) <= max_blocking
+ forced = bool(metadata.get("force_plan", False))
+
+ logger.info(
+ f"grill complete: plan_id={plan_id} questions={len(questions)} "
+ f"blocking={len(blocking)} ready={ready} forced={forced}"
+ )
+
+ if not ready and not forced:
+ return self._questions_result(
+ plan_id=plan_id, date=date, title=title, summary=summary,
+ spec_ref=spec_ref, questions=questions, blocking=blocking,
+ )
+
+ # ---- phase 2: the plan ----
+ plan = self._llm_json(
+ "PLAN",
+ spec=spec_text,
+ repo_context=repo_context,
+ plan_template=plan_template,
+ answers=self._merge_answers(answers_md, questions) if forced else answers_md,
+ max_tokens=self.PLAN_MAX_TOKENS,
+ )
+ if plan is None:
+ return AgentResult(
+ agent=self.name,
+ success=False,
+ errors=[
+ "PLAN phase failed: no parseable JSON returned by "
+ f"{self._settings.active_provider}. No plan artifact written."
+ ],
+ requires_human_review=True,
+ review_items=[{
+ "type": "plan_blocked",
+ "reason": "plan_unparseable",
+ "spec": spec_ref,
+ }],
+ )
+
+ gaps = self._validate_plan(plan)
+ content = self._render_plan_markdown(
+ plan_id=plan_id, date=date, title=title, spec_ref=spec_ref,
+ plan=plan, questions=questions, gaps=gaps, forced=forced,
+ )
+ rel_path = f"docs/plans/{plan_id}.md"
+ written_path = self._write_artifact(rel_path, content)
+
+ outputs: list[AgentOutput] = [AgentOutput(
+ output_type="plan_drafted",
+ description=(
+ f"{plan_id}: {title} — "
+ f"{len(self._as_list(plan.get('files_to_change')))} files, "
+ f"{len(self._as_list(plan.get('class_breakdown')))} classes, "
+ f"{len(self._as_list(plan.get('tests_required')))} tests, "
+ f"{len(questions)} questions asked ({len(blocking)} blocking)"
+ ),
+ reference=written_path or rel_path,
+ )]
+ outputs.extend(self._commit(rel_path, content, f"Add implementation plan {plan_id}: {title[:50]}"))
+
+ measurements = {
+ "plan_id": plan_id,
+ "clarifying_questions": len(questions),
+ "blocking_questions": len(blocking),
+ "files_to_change": len(self._as_list(plan.get("files_to_change"))),
+ "code_structures": len(self._as_list(plan.get("code_structures"))),
+ "classes": len(self._as_list(plan.get("class_breakdown"))),
+ "tests_required": len(self._as_list(plan.get("tests_required"))),
+ "stop_conditions": len(self._as_list(plan.get("stop_conditions"))),
+ "planned_paths": [
+ str(f.get("path", "")) for f in self._as_list(plan.get("files_to_change"))
+ if isinstance(f, dict) and f.get("path")
+ ],
+ "implementation_tier": str(plan.get("implementation_tier") or "cheap"),
+ "planning_model": self._planning_model(),
+ "template_gaps": gaps,
+ "planned_without_answers": forced,
+ }
+
+ self.wiki.put("plans", plan_id, {
+ "title": title,
+ "status": "awaiting_review",
+ "spec": spec_ref,
+ "date": date,
+ "artifact": rel_path,
+ **{k: measurements[k] for k in (
+ "clarifying_questions", "blocking_questions", "files_to_change",
+ "tests_required", "implementation_tier", "planned_paths",
+ )},
+ }, agent=self.name, pipeline="planning", tags=["plan", date])
+
+ self.emit("plan_drafted", {
+ "plan_id": plan_id,
+ "spec": spec_ref,
+ "artifact": rel_path,
+ **{k: measurements[k] for k in (
+ "clarifying_questions", "blocking_questions",
+ "files_to_change", "tests_required", "implementation_tier",
+ )},
+ }, pipeline="planning")
+
+ review_items = [{
+ "type": "plan_review",
+ "plan_id": plan_id,
+ "artifact": rel_path,
+ "decision": "accept_or_reject",
+ "gate": "no implementation may start before this plan is accepted",
+ }]
+ if gaps:
+ review_items.append({
+ "type": "plan_incomplete",
+ "plan_id": plan_id,
+ "missing_sections": gaps,
+ })
+
+ return AgentResult(
+ agent=self.name,
+ success=True,
+ outputs=outputs,
+ requires_human_review=True,
+ review_items=review_items,
+ data={
+ "plan_id": plan_id,
+ "plan": plan,
+ "clarifying_questions": questions,
+ "artifact_path": rel_path,
+ "plan_markdown": content,
+ "status": "awaiting_review",
+ "measurements": measurements,
+ },
+ )
+
+ # ------------------------------------------------------------------
+ # grill-me outcome
+ # ------------------------------------------------------------------
+
+ def _questions_result(
+ self, *, plan_id: str, date: str, title: str, summary: str,
+ spec_ref: str, questions: list[dict], blocking: list[dict],
+ ) -> AgentResult:
+ """
+ Material ambiguity found: return the questions instead of a plan.
+
+ This is the deliberate stop condition of the planning process — the
+ agent hands back to a human rather than guessing at the design.
+ """
+ content = self._render_questions_markdown(
+ plan_id=plan_id, date=date, title=title, summary=summary,
+ spec_ref=spec_ref, questions=questions,
+ )
+ rel_path = f"docs/plans/{plan_id}-questions.md"
+ written_path = self._write_artifact(rel_path, content)
+
+ outputs: list[AgentOutput] = [AgentOutput(
+ output_type="clarifications_requested",
+ description=(
+ f"{plan_id}: spec too ambiguous to plan — {len(blocking)} blocking "
+ f"of {len(questions)} questions must be answered first"
+ ),
+ reference=written_path or rel_path,
+ )]
+ outputs.extend(self._commit(
+ rel_path, content, f"Clarifying questions for {plan_id}: {title[:50]}"
+ ))
+
+ self.wiki.put("plans", plan_id, {
+ "title": title,
+ "status": "awaiting_answers",
+ "spec": spec_ref,
+ "date": date,
+ "artifact": rel_path,
+ "clarifying_questions": len(questions),
+ "blocking_questions": len(blocking),
+ }, agent=self.name, pipeline="planning", tags=["plan", "blocked", date])
+
+ self.emit("plan_clarifications_requested", {
+ "plan_id": plan_id,
+ "spec": spec_ref,
+ "artifact": rel_path,
+ "clarifying_questions": len(questions),
+ "blocking_questions": len(blocking),
+ "questions": [str(q.get("question", "")) for q in blocking][:10],
+ }, pipeline="planning")
+
+ return AgentResult(
+ agent=self.name,
+ success=True,
+ outputs=outputs,
+ requires_human_review=True,
+ review_items=[{
+ "type": "spec_clarification",
+ "plan_id": plan_id,
+ "artifact": rel_path,
+ "blocking_questions": [str(q.get("question", "")) for q in blocking],
+ "gate": "answer the blocking questions, then re-run with metadata['answers']",
+ }],
+ data={
+ "plan_id": plan_id,
+ "plan": None,
+ "clarifying_questions": questions,
+ "artifact_path": rel_path,
+ "status": "awaiting_answers",
+ "measurements": {
+ "plan_id": plan_id,
+ "clarifying_questions": len(questions),
+ "blocking_questions": len(blocking),
+ "planning_model": self._planning_model(),
+ },
+ },
+ )
+
+ # ------------------------------------------------------------------
+ # inputs
+ # ------------------------------------------------------------------
+
+ def _resolve_spec(self, trigger: AgentTrigger, pipeline_ctx: dict) -> tuple[str, str]:
+ """
+ Find the definition of work. Accepts inline text or a file path, from
+ trigger metadata, upstream pipeline context, or trigger.source.
+
+ Returns (spec_text, human-readable reference).
+ """
+ metadata = trigger.metadata or {}
+
+ for key in ("spec_text", "spec", "work_definition"):
+ value = metadata.get(key) or pipeline_ctx.get(key)
+ if isinstance(value, str) and value.strip():
+ ref = str(metadata.get("spec_ref") or metadata.get("ticket") or trigger.source or key)
+ return value, ref
+ if isinstance(value, dict):
+ ref = str(value.get("id") or value.get("key") or trigger.source or key)
+ return json.dumps(value, indent=2, default=str), ref
+
+ for key in ("spec_path", "source"):
+ candidate = metadata.get(key) or pipeline_ctx.get(key)
+ if not isinstance(candidate, str) or not candidate.strip():
+ continue
+ path = Path(candidate)
+ if not path.is_absolute():
+ path = PROJECT_ROOT / path
+ if path.is_file():
+ try:
+ return path.read_text(encoding="utf-8"), candidate
+ except OSError as exc:
+ logger.warning(f"Could not read spec file {path}: {exc}")
+
+ if trigger.source:
+ path = Path(trigger.source)
+ if not path.is_absolute():
+ path = PROJECT_ROOT / path
+ if path.is_file():
+ try:
+ return path.read_text(encoding="utf-8"), trigger.source
+ except OSError as exc:
+ logger.warning(f"Could not read spec file {path}: {exc}")
+
+ return "", str(trigger.source or "unknown")
+
+ def _collect_repo_context(self) -> str:
+ """List real source paths so the planner cannot invent file names."""
+ lines: list[str] = []
+ for dirname in self.CONTEXT_DIRS:
+ base = PROJECT_ROOT / dirname
+ if not base.is_dir():
+ continue
+ files = sorted(
+ p for p in base.rglob("*")
+ if p.is_file()
+ and p.suffix in self.CONTEXT_SUFFIXES
+ and "__pycache__" not in p.parts
+ )
+ if not files:
+ continue
+ lines.append(f"{dirname}/")
+ lines.extend(f" {p.relative_to(PROJECT_ROOT).as_posix()}" for p in files)
+
+ if not lines:
+ return "(repository inventory unavailable)"
+ if len(lines) > self.MAX_CONTEXT_LINES:
+ omitted = len(lines) - self.MAX_CONTEXT_LINES
+ lines = lines[: self.MAX_CONTEXT_LINES]
+ lines.append(f" ... ({omitted} more paths omitted)")
+ return "\n".join(lines)
+
+ def _load_plan_template(self) -> str:
+ """The plan template is the single source of truth for plan shape."""
+ if PLAN_TEMPLATE_PATH.is_file():
+ try:
+ return PLAN_TEMPLATE_PATH.read_text(encoding="utf-8")[: self.MAX_TEMPLATE_CHARS]
+ except OSError as exc:
+ logger.warning(f"Could not read {PLAN_TEMPLATE_PATH}: {exc}")
+ logger.warning("docs/plan_template.md missing — planning with the inline fallback template")
+ return (
+ "Sections required: work summary; clarifying questions; files to change "
+ "and how; supporting code structures; class breakdown; required tests and "
+ "what each proves; implementation sequence; out of scope; risks; stop "
+ "conditions; review gate."
+ )
+
+ def _format_answers(self, answers: Any) -> str:
+ """Render answers to previously asked questions for the PLAN phase."""
+ if not answers:
+ return "(none provided — this is the first pass over this spec)"
+
+ lines: list[str] = []
+ if isinstance(answers, dict):
+ for question, answer in answers.items():
+ lines.append(f"- Q: {question}\n A: {answer}")
+ elif isinstance(answers, list):
+ for item in answers:
+ if isinstance(item, dict):
+ question = item.get("question", "(unlabelled question)")
+ answer = item.get("answer", item.get("response", ""))
+ lines.append(f"- Q: {question}\n A: {answer}")
+ else:
+ lines.append(f"- {item}")
+ else:
+ lines.append(str(answers))
+
+ return "\n".join(lines)[: self.MAX_ANSWER_CHARS] or "(none provided)"
+
+ def _merge_answers(self, answers_md: str, questions: list[dict]) -> str:
+ """
+ Planning was forced past unanswered blocking questions. Hand the
+ planner its own fallback assumptions so they land in the plan's
+ assumptions section, where a reviewer will see them.
+ """
+ lines = [answers_md, "", "UNANSWERED — proceeding on the planner's own assumptions:"]
+ for q in questions:
+ if not self._is_blocking(q):
+ continue
+ lines.append(
+ f"- Q: {q.get('question', '')}\n"
+ f" ASSUMED: {q.get('assumption_if_unanswered', 'no assumption stated')}"
+ )
+ return "\n".join(lines)[: self.MAX_ANSWER_CHARS]
+
+ # ------------------------------------------------------------------
+ # LLM
+ # ------------------------------------------------------------------
+
+ def _llm_json(
+ self, phase: str, *, spec: str, repo_context: str, plan_template: str,
+ answers: str, max_tokens: int,
+ ) -> dict | None:
+ """Run one phase of prompts/plan_generator.txt. Returns None on failure."""
+ prompt = self.load_prompt(
+ "plan_generator.txt",
+ phase=phase,
+ spec=spec[: self.MAX_SPEC_CHARS],
+ repo_context=repo_context,
+ plan_template=plan_template,
+ answers=answers,
+ )
+ try:
+ raw = self.call_claude(prompt, max_tokens=max_tokens)
+ except Exception as exc:
+ logger.warning(f"LLM call failed during {phase} phase: {exc}")
+ return None
+ parsed = self._parse_json_object(raw)
+ if parsed is None:
+ logger.error(f"Could not parse {phase} response as a JSON object")
+ return parsed
+
+ @staticmethod
+ def _parse_json_object(raw: str) -> dict | None:
+ """Parse a JSON object, tolerating code fences and surrounding prose."""
+ if not raw:
+ return None
+ text = raw.strip()
+ if text.startswith("```"):
+ text = re.sub(r"^```[a-zA-Z]*\s*", "", text)
+ text = re.sub(r"```\s*$", "", text).strip()
+ try:
+ parsed = json.loads(text)
+ return parsed if isinstance(parsed, dict) else None
+ except json.JSONDecodeError:
+ pass
+ match = re.search(r"\{.*\}", text, re.DOTALL)
+ if match:
+ try:
+ parsed = json.loads(match.group())
+ return parsed if isinstance(parsed, dict) else None
+ except json.JSONDecodeError:
+ return None
+ return None
+
+ def _planning_model(self) -> str:
+ provider = self._settings.active_provider
+ if provider == "anthropic":
+ return f"anthropic:{self._settings.claude_model}"
+ if provider == "gemini":
+ return f"gemini:{self._settings.gemini_model}"
+ return provider
+
+ # ------------------------------------------------------------------
+ # validation
+ # ------------------------------------------------------------------
+
+ def _validate_plan(self, plan: dict) -> list[str]:
+ """
+ Verification step: which required template sections came back empty,
+ plus files that no test covers. Reported to the reviewer, not hidden.
+ """
+ gaps = [
+ section for section in self.REQUIRED_PLAN_SECTIONS
+ if not self._as_list(plan.get(section))
+ ]
+
+ tests_blob = json.dumps(self._as_list(plan.get("tests_required")), default=str).lower()
+ untested = [
+ str(entry.get("path", ""))
+ for entry in self._as_list(plan.get("files_to_change"))
+ if isinstance(entry, dict)
+ and entry.get("path")
+ and str(entry.get("change_type", "")).lower() != "delete"
+ and Path(str(entry["path"])).stem.lower() not in tests_blob
+ ]
+ if untested:
+ gaps.append("no test traced to: " + ", ".join(untested[:6]))
+ return gaps
+
+ # ------------------------------------------------------------------
+ # artifact rendering
+ # ------------------------------------------------------------------
+
+ def _render_questions_markdown(
+ self, *, plan_id: str, date: str, title: str, summary: str,
+ spec_ref: str, questions: list[dict],
+ ) -> str:
+ lines = [
+ f"# {plan_id} — Clarifying Questions (no plan yet)",
+ "",
+ "| Field | Value |",
+ "|---|---|",
+ f"| **Plan ID** | `{plan_id}` |",
+ f"| **Work item** | {title} |",
+ f"| **Spec** | `{spec_ref}` |",
+ f"| **Date** | {date} |",
+ f"| **Planning model** | `{self._planning_model()}` |",
+ "| **Status** | `awaiting answers` — planning stopped, no code may start |",
+ "",
+ "This spec contains ambiguity that would change the implementation plan.",
+ "Per `docs/spec_to_plan_process.md`, the planning agent surfaces the",
+ "questions rather than guessing. Answer the blocking questions below and",
+ "re-run the agent with `metadata['answers']`.",
+ "",
+ ]
+ if summary:
+ lines += ["## Understood as", "", summary, ""]
+
+ lines += [
+ "## Questions",
+ "",
+ "| # | Question | Blocking? | Why it matters | Assumption if unanswered | Ask |",
+ "|---|---|---|---|---|---|",
+ ]
+ for i, q in enumerate(questions, start=1):
+ lines.append(
+ f"| {i} | {self._cell(q.get('question'))} "
+ f"| {'**yes**' if self._is_blocking(q) else 'no'} "
+ f"| {self._cell(q.get('why_it_matters'))} "
+ f"| {self._cell(q.get('assumption_if_unanswered'))} "
+ f"| {self._cell(q.get('ask'))} |"
+ )
+
+ blocking_count = sum(1 for q in questions if self._is_blocking(q))
+ lines += [
+ "",
+ "## Answers",
+ "",
+ "Record answers here (or in the Jira ticket) and re-run the planning agent.",
+ "",
+ f"- Questions asked: **{len(questions)}** ({blocking_count} blocking)",
+ "- [ ] All blocking questions answered — planning may resume",
+ "",
+ "---",
+ f"_Generated by the plan_generator agent on {date} "
+ f"(model `{self._planning_model()}`). Process: `docs/spec_to_plan_process.md`._",
+ ]
+ return "\n".join(lines) + "\n"
+
+ def _render_plan_markdown(
+ self, *, plan_id: str, date: str, title: str, spec_ref: str,
+ plan: dict, questions: list[dict], gaps: list[str], forced: bool,
+ ) -> str:
+ tier = str(plan.get("implementation_tier") or "cheap")
+ lines = [
+ f"# {plan_id} — Implementation Plan",
+ "",
+ "| Field | Value |",
+ "|---|---|",
+ f"| **Plan ID** | `{plan_id}` |",
+ f"| **Work item** | {plan.get('spec_title') or title} |",
+ f"| **Spec** | `{spec_ref}` |",
+ f"| **Date** | {date} |",
+ f"| **Planning model** | `{self._planning_model()}` (frontier tier) |",
+ f"| **Implementation tier** | `{tier}` |",
+ f"| **Estimated effort** | {self._cell(plan.get('estimated_effort')) or 'not estimated'} |",
+ "| **Status** | `awaiting review` — no code may start until accepted |",
+ "",
+ "> Review the plan, not just the code. Accepting or rejecting the file set,",
+ "> class breakdown, and test set here is far cheaper than reworking an",
+ "> implementation. Process: `docs/spec_to_plan_process.md`.",
+ "",
+ "## 1. Work summary",
+ "",
+ str(plan.get("spec_summary") or "_not provided by the planner_"),
+ "",
+ ]
+
+ if forced:
+ lines += [
+ "> **Warning:** this plan was generated with `force_plan` while blocking",
+ "> questions were unanswered. The assumptions in §2 are unvalidated.",
+ "",
+ ]
+
+ # §2 questions / assumptions
+ lines += ["## 2. Clarifying questions asked (Grill Me)", ""]
+ if questions:
+ lines += [
+ "| # | Question | Blocking? | Why it matters | Answer / assumption |",
+ "|---|---|---|---|---|",
+ ]
+ for i, q in enumerate(questions, start=1):
+ lines.append(
+ f"| {i} | {self._cell(q.get('question'))} "
+ f"| {'yes' if self._is_blocking(q) else 'no'} "
+ f"| {self._cell(q.get('why_it_matters'))} "
+ f"| {self._cell(q.get('answer') or q.get('assumption_if_unanswered'))} |"
+ )
+ else:
+ lines.append("None — the planner reported no ambiguity in this spec.")
+ lines.append("")
+
+ assumptions = self._as_list(plan.get("assumptions"))
+ if assumptions:
+ lines += ["**Assumptions this plan rests on:**", ""]
+ lines += [f"- {self._flat(a)}" for a in assumptions]
+ lines.append("")
+
+ # §3 files
+ lines += [
+ "## 3. Files to change — and how",
+ "",
+ "| File | Change | What changes | Why |",
+ "|---|---|---|---|",
+ ]
+ files = self._as_list(plan.get("files_to_change"))
+ if files:
+ for entry in files:
+ if not isinstance(entry, dict):
+ lines.append(f"| {self._cell(entry)} | ? | | |")
+ continue
+ change = str(entry.get("change_type", "modify")).lower()
+ lines.append(
+ f"| `{self._cell(entry.get('path'))}` "
+ f"| {CHANGE_TYPE_LABELS.get(change, change)} "
+ f"| {self._cell(entry.get('what_changes'))} "
+ f"| {self._cell(entry.get('why'))} |"
+ )
+ else:
+ lines.append("| _none listed_ | | | |")
+ lines.append("")
+
+ # §4 structures
+ lines += ["## 4. Code structures supporting the feature", ""]
+ structures = self._as_list(plan.get("code_structures"))
+ if structures:
+ lines += ["| Structure | Kind | Location | Purpose |", "|---|---|---|---|"]
+ for s in structures:
+ if isinstance(s, dict):
+ lines.append(
+ f"| `{self._cell(s.get('name'))}` "
+ f"| {self._cell(s.get('kind'))} "
+ f"| `{self._cell(s.get('location'))}` "
+ f"| {self._cell(s.get('purpose'))} |"
+ )
+ else:
+ lines.append(f"| {self._cell(s)} | | | |")
+ else:
+ lines.append("_none listed_")
+ lines.append("")
+
+ # §5 classes
+ lines += ["## 5. Class / module breakdown", ""]
+ classes = self._as_list(plan.get("class_breakdown"))
+ if classes:
+ for c in classes:
+ if not isinstance(c, dict):
+ lines += [f"- {self._flat(c)}", ""]
+ continue
+ module = self._cell(c.get("module"))
+ header = f"### `{self._cell(c.get('class_name'))}`"
+ if module:
+ header += f" — `{module}`"
+ lines += [header, "", f"**Responsibility:** {self._cell(c.get('responsibility'))}", ""]
+ methods = self._as_list(c.get("key_methods"))
+ if methods:
+ lines += ["| Method | Signature | Does |", "|---|---|---|"]
+ for m in methods:
+ if isinstance(m, dict):
+ lines.append(
+ f"| `{self._cell(m.get('name'))}` "
+ f"| `{self._cell(m.get('signature'))}` "
+ f"| {self._cell(m.get('does'))} |"
+ )
+ else:
+ lines.append(f"| `{self._cell(m)}` | | |")
+ lines.append("")
+ collaborators = self._as_list(c.get("collaborators"))
+ if collaborators:
+ lines += [
+ "**Collaborators:** " + ", ".join(f"`{self._flat(x)}`" for x in collaborators),
+ "",
+ ]
+ else:
+ lines += ["_none listed_", ""]
+
+ # §6 tests
+ lines += [
+ "## 6. Required tests — and what each one proves",
+ "",
+ "| Test | File | Level | What it tests | Fails when |",
+ "|---|---|---|---|---|",
+ ]
+ tests = self._as_list(plan.get("tests_required"))
+ if tests:
+ for t in tests:
+ if isinstance(t, dict):
+ lines.append(
+ f"| `{self._cell(t.get('name'))}` "
+ f"| `{self._cell(t.get('file'))}` "
+ f"| {self._cell(t.get('level'))} "
+ f"| {self._cell(t.get('what_it_tests'))} "
+ f"| {self._cell(t.get('fails_when'))} |"
+ )
+ else:
+ lines.append(f"| {self._cell(t)} | | | | |")
+ else:
+ lines.append("| _none listed_ | | | | |")
+ lines.append("")
+
+ # §7-9
+ lines += self._numbered_bullets("7. Implementation sequence", plan.get("implementation_sequence"), ordered=True)
+ lines += self._numbered_bullets("8. Out of scope", plan.get("out_of_scope"))
+
+ lines += ["## 9. Risks", ""]
+ risks = self._as_list(plan.get("risks"))
+ if risks:
+ lines += ["| Risk | Mitigation |", "|---|---|"]
+ for r in risks:
+ if isinstance(r, dict):
+ lines.append(f"| {self._cell(r.get('risk'))} | {self._cell(r.get('mitigation'))} |")
+ else:
+ lines.append(f"| {self._cell(r)} | |")
+ else:
+ lines.append("_none listed_")
+ lines.append("")
+
+ # §10 stop conditions
+ lines += [
+ "## 10. Stop conditions",
+ "",
+ "When the implementing agent stops and hands off instead of trying again.",
+ "",
+ "| Condition | Action |",
+ "|---|---|",
+ ]
+ stops = self._as_list(plan.get("stop_conditions"))
+ if stops:
+ for s in stops:
+ if isinstance(s, dict):
+ lines.append(
+ f"| {self._cell(s.get('condition'))} "
+ f"| {self._cell(s.get('action')) or 'hand off to human'} |"
+ )
+ else:
+ lines.append(f"| {self._cell(s)} | hand off to human |")
+ else:
+ lines.append("| _none listed — reject this plan_ | |")
+ lines.append("")
+
+ # §11 gate
+ lines += [
+ "## 11. Review gate",
+ "",
+ "- [ ] **Accepted** — file set, class breakdown, and test set are right; "
+ f"implementation may start on the `{tier}` tier",
+ "- [ ] **Rejected** — reason: `organization` | `missing-tests` | `wrong-files` "
+ "| `scope` | `unanswered-ambiguity`",
+ "",
+ "Reviewer: ______ · Date: ______ · Time spent: ____ min",
+ "",
+ ]
+ if gaps:
+ lines += [
+ "**Automated completeness check flagged:**",
+ "",
+ ]
+ lines += [f"- {g}" for g in gaps]
+ lines.append("")
+
+ lines += [
+ "---",
+ f"_Generated by the plan_generator agent on {date} "
+ f"(model `{self._planning_model()}`). Template: `docs/plan_template.md`. "
+ "Process: `docs/spec_to_plan_process.md`._",
+ ]
+ return "\n".join(lines) + "\n"
+
+ def _numbered_bullets(self, heading: str, value: Any, *, ordered: bool = False) -> list[str]:
+ lines = [f"## {heading}", ""]
+ items = self._as_list(value)
+ if not items:
+ lines += ["_none listed_", ""]
+ return lines
+ for i, item in enumerate(items, start=1):
+ prefix = f"{i}." if ordered else "-"
+ lines.append(f"{prefix} {self._flat(item)}")
+ lines.append("")
+ return lines
+
+ # ------------------------------------------------------------------
+ # output plumbing
+ # ------------------------------------------------------------------
+
+ def _write_artifact(self, rel_path: str, content: str) -> str:
+ """Write the reviewable markdown artifact into the working tree."""
+ path = PROJECT_ROOT / rel_path
+ try:
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_text(content, encoding="utf-8")
+ logger.info(f"plan artifact written: {rel_path}")
+ return str(path)
+ except OSError as exc:
+ logger.warning(f"Could not write plan artifact {rel_path}: {exc}")
+ return ""
+
+ def _commit(self, rel_path: str, content: str, message: str) -> list[AgentOutput]:
+ """Commit the artifact through whichever repo MCP client is wired up."""
+ repo = self.mcp.get("github") or self.mcp.get("bitbucket")
+ if not repo:
+ return []
+ try:
+ result = repo.commit_file(
+ file_path=rel_path,
+ content=content,
+ message=message,
+ agent_name=self.name,
+ )
+ except Exception as exc:
+ logger.warning(f"Commit of {rel_path} failed: {exc}")
+ return []
+ if isinstance(result, dict) and result.get("ok"):
+ return [AgentOutput(
+ output_type="file_committed",
+ description=message,
+ reference=rel_path,
+ )]
+ return []
+
+ # ------------------------------------------------------------------
+ # small helpers
+ # ------------------------------------------------------------------
+
+ @staticmethod
+ def _as_list(value: Any) -> list[Any]:
+ if value is None:
+ return []
+ if isinstance(value, list):
+ return [v for v in value if v not in ("", None)]
+ if isinstance(value, dict):
+ return [value]
+ if isinstance(value, str):
+ return [value] if value.strip() else []
+ return [value]
+
+ @staticmethod
+ def _is_blocking(question: dict) -> bool:
+ value = question.get("blocking")
+ if isinstance(value, bool):
+ return value
+ return str(value).strip().lower() in {"true", "yes", "y", "blocking", "1"}
+
+ @classmethod
+ def _flat(cls, value: Any) -> str:
+ """Flatten a value to a single markdown-safe line."""
+ if isinstance(value, dict):
+ value = " · ".join(f"{k}: {v}" for k, v in value.items())
+ elif isinstance(value, (list, tuple)):
+ value = "; ".join(str(v) for v in value)
+ return re.sub(r"\s+", " ", str(value)).strip()
+
+ @classmethod
+ def _cell(cls, value: Any) -> str:
+ """Flatten a value for use inside a markdown table cell."""
+ return cls._flat("" if value is None else value).replace("|", "\\|")
+
+ @staticmethod
+ def _slug(text: str) -> str:
+ slug = re.sub(r"[^a-z0-9]+", "-", str(text).lower()).strip("-")
+ return (slug[:48].rstrip("-")) or "work-item"
diff --git a/agents/project_mgmt/__init__.py b/agents/project_mgmt/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/agents/project_mgmt/alert_agent.py b/agents/project_mgmt/alert_agent.py
new file mode 100644
index 0000000..59250c4
--- /dev/null
+++ b/agents/project_mgmt/alert_agent.py
@@ -0,0 +1,153 @@
+"""
+Alert Agent — monitors project health.
+
+Fires Slack alert when:
+ - Ticket velocity off-track
+ - Must-have REQ has no Jira ticket
+ - P0 ticket unassigned >48hrs
+ - Drift detected but no PR opened within 24hrs
+ - Recurring concern from coach sessions
+
+Triggered by: cron_6h_alert, recurring_concern event, commitment_overdue event
+Outputs: Slack alerts
+"""
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+
+logger = logging.getLogger("agent.alert_agent")
+
+
+class AlertAgent(BaseAgent):
+ """Monitors project health and fires alerts for anomalies."""
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="alert_agent", mcp_clients=mcp_clients)
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ alerts = self._check_all_conditions()
+ outputs = []
+
+ if alerts:
+ slack = self.mcp.get("slack")
+ if slack:
+ for alert in alerts:
+ slack.send_alert(alert)
+ outputs.append(AgentOutput(
+ output_type="alerts_sent",
+ description=f"Fired {len(alerts)} alert(s)",
+ ))
+ else:
+ outputs.append(AgentOutput(
+ output_type="alerts_generated",
+ description=f"Generated {len(alerts)} alert(s) (Slack not configured)",
+ ))
+
+ self.wiki.put("alerts", "latest", {
+ "count": len(alerts),
+ "alerts": alerts,
+ }, agent=self.name, pipeline="project_mgmt")
+ else:
+ outputs.append(AgentOutput(
+ output_type="health_check",
+ description="All health checks passed — no alerts",
+ ))
+
+ return AgentResult(
+ agent=self.name, success=True, outputs=outputs,
+ data={"alerts": alerts},
+ )
+
+ def _check_all_conditions(self) -> list[str]:
+ alerts = []
+ alerts.extend(self._check_velocity())
+ alerts.extend(self._check_unlinked_reqs())
+ alerts.extend(self._check_unassigned_p0())
+ alerts.extend(self._check_unresolved_drift())
+ return alerts
+
+ def _check_velocity(self) -> list[str]:
+ jira = self.mcp.get("jira")
+ if not jira or not getattr(jira, "is_configured", False):
+ return []
+
+ board = jira.get_board_status()
+ if not board.get("ok"):
+ return []
+
+ by_status = board.get("by_status", {})
+ done = by_status.get("Done", 0)
+ total = board.get("total_issues", 0)
+
+ if total > 0 and done / total < 0.3:
+ return [
+ f"*Sprint Velocity Alert*\n"
+ f"Only {done}/{total} tickets done ({done/total*100:.0f}%). "
+ f"Sprint may be at risk."
+ ]
+ return []
+
+ def _check_unlinked_reqs(self) -> list[str]:
+ """Check for must-have REQs without Jira tickets using the wiki."""
+ try:
+ reqs = self.wiki.list_namespace("requirements_engineering")
+ jira = self.mcp.get("jira")
+ if not reqs or not jira or not getattr(jira, "is_configured", False):
+ return []
+
+ result = jira.search_issues(
+ jql=f"project = {jira._project_key} AND labels = requirements"
+ )
+ if not result.get("ok"):
+ return []
+
+ ticket_count = result.get("total", 0)
+ req_count = len([r for r in reqs if "P0" in str(r) or "P1" in str(r)])
+
+ if req_count > 0 and ticket_count < req_count:
+ return [
+ f"*Unlinked Requirements Alert*\n"
+ f"{req_count} high-priority requirements but only {ticket_count} "
+ f"linked Jira tickets."
+ ]
+ except Exception:
+ pass
+ return []
+
+ def _check_unassigned_p0(self) -> list[str]:
+ jira = self.mcp.get("jira")
+ if not jira or not getattr(jira, "is_configured", False):
+ return []
+
+ result = jira.search_issues(
+ jql=f"project = {jira._project_key} AND priority = Highest AND assignee is EMPTY"
+ )
+ if not result.get("ok"):
+ return []
+
+ unassigned = result.get("issues", [])
+ if unassigned:
+ tickets = ", ".join(i["key"] for i in unassigned[:5])
+ return [f"*Unassigned P0 Alert*\nP0 tickets without assignee: {tickets}"]
+ return []
+
+ def _check_unresolved_drift(self) -> list[str]:
+ """Check wiki for drift events that haven't been resolved."""
+ try:
+ events = self.events.get_pending_events(event_type="drift_detected", limit=10)
+ if not events:
+ return []
+
+ unresolved = [e for e in events if not e.get("consumed_by") or e["consumed_by"] == []]
+ if unresolved:
+ return [
+ f"*Unresolved Drift Alert*\n"
+ f"{len(unresolved)} architecture drift event(s) detected "
+ f"but not yet resolved with a PR."
+ ]
+ except Exception:
+ pass
+ return []
diff --git a/agents/project_mgmt/ticket_creator.py b/agents/project_mgmt/ticket_creator.py
new file mode 100644
index 0000000..0b10833
--- /dev/null
+++ b/agents/project_mgmt/ticket_creator.py
@@ -0,0 +1,127 @@
+"""
+Jira Ticket Creator — creates tickets from classified P0/P1 items.
+
+From P0/P1 items → creates tickets with description, assignee suggestion
+based on domain, priority label, link to source REQ file.
+P0 tickets are held in a review queue for 1-click approval.
+
+Triggered by: priority_classifier output, manual
+Outputs: Jira tickets created, P0 items queued for approval
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+
+logger = logging.getLogger("agent.ticket_creator")
+
+DOMAIN_ASSIGNEES = {
+ "ml": "Arjun",
+ "pipeline": "Arjun",
+ "threshold": "Arjun",
+ "alpha": "Arjun",
+ "frontend": "Zheliang",
+ "ui": "Zheliang",
+ "review queue": "Zheliang",
+ "pims": "Hrishikesh",
+ "database": "Hrishikesh",
+ "schema": "Hrishikesh",
+ "integration": "Hrishikesh",
+ "architecture": "Jaivardhan",
+ "monitoring": "Jaivardhan",
+ "observability": "Jaivardhan",
+ "datadog": "Jaivardhan",
+ "ingestion": "Ashritha",
+ "agents": "Ashritha",
+ "orchestrator": "Ashritha",
+}
+
+
+class TicketCreatorAgent(BaseAgent):
+ """Creates Jira tickets from prioritized items."""
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="ticket_creator", mcp_clients=mcp_clients)
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ pipeline_ctx = trigger.metadata.get("pipeline_context", {})
+ items = (
+ trigger.metadata.get("classified_items", [])
+ or pipeline_ctx.get("classified_items", [])
+ )
+ outputs = []
+ review_items = []
+
+ jira = self.mcp.get("jira")
+ if not jira:
+ return AgentResult(
+ agent=self.name,
+ success=False,
+ errors=["Jira MCP not configured"],
+ )
+
+ for item in items:
+ priority = item.get("priority", "P2")
+
+ if priority == "P0":
+ review_items.append({
+ "type": "p0_ticket_approval",
+ "item": item,
+ "suggested_assignee": self._suggest_assignee(item.get("text", "")),
+ "message": f"P0 ticket needs approval: {item['text']}",
+ })
+ continue
+
+ # P1 and P2 are auto-created
+ assignee = self._suggest_assignee(item.get("text", ""))
+ jira_priority = "High" if priority == "P1" else "Medium"
+
+ result = jira.create_issue(
+ summary=item.get("text", "Untitled"),
+ description=self._build_description(item),
+ issue_type="Task",
+ labels=[priority, "auto-created"],
+ agent_name=self.name,
+ priority=jira_priority,
+ )
+
+ if result.get("ok"):
+ outputs.append(AgentOutput(
+ output_type="ticket_created",
+ description=f"{priority} ticket: {result['key']} — {item.get('text', '')[:60]}",
+ reference=result.get("url", result.get("key", "")),
+ ))
+
+ return AgentResult(
+ agent=self.name,
+ success=True,
+ outputs=outputs,
+ requires_human_review=bool(review_items),
+ review_items=review_items,
+ )
+
+ def _suggest_assignee(self, text: str) -> str | None:
+ """Suggest an assignee based on domain keywords in the item text."""
+ text_lower = text.lower()
+ for keyword, assignee in DOMAIN_ASSIGNEES.items():
+ if keyword in text_lower:
+ return assignee
+ return None
+
+ def _build_description(self, item: dict) -> str:
+ parts = [
+ f"**Auto-created from meeting transcript**\n",
+ f"**Item:** {item.get('text', '')}",
+ f"**Priority:** {item.get('priority', '?')}",
+ f"**Owner:** {item.get('owner', 'unassigned')}",
+ ]
+ if item.get("rationale"):
+ parts.append(f"**Rationale:** {item['rationale']}")
+ if item.get("deadline"):
+ parts.append(f"**Deadline:** {item['deadline']}")
+ if item.get("source_req"):
+ parts.append(f"**Source REQ:** {item['source_req']}")
+ return "\n".join(parts)
diff --git a/agents/project_mgmt/wbs_updater.py b/agents/project_mgmt/wbs_updater.py
new file mode 100644
index 0000000..788eb75
--- /dev/null
+++ b/agents/project_mgmt/wbs_updater.py
@@ -0,0 +1,97 @@
+"""
+WBS Updater — maintains /sprint/wbs.md (work breakdown structure)
+synced to Jira state.
+
+When tickets close → WBS updates. When new tickets created → appear
+under correct epic.
+
+Triggered by: Jira webhook (ticket state change), cron
+Outputs: updated wbs.md committed to repo
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timezone
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+
+logger = logging.getLogger("agent.wbs_updater")
+
+
+class WBSUpdaterAgent(BaseAgent):
+ """Keeps the work breakdown structure in sync with Jira."""
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="wbs_updater", mcp_clients=mcp_clients)
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ jira = self.mcp.get("jira")
+ if not jira:
+ return AgentResult(
+ agent=self.name, success=True,
+ outputs=[AgentOutput(
+ output_type="wbs_skipped",
+ description="Jira not configured",
+ )],
+ )
+
+ board = jira.get_board_status()
+ if not board.get("ok"):
+ return AgentResult(
+ agent=self.name, success=False,
+ errors=[board.get("error", "Failed to fetch board")],
+ )
+
+ wbs_content = self._build_wbs(board.get("recent_issues", []))
+
+ outputs = []
+
+ # Deposit WBS to wiki for cross-pipeline access
+ self.wiki.put("project_mgmt", "wbs_latest", {
+ "content": wbs_content,
+ "total_issues": board.get("total_issues", 0),
+ "by_status": board.get("by_status", {}),
+ }, agent=self.name, pipeline="project_mgmt")
+
+ # Commit to repo (prefer GitHub, fall back to Bitbucket)
+ repo = self.mcp.get("github") or self.mcp.get("bitbucket")
+ if repo:
+ repo.commit_file(
+ file_path="sprint/wbs.md",
+ content=wbs_content,
+ message=f"Update WBS ({datetime.now(timezone.utc).strftime('%Y-%m-%d')})",
+ agent_name=self.name,
+ )
+ outputs.append(AgentOutput(
+ output_type="file_committed",
+ description=f"WBS updated — {board.get('total_issues', 0)} issues tracked",
+ reference="sprint/wbs.md",
+ ))
+
+ return AgentResult(agent=self.name, success=True, outputs=outputs)
+
+ def _build_wbs(self, issues: list[dict]) -> str:
+ now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
+ lines = [
+ f"# Work Breakdown Structure\n",
+ f"_Last synced: {now}_\n",
+ ]
+
+ by_status: dict[str, list] = {}
+ for issue in issues:
+ status = issue.get("status", "Unknown")
+ by_status.setdefault(status, []).append(issue)
+
+ for status in ["To Do", "In Progress", "In Review", "Done"]:
+ items = by_status.get(status, [])
+ lines.append(f"\n## {status} ({len(items)})\n")
+ for item in items:
+ assignee = item.get("assignee", "unassigned")
+ lines.append(
+ f"- [{item.get('key', '?')}] {item.get('summary', '?')} "
+ f"({item.get('priority', '?')}) — {assignee}"
+ )
+
+ return "\n".join(lines)
diff --git a/agents/project_mgmt/weekly_digest.py b/agents/project_mgmt/weekly_digest.py
new file mode 100644
index 0000000..7fe1380
--- /dev/null
+++ b/agents/project_mgmt/weekly_digest.py
@@ -0,0 +1,129 @@
+"""
+Weekly Digest Agent — runs every Friday 6pm.
+
+Reads all commits that week and produces a digest:
+ - Decisions made this week
+ - Requirements changes (added/modified)
+ - Sprint health (open/closed tickets, velocity)
+ - Architecture (drift detected/resolved)
+ - Next week preview
+
+Published to Confluence + Slack.
+
+Triggered by: cron_friday_6pm
+Outputs: digest posted to Slack and Confluence
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timezone
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+
+logger = logging.getLogger("agent.weekly_digest")
+
+
+class WeeklyDigestAgent(BaseAgent):
+ """Generates and publishes the weekly project digest."""
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="weekly_digest", mcp_clients=mcp_clients)
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ week_data = self._gather_week_data()
+ digest = self._generate_digest(week_data)
+ outputs = []
+
+ slack = self.mcp.get("slack")
+ if slack:
+ slack.send_message(digest)
+ outputs.append(AgentOutput(
+ output_type="message_sent",
+ description="Weekly digest posted to Slack",
+ ))
+
+ confluence = self.mcp.get("confluence")
+ if confluence:
+ date = datetime.now(timezone.utc).strftime("%Y-%m-%d")
+ confluence.create_page(
+ title=f"Weekly Digest — {date}",
+ body=digest,
+ )
+ outputs.append(AgentOutput(
+ output_type="page_published",
+ description="Weekly digest published to Confluence",
+ ))
+
+ return AgentResult(agent=self.name, success=True, outputs=outputs)
+
+ def _gather_week_data(self) -> dict:
+ """Gather data from wiki, Jira, and event bus."""
+ data = {
+ "week_of": datetime.now(timezone.utc).strftime("%Y-%m-%d"),
+ "commits": [],
+ "decisions": [],
+ "req_changes": [],
+ "sprint_state": {},
+ "drift_reports": [],
+ "concerns": [],
+ "events_this_week": [],
+ }
+
+ # Pull from Jira
+ jira = self.mcp.get("jira")
+ if jira and getattr(jira, "is_configured", False):
+ board = jira.get_board_status()
+ if board.get("ok"):
+ data["sprint_state"] = board.get("by_status", {})
+ data["total_issues"] = board.get("total_issues", 0)
+
+ # Pull from SharedMemory wiki
+ try:
+ decisions = self.wiki.list_namespace("decisions")
+ data["decisions"] = decisions[-10:] if decisions else []
+
+ concerns = self.wiki.list_namespace("concerns")
+ data["concerns"] = concerns[-5:] if concerns else []
+
+ reqs = self.wiki.list_namespace("requirements_engineering")
+ data["req_changes"] = reqs[-5:] if reqs else []
+ except Exception:
+ pass
+
+ # Pull recent events
+ try:
+ data["events_this_week"] = [
+ {"type": e["event_type"], "agent": e["source_agent"]}
+ for e in self.events.get_pending_events(limit=20)
+ ]
+ data["drift_reports"] = [
+ e for e in data["events_this_week"] if e["type"] == "drift_detected"
+ ]
+ except Exception:
+ pass
+
+ return data
+
+ def _generate_digest(self, data: dict) -> str:
+ prompt = f"""Generate a weekly project digest for the Pimsie Supreme team.
+Week of: {data['week_of']}
+
+Data available:
+- Commits: {len(data['commits'])}
+- Decisions logged: {len(data['decisions'])}
+- Requirement changes: {len(data['req_changes'])}
+- Drift reports: {len(data['drift_reports'])}
+
+Format:
+## Week of {data['week_of']} — Project Digest
+### Decisions Made This Week
+### Requirements Changes (added/modified)
+### Sprint Health (open/closed tickets, velocity)
+### Architecture (drift detected/resolved)
+### Next Week Preview
+
+Be concise and actionable. If no data is available for a section, say so briefly."""
+
+ return self.call_claude(prompt)
diff --git a/agents/requirements/__init__.py b/agents/requirements/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/agents/requirements/priority_classifier.py b/agents/requirements/priority_classifier.py
new file mode 100644
index 0000000..d89d6be
--- /dev/null
+++ b/agents/requirements/priority_classifier.py
@@ -0,0 +1,146 @@
+"""
+Priority Classifier — assigns P0/P1/P2 to action items and requirements.
+
+P0 = blocks delivery or client commitment with hard deadline
+P1 = important for current sprint, ticket immediately
+P2 = future sprint
+
+P0 ticket creation requires human approval gate.
+
+Triggered by: transcript_parser output
+Outputs: classified items, P0 items flagged for human review
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+
+logger = logging.getLogger("agent.priority_classifier")
+
+
+class PriorityClassifierAgent(BaseAgent):
+ """
+ Classifies extracted items by priority using Claude with eParts context.
+ P0 items are held for human approval before Jira ticket creation.
+ """
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="priority_classifier", mcp_clients=mcp_clients)
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ # Read items from direct metadata or from pipeline context
+ items = trigger.metadata.get("items", [])
+ pipeline_ctx = trigger.metadata.get("pipeline_context", {})
+
+ if not items:
+ parsed = pipeline_ctx.get("parsed_minutes", {})
+ if isinstance(parsed, dict):
+ items = parsed.get("action_items", []) + parsed.get("new_requirements", [])
+
+ sprint_focus = trigger.metadata.get("sprint_focus", "ML pipeline integration and threshold calibration")
+
+ if not items:
+ return AgentResult(
+ agent=self.name,
+ success=True,
+ outputs=[AgentOutput(
+ output_type="classification_skipped",
+ description="No items to classify (upstream produced none)",
+ )],
+ )
+
+ # Online: use Claude. Offline: heuristic classification
+ if self._settings.has_llm:
+ classified = self._classify_items(items, sprint_focus)
+ else:
+ classified = self._classify_offline(items)
+
+ p0_items = [i for i in classified if i.get("priority") == "P0"]
+ p1_items = [i for i in classified if i.get("priority") == "P1"]
+ p2_items = [i for i in classified if i.get("priority") == "P2"]
+
+ outputs = [
+ AgentOutput(
+ output_type="items_classified",
+ description=f"Classified {len(classified)} items: "
+ f"{len(p0_items)} P0, {len(p1_items)} P1, {len(p2_items)} P2",
+ )
+ ]
+
+ review_items = []
+ if p0_items:
+ review_items = [
+ {
+ "type": "p0_ticket_approval",
+ "item": item,
+ "message": f"P0 ticket needs approval: {item['text'][:100]}"
+ }
+ for item in p0_items
+ ]
+
+ return AgentResult(
+ agent=self.name,
+ success=True,
+ outputs=outputs,
+ requires_human_review=bool(p0_items),
+ review_items=review_items,
+ data={
+ "classified_items": classified,
+ "p0_items": p0_items,
+ "p1_items": p1_items,
+ "p2_items": p2_items,
+ },
+ )
+
+ def _classify_offline(self, items: list[dict]) -> list[dict]:
+ """Heuristic classification when no API key is available."""
+ p0_keywords = {"deadline", "demo", "block", "urgent", "critical", "p0", "client"}
+ p1_keywords = {"should", "need", "sprint", "important", "this week"}
+
+ classified = []
+ for item in items:
+ text = (item.get("text", "") or str(item)).lower()
+ if any(kw in text for kw in p0_keywords):
+ priority = "P0"
+ elif any(kw in text for kw in p1_keywords):
+ priority = "P1"
+ else:
+ priority = "P2"
+ classified.append({**item, "priority": priority})
+ return classified
+
+ def _classify_items(self, items: list[dict], sprint_focus: str) -> list[dict]:
+ """Send items to Claude for priority classification."""
+ items_text = "\n".join(
+ f"- {i.get('text', i)} (owner: {i.get('owner', 'unassigned')})"
+ for i in items
+ )
+
+ prompt = self.load_prompt(
+ "priority_classifier.txt",
+ items=items_text,
+ sprint_focus=sprint_focus,
+ )
+
+ try:
+ raw_response = self.call_claude(prompt)
+ except Exception as exc:
+ logger.warning(f"LLM call failed, falling back to offline: {exc}")
+ return self._classify_offline(items)
+
+ try:
+ return json.loads(raw_response)
+ except json.JSONDecodeError:
+ json_match = re.search(r"\[.*\]", raw_response, re.DOTALL)
+ if json_match:
+ try:
+ return json.loads(json_match.group())
+ except json.JSONDecodeError:
+ pass
+ logger.error("Failed to parse priority classification response")
+ return items
diff --git a/agents/requirements/req_extractor.py b/agents/requirements/req_extractor.py
new file mode 100644
index 0000000..f84df46
--- /dev/null
+++ b/agents/requirements/req_extractor.py
@@ -0,0 +1,468 @@
+"""
+REQ Extractor — synthesizes formal, categorized requirements from
+parsed meeting data.
+
+Takes raw action items, decisions, and discussion points from the
+transcript parser and produces proper requirements with categories:
+ FUNCTIONAL, NON_FUNCTIONAL, USER_GOAL, SOFT_GOAL, CONSTRAINT
+
+Uses LLM when available for intelligent synthesis; falls back to
+domain-aware extraction for eParts using keyword patterns.
+
+Triggered by: transcript_parser + priority_classifier output
+Outputs: REQ-XXX.md files committed to GitHub
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+
+logger = logging.getLogger("agent.req_extractor")
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent
+
+# Domain knowledge for offline extraction
+EPARTS_REQUIREMENT_PATTERNS: list[dict[str, Any]] = [
+ {
+ "keywords": ["extract", "attribute", "spec sheet", "pdf", "catalog", "parse", "ingestion"],
+ "id": "REQ-001",
+ "title": "Automated product attribute extraction",
+ "statement": "The system shall automatically extract product attributes (name, description, specifications, category) from vendor spec sheets in PDF, CSV, and Excel formats.",
+ "category": "FUNCTIONAL",
+ "rationale": "Core project objective — eParts receives catalogs from 50+ vendors in inconsistent formats.",
+ "acceptance_criteria": "Given a vendor spec sheet, the system extracts at least 5 key attributes with >80% accuracy.",
+ },
+ {
+ "keywords": ["confidence", "score", "threshold", "accuracy", "precision"],
+ "id": "REQ-002",
+ "title": "ML confidence scoring",
+ "statement": "The system shall assign a confidence score (0.0-1.0) to every ML-predicted attribute value.",
+ "category": "NON_FUNCTIONAL",
+ "rationale": "Client emphasized need for transparency — operators must know which predictions to trust vs review.",
+ "acceptance_criteria": "Every extracted attribute includes a confidence score; scores correlate with actual accuracy (calibration within 10%).",
+ },
+ {
+ "keywords": ["review", "human", "correct", "approve", "manual", "operator", "queue"],
+ "id": "REQ-003",
+ "title": "Human-in-the-loop review workflow",
+ "statement": "The system shall route low-confidence predictions (below configurable threshold) to a human review queue where operators can approve, correct, or reject values.",
+ "category": "USER_GOAL",
+ "rationale": "Client requires human oversight for business-critical data — cannot auto-publish uncertain predictions.",
+ "acceptance_criteria": "Predictions below threshold appear in review queue; operator can approve/edit/reject; corrections feed back to model.",
+ },
+ {
+ "keywords": ["format", "vendor", "inconsistent", "variation", "standard", "normalize"],
+ "id": "REQ-004",
+ "title": "Multi-format vendor data support",
+ "statement": "The system shall support ingestion of vendor catalogs in at least 3 formats: PDF, CSV, and Excel, normalizing data into a unified schema.",
+ "category": "FUNCTIONAL",
+ "rationale": "Vendors submit data in different formats — the system must handle this variation without manual conversion.",
+ "acceptance_criteria": "System successfully ingests and normalizes test files in PDF, CSV, and XLSX formats.",
+ },
+ {
+ "keywords": ["azure", "cloud", "deploy", "infrastructure", "hosting"],
+ "id": "REQ-005",
+ "title": "Azure cloud deployment",
+ "statement": "The system shall be deployable on Microsoft Azure cloud infrastructure using Azure App Service.",
+ "category": "CONSTRAINT",
+ "rationale": "eParts' existing infrastructure runs on Azure — non-negotiable deployment target.",
+ "acceptance_criteria": "System runs successfully on Azure App Service with all endpoints accessible.",
+ },
+ {
+ "keywords": ["staging", "table", "validation", "before", "production", "write"],
+ "id": "REQ-006",
+ "title": "Staging tables for data validation",
+ "statement": "The system shall write all ML-extracted data to staging tables first, never directly to production, allowing validation before promotion.",
+ "category": "FUNCTIONAL",
+ "rationale": "Architecture decision to prevent bad ML predictions from corrupting production catalog data.",
+ "acceptance_criteria": "No pipeline path writes directly to production tables; all data passes through staging with validation.",
+ },
+ {
+ "keywords": ["metric", "dashboard", "monitor", "report", "track", "performance"],
+ "id": "REQ-007",
+ "title": "Pipeline performance monitoring",
+ "statement": "The system should provide a monitoring dashboard showing pipeline throughput, ML accuracy metrics, and error rates.",
+ "category": "NON_FUNCTIONAL",
+ "rationale": "Client wants visibility into system health and ML model performance over time.",
+ "acceptance_criteria": "Dashboard displays: records processed/day, accuracy by attribute type, error count, and trend charts.",
+ },
+ {
+ "keywords": ["manual", "effort", "automate", "reduce", "time", "efficiency", "save"],
+ "id": "REQ-008",
+ "title": "Reduce manual data entry effort",
+ "statement": "The system should reduce manual product data entry effort by at least 60% compared to the current fully manual process.",
+ "category": "SOFT_GOAL",
+ "rationale": "Primary business value proposition — eParts currently has staff manually keying in vendor data.",
+ "acceptance_criteria": "Measured time-to-catalog for 100 products: AI-assisted < 40% of manual baseline.",
+ },
+ {
+ "keywords": ["per.attribute", "routing", "classification", "category", "predict"],
+ "id": "REQ-009",
+ "title": "Per-attribute ML routing",
+ "statement": "The system shall apply ML classification at the individual attribute level (not per-record), allowing different models/thresholds per attribute type.",
+ "category": "FUNCTIONAL",
+ "rationale": "Architecture decision — different attributes (description vs. specs vs. category) need different ML approaches.",
+ "acceptance_criteria": "Each attribute type can have independent model and threshold configuration.",
+ },
+ {
+ "keywords": ["training", "data", "label", "sample", "200", "dataset"],
+ "id": "REQ-010",
+ "title": "Training data requirements",
+ "statement": "The ML models shall be trainable on a minimum of 200 labeled product examples provided by eParts.",
+ "category": "CONSTRAINT",
+ "rationale": "Client committed to providing labeled training data; team needs minimum viable dataset for model training.",
+ "acceptance_criteria": "Models train and evaluate successfully on provided labeled dataset of >=200 examples.",
+ },
+ {
+ "keywords": ["pricing", "sensitive", "exclude", "not", "price"],
+ "id": "REQ-011",
+ "title": "Exclude pricing from ML pipeline",
+ "statement": "The system shall not include pricing data in the ML extraction pipeline — pricing remains a manual process.",
+ "category": "CONSTRAINT",
+ "rationale": "Client explicitly stated pricing is too sensitive for automated extraction; business risk too high.",
+ "acceptance_criteria": "No pricing fields appear in ML pipeline output; pricing columns remain untouched.",
+ },
+ {
+ "keywords": ["feedback", "loop", "retrain", "improve", "learn", "correction"],
+ "id": "REQ-012",
+ "title": "Feedback loop for model improvement",
+ "statement": "The system should incorporate human corrections back into model retraining to improve accuracy over time.",
+ "category": "USER_GOAL",
+ "rationale": "Operators correcting predictions should make the system smarter — not just fix individual records.",
+ "acceptance_criteria": "After 50+ corrections on an attribute type, retraining measurably improves accuracy on that type.",
+ },
+]
+
+CATEGORY_LABELS = {
+ "FUNCTIONAL": "Functional Requirement",
+ "NON_FUNCTIONAL": "Non-Functional Requirement (Quality Attribute)",
+ "USER_GOAL": "User Goal",
+ "SOFT_GOAL": "Soft Goal",
+ "CONSTRAINT": "Constraint",
+}
+
+
+class ReqExtractorAgent(BaseAgent):
+ """Synthesizes formal, categorized requirements from parsed meeting data."""
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="req_extractor", mcp_clients=mcp_clients)
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ pipeline_ctx = trigger.metadata.get("pipeline_context", {})
+ parsed_minutes = pipeline_ctx.get("parsed_minutes", {})
+ classified_items = pipeline_ctx.get("classified_items", [])
+
+ date = (
+ trigger.metadata.get("date")
+ or pipeline_ctx.get("meeting_date")
+ or datetime.now(timezone.utc).strftime("%Y-%m-%d")
+ )
+ meeting_type = pipeline_ctx.get("meeting_type", "client")
+
+ meeting_data = self._build_meeting_summary(parsed_minutes, classified_items)
+
+ if not meeting_data.strip():
+ return AgentResult(
+ agent=self.name, success=True,
+ outputs=[AgentOutput(
+ output_type="extraction_skipped",
+ description="No meeting data to extract requirements from",
+ )],
+ )
+
+ existing_reqs = self._get_existing_reqs()
+
+ requirements = None
+ if self._settings.has_llm:
+ requirements = self._extract_with_llm(meeting_data, existing_reqs, date)
+
+ if not requirements:
+ requirements = self._extract_offline(parsed_minutes, classified_items, existing_reqs)
+
+ if not requirements:
+ return AgentResult(
+ agent=self.name, success=True,
+ outputs=[AgentOutput(
+ output_type="extraction_skipped",
+ description="No new requirements identified from this meeting",
+ )],
+ )
+
+ outputs = []
+ repo = self.mcp.get("github") or self.mcp.get("bitbucket")
+
+ for req in requirements:
+ req_id = req.get("id", f"REQ-{hash(req.get('title',''))%1000:03d}")
+ content = self._format_req_file(req, date, meeting_type)
+ filename = f"requirements/parsed/{req_id}.md"
+
+ self.wiki.put("requirements", req_id, {
+ "title": req.get("title", ""),
+ "statement": req.get("statement", ""),
+ "category": req.get("category", "FUNCTIONAL"),
+ "priority": req.get("priority", "P1"),
+ "date": date,
+ "meeting_type": meeting_type,
+ }, agent=self.name, pipeline="requirements")
+
+ if repo:
+ result = repo.commit_file(
+ file_path=filename,
+ content=content,
+ message=f"Add {req.get('category', 'REQ')} {req_id}: {req.get('title', '')[:50]}",
+ agent_name=self.name,
+ )
+ if result.get("ok"):
+ outputs.append(AgentOutput(
+ output_type="file_committed",
+ description=f"{req_id} [{req.get('category', '?')}] {req.get('title', '')}",
+ reference=filename,
+ ))
+ else:
+ outputs.append(AgentOutput(
+ output_type="req_extracted",
+ description=f"{req_id} [{req.get('category', '?')}] {req.get('title', '')}",
+ reference=req_id,
+ ))
+ else:
+ outputs.append(AgentOutput(
+ output_type="req_extracted",
+ description=f"{req_id} [{req.get('category', '?')}] {req.get('title', '')}",
+ reference=req_id,
+ ))
+
+ categories = {}
+ for r in requirements:
+ cat = r.get("category", "UNKNOWN")
+ categories[cat] = categories.get(cat, 0) + 1
+ cat_summary = ", ".join(f"{c}: {n}" for c, n in sorted(categories.items()))
+
+ self.emit("requirements_extracted", {
+ "count": len(requirements),
+ "categories": categories,
+ "date": date,
+ })
+
+ return AgentResult(
+ agent=self.name, success=True, outputs=outputs,
+ data={
+ "requirements": requirements,
+ "requirements_count": len(requirements),
+ "categories": categories,
+ },
+ )
+
+ def _build_meeting_summary(self, parsed_minutes: dict, classified_items: list) -> str:
+ """Build a text summary of meeting data for the LLM."""
+ parts = []
+ if isinstance(parsed_minutes, dict):
+ for key in ["decisions", "action_items", "open_questions",
+ "new_requirements", "key_discussion_points"]:
+ items = parsed_minutes.get(key, [])
+ if items:
+ parts.append(f"\n{key.upper()}:")
+ for item in items:
+ if isinstance(item, dict):
+ parts.append(f" - {item.get('text', str(item))}")
+ else:
+ parts.append(f" - {item}")
+
+ if classified_items:
+ parts.append("\nCLASSIFIED ITEMS:")
+ for item in classified_items:
+ if isinstance(item, dict):
+ parts.append(f" - [{item.get('priority', '?')}] {item.get('text', str(item))}")
+
+ return "\n".join(parts)
+
+ def _get_existing_reqs(self) -> str:
+ """Get already-extracted requirements to avoid duplicates."""
+ try:
+ entries = self.wiki.list_namespace("requirements")
+ if entries:
+ lines = []
+ for e in entries[:20]:
+ val = e.get("value", {})
+ if isinstance(val, dict):
+ lines.append(f"- {e.get('key', '?')}: {val.get('title', val.get('text', ''))[:80]}")
+ return "\n".join(lines)
+ except Exception:
+ pass
+ return "(none yet)"
+
+ def _extract_with_llm(self, meeting_data: str, existing_reqs: str, date: str) -> list[dict] | None:
+ """Use LLM to synthesize proper requirements from meeting data."""
+ prompt = self.load_prompt(
+ "req_extractor.txt",
+ meeting_data=meeting_data[:6000],
+ existing_reqs=existing_reqs[:1000],
+ )
+
+ try:
+ raw = self.call_claude(prompt)
+ except Exception as exc:
+ logger.warning(f"LLM call failed, falling back to offline: {exc}")
+ return None
+
+ try:
+ reqs = json.loads(raw)
+ if isinstance(reqs, list):
+ return reqs
+ except json.JSONDecodeError:
+ match = re.search(r"\[.*\]", raw, re.DOTALL)
+ if match:
+ try:
+ return json.loads(match.group())
+ except json.JSONDecodeError:
+ pass
+ logger.warning("Failed to parse LLM response for requirements")
+ return None
+
+ def _extract_offline(self, parsed_minutes: dict, classified_items: list,
+ existing_reqs: str) -> list[dict]:
+ """
+ Domain-aware offline extraction using eParts knowledge.
+
+ Matches meeting discussion keywords against known requirement patterns.
+ Also pulls context from ALL previous meeting JSONs in minutes/ so even
+ a thin transcript produces meaningful requirements.
+ """
+ all_text = ""
+ if isinstance(parsed_minutes, dict):
+ for key in ["decisions", "action_items", "open_questions",
+ "new_requirements", "key_discussion_points"]:
+ for item in parsed_minutes.get(key, []):
+ if isinstance(item, dict):
+ all_text += " " + item.get("text", "")
+ else:
+ all_text += " " + str(item)
+
+ for item in classified_items:
+ if isinstance(item, dict):
+ all_text += " " + item.get("text", "")
+
+ # Pull context from all previous meeting JSONs for richer matching
+ all_text += " " + self._load_all_meeting_context()
+
+ all_text = all_text.lower()
+
+ already_extracted = set()
+ for line in existing_reqs.split("\n"):
+ match = re.search(r"(REQ-\d+)", line)
+ if match:
+ already_extracted.add(match.group(1))
+
+ matched = []
+ for pattern in EPARTS_REQUIREMENT_PATTERNS:
+ if pattern["id"] in already_extracted:
+ continue
+
+ score = sum(1 for kw in pattern["keywords"] if re.search(kw, all_text))
+ if score >= 1:
+ matched.append((score, pattern))
+
+ matched.sort(key=lambda x: -x[0])
+ requirements = []
+ for score, pattern in matched[:8]:
+ req = {
+ "id": pattern["id"],
+ "title": pattern["title"],
+ "statement": pattern["statement"],
+ "category": pattern["category"],
+ "priority": "P0" if score >= 3 else "P1" if score >= 2 else "P2",
+ "rationale": pattern["rationale"],
+ "source_speaker": "team discussion",
+ "acceptance_criteria": pattern["acceptance_criteria"],
+ "related_concerns": [],
+ }
+ requirements.append(req)
+
+ return requirements
+
+ def _load_all_meeting_context(self) -> str:
+ """Load text from all meeting JSONs for comprehensive keyword matching."""
+ minutes_dir = PROJECT_ROOT / "minutes"
+ if not minutes_dir.exists():
+ return ""
+ texts = []
+ for jf in sorted(minutes_dir.glob("*.json")):
+ try:
+ data = json.loads(jf.read_text())
+ for key in ["detected_topics", "questions_sample", "decisions_sample",
+ "actions_sample"]:
+ items = data.get(key, []) if isinstance(data.get(key), list) else []
+ for item in items:
+ if isinstance(item, dict):
+ texts.append(item.get("text", ""))
+ elif isinstance(item, str):
+ texts.append(item)
+ if isinstance(data.get("detected_topics"), dict):
+ texts.extend(data["detected_topics"].keys())
+ except Exception:
+ continue
+ return " ".join(texts)
+
+ def _format_req_file(self, req: dict, date: str, meeting_type: str) -> str:
+ """Format a requirement as a professional markdown document."""
+ req_id = req.get("id", "REQ-???")
+ title = req.get("title", "Untitled")
+ category = req.get("category", "FUNCTIONAL")
+ category_label = CATEGORY_LABELS.get(category, category)
+ priority = req.get("priority", "P1")
+ statement = req.get("statement", "")
+ rationale = req.get("rationale", "")
+ acceptance = req.get("acceptance_criteria", "To be defined.")
+ source = req.get("source_speaker", "team discussion")
+ concerns = req.get("related_concerns", [])
+
+ concerns_md = ""
+ if concerns:
+ concerns_md = "\n".join(f"- {c}" for c in concerns)
+ else:
+ concerns_md = "None identified."
+
+ return f"""# {req_id}: {title}
+
+| Field | Value |
+|-------|-------|
+| **ID** | {req_id} |
+| **Category** | {category_label} |
+| **Priority** | {priority} |
+| **Date Identified** | {date} |
+| **Source Meeting** | {meeting_type} |
+| **Source** | {source} |
+| **Status** | draft |
+
+## Requirement Statement
+
+{statement}
+
+## Rationale
+
+{rationale}
+
+## Acceptance Criteria
+
+{acceptance}
+
+## Related Concerns / Open Questions
+
+{concerns_md}
+
+## Traceability
+
+- Jira Ticket: _pending auto-link_
+- Architecture Decision: _pending_
+- Test Coverage: _pending_
+
+---
+_Auto-generated by req_extractor agent on {date}_
+"""
diff --git a/agents/requirements/stale_detector.py b/agents/requirements/stale_detector.py
new file mode 100644
index 0000000..faace61
--- /dev/null
+++ b/agents/requirements/stale_detector.py
@@ -0,0 +1,125 @@
+"""
+Stale REQ Detector — flags requirements that have gone stale.
+
+Runs Monday 8am via cron. Flags REQs with no Jira ticket and not
+mentioned in any meeting transcript in the past 14 days.
+
+Triggered by: cron_monday_8am
+Outputs: Slack alert + /docs/stale-requirements.md
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from datetime import datetime, timedelta, timezone
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+
+logger = logging.getLogger("agent.stale_detector")
+
+STALE_THRESHOLD_DAYS = int(os.getenv("STALE_REQ_THRESHOLD_DAYS", "14"))
+
+
+class StaleDetectorAgent(BaseAgent):
+ """
+ Detects requirements that have gone stale — no Jira ticket
+ and no mention in recent meetings.
+ """
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="stale_detector", mcp_clients=mcp_clients)
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ # In production: scan /requirements/parsed/ in Bitbucket,
+ # cross-reference against Jira tickets and recent meeting minutes
+ stale_reqs = self._find_stale_requirements()
+ outputs = []
+
+ if stale_reqs:
+ report = self._format_stale_report(stale_reqs)
+
+ slack = self.mcp.get("slack")
+ if slack:
+ slack.send_alert(
+ f"*Stale Requirements Alert*\n"
+ f"{len(stale_reqs)} requirement(s) with no activity in "
+ f"{STALE_THRESHOLD_DAYS} days.\n\n{report}"
+ )
+ outputs.append(AgentOutput(
+ output_type="message_sent",
+ description=f"Stale REQ alert: {len(stale_reqs)} items",
+ ))
+
+ bitbucket = self.mcp.get("bitbucket")
+ if bitbucket:
+ bitbucket.commit_file(
+ file_path="docs/stale-requirements.md",
+ content=f"# Stale Requirements — {datetime.now(timezone.utc).strftime('%Y-%m-%d')}\n\n{report}",
+ message=f"Update stale requirements report ({len(stale_reqs)} items)",
+ agent_name=self.name,
+ )
+ outputs.append(AgentOutput(
+ output_type="file_committed",
+ description="docs/stale-requirements.md updated",
+ ))
+ else:
+ outputs.append(AgentOutput(
+ output_type="stale_check",
+ description="No stale requirements detected",
+ ))
+
+ return AgentResult(agent=self.name, success=True, outputs=outputs)
+
+ def _find_stale_requirements(self) -> list[dict]:
+ """
+ Scan the wiki for requirements with no recent activity,
+ cross-reference against Jira tickets.
+ """
+ stale = []
+ try:
+ reqs = self.wiki.list_namespace("requirements_engineering")
+ if not reqs:
+ return []
+
+ cutoff = (datetime.now(timezone.utc) - timedelta(days=STALE_THRESHOLD_DAYS)).isoformat()
+
+ jira = self.mcp.get("jira")
+ jira_keys = set()
+ if jira and getattr(jira, "is_configured", False):
+ result = jira.search_issues(
+ jql=f"project = {jira._project_key} AND labels = requirements"
+ )
+ if result.get("ok"):
+ jira_keys = {i["summary"].lower() for i in result.get("issues", [])}
+
+ for entry in reqs:
+ if not isinstance(entry, dict):
+ continue
+ updated = entry.get("updated_at", entry.get("timestamp", ""))
+ text = str(entry.get("value", entry.get("key", "")))
+
+ has_ticket = any(kw in text.lower() for kw in jira_keys) if jira_keys else False
+
+ if updated < cutoff and not has_ticket:
+ stale.append({
+ "req_id": entry.get("key", "unknown"),
+ "text": text[:100],
+ "last_activity": updated[:10] if updated else "unknown",
+ "jira_ticket": "none",
+ })
+ except Exception as exc:
+ logger.debug(f"Stale detection error: {exc}")
+
+ return stale
+
+ def _format_stale_report(self, stale_reqs: list[dict]) -> str:
+ lines = []
+ for req in stale_reqs:
+ lines.append(
+ f"- **{req.get('req_id', '?')}**: {req.get('text', '?')}\n"
+ f" Last activity: {req.get('last_activity', 'unknown')} | "
+ f"Jira: {req.get('jira_ticket', 'none')}"
+ )
+ return "\n".join(lines) if lines else "No stale requirements."
diff --git a/agents/requirements/transcript_parser.py b/agents/requirements/transcript_parser.py
new file mode 100644
index 0000000..0b317cf
--- /dev/null
+++ b/agents/requirements/transcript_parser.py
@@ -0,0 +1,268 @@
+"""
+Transcript Parser — extracts structured data from meeting transcripts.
+
+Sends .vtt/.txt transcripts to Claude. Extracts: meeting date, attendees,
+decisions, action items with owners, open questions, new requirements.
+Output: structured JSON committed as markdown.
+
+Triggered by: transcript upload (Google Drive poll or manual)
+Outputs: parsed meeting minutes committed to /minutes/
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from agents.base import AgentOutput, AgentResult, AgentTrigger, BaseAgent
+
+logger = logging.getLogger("agent.transcript_parser")
+
+
+class TranscriptParserAgent(BaseAgent):
+ """
+ Parses meeting transcripts into structured data using Claude.
+ Produces meeting minutes in markdown format.
+ """
+
+ def __init__(self, mcp_clients: dict[str, Any] | None = None):
+ super().__init__(name="transcript_parser", mcp_clients=mcp_clients)
+
+ def run(self, trigger: AgentTrigger) -> AgentResult:
+ source = trigger.source
+ metadata = trigger.metadata
+
+ # Support both direct source path and pipeline context
+ pipeline_ctx = metadata.get("pipeline_context", {})
+ source_path = pipeline_ctx.get("source", source)
+
+ transcript_path = Path(source_path)
+ if not transcript_path.exists():
+ return AgentResult(
+ agent=self.name,
+ success=False,
+ errors=[f"Transcript not found: {source_path}"],
+ )
+
+ raw_text = transcript_path.read_text(encoding="utf-8")
+ date = metadata.get("date", datetime.now(timezone.utc).strftime("%Y-%m-%d"))
+ meeting_type = metadata.get("meeting_type", "client")
+
+ # Clean VTT formatting if present
+ cleaned = self._clean_vtt(raw_text)
+
+ # Try Claude-powered extraction (online) or fall back to structural (offline)
+ parsed = None
+ if self._settings.has_llm:
+ parsed = self._parse_with_claude(cleaned, date, meeting_type)
+
+ if not parsed:
+ parsed = self._parse_offline(raw_text, date, transcript_path.name)
+
+ if not parsed:
+ return AgentResult(
+ agent=self.name,
+ success=False,
+ errors=["Failed to parse transcript"],
+ )
+
+ # Format as markdown
+ minutes_md = self._format_minutes(parsed, date, meeting_type)
+
+ # Commit to repo
+ outputs = []
+ bitbucket = self.mcp.get("bitbucket")
+ if bitbucket:
+ filename = f"minutes/{date}-{meeting_type}.md"
+ result = bitbucket.commit_file(
+ file_path=filename,
+ content=minutes_md,
+ message=f"Parsed minutes from {meeting_type} on {date}",
+ agent_name=self.name,
+ )
+ if result.get("ok"):
+ outputs.append(AgentOutput(
+ output_type="file_committed",
+ description=f"Meeting minutes committed: {filename}",
+ reference=filename,
+ ))
+
+ action_count = len(parsed.get("action_items", []))
+ decision_count = len(parsed.get("decisions", []))
+ req_count = len(parsed.get("new_requirements", []))
+ mode = f"online ({self._settings.active_provider})" if self._settings.has_llm else "offline (structural)"
+
+ outputs.append(AgentOutput(
+ output_type="transcript_parsed",
+ description=f"[{mode}] Extracted {action_count} action items, "
+ f"{decision_count} decisions, {req_count} new requirements",
+ reference=str(transcript_path),
+ ))
+
+ # Deposit to shared wiki and emit cross-pipeline events
+ if action_count > 0:
+ self.emit("action_items_extracted", {
+ "count": action_count,
+ "meeting_date": date,
+ "meeting_type": meeting_type,
+ "source": str(transcript_path),
+ "items": parsed.get("action_items", [])[:10],
+ })
+ if decision_count > 0:
+ self.emit("decision_logged", {
+ "count": decision_count,
+ "meeting_date": date,
+ "decisions": parsed.get("decisions", [])[:10],
+ })
+ self.wiki.put("meetings", f"{date}-{meeting_type}", {
+ "date": date,
+ "type": meeting_type,
+ "source": str(transcript_path),
+ "action_items": action_count,
+ "decisions": decision_count,
+ "new_requirements": req_count,
+ "participants": parsed.get("attendees", []),
+ }, agent=self.name, tags=[meeting_type, date])
+
+ return AgentResult(
+ agent=self.name,
+ success=True,
+ outputs=outputs,
+ requires_human_review=False,
+ data={
+ "parsed_minutes": parsed,
+ "transcript_cleaned": cleaned[:5000],
+ "meeting_date": date,
+ "meeting_type": meeting_type,
+ "source_file": str(transcript_path),
+ },
+ )
+
+ def _parse_offline(self, transcript: str, date: str, filename: str) -> dict | None:
+ """Structural extraction without LLM — used when no API key is set."""
+ from pipeline.vtt_processor import parse_vtt, generate_offline_summary
+ meeting = parse_vtt(transcript, filename)
+ summary = generate_offline_summary(meeting)
+
+ decisions = [
+ {"text": d["text"][:200], "context": f"said by {d['speaker']}"}
+ for d in summary.get("decisions_sample", [])
+ ]
+ action_items = [
+ {"text": a["text"][:200], "owner": a["speaker"], "deadline": ""}
+ for a in summary.get("actions_sample", [])
+ ]
+ questions = [
+ {"text": q["text"], "context": "", "assigned_to": q["speaker"]}
+ for q in summary.get("questions_sample", [])
+ ]
+
+ return {
+ "meeting_date": date,
+ "meeting_type": "client",
+ "attendees": summary.get("participants", []),
+ "decisions": decisions,
+ "action_items": action_items,
+ "open_questions": questions,
+ "new_requirements": [],
+ "key_discussion_points": [
+ f"Topics discussed: {', '.join(summary.get('detected_topics', {}).keys())}",
+ f"Duration: {summary.get('duration_minutes', 0)} minutes",
+ f"Total words: {summary.get('total_words', 0)} across {summary.get('total_turns', 0)} turns",
+ ],
+ "_analysis_mode": "offline",
+ "_speaker_stats": summary.get("speaker_stats", {}),
+ }
+
+ def _clean_vtt(self, text: str) -> str:
+ """Strip WebVTT timestamps and metadata, keeping only speech content."""
+ lines = text.split("\n")
+ cleaned = []
+ for line in lines:
+ line = line.strip()
+ if not line:
+ continue
+ if line.startswith("WEBVTT") or line.startswith("NOTE"):
+ continue
+ if re.match(r"^\d+$", line):
+ continue
+ if re.match(r"\d{2}:\d{2}:\d{2}\.\d{3}\s*-->", line):
+ continue
+ cleaned.append(line)
+ return "\n".join(cleaned)
+
+ def _parse_with_claude(self, transcript: str, date: str, meeting_type: str) -> dict | None:
+ """Send transcript to LLM for structured extraction. Returns None on failure."""
+ prompt = self.load_prompt(
+ "transcript_parser.txt",
+ transcript=transcript[:12000],
+ date=date,
+ meeting_type=meeting_type,
+ )
+
+ try:
+ raw_response = self.call_claude(prompt)
+ except Exception as exc:
+ logger.warning(f"LLM call failed, will fall back to offline: {exc}")
+ return None
+
+ try:
+ return json.loads(raw_response)
+ except json.JSONDecodeError:
+ json_match = re.search(r"\{.*\}", raw_response, re.DOTALL)
+ if json_match:
+ try:
+ return json.loads(json_match.group())
+ except json.JSONDecodeError:
+ pass
+ logger.error("Failed to parse Claude response as JSON")
+ return None
+
+ def _format_minutes(self, parsed: dict, date: str, meeting_type: str) -> str:
+ """Format parsed transcript data as markdown minutes."""
+ lines = [
+ f"# Meeting Minutes — {date}",
+ f"**Type:** {meeting_type}",
+ f"**Attendees:** {', '.join(parsed.get('attendees', ['unknown']))}",
+ "",
+ ]
+
+ if parsed.get("key_discussion_points"):
+ lines.append("## Key Discussion Points")
+ for point in parsed["key_discussion_points"]:
+ lines.append(f"- {point}")
+ lines.append("")
+
+ if parsed.get("decisions"):
+ lines.append("## Decisions")
+ for d in parsed["decisions"]:
+ lines.append(f"- **{d['text']}**")
+ if d.get("context"):
+ lines.append(f" - Context: {d['context']}")
+ lines.append("")
+
+ if parsed.get("action_items"):
+ lines.append("## Action Items")
+ for a in parsed["action_items"]:
+ deadline = f" (due: {a['deadline']})" if a.get("deadline") else ""
+ lines.append(f"- [ ] {a['text']} — **{a.get('owner', 'unassigned')}**{deadline}")
+ lines.append("")
+
+ if parsed.get("open_questions"):
+ lines.append("## Open Questions")
+ for q in parsed["open_questions"]:
+ assigned = f" → {q['assigned_to']}" if q.get("assigned_to") else ""
+ lines.append(f"- {q['text']}{assigned}")
+ lines.append("")
+
+ if parsed.get("new_requirements"):
+ lines.append("## New Requirements Identified")
+ for r in parsed["new_requirements"]:
+ lines.append(f"- {r['text']} (source: {r.get('source', 'unknown')})")
+ lines.append("")
+
+ return "\n".join(lines)
diff --git a/coach_meetings/GMT20260220-180425_Recording.transcript (1).vtt b/coach_meetings/GMT20260220-180425_Recording.transcript (1).vtt
new file mode 100644
index 0000000..dfa3a77
--- /dev/null
+++ b/coach_meetings/GMT20260220-180425_Recording.transcript (1).vtt
@@ -0,0 +1,1794 @@
+WEBVTT
+
+1
+00:00:00.000 --> 00:00:03.899
+hrishikb@andrew.cmu.edu: Consequence format, and, let's start off then.
+
+2
+00:00:04.250 --> 00:00:11.060
+hrishikb@andrew.cmu.edu: The first one is, we are… the development cannot proceed due to lack of data that we're having.
+
+3
+00:00:11.270 --> 00:00:19.769
+hrishikb@andrew.cmu.edu: So… the… first of all, we… and there's also some other access issues that we are facing.
+
+4
+00:00:19.950 --> 00:00:23.700
+hrishikb@andrew.cmu.edu: So we… I don't think we still have any access to Purser.
+
+5
+00:00:24.580 --> 00:00:31.099
+hrishikb@andrew.cmu.edu: And we also don't have any of the sample data that we need to proceed with any of the…
+
+6
+00:00:31.500 --> 00:00:36.760
+hrishikb@andrew.cmu.edu: MLT work, and… Any of the other. So…
+
+7
+00:00:37.860 --> 00:00:41.490
+hrishikb@andrew.cmu.edu: This is, decline-dependent, and, this could,
+
+8
+00:00:41.630 --> 00:00:45.979
+hrishikb@andrew.cmu.edu: Like, protect how we start the initial pages of the group, basically.
+
+9
+00:00:46.720 --> 00:00:52.699
+hrishikb@andrew.cmu.edu: So… The second risk relates to the first one.
+
+10
+00:00:52.960 --> 00:01:12.409
+hrishikb@andrew.cmu.edu: As we do not have the data, there are several different kinds of ML models that we wanted to try, and to see which has the best performance. So, like, the goal of the entire project is to be better than the manual one, but if we do not test the different ML models, we will not have a good enough idea of which to proceed with.
+
+11
+00:01:12.480 --> 00:01:16.560
+hrishikb@andrew.cmu.edu: And that just increases the uncertainty a lot.
+
+12
+00:01:19.370 --> 00:01:26.839
+hrishikb@andrew.cmu.edu: Third one, I think, is… More of a… future concern. If,
+
+13
+00:01:27.020 --> 00:01:31.010
+hrishikb@andrew.cmu.edu: Depending on if we make the… how we make the project, and how…
+
+14
+00:01:31.340 --> 00:01:41.000
+hrishikb@andrew.cmu.edu: whether scope creep does happen, because we've kept it very minimal right now. First of all, because we don't have, we've not gotten our hands dirty yet.
+
+15
+00:01:41.120 --> 00:01:50.219
+hrishikb@andrew.cmu.edu: And also because we don't know how the data looks, so that might happen, but this is more of a future concern, rather than something we're facing.
+
+16
+00:01:53.050 --> 00:01:55.410
+hrishikb@andrew.cmu.edu: Fourth one is…
+
+17
+00:01:57.180 --> 00:02:11.610
+hrishikb@andrew.cmu.edu: the client is pretty much, has strong preferences, and, is pretty much locked into the Azure environment. So, we… we will not be using any tools that, basically
+
+18
+00:02:12.700 --> 00:02:32.649
+hrishikb@andrew.cmu.edu: has to, like, force them to learn new things that they don't want to learn. And as a consequence, basically, we're, like, making our choices such that we're not using Terraform, we're using Bicep. Alright, is that not making them learn things through the online environment? We can go back, we'll go back over a bit. We'll go back there. And…
+
+19
+00:02:36.950 --> 00:02:46.790
+hrishikb@andrew.cmu.edu: The fifth one is not that big of an issue now, but might become one in the future. There could be some schema volatility, depending on,
+
+20
+00:02:47.130 --> 00:02:53.449
+hrishikb@andrew.cmu.edu: If, like, how much, changes we're gonna have to make to the ML output that we get.
+
+21
+00:02:53.650 --> 00:02:57.360
+hrishikb@andrew.cmu.edu: And how that goes into films.
+
+22
+00:02:57.660 --> 00:03:02.720
+hrishikb@andrew.cmu.edu: Okay. The rest is just a… my take on…
+
+23
+00:03:03.030 --> 00:03:06.610
+hrishikb@andrew.cmu.edu: The risk mitigation strategies we can have for each one of them.
+
+24
+00:03:07.320 --> 00:03:08.430
+hrishikb@andrew.cmu.edu: So…
+
+25
+00:03:08.950 --> 00:03:24.119
+hrishikb@andrew.cmu.edu: Scheduling calls with basically the client, and, like, sending changes to them so that we get the access to the cursor, and even more importantly than that, that we get access to the data.
+
+26
+00:03:24.660 --> 00:03:36.219
+hrishikb@andrew.cmu.edu: Second one, I did write something, but it's mainly just, like, dependent on the first being resolved, so that we can just try out the… I think we have 3 ML candidates, Brittany.
+
+27
+00:03:36.890 --> 00:03:45.479
+hrishikb@andrew.cmu.edu: Third one is, we will start missing milestones if, if, we, this continues, and,
+
+28
+00:03:45.580 --> 00:03:51.689
+hrishikb@andrew.cmu.edu: you're not able to, like, start work on… well, that's not… scope creep is not… it's different, right?
+
+29
+00:03:52.610 --> 00:04:01.720
+hrishikb@andrew.cmu.edu: You may have smart as risk number one. This one, this would be more of a futuristic. I think I should change it and just,
+
+30
+00:04:01.850 --> 00:04:15.010
+hrishikb@andrew.cmu.edu: Instead of being scope creep, right? For right now, scope creep is far outside than what we are currently doing. Yeah, scope creep is what? Scope creep would be if you were designing the system, and instead of having
+
+31
+00:04:15.010 --> 00:04:25.790
+hrishikb@andrew.cmu.edu: you know, a certain number of formats that they expect us to. Okay, sure, sure, okay, yep. They, they, we suddenly decide, or they decide that it would be better if we could handle more. Right.
+
+32
+00:04:26.170 --> 00:04:27.969
+hrishikb@andrew.cmu.edu: Or different tech kinds.
+
+33
+00:04:30.500 --> 00:04:44.530
+hrishikb@andrew.cmu.edu: Architectural complexity is, is, we have already decided that, when we were talking with them, that we would only do the minimal amount of darkerization, and…
+
+34
+00:04:45.400 --> 00:04:51.750
+hrishikb@andrew.cmu.edu: keep anything, like, open television and such into, like, stress goes, so that we… we're not…
+
+35
+00:04:51.980 --> 00:04:54.449
+hrishikb@andrew.cmu.edu: Going to start with the reward.
+
+36
+00:04:55.120 --> 00:05:03.059
+hrishikb@andrew.cmu.edu: And this overhead from schema is not relevant. So, so let's look at these one by one, okay? So,
+
+37
+00:05:03.200 --> 00:05:09.209
+hrishikb@andrew.cmu.edu: Well, two things. Let's do the risk, all right, and then what I also want to do is sort of share with you
+
+38
+00:05:10.310 --> 00:05:18.210
+hrishikb@andrew.cmu.edu: And I'm actually going to spend this weekend reflecting on it, make sure what I'm sharing is actually correct, but I'll share it anyway. It kind of what…
+
+39
+00:05:18.670 --> 00:05:23.840
+hrishikb@andrew.cmu.edu: You know, what are those kinds of expectations that we would have from a project management perspective?
+
+40
+00:05:24.070 --> 00:05:36.440
+hrishikb@andrew.cmu.edu: by, you know, by soon, by the end of the semester, etc, so you can make sure you queue that stuff up, right? So we'll do that as well. But happy to focus on… on the risk part.
+
+41
+00:05:37.000 --> 00:05:43.489
+hrishikb@andrew.cmu.edu: the… So, I'm just gonna give my, like, direct feedback, right? So…
+
+42
+00:05:43.720 --> 00:05:49.020
+hrishikb@andrew.cmu.edu: Risk one, I think, is actually two different risks here, right? So it's really… there's one about
+
+43
+00:05:49.220 --> 00:05:59.059
+hrishikb@andrew.cmu.edu: getting, you know, some access to some tooling, and the other is access to the data. Those are… I would… I would put those as two different risks, because
+
+44
+00:05:59.280 --> 00:06:05.250
+hrishikb@andrew.cmu.edu: The… there's a… the consequences are eroded as one consequence, but they are really different consequences.
+
+45
+00:06:05.340 --> 00:06:19.449
+hrishikb@andrew.cmu.edu: Depending on… depending on the access. And… and also, potentially, the mitigations for them could be a little bit different as well. But those are actually very legitimate… legitimate risks, right? 100% agree with you there.
+
+46
+00:06:20.830 --> 00:06:39.239
+hrishikb@andrew.cmu.edu: Now, let's go to the mitigation for risk number one, and I think… by the way, I also think you did a great job of, sort of, you know, the way you wrote the risks as well. I think it's really well done. If you ever do, sort of, a presentation where you want to show risks, it'll be a lot… need to be a lot less wordy, right?
+
+47
+00:06:39.310 --> 00:06:43.710
+hrishikb@andrew.cmu.edu: But having this as the backup is really good. Did a really nice job there.
+
+48
+00:06:45.090 --> 00:06:50.980
+hrishikb@andrew.cmu.edu: So, if I think about… Just in general, about risk.
+
+49
+00:06:51.120 --> 00:06:54.360
+hrishikb@andrew.cmu.edu: Risks and how to… how to manage their risks.
+
+50
+00:06:55.820 --> 00:06:56.890
+hrishikb@andrew.cmu.edu: there's…
+
+51
+00:06:57.370 --> 00:07:07.729
+hrishikb@andrew.cmu.edu: how do you… you know, there's multiple ways to attack risks, right? One, we can just say, okay, good, no problem, all of it, right? But that's not what you want to do here. You can…
+
+52
+00:07:08.570 --> 00:07:15.640
+hrishikb@andrew.cmu.edu: mitigate risks, right? What does it mean… what does it mean to mitigate a risk? Let me ask that question. What does that really mean?
+
+53
+00:07:17.150 --> 00:07:34.399
+hrishikb@andrew.cmu.edu: I mean, so to mitigate the effects of a risk, so it doesn't negatively impact our team or our project? Okay, right. There's also something where you could reduce the likelihood of something occurring. Those are two different things, right? So one is, hey, it may occur in one of the impact, and the other's a likelihood. So…
+
+54
+00:07:34.770 --> 00:07:43.250
+hrishikb@andrew.cmu.edu: you know, schedule the 30-45 minute meeting, right? What is that? Is that really a mitigation, or is that a reduced likelihood of it occurring?
+
+55
+00:07:44.140 --> 00:07:51.800
+hrishikb@andrew.cmu.edu: I guess it would be reduced likelihood, because even after the meeting, there's still a chance that, you know, no further…
+
+56
+00:07:51.940 --> 00:07:52.680
+hrishikb@andrew.cmu.edu: Right.
+
+57
+00:07:52.850 --> 00:07:59.240
+hrishikb@andrew.cmu.edu: What would reduce… what would be an example of, like, a reduced, sort of, impact if it… if it does happen?
+
+58
+00:08:00.620 --> 00:08:14.110
+hrishikb@andrew.cmu.edu: And I don't care about, like, the fine… you know, I don't care about the nuances, this, reduced likelihood or reduced impact, but we should think about… we should… when we think about a risk, we should think about both of those, right, because they're both strategies.
+
+59
+00:08:14.280 --> 00:08:19.379
+hrishikb@andrew.cmu.edu: That we can employ. Sometimes we can do one, sometimes we can do the other, sometimes we can do both.
+
+60
+00:08:20.070 --> 00:08:25.130
+hrishikb@andrew.cmu.edu: So what might be, A way to reduce impact.
+
+61
+00:08:25.880 --> 00:08:30.579
+hrishikb@andrew.cmu.edu: Wait to release… it's not to wait for the, like, the…
+
+62
+00:08:30.980 --> 00:08:39.809
+hrishikb@andrew.cmu.edu: A large amount of data to come in and just ask them for, like, a much smaller amount, and then just kind of go with that first.
+
+63
+00:08:39.950 --> 00:08:52.939
+hrishikb@andrew.cmu.edu: That would reduce… like, there would still be an impact, because we would not be able to, like, train any exploratory models, but we would kind of get a feel of what the data is structured like, or what are, like, the usual entries in some of them.
+
+64
+00:08:54.120 --> 00:08:55.660
+hrishikb@andrew.cmu.edu: And anyone else?
+
+65
+00:08:55.960 --> 00:09:00.599
+hrishikb@andrew.cmu.edu: I think for the cursor issue, it might be that we can use our own
+
+66
+00:09:00.810 --> 00:09:16.960
+hrishikb@andrew.cmu.edu: LLMs to help us, like, we currently are, without using clients' proprietary data. Just to, like, we can hold off working on the proprietary data, like, we don't have any… You don't have the data, so whatever reason? So, yeah, right now we're using our own LLM, so that part is, I guess, currently being mitigated.
+
+67
+00:09:17.610 --> 00:09:25.120
+hrishikb@andrew.cmu.edu: As for the data, I'm not sure what we can do, like, we need something from them.
+
+68
+00:09:25.450 --> 00:09:37.920
+hrishikb@andrew.cmu.edu: is there any way to… you know, so I realize that for those sort of machine learning models, you really want to see the data from them, but for a lot of the other work… so…
+
+69
+00:09:38.910 --> 00:09:40.430
+hrishikb@andrew.cmu.edu: There's a…
+
+70
+00:09:41.980 --> 00:09:47.550
+hrishikb@andrew.cmu.edu: Ideally, you'd be in a situation, and I certainly remember from your requirements document, where you were trying to…
+
+71
+00:09:48.040 --> 00:10:02.640
+hrishikb@andrew.cmu.edu: I forget what… I forget which quality attribute you called it. Was it extensibility? I can't remember, or you need to, you know… you're doing this for three types of data sources, and it needs to be relatively simple to add a fourth, a fifth, a sixth on, right? There's… there's something there.
+
+72
+00:10:03.220 --> 00:10:07.080
+hrishikb@andrew.cmu.edu: is there any way to use synthetic data?
+
+73
+00:10:07.420 --> 00:10:12.070
+hrishikb@andrew.cmu.edu: Like, just… you know what? You go… Because, cause, yes.
+
+74
+00:10:12.080 --> 00:10:31.420
+hrishikb@andrew.cmu.edu: that's not gonna… But then you have to know the format, right? Yeah, so you're gonna make something up, right? You're gonna make… you're gonna… again, I'm just throwing an idea out there, right? You totally make it up, right? Because that training part is just a piece of the whole… the whole pipeline, right? So there's probably a lot you can do.
+
+75
+00:10:31.810 --> 00:10:38.629
+hrishikb@andrew.cmu.edu: And you may need to do some rework, sure, but there's probably a bunch that you can do, even if you don't know… you don't have any real data.
+
+76
+00:10:38.740 --> 00:10:41.149
+hrishikb@andrew.cmu.edu: And it's not uncommon in…
+
+77
+00:10:42.590 --> 00:10:51.899
+hrishikb@andrew.cmu.edu: You know, getting data sometimes is very, you know, doesn't happen quickly, and there's a lot of times where people need to sort of make some forward progress, even before they have it.
+
+78
+00:10:53.630 --> 00:10:56.930
+hrishikb@andrew.cmu.edu: Not ideal, I understand, but I think also.
+
+79
+00:10:57.310 --> 00:11:11.440
+hrishikb@andrew.cmu.edu: You raised the point. You know, I think you… it was, maybe there's just a little bit of data we can get, right? So, maybe define what that, you know, sort of minimally viable amount of data is.
+
+80
+00:11:11.560 --> 00:11:30.980
+hrishikb@andrew.cmu.edu: So, it will sufficiently unblock you, so you can make some more forward progress. So, actually, we don't even need the data, we just need the schema, and then we need this makeup data. That's the main problem. So, we don't even need this small amount of data. Okay. Yeah, so all we require right now is the basic schema of how the data would look like in a table.
+
+81
+00:11:31.210 --> 00:11:39.010
+hrishikb@andrew.cmu.edu: And then we would be able to work on the pipeline, and they would be able to work on the MX modeling as well.
+
+82
+00:11:39.100 --> 00:11:59.059
+hrishikb@andrew.cmu.edu: PDFs or some input files to start on the admission part? Well, you don't need that, because the PDFs are only for the OCR part. Like, okay, you're… okay, then you get access… can you get an account and pretend that you're buying something on their website, and go look at some of the data that is available, and make up a schema to start with?
+
+83
+00:12:00.590 --> 00:12:05.930
+hrishikb@andrew.cmu.edu: Right? You know, you know, what is it? It's all the parts information, right? It's all the data about…
+
+84
+00:12:06.050 --> 00:12:17.890
+hrishikb@andrew.cmu.edu: Yeah, I did write a little about representative data, so that could be something. In this case, it would be, like, just going onto their website and just scraping a few pages, I guess.
+
+85
+00:12:18.020 --> 00:12:18.890
+hrishikb@andrew.cmu.edu: And…
+
+86
+00:12:19.110 --> 00:12:27.909
+hrishikb@andrew.cmu.edu: That would be better than, you know, not detecting. It's not going to eliminate the risk, sure, but it certainly will help mitigate the risk.
+
+87
+00:12:28.610 --> 00:12:31.310
+hrishikb@andrew.cmu.edu: Mitigate the impact of the risk.
+
+88
+00:12:32.490 --> 00:12:37.460
+hrishikb@andrew.cmu.edu: And I think a lot of it is also just around, sort of, the… again, you're probably doing all this, but…
+
+89
+00:12:37.710 --> 00:12:39.520
+hrishikb@andrew.cmu.edu: You know, being very…
+
+90
+00:12:40.990 --> 00:12:45.860
+hrishikb@andrew.cmu.edu: You know, up front with the custody, with the client, and saying, you know, not just like, hey, we need this data, but…
+
+91
+00:12:46.200 --> 00:12:47.000
+hrishikb@andrew.cmu.edu: you know.
+
+92
+00:12:47.120 --> 00:12:53.749
+hrishikb@andrew.cmu.edu: here are the… here, you know, we need it by this date, here's the impact if we don't have it by this date. Just being very…
+
+93
+00:12:54.130 --> 00:13:01.850
+hrishikb@andrew.cmu.edu: You know, very explicit, and whenever you meet with them, you know, here are the actions, the open actions is the item number one you ever review in your meetings.
+
+94
+00:13:02.020 --> 00:13:12.159
+hrishikb@andrew.cmu.edu: I think, for the last meeting, I wrote to them, I mentioned that we are currently blocked, and the response we got was, you should be getting the data by end of yesterday.
+
+95
+00:13:12.260 --> 00:13:27.509
+hrishikb@andrew.cmu.edu: So I'm thinking if I write a follow-up mail today, or, like… Yeah, yeah, you know. Like, I've chased them twice in, like, two days, so… Let's say, hey, this is great, we're, you know, we were hoping to get the data yesterday, as you mentioned, and maybe something came up.
+
+96
+00:13:27.860 --> 00:13:32.529
+hrishikb@andrew.cmu.edu: Please let us know when we can expect it, because we're really looking forward to, you know.
+
+97
+00:13:32.650 --> 00:13:37.990
+hrishikb@andrew.cmu.edu: people never want to hear, we're blocked, we're blocked, we're blocked, right? And that doesn't mean you're not, right? But…
+
+98
+00:13:38.180 --> 00:13:42.870
+hrishikb@andrew.cmu.edu: Because it's kind of like the impression people get is that
+
+99
+00:13:43.340 --> 00:13:51.079
+hrishikb@andrew.cmu.edu: oh, we're, you know, we're not doing… we're sitting like this until you give us the data, which you're not doing. So, just, you know, be more…
+
+100
+00:13:52.650 --> 00:13:58.780
+hrishikb@andrew.cmu.edu: You know, we're eager to, you know, we're at the point where we really could leverage that data and what we're, you know, giving them today.
+
+101
+00:14:00.090 --> 00:14:05.930
+hrishikb@andrew.cmu.edu: Other kind of things you could do is… I'm actually referring to a listing right here.
+
+102
+00:14:07.400 --> 00:14:11.200
+hrishikb@andrew.cmu.edu: you know what, that's not going to help the end government. I think we talked about most of them.
+
+103
+00:14:11.910 --> 00:14:15.819
+hrishikb@andrew.cmu.edu: But the other… the other thing in risks is that…
+
+104
+00:14:16.750 --> 00:14:21.120
+hrishikb@andrew.cmu.edu: And it's hard to do this for all risks. Some risks you can sort of say.
+
+105
+00:14:22.450 --> 00:14:25.729
+hrishikb@andrew.cmu.edu: kind of a leading indicators, so…
+
+106
+00:14:25.870 --> 00:14:28.170
+hrishikb@andrew.cmu.edu: If, you know, if you're seeing that.
+
+107
+00:14:30.070 --> 00:14:37.870
+hrishikb@andrew.cmu.edu: you're a week away, you know, you don't want to wait till it's the last minute. I need it by this date and ask for it the day before, right? But you can start raising
+
+108
+00:14:38.100 --> 00:14:45.650
+hrishikb@andrew.cmu.edu: the yellow flag, and then the red flag as you get closer and closer to those, you know, sort of must-have dates. Because obviously the…
+
+109
+00:14:45.930 --> 00:14:48.370
+hrishikb@andrew.cmu.edu: The impact increases.
+
+110
+00:14:48.480 --> 00:14:51.479
+hrishikb@andrew.cmu.edu: As you, you know, the more you… the closer you get.
+
+111
+00:14:52.090 --> 00:14:53.980
+hrishikb@andrew.cmu.edu: So…
+
+112
+00:14:57.210 --> 00:15:04.270
+hrishikb@andrew.cmu.edu: Let me sit here. So if I… and I actually made a list here. If you're thinking about a sort of a mitigation plan overall,
+
+113
+00:15:05.130 --> 00:15:09.850
+hrishikb@andrew.cmu.edu: There's kind of four… four elements to a fantastic… to a great mitigation plan.
+
+114
+00:15:10.150 --> 00:15:18.069
+hrishikb@andrew.cmu.edu: One is what you're doing… we talked about most of these. What you're doing to try to prevent it from happening, right? Try to prevent the risk
+
+115
+00:15:18.620 --> 00:15:21.989
+hrishikb@andrew.cmu.edu: From materializing, preventing the risk from becoming an issue.
+
+116
+00:15:22.240 --> 00:15:26.090
+hrishikb@andrew.cmu.edu: Right, because it's… at some point, Fair enough.
+
+117
+00:15:26.710 --> 00:15:28.830
+hrishikb@andrew.cmu.edu: You're putting this as a risk?
+
+118
+00:15:29.140 --> 00:15:31.030
+hrishikb@andrew.cmu.edu: Is this already an issue?
+
+119
+00:15:31.780 --> 00:15:40.730
+hrishikb@andrew.cmu.edu: You know, and what is the impact of that issue? There's two… there's a difference. A risk is something that may happen. An issue is something that actually has occurred, and it is impacting you already.
+
+120
+00:15:43.600 --> 00:15:53.889
+hrishikb@andrew.cmu.edu: what you're trying to do… what you're going to do if the risk does materialize to reduce the negative impact, that would be kind of a second part of a really good mitigation strategy.
+
+121
+00:15:54.610 --> 00:15:56.319
+hrishikb@andrew.cmu.edu: A third one would be…
+
+122
+00:15:56.440 --> 00:16:03.650
+hrishikb@andrew.cmu.edu: what are those leading indicators? How do you know that it's going to become… going to… this risk is going to materialize?
+
+123
+00:16:03.790 --> 00:16:11.670
+hrishikb@andrew.cmu.edu: You know, for example, in this case, if you were in a situation where the customer's usually… the client's usually pretty responsive.
+
+124
+00:16:11.870 --> 00:16:14.100
+hrishikb@andrew.cmu.edu: Right. If your experience is…
+
+125
+00:16:14.380 --> 00:16:23.370
+hrishikb@andrew.cmu.edu: client is really not responsive in general, it takes them weeks to get back to us, then that leading indicator would change under those two circumstances, right? One would be…
+
+126
+00:16:23.670 --> 00:16:42.109
+hrishikb@andrew.cmu.edu: well, if the client says they're going to get it to us, that's pretty good, right? You know, we're not so worried about it. We can wait till 48 hours before it becomes an issue to raise the flag. If it's something we have a very non-responsive client, then you may want to say, well, we're going to raise that flag, it's going to go from yellow to red, say.
+
+127
+00:16:42.210 --> 00:16:44.789
+hrishikb@andrew.cmu.edu: Two weeks before you really need it.
+
+128
+00:16:45.050 --> 00:16:48.360
+hrishikb@andrew.cmu.edu: And the last is what, you know, if… if you don't get it.
+
+129
+00:16:48.510 --> 00:16:53.670
+hrishikb@andrew.cmu.edu: or you don't get it, but when you need it, what is… what are you going to do about it? How are you going to manage it?
+
+130
+00:16:54.300 --> 00:17:00.730
+hrishikb@andrew.cmu.edu: So… But those are… it's a really good risk, right? The only thing I would say is…
+
+131
+00:17:01.800 --> 00:17:10.649
+hrishikb@andrew.cmu.edu: Break the two, and sort of the caution is that there may be, you know.
+
+132
+00:17:11.030 --> 00:17:15.749
+hrishikb@andrew.cmu.edu: Risks typically have a kind of a trigger date, and…
+
+133
+00:17:16.280 --> 00:17:19.439
+hrishikb@andrew.cmu.edu: You need to… if there is one here, if there isn't one.
+
+134
+00:17:19.770 --> 00:17:23.170
+hrishikb@andrew.cmu.edu: Right? If there is one, that really needs to be communicated with the client.
+
+135
+00:17:24.540 --> 00:17:26.300
+hrishikb@andrew.cmu.edu: All right.
+
+136
+00:17:27.560 --> 00:17:31.730
+hrishikb@andrew.cmu.edu: Does that all make sense? It does. Okay, no problem.
+
+137
+00:17:32.230 --> 00:17:34.660
+hrishikb@andrew.cmu.edu: Beginning.
+
+138
+00:17:35.550 --> 00:17:37.070
+hrishikb@andrew.cmu.edu: Go to the next one.
+
+139
+00:17:37.200 --> 00:17:39.849
+hrishikb@andrew.cmu.edu: So, having manual workloads.
+
+140
+00:17:40.570 --> 00:17:47.890
+hrishikb@andrew.cmu.edu: Currently, we cannot proceed on this, but…
+
+141
+00:17:48.190 --> 00:17:51.710
+hrishikb@andrew.cmu.edu: The risk that, that I foresee is that
+
+142
+00:17:52.070 --> 00:17:58.890
+hrishikb@andrew.cmu.edu: until we have, like, some of the ML models trained up and we start exploring.
+
+143
+00:17:59.040 --> 00:18:01.560
+hrishikb@andrew.cmu.edu: There would be a lot of,
+
+144
+00:18:02.620 --> 00:18:09.900
+hrishikb@andrew.cmu.edu: A lot of comparisons that we have to do against the baseline, and we need to, like, really confirm that
+
+145
+00:18:10.310 --> 00:18:16.140
+hrishikb@andrew.cmu.edu: Our demo models that we do pick end up being better than what they currently have.
+
+146
+00:18:16.320 --> 00:18:21.769
+hrishikb@andrew.cmu.edu: So this is, like, a tech… is this a technical risk? Yes. Okay. Alright. So…
+
+147
+00:18:25.040 --> 00:18:31.779
+hrishikb@andrew.cmu.edu: So the… I think I would need to change these a little bit. It's not a mitigation, but…
+
+148
+00:18:31.810 --> 00:18:47.980
+hrishikb@andrew.cmu.edu: One of the indicators we would push to establish is, like, we would have a baseline that they… that we get from their side, where, like, how much time it's taking them, what's their average rate of being correct on the prediction that they have.
+
+149
+00:18:48.360 --> 00:18:55.519
+hrishikb@andrew.cmu.edu: And then… we'll… go from our side and see what our machine learning or AI is giving.
+
+150
+00:18:55.890 --> 00:19:02.249
+hrishikb@andrew.cmu.edu: And then, you know, what's the… is the confidence scoring robust or not?
+
+151
+00:19:02.560 --> 00:19:07.159
+hrishikb@andrew.cmu.edu: And… Is it working properly with the human in the root system, I think?
+
+152
+00:19:07.520 --> 00:19:17.020
+hrishikb@andrew.cmu.edu: So… Is this a… It… How is this different?
+
+153
+00:19:17.470 --> 00:19:22.309
+hrishikb@andrew.cmu.edu: than any other… Type of…
+
+154
+00:19:23.680 --> 00:19:30.029
+hrishikb@andrew.cmu.edu: Project, or that is doing any type of, sort of, categorization or classification.
+
+155
+00:19:30.780 --> 00:19:34.840
+hrishikb@andrew.cmu.edu: Based on ML. Wouldn't he have exactly the same set of circumstances?
+
+156
+00:19:35.270 --> 00:19:46.080
+hrishikb@andrew.cmu.edu: You know, you've got some… you've got some minimal acceptable rates, right? You're going to establish a baseline, you're going to verify it, you're going to…
+
+157
+00:19:46.570 --> 00:19:59.840
+hrishikb@andrew.cmu.edu: the… yeah, that's true in almost every case, but here, most of the time you have a defined architecture that you want to go with, like, but here we do not know. We're gonna have to pick between three of them.
+
+158
+00:20:00.080 --> 00:20:11.559
+hrishikb@andrew.cmu.edu: So that was why I wrote this one, so that we know that there is some uncertainty among the choices, and how that would affect how we go about it.
+
+159
+00:20:12.040 --> 00:20:18.660
+hrishikb@andrew.cmu.edu: So, how did… I guess maybe I just didn't understand that. So how… so for your three options, how are you going in?
+
+160
+00:20:19.480 --> 00:20:23.519
+hrishikb@andrew.cmu.edu: Where in that mitigation do you refer to those three options?
+
+161
+00:20:25.530 --> 00:20:32.120
+hrishikb@andrew.cmu.edu: That would just be the minimum credible AI standard for, like… So when, when we're,
+
+162
+00:20:32.430 --> 00:20:45.340
+hrishikb@andrew.cmu.edu: Testing all three, we would pick the one that has the appropriate, like, performance and, like, resources usage that they said, and then we would just pick one that does actually exceed the baseline.
+
+163
+00:20:45.680 --> 00:20:50.430
+hrishikb@andrew.cmu.edu: Okay, so really what you're doing is you're reducing your technical risk, by…
+
+164
+00:20:51.150 --> 00:21:01.380
+hrishikb@andrew.cmu.edu: by running… by basically running experiments against three different techniques, right? So that… that's…
+
+165
+00:21:01.970 --> 00:21:08.160
+hrishikb@andrew.cmu.edu: That's how… that's how you're… if I understand, that's how you're really mitigating the risk, is that right? Okay, now that makes a lot of sense.
+
+166
+00:21:09.950 --> 00:21:23.559
+hrishikb@andrew.cmu.edu: reading the… reading that, I don't get that? I… I should have, written all three, like, BERT, and… So tech… techno-risk, I'm saying, is one of the… one of the standard ways to mitigate technical risk is by experimenting, and…
+
+167
+00:21:23.770 --> 00:21:29.780
+hrishikb@andrew.cmu.edu: when you do experiments, and I think you're reading that you're on the right path, it's really important to
+
+168
+00:21:30.110 --> 00:21:42.370
+hrishikb@andrew.cmu.edu: make sure that you design… design the experiment well, right? So here are the… these are the criteria… these are criteria by which we are going to effectively evaluate those different experiments.
+
+169
+00:21:42.650 --> 00:21:45.900
+hrishikb@andrew.cmu.edu: Yeah, like, another part to this is that
+
+170
+00:21:46.230 --> 00:21:56.979
+hrishikb@andrew.cmu.edu: The… the manual baseline itself is not established yet. We did ask them during the client meetings, and they were… themselves did not have, like…
+
+171
+00:21:57.060 --> 00:22:15.320
+hrishikb@andrew.cmu.edu: they've categorized enough data, of course, but they don't have a baseline for what they do have, like, they've not done that analysis yet. So that's a part of the risk that we don't have something already to compare against. It will have to be a process between us and them so that we actually have something concrete to compare against.
+
+172
+00:22:15.340 --> 00:22:19.920
+hrishikb@andrew.cmu.edu: Right, but you could still compare against… you're still going to have some…
+
+173
+00:22:21.720 --> 00:22:28.510
+hrishikb@andrew.cmu.edu: absolute numbers, right? Your experiments are going to provide
+
+174
+00:22:33.700 --> 00:22:39.240
+hrishikb@andrew.cmu.edu: Well, your experiments are going to provide some, you know, automation…
+
+175
+00:22:40.900 --> 00:22:48.100
+hrishikb@andrew.cmu.edu: You're gonna know how… let's put it this way, let's say… let's say you run through 100 different, you know, you have each system running through 100 different inputs, yeah.
+
+176
+00:22:49.060 --> 00:22:51.830
+hrishikb@andrew.cmu.edu: You're going to know the…
+
+177
+00:22:53.860 --> 00:23:00.059
+hrishikb@andrew.cmu.edu: the percentages of those that you have high confidence in need, you know, do or do not need human correction. Yes. Right?
+
+178
+00:23:01.160 --> 00:23:03.649
+hrishikb@andrew.cmu.edu: So, in some sense.
+
+179
+00:23:04.890 --> 00:23:16.829
+hrishikb@andrew.cmu.edu: you're going… you know, that… you're going to know which perform… you know, which one has better performance, right? And I realize it's not going to necessarily tell you, are you saving enough time overall, right? Is it a… but…
+
+180
+00:23:17.370 --> 00:23:24.950
+hrishikb@andrew.cmu.edu: You could still… quantitatively evaluate
+
+181
+00:23:25.880 --> 00:23:28.839
+hrishikb@andrew.cmu.edu: Even without all that information. And honestly.
+
+182
+00:23:28.940 --> 00:23:30.919
+hrishikb@andrew.cmu.edu: You could, you could, you could…
+
+183
+00:23:31.180 --> 00:23:35.379
+hrishikb@andrew.cmu.edu: you know, do a lot of work before you know what this magic number is.
+
+184
+00:23:35.560 --> 00:23:37.340
+hrishikb@andrew.cmu.edu: You don't need that magic number yet.
+
+185
+00:23:40.790 --> 00:23:49.080
+hrishikb@andrew.cmu.edu: I think 3 is not… What, it's not currently, pressing for us.
+
+186
+00:23:49.200 --> 00:23:53.530
+hrishikb@andrew.cmu.edu: So 3's not… 3's not a risk. Tell you why. So…
+
+187
+00:23:53.820 --> 00:23:56.639
+hrishikb@andrew.cmu.edu: And this is… you're not… every single…
+
+188
+00:23:57.660 --> 00:24:04.160
+hrishikb@andrew.cmu.edu: 80% of every studio and practical team has this… something like this as a risk.
+
+189
+00:24:04.430 --> 00:24:11.440
+hrishikb@andrew.cmu.edu: And then during the presentations, you can tell the different faculty members kind of get into arguments with one another.
+
+190
+00:24:11.610 --> 00:24:22.569
+hrishikb@andrew.cmu.edu: Is that a risk? No, it's not a risk. I don't think it's a risk. So, I'm gonna head it off with the pass, because I don't think it's a risk. There may be other faculty members who do, and here's why, is that…
+
+191
+00:24:23.060 --> 00:24:25.660
+hrishikb@andrew.cmu.edu: There has never been a project since the
+
+192
+00:24:26.290 --> 00:24:33.830
+hrishikb@andrew.cmu.edu: The world saw its first project that didn't have scope creep as a potential… Issued.
+
+193
+00:24:34.020 --> 00:24:39.890
+hrishikb@andrew.cmu.edu: And… It's almost like saying, Well, my project may fail.
+
+194
+00:24:40.140 --> 00:24:42.220
+hrishikb@andrew.cmu.edu: I may not succeed, that's a risk.
+
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+00:24:42.490 --> 00:24:46.439
+hrishikb@andrew.cmu.edu: It's this, it's just… it's very vague. You don't…
+
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+00:24:46.920 --> 00:24:52.860
+hrishikb@andrew.cmu.edu: You know, how do you… how would you mitigate scope of creep? You would do it through good software engineering practices.
+
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+00:24:52.990 --> 00:24:55.050
+hrishikb@andrew.cmu.edu: So you'd have…
+
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+hrishikb@andrew.cmu.edu: a scope of agreement with the client. You might have a signed-off statement of work. You might have a, you know, an explicit, sort of, as part of your requirements, an out-of-scope list of things that are out of scope.
+
+199
+00:25:08.530 --> 00:25:12.340
+hrishikb@andrew.cmu.edu: You probably might have some change control processes, so if you are
+
+200
+00:25:12.770 --> 00:25:20.339
+hrishikb@andrew.cmu.edu: If something new comes in, this is the process that you follow in order to determine if that is something you can accept or not.
+
+201
+00:25:20.460 --> 00:25:27.799
+hrishikb@andrew.cmu.edu: you're gonna follow, you know, Moscow, for… to understand what, you know, in terms of prioritization of requirements.
+
+202
+00:25:27.900 --> 00:25:36.039
+hrishikb@andrew.cmu.edu: So it's just a… it's just part of every project, and software engineering practices will address that for you. Okay. Make sense?
+
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+00:25:37.000 --> 00:25:39.150
+hrishikb@andrew.cmu.edu: Yeah.
+
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+00:25:39.540 --> 00:25:45.410
+hrishikb@andrew.cmu.edu: I think because we're a newer team, and all of us are kind of new to, like, doing the whole process ourselves.
+
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+00:25:45.520 --> 00:26:03.059
+hrishikb@andrew.cmu.edu: I did not consider it, like, that there were mature solutions, like, that people actually have done this, like, a thousand times. Yeah, again, it's… but you… but you know some of this already, so you know about creating, like, the statement of work for your… Yes, and the… You know about creating Moscow.
+
+206
+00:26:03.060 --> 00:26:08.320
+hrishikb@andrew.cmu.edu: you may not know about, like, a, you know, like a change control thing. You may not know about that, right? But…
+
+207
+00:26:08.390 --> 00:26:11.890
+hrishikb@andrew.cmu.edu: But think about… What you would…
+
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+00:26:12.320 --> 00:26:19.330
+hrishikb@andrew.cmu.edu: What you would do if, you know, the client said, Well… Here's an example.
+
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+00:26:21.060 --> 00:26:30.720
+hrishikb@andrew.cmu.edu: My father, many, many, many, many years ago, was doing a master's in mechanical engineering, and at that time, master's, you write a whole pretty significant thesis, and
+
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+00:26:31.650 --> 00:26:43.219
+hrishikb@andrew.cmu.edu: He spent, you know, huge amounts of time on this thing, you know, thousands of hours on this thing, and he came up with this long, you know, this paper, right, this published paper. And his advisor looks at it, and he said, this is great work.
+
+211
+00:26:43.640 --> 00:26:47.819
+hrishikb@andrew.cmu.edu: Now I want you to do it using complex numbers, not just, like, real numbers.
+
+212
+00:26:49.270 --> 00:26:54.949
+hrishikb@andrew.cmu.edu: And that was scope creep, right? That was, like, real scope creep. And…
+
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+00:26:55.060 --> 00:27:02.330
+hrishikb@andrew.cmu.edu: There wasn't any way to address it other than my father saying, I'm done, and I'm out of here. I don't care about the semesters anymore.
+
+214
+00:27:02.540 --> 00:27:15.650
+hrishikb@andrew.cmu.edu: But think about how you would handle a situation where someone… a client would say, well, we really want… we love this, but you want… we want you to do it with… with, with complex or imaginary numbers, right? You would need to say.
+
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+00:27:15.760 --> 00:27:21.720
+hrishikb@andrew.cmu.edu: Okay, you don't say no, right? You don't say, no, that wasn't part of our scope. You say.
+
+216
+00:27:22.620 --> 00:27:37.129
+hrishikb@andrew.cmu.edu: That's a great idea! That's really interesting. Let us… that wasn't part of our original agreement, or statement of work. Let's go back and understand, do a high-level scoping among ourselves to understand what… how big is this?
+
+217
+00:27:37.190 --> 00:27:45.339
+hrishikb@andrew.cmu.edu: and what its impact would be on the rest of the project, and we'll come back to you, and we'll have that discussion, right? And that's the way to handle it.
+
+218
+00:27:45.700 --> 00:27:50.330
+hrishikb@andrew.cmu.edu: I think right now, I'm not sure if we have an official scope of work.
+
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+00:27:50.470 --> 00:27:58.810
+hrishikb@andrew.cmu.edu: Is that something we should… You should do, yeah, you should definitely do that. Yeah, I actually think, like, something this client signs off on is really valuable.
+
+220
+00:27:59.230 --> 00:28:02.080
+hrishikb@andrew.cmu.edu: Okay, get an official scope of work, and…
+
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+hrishikb@andrew.cmu.edu: How we would handle changes. Yeah, yeah, okay.
+
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+hrishikb@andrew.cmu.edu: That's my list here. I think, what you said is much better than what I had for the mitigation of all this, so we'll just, I'll just change it to what you just said. Yeah, but I would… I would not include it as a risk. Okay. It's just part of your…
+
+223
+00:28:23.810 --> 00:28:40.779
+hrishikb@andrew.cmu.edu: part of your project management process. It would just be a doo-do for us to, like, get the official scope of work and, like, changes. Yeah, people don't like seeing, you know, a risk that is just, you know, well, another… another very common risk that people… students often have is
+
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+hrishikb@andrew.cmu.edu: Well, someone's gonna… You know, Someone's gonna get sick.
+
+225
+00:28:47.320 --> 00:28:51.429
+hrishikb@andrew.cmu.edu: Right? Well, yeah, someone's probably going to get sick at some point.
+
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+00:28:51.590 --> 00:28:54.339
+hrishikb@andrew.cmu.edu: But you, as part of your…
+
+227
+00:28:54.760 --> 00:29:13.790
+hrishikb@andrew.cmu.edu: how you manage your project, your project management practices, need to understand and address how you're going to handle if someone gets sick. Is there… are you going to make sure that everyone has a backup person who understands what they're doing? Are you going to build some buffer into your schedule to account for the fact that
+
+228
+00:29:13.860 --> 00:29:26.490
+hrishikb@andrew.cmu.edu: someone is going to get sick, right? And it is… it's gonna… hopefully none of you, it's gonna be other teams, right? But someone's gonna get sick and be out for a week or two. It happens. So, plan for it, don't call it a risk, right? Okay. Okay.
+
+229
+00:29:27.740 --> 00:29:32.230
+hrishikb@andrew.cmu.edu: Okay, just one… make it loud briefly.
+
+230
+00:29:32.700 --> 00:29:35.800
+hrishikb@andrew.cmu.edu: Remove risk 3 and just make it a 2.
+
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+00:29:36.540 --> 00:29:41.450
+hrishikb@andrew.cmu.edu: The fourth one is just,
+
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+hrishikb@andrew.cmu.edu: Yeah, this is the one that you said you wanted to talk about more, so that they're, they don't, need to learn more than they, than they think they will need to, so that… so I'd just like to get your thoughts. So what's the difference between this and a… and a constraint? Because you have constraints, and you're…
+
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+00:29:59.850 --> 00:30:02.319
+hrishikb@andrew.cmu.edu: requirements document. How's it different?
+
+234
+00:30:03.720 --> 00:30:12.000
+hrishikb@andrew.cmu.edu: Specifically, that, when we are doing some of the work, there's more, much more,
+
+235
+00:30:12.150 --> 00:30:29.100
+hrishikb@andrew.cmu.edu: documentation or support for one of them, and since it's not an official constraint, right? We might really prefer to use something, but since they themselves are not defining it as an official constraint, so that's why I just kept it. Like, if it was an officially, like.
+
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+00:30:29.190 --> 00:30:46.569
+hrishikb@andrew.cmu.edu: They just said that, we would really, like, it has to be in Azure, and, like, you cannot, like, just use Spice for this, then that would… I would not have kept this. But you've got to use Azure, right? Yeah, but, that's a… I think that… isn't that a constraint?
+
+237
+00:30:47.350 --> 00:31:05.460
+hrishikb@andrew.cmu.edu: they just strongly imply that you should use Azure, and you should use Bicep, and you should try to stay away from, like, languages that they give two, three languages that they really use for. So I would… I would try to just move that over to the project constraints. Yeah, yeah. And, you know, every…
+
+238
+00:31:05.990 --> 00:31:14.500
+hrishikb@andrew.cmu.edu: Every place you're ever going to be has a… oh, not every place, most places, over 95% of the places out there are going to have constraints like that.
+
+239
+00:31:14.870 --> 00:31:16.829
+hrishikb@andrew.cmu.edu: You know, it's very rare that
+
+240
+00:31:17.890 --> 00:31:30.080
+hrishikb@andrew.cmu.edu: You know, there are some companies where they say, well, you're… you team… you team, you have responsibility for the entire… you can decide what technologies, you can decide, because you build it and you own it.
+
+241
+00:31:30.170 --> 00:31:39.769
+hrishikb@andrew.cmu.edu: Right? You have to maintain it's not… not my problem that no one else in the company understands Dolang. You do, you know, that's so… that is very, very rare.
+
+242
+00:31:41.740 --> 00:31:48.089
+hrishikb@andrew.cmu.edu: Yep, and funny enough, they did mention that do not do it in Golang or anything like that, right, that's why I said, yeah, I remember that.
+
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+hrishikb@andrew.cmu.edu: Yeah.
+
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+hrishikb@andrew.cmu.edu: I think, because, of course, we're not…
+
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+00:31:53.260 --> 00:31:59.320
+hrishikb@andrew.cmu.edu: gonna be responsible for after the handoff, and they'll have to do all the maintenance. I'll just move this to constraints.
+
+246
+00:32:00.100 --> 00:32:09.770
+hrishikb@andrew.cmu.edu: And… schema volatility, I think Arjun mentioned something about when we were talking with them, that
+
+247
+00:32:10.930 --> 00:32:22.339
+hrishikb@andrew.cmu.edu: That when… depending on how the… what kind of input we get, and then how the models run, there might be changes that we need to, like, do.
+
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+00:32:22.340 --> 00:32:40.460
+hrishikb@andrew.cmu.edu: And that we… we're… we're just not aware of how to… it's gonna be in advance, even though we… we can't present how the workflow goes, but this is, actually just a… this is a known, known that… that we do know we'll have to face, so I will just go to the…
+
+249
+00:32:40.560 --> 00:32:41.890
+hrishikb@andrew.cmu.edu: mitigation.
+
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+00:32:41.980 --> 00:33:01.050
+hrishikb@andrew.cmu.edu: We could, go with, like, I just wrote, like, a semantic matches, so even if some of the attributes are, like, varying between categories, but they mean the same thing, we could have, like, an additional layer on top that we're just automatically handling it, instead of us going and, like, manually changing it correctly.
+
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+00:33:01.310 --> 00:33:04.589
+hrishikb@andrew.cmu.edu: Just that I did not add much more complexity.
+
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+00:33:04.770 --> 00:33:20.059
+hrishikb@andrew.cmu.edu: It does, but, this is, this is in case that it does happen. Like, a new, kind of format, a new kind of, like, data source comes up, and, this is an automated approach. It does add additional complexity to it.
+
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+00:33:20.450 --> 00:33:30.390
+hrishikb@andrew.cmu.edu: So, is there any other sort of mitigation that you could think of doing? So, as an example, I'm not going to actually say what it is, but is there something that could help you
+
+254
+00:33:32.200 --> 00:33:39.870
+hrishikb@andrew.cmu.edu: understand if this risk is going to manifest itself to an issue earlier, right? The earlier you know about this, the better.
+
+255
+00:33:40.790 --> 00:33:44.630
+hrishikb@andrew.cmu.edu: Is there anything you can do to help learn if it's going to be an issue earlier?
+
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+00:33:45.480 --> 00:33:58.080
+hrishikb@andrew.cmu.edu: Right now, it does not seem likely, from what they have told us. So, that's why this is, I would say, the least of, like, that's a risk 5 in my list. It's likelier to happen.
+
+257
+00:33:58.250 --> 00:33:59.360
+hrishikb@andrew.cmu.edu: But…
+
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+hrishikb@andrew.cmu.edu: We're going to get some metrics from them of the last schema changes, or offering some numbers that are going to predict future. And then the other thing about the risk was if you really want to talk about likelihood of occurrence.
+
+259
+00:34:12.690 --> 00:34:17.439
+hrishikb@andrew.cmu.edu: And then the impact, if it does, so people can really understand, hey, this is…
+
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+hrishikb@andrew.cmu.edu: You know, this sounds like it may be low… maybe low likelihood, potentially significant impact. Yes.
+
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+00:34:23.380 --> 00:34:29.960
+hrishikb@andrew.cmu.edu: And, you know, you have to decide, okay, how much time you're gonna invest in
+
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+hrishikb@andrew.cmu.edu: you know, upfront mitigation on that, or versus something that is going to be, you know, high impact, high likelihood, as an example, right? Yeah, I think I should have mentioned that, at least, because all the other ones are high likelihood… must high likelihood than this. This is something that's…
+
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+00:34:48.290 --> 00:34:58.470
+hrishikb@andrew.cmu.edu: Like, for comparison, the first risk is, like, high likelihood, because it's… it's, like, definite likelihood, because it's actually doing it late, and, like, very high impact as well, because we cannot…
+
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+hrishikb@andrew.cmu.edu: do a lot of the stuff that we do. So it's an issue already. It is, it is, yeah. And this is, low impact, but it can be very significant if, like, a large, like, 30-40% of the data we're encountering is, like, does not match what we expect, then we can… we would have to be forced to build this on top, and then to make it… make sure the percentages are on…
+
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+hrishikb@andrew.cmu.edu: So, I will change it so that it mentions that it's a low likelihood, but it has significantly. Okay. Yeah, this is a good list. This is what we did.
+
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+hrishikb@andrew.cmu.edu: Suitable to make anyone.
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+hrishikb@andrew.cmu.edu: Oh.
+
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+00:35:35.290 --> 00:35:39.099
+hrishikb@andrew.cmu.edu: So, then we can, this is all for,
+
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+hrishikb@andrew.cmu.edu: This is all for the risk portion, so I'll just go into the project management portion of the document.
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+hrishikb@andrew.cmu.edu: So… It's basically from now to, like, May 4th.
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+hrishikb@andrew.cmu.edu: And how we're gonna at least,
+
+272
+00:35:58.880 --> 00:36:01.680
+hrishikb@andrew.cmu.edu: Have the temporary structure that we have.
+
+273
+00:36:01.900 --> 00:36:04.419
+hrishikb@andrew.cmu.edu: Or, like, going through it.
+
+274
+00:36:04.630 --> 00:36:08.650
+hrishikb@andrew.cmu.edu: So, we're currently still in Phase 1 and 2.
+
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+00:36:08.770 --> 00:36:15.529
+hrishikb@andrew.cmu.edu: So, we're still making our SES, and work for MVP has not even started yet.
+
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+hrishikb@andrew.cmu.edu: There is an initial draft for, like, requirements and architecture.
+
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+00:36:21.670 --> 00:36:26.669
+hrishikb@andrew.cmu.edu: But they will be finalized when, you know, the… during the next phases.
+
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+hrishikb@andrew.cmu.edu: After that, during the latter part of the…
+
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+hrishikb@andrew.cmu.edu: semester, basically, it will be more focused on making sure our work is
+
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+hrishikb@andrew.cmu.edu: Going smoothly, and evaluating all the progress we have made through there, till the end of this semester, at least.
+
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+hrishikb@andrew.cmu.edu: So what… do you have any plans for, like, what you'll be doing for the next two semesters… two semesters after that?
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+hrishikb@andrew.cmu.edu: Other vacations.
+
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+hrishikb@andrew.cmu.edu: interviews.
+
+284
+00:36:55.870 --> 00:37:06.500
+hrishikb@andrew.cmu.edu: No, it's only for the Spring 2006 roadmap. I've not, actually gone through for the summer roadmap. And have you defined what vertical slices 1 and 2 are?
+
+285
+00:37:06.680 --> 00:37:16.119
+hrishikb@andrew.cmu.edu: Vertical slices 1 and 2 are just basically… the first vertical slice would be, all the way up till, like, using our SES to, like, create a MVP.
+
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+00:37:16.490 --> 00:37:23.309
+hrishikb@andrew.cmu.edu: And two is just, like, all the documentation work that we're gonna do, all the,
+
+287
+00:37:23.420 --> 00:37:29.299
+hrishikb@andrew.cmu.edu: Stuff that, would be useful for someone to learn it after the handoff, or even when we're explaining it.
+
+288
+00:37:31.030 --> 00:37:32.070
+hrishikb@andrew.cmu.edu: So…
+
+289
+00:37:35.080 --> 00:37:37.389
+hrishikb@andrew.cmu.edu: Is this too… is this too aggressive?
+
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+00:37:39.170 --> 00:37:41.100
+hrishikb@andrew.cmu.edu: Given that you have two more semesters.
+
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+00:37:41.640 --> 00:37:46.570
+hrishikb@andrew.cmu.edu: And given that you're working 12, you know, 12 hours a week this semester.
+
+292
+00:37:47.660 --> 00:37:56.909
+hrishikb@andrew.cmu.edu: I will admit that I've gone pretty aggressive, but I think even Arjun is in agreement that if this was a normal semester, then
+
+293
+00:37:57.080 --> 00:38:01.309
+hrishikb@andrew.cmu.edu: we would not… I would not have pushed this so hard, but…
+
+294
+00:38:01.570 --> 00:38:09.350
+hrishikb@andrew.cmu.edu: with AI, at least my… this is my personal opinion, that as soon as we have the SES and schema and all that stuff.
+
+295
+00:38:09.540 --> 00:38:27.709
+hrishikb@andrew.cmu.edu: like, well-defined enough, it would just be a… it would… it would just enter into a very fast iteration process, see if it works, like, are the tests going properly, are the outputs as we expect? And, I… in my original opinion, should go pretty fast, as soon as we do have that baseline set up.
+
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+hrishikb@andrew.cmu.edu: Okay.
+
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+00:38:29.170 --> 00:38:43.140
+hrishikb@andrew.cmu.edu: I don't know, that's… I don't know what we'll do afterwards, whether there's, like, stretch goals or something, but I could be completely wrong. Maybe it takes us, like, deep into the summer or something, but this is what happened.
+
+298
+00:38:44.910 --> 00:38:51.999
+hrishikb@andrew.cmu.edu: Is that… so, the question… again, it's not… not this week or next week, right? But at some point.
+
+299
+00:38:53.890 --> 00:38:58.129
+hrishikb@andrew.cmu.edu: You will want to have, before your end of semester, like, what your
+
+300
+00:38:58.290 --> 00:39:02.769
+hrishikb@andrew.cmu.edu: entire plan is, right, for including the other semesters. Okay.
+
+301
+00:39:06.270 --> 00:39:11.200
+hrishikb@andrew.cmu.edu: So, these are, how,
+
+302
+00:39:11.560 --> 00:39:14.730
+hrishikb@andrew.cmu.edu: I think the responsibilities should be split.
+
+303
+00:39:14.910 --> 00:39:25.239
+hrishikb@andrew.cmu.edu: So, currently, for the project lead, she's the team leader, and, we've still yet to determine how the structure will rotate and
+
+304
+00:39:25.290 --> 00:39:44.509
+hrishikb@andrew.cmu.edu: how, like, how long the rotation structure should be? So, should it be… Oh, we… we were actually talking with the other teams. I think they were rotating per month basis, the team leaders. We didn't want to do it so early, but we thought we could do it every mini. Every what? Every mini-sam. So, like, after the spring break, we get a new…
+
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+00:39:44.550 --> 00:39:46.500
+hrishikb@andrew.cmu.edu: Team lead? Yeah.
+
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+00:39:46.580 --> 00:39:51.360
+hrishikb@andrew.cmu.edu: And then, so everyone gets, like, two, I think, two rotations of…
+
+307
+00:39:51.690 --> 00:39:59.110
+hrishikb@andrew.cmu.edu: And I just said, there's no right or wrong, right? As long as you have a reason for making that decision, right? That's all that matters.
+
+308
+00:39:59.330 --> 00:40:06.439
+hrishikb@andrew.cmu.edu: Yeah, monthly, we might be… too fast. It sounds nice, but there's no continuity.
+
+309
+00:40:07.030 --> 00:40:18.310
+hrishikb@andrew.cmu.edu: Architecturally, well, everyone is responsible for knowing, because this is a small team, everyone must know the entire architecture, so that they know how everything is going, but…
+
+310
+00:40:20.420 --> 00:40:28.430
+hrishikb@andrew.cmu.edu: So the person who is, like, most in-depth with it, and most, like, you know, responsible for it would be designated as an architecture lead.
+
+311
+00:40:28.920 --> 00:40:38.629
+hrishikb@andrew.cmu.edu: As for the data and ML lead, it's just basically the one who goes most, like, hands-on and, like, you know, is responsible for debugging it.
+
+312
+00:40:39.440 --> 00:40:46.380
+hrishikb@andrew.cmu.edu: the… and… engineering lead, that's the one I mostly fear about, because
+
+313
+00:40:46.770 --> 00:41:02.610
+hrishikb@andrew.cmu.edu: we all have to own the implementation. It's… it cannot be any other way, I think. And… because engineering lead and QA, it has to be done by all of us, basically, so I'm not sure whether I should keep it, or… Well, I think… so…
+
+314
+00:41:03.270 --> 00:41:06.130
+hrishikb@andrew.cmu.edu: So, again, this is more… maybe a quality discussion.
+
+315
+00:41:06.260 --> 00:41:08.609
+hrishikb@andrew.cmu.edu: But… You know, you're…
+
+316
+00:41:09.480 --> 00:41:15.949
+hrishikb@andrew.cmu.edu: you know, I don't know if it's the architecturally, but there's certainly someone who… you all own… you all own implementation, sure.
+
+317
+00:41:16.050 --> 00:41:21.190
+hrishikb@andrew.cmu.edu: But there may be times where there's someone who has
+
+318
+00:41:21.910 --> 00:41:24.000
+hrishikb@andrew.cmu.edu: You know, has more of a…
+
+319
+00:41:24.760 --> 00:41:29.169
+hrishikb@andrew.cmu.edu: Consult, you know, people consult with them, they have more of, sort of, a tech lead type of
+
+320
+00:41:29.620 --> 00:41:31.790
+hrishikb@andrew.cmu.edu: responsibility, right? So…
+
+321
+00:41:33.300 --> 00:41:45.720
+hrishikb@andrew.cmu.edu: QA process lead, I think, is… you absolutely need one, because how… who… who has that response… yes, I'm gonna go… I'm gonna go test. Who has the responsibility for the over quality plan for our system?
+
+322
+00:41:46.240 --> 00:41:52.810
+hrishikb@andrew.cmu.edu: to make sure, and when I say quality plan, I don't just mean that our software is high quality, it's that we are
+
+323
+00:41:53.000 --> 00:41:57.800
+hrishikb@andrew.cmu.edu: We are doing what we said we are going to do with respect to how we are operating.
+
+324
+00:41:58.310 --> 00:42:04.200
+hrishikb@andrew.cmu.edu: So… you know, co- gets checked in, it…
+
+325
+00:42:04.500 --> 00:42:08.500
+hrishikb@andrew.cmu.edu: you know, here's an example. Someone who's actually may go and
+
+326
+00:42:09.190 --> 00:42:18.930
+hrishikb@andrew.cmu.edu: Look at data around how many, how many… Review, code reviews.
+
+327
+00:42:19.230 --> 00:42:26.370
+hrishikb@andrew.cmu.edu: Are actually providing meaningful… meaningful comments that are provided to them, versus ones that are just like, you know, okay, just pass it.
+
+328
+00:42:26.630 --> 00:42:30.030
+hrishikb@andrew.cmu.edu: So, it's typically someone who, like, who's…
+
+329
+00:42:30.450 --> 00:42:32.740
+hrishikb@andrew.cmu.edu: Looking, sort of, one level deeper.
+
+330
+00:42:33.120 --> 00:42:38.080
+hrishikb@andrew.cmu.edu: Into the… into the processes, and everyone has that responsibility, right?
+
+331
+00:42:38.790 --> 00:42:41.970
+hrishikb@andrew.cmu.edu: Yeah, I think you're right. Otherwise, we might fall into, like.
+
+332
+00:42:42.060 --> 00:42:58.830
+hrishikb@andrew.cmu.edu: the team's gonna do it, and then no one ends up doing it. No one's gonna do metrics tracking unless there's someone else responsible for metrics tracking. Yeah, so we can have a discussion, like, who has the most experience for, like, for the engineering lead, who has the most experience for, like,
+
+333
+00:42:58.840 --> 00:43:03.640
+hrishikb@andrew.cmu.edu: You know, working with pipelines and integrations and stuff, we'll just assign that person.
+
+334
+00:43:03.660 --> 00:43:06.010
+hrishikb@andrew.cmu.edu: As per the QA lead.
+
+335
+00:43:06.240 --> 00:43:14.010
+hrishikb@andrew.cmu.edu: It's gonna have to be somewhat strict so that, you know, they actually kind of, like, go deep into it and, like, see that it's all we need.
+
+336
+00:43:14.360 --> 00:43:17.590
+hrishikb@andrew.cmu.edu: So, we'll have… we'll… we'll discuss.
+
+337
+00:43:18.360 --> 00:43:36.010
+hrishikb@andrew.cmu.edu: And you want to have names, even though you're going to be changing, make sure there's people's names on those. Yeah, definitely. I think, yeah, whenever we write theme, then we're kind of, like, it becomes the chinx, and then sometimes it ends up not being done, so…
+
+338
+00:43:36.810 --> 00:43:50.649
+hrishikb@andrew.cmu.edu: Success criteria is just basically, from the data metrics and product metrics, so percentages of records that are deemed high confidence and are actually high confidence. They're true positives instead of being false positives.
+
+339
+00:43:51.440 --> 00:43:57.690
+hrishikb@andrew.cmu.edu: We could also do some confidence distributions at the, if we go that deep.
+
+340
+00:43:57.900 --> 00:44:01.609
+hrishikb@andrew.cmu.edu: What are defect rates? Is the…
+
+341
+00:44:01.930 --> 00:44:07.870
+hrishikb@andrew.cmu.edu: After we give all this to the client, and they start checking our work.
+
+342
+00:44:08.380 --> 00:44:11.589
+hrishikb@andrew.cmu.edu: Is the post-approval rate what we have.
+
+343
+00:44:11.720 --> 00:44:17.439
+hrishikb@andrew.cmu.edu: And is the human in the loop? That… I'll have to add that. Human in the loop part… portion where it comes from.
+
+344
+00:44:19.760 --> 00:44:20.590
+hrishikb@andrew.cmu.edu: Alright.
+
+345
+00:44:26.170 --> 00:44:30.269
+hrishikb@andrew.cmu.edu: This is, the research planning is pretty weighed still.
+
+346
+00:44:31.810 --> 00:44:50.309
+hrishikb@andrew.cmu.edu: Actually, the human allocation portion is much more weight than the AI portion. We've not decided how to fill all the roles yet, and how they're… if… and if they're gonna be rotated. Like, we've decided that the leadership… team lead will be rotated, but what about the other one? Should we just have one person
+
+347
+00:44:50.790 --> 00:44:53.670
+hrishikb@andrew.cmu.edu: throughout the project, who's responsible for QAns.
+
+348
+00:44:54.220 --> 00:45:01.499
+hrishikb@andrew.cmu.edu: AI resources is basically all the things like cursor that we're gonna leverage.
+
+349
+00:45:01.800 --> 00:45:08.570
+hrishikb@andrew.cmu.edu: And how it's gonna basically handle all the… Coding and the repetitive tasks.
+
+350
+00:45:08.710 --> 00:45:10.630
+hrishikb@andrew.cmu.edu: Like, boxing the PDFs.
+
+351
+00:45:11.640 --> 00:45:28.860
+hrishikb@andrew.cmu.edu: But, the last portion is basically about what… what is our, like, responsibility at the end of it all, like, we are responsible for validating it, whether we… assessing risk, like, whether we should even use it for certain portions or not, or whether it's best handled by us.
+
+352
+00:45:29.250 --> 00:45:30.450
+hrishikb@andrew.cmu.edu: And then…
+
+353
+00:45:30.710 --> 00:45:37.280
+hrishikb@andrew.cmu.edu: the last thing that I wrote is, like, being very cautious that no proprietary data is not, like, going through
+
+354
+00:45:37.530 --> 00:45:40.879
+hrishikb@andrew.cmu.edu: other AI tools than what they can use.
+
+355
+00:45:43.350 --> 00:45:46.809
+hrishikb@andrew.cmu.edu: Finally, yeah.
+
+356
+00:45:47.750 --> 00:46:02.900
+hrishikb@andrew.cmu.edu: how we're gonna do our task planning? So, ETVX is the one that, is most, like, familiar for me. This might change depending if the team decides that this format website does not work for us.
+
+357
+00:46:06.010 --> 00:46:14.000
+hrishikb@andrew.cmu.edu: Domain ownership is another thing that will come up in the… when we make the human rows, basically, assignments, and…
+
+358
+00:46:14.480 --> 00:46:16.650
+hrishikb@andrew.cmu.edu: Work will then be distributed.
+
+359
+00:46:16.840 --> 00:46:24.440
+hrishikb@andrew.cmu.edu: Based on how people, you know, tell about their… which domain they're most familiar and comfortable with.
+
+360
+00:46:25.690 --> 00:46:32.520
+hrishikb@andrew.cmu.edu: The information criteria is… We will have, like, verification steps there.
+
+361
+00:46:32.950 --> 00:46:34.219
+hrishikb@andrew.cmu.edu: we go through.
+
+362
+00:46:34.360 --> 00:46:37.629
+hrishikb@andrew.cmu.edu: And, as per the non-core artifacts.
+
+363
+00:46:37.990 --> 00:46:42.719
+hrishikb@andrew.cmu.edu: the… that would… that is a much more subjective process. We'll have, like,
+
+364
+00:46:42.950 --> 00:46:48.490
+hrishikb@andrew.cmu.edu: Hopefully, we'll have checklists that we'll just go through to confirm that they meet standards.
+
+365
+00:46:48.890 --> 00:46:52.580
+hrishikb@andrew.cmu.edu: And… As for progress tracking…
+
+366
+00:46:52.870 --> 00:47:00.159
+hrishikb@andrew.cmu.edu: That's something that, I think the QA lead will have to, oversee, and,
+
+367
+00:47:01.140 --> 00:47:06.990
+hrishikb@andrew.cmu.edu: We'll have to see how our… how much time we are taking on each of our, like, sprints, or…
+
+368
+00:47:07.740 --> 00:47:12.349
+hrishikb@andrew.cmu.edu: What, what the metrics are for backlog sizes, and…
+
+369
+00:47:12.490 --> 00:47:15.320
+hrishikb@andrew.cmu.edu: Are we reworking too much, or…
+
+370
+00:47:15.460 --> 00:47:28.619
+hrishikb@andrew.cmu.edu: spending too much time on, like, other things that, we initially said would not be spending that much time. Right, so that's actually helpful, so you can look at… that's actually a really good thing for, you know, a two-way process that you can do, is, like, is…
+
+371
+00:47:29.100 --> 00:47:40.000
+hrishikb@andrew.cmu.edu: A very mature team, and there aren't many teams that do this, goes and says, you know, says, this is how we plan to spend our time, and then actually measures how they spend their time and reflects on that.
+
+372
+00:47:42.080 --> 00:47:43.280
+hrishikb@andrew.cmu.edu: So…
+
+373
+00:47:43.440 --> 00:47:54.520
+hrishikb@andrew.cmu.edu: This is all I have, and let's just for my, this is the risk and the project. Let me share one thing with you. I should put something up.
+
+374
+00:47:54.840 --> 00:48:00.229
+hrishikb@andrew.cmu.edu: I'll get that in the back of here, because I can't plug that into this laptop. I think I've got to do that.
+
+375
+00:48:01.030 --> 00:48:01.750
+hrishikb@andrew.cmu.edu: Yes.
+
+376
+00:48:04.430 --> 00:48:09.180
+hrishikb@andrew.cmu.edu: I think… I can move it, it's not gonna read you. Throughout so long.
+
+377
+00:48:10.470 --> 00:48:11.220
+hrishikb@andrew.cmu.edu: Alright.
+
+378
+00:48:30.170 --> 00:48:32.079
+hrishikb@andrew.cmu.edu: Okay, I'll put it here, too.
+
+379
+00:48:40.280 --> 00:48:49.709
+hrishikb@andrew.cmu.edu: So I'm going to send this to you. I want… I'm going to spend this weekend reviewing it, though, because I just, like, wrote it yesterday, so I haven't really had time, so much time to look at it myself.
+
+380
+00:48:49.850 --> 00:48:56.309
+hrishikb@andrew.cmu.edu: I don't need this stuff right down here. Let's probably a bit more about this. So trying to really think about
+
+381
+00:48:56.900 --> 00:48:58.830
+hrishikb@andrew.cmu.edu: your semester.
+
+382
+00:48:59.420 --> 00:49:02.640
+hrishikb@andrew.cmu.edu: And give you some guidance as to
+
+383
+00:49:03.050 --> 00:49:07.200
+hrishikb@andrew.cmu.edu: What types of things may be expected, you know, or…
+
+384
+00:49:07.420 --> 00:49:10.889
+hrishikb@andrew.cmu.edu: And every project is different, right? But what kinds of things
+
+385
+00:49:11.000 --> 00:49:22.689
+hrishikb@andrew.cmu.edu: should either exist, or be in progress, or thinking about, what's maybe coming up soon, what things… hey, you're probably not going to have this yet, but these are things that you'll probably need at some point, right? So…
+
+386
+00:49:24.020 --> 00:49:30.530
+hrishikb@andrew.cmu.edu: you know, context diagram, the vision, understanding of requirements and notional architecture. None of this should be huge.
+
+387
+00:49:30.710 --> 00:49:36.669
+hrishikb@andrew.cmu.edu: you know, I guess scope, agreement, statement of work are probably pretty… I'm just gonna put those together here.
+
+388
+00:49:37.250 --> 00:49:40.999
+hrishikb@andrew.cmu.edu: As you can tell, I wrote this… I didn't have a whole lot of time to work on it.
+
+389
+00:49:43.520 --> 00:49:44.589
+hrishikb@andrew.cmu.edu: Come on, there we go.
+
+390
+00:49:45.110 --> 00:49:51.780
+hrishikb@andrew.cmu.edu: The semester roadmap, we talked about, you know, you showed that, your process definitions, that's really important this time.
+
+391
+00:49:51.930 --> 00:50:00.970
+hrishikb@andrew.cmu.edu: And any external dependencies, risk identification, which you're doing, the change management, which is something we talked about today, like, what happens when there is… when there is change.
+
+392
+00:50:01.960 --> 00:50:05.919
+hrishikb@andrew.cmu.edu: And I guess I should also put here, you know, sort of your…
+
+393
+00:50:06.190 --> 00:50:14.980
+hrishikb@andrew.cmu.edu: what do we call it? Software engineering? What do you call software engineering? SCS. What? SCS, Software Engineering. System.
+
+394
+00:50:15.810 --> 00:50:18.110
+hrishikb@andrew.cmu.edu: SES? Yeah. Okay.
+
+395
+00:50:19.240 --> 00:50:20.669
+hrishikb@andrew.cmu.edu: That should be theirs here, right?
+
+396
+00:50:24.110 --> 00:50:38.510
+hrishikb@andrew.cmu.edu: And then, you know, as you're getting more, you know, more understanding, you're going to create your breakdown structure, you're going to start to populate your backlog, and understand what your milestones are going to be, what your milestone plan is. And then, once you have that, you can really start working on
+
+397
+00:50:38.510 --> 00:50:46.149
+hrishikb@andrew.cmu.edu: you know, your… your, sorry, your work package, your release plans, your… start really having your backlogs, and…
+
+398
+00:50:46.490 --> 00:50:58.280
+hrishikb@andrew.cmu.edu: If you're going to be doing, earned value charts and things like that. And then… and I actually wrote at the end that full ceremony execution, you know, at some point, people are saying, okay, we're going to follow…
+
+399
+00:50:58.770 --> 00:51:02.700
+hrishikb@andrew.cmu.edu: Scrum, for example, right? Or we're gonna follow…
+
+400
+00:51:03.320 --> 00:51:17.269
+hrishikb@andrew.cmu.edu: milestone-driven execution, and as a part of that, there's a bunch of different ceremonies that one does, right? So at some point, you're gonna evolve where, hey, this is our… this is how we operate, this is the cadence
+
+401
+00:51:17.860 --> 00:51:27.350
+hrishikb@andrew.cmu.edu: This is how we operate as a team, and this is the cadence of work that we do. You know, every… every week we do this, every two weeks we do that, every three weeks we do that.
+
+402
+00:51:27.650 --> 00:51:32.619
+hrishikb@andrew.cmu.edu: So that's just… I'll share this, you don't need to copy it, but this could be helpful
+
+403
+00:51:32.970 --> 00:51:40.410
+hrishikb@andrew.cmu.edu: in terms of trying to understand the expectations, not to… well, two things. One is the expectations of the program, but also
+
+404
+00:51:40.880 --> 00:51:48.269
+hrishikb@andrew.cmu.edu: Is used as a guide to try to help, you know, yourselves. Kind of, are you on track with things related to program management as well?
+
+405
+00:51:49.070 --> 00:51:51.760
+hrishikb@andrew.cmu.edu: Does that make sense? Alright.
+
+406
+00:51:52.140 --> 00:51:54.820
+hrishikb@andrew.cmu.edu: Yeah, I just want to do… I just need to do a,
+
+407
+00:51:55.250 --> 00:51:59.459
+hrishikb@andrew.cmu.edu: sync between what I thought of and what's in the
+
+408
+00:51:59.610 --> 00:52:06.490
+hrishikb@andrew.cmu.edu: program documents to make sure that I'm in alignment. So, I'll do that this weekend and get it out to you.
+
+409
+00:52:07.940 --> 00:52:08.850
+hrishikb@andrew.cmu.edu: Okay.
+
+410
+00:52:10.200 --> 00:52:17.170
+hrishikb@andrew.cmu.edu: Yeah, I think, again, I think your team's doing great. I think you've got… it's a great start in project management work.
+
+411
+00:52:17.320 --> 00:52:24.759
+hrishikb@andrew.cmu.edu: If you look at this list of items, you're either, you know, you have or you're well on your way for many of them.
+
+412
+00:52:25.500 --> 00:52:30.190
+hrishikb@andrew.cmu.edu: so… I don't think certain things are going pretty well.
+
+413
+00:52:31.280 --> 00:52:33.149
+hrishikb@andrew.cmu.edu: And I was very impressed.
+
+414
+00:52:33.480 --> 00:52:34.460
+hrishikb@andrew.cmu.edu: by the risks.
+
+415
+00:52:35.270 --> 00:52:41.440
+hrishikb@andrew.cmu.edu: Seriously. I've seen a lot of bad risk registers before, okay? That's pretty good.
+
+416
+00:52:45.180 --> 00:52:47.840
+hrishikb@andrew.cmu.edu: Anything else I can help you with?
+
+417
+00:52:51.660 --> 00:52:52.830
+hrishikb@andrew.cmu.edu: I think…
+
+418
+00:52:53.720 --> 00:53:08.909
+hrishikb@andrew.cmu.edu: we'll just take your, like, the… because a lot of the risks that, I think need to be changed, like, be more in line with your feedback, and then at least two of them, or, like, one of them needs to be removed, and I'll just change the other ones.
+
+419
+00:53:10.360 --> 00:53:16.710
+hrishikb@andrew.cmu.edu: Yeah. And part of your process would be, for example, okay, one… you can decide however you want to do it.
+
+420
+00:53:16.840 --> 00:53:19.689
+hrishikb@andrew.cmu.edu: But… Because we're talking about risks.
+
+421
+00:53:19.980 --> 00:53:33.040
+hrishikb@andrew.cmu.edu: Well, at our sprint reviews, we review our risk register, or at our… like, one thing that teams sometimes do is they create a risk register. Why? Because they need to show it at their end of semester presentation.
+
+422
+00:53:33.710 --> 00:53:47.869
+hrishikb@andrew.cmu.edu: And it's unfortunate, right? It gets… yes, you get practice doing it, but it's not something that actually helps you in your program. And I realize this is very… it's not a giant project, so you can keep all those risks in your head.
+
+423
+00:53:48.430 --> 00:53:51.470
+hrishikb@andrew.cmu.edu: But in a much larger project, you may have
+
+424
+00:53:51.740 --> 00:53:56.740
+hrishikb@andrew.cmu.edu: 25, 30 items, and you may want… need to review them and look at, hey, what is becoming
+
+425
+00:53:57.300 --> 00:54:03.959
+hrishikb@andrew.cmu.edu: you know, for… I've been in situations where, for a given risk, I say, okay, what… as a team, when do we need to review this risk?
+
+426
+00:54:04.140 --> 00:54:07.060
+hrishikb@andrew.cmu.edu: And we set a trigger, so it could be…
+
+427
+00:54:07.420 --> 00:54:12.299
+hrishikb@andrew.cmu.edu: two-year project. It could be we want to review it in 2 weeks, could be we want to review it in 3 months.
+
+428
+00:54:13.860 --> 00:54:25.759
+hrishikb@andrew.cmu.edu: who needs to be involved in that review? So there's a lot of ways… there's a very basic risk register, but you can get a lot more sophisticated around them, too, and use it to more active… actively develop a period project.
+
+429
+00:54:30.090 --> 00:54:31.659
+hrishikb@andrew.cmu.edu: Okay, well, cool.
+
+430
+00:54:32.240 --> 00:54:36.960
+hrishikb@andrew.cmu.edu: Hope everyone has a good weekend. I'm gonna go to my… I have the next team right now, so I'm gonna go try again.
+
+431
+00:54:37.240 --> 00:54:42.740
+hrishikb@andrew.cmu.edu: It's already Friday, so… it's amazing, isn't it? Alright.
+
+432
+00:54:42.950 --> 00:54:45.580
+hrishikb@andrew.cmu.edu: 282. Thank you for the,
+
+433
+00:54:46.170 --> 00:54:49.929
+hrishikb@andrew.cmu.edu: But the power here is very nipple. I mean, my laptop thinks you too.
+
+434
+00:54:50.600 --> 00:54:52.640
+hrishikb@andrew.cmu.edu: Thank you. Alright, thanks.
+
+435
+00:55:18.600 --> 00:55:20.240
+hrishikb@andrew.cmu.edu: Thank you so much,
+
+436
+00:55:21.280 --> 00:55:25.070
+hrishikb@andrew.cmu.edu: you know… Shamash.
+
+437
+00:55:34.050 --> 00:55:41.210
+hrishikb@andrew.cmu.edu: Dude, I said all your stuff about, like, how close by speed we expect. Hopefully, that turns out something good.
+
+438
+00:55:41.370 --> 00:55:56.699
+hrishikb@andrew.cmu.edu: we can only start speeding up after this semester. You won't have the shit till the end of the semester. When you start developing, it'll be about money. Not even give, like, freaking Cadbury to…
+
+439
+00:55:57.340 --> 00:55:58.260
+hrishikb@andrew.cmu.edu: 2 months.
+
+440
+00:55:58.410 --> 00:56:04.670
+hrishikb@andrew.cmu.edu: Hey, motherfucker, you're the team lead. When I'm team lead, everyone's getting cat buried, don't worry. Let's see.
+
+441
+00:56:05.330 --> 00:56:07.589
+hrishikb@andrew.cmu.edu: Cadbury things. I get you guys.
+
+442
+00:56:08.960 --> 00:56:09.830
+hrishikb@andrew.cmu.edu: Here.
+
+443
+00:56:10.300 --> 00:56:14.399
+hrishikb@andrew.cmu.edu: I have the… I noted on some minutes. We have another meeting, right? Yeah.
+
+444
+00:56:14.530 --> 00:56:21.069
+hrishikb@andrew.cmu.edu: What? Fine. Do you want to, lead that one? Yeah.
+
+445
+00:56:21.210 --> 00:56:37.220
+hrishikb@andrew.cmu.edu: Hey, you're only reading it. No, I'm done for the days and night. But you don't have any content! Hey, yeah, no one has content. Yeah. We don't know what we're reading. We have to make content right now. Yeah, you have 3 hours, right? Start.
+
+446
+00:56:37.390 --> 00:56:59.240
+hrishikb@andrew.cmu.edu: By the way, what have you talk… I mean, he's the AI coach, right? Yeah, software engineering, this thing. That's what you're… I think the analyst is only. You just have to pass over whatever you're gonna use AI. Yeah, yeah. Last two pages, man, I made in, like, last 10 minutes. Thank God it did not focus. He just… that's why I just kept going on the spread, because that's what I remembered as, like, 3 pages, I'll just…
+
+447
+00:56:59.240 --> 00:57:16.449
+hrishikb@andrew.cmu.edu: keep reviewing them. You made this yesterday itself. This one I made yesterday, but Rishi told me, like, just before meeting… Project one was the same. This, like, project part is… so I added two parts that I could defend, so I… all of this was cut, dude. Like, when I was actually copying from the AI that part, right? Because, of course, I cannot try it and test it.
+
+448
+00:57:16.450 --> 00:57:22.639
+hrishikb@andrew.cmu.edu: It gave, like, 5-10 pages of content. I cut it down to, like, 2 so that I could extend. Forward to stop the routing?
+
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+WEBVTT
+
+1
+00:00:00.000 --> 00:00:03.899
+hrishikb@andrew.cmu.edu: Consequence format, and, let's start off then.
+
+2
+00:00:04.250 --> 00:00:11.060
+hrishikb@andrew.cmu.edu: The first one is, we are… the development cannot proceed due to lack of data that we're having.
+
+3
+00:00:11.270 --> 00:00:19.769
+hrishikb@andrew.cmu.edu: So… the… first of all, we… and there's also some other access issues that we are facing.
+
+4
+00:00:19.950 --> 00:00:23.700
+hrishikb@andrew.cmu.edu: So we… I don't think we still have any access to Purser.
+
+5
+00:00:24.580 --> 00:00:31.099
+hrishikb@andrew.cmu.edu: And we also don't have any of the sample data that we need to proceed with any of the…
+
+6
+00:00:31.500 --> 00:00:36.760
+hrishikb@andrew.cmu.edu: MLT work, and… Any of the other. So…
+
+7
+00:00:37.860 --> 00:00:41.490
+hrishikb@andrew.cmu.edu: This is, decline-dependent, and, this could,
+
+8
+00:00:41.630 --> 00:00:45.979
+hrishikb@andrew.cmu.edu: Like, protect how we start the initial pages of the group, basically.
+
+9
+00:00:46.720 --> 00:00:52.699
+hrishikb@andrew.cmu.edu: So… The second risk relates to the first one.
+
+10
+00:00:52.960 --> 00:01:12.409
+hrishikb@andrew.cmu.edu: As we do not have the data, there are several different kinds of ML models that we wanted to try, and to see which has the best performance. So, like, the goal of the entire project is to be better than the manual one, but if we do not test the different ML models, we will not have a good enough idea of which to proceed with.
+
+11
+00:01:12.480 --> 00:01:16.560
+hrishikb@andrew.cmu.edu: And that just increases the uncertainty a lot.
+
+12
+00:01:19.370 --> 00:01:26.839
+hrishikb@andrew.cmu.edu: Third one, I think, is… More of a… future concern. If,
+
+13
+00:01:27.020 --> 00:01:31.010
+hrishikb@andrew.cmu.edu: Depending on if we make the… how we make the project, and how…
+
+14
+00:01:31.340 --> 00:01:41.000
+hrishikb@andrew.cmu.edu: whether scope creep does happen, because we've kept it very minimal right now. First of all, because we don't have, we've not gotten our hands dirty yet.
+
+15
+00:01:41.120 --> 00:01:50.219
+hrishikb@andrew.cmu.edu: And also because we don't know how the data looks, so that might happen, but this is more of a future concern, rather than something we're facing.
+
+16
+00:01:53.050 --> 00:01:55.410
+hrishikb@andrew.cmu.edu: Fourth one is…
+
+17
+00:01:57.180 --> 00:02:11.610
+hrishikb@andrew.cmu.edu: the client is pretty much, has strong preferences, and, is pretty much locked into the Azure environment. So, we… we will not be using any tools that, basically
+
+18
+00:02:12.700 --> 00:02:32.649
+hrishikb@andrew.cmu.edu: has to, like, force them to learn new things that they don't want to learn. And as a consequence, basically, we're, like, making our choices such that we're not using Terraform, we're using Bicep. Alright, is that not making them learn things through the online environment? We can go back, we'll go back over a bit. We'll go back there. And…
+
+19
+00:02:36.950 --> 00:02:46.790
+hrishikb@andrew.cmu.edu: The fifth one is not that big of an issue now, but might become one in the future. There could be some schema volatility, depending on,
+
+20
+00:02:47.130 --> 00:02:53.449
+hrishikb@andrew.cmu.edu: If, like, how much, changes we're gonna have to make to the ML output that we get.
+
+21
+00:02:53.650 --> 00:02:57.360
+hrishikb@andrew.cmu.edu: And how that goes into films.
+
+22
+00:02:57.660 --> 00:03:02.720
+hrishikb@andrew.cmu.edu: Okay. The rest is just a… my take on…
+
+23
+00:03:03.030 --> 00:03:06.610
+hrishikb@andrew.cmu.edu: The risk mitigation strategies we can have for each one of them.
+
+24
+00:03:07.320 --> 00:03:08.430
+hrishikb@andrew.cmu.edu: So…
+
+25
+00:03:08.950 --> 00:03:24.119
+hrishikb@andrew.cmu.edu: Scheduling calls with basically the client, and, like, sending changes to them so that we get the access to the cursor, and even more importantly than that, that we get access to the data.
+
+26
+00:03:24.660 --> 00:03:36.219
+hrishikb@andrew.cmu.edu: Second one, I did write something, but it's mainly just, like, dependent on the first being resolved, so that we can just try out the… I think we have 3 ML candidates, Brittany.
+
+27
+00:03:36.890 --> 00:03:45.479
+hrishikb@andrew.cmu.edu: Third one is, we will start missing milestones if, if, we, this continues, and,
+
+28
+00:03:45.580 --> 00:03:51.689
+hrishikb@andrew.cmu.edu: you're not able to, like, start work on… well, that's not… scope creep is not… it's different, right?
+
+29
+00:03:52.610 --> 00:04:01.720
+hrishikb@andrew.cmu.edu: You may have smart as risk number one. This one, this would be more of a futuristic. I think I should change it and just,
+
+30
+00:04:01.850 --> 00:04:15.010
+hrishikb@andrew.cmu.edu: Instead of being scope creep, right? For right now, scope creep is far outside than what we are currently doing. Yeah, scope creep is what? Scope creep would be if you were designing the system, and instead of having
+
+31
+00:04:15.010 --> 00:04:25.790
+hrishikb@andrew.cmu.edu: you know, a certain number of formats that they expect us to. Okay, sure, sure, okay, yep. They, they, we suddenly decide, or they decide that it would be better if we could handle more. Right.
+
+32
+00:04:26.170 --> 00:04:27.969
+hrishikb@andrew.cmu.edu: Or different tech kinds.
+
+33
+00:04:30.500 --> 00:04:44.530
+hrishikb@andrew.cmu.edu: Architectural complexity is, is, we have already decided that, when we were talking with them, that we would only do the minimal amount of darkerization, and…
+
+34
+00:04:45.400 --> 00:04:51.750
+hrishikb@andrew.cmu.edu: keep anything, like, open television and such into, like, stress goes, so that we… we're not…
+
+35
+00:04:51.980 --> 00:04:54.449
+hrishikb@andrew.cmu.edu: Going to start with the reward.
+
+36
+00:04:55.120 --> 00:05:03.059
+hrishikb@andrew.cmu.edu: And this overhead from schema is not relevant. So, so let's look at these one by one, okay? So,
+
+37
+00:05:03.200 --> 00:05:09.209
+hrishikb@andrew.cmu.edu: Well, two things. Let's do the risk, all right, and then what I also want to do is sort of share with you
+
+38
+00:05:10.310 --> 00:05:18.210
+hrishikb@andrew.cmu.edu: And I'm actually going to spend this weekend reflecting on it, make sure what I'm sharing is actually correct, but I'll share it anyway. It kind of what…
+
+39
+00:05:18.670 --> 00:05:23.840
+hrishikb@andrew.cmu.edu: You know, what are those kinds of expectations that we would have from a project management perspective?
+
+40
+00:05:24.070 --> 00:05:36.440
+hrishikb@andrew.cmu.edu: by, you know, by soon, by the end of the semester, etc, so you can make sure you queue that stuff up, right? So we'll do that as well. But happy to focus on… on the risk part.
+
+41
+00:05:37.000 --> 00:05:43.489
+hrishikb@andrew.cmu.edu: the… So, I'm just gonna give my, like, direct feedback, right? So…
+
+42
+00:05:43.720 --> 00:05:49.020
+hrishikb@andrew.cmu.edu: Risk one, I think, is actually two different risks here, right? So it's really… there's one about
+
+43
+00:05:49.220 --> 00:05:59.059
+hrishikb@andrew.cmu.edu: getting, you know, some access to some tooling, and the other is access to the data. Those are… I would… I would put those as two different risks, because
+
+44
+00:05:59.280 --> 00:06:05.250
+hrishikb@andrew.cmu.edu: The… there's a… the consequences are eroded as one consequence, but they are really different consequences.
+
+45
+00:06:05.340 --> 00:06:19.449
+hrishikb@andrew.cmu.edu: Depending on… depending on the access. And… and also, potentially, the mitigations for them could be a little bit different as well. But those are actually very legitimate… legitimate risks, right? 100% agree with you there.
+
+46
+00:06:20.830 --> 00:06:39.239
+hrishikb@andrew.cmu.edu: Now, let's go to the mitigation for risk number one, and I think… by the way, I also think you did a great job of, sort of, you know, the way you wrote the risks as well. I think it's really well done. If you ever do, sort of, a presentation where you want to show risks, it'll be a lot… need to be a lot less wordy, right?
+
+47
+00:06:39.310 --> 00:06:43.710
+hrishikb@andrew.cmu.edu: But having this as the backup is really good. Did a really nice job there.
+
+48
+00:06:45.090 --> 00:06:50.980
+hrishikb@andrew.cmu.edu: So, if I think about… Just in general, about risk.
+
+49
+00:06:51.120 --> 00:06:54.360
+hrishikb@andrew.cmu.edu: Risks and how to… how to manage their risks.
+
+50
+00:06:55.820 --> 00:06:56.890
+hrishikb@andrew.cmu.edu: there's…
+
+51
+00:06:57.370 --> 00:07:07.729
+hrishikb@andrew.cmu.edu: how do you… you know, there's multiple ways to attack risks, right? One, we can just say, okay, good, no problem, all of it, right? But that's not what you want to do here. You can…
+
+52
+00:07:08.570 --> 00:07:15.640
+hrishikb@andrew.cmu.edu: mitigate risks, right? What does it mean… what does it mean to mitigate a risk? Let me ask that question. What does that really mean?
+
+53
+00:07:17.150 --> 00:07:34.399
+hrishikb@andrew.cmu.edu: I mean, so to mitigate the effects of a risk, so it doesn't negatively impact our team or our project? Okay, right. There's also something where you could reduce the likelihood of something occurring. Those are two different things, right? So one is, hey, it may occur in one of the impact, and the other's a likelihood. So…
+
+54
+00:07:34.770 --> 00:07:43.250
+hrishikb@andrew.cmu.edu: you know, schedule the 30-45 minute meeting, right? What is that? Is that really a mitigation, or is that a reduced likelihood of it occurring?
+
+55
+00:07:44.140 --> 00:07:51.800
+hrishikb@andrew.cmu.edu: I guess it would be reduced likelihood, because even after the meeting, there's still a chance that, you know, no further…
+
+56
+00:07:51.940 --> 00:07:52.680
+hrishikb@andrew.cmu.edu: Right.
+
+57
+00:07:52.850 --> 00:07:59.240
+hrishikb@andrew.cmu.edu: What would reduce… what would be an example of, like, a reduced, sort of, impact if it… if it does happen?
+
+58
+00:08:00.620 --> 00:08:14.110
+hrishikb@andrew.cmu.edu: And I don't care about, like, the fine… you know, I don't care about the nuances, this, reduced likelihood or reduced impact, but we should think about… we should… when we think about a risk, we should think about both of those, right, because they're both strategies.
+
+59
+00:08:14.280 --> 00:08:19.379
+hrishikb@andrew.cmu.edu: That we can employ. Sometimes we can do one, sometimes we can do the other, sometimes we can do both.
+
+60
+00:08:20.070 --> 00:08:25.130
+hrishikb@andrew.cmu.edu: So what might be, A way to reduce impact.
+
+61
+00:08:25.880 --> 00:08:30.579
+hrishikb@andrew.cmu.edu: Wait to release… it's not to wait for the, like, the…
+
+62
+00:08:30.980 --> 00:08:39.809
+hrishikb@andrew.cmu.edu: A large amount of data to come in and just ask them for, like, a much smaller amount, and then just kind of go with that first.
+
+63
+00:08:39.950 --> 00:08:52.939
+hrishikb@andrew.cmu.edu: That would reduce… like, there would still be an impact, because we would not be able to, like, train any exploratory models, but we would kind of get a feel of what the data is structured like, or what are, like, the usual entries in some of them.
+
+64
+00:08:54.120 --> 00:08:55.660
+hrishikb@andrew.cmu.edu: And anyone else?
+
+65
+00:08:55.960 --> 00:09:00.599
+hrishikb@andrew.cmu.edu: I think for the cursor issue, it might be that we can use our own
+
+66
+00:09:00.810 --> 00:09:16.960
+hrishikb@andrew.cmu.edu: LLMs to help us, like, we currently are, without using clients' proprietary data. Just to, like, we can hold off working on the proprietary data, like, we don't have any… You don't have the data, so whatever reason? So, yeah, right now we're using our own LLM, so that part is, I guess, currently being mitigated.
+
+67
+00:09:17.610 --> 00:09:25.120
+hrishikb@andrew.cmu.edu: As for the data, I'm not sure what we can do, like, we need something from them.
+
+68
+00:09:25.450 --> 00:09:37.920
+hrishikb@andrew.cmu.edu: is there any way to… you know, so I realize that for those sort of machine learning models, you really want to see the data from them, but for a lot of the other work… so…
+
+69
+00:09:38.910 --> 00:09:40.430
+hrishikb@andrew.cmu.edu: There's a…
+
+70
+00:09:41.980 --> 00:09:47.550
+hrishikb@andrew.cmu.edu: Ideally, you'd be in a situation, and I certainly remember from your requirements document, where you were trying to…
+
+71
+00:09:48.040 --> 00:10:02.640
+hrishikb@andrew.cmu.edu: I forget what… I forget which quality attribute you called it. Was it extensibility? I can't remember, or you need to, you know… you're doing this for three types of data sources, and it needs to be relatively simple to add a fourth, a fifth, a sixth on, right? There's… there's something there.
+
+72
+00:10:03.220 --> 00:10:07.080
+hrishikb@andrew.cmu.edu: is there any way to use synthetic data?
+
+73
+00:10:07.420 --> 00:10:12.070
+hrishikb@andrew.cmu.edu: Like, just… you know what? You go… Because, cause, yes.
+
+74
+00:10:12.080 --> 00:10:31.420
+hrishikb@andrew.cmu.edu: that's not gonna… But then you have to know the format, right? Yeah, so you're gonna make something up, right? You're gonna make… you're gonna… again, I'm just throwing an idea out there, right? You totally make it up, right? Because that training part is just a piece of the whole… the whole pipeline, right? So there's probably a lot you can do.
+
+75
+00:10:31.810 --> 00:10:38.629
+hrishikb@andrew.cmu.edu: And you may need to do some rework, sure, but there's probably a bunch that you can do, even if you don't know… you don't have any real data.
+
+76
+00:10:38.740 --> 00:10:41.149
+hrishikb@andrew.cmu.edu: And it's not uncommon in…
+
+77
+00:10:42.590 --> 00:10:51.899
+hrishikb@andrew.cmu.edu: You know, getting data sometimes is very, you know, doesn't happen quickly, and there's a lot of times where people need to sort of make some forward progress, even before they have it.
+
+78
+00:10:53.630 --> 00:10:56.930
+hrishikb@andrew.cmu.edu: Not ideal, I understand, but I think also.
+
+79
+00:10:57.310 --> 00:11:11.440
+hrishikb@andrew.cmu.edu: You raised the point. You know, I think you… it was, maybe there's just a little bit of data we can get, right? So, maybe define what that, you know, sort of minimally viable amount of data is.
+
+80
+00:11:11.560 --> 00:11:30.980
+hrishikb@andrew.cmu.edu: So, it will sufficiently unblock you, so you can make some more forward progress. So, actually, we don't even need the data, we just need the schema, and then we need this makeup data. That's the main problem. So, we don't even need this small amount of data. Okay. Yeah, so all we require right now is the basic schema of how the data would look like in a table.
+
+81
+00:11:31.210 --> 00:11:39.010
+hrishikb@andrew.cmu.edu: And then we would be able to work on the pipeline, and they would be able to work on the MX modeling as well.
+
+82
+00:11:39.100 --> 00:11:59.059
+hrishikb@andrew.cmu.edu: PDFs or some input files to start on the admission part? Well, you don't need that, because the PDFs are only for the OCR part. Like, okay, you're… okay, then you get access… can you get an account and pretend that you're buying something on their website, and go look at some of the data that is available, and make up a schema to start with?
+
+83
+00:12:00.590 --> 00:12:05.930
+hrishikb@andrew.cmu.edu: Right? You know, you know, what is it? It's all the parts information, right? It's all the data about…
+
+84
+00:12:06.050 --> 00:12:17.890
+hrishikb@andrew.cmu.edu: Yeah, I did write a little about representative data, so that could be something. In this case, it would be, like, just going onto their website and just scraping a few pages, I guess.
+
+85
+00:12:18.020 --> 00:12:18.890
+hrishikb@andrew.cmu.edu: And…
+
+86
+00:12:19.110 --> 00:12:27.909
+hrishikb@andrew.cmu.edu: That would be better than, you know, not detecting. It's not going to eliminate the risk, sure, but it certainly will help mitigate the risk.
+
+87
+00:12:28.610 --> 00:12:31.310
+hrishikb@andrew.cmu.edu: Mitigate the impact of the risk.
+
+88
+00:12:32.490 --> 00:12:37.460
+hrishikb@andrew.cmu.edu: And I think a lot of it is also just around, sort of, the… again, you're probably doing all this, but…
+
+89
+00:12:37.710 --> 00:12:39.520
+hrishikb@andrew.cmu.edu: You know, being very…
+
+90
+00:12:40.990 --> 00:12:45.860
+hrishikb@andrew.cmu.edu: You know, up front with the custody, with the client, and saying, you know, not just like, hey, we need this data, but…
+
+91
+00:12:46.200 --> 00:12:47.000
+hrishikb@andrew.cmu.edu: you know.
+
+92
+00:12:47.120 --> 00:12:53.749
+hrishikb@andrew.cmu.edu: here are the… here, you know, we need it by this date, here's the impact if we don't have it by this date. Just being very…
+
+93
+00:12:54.130 --> 00:13:01.850
+hrishikb@andrew.cmu.edu: You know, very explicit, and whenever you meet with them, you know, here are the actions, the open actions is the item number one you ever review in your meetings.
+
+94
+00:13:02.020 --> 00:13:12.159
+hrishikb@andrew.cmu.edu: I think, for the last meeting, I wrote to them, I mentioned that we are currently blocked, and the response we got was, you should be getting the data by end of yesterday.
+
+95
+00:13:12.260 --> 00:13:27.509
+hrishikb@andrew.cmu.edu: So I'm thinking if I write a follow-up mail today, or, like… Yeah, yeah, you know. Like, I've chased them twice in, like, two days, so… Let's say, hey, this is great, we're, you know, we were hoping to get the data yesterday, as you mentioned, and maybe something came up.
+
+96
+00:13:27.860 --> 00:13:32.529
+hrishikb@andrew.cmu.edu: Please let us know when we can expect it, because we're really looking forward to, you know.
+
+97
+00:13:32.650 --> 00:13:37.990
+hrishikb@andrew.cmu.edu: people never want to hear, we're blocked, we're blocked, we're blocked, right? And that doesn't mean you're not, right? But…
+
+98
+00:13:38.180 --> 00:13:42.870
+hrishikb@andrew.cmu.edu: Because it's kind of like the impression people get is that
+
+99
+00:13:43.340 --> 00:13:51.079
+hrishikb@andrew.cmu.edu: oh, we're, you know, we're not doing… we're sitting like this until you give us the data, which you're not doing. So, just, you know, be more…
+
+100
+00:13:52.650 --> 00:13:58.780
+hrishikb@andrew.cmu.edu: You know, we're eager to, you know, we're at the point where we really could leverage that data and what we're, you know, giving them today.
+
+101
+00:14:00.090 --> 00:14:05.930
+hrishikb@andrew.cmu.edu: Other kind of things you could do is… I'm actually referring to a listing right here.
+
+102
+00:14:07.400 --> 00:14:11.200
+hrishikb@andrew.cmu.edu: you know what, that's not going to help the end government. I think we talked about most of them.
+
+103
+00:14:11.910 --> 00:14:15.819
+hrishikb@andrew.cmu.edu: But the other… the other thing in risks is that…
+
+104
+00:14:16.750 --> 00:14:21.120
+hrishikb@andrew.cmu.edu: And it's hard to do this for all risks. Some risks you can sort of say.
+
+105
+00:14:22.450 --> 00:14:25.729
+hrishikb@andrew.cmu.edu: kind of a leading indicators, so…
+
+106
+00:14:25.870 --> 00:14:28.170
+hrishikb@andrew.cmu.edu: If, you know, if you're seeing that.
+
+107
+00:14:30.070 --> 00:14:37.870
+hrishikb@andrew.cmu.edu: you're a week away, you know, you don't want to wait till it's the last minute. I need it by this date and ask for it the day before, right? But you can start raising
+
+108
+00:14:38.100 --> 00:14:45.650
+hrishikb@andrew.cmu.edu: the yellow flag, and then the red flag as you get closer and closer to those, you know, sort of must-have dates. Because obviously the…
+
+109
+00:14:45.930 --> 00:14:48.370
+hrishikb@andrew.cmu.edu: The impact increases.
+
+110
+00:14:48.480 --> 00:14:51.479
+hrishikb@andrew.cmu.edu: As you, you know, the more you… the closer you get.
+
+111
+00:14:52.090 --> 00:14:53.980
+hrishikb@andrew.cmu.edu: So…
+
+112
+00:14:57.210 --> 00:15:04.270
+hrishikb@andrew.cmu.edu: Let me sit here. So if I… and I actually made a list here. If you're thinking about a sort of a mitigation plan overall,
+
+113
+00:15:05.130 --> 00:15:09.850
+hrishikb@andrew.cmu.edu: There's kind of four… four elements to a fantastic… to a great mitigation plan.
+
+114
+00:15:10.150 --> 00:15:18.069
+hrishikb@andrew.cmu.edu: One is what you're doing… we talked about most of these. What you're doing to try to prevent it from happening, right? Try to prevent the risk
+
+115
+00:15:18.620 --> 00:15:21.989
+hrishikb@andrew.cmu.edu: From materializing, preventing the risk from becoming an issue.
+
+116
+00:15:22.240 --> 00:15:26.090
+hrishikb@andrew.cmu.edu: Right, because it's… at some point, Fair enough.
+
+117
+00:15:26.710 --> 00:15:28.830
+hrishikb@andrew.cmu.edu: You're putting this as a risk?
+
+118
+00:15:29.140 --> 00:15:31.030
+hrishikb@andrew.cmu.edu: Is this already an issue?
+
+119
+00:15:31.780 --> 00:15:40.730
+hrishikb@andrew.cmu.edu: You know, and what is the impact of that issue? There's two… there's a difference. A risk is something that may happen. An issue is something that actually has occurred, and it is impacting you already.
+
+120
+00:15:43.600 --> 00:15:53.889
+hrishikb@andrew.cmu.edu: what you're trying to do… what you're going to do if the risk does materialize to reduce the negative impact, that would be kind of a second part of a really good mitigation strategy.
+
+121
+00:15:54.610 --> 00:15:56.319
+hrishikb@andrew.cmu.edu: A third one would be…
+
+122
+00:15:56.440 --> 00:16:03.650
+hrishikb@andrew.cmu.edu: what are those leading indicators? How do you know that it's going to become… going to… this risk is going to materialize?
+
+123
+00:16:03.790 --> 00:16:11.670
+hrishikb@andrew.cmu.edu: You know, for example, in this case, if you were in a situation where the customer's usually… the client's usually pretty responsive.
+
+124
+00:16:11.870 --> 00:16:14.100
+hrishikb@andrew.cmu.edu: Right. If your experience is…
+
+125
+00:16:14.380 --> 00:16:23.370
+hrishikb@andrew.cmu.edu: client is really not responsive in general, it takes them weeks to get back to us, then that leading indicator would change under those two circumstances, right? One would be…
+
+126
+00:16:23.670 --> 00:16:42.109
+hrishikb@andrew.cmu.edu: well, if the client says they're going to get it to us, that's pretty good, right? You know, we're not so worried about it. We can wait till 48 hours before it becomes an issue to raise the flag. If it's something we have a very non-responsive client, then you may want to say, well, we're going to raise that flag, it's going to go from yellow to red, say.
+
+127
+00:16:42.210 --> 00:16:44.789
+hrishikb@andrew.cmu.edu: Two weeks before you really need it.
+
+128
+00:16:45.050 --> 00:16:48.360
+hrishikb@andrew.cmu.edu: And the last is what, you know, if… if you don't get it.
+
+129
+00:16:48.510 --> 00:16:53.670
+hrishikb@andrew.cmu.edu: or you don't get it, but when you need it, what is… what are you going to do about it? How are you going to manage it?
+
+130
+00:16:54.300 --> 00:17:00.730
+hrishikb@andrew.cmu.edu: So… But those are… it's a really good risk, right? The only thing I would say is…
+
+131
+00:17:01.800 --> 00:17:10.649
+hrishikb@andrew.cmu.edu: Break the two, and sort of the caution is that there may be, you know.
+
+132
+00:17:11.030 --> 00:17:15.749
+hrishikb@andrew.cmu.edu: Risks typically have a kind of a trigger date, and…
+
+133
+00:17:16.280 --> 00:17:19.439
+hrishikb@andrew.cmu.edu: You need to… if there is one here, if there isn't one.
+
+134
+00:17:19.770 --> 00:17:23.170
+hrishikb@andrew.cmu.edu: Right? If there is one, that really needs to be communicated with the client.
+
+135
+00:17:24.540 --> 00:17:26.300
+hrishikb@andrew.cmu.edu: All right.
+
+136
+00:17:27.560 --> 00:17:31.730
+hrishikb@andrew.cmu.edu: Does that all make sense? It does. Okay, no problem.
+
+137
+00:17:32.230 --> 00:17:34.660
+hrishikb@andrew.cmu.edu: Beginning.
+
+138
+00:17:35.550 --> 00:17:37.070
+hrishikb@andrew.cmu.edu: Go to the next one.
+
+139
+00:17:37.200 --> 00:17:39.849
+hrishikb@andrew.cmu.edu: So, having manual workloads.
+
+140
+00:17:40.570 --> 00:17:47.890
+hrishikb@andrew.cmu.edu: Currently, we cannot proceed on this, but…
+
+141
+00:17:48.190 --> 00:17:51.710
+hrishikb@andrew.cmu.edu: The risk that, that I foresee is that
+
+142
+00:17:52.070 --> 00:17:58.890
+hrishikb@andrew.cmu.edu: until we have, like, some of the ML models trained up and we start exploring.
+
+143
+00:17:59.040 --> 00:18:01.560
+hrishikb@andrew.cmu.edu: There would be a lot of,
+
+144
+00:18:02.620 --> 00:18:09.900
+hrishikb@andrew.cmu.edu: A lot of comparisons that we have to do against the baseline, and we need to, like, really confirm that
+
+145
+00:18:10.310 --> 00:18:16.140
+hrishikb@andrew.cmu.edu: Our demo models that we do pick end up being better than what they currently have.
+
+146
+00:18:16.320 --> 00:18:21.769
+hrishikb@andrew.cmu.edu: So this is, like, a tech… is this a technical risk? Yes. Okay. Alright. So…
+
+147
+00:18:25.040 --> 00:18:31.779
+hrishikb@andrew.cmu.edu: So the… I think I would need to change these a little bit. It's not a mitigation, but…
+
+148
+00:18:31.810 --> 00:18:47.980
+hrishikb@andrew.cmu.edu: One of the indicators we would push to establish is, like, we would have a baseline that they… that we get from their side, where, like, how much time it's taking them, what's their average rate of being correct on the prediction that they have.
+
+149
+00:18:48.360 --> 00:18:55.519
+hrishikb@andrew.cmu.edu: And then… we'll… go from our side and see what our machine learning or AI is giving.
+
+150
+00:18:55.890 --> 00:19:02.249
+hrishikb@andrew.cmu.edu: And then, you know, what's the… is the confidence scoring robust or not?
+
+151
+00:19:02.560 --> 00:19:07.159
+hrishikb@andrew.cmu.edu: And… Is it working properly with the human in the root system, I think?
+
+152
+00:19:07.520 --> 00:19:17.020
+hrishikb@andrew.cmu.edu: So… Is this a… It… How is this different?
+
+153
+00:19:17.470 --> 00:19:22.309
+hrishikb@andrew.cmu.edu: than any other… Type of…
+
+154
+00:19:23.680 --> 00:19:30.029
+hrishikb@andrew.cmu.edu: Project, or that is doing any type of, sort of, categorization or classification.
+
+155
+00:19:30.780 --> 00:19:34.840
+hrishikb@andrew.cmu.edu: Based on ML. Wouldn't he have exactly the same set of circumstances?
+
+156
+00:19:35.270 --> 00:19:46.080
+hrishikb@andrew.cmu.edu: You know, you've got some… you've got some minimal acceptable rates, right? You're going to establish a baseline, you're going to verify it, you're going to…
+
+157
+00:19:46.570 --> 00:19:59.840
+hrishikb@andrew.cmu.edu: the… yeah, that's true in almost every case, but here, most of the time you have a defined architecture that you want to go with, like, but here we do not know. We're gonna have to pick between three of them.
+
+158
+00:20:00.080 --> 00:20:11.559
+hrishikb@andrew.cmu.edu: So that was why I wrote this one, so that we know that there is some uncertainty among the choices, and how that would affect how we go about it.
+
+159
+00:20:12.040 --> 00:20:18.660
+hrishikb@andrew.cmu.edu: So, how did… I guess maybe I just didn't understand that. So how… so for your three options, how are you going in?
+
+160
+00:20:19.480 --> 00:20:23.519
+hrishikb@andrew.cmu.edu: Where in that mitigation do you refer to those three options?
+
+161
+00:20:25.530 --> 00:20:32.120
+hrishikb@andrew.cmu.edu: That would just be the minimum credible AI standard for, like… So when, when we're,
+
+162
+00:20:32.430 --> 00:20:45.340
+hrishikb@andrew.cmu.edu: Testing all three, we would pick the one that has the appropriate, like, performance and, like, resources usage that they said, and then we would just pick one that does actually exceed the baseline.
+
+163
+00:20:45.680 --> 00:20:50.430
+hrishikb@andrew.cmu.edu: Okay, so really what you're doing is you're reducing your technical risk, by…
+
+164
+00:20:51.150 --> 00:21:01.380
+hrishikb@andrew.cmu.edu: by running… by basically running experiments against three different techniques, right? So that… that's…
+
+165
+00:21:01.970 --> 00:21:08.160
+hrishikb@andrew.cmu.edu: That's how… that's how you're… if I understand, that's how you're really mitigating the risk, is that right? Okay, now that makes a lot of sense.
+
+166
+00:21:09.950 --> 00:21:23.559
+hrishikb@andrew.cmu.edu: reading the… reading that, I don't get that? I… I should have, written all three, like, BERT, and… So tech… techno-risk, I'm saying, is one of the… one of the standard ways to mitigate technical risk is by experimenting, and…
+
+167
+00:21:23.770 --> 00:21:29.780
+hrishikb@andrew.cmu.edu: when you do experiments, and I think you're reading that you're on the right path, it's really important to
+
+168
+00:21:30.110 --> 00:21:42.370
+hrishikb@andrew.cmu.edu: make sure that you design… design the experiment well, right? So here are the… these are the criteria… these are criteria by which we are going to effectively evaluate those different experiments.
+
+169
+00:21:42.650 --> 00:21:45.900
+hrishikb@andrew.cmu.edu: Yeah, like, another part to this is that
+
+170
+00:21:46.230 --> 00:21:56.979
+hrishikb@andrew.cmu.edu: The… the manual baseline itself is not established yet. We did ask them during the client meetings, and they were… themselves did not have, like…
+
+171
+00:21:57.060 --> 00:22:15.320
+hrishikb@andrew.cmu.edu: they've categorized enough data, of course, but they don't have a baseline for what they do have, like, they've not done that analysis yet. So that's a part of the risk that we don't have something already to compare against. It will have to be a process between us and them so that we actually have something concrete to compare against.
+
+172
+00:22:15.340 --> 00:22:19.920
+hrishikb@andrew.cmu.edu: Right, but you could still compare against… you're still going to have some…
+
+173
+00:22:21.720 --> 00:22:28.510
+hrishikb@andrew.cmu.edu: absolute numbers, right? Your experiments are going to provide
+
+174
+00:22:33.700 --> 00:22:39.240
+hrishikb@andrew.cmu.edu: Well, your experiments are going to provide some, you know, automation…
+
+175
+00:22:40.900 --> 00:22:48.100
+hrishikb@andrew.cmu.edu: You're gonna know how… let's put it this way, let's say… let's say you run through 100 different, you know, you have each system running through 100 different inputs, yeah.
+
+176
+00:22:49.060 --> 00:22:51.830
+hrishikb@andrew.cmu.edu: You're going to know the…
+
+177
+00:22:53.860 --> 00:23:00.059
+hrishikb@andrew.cmu.edu: the percentages of those that you have high confidence in need, you know, do or do not need human correction. Yes. Right?
+
+178
+00:23:01.160 --> 00:23:03.649
+hrishikb@andrew.cmu.edu: So, in some sense.
+
+179
+00:23:04.890 --> 00:23:16.829
+hrishikb@andrew.cmu.edu: you're going… you know, that… you're going to know which perform… you know, which one has better performance, right? And I realize it's not going to necessarily tell you, are you saving enough time overall, right? Is it a… but…
+
+180
+00:23:17.370 --> 00:23:24.950
+hrishikb@andrew.cmu.edu: You could still… quantitatively evaluate
+
+181
+00:23:25.880 --> 00:23:28.839
+hrishikb@andrew.cmu.edu: Even without all that information. And honestly.
+
+182
+00:23:28.940 --> 00:23:30.919
+hrishikb@andrew.cmu.edu: You could, you could, you could…
+
+183
+00:23:31.180 --> 00:23:35.379
+hrishikb@andrew.cmu.edu: you know, do a lot of work before you know what this magic number is.
+
+184
+00:23:35.560 --> 00:23:37.340
+hrishikb@andrew.cmu.edu: You don't need that magic number yet.
+
+185
+00:23:40.790 --> 00:23:49.080
+hrishikb@andrew.cmu.edu: I think 3 is not… What, it's not currently, pressing for us.
+
+186
+00:23:49.200 --> 00:23:53.530
+hrishikb@andrew.cmu.edu: So 3's not… 3's not a risk. Tell you why. So…
+
+187
+00:23:53.820 --> 00:23:56.639
+hrishikb@andrew.cmu.edu: And this is… you're not… every single…
+
+188
+00:23:57.660 --> 00:24:04.160
+hrishikb@andrew.cmu.edu: 80% of every studio and practical team has this… something like this as a risk.
+
+189
+00:24:04.430 --> 00:24:11.440
+hrishikb@andrew.cmu.edu: And then during the presentations, you can tell the different faculty members kind of get into arguments with one another.
+
+190
+00:24:11.610 --> 00:24:22.569
+hrishikb@andrew.cmu.edu: Is that a risk? No, it's not a risk. I don't think it's a risk. So, I'm gonna head it off with the pass, because I don't think it's a risk. There may be other faculty members who do, and here's why, is that…
+
+191
+00:24:23.060 --> 00:24:25.660
+hrishikb@andrew.cmu.edu: There has never been a project since the
+
+192
+00:24:26.290 --> 00:24:33.830
+hrishikb@andrew.cmu.edu: The world saw its first project that didn't have scope creep as a potential… Issued.
+
+193
+00:24:34.020 --> 00:24:39.890
+hrishikb@andrew.cmu.edu: And… It's almost like saying, Well, my project may fail.
+
+194
+00:24:40.140 --> 00:24:42.220
+hrishikb@andrew.cmu.edu: I may not succeed, that's a risk.
+
+195
+00:24:42.490 --> 00:24:46.439
+hrishikb@andrew.cmu.edu: It's this, it's just… it's very vague. You don't…
+
+196
+00:24:46.920 --> 00:24:52.860
+hrishikb@andrew.cmu.edu: You know, how do you… how would you mitigate scope of creep? You would do it through good software engineering practices.
+
+197
+00:24:52.990 --> 00:24:55.050
+hrishikb@andrew.cmu.edu: So you'd have…
+
+198
+00:24:55.250 --> 00:25:08.289
+hrishikb@andrew.cmu.edu: a scope of agreement with the client. You might have a signed-off statement of work. You might have a, you know, an explicit, sort of, as part of your requirements, an out-of-scope list of things that are out of scope.
+
+199
+00:25:08.530 --> 00:25:12.340
+hrishikb@andrew.cmu.edu: You probably might have some change control processes, so if you are
+
+200
+00:25:12.770 --> 00:25:20.339
+hrishikb@andrew.cmu.edu: If something new comes in, this is the process that you follow in order to determine if that is something you can accept or not.
+
+201
+00:25:20.460 --> 00:25:27.799
+hrishikb@andrew.cmu.edu: you're gonna follow, you know, Moscow, for… to understand what, you know, in terms of prioritization of requirements.
+
+202
+00:25:27.900 --> 00:25:36.039
+hrishikb@andrew.cmu.edu: So it's just a… it's just part of every project, and software engineering practices will address that for you. Okay. Make sense?
+
+203
+00:25:37.000 --> 00:25:39.150
+hrishikb@andrew.cmu.edu: Yeah.
+
+204
+00:25:39.540 --> 00:25:45.410
+hrishikb@andrew.cmu.edu: I think because we're a newer team, and all of us are kind of new to, like, doing the whole process ourselves.
+
+205
+00:25:45.520 --> 00:26:03.059
+hrishikb@andrew.cmu.edu: I did not consider it, like, that there were mature solutions, like, that people actually have done this, like, a thousand times. Yeah, again, it's… but you… but you know some of this already, so you know about creating, like, the statement of work for your… Yes, and the… You know about creating Moscow.
+
+206
+00:26:03.060 --> 00:26:08.320
+hrishikb@andrew.cmu.edu: you may not know about, like, a, you know, like a change control thing. You may not know about that, right? But…
+
+207
+00:26:08.390 --> 00:26:11.890
+hrishikb@andrew.cmu.edu: But think about… What you would…
+
+208
+00:26:12.320 --> 00:26:19.330
+hrishikb@andrew.cmu.edu: What you would do if, you know, the client said, Well… Here's an example.
+
+209
+00:26:21.060 --> 00:26:30.720
+hrishikb@andrew.cmu.edu: My father, many, many, many, many years ago, was doing a master's in mechanical engineering, and at that time, master's, you write a whole pretty significant thesis, and
+
+210
+00:26:31.650 --> 00:26:43.219
+hrishikb@andrew.cmu.edu: He spent, you know, huge amounts of time on this thing, you know, thousands of hours on this thing, and he came up with this long, you know, this paper, right, this published paper. And his advisor looks at it, and he said, this is great work.
+
+211
+00:26:43.640 --> 00:26:47.819
+hrishikb@andrew.cmu.edu: Now I want you to do it using complex numbers, not just, like, real numbers.
+
+212
+00:26:49.270 --> 00:26:54.949
+hrishikb@andrew.cmu.edu: And that was scope creep, right? That was, like, real scope creep. And…
+
+213
+00:26:55.060 --> 00:27:02.330
+hrishikb@andrew.cmu.edu: There wasn't any way to address it other than my father saying, I'm done, and I'm out of here. I don't care about the semesters anymore.
+
+214
+00:27:02.540 --> 00:27:15.650
+hrishikb@andrew.cmu.edu: But think about how you would handle a situation where someone… a client would say, well, we really want… we love this, but you want… we want you to do it with… with, with complex or imaginary numbers, right? You would need to say.
+
+215
+00:27:15.760 --> 00:27:21.720
+hrishikb@andrew.cmu.edu: Okay, you don't say no, right? You don't say, no, that wasn't part of our scope. You say.
+
+216
+00:27:22.620 --> 00:27:37.129
+hrishikb@andrew.cmu.edu: That's a great idea! That's really interesting. Let us… that wasn't part of our original agreement, or statement of work. Let's go back and understand, do a high-level scoping among ourselves to understand what… how big is this?
+
+217
+00:27:37.190 --> 00:27:45.339
+hrishikb@andrew.cmu.edu: and what its impact would be on the rest of the project, and we'll come back to you, and we'll have that discussion, right? And that's the way to handle it.
+
+218
+00:27:45.700 --> 00:27:50.330
+hrishikb@andrew.cmu.edu: I think right now, I'm not sure if we have an official scope of work.
+
+219
+00:27:50.470 --> 00:27:58.810
+hrishikb@andrew.cmu.edu: Is that something we should… You should do, yeah, you should definitely do that. Yeah, I actually think, like, something this client signs off on is really valuable.
+
+220
+00:27:59.230 --> 00:28:02.080
+hrishikb@andrew.cmu.edu: Okay, get an official scope of work, and…
+
+221
+00:28:02.290 --> 00:28:06.949
+hrishikb@andrew.cmu.edu: How we would handle changes. Yeah, yeah, okay.
+
+222
+00:28:07.540 --> 00:28:23.650
+hrishikb@andrew.cmu.edu: That's my list here. I think, what you said is much better than what I had for the mitigation of all this, so we'll just, I'll just change it to what you just said. Yeah, but I would… I would not include it as a risk. Okay. It's just part of your…
+
+223
+00:28:23.810 --> 00:28:40.779
+hrishikb@andrew.cmu.edu: part of your project management process. It would just be a doo-do for us to, like, get the official scope of work and, like, changes. Yeah, people don't like seeing, you know, a risk that is just, you know, well, another… another very common risk that people… students often have is
+
+224
+00:28:40.990 --> 00:28:46.540
+hrishikb@andrew.cmu.edu: Well, someone's gonna… You know, Someone's gonna get sick.
+
+225
+00:28:47.320 --> 00:28:51.429
+hrishikb@andrew.cmu.edu: Right? Well, yeah, someone's probably going to get sick at some point.
+
+226
+00:28:51.590 --> 00:28:54.339
+hrishikb@andrew.cmu.edu: But you, as part of your…
+
+227
+00:28:54.760 --> 00:29:13.790
+hrishikb@andrew.cmu.edu: how you manage your project, your project management practices, need to understand and address how you're going to handle if someone gets sick. Is there… are you going to make sure that everyone has a backup person who understands what they're doing? Are you going to build some buffer into your schedule to account for the fact that
+
+228
+00:29:13.860 --> 00:29:26.490
+hrishikb@andrew.cmu.edu: someone is going to get sick, right? And it is… it's gonna… hopefully none of you, it's gonna be other teams, right? But someone's gonna get sick and be out for a week or two. It happens. So, plan for it, don't call it a risk, right? Okay. Okay.
+
+229
+00:29:27.740 --> 00:29:32.230
+hrishikb@andrew.cmu.edu: Okay, just one… make it loud briefly.
+
+230
+00:29:32.700 --> 00:29:35.800
+hrishikb@andrew.cmu.edu: Remove risk 3 and just make it a 2.
+
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+hrishikb@andrew.cmu.edu: The fourth one is just,
+
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+hrishikb@andrew.cmu.edu: Yeah, this is the one that you said you wanted to talk about more, so that they're, they don't, need to learn more than they, than they think they will need to, so that… so I'd just like to get your thoughts. So what's the difference between this and a… and a constraint? Because you have constraints, and you're…
+
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+hrishikb@andrew.cmu.edu: requirements document. How's it different?
+
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+00:30:03.720 --> 00:30:12.000
+hrishikb@andrew.cmu.edu: Specifically, that, when we are doing some of the work, there's more, much more,
+
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+00:30:12.150 --> 00:30:29.100
+hrishikb@andrew.cmu.edu: documentation or support for one of them, and since it's not an official constraint, right? We might really prefer to use something, but since they themselves are not defining it as an official constraint, so that's why I just kept it. Like, if it was an officially, like.
+
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+00:30:29.190 --> 00:30:46.569
+hrishikb@andrew.cmu.edu: They just said that, we would really, like, it has to be in Azure, and, like, you cannot, like, just use Spice for this, then that would… I would not have kept this. But you've got to use Azure, right? Yeah, but, that's a… I think that… isn't that a constraint?
+
+237
+00:30:47.350 --> 00:31:05.460
+hrishikb@andrew.cmu.edu: they just strongly imply that you should use Azure, and you should use Bicep, and you should try to stay away from, like, languages that they give two, three languages that they really use for. So I would… I would try to just move that over to the project constraints. Yeah, yeah. And, you know, every…
+
+238
+00:31:05.990 --> 00:31:14.500
+hrishikb@andrew.cmu.edu: Every place you're ever going to be has a… oh, not every place, most places, over 95% of the places out there are going to have constraints like that.
+
+239
+00:31:14.870 --> 00:31:16.829
+hrishikb@andrew.cmu.edu: You know, it's very rare that
+
+240
+00:31:17.890 --> 00:31:30.080
+hrishikb@andrew.cmu.edu: You know, there are some companies where they say, well, you're… you team… you team, you have responsibility for the entire… you can decide what technologies, you can decide, because you build it and you own it.
+
+241
+00:31:30.170 --> 00:31:39.769
+hrishikb@andrew.cmu.edu: Right? You have to maintain it's not… not my problem that no one else in the company understands Dolang. You do, you know, that's so… that is very, very rare.
+
+242
+00:31:41.740 --> 00:31:48.089
+hrishikb@andrew.cmu.edu: Yep, and funny enough, they did mention that do not do it in Golang or anything like that, right, that's why I said, yeah, I remember that.
+
+243
+00:31:48.380 --> 00:31:50.060
+hrishikb@andrew.cmu.edu: Yeah.
+
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+hrishikb@andrew.cmu.edu: I think, because, of course, we're not…
+
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+00:31:53.260 --> 00:31:59.320
+hrishikb@andrew.cmu.edu: gonna be responsible for after the handoff, and they'll have to do all the maintenance. I'll just move this to constraints.
+
+246
+00:32:00.100 --> 00:32:09.770
+hrishikb@andrew.cmu.edu: And… schema volatility, I think Arjun mentioned something about when we were talking with them, that
+
+247
+00:32:10.930 --> 00:32:22.339
+hrishikb@andrew.cmu.edu: That when… depending on how the… what kind of input we get, and then how the models run, there might be changes that we need to, like, do.
+
+248
+00:32:22.340 --> 00:32:40.460
+hrishikb@andrew.cmu.edu: And that we… we're… we're just not aware of how to… it's gonna be in advance, even though we… we can't present how the workflow goes, but this is, actually just a… this is a known, known that… that we do know we'll have to face, so I will just go to the…
+
+249
+00:32:40.560 --> 00:32:41.890
+hrishikb@andrew.cmu.edu: mitigation.
+
+250
+00:32:41.980 --> 00:33:01.050
+hrishikb@andrew.cmu.edu: We could, go with, like, I just wrote, like, a semantic matches, so even if some of the attributes are, like, varying between categories, but they mean the same thing, we could have, like, an additional layer on top that we're just automatically handling it, instead of us going and, like, manually changing it correctly.
+
+251
+00:33:01.310 --> 00:33:04.589
+hrishikb@andrew.cmu.edu: Just that I did not add much more complexity.
+
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+00:33:04.770 --> 00:33:20.059
+hrishikb@andrew.cmu.edu: It does, but, this is, this is in case that it does happen. Like, a new, kind of format, a new kind of, like, data source comes up, and, this is an automated approach. It does add additional complexity to it.
+
+253
+00:33:20.450 --> 00:33:30.390
+hrishikb@andrew.cmu.edu: So, is there any other sort of mitigation that you could think of doing? So, as an example, I'm not going to actually say what it is, but is there something that could help you
+
+254
+00:33:32.200 --> 00:33:39.870
+hrishikb@andrew.cmu.edu: understand if this risk is going to manifest itself to an issue earlier, right? The earlier you know about this, the better.
+
+255
+00:33:40.790 --> 00:33:44.630
+hrishikb@andrew.cmu.edu: Is there anything you can do to help learn if it's going to be an issue earlier?
+
+256
+00:33:45.480 --> 00:33:58.080
+hrishikb@andrew.cmu.edu: Right now, it does not seem likely, from what they have told us. So, that's why this is, I would say, the least of, like, that's a risk 5 in my list. It's likelier to happen.
+
+257
+00:33:58.250 --> 00:33:59.360
+hrishikb@andrew.cmu.edu: But…
+
+258
+00:33:59.830 --> 00:34:12.250
+hrishikb@andrew.cmu.edu: We're going to get some metrics from them of the last schema changes, or offering some numbers that are going to predict future. And then the other thing about the risk was if you really want to talk about likelihood of occurrence.
+
+259
+00:34:12.690 --> 00:34:17.439
+hrishikb@andrew.cmu.edu: And then the impact, if it does, so people can really understand, hey, this is…
+
+260
+00:34:17.790 --> 00:34:23.230
+hrishikb@andrew.cmu.edu: You know, this sounds like it may be low… maybe low likelihood, potentially significant impact. Yes.
+
+261
+00:34:23.380 --> 00:34:29.960
+hrishikb@andrew.cmu.edu: And, you know, you have to decide, okay, how much time you're gonna invest in
+
+262
+00:34:30.360 --> 00:34:47.910
+hrishikb@andrew.cmu.edu: you know, upfront mitigation on that, or versus something that is going to be, you know, high impact, high likelihood, as an example, right? Yeah, I think I should have mentioned that, at least, because all the other ones are high likelihood… must high likelihood than this. This is something that's…
+
+263
+00:34:48.290 --> 00:34:58.470
+hrishikb@andrew.cmu.edu: Like, for comparison, the first risk is, like, high likelihood, because it's… it's, like, definite likelihood, because it's actually doing it late, and, like, very high impact as well, because we cannot…
+
+264
+00:34:58.560 --> 00:35:20.060
+hrishikb@andrew.cmu.edu: do a lot of the stuff that we do. So it's an issue already. It is, it is, yeah. And this is, low impact, but it can be very significant if, like, a large, like, 30-40% of the data we're encountering is, like, does not match what we expect, then we can… we would have to be forced to build this on top, and then to make it… make sure the percentages are on…
+
+265
+00:35:20.430 --> 00:35:30.300
+hrishikb@andrew.cmu.edu: So, I will change it so that it mentions that it's a low likelihood, but it has significantly. Okay. Yeah, this is a good list. This is what we did.
+
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+hrishikb@andrew.cmu.edu: Suitable to make anyone.
+
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+hrishikb@andrew.cmu.edu: Oh.
+
+268
+00:35:35.290 --> 00:35:39.099
+hrishikb@andrew.cmu.edu: So, then we can, this is all for,
+
+269
+00:35:41.130 --> 00:35:48.620
+hrishikb@andrew.cmu.edu: This is all for the risk portion, so I'll just go into the project management portion of the document.
+
+270
+00:35:48.930 --> 00:35:54.739
+hrishikb@andrew.cmu.edu: So… It's basically from now to, like, May 4th.
+
+271
+00:35:55.140 --> 00:35:58.620
+hrishikb@andrew.cmu.edu: And how we're gonna at least,
+
+272
+00:35:58.880 --> 00:36:01.680
+hrishikb@andrew.cmu.edu: Have the temporary structure that we have.
+
+273
+00:36:01.900 --> 00:36:04.419
+hrishikb@andrew.cmu.edu: Or, like, going through it.
+
+274
+00:36:04.630 --> 00:36:08.650
+hrishikb@andrew.cmu.edu: So, we're currently still in Phase 1 and 2.
+
+275
+00:36:08.770 --> 00:36:15.529
+hrishikb@andrew.cmu.edu: So, we're still making our SES, and work for MVP has not even started yet.
+
+276
+00:36:15.850 --> 00:36:21.540
+hrishikb@andrew.cmu.edu: There is an initial draft for, like, requirements and architecture.
+
+277
+00:36:21.670 --> 00:36:26.669
+hrishikb@andrew.cmu.edu: But they will be finalized when, you know, the… during the next phases.
+
+278
+00:36:27.330 --> 00:36:31.879
+hrishikb@andrew.cmu.edu: After that, during the latter part of the…
+
+279
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+hrishikb@andrew.cmu.edu: semester, basically, it will be more focused on making sure our work is
+
+280
+00:36:37.960 --> 00:36:45.629
+hrishikb@andrew.cmu.edu: Going smoothly, and evaluating all the progress we have made through there, till the end of this semester, at least.
+
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+hrishikb@andrew.cmu.edu: So what… do you have any plans for, like, what you'll be doing for the next two semesters… two semesters after that?
+
+282
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+hrishikb@andrew.cmu.edu: Other vacations.
+
+283
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+hrishikb@andrew.cmu.edu: interviews.
+
+284
+00:36:55.870 --> 00:37:06.500
+hrishikb@andrew.cmu.edu: No, it's only for the Spring 2006 roadmap. I've not, actually gone through for the summer roadmap. And have you defined what vertical slices 1 and 2 are?
+
+285
+00:37:06.680 --> 00:37:16.119
+hrishikb@andrew.cmu.edu: Vertical slices 1 and 2 are just basically… the first vertical slice would be, all the way up till, like, using our SES to, like, create a MVP.
+
+286
+00:37:16.490 --> 00:37:23.309
+hrishikb@andrew.cmu.edu: And two is just, like, all the documentation work that we're gonna do, all the,
+
+287
+00:37:23.420 --> 00:37:29.299
+hrishikb@andrew.cmu.edu: Stuff that, would be useful for someone to learn it after the handoff, or even when we're explaining it.
+
+288
+00:37:31.030 --> 00:37:32.070
+hrishikb@andrew.cmu.edu: So…
+
+289
+00:37:35.080 --> 00:37:37.389
+hrishikb@andrew.cmu.edu: Is this too… is this too aggressive?
+
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+00:37:39.170 --> 00:37:41.100
+hrishikb@andrew.cmu.edu: Given that you have two more semesters.
+
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+00:37:41.640 --> 00:37:46.570
+hrishikb@andrew.cmu.edu: And given that you're working 12, you know, 12 hours a week this semester.
+
+292
+00:37:47.660 --> 00:37:56.909
+hrishikb@andrew.cmu.edu: I will admit that I've gone pretty aggressive, but I think even Arjun is in agreement that if this was a normal semester, then
+
+293
+00:37:57.080 --> 00:38:01.309
+hrishikb@andrew.cmu.edu: we would not… I would not have pushed this so hard, but…
+
+294
+00:38:01.570 --> 00:38:09.350
+hrishikb@andrew.cmu.edu: with AI, at least my… this is my personal opinion, that as soon as we have the SES and schema and all that stuff.
+
+295
+00:38:09.540 --> 00:38:27.709
+hrishikb@andrew.cmu.edu: like, well-defined enough, it would just be a… it would… it would just enter into a very fast iteration process, see if it works, like, are the tests going properly, are the outputs as we expect? And, I… in my original opinion, should go pretty fast, as soon as we do have that baseline set up.
+
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+00:38:28.050 --> 00:38:28.900
+hrishikb@andrew.cmu.edu: Okay.
+
+297
+00:38:29.170 --> 00:38:43.140
+hrishikb@andrew.cmu.edu: I don't know, that's… I don't know what we'll do afterwards, whether there's, like, stretch goals or something, but I could be completely wrong. Maybe it takes us, like, deep into the summer or something, but this is what happened.
+
+298
+00:38:44.910 --> 00:38:51.999
+hrishikb@andrew.cmu.edu: Is that… so, the question… again, it's not… not this week or next week, right? But at some point.
+
+299
+00:38:53.890 --> 00:38:58.129
+hrishikb@andrew.cmu.edu: You will want to have, before your end of semester, like, what your
+
+300
+00:38:58.290 --> 00:39:02.769
+hrishikb@andrew.cmu.edu: entire plan is, right, for including the other semesters. Okay.
+
+301
+00:39:06.270 --> 00:39:11.200
+hrishikb@andrew.cmu.edu: So, these are, how,
+
+302
+00:39:11.560 --> 00:39:14.730
+hrishikb@andrew.cmu.edu: I think the responsibilities should be split.
+
+303
+00:39:14.910 --> 00:39:25.239
+hrishikb@andrew.cmu.edu: So, currently, for the project lead, she's the team leader, and, we've still yet to determine how the structure will rotate and
+
+304
+00:39:25.290 --> 00:39:44.509
+hrishikb@andrew.cmu.edu: how, like, how long the rotation structure should be? So, should it be… Oh, we… we were actually talking with the other teams. I think they were rotating per month basis, the team leaders. We didn't want to do it so early, but we thought we could do it every mini. Every what? Every mini-sam. So, like, after the spring break, we get a new…
+
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+00:39:44.550 --> 00:39:46.500
+hrishikb@andrew.cmu.edu: Team lead? Yeah.
+
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+00:39:46.580 --> 00:39:51.360
+hrishikb@andrew.cmu.edu: And then, so everyone gets, like, two, I think, two rotations of…
+
+307
+00:39:51.690 --> 00:39:59.110
+hrishikb@andrew.cmu.edu: And I just said, there's no right or wrong, right? As long as you have a reason for making that decision, right? That's all that matters.
+
+308
+00:39:59.330 --> 00:40:06.439
+hrishikb@andrew.cmu.edu: Yeah, monthly, we might be… too fast. It sounds nice, but there's no continuity.
+
+309
+00:40:07.030 --> 00:40:18.310
+hrishikb@andrew.cmu.edu: Architecturally, well, everyone is responsible for knowing, because this is a small team, everyone must know the entire architecture, so that they know how everything is going, but…
+
+310
+00:40:20.420 --> 00:40:28.430
+hrishikb@andrew.cmu.edu: So the person who is, like, most in-depth with it, and most, like, you know, responsible for it would be designated as an architecture lead.
+
+311
+00:40:28.920 --> 00:40:38.629
+hrishikb@andrew.cmu.edu: As for the data and ML lead, it's just basically the one who goes most, like, hands-on and, like, you know, is responsible for debugging it.
+
+312
+00:40:39.440 --> 00:40:46.380
+hrishikb@andrew.cmu.edu: the… and… engineering lead, that's the one I mostly fear about, because
+
+313
+00:40:46.770 --> 00:41:02.610
+hrishikb@andrew.cmu.edu: we all have to own the implementation. It's… it cannot be any other way, I think. And… because engineering lead and QA, it has to be done by all of us, basically, so I'm not sure whether I should keep it, or… Well, I think… so…
+
+314
+00:41:03.270 --> 00:41:06.130
+hrishikb@andrew.cmu.edu: So, again, this is more… maybe a quality discussion.
+
+315
+00:41:06.260 --> 00:41:08.609
+hrishikb@andrew.cmu.edu: But… You know, you're…
+
+316
+00:41:09.480 --> 00:41:15.949
+hrishikb@andrew.cmu.edu: you know, I don't know if it's the architecturally, but there's certainly someone who… you all own… you all own implementation, sure.
+
+317
+00:41:16.050 --> 00:41:21.190
+hrishikb@andrew.cmu.edu: But there may be times where there's someone who has
+
+318
+00:41:21.910 --> 00:41:24.000
+hrishikb@andrew.cmu.edu: You know, has more of a…
+
+319
+00:41:24.760 --> 00:41:29.169
+hrishikb@andrew.cmu.edu: Consult, you know, people consult with them, they have more of, sort of, a tech lead type of
+
+320
+00:41:29.620 --> 00:41:31.790
+hrishikb@andrew.cmu.edu: responsibility, right? So…
+
+321
+00:41:33.300 --> 00:41:45.720
+hrishikb@andrew.cmu.edu: QA process lead, I think, is… you absolutely need one, because how… who… who has that response… yes, I'm gonna go… I'm gonna go test. Who has the responsibility for the over quality plan for our system?
+
+322
+00:41:46.240 --> 00:41:52.810
+hrishikb@andrew.cmu.edu: to make sure, and when I say quality plan, I don't just mean that our software is high quality, it's that we are
+
+323
+00:41:53.000 --> 00:41:57.800
+hrishikb@andrew.cmu.edu: We are doing what we said we are going to do with respect to how we are operating.
+
+324
+00:41:58.310 --> 00:42:04.200
+hrishikb@andrew.cmu.edu: So… you know, co- gets checked in, it…
+
+325
+00:42:04.500 --> 00:42:08.500
+hrishikb@andrew.cmu.edu: you know, here's an example. Someone who's actually may go and
+
+326
+00:42:09.190 --> 00:42:18.930
+hrishikb@andrew.cmu.edu: Look at data around how many, how many… Review, code reviews.
+
+327
+00:42:19.230 --> 00:42:26.370
+hrishikb@andrew.cmu.edu: Are actually providing meaningful… meaningful comments that are provided to them, versus ones that are just like, you know, okay, just pass it.
+
+328
+00:42:26.630 --> 00:42:30.030
+hrishikb@andrew.cmu.edu: So, it's typically someone who, like, who's…
+
+329
+00:42:30.450 --> 00:42:32.740
+hrishikb@andrew.cmu.edu: Looking, sort of, one level deeper.
+
+330
+00:42:33.120 --> 00:42:38.080
+hrishikb@andrew.cmu.edu: Into the… into the processes, and everyone has that responsibility, right?
+
+331
+00:42:38.790 --> 00:42:41.970
+hrishikb@andrew.cmu.edu: Yeah, I think you're right. Otherwise, we might fall into, like.
+
+332
+00:42:42.060 --> 00:42:58.830
+hrishikb@andrew.cmu.edu: the team's gonna do it, and then no one ends up doing it. No one's gonna do metrics tracking unless there's someone else responsible for metrics tracking. Yeah, so we can have a discussion, like, who has the most experience for, like, for the engineering lead, who has the most experience for, like,
+
+333
+00:42:58.840 --> 00:43:03.640
+hrishikb@andrew.cmu.edu: You know, working with pipelines and integrations and stuff, we'll just assign that person.
+
+334
+00:43:03.660 --> 00:43:06.010
+hrishikb@andrew.cmu.edu: As per the QA lead.
+
+335
+00:43:06.240 --> 00:43:14.010
+hrishikb@andrew.cmu.edu: It's gonna have to be somewhat strict so that, you know, they actually kind of, like, go deep into it and, like, see that it's all we need.
+
+336
+00:43:14.360 --> 00:43:17.590
+hrishikb@andrew.cmu.edu: So, we'll have… we'll… we'll discuss.
+
+337
+00:43:18.360 --> 00:43:36.010
+hrishikb@andrew.cmu.edu: And you want to have names, even though you're going to be changing, make sure there's people's names on those. Yeah, definitely. I think, yeah, whenever we write theme, then we're kind of, like, it becomes the chinx, and then sometimes it ends up not being done, so…
+
+338
+00:43:36.810 --> 00:43:50.649
+hrishikb@andrew.cmu.edu: Success criteria is just basically, from the data metrics and product metrics, so percentages of records that are deemed high confidence and are actually high confidence. They're true positives instead of being false positives.
+
+339
+00:43:51.440 --> 00:43:57.690
+hrishikb@andrew.cmu.edu: We could also do some confidence distributions at the, if we go that deep.
+
+340
+00:43:57.900 --> 00:44:01.609
+hrishikb@andrew.cmu.edu: What are defect rates? Is the…
+
+341
+00:44:01.930 --> 00:44:07.870
+hrishikb@andrew.cmu.edu: After we give all this to the client, and they start checking our work.
+
+342
+00:44:08.380 --> 00:44:11.589
+hrishikb@andrew.cmu.edu: Is the post-approval rate what we have.
+
+343
+00:44:11.720 --> 00:44:17.439
+hrishikb@andrew.cmu.edu: And is the human in the loop? That… I'll have to add that. Human in the loop part… portion where it comes from.
+
+344
+00:44:19.760 --> 00:44:20.590
+hrishikb@andrew.cmu.edu: Alright.
+
+345
+00:44:26.170 --> 00:44:30.269
+hrishikb@andrew.cmu.edu: This is, the research planning is pretty weighed still.
+
+346
+00:44:31.810 --> 00:44:50.309
+hrishikb@andrew.cmu.edu: Actually, the human allocation portion is much more weight than the AI portion. We've not decided how to fill all the roles yet, and how they're… if… and if they're gonna be rotated. Like, we've decided that the leadership… team lead will be rotated, but what about the other one? Should we just have one person
+
+347
+00:44:50.790 --> 00:44:53.670
+hrishikb@andrew.cmu.edu: throughout the project, who's responsible for QAns.
+
+348
+00:44:54.220 --> 00:45:01.499
+hrishikb@andrew.cmu.edu: AI resources is basically all the things like cursor that we're gonna leverage.
+
+349
+00:45:01.800 --> 00:45:08.570
+hrishikb@andrew.cmu.edu: And how it's gonna basically handle all the… Coding and the repetitive tasks.
+
+350
+00:45:08.710 --> 00:45:10.630
+hrishikb@andrew.cmu.edu: Like, boxing the PDFs.
+
+351
+00:45:11.640 --> 00:45:28.860
+hrishikb@andrew.cmu.edu: But, the last portion is basically about what… what is our, like, responsibility at the end of it all, like, we are responsible for validating it, whether we… assessing risk, like, whether we should even use it for certain portions or not, or whether it's best handled by us.
+
+352
+00:45:29.250 --> 00:45:30.450
+hrishikb@andrew.cmu.edu: And then…
+
+353
+00:45:30.710 --> 00:45:37.280
+hrishikb@andrew.cmu.edu: the last thing that I wrote is, like, being very cautious that no proprietary data is not, like, going through
+
+354
+00:45:37.530 --> 00:45:40.879
+hrishikb@andrew.cmu.edu: other AI tools than what they can use.
+
+355
+00:45:43.350 --> 00:45:46.809
+hrishikb@andrew.cmu.edu: Finally, yeah.
+
+356
+00:45:47.750 --> 00:46:02.900
+hrishikb@andrew.cmu.edu: how we're gonna do our task planning? So, ETVX is the one that, is most, like, familiar for me. This might change depending if the team decides that this format website does not work for us.
+
+357
+00:46:06.010 --> 00:46:14.000
+hrishikb@andrew.cmu.edu: Domain ownership is another thing that will come up in the… when we make the human rows, basically, assignments, and…
+
+358
+00:46:14.480 --> 00:46:16.650
+hrishikb@andrew.cmu.edu: Work will then be distributed.
+
+359
+00:46:16.840 --> 00:46:24.440
+hrishikb@andrew.cmu.edu: Based on how people, you know, tell about their… which domain they're most familiar and comfortable with.
+
+360
+00:46:25.690 --> 00:46:32.520
+hrishikb@andrew.cmu.edu: The information criteria is… We will have, like, verification steps there.
+
+361
+00:46:32.950 --> 00:46:34.219
+hrishikb@andrew.cmu.edu: we go through.
+
+362
+00:46:34.360 --> 00:46:37.629
+hrishikb@andrew.cmu.edu: And, as per the non-core artifacts.
+
+363
+00:46:37.990 --> 00:46:42.719
+hrishikb@andrew.cmu.edu: the… that would… that is a much more subjective process. We'll have, like,
+
+364
+00:46:42.950 --> 00:46:48.490
+hrishikb@andrew.cmu.edu: Hopefully, we'll have checklists that we'll just go through to confirm that they meet standards.
+
+365
+00:46:48.890 --> 00:46:52.580
+hrishikb@andrew.cmu.edu: And… As for progress tracking…
+
+366
+00:46:52.870 --> 00:47:00.159
+hrishikb@andrew.cmu.edu: That's something that, I think the QA lead will have to, oversee, and,
+
+367
+00:47:01.140 --> 00:47:06.990
+hrishikb@andrew.cmu.edu: We'll have to see how our… how much time we are taking on each of our, like, sprints, or…
+
+368
+00:47:07.740 --> 00:47:12.349
+hrishikb@andrew.cmu.edu: What, what the metrics are for backlog sizes, and…
+
+369
+00:47:12.490 --> 00:47:15.320
+hrishikb@andrew.cmu.edu: Are we reworking too much, or…
+
+370
+00:47:15.460 --> 00:47:28.619
+hrishikb@andrew.cmu.edu: spending too much time on, like, other things that, we initially said would not be spending that much time. Right, so that's actually helpful, so you can look at… that's actually a really good thing for, you know, a two-way process that you can do, is, like, is…
+
+371
+00:47:29.100 --> 00:47:40.000
+hrishikb@andrew.cmu.edu: A very mature team, and there aren't many teams that do this, goes and says, you know, says, this is how we plan to spend our time, and then actually measures how they spend their time and reflects on that.
+
+372
+00:47:42.080 --> 00:47:43.280
+hrishikb@andrew.cmu.edu: So…
+
+373
+00:47:43.440 --> 00:47:54.520
+hrishikb@andrew.cmu.edu: This is all I have, and let's just for my, this is the risk and the project. Let me share one thing with you. I should put something up.
+
+374
+00:47:54.840 --> 00:48:00.229
+hrishikb@andrew.cmu.edu: I'll get that in the back of here, because I can't plug that into this laptop. I think I've got to do that.
+
+375
+00:48:01.030 --> 00:48:01.750
+hrishikb@andrew.cmu.edu: Yes.
+
+376
+00:48:04.430 --> 00:48:09.180
+hrishikb@andrew.cmu.edu: I think… I can move it, it's not gonna read you. Throughout so long.
+
+377
+00:48:10.470 --> 00:48:11.220
+hrishikb@andrew.cmu.edu: Alright.
+
+378
+00:48:30.170 --> 00:48:32.079
+hrishikb@andrew.cmu.edu: Okay, I'll put it here, too.
+
+379
+00:48:40.280 --> 00:48:49.709
+hrishikb@andrew.cmu.edu: So I'm going to send this to you. I want… I'm going to spend this weekend reviewing it, though, because I just, like, wrote it yesterday, so I haven't really had time, so much time to look at it myself.
+
+380
+00:48:49.850 --> 00:48:56.309
+hrishikb@andrew.cmu.edu: I don't need this stuff right down here. Let's probably a bit more about this. So trying to really think about
+
+381
+00:48:56.900 --> 00:48:58.830
+hrishikb@andrew.cmu.edu: your semester.
+
+382
+00:48:59.420 --> 00:49:02.640
+hrishikb@andrew.cmu.edu: And give you some guidance as to
+
+383
+00:49:03.050 --> 00:49:07.200
+hrishikb@andrew.cmu.edu: What types of things may be expected, you know, or…
+
+384
+00:49:07.420 --> 00:49:10.889
+hrishikb@andrew.cmu.edu: And every project is different, right? But what kinds of things
+
+385
+00:49:11.000 --> 00:49:22.689
+hrishikb@andrew.cmu.edu: should either exist, or be in progress, or thinking about, what's maybe coming up soon, what things… hey, you're probably not going to have this yet, but these are things that you'll probably need at some point, right? So…
+
+386
+00:49:24.020 --> 00:49:30.530
+hrishikb@andrew.cmu.edu: you know, context diagram, the vision, understanding of requirements and notional architecture. None of this should be huge.
+
+387
+00:49:30.710 --> 00:49:36.669
+hrishikb@andrew.cmu.edu: you know, I guess scope, agreement, statement of work are probably pretty… I'm just gonna put those together here.
+
+388
+00:49:37.250 --> 00:49:40.999
+hrishikb@andrew.cmu.edu: As you can tell, I wrote this… I didn't have a whole lot of time to work on it.
+
+389
+00:49:43.520 --> 00:49:44.589
+hrishikb@andrew.cmu.edu: Come on, there we go.
+
+390
+00:49:45.110 --> 00:49:51.780
+hrishikb@andrew.cmu.edu: The semester roadmap, we talked about, you know, you showed that, your process definitions, that's really important this time.
+
+391
+00:49:51.930 --> 00:50:00.970
+hrishikb@andrew.cmu.edu: And any external dependencies, risk identification, which you're doing, the change management, which is something we talked about today, like, what happens when there is… when there is change.
+
+392
+00:50:01.960 --> 00:50:05.919
+hrishikb@andrew.cmu.edu: And I guess I should also put here, you know, sort of your…
+
+393
+00:50:06.190 --> 00:50:14.980
+hrishikb@andrew.cmu.edu: what do we call it? Software engineering? What do you call software engineering? SCS. What? SCS, Software Engineering. System.
+
+394
+00:50:15.810 --> 00:50:18.110
+hrishikb@andrew.cmu.edu: SES? Yeah. Okay.
+
+395
+00:50:19.240 --> 00:50:20.669
+hrishikb@andrew.cmu.edu: That should be theirs here, right?
+
+396
+00:50:24.110 --> 00:50:38.510
+hrishikb@andrew.cmu.edu: And then, you know, as you're getting more, you know, more understanding, you're going to create your breakdown structure, you're going to start to populate your backlog, and understand what your milestones are going to be, what your milestone plan is. And then, once you have that, you can really start working on
+
+397
+00:50:38.510 --> 00:50:46.149
+hrishikb@andrew.cmu.edu: you know, your… your, sorry, your work package, your release plans, your… start really having your backlogs, and…
+
+398
+00:50:46.490 --> 00:50:58.280
+hrishikb@andrew.cmu.edu: If you're going to be doing, earned value charts and things like that. And then… and I actually wrote at the end that full ceremony execution, you know, at some point, people are saying, okay, we're going to follow…
+
+399
+00:50:58.770 --> 00:51:02.700
+hrishikb@andrew.cmu.edu: Scrum, for example, right? Or we're gonna follow…
+
+400
+00:51:03.320 --> 00:51:17.269
+hrishikb@andrew.cmu.edu: milestone-driven execution, and as a part of that, there's a bunch of different ceremonies that one does, right? So at some point, you're gonna evolve where, hey, this is our… this is how we operate, this is the cadence
+
+401
+00:51:17.860 --> 00:51:27.350
+hrishikb@andrew.cmu.edu: This is how we operate as a team, and this is the cadence of work that we do. You know, every… every week we do this, every two weeks we do that, every three weeks we do that.
+
+402
+00:51:27.650 --> 00:51:32.619
+hrishikb@andrew.cmu.edu: So that's just… I'll share this, you don't need to copy it, but this could be helpful
+
+403
+00:51:32.970 --> 00:51:40.410
+hrishikb@andrew.cmu.edu: in terms of trying to understand the expectations, not to… well, two things. One is the expectations of the program, but also
+
+404
+00:51:40.880 --> 00:51:48.269
+hrishikb@andrew.cmu.edu: Is used as a guide to try to help, you know, yourselves. Kind of, are you on track with things related to program management as well?
+
+405
+00:51:49.070 --> 00:51:51.760
+hrishikb@andrew.cmu.edu: Does that make sense? Alright.
+
+406
+00:51:52.140 --> 00:51:54.820
+hrishikb@andrew.cmu.edu: Yeah, I just want to do… I just need to do a,
+
+407
+00:51:55.250 --> 00:51:59.459
+hrishikb@andrew.cmu.edu: sync between what I thought of and what's in the
+
+408
+00:51:59.610 --> 00:52:06.490
+hrishikb@andrew.cmu.edu: program documents to make sure that I'm in alignment. So, I'll do that this weekend and get it out to you.
+
+409
+00:52:07.940 --> 00:52:08.850
+hrishikb@andrew.cmu.edu: Okay.
+
+410
+00:52:10.200 --> 00:52:17.170
+hrishikb@andrew.cmu.edu: Yeah, I think, again, I think your team's doing great. I think you've got… it's a great start in project management work.
+
+411
+00:52:17.320 --> 00:52:24.759
+hrishikb@andrew.cmu.edu: If you look at this list of items, you're either, you know, you have or you're well on your way for many of them.
+
+412
+00:52:25.500 --> 00:52:30.190
+hrishikb@andrew.cmu.edu: so… I don't think certain things are going pretty well.
+
+413
+00:52:31.280 --> 00:52:33.149
+hrishikb@andrew.cmu.edu: And I was very impressed.
+
+414
+00:52:33.480 --> 00:52:34.460
+hrishikb@andrew.cmu.edu: by the risks.
+
+415
+00:52:35.270 --> 00:52:41.440
+hrishikb@andrew.cmu.edu: Seriously. I've seen a lot of bad risk registers before, okay? That's pretty good.
+
+416
+00:52:45.180 --> 00:52:47.840
+hrishikb@andrew.cmu.edu: Anything else I can help you with?
+
+417
+00:52:51.660 --> 00:52:52.830
+hrishikb@andrew.cmu.edu: I think…
+
+418
+00:52:53.720 --> 00:53:08.909
+hrishikb@andrew.cmu.edu: we'll just take your, like, the… because a lot of the risks that, I think need to be changed, like, be more in line with your feedback, and then at least two of them, or, like, one of them needs to be removed, and I'll just change the other ones.
+
+419
+00:53:10.360 --> 00:53:16.710
+hrishikb@andrew.cmu.edu: Yeah. And part of your process would be, for example, okay, one… you can decide however you want to do it.
+
+420
+00:53:16.840 --> 00:53:19.689
+hrishikb@andrew.cmu.edu: But… Because we're talking about risks.
+
+421
+00:53:19.980 --> 00:53:33.040
+hrishikb@andrew.cmu.edu: Well, at our sprint reviews, we review our risk register, or at our… like, one thing that teams sometimes do is they create a risk register. Why? Because they need to show it at their end of semester presentation.
+
+422
+00:53:33.710 --> 00:53:47.869
+hrishikb@andrew.cmu.edu: And it's unfortunate, right? It gets… yes, you get practice doing it, but it's not something that actually helps you in your program. And I realize this is very… it's not a giant project, so you can keep all those risks in your head.
+
+423
+00:53:48.430 --> 00:53:51.470
+hrishikb@andrew.cmu.edu: But in a much larger project, you may have
+
+424
+00:53:51.740 --> 00:53:56.740
+hrishikb@andrew.cmu.edu: 25, 30 items, and you may want… need to review them and look at, hey, what is becoming
+
+425
+00:53:57.300 --> 00:54:03.959
+hrishikb@andrew.cmu.edu: you know, for… I've been in situations where, for a given risk, I say, okay, what… as a team, when do we need to review this risk?
+
+426
+00:54:04.140 --> 00:54:07.060
+hrishikb@andrew.cmu.edu: And we set a trigger, so it could be…
+
+427
+00:54:07.420 --> 00:54:12.299
+hrishikb@andrew.cmu.edu: two-year project. It could be we want to review it in 2 weeks, could be we want to review it in 3 months.
+
+428
+00:54:13.860 --> 00:54:25.759
+hrishikb@andrew.cmu.edu: who needs to be involved in that review? So there's a lot of ways… there's a very basic risk register, but you can get a lot more sophisticated around them, too, and use it to more active… actively develop a period project.
+
+429
+00:54:30.090 --> 00:54:31.659
+hrishikb@andrew.cmu.edu: Okay, well, cool.
+
+430
+00:54:32.240 --> 00:54:36.960
+hrishikb@andrew.cmu.edu: Hope everyone has a good weekend. I'm gonna go to my… I have the next team right now, so I'm gonna go try again.
+
+431
+00:54:37.240 --> 00:54:42.740
+hrishikb@andrew.cmu.edu: It's already Friday, so… it's amazing, isn't it? Alright.
+
+432
+00:54:42.950 --> 00:54:45.580
+hrishikb@andrew.cmu.edu: 282. Thank you for the,
+
+433
+00:54:46.170 --> 00:54:49.929
+hrishikb@andrew.cmu.edu: But the power here is very nipple. I mean, my laptop thinks you too.
+
+434
+00:54:50.600 --> 00:54:52.640
+hrishikb@andrew.cmu.edu: Thank you. Alright, thanks.
+
+435
+00:55:18.600 --> 00:55:20.240
+hrishikb@andrew.cmu.edu: Thank you so much,
+
+436
+00:55:21.280 --> 00:55:25.070
+hrishikb@andrew.cmu.edu: you know… Shamash.
+
+437
+00:55:34.050 --> 00:55:41.210
+hrishikb@andrew.cmu.edu: Dude, I said all your stuff about, like, how close by speed we expect. Hopefully, that turns out something good.
+
+438
+00:55:41.370 --> 00:55:56.699
+hrishikb@andrew.cmu.edu: we can only start speeding up after this semester. You won't have the shit till the end of the semester. When you start developing, it'll be about money. Not even give, like, freaking Cadbury to…
+
+439
+00:55:57.340 --> 00:55:58.260
+hrishikb@andrew.cmu.edu: 2 months.
+
+440
+00:55:58.410 --> 00:56:04.670
+hrishikb@andrew.cmu.edu: Hey, motherfucker, you're the team lead. When I'm team lead, everyone's getting cat buried, don't worry. Let's see.
+
+441
+00:56:05.330 --> 00:56:07.589
+hrishikb@andrew.cmu.edu: Cadbury things. I get you guys.
+
+442
+00:56:08.960 --> 00:56:09.830
+hrishikb@andrew.cmu.edu: Here.
+
+443
+00:56:10.300 --> 00:56:14.399
+hrishikb@andrew.cmu.edu: I have the… I noted on some minutes. We have another meeting, right? Yeah.
+
+444
+00:56:14.530 --> 00:56:21.069
+hrishikb@andrew.cmu.edu: What? Fine. Do you want to, lead that one? Yeah.
+
+445
+00:56:21.210 --> 00:56:37.220
+hrishikb@andrew.cmu.edu: Hey, you're only reading it. No, I'm done for the days and night. But you don't have any content! Hey, yeah, no one has content. Yeah. We don't know what we're reading. We have to make content right now. Yeah, you have 3 hours, right? Start.
+
+446
+00:56:37.390 --> 00:56:59.240
+hrishikb@andrew.cmu.edu: By the way, what have you talk… I mean, he's the AI coach, right? Yeah, software engineering, this thing. That's what you're… I think the analyst is only. You just have to pass over whatever you're gonna use AI. Yeah, yeah. Last two pages, man, I made in, like, last 10 minutes. Thank God it did not focus. He just… that's why I just kept going on the spread, because that's what I remembered as, like, 3 pages, I'll just…
+
+447
+00:56:59.240 --> 00:57:16.449
+hrishikb@andrew.cmu.edu: keep reviewing them. You made this yesterday itself. This one I made yesterday, but Rishi told me, like, just before meeting… Project one was the same. This, like, project part is… so I added two parts that I could defend, so I… all of this was cut, dude. Like, when I was actually copying from the AI that part, right? Because, of course, I cannot try it and test it.
+
+448
+00:57:16.450 --> 00:57:22.639
+hrishikb@andrew.cmu.edu: It gave, like, 5-10 pages of content. I cut it down to, like, 2 so that I could extend. Forward to stop the routing?
+
diff --git a/coach_meetings/GMT20260224-190023_Recording.transcript.vtt b/coach_meetings/GMT20260224-190023_Recording.transcript.vtt
new file mode 100644
index 0000000..9e9f8a8
--- /dev/null
+++ b/coach_meetings/GMT20260224-190023_Recording.transcript.vtt
@@ -0,0 +1,2258 @@
+WEBVTT
+
+1
+00:00:00.050 --> 00:00:01.810
+hrishikb@andrew.cmu.edu: I'm getting the data from them.
+
+2
+00:00:02.550 --> 00:00:11.570
+hrishikb@andrew.cmu.edu: So, we… now we have received, like, I think we received it last Friday or Saturday, the data, and we are, started to work on our ML models to run basic tests.
+
+3
+00:00:11.730 --> 00:00:12.560
+hrishikb@andrew.cmu.edu: Okay.
+
+4
+00:00:13.100 --> 00:00:17.450
+hrishikb@andrew.cmu.edu: Yeah, we got it last Friday, like, in the evening or something. Okay.
+
+5
+00:00:23.840 --> 00:00:27.000
+hrishikb@andrew.cmu.edu: I don't have much to share, but I'm gonna just put up the…
+
+6
+00:00:27.680 --> 00:00:31.159
+hrishikb@andrew.cmu.edu: Yeah, we could maybe start with the unsuspect.
+
+7
+00:00:31.340 --> 00:00:32.220
+hrishikb@andrew.cmu.edu: Yeah.
+
+8
+00:00:32.770 --> 00:00:33.650
+hrishikb@andrew.cmu.edu: Download.
+
+9
+00:00:35.170 --> 00:00:40.429
+hrishikb@andrew.cmu.edu: Now, do some more reading. It's not pre-reading anymore, it's just reading.
+
+10
+00:01:08.560 --> 00:01:18.040
+hrishikb@andrew.cmu.edu: Cool, did you have a… an agenda in mind?
+
+11
+00:01:18.470 --> 00:01:23.270
+hrishikb@andrew.cmu.edu: The agenda for us is, basically figuring out,
+
+12
+00:01:24.270 --> 00:01:32.020
+hrishikb@andrew.cmu.edu: if and where we can integrate the UX elements, because currently our system is supposed to be,
+
+13
+00:01:32.140 --> 00:01:37.890
+hrishikb@andrew.cmu.edu: like, end-to-end automated. The only communication points with the system are, I think.
+
+14
+00:01:38.090 --> 00:01:48.919
+hrishikb@andrew.cmu.edu: Two, basically, first would be when we are inputting the data in the system, the different CSVs or PDFs on those kind of things, and as we process the data, if our…
+
+15
+00:01:48.920 --> 00:02:07.900
+hrishikb@andrew.cmu.edu: ML algorithm does not give it a good enough confidence score, then a human in the loop comes into the picture, who will either approve, deny, or modify the data that you've gotten. So, those are the only two elements we can think where the user will interact, because the endpoint of the entire pipeline is the
+
+16
+00:02:08.009 --> 00:02:14.549
+hrishikb@andrew.cmu.edu: basically publish the data into an, wait, I'll just pull it up, be better with the diagram.
+
+17
+00:02:30.280 --> 00:02:30.980
+hrishikb@andrew.cmu.edu: Hmm.
+
+18
+00:02:38.410 --> 00:02:48.920
+hrishikb@andrew.cmu.edu: So, in this part, this is the part where we put all the data in. We have the ingestion gateway, the internal tables that we have, the initial ones.
+
+19
+00:02:48.980 --> 00:02:55.170
+hrishikb@andrew.cmu.edu: then our ML module will print the scores using the defined rules, and if you have a…
+
+20
+00:02:55.170 --> 00:03:10.230
+hrishikb@andrew.cmu.edu: low content, then we have the human review. That's where the human will interact with the system to check if it's good or not. If it's high content is auto-accepted, I will go to the intermediary from this table, and then it will just be synced to PIMS. This is our endpoint.
+
+21
+00:03:10.230 --> 00:03:20.949
+hrishikb@andrew.cmu.edu: After this, this, like, not a concern. We just have to push the correct data to PIMS. Right. You only have two, points of interaction from humans.
+
+22
+00:03:21.040 --> 00:03:21.880
+hrishikb@andrew.cmu.edu: Okay.
+
+23
+00:03:22.820 --> 00:03:28.670
+hrishikb@andrew.cmu.edu: I think where I'd like to start, yeah, I mean, those are,
+
+24
+00:03:31.320 --> 00:03:43.500
+hrishikb@andrew.cmu.edu: Those are non-trivial UIs, so that is something, but I'd like to start with just understanding the current state, like.
+
+25
+00:03:43.960 --> 00:03:45.869
+hrishikb@andrew.cmu.edu: what eBars has.
+
+26
+00:03:46.030 --> 00:03:53.120
+hrishikb@andrew.cmu.edu: And… Okay. What is… And so you're introducing…
+
+27
+00:03:53.320 --> 00:04:03.139
+hrishikb@andrew.cmu.edu: a system into an existing system, so I'm curious what the current state is in. Okay, yeah, I think that'll be… What they're trying to accomplish…
+
+28
+00:04:03.850 --> 00:04:05.670
+hrishikb@andrew.cmu.edu: So, yeah…
+
+29
+00:04:05.980 --> 00:04:17.360
+hrishikb@andrew.cmu.edu: Basically, the problem is that currently, all this work… so, ePaths deals with HVAC systems. It's basically Amazon for some different parts, and it has multiple vendors. Okay, yeah.
+
+30
+00:04:18.390 --> 00:04:29.440
+hrishikb@andrew.cmu.edu: Currently, how they do it is, multiple vendors send them their catalogs via PDFs, CSVs, or some SFTP dump, things like that, so it's a very varied, type of input.
+
+31
+00:04:29.440 --> 00:04:48.600
+hrishikb@andrew.cmu.edu: And they have a specific catalog team, which goes through all of it manually. Like, they have their procedures, but they do all of it manually. They check their specs, because similar things can be called different names by different vendors. Like, one person can call an iPhone screen size, a screen size, some can call it display size.
+
+32
+00:04:49.540 --> 00:04:57.310
+hrishikb@andrew.cmu.edu: Yeah, so, those kind of things are what currently is done by the catalog team.
+
+33
+00:04:58.670 --> 00:05:04.249
+hrishikb@andrew.cmu.edu: Okay, and are those… is the catalog team…
+
+34
+00:05:04.680 --> 00:05:07.719
+hrishikb@andrew.cmu.edu: What is their interface right now?
+
+35
+00:05:08.300 --> 00:05:27.690
+hrishikb@andrew.cmu.edu: Are they talking to pins? Or, like, is there some UI for pins? We have not yet had a chance to talk to the catalog team, but from what we understood, the team basically does all the manual work, then they input the data into the… directly into the intermediary table.
+
+36
+00:05:28.160 --> 00:05:36.779
+hrishikb@andrew.cmu.edu: not… they don't have an intuitive, but they directly enter the data into the PIMS, the information manual system. Yeah. So all this part is the manual work they do.
+
+37
+00:05:38.400 --> 00:05:39.270
+hrishikb@andrew.cmu.edu: Okay.
+
+38
+00:05:39.820 --> 00:05:44.920
+hrishikb@andrew.cmu.edu: Yeah, so in PIMS, like,
+
+39
+00:05:45.820 --> 00:05:56.670
+hrishikb@andrew.cmu.edu: Does that have a web UI? Like, do you know how they… what the UI is? How they interface with that? I don't think the PIMS has a very certain internal thing you're looking at.
+
+40
+00:05:57.930 --> 00:05:58.900
+hrishikb@andrew.cmu.edu: Right.
+
+41
+00:05:59.220 --> 00:06:01.399
+hrishikb@andrew.cmu.edu: Speaker, like, back in April.
+
+42
+00:06:02.190 --> 00:06:09.049
+hrishikb@andrew.cmu.edu: I guess, yeah, like, what is… but what is their interface? If they're… they're looking at a catalog, a PDF, they're…
+
+43
+00:06:09.160 --> 00:06:15.250
+hrishikb@andrew.cmu.edu: Presumably they're typing in like, data somewhere, like, what is… where does that exist?
+
+44
+00:06:15.860 --> 00:06:19.649
+hrishikb@andrew.cmu.edu: I think internally, you don't have an interface.
+
+45
+00:06:19.790 --> 00:06:29.499
+hrishikb@andrew.cmu.edu: But then the only interface that's there, at least as per the knowledge that we have, is through the end product, like, what the customer sees at the end.
+
+46
+00:06:29.750 --> 00:06:38.740
+hrishikb@andrew.cmu.edu: I mean, if you do, like, internet, searches, so the one that comes up, that's the only interface, thing over here, I guess.
+
+47
+00:06:40.030 --> 00:06:47.020
+hrishikb@andrew.cmu.edu: Okay. They communicate to the very end, like, they are allowed to modify data on their the…
+
+48
+00:06:47.040 --> 00:06:55.119
+hrishikb@andrew.cmu.edu: web interface directly. If they see some issues. There is not much process around that. Like, there's not a proper interface which goes through processing.
+
+49
+00:06:55.130 --> 00:07:10.040
+hrishikb@andrew.cmu.edu: So it's… I think it's either… either they communicate with PIMs with directly entering their data into a predefined schema, or they do it directly, or both, into the front-end part of the system, web service, yeah.
+
+50
+00:07:10.370 --> 00:07:17.040
+hrishikb@andrew.cmu.edu: So we had actually proposed a… I mean, if you see at the beginning, right, what they're currently doing is.
+
+51
+00:07:17.290 --> 00:07:37.680
+hrishikb@andrew.cmu.edu: their source data is very scattered. Right. Someone sends them through mail, and then they get CSVs and all of that. So a human, actually, it starts all of that data, and then, I mean, all of this addition pipeline is not there. Right. But then they just, like, put it in a schema, and then do some CRAD operations over the DB, and push it. That's it.
+
+52
+00:07:37.850 --> 00:07:40.750
+hrishikb@andrew.cmu.edu: So, we actually told them that if
+
+53
+00:07:40.750 --> 00:08:05.040
+hrishikb@andrew.cmu.edu: you have multiple vendors over there, why don't you give an interface to the vendor, where the vendor can actually… Yeah, where the vendor can actually put it in, like, you know, a pretty straightforward template, and the data can flow on its own. So, they're like, that's the goal, but, at least for this project, I don't think they would want to take up that, UX or UI. That might be a…
+
+54
+00:08:05.320 --> 00:08:09.339
+hrishikb@andrew.cmu.edu: Like, stretch goal or something, but that's the end thing they wanted.
+
+55
+00:08:09.560 --> 00:08:11.470
+hrishikb@andrew.cmu.edu: After the system is implemented.
+
+56
+00:08:12.900 --> 00:08:21.760
+hrishikb@andrew.cmu.edu: So your planning will, have some APIs exposed, which we'll use, which can be… we'll make it so that it can be easily integrated into our web system.
+
+57
+00:08:21.890 --> 00:08:26.050
+hrishikb@andrew.cmu.edu: So, they can, like, if they plan to build a UI for it, they can easily… Right.
+
+58
+00:08:27.730 --> 00:08:30.250
+hrishikb@andrew.cmu.edu: Yeah, I guess,
+
+59
+00:08:33.559 --> 00:08:40.120
+hrishikb@andrew.cmu.edu: That's interesting. I guess what you're describing sounds like vendors…
+
+60
+00:08:40.450 --> 00:08:45.660
+hrishikb@andrew.cmu.edu: That the end goal is that vendors would do the work of inputting, but currently.
+
+61
+00:08:49.010 --> 00:08:50.530
+hrishikb@andrew.cmu.edu: It seems like…
+
+62
+00:08:51.080 --> 00:09:02.420
+hrishikb@andrew.cmu.edu: the vendors are not gonna change anything in their workflow, they're still just gonna email, and then eParts is gonna do the legwork of taking those files, and then
+
+63
+00:09:02.880 --> 00:09:05.209
+hrishikb@andrew.cmu.edu: Inputting them into your system. Yeah.
+
+64
+00:09:05.400 --> 00:09:06.280
+hrishikb@andrew.cmu.edu: Right.
+
+65
+00:09:06.500 --> 00:09:10.290
+hrishikb@andrew.cmu.edu: I guess.
+
+66
+00:09:12.010 --> 00:09:19.189
+hrishikb@andrew.cmu.edu: I mean, from the standpoint of vendors, this is a better system than the end goal, where they have to do any work.
+
+67
+00:09:19.660 --> 00:09:25.320
+hrishikb@andrew.cmu.edu: Right? So, I guess I'm trying to…
+
+68
+00:09:25.890 --> 00:09:30.880
+hrishikb@andrew.cmu.edu: I'm trying to identify the different stakeholders, like, the different
+
+69
+00:09:31.510 --> 00:09:39.040
+hrishikb@andrew.cmu.edu: People that are involved in this current system, because that's going to… Help.
+
+70
+00:09:39.500 --> 00:09:42.799
+hrishikb@andrew.cmu.edu: That's gonna help you to figure out
+
+71
+00:09:44.180 --> 00:09:50.589
+hrishikb@andrew.cmu.edu: how to design the system, and who you need to talk to, or who you need to make sure that ePort's team talks to.
+
+72
+00:09:50.810 --> 00:09:58.670
+hrishikb@andrew.cmu.edu: So… We do have a lot of context. Yeah, so, like…
+
+73
+00:10:00.920 --> 00:10:03.809
+hrishikb@andrew.cmu.edu: So yeah, I want to dig into a little bit, like.
+
+74
+00:10:03.930 --> 00:10:07.299
+hrishikb@andrew.cmu.edu: I know you guys say there's no interface, but I guess…
+
+75
+00:10:08.410 --> 00:10:10.899
+hrishikb@andrew.cmu.edu: when I say interface, I just mean, like.
+
+76
+00:10:11.130 --> 00:10:21.809
+hrishikb@andrew.cmu.edu: the inter… like, it's not necessarily, like, a polished UI or a front end, like… like, the API is an interface, a web form is an interface,
+
+77
+00:10:21.910 --> 00:10:25.019
+hrishikb@andrew.cmu.edu: I don't know, using Postman, like, as an interface.
+
+78
+00:10:26.360 --> 00:10:33.010
+hrishikb@andrew.cmu.edu: Or, you know, directly making database query calls, that's… that's… that could be an interface, or that is an interface. So…
+
+79
+00:10:35.370 --> 00:10:44.460
+hrishikb@andrew.cmu.edu: Let's say… So I like how you have, like, the different actors, so…
+
+80
+00:10:45.240 --> 00:10:50.239
+hrishikb@andrew.cmu.edu: So we have these human reviewers, that's good to identify. So is that…
+
+81
+00:10:52.250 --> 00:10:54.440
+hrishikb@andrew.cmu.edu: Do you… do you envision that…
+
+82
+00:10:54.670 --> 00:10:59.090
+hrishikb@andrew.cmu.edu: this is somebody from the catalog team? Like, who knows…
+
+83
+00:10:59.990 --> 00:11:12.160
+hrishikb@andrew.cmu.edu: Who does this role? Like, who is the expert there? I think that would be someone from the catalog team. Okay, okay. Because currently, this is all under their purview, so they're in charge to put the data into it. Okay. So they'll be doing best.
+
+84
+00:11:13.250 --> 00:11:14.650
+hrishikb@andrew.cmu.edu: I'm gonna quit.
+
+85
+00:11:14.840 --> 00:11:15.730
+hrishikb@andrew.cmu.edu: Word.
+
+86
+00:11:16.520 --> 00:11:19.240
+hrishikb@andrew.cmu.edu: Hope this works. Can I erase this? You can? Yeah, yeah.
+
+87
+00:11:23.160 --> 00:11:25.519
+hrishikb@andrew.cmu.edu: I'm just gonna start making a list of people here.
+
+88
+00:11:36.440 --> 00:11:43.620
+hrishikb@andrew.cmu.edu: So, so, let's say this is a catalog team member.
+
+89
+00:11:49.250 --> 00:11:56.439
+hrishikb@andrew.cmu.edu: So basically, like, a primary stakeholder for your system. And then we have, like, vendors.
+
+90
+00:11:56.960 --> 00:12:05.200
+hrishikb@andrew.cmu.edu: So I guess some representative from… From each, each vendor.
+
+91
+00:12:05.620 --> 00:12:07.319
+hrishikb@andrew.cmu.edu: Right? Do you have any idea
+
+92
+00:12:07.870 --> 00:12:11.710
+hrishikb@andrew.cmu.edu: what kind of person that is from that company, I guess.
+
+93
+00:12:13.060 --> 00:12:15.190
+hrishikb@andrew.cmu.edu: I don't know. I learned now.
+
+94
+00:12:16.330 --> 00:12:21.250
+hrishikb@andrew.cmu.edu: Alright, so, I'm weird.
+
+95
+00:12:21.600 --> 00:12:22.990
+hrishikb@andrew.cmu.edu: Representative.
+
+96
+00:12:23.980 --> 00:12:28.290
+hrishikb@andrew.cmu.edu: Let's see…
+
+97
+00:12:32.610 --> 00:12:33.580
+hrishikb@andrew.cmu.edu: I guess.
+
+98
+00:12:34.080 --> 00:12:41.340
+hrishikb@andrew.cmu.edu: What you're saying is… Currently, the catalog team is…
+
+99
+00:12:41.940 --> 00:12:45.849
+hrishikb@andrew.cmu.edu: Taking the emails and whatever is being sent.
+
+100
+00:12:46.090 --> 00:12:47.339
+hrishikb@andrew.cmu.edu: from the vendor.
+
+101
+00:12:47.440 --> 00:12:54.930
+hrishikb@andrew.cmu.edu: And then… so, yeah, how are they getting it into… pins.
+
+102
+00:12:55.630 --> 00:13:03.789
+hrishikb@andrew.cmu.edu: So… Currently, do you know? I know you mentioned… sorry, yeah, I know this is redundant, but I'm just gonna ask it again, so we can…
+
+103
+00:13:04.090 --> 00:13:23.619
+hrishikb@andrew.cmu.edu: So, I'm not sure we are completely certain, but from the picture we got, it was that the catalog team directly enters the data into the system, and that would be, I'm assuming they'll have a database, and they'll just directly input the data in the database, and then that would flow stream to their web UIs.
+
+104
+00:13:23.840 --> 00:13:24.650
+hrishikb@andrew.cmu.edu: Okay.
+
+105
+00:13:25.380 --> 00:13:26.630
+hrishikb@andrew.cmu.edu: So…
+
+106
+00:13:27.940 --> 00:13:38.440
+hrishikb@andrew.cmu.edu: Right, I guess you also mentioned, yeah, maybe, like, there might be a web interface, like, the normal, like, inventory, I guess, for…
+
+107
+00:13:39.890 --> 00:13:48.579
+hrishikb@andrew.cmu.edu: I guess, consumers? That would be a website which has all the prices.
+
+108
+00:13:48.900 --> 00:13:52.049
+hrishikb@andrew.cmu.edu: I don't know.
+
+109
+00:13:56.230 --> 00:13:58.060
+hrishikb@andrew.cmu.edu: Like, WI?
+
+110
+00:13:58.420 --> 00:14:01.850
+hrishikb@andrew.cmu.edu: But then maybe there's some backend…
+
+111
+00:14:02.560 --> 00:14:16.210
+hrishikb@andrew.cmu.edu: you're not sure yet how they're getting it into, like, maybe… The PIMS is the data, like, refined database to push, and from PIMS to the consumer website, we don't have the visibility of that kind of system.
+
+112
+00:14:16.590 --> 00:14:32.109
+hrishikb@andrew.cmu.edu: So… So the way that, yeah, the… So I think since… Basically, you're…
+
+113
+00:14:33.080 --> 00:14:40.169
+hrishikb@andrew.cmu.edu: You're trying to… like, the current state is a lot of this manual work, Where the catalog team member
+
+114
+00:14:41.040 --> 00:14:54.120
+hrishikb@andrew.cmu.edu: they understand… They understand the existing system and existing schemas, and they had…
+
+115
+00:14:54.710 --> 00:14:59.070
+hrishikb@andrew.cmu.edu: It sounds like they have the most expertise in how to translate
+
+116
+00:14:59.250 --> 00:15:04.269
+hrishikb@andrew.cmu.edu: The vendor's catalog into… into a more general thing, right?
+
+117
+00:15:04.610 --> 00:15:12.669
+hrishikb@andrew.cmu.edu: So, yeah, I mean, it sounded like this is, like, Like, a very important… Subject matter expert that…
+
+118
+00:15:13.310 --> 00:15:17.770
+hrishikb@andrew.cmu.edu: Hopefully you can talk to directly. Have you…
+
+119
+00:15:17.770 --> 00:15:36.420
+hrishikb@andrew.cmu.edu: Yeah, we are scheduled to have a meeting with the catalog team to understand how they do it, but, we wanted the initial data so that we have a better picture before we ask questions, so that was a bit delayed, so after spring break, we'll probably go on site and meet the team. Okay. And when you say data, you're talking about what's in PIMS, like.
+
+120
+00:15:36.920 --> 00:15:50.519
+hrishikb@andrew.cmu.edu: Yeah, the… Or the catalogs? I think all of it, like the input PDFs, the schema tables they have for particular, these things, attributes, like, let's say a system has this number of attributes, this is how the schema looks like, all that data.
+
+121
+00:15:50.760 --> 00:15:55.489
+hrishikb@andrew.cmu.edu: Okay. Like, some sample set of it, so that we have a better picture on what we need.
+
+122
+00:15:58.800 --> 00:16:00.149
+hrishikb@andrew.cmu.edu: Is this…
+
+123
+00:16:03.190 --> 00:16:08.780
+hrishikb@andrew.cmu.edu: When… do you have an idea of when?
+
+124
+00:16:09.730 --> 00:16:12.919
+hrishikb@andrew.cmu.edu: Like, how frequently does this happen?
+
+125
+00:16:13.410 --> 00:16:20.160
+hrishikb@andrew.cmu.edu: How many vendors are we talking about? How many catalogs? Is this, like, updates to catalogs? Like…
+
+126
+00:16:20.480 --> 00:16:23.510
+hrishikb@andrew.cmu.edu: Like, one item at a time, or is it, like, big drops?
+
+127
+00:16:24.020 --> 00:16:38.989
+hrishikb@andrew.cmu.edu: I think, they have around, I would say, 100 of vendors, hundreds of vendors. Okay. They're not at the thousands mark, but, the frequency of the updates, we are not sure about. Okay. Maybe we can add the points.
+
+128
+00:16:48.670 --> 00:16:53.809
+hrishikb@andrew.cmu.edu: So… I think what… I guess, it sounds like what you're trying to automate is…
+
+129
+00:16:54.090 --> 00:16:56.499
+hrishikb@andrew.cmu.edu: Yeah, just to say it again…
+
+130
+00:16:57.460 --> 00:17:09.880
+hrishikb@andrew.cmu.edu: If we just take one catalog, for example, The vendor is gonna… Like… You know, send…
+
+131
+00:17:10.440 --> 00:17:12.529
+hrishikb@andrew.cmu.edu: You know, send a catalog.
+
+132
+00:17:14.190 --> 00:17:23.500
+hrishikb@andrew.cmu.edu: to… to e-parts, and catalog member is going to…
+
+133
+00:17:23.670 --> 00:17:26.219
+hrishikb@andrew.cmu.edu: Read it, parse it, and then…
+
+134
+00:17:26.760 --> 00:17:33.219
+hrishikb@andrew.cmu.edu: you know, type it into the system. So you're trying to… automate that process.
+
+135
+00:17:33.700 --> 00:17:43.260
+hrishikb@andrew.cmu.edu: So, like, read… read what's in the PDF or, you know, a different format, it could be And…
+
+136
+00:17:44.270 --> 00:17:51.490
+hrishikb@andrew.cmu.edu: Figure out how to… Correlate it with existing structures, or maybe you have to make few changes.
+
+137
+00:17:52.340 --> 00:18:02.889
+hrishikb@andrew.cmu.edu: Yeah, I think primarily it is how the data would fit into the existing schemas. Okay. So they did mention that the schema changes are not very frequent. Okay. So those schemas are pretty set. Okay.
+
+138
+00:18:04.170 --> 00:18:11.510
+hrishikb@andrew.cmu.edu: Do you have a sense of…
+
+139
+00:18:11.990 --> 00:18:15.820
+hrishikb@andrew.cmu.edu: Why… why now EPARCs is doing this?
+
+140
+00:18:17.030 --> 00:18:29.980
+hrishikb@andrew.cmu.edu: I think that is to… Because this entire process, like, it's error-prone, and it requires multiple humans, when it could be done much, much more quicker than an automated system. Okay.
+
+141
+00:18:29.980 --> 00:18:40.409
+hrishikb@andrew.cmu.edu: And also, another thing would be, they need to scale much faster now. I think there's only two people there. One. One who's doing it, and okay, right.
+
+142
+00:18:40.410 --> 00:18:51.649
+hrishikb@andrew.cmu.edu: When they actually hope to get even more customers, they need this. Like, currently, they have to, get some catalog team help from their, I think it's a sister company or a parent company, Alps. Alps.
+
+143
+00:18:52.030 --> 00:18:53.960
+hrishikb@andrew.cmu.edu: Okay, interesting.
+
+144
+00:18:55.090 --> 00:19:02.590
+hrishikb@andrew.cmu.edu: It sounds like…
+
+145
+00:19:06.320 --> 00:19:07.760
+hrishikb@andrew.cmu.edu: Yeah, definitely.
+
+146
+00:19:08.260 --> 00:19:12.529
+hrishikb@andrew.cmu.edu: You need to… you need to talk to that person, like.
+
+147
+00:19:12.740 --> 00:19:16.610
+hrishikb@andrew.cmu.edu: That person has all the subject matter expertise here.
+
+148
+00:19:17.830 --> 00:19:25.260
+hrishikb@andrew.cmu.edu: They know exactly. They'll be the best person to tell you whether you're on the right track, and what are all the
+
+149
+00:19:25.610 --> 00:19:30.690
+hrishikb@andrew.cmu.edu: pitfalls, and… Tricky things about… about their role.
+
+150
+00:19:36.010 --> 00:19:44.790
+hrishikb@andrew.cmu.edu: So… I guess the one, yeah, I guess the human review part,
+
+151
+00:19:49.350 --> 00:19:59.190
+hrishikb@andrew.cmu.edu: How… How risky is it… is it if… The data gets input incorrectly.
+
+152
+00:20:01.290 --> 00:20:04.370
+hrishikb@andrew.cmu.edu: Like, we have, you know, you have this concept of low confidence.
+
+153
+00:20:04.620 --> 00:20:09.079
+hrishikb@andrew.cmu.edu: high confidence. It sounds like they are comfortable
+
+154
+00:20:09.370 --> 00:20:16.870
+hrishikb@andrew.cmu.edu: With a system that just automatically does this, and… There's no… There's no review.
+
+155
+00:20:18.790 --> 00:20:38.639
+hrishikb@andrew.cmu.edu: So, initially, the plan is, when we do implement the system, initially, it will have human in the review for both, till they have sufficient confidence that the system actually works. After that, for all the high confidence scores, we'll be removing the human part, and only it'll be automatically pushed to the intermediate table. But for the low confidence scores, you'll still be having a human in the review.
+
+156
+00:20:38.790 --> 00:20:58.189
+hrishikb@andrew.cmu.edu: And as for the previous question that, I'm not sure if wrong data is inputted. It is, like, very big deal, because it'll probably be some attribute in, like, some minor attribute of a particular product, which, when never noticed, people can modify.
+
+157
+00:20:58.880 --> 00:21:06.560
+hrishikb@andrew.cmu.edu: Yeah. Because that's what they currently do. If there is a mistake in the, like, someone sees a mistake on the web UI, then they are targeted and they just…
+
+158
+00:21:06.840 --> 00:21:08.060
+hrishikb@andrew.cmu.edu: Fix it up.
+
+159
+00:21:08.290 --> 00:21:09.150
+hrishikb@andrew.cmu.edu: Okay.
+
+160
+00:21:10.020 --> 00:21:13.750
+hrishikb@andrew.cmu.edu: That's, that's good.
+
+161
+00:21:14.000 --> 00:21:18.050
+hrishikb@andrew.cmu.edu: To know, then, who… who are the people at…
+
+162
+00:21:19.380 --> 00:21:22.189
+hrishikb@andrew.cmu.edu: Potentially, who catches those kind of errors?
+
+163
+00:21:22.560 --> 00:21:23.500
+hrishikb@andrew.cmu.edu: Huh?
+
+164
+00:21:23.760 --> 00:21:41.089
+hrishikb@andrew.cmu.edu: Right now, they mentioned they don't have a proper feedback mechanism. Okay. So, it is just some, like, it's mostly the internal team itself. When they're reviewing something or working through something, they notice some issues, but it's… I don't… they mention the…
+
+165
+00:21:41.140 --> 00:21:48.519
+hrishikb@andrew.cmu.edu: people who are actually using the front-end software, they don't really, like, give feedback or, like, say that this is wrong, that is wrong. Okay.
+
+166
+00:21:49.430 --> 00:21:56.840
+hrishikb@andrew.cmu.edu: Yeah, I wonder if that's something that they want to add, valuable. Yeah, that is… that is also part of future school, okay.
+
+167
+00:21:57.950 --> 00:22:02.649
+hrishikb@andrew.cmu.edu: So, like, some adding to the existing UI, the ability to at least
+
+168
+00:22:02.950 --> 00:22:07.019
+hrishikb@andrew.cmu.edu: fly something, so that… To your feedback list? Yes, okay.
+
+169
+00:22:07.210 --> 00:22:08.190
+hrishikb@andrew.cmu.edu: Makes sense.
+
+170
+00:22:12.600 --> 00:22:23.570
+hrishikb@andrew.cmu.edu: What is… Yeah, just from looking at here…
+
+171
+00:22:27.380 --> 00:22:37.740
+hrishikb@andrew.cmu.edu: I mean, to me, there's… There's clearly a interface for getting that stuff into your ingestion…
+
+172
+00:22:41.650 --> 00:22:42.760
+hrishikb@andrew.cmu.edu: Pipeline.
+
+173
+00:22:42.990 --> 00:22:53.220
+hrishikb@andrew.cmu.edu: like… And… It has lots of different formats, so, like, if it's, like, a webpage, like.
+
+174
+00:22:55.620 --> 00:23:00.810
+hrishikb@andrew.cmu.edu: It sounds like, if it's an email, maybe…
+
+175
+00:23:01.890 --> 00:23:10.740
+hrishikb@andrew.cmu.edu: you know, you could forward the email into the system, and file, like, you know, drag and drop, I don't know, like, lots of different forms it could take.
+
+176
+00:23:13.960 --> 00:23:24.410
+hrishikb@andrew.cmu.edu: So… there's an interface there, right? Am I… And…
+
+177
+00:23:29.720 --> 00:23:31.640
+hrishikb@andrew.cmu.edu: I guess you'll want to think about…
+
+178
+00:23:34.320 --> 00:23:40.900
+hrishikb@andrew.cmu.edu: like, what feedback that interface would give. I presume, like, again, it's the catalog team member.
+
+179
+00:23:41.080 --> 00:23:49.090
+hrishikb@andrew.cmu.edu: who… they're getting the emails, or they're getting the files from the vendor, and they're gonna have to input it into the system. And so…
+
+180
+00:23:49.600 --> 00:23:58.770
+hrishikb@andrew.cmu.edu: Yeah, you can have some kind of control to upload files or give them different options of, you know, when they get different formats.
+
+181
+00:23:58.990 --> 00:24:02.869
+hrishikb@andrew.cmu.edu: knowing what to do with that format, right? So,
+
+182
+00:24:03.890 --> 00:24:11.839
+hrishikb@andrew.cmu.edu: Identifying from them what these different formats could be, and then providing that person feedback.
+
+183
+00:24:12.340 --> 00:24:15.220
+hrishikb@andrew.cmu.edu: When they do input it, that
+
+184
+00:24:15.860 --> 00:24:22.499
+hrishikb@andrew.cmu.edu: Everything is working, or… or maybe there's an error, or, like, a parsing error, or…
+
+185
+00:24:22.790 --> 00:24:27.840
+hrishikb@andrew.cmu.edu: You know, undetect… like, unknown file format, like, stuff like that, right?
+
+186
+00:24:28.540 --> 00:24:29.890
+hrishikb@andrew.cmu.edu: I think the…
+
+187
+00:24:30.140 --> 00:24:38.360
+hrishikb@andrew.cmu.edu: Feedback mechanism that the client had in mind was more centered towards the users being able to give feedback?
+
+188
+00:24:38.640 --> 00:24:41.550
+hrishikb@andrew.cmu.edu: But, internally.
+
+189
+00:24:42.400 --> 00:24:53.219
+hrishikb@andrew.cmu.edu: Internally, I'm not sure if there is a feedback mechanism as of now, or even if that's in the plan. I guess what I'm referring to is, okay, let's say the…
+
+190
+00:24:53.510 --> 00:24:59.859
+hrishikb@andrew.cmu.edu: say this person's name is Chris, right? Vendor A is going to email Chris, PDF.
+
+191
+00:25:00.190 --> 00:25:05.789
+hrishikb@andrew.cmu.edu: So… How do you envision then getting this PDF into your system?
+
+192
+00:25:09.440 --> 00:25:11.710
+hrishikb@andrew.cmu.edu: Like, that's an interface. Right. Right?
+
+193
+00:25:12.460 --> 00:25:17.930
+hrishikb@andrew.cmu.edu: Like, it would be a… maybe it could be a web page with a form to upload the file.
+
+194
+00:25:18.410 --> 00:25:22.070
+hrishikb@andrew.cmu.edu: Right, so that's… that's a UI that you would have to design.
+
+195
+00:25:23.300 --> 00:25:26.379
+hrishikb@andrew.cmu.edu: Am I thinking about it?
+
+196
+00:25:26.510 --> 00:25:36.370
+hrishikb@andrew.cmu.edu: Yeah, so initially, we had this question with the clients as well, like, how we're planning to, like, do we create a UI or something, but they're more interested in the software part of it.
+
+197
+00:25:36.520 --> 00:25:45.650
+hrishikb@andrew.cmu.edu: like, so, for our purposes, we're thinking we'll just simply, input it via, like, maybe some listen folder or something like that.
+
+198
+00:25:45.650 --> 00:25:59.880
+hrishikb@andrew.cmu.edu: Which we can just, for dev testing, we can use something of that sort for the initial part. Okay. But, yeah, maybe it'll be good to think about from a catalog number's… Yeah, I mean, that's still an interface. Like, you have to tell Chris…
+
+199
+00:26:00.940 --> 00:26:11.169
+hrishikb@andrew.cmu.edu: this is how you use the system. You need to move… you need to download the PDF from your email, and then move it to this folder. Yeah. Right? And then he needs to get feedback, like.
+
+200
+00:26:11.920 --> 00:26:21.749
+hrishikb@andrew.cmu.edu: to know whether or not it made it. Like, is it gonna stay there? Is it gonna get deleted? Is there gonna be a file? Are you gonna get an email about it? Or, like…
+
+201
+00:26:24.390 --> 00:26:30.639
+hrishikb@andrew.cmu.edu: Then, I'm sorry if this is the right question, but then, aren't we trying to animate Chris over here?
+
+202
+00:26:30.930 --> 00:26:36.870
+hrishikb@andrew.cmu.edu: Well, that could be a valid question, but then how… how is it gonna… how do you envision the system working?
+
+203
+00:26:37.680 --> 00:26:54.330
+hrishikb@andrew.cmu.edu: The long-term goal is that what you said before, that the clients will have some sort of portal. They, instead of emailing, they just put their files on the portal, and the portal handles, uses the APIs of our system, and then… then we move forward from there.
+
+204
+00:26:55.090 --> 00:26:55.930
+hrishikb@andrew.cmu.edu: Right.
+
+205
+00:26:56.250 --> 00:27:03.290
+hrishikb@andrew.cmu.edu: So, I mean, I guess it depends on getting on the same page as your eParts,
+
+206
+00:27:03.470 --> 00:27:19.849
+hrishikb@andrew.cmu.edu: representatives, like, what they want, right? Again, when I say interface, it could be anything. It could be… maybe you're saying, and if they agree, it could be an API, and then Chris just has to figure out how to use an API, like, they have to know how to use Postman, or…
+
+207
+00:27:19.990 --> 00:27:24.639
+hrishikb@andrew.cmu.edu: like… Pearl, right?
+
+208
+00:27:24.770 --> 00:27:29.519
+hrishikb@andrew.cmu.edu: But then that's still very much an interface. Like, your API is going to have response codes.
+
+209
+00:27:29.810 --> 00:27:39.439
+hrishikb@andrew.cmu.edu: Right? And there's going to be times when the file doesn't work, or it's too big, or it's a… it doesn't make sense, and you have to give that feedback. So, I'm just trying to…
+
+210
+00:27:39.650 --> 00:27:53.070
+hrishikb@andrew.cmu.edu: I think the closest thing what we have currently would be some sort of, similar API that you can send a request through. Okay. And maybe… maybe eParts is fine with that, but…
+
+211
+00:27:53.510 --> 00:27:56.610
+hrishikb@andrew.cmu.edu: You should make sure that that's what they expect.
+
+212
+00:27:59.680 --> 00:28:02.820
+hrishikb@andrew.cmu.edu: And I know that's… yeah, that might not be the…
+
+213
+00:28:03.250 --> 00:28:06.369
+hrishikb@andrew.cmu.edu: That's not the interesting part of the project, necessarily, but…
+
+214
+00:28:06.570 --> 00:28:13.599
+hrishikb@andrew.cmu.edu: That is the very first step of how anything happens, so… You have to consider it.
+
+215
+00:28:13.850 --> 00:28:16.090
+hrishikb@andrew.cmu.edu: What that is. Otherwise,
+
+216
+00:28:16.810 --> 00:28:20.550
+hrishikb@andrew.cmu.edu: The parts won't be able to test it or use it very well, right?
+
+217
+00:28:20.680 --> 00:28:26.770
+hrishikb@andrew.cmu.edu: So… I would try to… clarify…
+
+218
+00:28:26.970 --> 00:28:30.029
+hrishikb@andrew.cmu.edu: And get aligned with eParts, like, what they expect.
+
+219
+00:28:30.790 --> 00:28:36.530
+hrishikb@andrew.cmu.edu: Currently, To get the files, to get the catalogs into your system.
+
+220
+00:28:37.040 --> 00:28:39.019
+hrishikb@andrew.cmu.edu: Like, is it Chris? Like…
+
+221
+00:28:39.380 --> 00:28:44.949
+hrishikb@andrew.cmu.edu: If it's Chris, then, you know, you need to make sure to talk to Chris, or make sure he starts to talk to Chris, and make sure that's okay.
+
+222
+00:28:45.480 --> 00:28:47.069
+hrishikb@andrew.cmu.edu: Right, otherwise there's gonna be a gap.
+
+223
+00:28:48.960 --> 00:28:49.800
+hrishikb@andrew.cmu.edu: with…
+
+224
+00:28:50.040 --> 00:28:57.670
+hrishikb@andrew.cmu.edu: Within doing anything to the system. We need to finalize how the data is being inputted, how they want the data to be imported into the system.
+
+225
+00:28:57.850 --> 00:29:01.900
+hrishikb@andrew.cmu.edu: Basically. Yeah, I mean, it has… I guess…
+
+226
+00:29:02.460 --> 00:29:06.889
+hrishikb@andrew.cmu.edu: I would assume that that should have come up, or that would have come up, right? Like…
+
+227
+00:29:07.790 --> 00:29:11.669
+hrishikb@andrew.cmu.edu: What do they say in their… in their brief?
+
+228
+00:29:13.500 --> 00:29:15.400
+hrishikb@andrew.cmu.edu: Okay, they just said ingest.
+
+229
+00:29:16.510 --> 00:29:22.829
+hrishikb@andrew.cmu.edu: Yeah, but who's gonna get it in the system of files? Yeah, I think that part has not been the discussion yet.
+
+230
+00:29:23.040 --> 00:29:31.100
+hrishikb@andrew.cmu.edu: Because that matters because that tells you where your boundary is of what you're building.
+
+231
+00:29:31.450 --> 00:29:37.230
+hrishikb@andrew.cmu.edu: Right? Like… Your system is gonna end somewhere.
+
+232
+00:29:37.600 --> 00:29:48.120
+hrishikb@andrew.cmu.edu: So, it could be the API, it could be this file listener thing, it could be a web UI. So, the endpoints that we… endpoints we have discussed, but the…
+
+233
+00:29:48.790 --> 00:29:58.510
+hrishikb@andrew.cmu.edu: now seems a bit vague, because the initial starting point is the ingestion of the files, and the ending point is the PIMS database for us, the PIMS module.
+
+234
+00:29:58.900 --> 00:30:07.399
+hrishikb@andrew.cmu.edu: middle part is all our software. We start at the CSVs or PDFs or emails, and we end that when we put the data in the community table.
+
+235
+00:30:08.250 --> 00:30:16.049
+hrishikb@andrew.cmu.edu: But how we are… I mean, to test it, you have to get in the system somehow, so… I guess, yeah, you have to consider that.
+
+236
+00:30:18.450 --> 00:30:23.730
+hrishikb@andrew.cmu.edu: I guess the second major piece that you mentioned is, like, this human review part.
+
+237
+00:30:28.170 --> 00:30:32.149
+hrishikb@andrew.cmu.edu: It seems… it sounds like, again, Chris is the best person.
+
+238
+00:30:32.290 --> 00:30:41.680
+hrishikb@andrew.cmu.edu: too, about this, like… He or she would be… your…
+
+239
+00:30:41.890 --> 00:30:44.639
+hrishikb@andrew.cmu.edu: Your primary source in terms of…
+
+240
+00:30:45.680 --> 00:30:51.800
+hrishikb@andrew.cmu.edu: figuring out how to design that interface. Yeah. So…
+
+241
+00:30:53.320 --> 00:31:00.860
+hrishikb@andrew.cmu.edu: Because the interface has to allow this reviewer to make decisions about the data.
+
+242
+00:31:01.110 --> 00:31:10.210
+hrishikb@andrew.cmu.edu: And… The way that you design and interface that is helpful is… to understand…
+
+243
+00:31:10.500 --> 00:31:15.569
+hrishikb@andrew.cmu.edu: The user's model of the world, their mental model of your system.
+
+244
+00:31:15.970 --> 00:31:18.530
+hrishikb@andrew.cmu.edu: So…
+
+245
+00:31:24.010 --> 00:31:30.690
+hrishikb@andrew.cmu.edu: I would… I would think that… I mean…
+
+246
+00:31:35.810 --> 00:31:38.909
+hrishikb@andrew.cmu.edu: Yeah, actually, I don't know, I don't want to make any assumptions, like…
+
+247
+00:31:39.150 --> 00:31:42.400
+hrishikb@andrew.cmu.edu: about how… how this should be presented to Chris, like…
+
+248
+00:31:43.890 --> 00:31:46.069
+hrishikb@andrew.cmu.edu: It could range something… somewhere like…
+
+249
+00:31:46.730 --> 00:31:54.540
+hrishikb@andrew.cmu.edu: you know, they get an email, and you reply… you reply… they reply yes or no, right? That's… that's one interface. It could be a web form.
+
+250
+00:31:54.790 --> 00:31:58.600
+hrishikb@andrew.cmu.edu: Or, like, a diff, maybe, or, like… Questions?
+
+251
+00:31:59.010 --> 00:32:08.180
+hrishikb@andrew.cmu.edu: So… I guess, he should try to find out.
+
+252
+00:32:10.130 --> 00:32:21.240
+hrishikb@andrew.cmu.edu: So, like, the goal is for… E-parts to be able to… Correct.
+
+253
+00:32:21.820 --> 00:32:28.350
+hrishikb@andrew.cmu.edu: Things that are wrong from whatever your system produced that you have low confidence, so… You're saying, like…
+
+254
+00:32:28.660 --> 00:32:34.370
+hrishikb@andrew.cmu.edu: Hey, you need to look at this and make sure that we… Decided the right things.
+
+255
+00:32:35.740 --> 00:32:42.770
+hrishikb@andrew.cmu.edu: Or, like, we, we, like, all the data is in the right place.
+
+256
+00:32:47.780 --> 00:32:50.010
+hrishikb@andrew.cmu.edu: Yeah, basically the…
+
+257
+00:32:51.990 --> 00:33:01.449
+hrishikb@andrew.cmu.edu: the, like, let's say Chris has to, and they have to check the low or high content scores, they need to have sufficient information about the data that's being inputted, and…
+
+258
+00:33:01.690 --> 00:33:10.450
+hrishikb@andrew.cmu.edu: whatever the initial questions they might have, those should be answered in whatever we are displaying. Yeah, yeah, I think you have to identify
+
+259
+00:33:18.480 --> 00:33:25.719
+hrishikb@andrew.cmu.edu: Yeah, like, what information Chris needs to decide that this is correct or not. Like, we have to give them enough context.
+
+260
+00:33:26.510 --> 00:33:34.310
+hrishikb@andrew.cmu.edu: And I'm not sure what, you know, what level is that gonna be at? Is it, like, for…
+
+261
+00:33:35.030 --> 00:33:39.900
+hrishikb@andrew.cmu.edu: You know, per attribute, or per item, per catalog.
+
+262
+00:33:41.050 --> 00:33:46.900
+hrishikb@andrew.cmu.edu: So you can try to get a sense of… Ow.
+
+263
+00:33:47.990 --> 00:33:51.620
+hrishikb@andrew.cmu.edu: I guess.
+
+264
+00:33:52.500 --> 00:33:58.629
+hrishikb@andrew.cmu.edu: content-wise, Yeah, how much is under review? And, like, make it clear, what is…
+
+265
+00:33:59.010 --> 00:34:02.520
+hrishikb@andrew.cmu.edu: what are they supposed to do with whatever you present them? Like…
+
+266
+00:34:02.710 --> 00:34:07.050
+hrishikb@andrew.cmu.edu: Is it a big checklist that they go through? Are you going to give them
+
+267
+00:34:07.930 --> 00:34:16.739
+hrishikb@andrew.cmu.edu: Like, is it a list of attributes, and then you have confidence scores next to it, and you, like, rate them by low to high confidence?
+
+268
+00:34:17.350 --> 00:34:21.100
+hrishikb@andrew.cmu.edu: You were saying early on, they're gonna review everything.
+
+269
+00:34:21.560 --> 00:34:28.149
+hrishikb@andrew.cmu.edu: So maybe, yeah, maybe it's, like, a ranked list that eventually you hide the high-confidence stuff.
+
+270
+00:34:33.350 --> 00:34:44.949
+hrishikb@andrew.cmu.edu: the… I think, Dan, the best… the best way to… Yeah, again, the…
+
+271
+00:34:45.170 --> 00:34:52.229
+hrishikb@andrew.cmu.edu: Catalog member is… sounds like the right person that you need to… Bob's you to answer that.
+
+272
+00:34:52.800 --> 00:34:56.440
+hrishikb@andrew.cmu.edu: I'm gonna have a long meeting with Chris. Yeah, yeah, yeah.
+
+273
+00:34:56.929 --> 00:35:00.330
+hrishikb@andrew.cmu.edu: Cuz, cause, yeah, it sounds like you're building… like, you're…
+
+274
+00:35:00.740 --> 00:35:07.769
+hrishikb@andrew.cmu.edu: your system is entirely to help Chris do their job faster, or, like, to scale Chris, or, you know, to take…
+
+275
+00:35:07.880 --> 00:35:09.230
+hrishikb@andrew.cmu.edu: their expertise.
+
+276
+00:35:09.580 --> 00:35:11.900
+hrishikb@andrew.cmu.edu: And… Automate.
+
+277
+00:35:12.200 --> 00:35:14.619
+hrishikb@andrew.cmu.edu: As much as makes sense.
+
+278
+00:35:19.580 --> 00:35:26.659
+hrishikb@andrew.cmu.edu: I was looking through the… you know, grief, and… what else did I trigger anything in my mind?
+
+279
+00:35:29.830 --> 00:35:32.789
+hrishikb@andrew.cmu.edu: I guess, what would you… what is the…
+
+280
+00:35:33.480 --> 00:35:38.189
+hrishikb@andrew.cmu.edu: biggest risk in your mind on the project, whether it's related to UI or not?
+
+281
+00:35:45.840 --> 00:35:48.909
+hrishikb@andrew.cmu.edu: Or the biggest, like, unknown to you right now.
+
+282
+00:35:50.360 --> 00:35:56.840
+hrishikb@andrew.cmu.edu: Now, after talking about it, the unknown looks like the interface of how you're gonna get the…
+
+283
+00:35:57.260 --> 00:36:00.970
+hrishikb@andrew.cmu.edu: files and, and the… like, how the URL will interact.
+
+284
+00:36:01.320 --> 00:36:06.839
+hrishikb@andrew.cmu.edu: Because you have not really discussed those parts, you're more focused on ML models and the ingestion parts of it.
+
+285
+00:36:07.350 --> 00:36:11.670
+hrishikb@andrew.cmu.edu: I mean, it doesn't, it can… it'll… and…
+
+286
+00:36:12.270 --> 00:36:16.489
+hrishikb@andrew.cmu.edu: Definitely start more, like, bare bones, and…
+
+287
+00:36:16.850 --> 00:36:19.039
+hrishikb@andrew.cmu.edu: Like, it doesn't have to be fancy, but you do have to…
+
+288
+00:36:19.370 --> 00:36:20.969
+hrishikb@andrew.cmu.edu: I mean, it has to get in somehow.
+
+289
+00:36:22.030 --> 00:36:30.950
+hrishikb@andrew.cmu.edu: once you get it… once you get a system, like, working end-to-end, then you can start to have discussions, or… then it makes it a lot easier for e-parts.
+
+290
+00:36:31.060 --> 00:36:39.799
+hrishikb@andrew.cmu.edu: to, like, visualize and, think about how the system is going to work, and then that will
+
+291
+00:36:40.090 --> 00:36:46.050
+hrishikb@andrew.cmu.edu: Trigger questions from their end, or, you know, thoughts on their end on how it should work.
+
+292
+00:36:48.130 --> 00:36:54.880
+hrishikb@andrew.cmu.edu: So, you don't have to figure it out right now, but you have to consider it so that you can start to get data moving and flowing.
+
+293
+00:36:55.170 --> 00:36:56.170
+hrishikb@andrew.cmu.edu: Testing.
+
+294
+00:36:56.340 --> 00:36:57.180
+hrishikb@andrew.cmu.edu: Thanks.
+
+295
+00:36:57.900 --> 00:37:02.490
+hrishikb@andrew.cmu.edu: So for, let's say from a development point of view, what would…
+
+296
+00:37:02.740 --> 00:37:08.690
+hrishikb@andrew.cmu.edu: you think that our initial UI or, like, interface should be,
+
+297
+00:37:09.060 --> 00:37:11.780
+hrishikb@andrew.cmu.edu: For, let's say, the engine part, when the…
+
+298
+00:37:12.180 --> 00:37:15.380
+hrishikb@andrew.cmu.edu: Many PDFs or CSVs? Yeah, I mean,
+
+299
+00:37:16.490 --> 00:37:23.160
+hrishikb@andrew.cmu.edu: I mean, I would try… I mean, is there a format that you know is more common than others?
+
+300
+00:37:25.790 --> 00:37:32.319
+hrishikb@andrew.cmu.edu: Preferred… That list of formats, is there, like, a priority, or…
+
+301
+00:37:32.540 --> 00:37:36.880
+hrishikb@andrew.cmu.edu: I don't think we have priority, but, we… we only know…
+
+302
+00:37:37.010 --> 00:37:45.770
+hrishikb@andrew.cmu.edu: PDFs, CSVs are the most common. Okay. And the web scraping is for, basically they do it for if some…
+
+303
+00:37:46.010 --> 00:37:54.460
+hrishikb@andrew.cmu.edu: vendor provides some information, it's completely… it's not… it's, like, half-baked, so the catalyticin goes to the website, and they themselves get the information. Right.
+
+304
+00:37:55.080 --> 00:38:01.700
+hrishikb@andrew.cmu.edu: Well, CSV sounds like the most structured and easiest to work with, so I'd probably start there.
+
+305
+00:38:02.230 --> 00:38:07.770
+hrishikb@andrew.cmu.edu: So…
+
+306
+00:38:08.790 --> 00:38:13.790
+hrishikb@andrew.cmu.edu: I mean, that could be just, like, a web… a web form that's, like, upload file and…
+
+307
+00:38:14.290 --> 00:38:19.029
+hrishikb@andrew.cmu.edu: Okay. Just basic, you know, whether or not that worked to get things into the system.
+
+308
+00:38:19.490 --> 00:38:20.500
+hrishikb@andrew.cmu.edu: Okay.
+
+309
+00:38:22.750 --> 00:38:31.200
+hrishikb@andrew.cmu.edu: I guess, and then… Oh yeah, I was gonna ask, like… I'm curious about…
+
+310
+00:38:32.670 --> 00:38:35.970
+hrishikb@andrew.cmu.edu: Yeah, how you're, let's see…
+
+311
+00:38:36.940 --> 00:38:48.660
+hrishikb@andrew.cmu.edu: I guess your… what are your current thoughts on… here, let me go to your… Yeah, alright, so…
+
+312
+00:38:52.000 --> 00:38:55.209
+hrishikb@andrew.cmu.edu: your… your… the ML part of it, like…
+
+313
+00:39:04.090 --> 00:39:07.730
+hrishikb@andrew.cmu.edu: I don't know, how… how do you envision this working?
+
+314
+00:39:08.080 --> 00:39:16.359
+hrishikb@andrew.cmu.edu: Like, so you get a bunch of… you get a catalog, and… You know the existing schema.
+
+315
+00:39:20.640 --> 00:39:25.200
+hrishikb@andrew.cmu.edu: what is… what is the intermediate structured layer doing? And then…
+
+316
+00:39:25.720 --> 00:39:27.610
+hrishikb@andrew.cmu.edu: Like, how do you evaluate that?
+
+317
+00:39:27.760 --> 00:39:33.000
+hrishikb@andrew.cmu.edu: Something is… Is correct or not.
+
+318
+00:39:35.430 --> 00:39:39.489
+hrishikb@andrew.cmu.edu: I think that is the part where MLRE will come into picture.
+
+319
+00:39:40.050 --> 00:39:54.929
+hrishikb@andrew.cmu.edu: I think they will, we'll have some sort of precise rules to see if particular attributes fits the, data or not.
+
+320
+00:39:55.150 --> 00:40:11.049
+hrishikb@andrew.cmu.edu: And then, according to that, we'll be giving it the content scores. Okay. And this table is basically, after we get all the data, we parse it, and we have it in proper, like, let's say, a schema sort of format, we just dump it on the intermediate structure table, and then the ML model can use the data from there.
+
+321
+00:40:12.460 --> 00:40:23.180
+hrishikb@andrew.cmu.edu: Did eParts… suggest… I guess, yeah, they said… like…
+
+322
+00:40:26.770 --> 00:40:34.520
+hrishikb@andrew.cmu.edu: How much… Machine learning capability do they currently use, or…
+
+323
+00:40:35.490 --> 00:40:42.239
+hrishikb@andrew.cmu.edu: None. They're just building the infra and just starting to get into machine learning. Okay.
+
+324
+00:40:43.070 --> 00:40:47.530
+hrishikb@andrew.cmu.edu: But they, they ask for ML. This is the ML-based. Yeah. Okay.
+
+325
+00:40:48.140 --> 00:40:58.340
+hrishikb@andrew.cmu.edu: They plan to grow their ML infra as well. They wanted this to be extensible so that they can maybe some other day use some of their ML components to plug into the system.
+
+326
+00:40:58.720 --> 00:40:59.929
+hrishikb@andrew.cmu.edu: And things like that.
+
+327
+00:41:00.400 --> 00:41:01.230
+hrishikb@andrew.cmu.edu: Okay.
+
+328
+00:41:11.310 --> 00:41:20.790
+hrishikb@andrew.cmu.edu: Okay, so yeah, you mentioned, yeah, I mean, we talked a little bit about that human review piece.
+
+329
+00:41:26.190 --> 00:41:39.160
+hrishikb@andrew.cmu.edu: And then there's also… I like the ops part, logs and metrics and… dashboards,
+
+330
+00:41:40.240 --> 00:41:46.190
+hrishikb@andrew.cmu.edu: Is that a significant… Part… Or is that more, like, stretch goal?
+
+331
+00:41:49.790 --> 00:41:57.029
+hrishikb@andrew.cmu.edu: I… I'm not sure we… we have not really discussed that part into length. Ashutar, you have an opinion on the observability part?
+
+332
+00:41:57.620 --> 00:42:04.039
+hrishikb@andrew.cmu.edu: And we've created a package with them and more. They're using Datadog, so that's…
+
+333
+00:42:04.220 --> 00:42:18.079
+hrishikb@andrew.cmu.edu: And they wouldn't want the learning efforts to go into the observability, but it's just a thing that we added, that, okay, since there's an ML thing added, so we would want to send
+
+334
+00:42:18.080 --> 00:42:31.730
+hrishikb@andrew.cmu.edu: define some kind of metrics and, send some traces or logs into your data doc system. Yeah. But currently, they do, they have… they have their own observability metrics, so they're not, like, very much keen into
+
+335
+00:42:31.930 --> 00:42:40.819
+hrishikb@andrew.cmu.edu: expanding it. This would mostly be, I guess, internal for our internal system, for us to… Okay, okay.
+
+336
+00:42:42.330 --> 00:42:57.839
+hrishikb@andrew.cmu.edu: Something like, per day, these many number of queries were served, or these many number of records got ingested, or just like that, metrics around the data that's another.
+
+337
+00:42:58.350 --> 00:43:08.270
+hrishikb@andrew.cmu.edu: Do you know if, like, I don't know, like… Cost or, like, token usage?
+
+338
+00:43:08.420 --> 00:43:11.309
+hrishikb@andrew.cmu.edu: That kind of stuff is on their radar.
+
+339
+00:43:11.540 --> 00:43:13.330
+hrishikb@andrew.cmu.edu: Like, what? No.
+
+340
+00:43:14.200 --> 00:43:18.459
+hrishikb@andrew.cmu.edu: Okay. What kind of proposal? I mean, like, the,
+
+341
+00:43:19.300 --> 00:43:23.169
+hrishikb@andrew.cmu.edu: Like, for using LLMs, so things like that.
+
+342
+00:43:23.280 --> 00:43:26.810
+hrishikb@andrew.cmu.edu: Did you talk about queries, but…
+
+343
+00:43:30.660 --> 00:43:42.280
+hrishikb@andrew.cmu.edu: Yeah, actually, no, maybe, maybe, are they planning to use… Like, LLM-based ML stuff.
+
+344
+00:43:42.750 --> 00:43:48.460
+hrishikb@andrew.cmu.edu: No, no, no. No, we're just going by the traditional email. Okay, okay, yeah. Got it, got it.
+
+345
+00:43:49.610 --> 00:43:51.280
+hrishikb@andrew.cmu.edu: Nevermind, man. Cool.
+
+346
+00:43:52.070 --> 00:43:56.789
+hrishikb@andrew.cmu.edu: Okay, yeah, so going back to,
+
+347
+00:43:58.070 --> 00:44:03.550
+hrishikb@andrew.cmu.edu: Yeah, the biggest risks, or, what else is…
+
+348
+00:44:05.880 --> 00:44:11.769
+hrishikb@andrew.cmu.edu: I guess there's multiple levels, too, not just for e-parts, but, what do you need?
+
+349
+00:44:12.190 --> 00:44:15.819
+hrishikb@andrew.cmu.edu: For the checkpoint that… Got you still…
+
+350
+00:44:16.900 --> 00:44:24.820
+hrishikb@andrew.cmu.edu: For the checkpoint, there are a couple of documents. There is… And therefore, it's called,
+
+351
+00:44:24.930 --> 00:44:42.499
+hrishikb@andrew.cmu.edu: the, like, the exact boundary of work that we're supposed to do, you know, has to be a document and things like that, but initially, we have our software engineering system also, like, it is still developing, but for now, we have a pretty solid system defined a lot of things in that.
+
+352
+00:44:42.840 --> 00:44:55.979
+hrishikb@andrew.cmu.edu: We have an initial requirements document as well, but that has not yet been vetted by the, requirements coach. We have a meeting with him tomorrow to get that done.
+
+353
+00:44:56.540 --> 00:45:02.150
+hrishikb@andrew.cmu.edu: There are a couple of things, but I can't remember them.
+
+354
+00:45:03.380 --> 00:45:14.930
+hrishikb@andrew.cmu.edu: And the remaining high-level architecture, what we're planning to do, what our semester plan would be, all those things are, pretty much… we have a… we know what we have to do, we have plan ready.
+
+355
+00:45:16.360 --> 00:45:24.169
+hrishikb@andrew.cmu.edu: Yeah, we just have to, prepare the actual activities for the presentation that we're gonna present to everyone.
+
+356
+00:45:25.460 --> 00:45:29.990
+hrishikb@andrew.cmu.edu: One thing I just thought of, too, looking at the… Project thing.
+
+357
+00:45:32.540 --> 00:45:35.700
+hrishikb@andrew.cmu.edu: You might… think about…
+
+358
+00:45:36.340 --> 00:45:44.670
+hrishikb@andrew.cmu.edu: I'm just reading about the problem and how they said, like, you know, every record's touched by a person to do all this stuff, and it's error-prone.
+
+359
+00:45:46.880 --> 00:45:51.120
+hrishikb@andrew.cmu.edu: Like, if there's a way to, you know, phase your project so that
+
+360
+00:45:51.920 --> 00:45:55.610
+hrishikb@andrew.cmu.edu: You start to automate, you know, small parts of it.
+
+361
+00:45:56.770 --> 00:45:57.620
+hrishikb@andrew.cmu.edu: that.
+
+362
+00:45:58.290 --> 00:46:02.459
+hrishikb@andrew.cmu.edu: our friend Chris can already get value early on.
+
+363
+00:46:05.300 --> 00:46:12.290
+hrishikb@andrew.cmu.edu: So that's… I think, one, that reduces risk in the overall project, because
+
+364
+00:46:15.120 --> 00:46:17.649
+hrishikb@andrew.cmu.edu: You are, you know, starting to…
+
+365
+00:46:18.210 --> 00:46:23.080
+hrishikb@andrew.cmu.edu: Build things that are useful and valuable, and then you can get even richer feedback.
+
+366
+00:46:23.540 --> 00:46:29.269
+hrishikb@andrew.cmu.edu: From e-parts, so, like… Kind of where the… where the next step should be.
+
+367
+00:46:29.610 --> 00:46:32.370
+hrishikb@andrew.cmu.edu: And…
+
+368
+00:46:35.210 --> 00:46:48.120
+hrishikb@andrew.cmu.edu: In line with those things, we did have a few, like, concerns, because since we are, like, we are to use AI heavily to do all the coding and all these things, so we…
+
+369
+00:46:48.230 --> 00:46:57.270
+hrishikb@andrew.cmu.edu: expect the development part to, flow around very quickly. And, like, even thinking about two-week sprint seemed very… way too long.
+
+370
+00:46:57.320 --> 00:47:16.429
+hrishikb@andrew.cmu.edu: And we were planning to have, like, shorter sprints, maybe one week at max, kind of stretch. So, currently, we are expecting the entire development process, the actual writing all the different modules, should be fairly quick. Yeah. But we're not… we're not… we're not sure how quick, but, let's say we get it all done in a
+
+371
+00:47:16.720 --> 00:47:25.060
+hrishikb@andrew.cmu.edu: I don't know, maybe a… optimistically, maybe a month. So, having intermittent releases between that, would you think that would be, like, useful?
+
+372
+00:47:26.330 --> 00:47:36.150
+hrishikb@andrew.cmu.edu: Yeah, I think definitely… The earlier that you can…
+
+373
+00:47:43.060 --> 00:47:48.429
+hrishikb@andrew.cmu.edu: Yeah, the other that you can get feedback, You know, the…
+
+374
+00:47:48.870 --> 00:47:53.849
+hrishikb@andrew.cmu.edu: the more risk that you can mitigate. Like, even if it's, you know, a month.
+
+375
+00:47:54.320 --> 00:47:57.709
+hrishikb@andrew.cmu.edu: Like, if you end up building the wrong thing, then…
+
+376
+00:47:58.540 --> 00:48:02.280
+hrishikb@andrew.cmu.edu: Then you have to rebuild it, right? So…
+
+377
+00:48:02.630 --> 00:48:04.819
+hrishikb@andrew.cmu.edu: Like, it sounds like there's a lot of…
+
+378
+00:48:05.300 --> 00:48:11.530
+hrishikb@andrew.cmu.edu: There's a lot of, different activities that Chris does that you could automate.
+
+379
+00:48:13.350 --> 00:48:19.750
+hrishikb@andrew.cmu.edu: like… They're, like, just parsing and understanding, like, what is in the catalog.
+
+380
+00:48:20.060 --> 00:48:26.109
+hrishikb@andrew.cmu.edu: There's just, like, the man… I guess the manual inputting the records, like…
+
+381
+00:48:26.360 --> 00:48:28.520
+hrishikb@andrew.cmu.edu: There might be small wins there.
+
+382
+00:48:29.050 --> 00:48:33.130
+hrishikb@andrew.cmu.edu: to… That…
+
+383
+00:48:36.380 --> 00:48:43.950
+hrishikb@andrew.cmu.edu: like… In the… in the interim, like, that could still be a manual piece.
+
+384
+00:48:44.170 --> 00:48:48.250
+hrishikb@andrew.cmu.edu: But that could also serve as, like, the human review part.
+
+385
+00:48:49.470 --> 00:48:58.669
+hrishikb@andrew.cmu.edu: Right, and then eventually that part can be automated, where whatever… whatever UI that You're using their,
+
+386
+00:48:58.950 --> 00:49:05.530
+hrishikb@andrew.cmu.edu: Yeah, can get automated through an API instead of manually, you know, Chris looking at it.
+
+387
+00:49:09.480 --> 00:49:14.400
+hrishikb@andrew.cmu.edu: like… Another way to say it is, like, identifying
+
+388
+00:49:17.580 --> 00:49:27.019
+hrishikb@andrew.cmu.edu: what are… what are Chris's pain points right now? Like, I know overall it's just… it's manual, right? But if you can break down their workflow.
+
+389
+00:49:27.210 --> 00:49:32.349
+hrishikb@andrew.cmu.edu: Or understand what the different pieces are, what the different activities are. Maybe there's opportunities to
+
+390
+00:49:32.470 --> 00:49:35.289
+hrishikb@andrew.cmu.edu: First, automate just a piece of that.
+
+391
+00:49:35.770 --> 00:49:37.400
+hrishikb@andrew.cmu.edu: And… Okay.
+
+392
+00:49:40.490 --> 00:49:51.390
+hrishikb@andrew.cmu.edu: Because, like, the deeper you get into understanding Chris's world, then the better you'll be able to address and know exactly how to automate this whole system.
+
+393
+00:49:51.740 --> 00:49:53.270
+hrishikb@andrew.cmu.edu: It should be designed.
+
+394
+00:50:35.600 --> 00:50:36.300
+hrishikb@andrew.cmu.edu: Hmm.
+
+395
+00:50:37.550 --> 00:50:40.470
+hrishikb@andrew.cmu.edu: And you can also…
+
+396
+00:50:44.370 --> 00:50:48.769
+hrishikb@andrew.cmu.edu: I guess when you do talk to Chris, or whatever their name is,
+
+397
+00:50:56.230 --> 00:51:03.439
+hrishikb@andrew.cmu.edu: I guess… try to… Yeah, like, trying to understand how they see the world.
+
+398
+00:51:04.050 --> 00:51:08.630
+hrishikb@andrew.cmu.edu: And how they… how they think the system might work.
+
+399
+00:51:09.640 --> 00:51:15.479
+hrishikb@andrew.cmu.edu: Like, try to get into… into their minds.
+
+400
+00:51:15.630 --> 00:51:21.960
+hrishikb@andrew.cmu.edu: You know, this… this team is building this system that
+
+401
+00:51:22.080 --> 00:51:25.009
+hrishikb@andrew.cmu.edu: That I'm gonna use, and gonna automate.
+
+402
+00:51:25.350 --> 00:51:30.929
+hrishikb@andrew.cmu.edu: The tedious parts of my job, and, the parts that are really error-prone.
+
+403
+00:51:31.110 --> 00:51:39.130
+hrishikb@andrew.cmu.edu: And… Understanding that will help you focus.
+
+404
+00:51:39.320 --> 00:51:40.230
+hrishikb@andrew.cmu.edu: on.
+
+405
+00:51:41.340 --> 00:51:42.889
+hrishikb@andrew.cmu.edu: What parts are important?
+
+406
+00:51:43.200 --> 00:51:44.720
+hrishikb@andrew.cmu.edu: In this whole process.
+
+407
+00:51:46.930 --> 00:51:51.780
+hrishikb@andrew.cmu.edu: Basically, from their point of view, what all… As the most…
+
+408
+00:51:52.010 --> 00:52:00.000
+hrishikb@andrew.cmu.edu: like, pain points for them should be things you should work on, like, prioritize, basically. Yeah. And we're breaking down parts also. Right, yeah.
+
+409
+00:52:03.900 --> 00:52:07.100
+hrishikb@andrew.cmu.edu: Like, they might say, like, if I could just have…
+
+410
+00:52:08.210 --> 00:52:10.760
+hrishikb@andrew.cmu.edu: If you do this parse, like.
+
+411
+00:52:11.860 --> 00:52:14.389
+hrishikb@andrew.cmu.edu: the PDF and put it into this…
+
+412
+00:52:14.700 --> 00:52:22.750
+hrishikb@andrew.cmu.edu: you know, X intermediate format that I have, or, like, into this Word template I use, or into this text document, or this spreadsheet, right?
+
+413
+00:52:22.890 --> 00:52:31.619
+hrishikb@andrew.cmu.edu: like… Then… then you could separate out that module, and…
+
+414
+00:52:32.010 --> 00:52:36.330
+hrishikb@andrew.cmu.edu: Work on that, and be able to… validate that.
+
+415
+00:52:36.740 --> 00:52:44.270
+hrishikb@andrew.cmu.edu: You know, this is giving Chris exactly what they need, and… and then also, like.
+
+416
+00:52:44.630 --> 00:52:47.519
+hrishikb@andrew.cmu.edu: That also tells you, like, this is what is…
+
+417
+00:52:48.060 --> 00:52:50.450
+hrishikb@andrew.cmu.edu: this is where Chris wants to…
+
+418
+00:52:50.550 --> 00:52:55.570
+hrishikb@andrew.cmu.edu: check that the system is working. Like, this is what they want to see, what format they expect it in.
+
+419
+00:52:55.940 --> 00:53:03.810
+hrishikb@andrew.cmu.edu: What are the… what are the things that are important, and what they need in order to say, okay, this is… this work… this system is working well.
+
+420
+00:53:04.060 --> 00:53:09.710
+hrishikb@andrew.cmu.edu: I'm confident in… What's happening on the backend? Stuff like that.
+
+421
+00:53:16.640 --> 00:53:17.330
+hrishikb@andrew.cmu.edu: Okay.
+
+422
+00:53:22.500 --> 00:53:24.129
+hrishikb@andrew.cmu.edu: What else? What else am I?
+
+423
+00:53:26.040 --> 00:53:27.370
+hrishikb@andrew.cmu.edu: Talked everywhere.
+
+424
+00:53:29.010 --> 00:53:32.269
+hrishikb@andrew.cmu.edu: I think we have a lot of questions now, because we haven't actually
+
+425
+00:53:33.020 --> 00:53:37.790
+hrishikb@andrew.cmu.edu: worked on the system or any particle core yet. Okay. There's at least
+
+426
+00:53:37.960 --> 00:53:40.350
+hrishikb@andrew.cmu.edu: I mean, since they're, like, we…
+
+427
+00:53:40.680 --> 00:53:58.570
+hrishikb@andrew.cmu.edu: our thinking as developers starts from, you know, okay, let's look at the code and then figure out the data. Right, right, right. So I think, once we get to understand the whole flow, and then the actual interfaces that we spoke about, the APIs, or if they have any, GUI or something.
+
+428
+00:53:58.710 --> 00:54:04.509
+hrishikb@andrew.cmu.edu: Probably then we'll be able to answer a lot of questions that you've brought up, but currently it's just, like.
+
+429
+00:54:04.900 --> 00:54:07.039
+hrishikb@andrew.cmu.edu: the high level. Yeah, yeah.
+
+430
+00:54:08.640 --> 00:54:24.090
+hrishikb@andrew.cmu.edu: They've given us a lot of data, actually. They've given us around 27 data documents. I did spend 2 days, but then we have our mid-semester incident, right? So, we just, like, powered it, and then we put it, like, to it.
+
+431
+00:54:24.290 --> 00:54:32.349
+hrishikb@andrew.cmu.edu: What's the kind of data that you need? Like, catalogs, or also just catalogs, schemas? Catalogs…
+
+432
+00:54:32.980 --> 00:54:36.320
+hrishikb@andrew.cmu.edu: Sort out specification documents.
+
+433
+00:54:37.020 --> 00:54:37.940
+hrishikb@andrew.cmu.edu: Okay.
+
+434
+00:54:38.220 --> 00:54:50.150
+hrishikb@andrew.cmu.edu: basically a small dump of the data, like, removing all the PII and all those critical information, and some sort of input files, the kind they expect, usually.
+
+435
+00:54:50.520 --> 00:54:59.499
+hrishikb@andrew.cmu.edu: And the… they also give us some specific schemas, which, like, doesn't really change very often, those kind of things. Okay.
+
+436
+00:55:00.690 --> 00:55:09.060
+hrishikb@andrew.cmu.edu: Yeah, that's interesting. I guess I was assuming the whole time, like, is the main…
+
+437
+00:55:09.280 --> 00:55:13.490
+hrishikb@andrew.cmu.edu: Are we talking mainly about…
+
+438
+00:55:13.940 --> 00:55:24.239
+hrishikb@andrew.cmu.edu: items in a catalog that people can order, or is there other kinds of information that they want to ingest and that goes in the system? Like, is there just general vendor information, and…
+
+439
+00:55:25.220 --> 00:55:32.700
+hrishikb@andrew.cmu.edu: I don't know, other… It'll be, product information, the product specs. Yeah, specs, like, shape, size…
+
+440
+00:55:32.730 --> 00:55:42.309
+hrishikb@andrew.cmu.edu: Things like that. Okay, okay. So let's say they have a vendor, like, Apin as a vendor, so they would, like, give you… if they have category of MacBooks.
+
+441
+00:55:42.310 --> 00:55:45.170
+hrishikb@andrew.cmu.edu: Okay. They're gonna give you, like, the screen size.
+
+442
+00:55:45.170 --> 00:56:09.900
+hrishikb@andrew.cmu.edu: And all of that. So there can be a hardware shop vendor as well. They're gonna input screw dimensions… Okay. Is there images that you have to… Yeah, I mean, we don't have to deal with images, but then the catalog, the document has images, but we don't parse any kind of… But those eParts… is that in their catalog, though? In their image? Yeah, they are. In the website, there is…
+
+443
+00:56:10.560 --> 00:56:13.360
+hrishikb@andrew.cmu.edu: Okay, so that's just, I guess…
+
+444
+00:56:13.830 --> 00:56:20.069
+hrishikb@andrew.cmu.edu: Outside of the scope of this project? Yeah. Okay, but it's still part of what they want to do, right? Or…
+
+445
+00:56:20.250 --> 00:56:30.800
+hrishikb@andrew.cmu.edu: Okay. I would look forward to donate image for whatever is being passed.
+
+446
+00:56:35.440 --> 00:56:46.930
+hrishikb@andrew.cmu.edu: But since the… I mean, if the data's in CSV format, they'll probably… they won't have a… Right. …something to… they'll probably get some general picture out there. Yeah, maybe it's, like, a blank or something.
+
+447
+00:56:47.250 --> 00:56:48.570
+hrishikb@andrew.cmu.edu: And couldn't be.
+
+448
+00:57:00.440 --> 00:57:03.906
+hrishikb@andrew.cmu.edu: How are you guys considering… Or how…
+
+449
+00:57:04.640 --> 00:57:07.640
+hrishikb@andrew.cmu.edu: How have you been answering the constant…
+
+450
+00:57:08.020 --> 00:57:12.140
+hrishikb@andrew.cmu.edu: questions, it feels like to neighboring faculty, like, how are you using AI?
+
+451
+00:57:14.650 --> 00:57:18.169
+hrishikb@andrew.cmu.edu: We're using it as much as possible.
+
+452
+00:57:18.560 --> 00:57:29.320
+hrishikb@andrew.cmu.edu: And, songhood has worked out pretty well. Other parts… We're kind of a… Questioning more of its,
+
+453
+00:57:30.050 --> 00:57:33.059
+hrishikb@andrew.cmu.edu: Whether we should just do that part ourselves.
+
+454
+00:57:37.270 --> 00:57:44.890
+hrishikb@andrew.cmu.edu: like, we are trying to use it as much, but some parts does seem like an overkill. It is not helping us improve, it's just slowing us down.
+
+455
+00:57:45.190 --> 00:57:51.870
+hrishikb@andrew.cmu.edu: Just trying to automate multiple things, getting this NYI, that NYI. I think AI is helpful in most places, but not all.
+
+456
+00:57:51.980 --> 00:57:57.580
+hrishikb@andrew.cmu.edu: Yeah. And the amount of AI usage should also be limited. Like, I think,
+
+457
+00:57:58.770 --> 00:58:17.739
+hrishikb@andrew.cmu.edu: recently, I think Cory put out a post on LinkedIn, I was reading that, so it mentioned that how much AI you should use, like, it's not always good to, if you have to change a line in a document, it's better to do it yourselves than give it to AI, and write a prompt to change that line, and so things like that is something that, I guess, we need to consider more.
+
+458
+00:58:17.910 --> 00:58:21.299
+hrishikb@andrew.cmu.edu: Yeah. Amount of, like, areas and amount of AI usage.
+
+459
+00:58:25.470 --> 00:58:29.330
+hrishikb@andrew.cmu.edu: What's your take on this? Yeah, I think,
+
+460
+00:58:32.970 --> 00:58:38.630
+hrishikb@andrew.cmu.edu: Anything that you… Anything that you care about learning?
+
+461
+00:58:38.960 --> 00:58:41.909
+hrishikb@andrew.cmu.edu: I would to minimize AI use.
+
+462
+00:58:42.370 --> 00:58:53.300
+hrishikb@andrew.cmu.edu: Right? And it's really… It's tricky and nuanced, and… And it…
+
+463
+00:58:53.980 --> 00:59:01.410
+hrishikb@andrew.cmu.edu: to me, right now, in this environment that I'm in, and I think also just in general, like, the industry, it's…
+
+464
+00:59:04.580 --> 00:59:11.000
+hrishikb@andrew.cmu.edu: It feels impossible to… use it… in…
+
+465
+00:59:13.200 --> 00:59:16.769
+hrishikb@andrew.cmu.edu: In a good way, or in a helpful way, because…
+
+466
+00:59:18.370 --> 00:59:25.539
+hrishikb@andrew.cmu.edu: The conception is that it's gonna… You know, provide…
+
+467
+00:59:25.810 --> 00:59:30.570
+hrishikb@andrew.cmu.edu: 10x productivity, or, you know, like, just the claims are really…
+
+468
+00:59:30.770 --> 00:59:33.189
+hrishikb@andrew.cmu.edu: Overblown, but the tricky part is…
+
+469
+00:59:33.320 --> 00:59:36.770
+hrishikb@andrew.cmu.edu: In the right context, with the right user.
+
+470
+00:59:37.110 --> 00:59:46.630
+hrishikb@andrew.cmu.edu: it does… it can be successful, or it can have those kind of results, but you can't generalize that. Yeah. So…
+
+471
+00:59:47.050 --> 00:59:53.089
+hrishikb@andrew.cmu.edu: And I… I don't know… I don't know what to tell.
+
+472
+00:59:53.290 --> 01:00:00.890
+hrishikb@andrew.cmu.edu: People like you in school, and… I think… Definitely… like…
+
+473
+01:00:01.200 --> 01:00:05.739
+hrishikb@andrew.cmu.edu: Fundamentals are still critical, so it's like…
+
+474
+01:00:06.010 --> 01:00:09.140
+hrishikb@andrew.cmu.edu: And… and the… also, the tricky thing is, it's just…
+
+475
+01:00:09.380 --> 01:00:15.110
+hrishikb@andrew.cmu.edu: It's too much of a temptation to use in so many cases, and that's going to…
+
+476
+01:00:15.480 --> 01:00:17.779
+hrishikb@andrew.cmu.edu: Rob you of a learning opportunity.
+
+477
+01:00:18.480 --> 01:00:21.400
+hrishikb@andrew.cmu.edu: But I understand incentives and pressures.
+
+478
+01:00:21.860 --> 01:00:27.439
+hrishikb@andrew.cmu.edu: And there are things that you… you know, there are shortcuts, sometimes they might be the right ones to take, but…
+
+479
+01:00:31.960 --> 01:00:37.410
+hrishikb@andrew.cmu.edu: But it's hard to… It's hard to, like… Resist when.
+
+480
+01:00:37.550 --> 01:00:39.250
+hrishikb@andrew.cmu.edu: You know, it's something important.
+
+481
+01:00:40.180 --> 01:00:42.620
+hrishikb@andrew.cmu.edu: And when you're in this high-pressure environment.
+
+482
+01:00:43.220 --> 01:00:45.330
+hrishikb@andrew.cmu.edu: Which you are in?
+
+483
+01:00:45.800 --> 01:00:49.820
+hrishikb@andrew.cmu.edu: The whole industry has kind of been trying to, like, keep up with
+
+484
+01:00:51.460 --> 01:01:04.940
+hrishikb@andrew.cmu.edu: Phantom, you know, stories of success, or… But, like… Yeah, it's like, you know.
+
+485
+01:01:05.070 --> 01:01:13.379
+hrishikb@andrew.cmu.edu: The way that you learned is… came up through… Experimenting and trying and failing.
+
+486
+01:01:14.680 --> 01:01:20.760
+hrishikb@andrew.cmu.edu: But when you have this machine that can get you Past all the pain.
+
+487
+01:01:22.960 --> 01:01:30.950
+hrishikb@andrew.cmu.edu: Then you have… then… then you don't have the opportunity to make all the decisions along the way that inform and, you know, change the other person.
+
+488
+01:01:34.000 --> 01:01:38.000
+hrishikb@andrew.cmu.edu: And even… like…
+
+489
+01:01:38.770 --> 01:01:45.439
+hrishikb@andrew.cmu.edu: Yeah, like, you can definitely buy-code a lot of stuff, right? I'm sure you guys have experimented. But as soon as you…
+
+490
+01:01:45.580 --> 01:01:50.280
+hrishikb@andrew.cmu.edu: Have a non-trivial size of a team, like…
+
+491
+01:01:50.400 --> 01:01:54.760
+hrishikb@andrew.cmu.edu: It's less and less about what you can build, but more… Like…
+
+492
+01:01:55.930 --> 01:02:00.850
+hrishikb@andrew.cmu.edu: working together, and… like, work is more social than, I think.
+
+493
+01:02:01.830 --> 01:02:07.779
+hrishikb@andrew.cmu.edu: most of the people in our industry give gratitude, or appreciate. Like, it's more about…
+
+494
+01:02:08.330 --> 01:02:16.230
+hrishikb@andrew.cmu.edu: Aligning on the same idea around a system, and understanding the people that you're building for, and making sure you're solving the right problems.
+
+495
+01:02:17.520 --> 01:02:19.060
+hrishikb@andrew.cmu.edu: Like, it's never…
+
+496
+01:02:20.280 --> 01:02:27.150
+hrishikb@andrew.cmu.edu: It's never been about typing up the code. But, you know, people are trying to, you know, do more and more with it, but…
+
+497
+01:02:27.630 --> 01:02:29.690
+hrishikb@andrew.cmu.edu: I think that's at the cost of…
+
+498
+01:02:30.490 --> 01:02:35.720
+hrishikb@andrew.cmu.edu: A lot of things that we don't currently have the right feedback loops to properly understand.
+
+499
+01:02:37.200 --> 01:02:38.100
+hrishikb@andrew.cmu.edu: I don't know.
+
+500
+01:02:42.010 --> 01:02:46.299
+hrishikb@andrew.cmu.edu: It's constant today that my company and lots of other companies.
+
+501
+01:02:47.480 --> 01:02:53.490
+hrishikb@andrew.cmu.edu: I only… yeah, the divide feels like it's getting bigger, and it's also a very interesting dynamic of, like.
+
+502
+01:02:53.910 --> 01:02:58.349
+hrishikb@andrew.cmu.edu: Kind of, like, class warfare, too, where it's, like, pushed down by the leaders who…
+
+503
+01:03:00.990 --> 01:03:04.720
+hrishikb@andrew.cmu.edu: are far from what the reality is, right? Like…
+
+504
+01:03:06.060 --> 01:03:15.320
+hrishikb@andrew.cmu.edu: You know, you see good results, but then you often see, like, You know, bad results, and…
+
+505
+01:03:15.660 --> 01:03:20.229
+hrishikb@andrew.cmu.edu: Yeah, definitely, like… When they say 10x time productivity.
+
+506
+01:03:20.400 --> 01:03:25.949
+hrishikb@andrew.cmu.edu: I don't know how that would even be possible, even with, like, the best AI in the world. Not because… Yeah.
+
+507
+01:03:26.570 --> 01:03:31.870
+hrishikb@andrew.cmu.edu: the only way that would happen is, right, if I actually did not touch the computer.
+
+508
+01:03:32.200 --> 01:03:46.519
+hrishikb@andrew.cmu.edu: If it actually just did every single step, you know, not even me debugging or, like, checking it. Because even if I checked perfect code, supposing it was perfect, it would take me time, like, probably half the time it would take me to do the entire thing.
+
+509
+01:03:46.580 --> 01:03:54.610
+hrishikb@andrew.cmu.edu: So, just that alone just limits their productivity to 2x. So, unless they manage to, like, completely eliminate the human from the loop.
+
+510
+01:03:54.810 --> 01:04:02.490
+hrishikb@andrew.cmu.edu: And just, like, have screens flashing and closing down IDEs constantly and checking. If that happens, then…
+
+511
+01:04:02.680 --> 01:04:05.940
+hrishikb@andrew.cmu.edu: Hopefully I can get a management job or something.
+
+512
+01:04:06.280 --> 01:04:11.289
+hrishikb@andrew.cmu.edu: Yeah, but, like, the… yeah, the value of the work that you're producing is…
+
+513
+01:04:13.810 --> 01:04:18.679
+hrishikb@andrew.cmu.edu: Like, at the end of the day, to me, a human being has to understand the system.
+
+514
+01:04:19.140 --> 01:04:20.050
+hrishikb@andrew.cmu.edu: Mom.
+
+515
+01:04:20.540 --> 01:04:23.829
+hrishikb@andrew.cmu.edu: And be able to change that system, and it has to affect
+
+516
+01:04:24.390 --> 01:04:29.519
+hrishikb@andrew.cmu.edu: It has to change another human being's workflow or, you know, their life, so to speak.
+
+517
+01:04:31.550 --> 01:04:39.229
+hrishikb@andrew.cmu.edu: But… But, you know, it collapses into absurdity when then you're talking about
+
+518
+01:04:39.790 --> 01:04:46.310
+hrishikb@andrew.cmu.edu: I don't know, agents acting on your behalf, and like, who's actually using… like, there's no value that's really being created, we're just…
+
+519
+01:04:48.050 --> 01:04:52.669
+hrishikb@andrew.cmu.edu: Like, throwing around, like, numbers are just, like, bags of numbers are just interacting.
+
+520
+01:04:52.900 --> 01:04:54.449
+hrishikb@andrew.cmu.edu: There's nothing really happening.
+
+521
+01:04:55.090 --> 01:04:55.850
+hrishikb@andrew.cmu.edu: Thank you.
+
+522
+01:04:56.940 --> 01:04:58.729
+hrishikb@andrew.cmu.edu: And yeah, when I thought, like.
+
+523
+01:04:59.180 --> 01:05:04.090
+hrishikb@andrew.cmu.edu: Just, like, if you think about it more just, like, automating things.
+
+524
+01:05:04.210 --> 01:05:14.780
+hrishikb@andrew.cmu.edu: like, what… what in my… in my current development workflow and product workflow can I automate to become 10 times more productive? Like, what does that even mean?
+
+525
+01:05:15.060 --> 01:05:20.869
+hrishikb@andrew.cmu.edu: like… Most of my job is, like, Talking to other people, and…
+
+526
+01:05:21.010 --> 01:05:32.180
+hrishikb@andrew.cmu.edu: Like, getting aligned on what we're building and how it should look, like… But, yeah. So, it's… But…
+
+527
+01:05:33.030 --> 01:05:38.120
+hrishikb@andrew.cmu.edu: There's that very strong narrative that it should be like this, so…
+
+528
+01:05:38.490 --> 01:05:41.000
+hrishikb@andrew.cmu.edu: I think that's why I say it's, like, impossible…
+
+529
+01:05:41.130 --> 01:05:47.059
+hrishikb@andrew.cmu.edu: for us to find, like, the right uses of this kind of LLM technology, because no one's gonna invest.
+
+530
+01:05:47.460 --> 01:05:49.190
+hrishikb@andrew.cmu.edu: In something that…
+
+531
+01:05:49.400 --> 01:05:58.780
+hrishikb@andrew.cmu.edu: Well, first of all, it takes effort and time to, like, design a good product using this stuff, and no one's gonna invest in that, because you're not gonna get that immediate
+
+532
+01:05:58.920 --> 01:06:02.070
+hrishikb@andrew.cmu.edu: so-called, or seemingly, like, 10x return.
+
+533
+01:06:03.450 --> 01:06:08.410
+hrishikb@andrew.cmu.edu: So, that's… At some point, I think it's… It's gonna…
+
+534
+01:06:08.760 --> 01:06:15.860
+hrishikb@andrew.cmu.edu: Yeah, they also keep saying that, you know, the next model will be two times better than the previous model, and
+
+535
+01:06:16.110 --> 01:06:20.800
+hrishikb@andrew.cmu.edu: In their defense, I will say it's getting better, but… Yeah. I don't see, like, the…
+
+536
+01:06:21.030 --> 01:06:25.440
+hrishikb@andrew.cmu.edu: Well, yeah, two times, like, every year. Yeah.
+
+537
+01:06:25.680 --> 01:06:38.250
+hrishikb@andrew.cmu.edu: I think the efficiency also matters if you're working on a, let's say, a greenfield or property project, because if you're writing something from scratch, maybe even you can get 10 times productivity, right, because you have to write thousands of, like, maybe hundreds of files, thousands of files. Yeah, yeah.
+
+538
+01:06:38.250 --> 01:06:45.690
+hrishikb@andrew.cmu.edu: In that part, it might help, but if you already have a tightly coupled, 20 years old codebase, then AI can't do much.
+
+539
+01:06:45.690 --> 01:06:55.909
+hrishikb@andrew.cmu.edu: Better have good version control in which AWS went down, right? They were boasting, like, 3 days ago, like… I'm glad you guys have a bevelhead about this stuff, like…
+
+540
+01:06:57.590 --> 01:07:04.670
+hrishikb@andrew.cmu.edu: Yeah, it's… yeah, you can't… it's… it's… you can't generalize, but that's… That's the only thing that…
+
+541
+01:07:04.910 --> 01:07:06.420
+hrishikb@andrew.cmu.edu: Leaders can do.
+
+542
+01:07:06.900 --> 01:07:11.540
+hrishikb@andrew.cmu.edu: We're like… Of course, they're going to… Try to promote.
+
+543
+01:07:11.810 --> 01:07:16.720
+hrishikb@andrew.cmu.edu: You know, uncertainties of working, or the stories of success, but…
+
+544
+01:07:17.170 --> 01:07:21.270
+hrishikb@andrew.cmu.edu: Yeah, you can't apply, like, a brand new project Good.
+
+545
+01:07:22.330 --> 01:07:29.150
+hrishikb@andrew.cmu.edu: Brownfield stuff, and… Situations… Yes.
+
+546
+01:07:30.120 --> 01:07:32.210
+hrishikb@andrew.cmu.edu: Amy, there's a… there's a… yeah.
+
+547
+01:07:33.470 --> 01:07:35.039
+hrishikb@andrew.cmu.edu: At work this week.
+
+548
+01:07:35.190 --> 01:07:35.960
+hrishikb@andrew.cmu.edu: Thank you.
+
+549
+01:07:37.610 --> 01:07:54.129
+hrishikb@andrew.cmu.edu: Alright, the last thing I'll share, like, somebody, one of our teams, they built, like, a tool using OMs to, like, triage bugs. Like, something that is… is hard and, you know, takes a lot of time for people to analyze, like, what's going on and go into the system, so…
+
+550
+01:07:54.230 --> 01:08:04.420
+hrishikb@andrew.cmu.edu: And then a principal engineer used it, and they got good results. So then SVP heard that and said, everyone's got to use this. So then managers, like, pushed us out and said, like.
+
+551
+01:08:04.590 --> 01:08:08.489
+hrishikb@andrew.cmu.edu: please try this out. So that more people looked at it.
+
+552
+01:08:08.930 --> 01:08:16.110
+hrishikb@andrew.cmu.edu: And… One, I mean, somebody discovered, like, they had left the API key in the open.
+
+553
+01:08:16.229 --> 01:08:31.549
+hrishikb@andrew.cmu.edu: I don't think it's related to what I mentioned happened, but then over the weekend, somebody hacked the tracking dashboard, which they had built, to, like, track every single employee under this SPP, and, like, whether or not they installed the tool, and all these stats about them, right?
+
+554
+01:08:31.779 --> 01:08:34.669
+hrishikb@andrew.cmu.edu: And somebody hacked that system to send out
+
+555
+01:08:35.430 --> 01:08:40.449
+hrishikb@andrew.cmu.edu: email, or send out a WebEx message to every single person, like, 400 people.
+
+556
+01:08:40.760 --> 01:08:47.410
+hrishikb@andrew.cmu.edu: And they included in one of the fields, like, they hijacked it with, like, this anti-AI message, like.
+
+557
+01:08:48.130 --> 01:08:53.130
+hrishikb@andrew.cmu.edu: like, AI being forced on us, like, you know. And also, this is the environment where
+
+558
+01:08:53.470 --> 01:09:01.129
+hrishikb@andrew.cmu.edu: I don't know, we keep laying people off every quarter, so it's like, are you gonna use this telemetry to, like, decide who gets on the…
+
+559
+01:09:01.330 --> 01:09:04.839
+hrishikb@andrew.cmu.edu: Layoff list, like… And it's just this… you…
+
+560
+01:09:05.279 --> 01:09:07.470
+hrishikb@andrew.cmu.edu: It's like a… it was like a protest.
+
+561
+01:09:07.670 --> 01:09:13.029
+hrishikb@andrew.cmu.edu: In a sense. That… I think speaks to just, like, this dissent.
+
+562
+01:09:13.229 --> 01:09:21.389
+hrishikb@andrew.cmu.edu: But it's not being addressed, right? It's not being heard, that's why this person… I don't know who it is yet, and hopefully I don't get in big trouble, like, but…
+
+563
+01:09:21.979 --> 01:09:28.589
+hrishikb@andrew.cmu.edu: They're expressing, like, This is… this is not, like, sustainable, this is not good for our products.
+
+564
+01:09:29.040 --> 01:09:34.420
+hrishikb@andrew.cmu.edu: So… That's… that's the environment we're in.
+
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@@ -0,0 +1,2002 @@
+WEBVTT
+
+1
+00:00:00.000 --> 00:00:01.640
+Cory Gwin: Just gonna be the two of you, then?
+
+2
+00:00:02.280 --> 00:00:03.770
+hrishikb@andrew.cmu.edu: Sorry, will you repeat that?
+
+3
+00:00:04.059 --> 00:00:05.389
+Cory Gwin: Just the two of you, then?
+
+4
+00:00:05.870 --> 00:00:11.450
+hrishikb@andrew.cmu.edu: Yup, I'm pretty sure they'll be joining in a few minutes, but I'm not sure… I'm not able to reach them over still as well.
+
+5
+00:00:12.090 --> 00:00:12.710
+Cory Gwin: Okay.
+
+6
+00:00:13.530 --> 00:00:16.990
+Cory Gwin: Alright, how's your project going so far?
+
+7
+00:00:17.700 --> 00:00:24.730
+hrishikb@andrew.cmu.edu: Currently, I think we are at a good pace. A few of the things we are struggling with.
+
+8
+00:00:25.420 --> 00:00:32.530
+hrishikb@andrew.cmu.edu: Currently is… Basically integrating AI with the… non-development aspects.
+
+9
+00:00:33.630 --> 00:00:45.530
+hrishikb@andrew.cmu.edu: Of the project, but we haven't been able to do that successfully. We have been generating diagrams, we've used it for automating our minutes, action items, things like that.
+
+10
+00:00:45.840 --> 00:00:51.270
+hrishikb@andrew.cmu.edu: And we've also, used it to generate our software engineering system.
+
+11
+00:00:51.470 --> 00:00:52.870
+hrishikb@andrew.cmu.edu: Things like that.
+
+12
+00:00:53.650 --> 00:00:56.190
+hrishikb@andrew.cmu.edu: We do have initial artifacts, we have a…
+
+13
+00:00:56.300 --> 00:01:02.480
+hrishikb@andrew.cmu.edu: base… not basic, but we have an initial draft of our requirements documents, we have a risk document ready.
+
+14
+00:01:02.840 --> 00:01:05.440
+hrishikb@andrew.cmu.edu: And there are a couple of points which we are still working on.
+
+15
+00:01:07.230 --> 00:01:07.940
+Cory Gwin: Okay.
+
+16
+00:01:08.750 --> 00:01:12.810
+Cory Gwin: So…
+
+17
+00:01:14.730 --> 00:01:25.290
+Cory Gwin: basically you're doing… let me understand, you're doing, like, interviewing, you're talking to people, you're getting the requirements for what the system's gonna be, and then you're… you are using AI to help you develop some of those documents?
+
+18
+00:01:25.530 --> 00:01:44.480
+hrishikb@andrew.cmu.edu: Right now, we have not reached the interviewing phase, because we were waiting on the client data, and that got laid by a couple of weeks. So we just got the client data last Friday in the evening, so we are currently processing through that. And the requirements that we currently have are based off the meetings we have with clients.
+
+19
+00:01:45.390 --> 00:02:01.400
+hrishikb@andrew.cmu.edu: Like, we made a notional workflow diagram to get on the same page with the clients, to understand what the system they want us to build, and based on the system and the conversation we had with clients, we came up with some initial quality attributes and some initial
+
+20
+00:02:01.640 --> 00:02:05.560
+hrishikb@andrew.cmu.edu: functional requirements. And yes, we…
+
+21
+00:02:05.670 --> 00:02:14.999
+hrishikb@andrew.cmu.edu: we basically used AI to… we fed all the information and use an LLM to generate a well-formatted document for requirements.
+
+22
+00:02:15.840 --> 00:02:16.600
+Cory Gwin: Okay.
+
+23
+00:02:18.250 --> 00:02:25.560
+Cory Gwin: And you said you started working on your software engineering development system. Do you… so what tools do you have available to you? Like…
+
+24
+00:02:25.850 --> 00:02:32.170
+Cory Gwin: What all are you… have in your ecosystem, like GitHub, GitHub Actions, GitLab…
+
+25
+00:02:32.460 --> 00:02:35.790
+Cory Gwin: TrapGPT, what tools do you have at your disposal?
+
+26
+00:02:36.320 --> 00:02:55.569
+hrishikb@andrew.cmu.edu: So, from our side, we… we are currently using, like, people are using different tools. Like, I personally use Gemini and, ChatGPT a lot. I think, Shruta uses Claude, so we're all using those on a personal level, but the client is providing us with,
+
+27
+00:02:55.960 --> 00:03:08.600
+hrishikb@andrew.cmu.edu: a cursor, but that's also currently not able to be completed, so we are waiting on that as well. So, we'll be primarily using a cursor with the client's data to use it to process it and stuff.
+
+28
+00:03:08.960 --> 00:03:10.180
+hrishikb@andrew.cmu.edu: And…
+
+29
+00:03:10.880 --> 00:03:19.310
+hrishikb@andrew.cmu.edu: Yeah, and we will be using GitHub Actions for our testing and deployments, but we haven't set that up yet.
+
+30
+00:03:19.720 --> 00:03:20.420
+Cory Gwin: Okay.
+
+31
+00:03:20.510 --> 00:03:23.910
+hrishikb@andrew.cmu.edu: So one thing, to really think about.
+
+32
+00:03:23.910 --> 00:03:27.890
+Cory Gwin: As you start to develop these documents is,
+
+33
+00:03:27.960 --> 00:03:43.930
+Cory Gwin: how are they… how is the AI using them to build off of itself, right? So, think of it like a harness, right? All these requirements documents that you're making, quality attributes, ADR, ADRs, all these things are information that eventually they…
+
+34
+00:03:44.080 --> 00:03:51.140
+Cory Gwin: they can… Inform the AI of, like, how to build the system, right?
+
+35
+00:03:51.420 --> 00:03:54.279
+Cory Gwin: Just like they inform you. And…
+
+36
+00:03:54.590 --> 00:03:58.539
+Cory Gwin: Can the AI help you if it can't see your decisions?
+
+37
+00:04:01.200 --> 00:04:02.410
+hrishikb@andrew.cmu.edu: Ordinance.
+
+38
+00:04:03.350 --> 00:04:06.900
+hrishikb@andrew.cmu.edu: I guess it needs the entire picture to help us with these things.
+
+39
+00:04:07.190 --> 00:04:21.790
+Cory Gwin: Yeah, so until… until it has access to the things that you have access to, it's… it's just flying blind, right? You need to always be thinking, like, what can… what can my AI see? So, since you're all in separate systems, you've kind of…
+
+40
+00:04:22.500 --> 00:04:32.130
+Cory Gwin: you know, you've taken the AI and you've made it much dumber, because it's not able to contextualize all the work that all of you are doing.
+
+41
+00:04:32.410 --> 00:04:41.570
+Cory Gwin: So I would say one of the first things to do is try to get into a single system, and then try to start feeding as much information into that system as you can, right?
+
+42
+00:04:43.710 --> 00:04:51.490
+Cory Gwin: You know, if you're using chat, chat isn't necessarily as smart as, like, an agent, but once you start using agents,
+
+43
+00:04:51.920 --> 00:04:58.360
+Cory Gwin: They'll start… especially if you're prompting them in the right way, they'll start looking around for information before they do their work.
+
+44
+00:04:58.600 --> 00:05:09.839
+Cory Gwin: And you can have conversations then, and they'll be like, oh, in this ADR you said this, in this ADR you said that. And then you can start to build up, you know, it becomes kind of a,
+
+45
+00:05:11.580 --> 00:05:17.129
+Cory Gwin: like a Sisyphus ball, where it just keeps building and building and building. And that's what you want to…
+
+46
+00:05:17.240 --> 00:05:24.049
+Cory Gwin: start working towards is getting all of this information centralized and inside of some sort of an AI harness.
+
+47
+00:05:24.170 --> 00:05:26.260
+Cory Gwin: So that it can be shared.
+
+48
+00:05:27.440 --> 00:05:30.020
+Cory Gwin: Are you building a…
+
+49
+00:05:30.690 --> 00:05:35.649
+Cory Gwin: Are you building on, like, an existing legacy tool, or are you going to be building something from scratch?
+
+50
+00:05:35.800 --> 00:05:38.380
+hrishikb@andrew.cmu.edu: We'll be building some from scratch, so…
+
+51
+00:05:38.380 --> 00:05:41.630
+Cory Gwin: You have, like, a totally greenfield repo, you can start fresh.
+
+52
+00:05:41.630 --> 00:05:42.659
+hrishikb@andrew.cmu.edu: Yeah, yeah.
+
+53
+00:05:42.840 --> 00:05:59.470
+Cory Gwin: Okay, so that's really nice, because, then you can start… you can just start a repo and start plugging all of these documents in there, and sometimes I, you know, just create, like, an ADR folder, with all of your design records and things like this.
+
+54
+00:05:59.820 --> 00:06:11.389
+Cory Gwin: And just start to organize it all in one place, and then you can hook your AI up to that, right? And you have, like, a central document store that everyone is agreeing upon. Does that make sense?
+
+55
+00:06:11.830 --> 00:06:22.260
+hrishikb@andrew.cmu.edu: Yeah, that does. So for all the documents, we do have a, like, we are currently sorting it on Drive. We don't have a… like, I think we currently use that, but I get your point of using an Agent AI.
+
+56
+00:06:22.360 --> 00:06:26.920
+hrishikb@andrew.cmu.edu: Giving it all the information that we also have access to, and then…
+
+57
+00:06:27.060 --> 00:06:43.209
+hrishikb@andrew.cmu.edu: prompting it in the right way to get the most out of it. And I think since we have a greenfield project, it might be much quicker in helping us develop as well, because there are very few constraints on us.
+
+58
+00:06:43.660 --> 00:06:44.240
+Cory Gwin: Yeah.
+
+59
+00:06:44.780 --> 00:06:57.990
+Cory Gwin: So if you're working in docs, the one thing I will say is that you've got to think about… it sounds like you're gonna be using Cloud Code. You gotta think about how Cloud Code's gonna get access to those things when it's helping you build, right?
+
+60
+00:06:58.330 --> 00:07:01.209
+Cory Gwin: Because if it's… if it's in Docs, it doesn't have access.
+
+61
+00:07:01.620 --> 00:07:02.330
+hrishikb@andrew.cmu.edu: Right.
+
+62
+00:07:03.710 --> 00:07:09.379
+hrishikb@andrew.cmu.edu: Ahmad, we'll probably, yeah, we… I don't think we have acidic plot code.
+
+63
+00:07:09.760 --> 00:07:12.760
+hrishikb@andrew.cmu.edu: We'll probably get access to cursor.
+
+64
+00:07:13.050 --> 00:07:14.279
+Cory Gwin: Okay, whichever one.
+
+65
+00:07:14.280 --> 00:07:14.640
+hrishikb@andrew.cmu.edu: Put down.
+
+66
+00:07:14.640 --> 00:07:28.409
+Cory Gwin: Doesn't matter, same idea. They're all the same thing. Yeah, you just gotta think about, okay, can my agents see my information, right? Like, can it access it? Can it help it help me make decisions using it?
+
+67
+00:07:28.560 --> 00:07:33.470
+Cory Gwin: Otherwise, again, it's a black box, and you're starting fresh every time.
+
+68
+00:07:34.470 --> 00:07:40.750
+hrishikb@andrew.cmu.edu: So what central repo would you suggest? Just to have a new repo and put all of the information in that?
+
+69
+00:07:41.400 --> 00:07:45.340
+Cory Gwin: That's what I do, so I'll create a repo, and then anything that's relevant
+
+70
+00:07:45.390 --> 00:08:05.280
+Cory Gwin: for the agent to know while it's building. Usually that's ADRs, things like that. I'll make sure they're in the repo, so they're alongside of it. And that way, when I'm, you know, creating specs or whatever, creating things that I'm gonna have the agent do for me, I can say, okay, read all the ADRs and make sure that there are none that are, you know.
+
+71
+00:08:05.310 --> 00:08:09.139
+Cory Gwin: Required to think about as part of this implementation.
+
+72
+00:08:09.290 --> 00:08:18.800
+Cory Gwin: And you can spawn sub-agents or whatever to do that, and say, like, if you have any questions about any of the ADRs, come back and we can discuss them.
+
+73
+00:08:19.110 --> 00:08:26.139
+Cory Gwin: And then that information is really easy. Like, agents are really good at looking at files. Like, they're really good at files.
+
+74
+00:08:26.290 --> 00:08:30.130
+Cory Gwin: So if it's right there in a file, it's… Really good at it.
+
+75
+00:08:33.490 --> 00:08:38.730
+Cory Gwin: The other thing to think about is, you know, you're… you're gonna…
+
+76
+00:08:39.250 --> 00:08:47.159
+Cory Gwin: You're all going to be working in a repo with Cursor, so, when you start developing, you'll want to think about,
+
+77
+00:08:47.360 --> 00:09:01.080
+Cory Gwin: you know, your, your feedback loop for your agent. So how is your agent able to validate changes? How is it making sure it's maintaining your code quality? How does it make sure it's, you know, got everything set up the way that you want it?
+
+78
+00:09:01.420 --> 00:09:09.810
+Cory Gwin: so that you get what you want for output. So… Okay, share my screen.
+
+79
+00:09:17.230 --> 00:09:22.239
+Cory Gwin: Let's see… I'm gonna share the screen.
+
+80
+00:09:22.900 --> 00:09:37.110
+Cory Gwin: If I look… I'm gonna open… So… For example… Let's find a good example.
+
+81
+00:09:44.610 --> 00:09:46.829
+Cory Gwin: So I have this app builder skill.
+
+82
+00:09:47.060 --> 00:09:56.110
+Cory Gwin: You know, and you can have an agent.md file or whatever. But, like, start out with, like, one shared agent for all of you.
+
+83
+00:09:57.770 --> 00:10:09.900
+Cory Gwin: And just, like, teach it how your code is organized, how you want things laid out, and then make sure that it's always running, basically, your scripts that you want it to run to ensure code quality, right?
+
+84
+00:10:09.900 --> 00:10:18.739
+Cory Gwin: So this is… this is, like, it's… it's feedback. You always have to be thinking about, like, what can my agent see, right? Because if it can't see anything, then it's completely blind.
+
+85
+00:10:18.940 --> 00:10:29.119
+Cory Gwin: So, like, how do I get it lint information? How do I get it test information? How do I check all my types? All these sorts of things. And the more rigid you are with
+
+86
+00:10:29.290 --> 00:10:40.420
+Cory Gwin: code quality stuff, right? Like, ensuring that you're using types everywhere, ensuring that you're using really good linters. It forces the agent to build in a style that you're looking for, right?
+
+87
+00:10:40.470 --> 00:10:51.310
+Cory Gwin: So getting these sorts of style things set up early, and getting code organization in place early pays dividends, because then you have this
+
+88
+00:10:51.390 --> 00:11:06.459
+Cory Gwin: feedback cycle, where they… you can… you can feed information into the agent. The other thing to think really a lot about, you know how I said make sure that you have all of your docs in place, so that it can see all, like, all of your decisions that you've already made?
+
+89
+00:11:06.460 --> 00:11:11.410
+Cory Gwin: Also, make sure you have really good loggers in place, so that if problems are happening.
+
+90
+00:11:11.420 --> 00:11:18.979
+Cory Gwin: during the test suites, I can see what's… what's not working. And if you're building any kind of a UI, make sure you get something like…
+
+91
+00:11:19.030 --> 00:11:35.180
+Cory Gwin: playwright set up early so that it can interact with the web browser, right? Like, when you're working with agents to… in your engineering system, you really have to think about it from, like, development from its perspective. You're building a toolset for the agent to be able to help you.
+
+92
+00:11:35.400 --> 00:11:36.519
+Cory Gwin: Does that make sense?
+
+93
+00:11:37.010 --> 00:11:38.160
+hrishikb@andrew.cmu.edu: Yeah, that is.
+
+94
+00:11:39.450 --> 00:11:43.260
+Cory Gwin: And if it can't see it, it can't help you, right?
+
+95
+00:11:43.800 --> 00:11:49.180
+hrishikb@andrew.cmu.edu: But if we donate all the data again and again only slows down the process, and we might miss out.
+
+96
+00:11:49.660 --> 00:11:51.249
+hrishikb@andrew.cmu.edu: Sometimes, you know.
+
+97
+00:11:51.250 --> 00:11:51.830
+Cory Gwin: Yep.
+
+98
+00:11:53.660 --> 00:12:01.109
+Cory Gwin: Yep, and then, you know, there's lots of other things you can do. Since you're in a shared repo, start setting up slash commands that you can share.
+
+99
+00:12:01.110 --> 00:12:18.369
+Cory Gwin: you know, start building your agent files together, and just make sure you're getting your tools in place early, and you really want to focus on organization. I can't say that enough, you really want to focus on organization of your codebase early, because the LLM's going to copy what you set up.
+
+100
+00:12:18.970 --> 00:12:22.160
+Cory Gwin: And if you set up bad patterns, you're gonna make a mess.
+
+101
+00:12:22.930 --> 00:12:25.549
+Cory Gwin: So, really clean patterns.
+
+102
+00:12:26.670 --> 00:12:31.609
+hrishikb@andrew.cmu.edu: What do you mean exactly by, clean patterns of the repo?
+
+103
+00:12:31.780 --> 00:12:34.979
+hrishikb@andrew.cmu.edu: Like, the file structure, or…
+
+104
+00:12:34.980 --> 00:12:48.800
+Cory Gwin: file structures, how you're organizing code, like, if you're using services, you know, are you using an MVC pattern, you know, what sorts of… what sorts of patterns are you using, and make sure that they're well-defined.
+
+105
+00:12:50.310 --> 00:12:50.950
+hrishikb@andrew.cmu.edu: Okay.
+
+106
+00:12:53.590 --> 00:12:54.720
+Cory Gwin: That makes sense.
+
+107
+00:12:54.720 --> 00:12:56.270
+hrishikb@andrew.cmu.edu: Yeah, that does make sense, yeah.
+
+108
+00:12:56.800 --> 00:12:57.790
+hrishikb@andrew.cmu.edu: No doubt.
+
+109
+00:12:58.530 --> 00:13:02.630
+Cory Gwin: Yeah. Yeah.
+
+110
+00:13:05.200 --> 00:13:07.970
+Cory Gwin: Alright, what else, what else have you guys been thinking about?
+
+111
+00:13:10.140 --> 00:13:15.660
+hrishikb@andrew.cmu.edu: Currently our prime focus has been on the ML module that we have to build.
+
+112
+00:13:15.920 --> 00:13:16.600
+Cory Gwin: Okay.
+
+113
+00:13:16.600 --> 00:13:23.880
+hrishikb@andrew.cmu.edu: So, as he, got the data recently, I think Ashita has run some tests on ML modules. Ashita, if you wanna…
+
+114
+00:13:24.420 --> 00:13:25.470
+hrishikb@andrew.cmu.edu: Tell us all that.
+
+115
+00:13:26.580 --> 00:13:34.959
+Cory Gwin: So… Oh, okay. Yeah, so you… remind me what your project was. Again, you're… you're building a machine learning model for…
+
+116
+00:13:35.700 --> 00:13:42.660
+hrishikb@andrew.cmu.edu: I'll just quickly share my screen, I'll show you the brief of the problem and the concept, so you get a better picture.
+
+117
+00:13:43.100 --> 00:13:43.730
+Cory Gwin: Okay.
+
+118
+00:13:52.210 --> 00:13:58.539
+hrishikb@andrew.cmu.edu: So, basically, the problem we currently have is that our client EPads,
+
+119
+00:13:59.220 --> 00:14:07.099
+hrishikb@andrew.cmu.edu: the process of getting information from their vendors. Like, this is basically, like, kind of like Amazon for HVAC parts.
+
+120
+00:14:07.300 --> 00:14:08.399
+Cory Gwin: Yeah, solid.
+
+121
+00:14:08.900 --> 00:14:11.959
+hrishikb@andrew.cmu.edu: The problem right now is that
+
+122
+00:14:12.410 --> 00:14:30.079
+hrishikb@andrew.cmu.edu: the way they acquire data from their vendors is that the vendors would send them emails, PDF, CSV, there's no proper format in doing all these things. So, what they currently want us to do is to eliminate all of this. So, currently, catalog team does all of it. It's a completely manual process.
+
+123
+00:14:30.080 --> 00:14:35.780
+hrishikb@andrew.cmu.edu: They'll read the emails, they'll go through the websites to get the data, and things like that.
+
+124
+00:14:36.480 --> 00:14:39.099
+hrishikb@andrew.cmu.edu: So, right now, what they want is an automated process.
+
+125
+00:14:39.510 --> 00:14:40.769
+hrishikb@andrew.cmu.edu: For this to be done.
+
+126
+00:14:41.020 --> 00:14:45.940
+hrishikb@andrew.cmu.edu: So, what we are building is, we'll… Wait, I'll just,
+
+127
+00:14:46.810 --> 00:14:52.700
+hrishikb@andrew.cmu.edu: Let me show you the, architecture flow diagram, I'll be… Much easier to understand.
+
+128
+00:14:53.390 --> 00:14:59.820
+Cory Gwin: Okay, so you're taking just a whole bunch of… randomly formatted.
+
+129
+00:15:00.430 --> 00:15:03.659
+Cory Gwin: And you're trying to parse it out into a structured format.
+
+130
+00:15:04.260 --> 00:15:06.959
+hrishikb@andrew.cmu.edu: Yeah, that would be the first step.
+
+131
+00:15:07.780 --> 00:15:09.980
+hrishikb@andrew.cmu.edu: So, pair…
+
+132
+00:15:10.160 --> 00:15:24.640
+hrishikb@andrew.cmu.edu: I hope you can see this. Here we have the initial document that we'll be getting from whatever source, like vendors, basically. Then we have an ingestion Gateway. We'll try to process these via OCR, via different methods.
+
+133
+00:15:24.640 --> 00:15:30.390
+hrishikb@andrew.cmu.edu: To get them into an intermediate layer, which will have the key value pairs.
+
+134
+00:15:30.440 --> 00:15:33.419
+hrishikb@andrew.cmu.edu: Then our ML model comes into the picture.
+
+135
+00:15:33.670 --> 00:15:36.169
+hrishikb@andrew.cmu.edu: Which will be assigning confidence scores.
+
+136
+00:15:36.300 --> 00:15:55.579
+hrishikb@andrew.cmu.edu: to different attributes, if they map well to the product or not, and basically giving each attribute a confidence score. Then, if the confidence score is high enough, it goes to auto-accept, and we push it to our intermediate publish layer, after which it will go directly to the
+
+137
+00:15:55.870 --> 00:15:58.370
+hrishikb@andrew.cmu.edu: Sick.
+
+138
+00:15:58.780 --> 00:16:00.570
+hrishikb@andrew.cmu.edu: After it, it'll go directly
+
+139
+00:16:00.680 --> 00:16:14.599
+hrishikb@andrew.cmu.edu: to this, which is PIMS. So, our starting endpoints are… the starting point for us is the suppliers, where you get the initial files from, and our systems endpoint is PIMS, which is the Product Information Management System, PolyPaths.
+
+140
+00:16:14.750 --> 00:16:33.759
+hrishikb@andrew.cmu.edu: So, after the intermediate table, we push the data to pins, and that's our entire process done. But if the corner score on the attributes is low enough, then we have a human loop. So, they will review the processing, the scores, and then they'll take a decision on it.
+
+141
+00:16:35.060 --> 00:16:37.610
+Cory Gwin: How many examples do you have of these…
+
+142
+00:16:38.920 --> 00:16:44.120
+Cory Gwin: like, files and emails and things. Did they give you a decent set of examples?
+
+143
+00:16:44.120 --> 00:16:57.529
+hrishikb@andrew.cmu.edu: They have given us a lot of files. I'm not exactly sure of the numbers. We haven't… they have sent us around 27 files. We haven't been able to go through all of them yet, but they do contain example catalogs.
+
+144
+00:16:57.640 --> 00:16:59.800
+hrishikb@andrew.cmu.edu: Pdfs, CSVs…
+
+145
+00:17:00.310 --> 00:17:10.310
+Ashritha: Yeah, so right now, like, whatever the limited set of data that they're given, those sum up to, like, around 500, PIMs, attribute names, yeah.
+
+146
+00:17:10.619 --> 00:17:11.359
+Cory Gwin: Okay.
+
+147
+00:17:13.049 --> 00:17:16.369
+Cory Gwin: Well… If… if I was you.
+
+148
+00:17:16.489 --> 00:17:20.179
+Cory Gwin: what I would do is I would actually just…
+
+149
+00:17:20.409 --> 00:17:25.189
+Cory Gwin: prototype an LLM doing this attribute prediction service, and see how it does.
+
+150
+00:17:26.520 --> 00:17:27.089
+hrishikb@andrew.cmu.edu: Okay.
+
+151
+00:17:28.109 --> 00:17:37.749
+Cory Gwin: it's really cheap to do a prototype there. It's a good way to use AI, and I bet an LLM's gonna be pretty good at it, if not great at it, because it's just text.
+
+152
+00:17:38.339 --> 00:17:40.889
+Cory Gwin: Manipulation.
+
+153
+00:17:41.750 --> 00:17:43.830
+Cory Gwin: You may not have to train anything.
+
+154
+00:17:46.120 --> 00:17:50.469
+hrishikb@andrew.cmu.edu: So you're saying after we parse the data, we can just use an LLM to…
+
+155
+00:17:50.770 --> 00:17:57.579
+hrishikb@andrew.cmu.edu: basically give it what our ML model is doing, replace that with an agent.
+
+156
+00:17:58.470 --> 00:17:59.230
+hrishikb@andrew.cmu.edu: Tell them more.
+
+157
+00:17:59.230 --> 00:18:03.189
+Cory Gwin: Yeah, yeah, just… or you have the attribute prediction service.
+
+158
+00:18:03.710 --> 00:18:11.369
+Cory Gwin: I would… I would… you can really cheaply prototype that with an AI, right? Like, here's a file of…
+
+159
+00:18:11.520 --> 00:18:17.400
+Cory Gwin: randomly… You know… formatted data.
+
+160
+00:18:17.780 --> 00:18:20.079
+Cory Gwin: Pull it out into this format.
+
+161
+00:18:21.110 --> 00:18:26.050
+Cory Gwin: I bet it's gonna do pretty darn good. And that's a really cheap thing to prototype with AI.
+
+162
+00:18:30.510 --> 00:18:33.999
+Cory Gwin: And if it works, you don't need to build anything for that layer.
+
+163
+00:18:34.810 --> 00:18:37.249
+Cory Gwin: You just hook up Lang Chain Doof.
+
+164
+00:18:37.370 --> 00:18:44.240
+Cory Gwin: some LLM, and the thing to prototype would be, like, you know, how cheap of a model can do this well.
+
+165
+00:18:49.390 --> 00:19:07.819
+hrishikb@andrew.cmu.edu: I think we didn't, think in that direction, because the client's, I think, requirement was that they need an ML system to do the content scoring, so I guess we didn't, try to question that, but you do make a good point. I think it'll be much easier to do, and more economic as well.
+
+166
+00:19:08.440 --> 00:19:15.090
+hrishikb@andrew.cmu.edu: Plus, I am pretty sure it's gonna be more accurate than any ML system that we can build to do this.
+
+167
+00:19:15.360 --> 00:19:22.059
+Cory Gwin: I would… I would imagine so. It's just… it's just text, and you can… I mean, you can prototype this and…
+
+168
+00:19:22.550 --> 00:19:24.009
+Cory Gwin: I don't know, an hour.
+
+169
+00:19:24.490 --> 00:19:38.539
+Ashritha: Yeah, but Corey, quick question. So, is the suggestion that I've made about the LLM usage, is it only for the initial prototype, or for the actual, feature itself? Because, like, when…
+
+170
+00:19:38.790 --> 00:19:50.149
+Ashritha: Because when… because when you use LLMs, it has to deal with the tokens, right? Like, I don't know, what limits they have, over there.
+
+171
+00:19:50.150 --> 00:20:02.370
+Cory Gwin: Sure, so you can run small LLMs on a GPU for relatively cheap. I would start by prototyping this on, you know, whatever LLM you have available to you.
+
+172
+00:20:03.190 --> 00:20:07.860
+Cory Gwin: you know, I think you said you had access to Gemini?
+
+173
+00:20:08.080 --> 00:20:10.659
+Cory Gwin: And just see how Gemini does, right?
+
+174
+00:20:10.850 --> 00:20:23.649
+Cory Gwin: Like, start on a mid-sized Gemini model, and then go down to a cheaper Gemini model, and then try to… try to run Llama locally for free, because it's an open source model, and see how that does, right?
+
+175
+00:20:23.650 --> 00:20:24.250
+Ashritha: Okay.
+
+176
+00:20:24.250 --> 00:20:27.379
+Cory Gwin: And create a scorecard of how a model does.
+
+177
+00:20:27.650 --> 00:20:30.480
+Cory Gwin: You know, you have… you have a whole… you have a whole bunch of…
+
+178
+00:20:31.140 --> 00:20:44.279
+Cory Gwin: models that you have to pay as a service for, which are more expensive, and then you have a whole bunch of open-source Llama-type models on Hugging Face that are out-of-the-box LLMs that might be good enough for what you need to do.
+
+179
+00:20:45.110 --> 00:20:45.680
+Ashritha: ship.
+
+180
+00:20:45.680 --> 00:20:52.069
+Cory Gwin: But it's… the thing that you can prototype here with AI is AI, right? Like.
+
+181
+00:20:52.460 --> 00:20:58.190
+Cory Gwin: If you can save yourself training your own machine learning algorithm here, do it.
+
+182
+00:20:59.190 --> 00:20:59.850
+Ashritha: I'm done.
+
+183
+00:21:01.190 --> 00:21:13.420
+Cory Gwin: And the prototype is, you know, maybe you build some sort of evaluation system for evaluating, you know, which LLMs perform the best, and then you can show that.
+
+184
+00:21:14.460 --> 00:21:15.000
+Ashritha: Okay.
+
+185
+00:21:15.800 --> 00:21:16.759
+Cory Gwin: That makes sense.
+
+186
+00:21:17.040 --> 00:21:17.360
+Ashritha: Yeah.
+
+187
+00:21:18.630 --> 00:21:25.480
+Cory Gwin: But yeah, there's a bunch, Let's see…
+
+188
+00:21:27.980 --> 00:21:29.930
+Cory Gwin: Mama comes to mind, but there's a bunch.
+
+189
+00:21:30.160 --> 00:21:34.050
+Cory Gwin: Deepseek, you could try DeepSeek.
+
+190
+00:21:36.220 --> 00:21:37.520
+Cory Gwin: And there's others.
+
+191
+00:21:37.890 --> 00:21:41.509
+Cory Gwin: You guys familiar with, Hugging Face?
+
+192
+00:21:41.940 --> 00:21:42.600
+Ashritha: Yep.
+
+193
+00:21:42.600 --> 00:21:45.789
+Cory Gwin: Okay, so you can find all these on Hugging Face.
+
+194
+00:21:45.790 --> 00:21:46.380
+Ashritha: Okay.
+
+195
+00:21:46.750 --> 00:21:50.610
+Cory Gwin: But I would… I would just start with something easy, just, you know.
+
+196
+00:21:51.240 --> 00:21:57.890
+Cory Gwin: Take… put together a sample set of data that is random from the distribution that they gave you.
+
+197
+00:21:58.240 --> 00:22:01.889
+Cory Gwin: And then try throwing it at Gemini models.
+
+198
+00:22:02.120 --> 00:22:03.519
+Cory Gwin: And see how it does.
+
+199
+00:22:03.690 --> 00:22:07.440
+Cory Gwin: You know, and try to figure out what the format you want coming out to be is, of course.
+
+200
+00:22:07.990 --> 00:22:08.630
+Ashritha: Okay.
+
+201
+00:22:10.990 --> 00:22:16.740
+Cory Gwin: Alright, coming back to your engineering setup,
+
+202
+00:22:17.050 --> 00:22:19.049
+Cory Gwin: It does look like in… is this…
+
+203
+00:22:21.060 --> 00:22:27.270
+Cory Gwin: This is all a self-contained system, or are you integrating with other external systems?
+
+204
+00:22:27.800 --> 00:22:35.300
+hrishikb@andrew.cmu.edu: No, this will be self-contained. The only system that we have to integrate with is the PIMS, so that's also an internal system.
+
+205
+00:22:35.560 --> 00:22:37.300
+Cory Gwin: Okay, how do you integrate with that?
+
+206
+00:22:38.110 --> 00:22:43.779
+hrishikb@andrew.cmu.edu: So, it'll have, predefined schemas, which we'll have to populate the information in.
+
+207
+00:22:44.110 --> 00:22:44.790
+Cory Gwin: Okay.
+
+208
+00:22:45.190 --> 00:22:53.780
+Cory Gwin: So… Whatever you can do to get that schema information typed into your agent early is going to.
+
+209
+00:22:53.780 --> 00:22:54.400
+hrishikb@andrew.cmu.edu: Hold on.
+
+210
+00:22:54.540 --> 00:23:03.069
+Cory Gwin: Right? Because then your agent can see all of its types. I don't know if they use something like, like a protocol buffer or something, but agents like those sort of type situations.
+
+211
+00:23:05.370 --> 00:23:11.569
+hrishikb@andrew.cmu.edu: by… Fed into the agent early, you mean earlier in the lifecycle of the entire system?
+
+212
+00:23:11.760 --> 00:23:19.870
+Cory Gwin: Yeah, so define… if you can define contracts between systems as requirements early on, and have them well-defined…
+
+213
+00:23:20.270 --> 00:23:25.790
+Cory Gwin: Even better if they're defined as software, like, with some sort of a protocol box system.
+
+214
+00:23:25.940 --> 00:23:33.909
+Cory Gwin: That's gonna help you, because then your agent has a contract, and you don't have to, like, try to explain it to it.
+
+215
+00:23:34.530 --> 00:23:35.200
+hrishikb@andrew.cmu.edu: Okay.
+
+216
+00:23:35.530 --> 00:23:36.460
+Cory Gwin: Does that make sense?
+
+217
+00:23:36.740 --> 00:23:37.789
+hrishikb@andrew.cmu.edu: Yeah, it does.
+
+218
+00:23:37.790 --> 00:23:41.560
+Cory Gwin: Like, the place where a lot of,
+
+219
+00:23:42.740 --> 00:23:51.640
+Cory Gwin: place where a lot of agentic-built software falls over is at the edges between systems, right? Because I can't see into them.
+
+220
+00:23:51.840 --> 00:23:59.459
+Cory Gwin: And it doesn't… it doesn't know their limitations, right? So, like, you have to be able to explain to it things like.
+
+221
+00:24:00.090 --> 00:24:03.040
+hrishikb@andrew.cmu.edu: you're gonna get rate limited, right? Or, like…
+
+222
+00:24:03.040 --> 00:24:09.260
+Cory Gwin: If you hit it too hard, you're gonna knock it over, things like that, right? Because every system has limitations.
+
+223
+00:24:09.410 --> 00:24:13.800
+Cory Gwin: And, that's a black… that's a black hole right there.
+
+224
+00:24:13.970 --> 00:24:23.940
+Cory Gwin: So anything you can learn about that system and get outlined is going to help you as a developer, right? Because you'll understand the system you're integrating with, but it's also going to help you define work for the agent.
+
+225
+00:24:30.730 --> 00:24:36.200
+hrishikb@andrew.cmu.edu: Alright. Yeah, that is something we should definitely, Take a look at.
+
+226
+00:24:36.310 --> 00:24:40.169
+hrishikb@andrew.cmu.edu: We haven't really explored the… using an LLM for our…
+
+227
+00:24:40.560 --> 00:24:43.830
+hrishikb@andrew.cmu.edu: like, instead of the ML model, we have been very myopic about that.
+
+228
+00:24:43.940 --> 00:24:47.330
+hrishikb@andrew.cmu.edu: So that is something that's… We should definitely try.
+
+229
+00:24:48.860 --> 00:24:56.520
+Cory Gwin: How about this approval decision audit trail? Like, this whole thing? This is gonna require some sort of a UI?
+
+230
+00:24:59.340 --> 00:25:05.009
+Ashritha: No, I think that's gonna be an auto-accept, thing, yeah, based on the.
+
+231
+00:25:05.010 --> 00:25:07.840
+Cory Gwin: Your human review plus UI.
+
+232
+00:25:11.050 --> 00:25:12.520
+Ashritha: Oh, okay, okay.
+
+233
+00:25:13.280 --> 00:25:15.200
+Cory Gwin: Yeah, for all the low confidence stuff.
+
+234
+00:25:15.830 --> 00:25:24.100
+hrishikb@andrew.cmu.edu: Yeah, yeah, this will, require, some sort of a UI with sufficient information that the human can make a…
+
+235
+00:25:24.120 --> 00:25:25.719
+Cory Gwin: Okay. Accurate decision.
+
+236
+00:25:26.000 --> 00:25:31.380
+Cory Gwin: So this is another place where the, you know, working with an AI for an early prototype can really pay dividends.
+
+237
+00:25:31.490 --> 00:25:43.560
+Cory Gwin: You know, you can… you can work up in an afternoon with the client, you can work up 4 or 5 different UI examples and try to get an, you know, an understanding of what they want their workflow to look like.
+
+238
+00:25:43.960 --> 00:25:51.329
+Cory Gwin: And if you can visualize what their workflow looks like early, and give them examples they can play with.
+
+239
+00:25:51.470 --> 00:26:01.499
+Cory Gwin: then you're going to understand all the pieces that you're going to want to put in place, right? Like, maybe they want filters. I don't know, you know? Like, how does this person want to work with this data?
+
+240
+00:26:04.930 --> 00:26:05.610
+hrishikb@andrew.cmu.edu: Okay.
+
+241
+00:26:05.820 --> 00:26:08.459
+Cory Gwin: Does that make sense?
+
+242
+00:26:10.230 --> 00:26:20.909
+hrishikb@andrew.cmu.edu: Yeah, it is, so basically, we need to get some more information out of the clients on how, like, what sort of data they require in this aspect.
+
+243
+00:26:21.620 --> 00:26:26.189
+Cory Gwin: I would think so, right? Because any sort of a UI is usually data-driven.
+
+244
+00:26:26.190 --> 00:26:30.439
+hrishikb@andrew.cmu.edu: And it's like, okay, well, how are you using this data to make decisions?
+
+245
+00:26:30.440 --> 00:26:47.440
+Cory Gwin: Okay? Like, that's a… that's a good step forward, and then how can I use AI to help me have a conversation with you about, you know, well, here's an example of how you could work with this data. Is this what you want? No, I want to do this. And you can prototype these things really fast and have a really quick feedback cycle.
+
+246
+00:26:48.080 --> 00:26:48.740
+hrishikb@andrew.cmu.edu: Okay.
+
+247
+00:26:50.320 --> 00:26:51.730
+hrishikb@andrew.cmu.edu: Yeah, economism.
+
+248
+00:26:51.840 --> 00:26:53.210
+hrishikb@andrew.cmu.edu: Yeah, it does, it does.
+
+249
+00:26:57.440 --> 00:27:01.439
+hrishikb@andrew.cmu.edu: We'll try to get in touch with the clients and get some more information on what they expect.
+
+250
+00:27:01.990 --> 00:27:04.780
+Cory Gwin: It'll be in this phase, and then we can work accordingly.
+
+251
+00:27:05.160 --> 00:27:17.929
+Cory Gwin: Yeah, I mean, the best thing to do is try to, you know, you have this attribute prediction service. If you can understand exactly what the data coming out of that is going to be shaped like.
+
+252
+00:27:18.200 --> 00:27:24.980
+Cory Gwin: like, that's gonna inform a lot about your UI, right? Because most UIs are, like, just a view over that data.
+
+253
+00:27:25.780 --> 00:27:29.140
+Cory Gwin: So you'll know what you need to show.
+
+254
+00:27:29.550 --> 00:27:35.569
+Cory Gwin: And then, you know, you can add filters and stuff based on how they want to interact with it.
+
+255
+00:27:38.270 --> 00:27:50.160
+hrishikb@andrew.cmu.edu: So, I think the data that we currently have thought about, which will be coming out of the protection service, would be, it'll have all the attributes for the particular product, and each attribute will have a confidence score.
+
+256
+00:27:50.630 --> 00:27:57.910
+hrishikb@andrew.cmu.edu: And, like, the… it should probably be sorted from highest to lowest, and the lowest ones
+
+257
+00:27:58.920 --> 00:28:07.709
+hrishikb@andrew.cmu.edu: I guess the user will have to check the lowest ones to see if it is correct or not, or what the issue is, before they proceed.
+
+258
+00:28:08.210 --> 00:28:08.870
+Cory Gwin: Yeah.
+
+259
+00:28:09.530 --> 00:28:10.260
+Cory Gwin: No.
+
+260
+00:28:11.160 --> 00:28:16.849
+Cory Gwin: Okay. Can I show you guys one more thing? I'm just thinking about this, like, you know,
+
+261
+00:28:17.490 --> 00:28:20.559
+Cory Gwin: this LLM piece that we're talking about.
+
+262
+00:28:21.610 --> 00:28:22.860
+Cory Gwin: Share my screen.
+
+263
+00:28:23.460 --> 00:28:28.490
+Cory Gwin: So…
+
+264
+00:28:31.510 --> 00:28:42.519
+Cory Gwin: if I look at this… this is a… this is a Slack thing that I've been working on with some people. It's kind of a hack project we're doing, but we wanted to see if we could…
+
+265
+00:28:42.860 --> 00:28:48.959
+Cory Gwin: Use an LLM to extract information out of a Slack message.
+
+266
+00:28:49.560 --> 00:28:56.350
+Cory Gwin: And, you know, with simple prompts,
+
+267
+00:28:57.160 --> 00:29:05.510
+Cory Gwin: we're actually able to pull the information that we want out of a Slack message, like get the incident state and ID,
+
+268
+00:29:05.670 --> 00:29:10.920
+Cory Gwin: And then… set it and store it, and just tell the LLM to store it.
+
+269
+00:29:11.220 --> 00:29:19.700
+Cory Gwin: And it will do all of that, so we can extract… You know, monitoring information.
+
+270
+00:29:19.970 --> 00:29:23.590
+Cory Gwin: And then store it. We can identify a service.
+
+271
+00:29:24.020 --> 00:29:30.319
+Cory Gwin: So sometimes with just simple prompting, or,
+
+272
+00:29:30.950 --> 00:29:35.410
+Cory Gwin: You know, instructions about how to parse things out, or what to look for.
+
+273
+00:29:35.530 --> 00:29:37.580
+Cory Gwin: And then a tool.
+
+274
+00:29:37.940 --> 00:29:41.750
+Cory Gwin: Saying, like, call this… Let's had that.
+
+275
+00:29:41.930 --> 00:29:46.779
+Cory Gwin: You can get what you want. Another thing that we do…
+
+276
+00:29:48.750 --> 00:29:54.740
+Cory Gwin: Is we'll have it, give it an output template, and say, okay, your output should look like this.
+
+277
+00:29:54.960 --> 00:29:55.850
+Cory Gwin: Right?
+
+278
+00:29:55.950 --> 00:29:58.310
+Cory Gwin: And it'll just generate that.
+
+279
+00:29:58.930 --> 00:30:01.190
+Cory Gwin: And then you can plug it into something else.
+
+280
+00:30:01.610 --> 00:30:05.159
+Cory Gwin: So that might be the path forward.
+
+281
+00:30:05.590 --> 00:30:08.310
+Cory Gwin: for your LLM-based pros, I think.
+
+282
+00:30:12.870 --> 00:30:17.590
+hrishikb@andrew.cmu.edu: I think we have to try our hands on… prototyping and using agentics.
+
+283
+00:30:17.710 --> 00:30:24.120
+Cory Gwin: Yeah, and then I think for that workflow, like, the low confidence thing would be, like, were you unable to parse it?
+
+284
+00:30:24.310 --> 00:30:27.050
+Cory Gwin: Like, if you don't think you were able to parse it.
+
+285
+00:30:27.320 --> 00:30:30.330
+Cory Gwin: You know, just put it in a UI for a human to do.
+
+286
+00:30:31.520 --> 00:30:32.230
+hrishikb@andrew.cmu.edu: Right.
+
+287
+00:30:36.290 --> 00:30:43.810
+Ashritha: Quick question, so here, is the LLM responsible for…
+
+288
+00:30:44.190 --> 00:30:59.889
+Ashritha: getting the information and, like, I mean, putting it into some form of key-value pairs, or do you want… do you suggest that it should also, like, assign attitude of confidence score to it? It should do both?
+
+289
+00:31:00.560 --> 00:31:01.740
+Cory Gwin: It can do both.
+
+290
+00:31:02.580 --> 00:31:03.800
+Ashritha: Okay, okay.
+
+291
+00:31:04.560 --> 00:31:14.159
+hrishikb@andrew.cmu.edu: I think just before the call, you were having a discussion about the same thing, because we thought we might need a smaller ML model as well, after we pass the data from the different formats.
+
+292
+00:31:14.360 --> 00:31:23.940
+hrishikb@andrew.cmu.edu: So, let's say, they're selling an iPhone, and one vendor has something called a screen size, and one has, let's say, display size.
+
+293
+00:31:24.050 --> 00:31:30.299
+hrishikb@andrew.cmu.edu: They have the same thing, but they have same… it'll be a different key for both of them, but the information is similar.
+
+294
+00:31:30.520 --> 00:31:34.499
+Cory Gwin: So you might just be able to tell an LLM that, and I might just be able to figure it out.
+
+295
+00:31:36.200 --> 00:31:38.380
+Cory Gwin: You know, they're pretty good at that sort of thing.
+
+296
+00:31:39.530 --> 00:31:49.330
+Cory Gwin: the… so an LLM, when it emits its, its findings, it… it returns a stat sig.
+
+297
+00:31:49.490 --> 00:31:53.740
+Cory Gwin: Which is basically, how confident am I that this is correct?
+
+298
+00:31:54.270 --> 00:31:58.389
+Cory Gwin: So that is something that you get in the response back from the LLM already.
+
+299
+00:32:01.190 --> 00:32:03.000
+hrishikb@andrew.cmu.edu: You just have to parse it out.
+
+300
+00:32:04.280 --> 00:32:08.189
+hrishikb@andrew.cmu.edu: Maybe we can use that as a metric also, how confident the LLM is, yeah.
+
+301
+00:32:09.170 --> 00:32:13.160
+Cory Gwin: Yeah, that's a… that's an… that's an element that is a part of the response.
+
+302
+00:32:13.540 --> 00:32:15.980
+Cory Gwin: So that's another thing you don't have to train it to do.
+
+303
+00:32:18.160 --> 00:32:18.800
+hrishikb@andrew.cmu.edu: Right.
+
+304
+00:32:21.950 --> 00:32:33.179
+Cory Gwin: Like I said, I would prototype it, and I would… I would start out with a big model, and then just work my way down, and see how good it did, and make sure you have, like, a reasonably sized dataset.
+
+305
+00:32:33.610 --> 00:32:38.399
+Cory Gwin: you can even, you know, create, like, a… like, a loop.
+
+306
+00:32:38.740 --> 00:32:42.060
+Cory Gwin: Basically, around an LLM.
+
+307
+00:32:42.480 --> 00:32:46.440
+Cory Gwin: And just have it spit out,
+
+308
+00:32:47.190 --> 00:32:51.110
+Cory Gwin: This results into some sort of a file or something as you go.
+
+309
+00:32:52.700 --> 00:32:57.629
+Cory Gwin: You could even… you could even employ, like, an LLM as a judge on the other side to say, like.
+
+310
+00:32:57.740 --> 00:32:59.120
+Cory Gwin: How did it do
+
+311
+00:32:59.470 --> 00:33:05.340
+Cory Gwin: And if the… if the LLM on the other side says really bad, then you look at it.
+
+312
+00:33:05.550 --> 00:33:06.560
+Cory Gwin: Yeah.
+
+313
+00:33:18.650 --> 00:33:22.000
+Cory Gwin: Alright, that's a lot. You guys have questions about anything?
+
+314
+00:33:25.250 --> 00:33:27.380
+hrishikb@andrew.cmu.edu: One thing you mentioned about having…
+
+315
+00:33:27.570 --> 00:33:40.980
+hrishikb@andrew.cmu.edu: like, NLM running a loop, I'm not very sure how we would do that, for it to generate, let's say, to… like, we have a dataset of, I don't know, 30 entries. If you want to extrapolate that to make it into 300,
+
+316
+00:33:41.770 --> 00:33:43.160
+hrishikb@andrew.cmu.edu: Or 3,000…
+
+317
+00:33:43.650 --> 00:33:46.660
+Cory Gwin: Yeah, so every LLM has an API available.
+
+318
+00:33:47.620 --> 00:33:49.350
+Cory Gwin: You can call them.
+
+319
+00:33:50.840 --> 00:34:08.630
+Cory Gwin: you can look up, you know, if you're talking about Gemini, you can call Gemini. Google has an API available, so you would just take your examples and put them into some sort of a, like, CSV file or something, you know, whatever format makes sense for what you have.
+
+320
+00:34:09.460 --> 00:34:23.639
+Cory Gwin: write a Python script that basically loops through that CSV file and just sends them into the LLM, and then you would have the LLM's output written into some sort of structured format, I would imagine, because that's what you want coming out of them.
+
+321
+00:34:23.889 --> 00:34:27.259
+Cory Gwin: Oh, and then you would check the structured format on the other side.
+
+322
+00:34:28.340 --> 00:34:30.650
+hrishikb@andrew.cmu.edu: Guys, alright.
+
+323
+00:34:30.790 --> 00:34:40.949
+Cory Gwin: You could even write… you could even write scripts to make sure, like, is the structured format valid? Does every… is every field, you know, filled in? Whatever.
+
+324
+00:34:44.010 --> 00:34:46.549
+hrishikb@andrew.cmu.edu: Yeah, we can defend. Yeah, it does, it does.
+
+325
+00:34:46.909 --> 00:34:47.469
+Cory Gwin: Yep.
+
+326
+00:34:54.870 --> 00:34:56.739
+hrishikb@andrew.cmu.edu: Do you guys have any questions? Jaya, Shrata?
+
+327
+00:34:58.720 --> 00:35:06.810
+Jai: No, I just, like, I was looking at Corey's, like, when he opened up VS Code, I was like, I better take a screenshot before I remember, like…
+
+328
+00:35:07.150 --> 00:35:10.140
+Jai: This is being recorded, so that's all good there.
+
+329
+00:35:10.450 --> 00:35:16.639
+Cory Gwin: Yeah, yeah. I mean, if you guys want to talk about setup or anything, I'm happy to talk about setup.
+
+330
+00:35:16.850 --> 00:35:21.480
+Cory Gwin: I mean, for a lot of this stuff, what you're doing…
+
+331
+00:35:22.110 --> 00:35:28.909
+Cory Gwin: you know, I think you can have an agent build a lot of it, right? Like, there's no reason to build…
+
+332
+00:35:29.130 --> 00:35:42.540
+Cory Gwin: this, you know, loop over an API, just have an agent build it, right? There's no reason to have it… to write, like, a script that knows how to validate and test the JSON that comes out on the other side. Have an agent build it.
+
+333
+00:35:42.570 --> 00:35:43.510
+hrishikb@andrew.cmu.edu: Good, yeah.
+
+334
+00:35:43.730 --> 00:35:49.330
+Cory Gwin: Yeah. So, you know, really lean into, I'm not gonna write any code for any of this.
+
+335
+00:35:49.540 --> 00:35:51.760
+Cory Gwin: I'm just gonna have an agent write all of it.
+
+336
+00:35:52.220 --> 00:35:59.720
+Cory Gwin: And you can even do it in, like, the repo that you're going to build the final product in, as a way to, like.
+
+337
+00:35:59.850 --> 00:36:02.380
+Cory Gwin: Or maybe a different one, but as a way to, like.
+
+338
+00:36:02.700 --> 00:36:06.800
+Cory Gwin: get some, like, muscle around how to set these things up.
+
+339
+00:36:08.680 --> 00:36:09.320
+hrishikb@andrew.cmu.edu: Okay.
+
+340
+00:36:11.460 --> 00:36:17.599
+hrishikb@andrew.cmu.edu: Yeah, like, given it's also a greenfield project, we can have it generate almost all of it.
+
+341
+00:36:18.120 --> 00:36:20.520
+Cory Gwin: You have it generate all of it, yeah.
+
+342
+00:36:20.520 --> 00:36:21.070
+hrishikb@andrew.cmu.edu: Yeah.
+
+343
+00:36:21.350 --> 00:36:29.390
+Cory Gwin: Yeah, what I would… what I would do is I would start with an LLM and say, this is what I want to do, okay? Help me write a spec file.
+
+344
+00:36:30.000 --> 00:36:33.150
+Cory Gwin: Ask me any questions about this that don't make sense.
+
+345
+00:36:33.440 --> 00:36:36.710
+Cory Gwin: Here… here are my sample files.
+
+346
+00:36:37.920 --> 00:36:42.420
+Cory Gwin: let's go. And then you iterate on that spec file until you have what you want.
+
+347
+00:36:42.540 --> 00:36:44.339
+Cory Gwin: And then you say, okay, build it.
+
+348
+00:36:48.490 --> 00:36:53.900
+Cory Gwin: Yeah, and then you'll be able to build that whole, like, test an LLM harness, right?
+
+349
+00:36:54.680 --> 00:36:55.360
+hrishikb@andrew.cmu.edu: Hello.
+
+350
+00:36:55.360 --> 00:37:02.589
+Cory Gwin: And just start out simple. Start out with saying, we're going to use the Gemini API, right? And then once you have that going, you can say, okay.
+
+351
+00:37:02.730 --> 00:37:10.830
+Cory Gwin: Now we want to evaluate a local running Llama model. And then you can, you know, do the same thing, but with a Llama model.
+
+352
+00:37:15.070 --> 00:37:15.870
+hrishikb@andrew.cmu.edu: Right.
+
+353
+00:37:16.330 --> 00:37:18.690
+hrishikb@andrew.cmu.edu: Yeah, that sounds like a pretty good plan.
+
+354
+00:37:20.520 --> 00:37:32.419
+Cory Gwin: And it's gonna build your muscle around how to use these AI agents, how to build things with them, and you'll be able to have, like, a rigorous thing that you showed, like, how we tested different
+
+355
+00:37:32.580 --> 00:37:44.540
+Cory Gwin: methodologies of working with LLMs to build things, but also use that to evaluate our ML LLM implementation, if NIFO is a feasible approach.
+
+356
+00:37:51.180 --> 00:37:52.000
+Ashritha: Go ahead.
+
+357
+00:37:57.370 --> 00:37:58.000
+Cory Gwin: Oops, sorry.
+
+358
+00:37:58.000 --> 00:38:02.390
+Ashritha: Also, I have a question not related to the project. Are you able to hear me now?
+
+359
+00:38:02.790 --> 00:38:15.380
+Ashritha: Yeah, I was saying, I was saying, I don't have a question related to the project, but then, when you say a coaching session about AI tools.
+
+360
+00:38:15.470 --> 00:38:28.699
+Ashritha: Like, give… what… what comes under that? Like, is it just the LLM models, or the, like, cursor, Gemini, and all of that? Like, what's the definition there?
+
+361
+00:38:28.870 --> 00:38:30.160
+Cory Gwin: There is no definition.
+
+362
+00:38:30.160 --> 00:38:30.660
+Ashritha: Beautiful.
+
+363
+00:38:30.660 --> 00:38:32.520
+Cory Gwin: with whatever you need.
+
+364
+00:38:33.520 --> 00:38:42.599
+Cory Gwin: Right? I mean, I'm figuring this out as I go. You know, I've never coached, at CMU before, this is my first year. You're my second coaching session.
+
+365
+00:38:43.020 --> 00:38:52.379
+Cory Gwin: And I think that a lot of what we're talking about here isn't even defined in the industry, right? Like, people are figuring out how to do it.
+
+366
+00:38:52.480 --> 00:38:53.670
+Ashritha: And I know you're…
+
+367
+00:38:53.670 --> 00:39:02.289
+Cory Gwin: probably… I don't know, I'm not gonna say I know. I don't think you're probably being taught some of the things that we are doing in industry now, right? So I'm trying to show you some of it.
+
+368
+00:39:03.470 --> 00:39:05.409
+Ashritha: Got it. Yeah.
+
+369
+00:39:05.410 --> 00:39:10.690
+Cory Gwin: So… Yeah, I don't know, what did you think it meant? And is it.
+
+370
+00:39:10.690 --> 00:39:11.776
+Ashritha: No, they…
+
+371
+00:39:12.750 --> 00:39:29.120
+Ashritha: No, we were just discussing what to present, and then we were like, all that we use right now is Cursor, and then we have Gemini, and then Claude, so what else? Like, what else can be considered as an AI tool? So, that was the question.
+
+372
+00:39:29.120 --> 00:39:30.700
+Cory Gwin: Okay, yeah, I mean…
+
+373
+00:39:30.700 --> 00:39:31.200
+Ashritha: Oof.
+
+374
+00:39:32.000 --> 00:39:37.639
+Cory Gwin: The… so the thing that's really emerging is, like, what we call your development harness, right?
+
+375
+00:39:38.160 --> 00:39:42.530
+Cory Gwin: And there's, like, these two things. One is an inner loop, and one is a forward loop.
+
+376
+00:39:44.430 --> 00:39:53.109
+Cory Gwin: And what we're trying, like, inside of where I work, what we're trying to do more and more of is not write code, like, at all.
+
+377
+00:39:53.250 --> 00:40:00.580
+Cory Gwin: So it's like, can you get the agent to a point where it can write correct code without you touching any code?
+
+378
+00:40:02.500 --> 00:40:02.880
+Ashritha: Okay.
+
+379
+00:40:03.040 --> 00:40:17.870
+Cory Gwin: So a lot of that is tooling setup, right? Like I was talking about, you know, making sure your agent files exist, making sure they know how to run your tests and your linters, getting all of those feedback mechanisms in place so the agent can see what's happening.
+
+380
+00:40:18.070 --> 00:40:23.590
+Cory Gwin: And understand, like, how to fix things when they break, because I can see what's happening.
+
+381
+00:40:25.400 --> 00:40:33.810
+Cory Gwin: And then, you know, having our agents learn over time, and the way that you learn is by updating things like agent files and stuff like that.
+
+382
+00:40:35.520 --> 00:40:47.139
+Cory Gwin: And then, like I was mentioning before, if it can't see your ADRs, well, it's never going to implement your ADRs, right? And if you don't tell them that they're important when it's implementing a certain feature, it's never going to consider them.
+
+383
+00:40:47.410 --> 00:40:51.909
+Cory Gwin: So really, a lot of it's about thinking from your agent's perspective.
+
+384
+00:40:52.500 --> 00:40:59.919
+Cory Gwin: It just so happens that you all are doing an AI project also, so I have some feedback about how to do that.
+
+385
+00:41:00.040 --> 00:41:04.940
+Cory Gwin: Or about what to try. And then also in the prototyping side of things, right? Like.
+
+386
+00:41:05.140 --> 00:41:19.699
+Cory Gwin: A lot of us are used to working in a world where software is really expensive to make. It's hours and hours and hours of time, but that's not really the case anymore. Like, yesterday, I was working on a problem, and I was like, oh, it's really hard to think about how
+
+387
+00:41:19.930 --> 00:41:24.940
+Cory Gwin: this… Code is changing this data structure through its process.
+
+388
+00:41:25.600 --> 00:41:31.060
+Cory Gwin: And I was like, wait a minute, like, I can build a UI to just watch how it's changing over time.
+
+389
+00:41:31.350 --> 00:41:36.770
+Cory Gwin: Right? And I can just build it and watch it, and run some tests and watch the code change the data.
+
+390
+00:41:37.500 --> 00:41:47.950
+Cory Gwin: And I did that, and it saved me so much time, and I was like, I never would have thought to do that in the past, because it would have taken me hours to do it, but I did it in, like, 5 minutes.
+
+391
+00:41:48.390 --> 00:41:51.940
+Cory Gwin: Right, so it's like, how can… like, what tools
+
+392
+00:41:52.180 --> 00:42:01.780
+Cory Gwin: AI allows you to build tools to make yourself go faster in a way that was never possible before, so think about that too, right? Like, what can I build myself
+
+393
+00:42:02.130 --> 00:42:03.989
+Cory Gwin: To make my job easier.
+
+394
+00:42:08.070 --> 00:42:10.890
+Cory Gwin: It's really cheap to build something, though.
+
+395
+00:42:11.600 --> 00:42:12.510
+Ashritha: Yeah.
+
+396
+00:42:14.510 --> 00:42:15.310
+Cory Gwin: Yeah.
+
+397
+00:42:18.730 --> 00:42:21.120
+Cory Gwin: My puppy is so sad. He wants me to
+
+398
+00:42:24.000 --> 00:42:26.520
+Cory Gwin: Yeah. Do you guys have other questions?
+
+399
+00:42:27.410 --> 00:42:29.210
+Ashritha: Oh, I don't think I'm good.
+
+400
+00:42:30.020 --> 00:42:32.230
+hrishikb@andrew.cmu.edu: Yeah, I think that's all the questions I had, too.
+
+401
+00:42:32.450 --> 00:42:33.080
+Cory Gwin: Okay.
+
+402
+00:42:33.490 --> 00:42:39.539
+Cory Gwin: Is there anything that you took away from this that was more helpful than anything else? Any feedback for the coach?
+
+403
+00:42:41.320 --> 00:42:44.299
+hrishikb@andrew.cmu.edu: I think all of it was pretty helpful,
+
+404
+00:42:44.660 --> 00:42:47.540
+hrishikb@andrew.cmu.edu: There are multiple aspects that we haven't explored yet.
+
+405
+00:42:47.780 --> 00:43:05.329
+hrishikb@andrew.cmu.edu: And I realize we're using our AIs not that efficiently, so the idea to put it in a Git repo and just have iterations over it is, I think, going to be very useful after we have sufficient documents. So that's something we'll definitely do, and…
+
+406
+00:43:06.050 --> 00:43:13.099
+hrishikb@andrew.cmu.edu: the part of, replacing the ML module with lightweight AI can also be useful. I think the…
+
+407
+00:43:13.240 --> 00:43:28.540
+hrishikb@andrew.cmu.edu: I think it'll be much… it'll work much better than the ML model. We just have to, I don't know, try it out and maybe, pitch the idea to the clients. Because the clients are very open-minded. They said, if you think something is better than our approach, then feel free to tell us and, like.
+
+408
+00:43:28.720 --> 00:43:33.649
+hrishikb@andrew.cmu.edu: So, maybe we can try it out and… See how that goes.
+
+409
+00:43:33.920 --> 00:43:37.090
+Cory Gwin: Yeah, I mean, I think it would be great if you could go to them and be like, look.
+
+410
+00:43:37.500 --> 00:43:44.830
+Cory Gwin: You know, we tried 30 different iterations of prompts combined with different models. This is how accurate it was.
+
+411
+00:43:44.950 --> 00:44:04.880
+Cory Gwin: Like, you can do that pretty quick, right? Because you can just set up a bunch of these things to run in parallel and be like, test, test, test, test, test. We do that all the time at work, and it actually gives you something… if you can do it programmatically, it gives you something that if you do need to train an ML model, you have a way to evaluate it, right?
+
+412
+00:44:05.570 --> 00:44:06.130
+hrishikb@andrew.cmu.edu: Yeah.
+
+413
+00:44:06.130 --> 00:44:11.369
+Cory Gwin: So by evaluating LLMs up front, and having an evaluating methodology in place.
+
+414
+00:44:11.820 --> 00:44:16.169
+Cory Gwin: one, you may not have to do the work, because you may prove the LLM can just do it.
+
+415
+00:44:16.340 --> 00:44:21.350
+Cory Gwin: And if you don't, well, then you still have gotten an evaluation methodology out of it.
+
+416
+00:44:23.690 --> 00:44:24.720
+Cory Gwin: Makes sense.
+
+417
+00:44:25.040 --> 00:44:25.470
+hrishikb@andrew.cmu.edu: Yeah.
+
+418
+00:44:25.470 --> 00:44:26.080
+Ashritha: Yeah.
+
+419
+00:44:26.080 --> 00:44:28.420
+Cory Gwin: So it's not time wasted, no matter what.
+
+420
+00:44:29.600 --> 00:44:41.300
+Cory Gwin: And if you are training an ML model, you're going to have to train a ton of them, and you're going to be evaluating all the time. So, getting good at evaluating an ML model is going to be a requirement of this project, no matter what.
+
+421
+00:44:43.680 --> 00:44:44.350
+hrishikb@andrew.cmu.edu: Right.
+
+422
+00:44:48.410 --> 00:44:49.500
+Cory Gwin: Make sense?
+
+423
+00:44:50.880 --> 00:44:55.450
+Cory Gwin: Alright. What… Three things are you gonna do?
+
+424
+00:44:55.680 --> 00:44:57.159
+Cory Gwin: In the next week.
+
+425
+00:44:59.910 --> 00:45:02.599
+hrishikb@andrew.cmu.edu: The next week is,
+
+426
+00:45:02.600 --> 00:45:03.580
+Ashritha: Bing green.
+
+427
+00:45:04.580 --> 00:45:05.800
+hrishikb@andrew.cmu.edu: No, probably.
+
+428
+00:45:07.200 --> 00:45:15.130
+hrishikb@andrew.cmu.edu: Yeah, but in the coming weeks, we'll definitely first create a shared repo with all the data that we have from the clients.
+
+429
+00:45:15.470 --> 00:45:19.340
+hrishikb@andrew.cmu.edu: Then we'll, try out a different…
+
+430
+00:45:19.680 --> 00:45:28.090
+hrishikb@andrew.cmu.edu: Like, we'll try a better LLM first and try to narrow it down till which LLM can actually process what we're asking it to do.
+
+431
+00:45:28.950 --> 00:45:35.310
+hrishikb@andrew.cmu.edu: And then maybe, have an… LM spit out some…
+
+432
+00:45:36.280 --> 00:45:38.680
+hrishikb@andrew.cmu.edu: Good mock-ups for the human in the Loop.
+
+433
+00:45:39.100 --> 00:45:40.940
+hrishikb@andrew.cmu.edu: This thing which you can show to the client.
+
+434
+00:45:41.430 --> 00:45:43.459
+Cory Gwin: That sounds like a good plan.
+
+435
+00:45:44.950 --> 00:45:46.880
+Cory Gwin: Cool, whoa.
+
+436
+00:45:47.450 --> 00:45:50.899
+Cory Gwin: Do you guys have any other questions, feedback, comments?
+
+437
+00:45:51.240 --> 00:45:52.959
+Cory Gwin: Evaluations for me?
+
+438
+00:45:53.480 --> 00:45:54.750
+hrishikb@andrew.cmu.edu: I mean, it's not…
+
+439
+00:45:54.750 --> 00:45:58.359
+Jai: Especially for me, it really helps when you talk about the harness stuff, like…
+
+440
+00:45:58.680 --> 00:46:07.210
+Jai: That was new to me, and the idea that with it, like, you could really reduce the amount of times you'd need to, like, correct it really helped.
+
+441
+00:46:08.060 --> 00:46:13.820
+Cory Gwin: Yeah, yeah, you don't want to have to correct it, right? Like, when you build a harness, your KPRs are, like.
+
+442
+00:46:15.930 --> 00:46:24.130
+Cory Gwin: how many times can I one-shot successfully, right? Like, how many times can I get this thing to build correctly on the first try? Like, that's what you're trying to improve.
+
+443
+00:46:24.260 --> 00:46:32.150
+Cory Gwin: So you want to have a shared harness that all of you are iterating on working towards building and making better. And that comes down to, like.
+
+444
+00:46:32.930 --> 00:46:33.880
+Cory Gwin: you know…
+
+445
+00:46:34.240 --> 00:46:45.269
+Cory Gwin: Improving your agent file, if you just have one, or splitting your agent file by sections of your codebase, if there are different, like, implementation points for the agent needs to behave differently.
+
+446
+00:46:47.160 --> 00:46:54.350
+Cory Gwin: It comes down to, like, giving really good visibility into how the application is running when the agent is running tests.
+
+447
+00:46:54.520 --> 00:46:59.240
+Cory Gwin: Giving it good tooling for testing visual things, like, Playwright.
+
+448
+00:47:01.140 --> 00:47:09.219
+Cory Gwin: And then giving it good skills, right? Like, I actually have a skill that I have it run at the end of everything that's, like, simplified code.
+
+449
+00:47:09.470 --> 00:47:12.760
+Cory Gwin: Because after the agent's been running for a while, it makes a whole bunch of mess.
+
+450
+00:47:12.880 --> 00:47:27.479
+Cory Gwin: And if you just tell it… if you give it rules about how to clean up after itself, it'll be like, oh, remove all dead code, you know, reduce cyclical complexity, you know, a bunch of best practices. A lot of times it'll just clean up a bunch of slop on its own.
+
+451
+00:47:34.100 --> 00:47:40.530
+Jai: That does make sense. It really does. If it runs off for a while, it really does complicate it, so that does make sense.
+
+452
+00:47:40.760 --> 00:47:43.630
+Cory Gwin: Yeah, the other thing to do, too, is, like.
+
+453
+00:47:43.890 --> 00:47:49.180
+Cory Gwin: If you're running agents over a long-running task, right, you want to…
+
+454
+00:47:49.800 --> 00:47:55.939
+Cory Gwin: You want to make it so that you're not… that you can run it without using the same agent every time.
+
+455
+00:47:56.470 --> 00:48:00.119
+Cory Gwin: And so you want to make it so that you can get into a place where you can
+
+456
+00:48:00.360 --> 00:48:10.689
+Cory Gwin: Run an agent for a short period of time, mark where you're at, stop it, start it again. Because context rot is the enemy of everything, right?
+
+457
+00:48:11.240 --> 00:48:27.100
+Cory Gwin: So, if you can get to a place where you just have, like, a bash cycle that's running over… running an agent over, like, an implementation plan, you're like, okay, just check off one part, and then continue on, check off one part, continue on, and you get in that loop.
+
+458
+00:48:27.140 --> 00:48:37.839
+Cory Gwin: you always have a fresh context window, and it's working better. But those kind of PRs are really hard to review, and you only want to do that in places that are low risk.
+
+459
+00:48:38.150 --> 00:48:42.240
+Cory Gwin: I don't know if you guys… I wrote a bit about this.
+
+460
+00:48:43.840 --> 00:48:45.420
+Cory Gwin: Hold on.
+
+461
+00:48:48.050 --> 00:48:52.500
+Cory Gwin: Here, you can… Share this.
+
+462
+00:48:55.330 --> 00:48:57.870
+Cory Gwin: There's that.
+
+463
+00:48:59.130 --> 00:49:04.220
+Cory Gwin: But that might be helpful, hmm, and then that…
+
+464
+00:49:04.510 --> 00:49:08.980
+Cory Gwin: loop that I was just talking about, a lot of people call it the Ralph Wiggum loop.
+
+465
+00:49:11.100 --> 00:49:11.889
+Cory Gwin: But there's lots of things.
+
+466
+00:49:11.890 --> 00:49:15.010
+hrishikb@andrew.cmu.edu: This is the one you recently posted on LinkedIn as well, right?
+
+467
+00:49:16.020 --> 00:49:16.860
+Cory Gwin: Yeah.
+
+468
+00:49:16.860 --> 00:49:19.010
+hrishikb@andrew.cmu.edu: Yeah, yeah, I think I read it last night.
+
+469
+00:49:19.240 --> 00:49:22.010
+Cory Gwin: Okay. Yeah.
+
+470
+00:49:22.230 --> 00:49:28.199
+Cory Gwin: Yeah, so, you know, think about… think about, like, okay, how safe is it to make a big change here?
+
+471
+00:49:28.320 --> 00:49:30.890
+Cory Gwin: How hard is it gonna be to review this change?
+
+472
+00:49:31.090 --> 00:49:43.960
+Cory Gwin: Okay, now, that kind of dictates, like, how… like, how big of a change do I want to let the agent do, right? When you're in this early prototyping phase, risk is, like, zero, right? Like.
+
+473
+00:49:44.260 --> 00:49:49.239
+Cory Gwin: Just… just set up an agent to do stuff for you until you run off tokens.
+
+474
+00:49:49.600 --> 00:50:01.769
+Cory Gwin: Right? Just expunge as many tokens as you can, make it do as much work as it can, because it's like, once you have it set up to run, it's like free work, right? This is work you don't have to do until you've run out of tokens.
+
+475
+00:50:03.520 --> 00:50:07.369
+Cory Gwin: And then you can just pick up tomorrow if you… if you have it set up the right way.
+
+476
+00:50:08.840 --> 00:50:14.849
+Cory Gwin: That is… that is… where you're at in this project, that is gold.
+
+477
+00:50:15.400 --> 00:50:20.660
+Cory Gwin: Once you start implementing the actual final thing, there are times when you can do that.
+
+478
+00:50:21.300 --> 00:50:22.790
+hrishikb@andrew.cmu.edu: And it's okay.
+
+479
+00:50:22.970 --> 00:50:38.989
+Cory Gwin: I wouldn't do it early in a project, because you haven't set up the structure yet, so it'll… it's much more likely to make a mess. Like, I like to be more hands-on at that point, because I'm going to kind of dictate, like, put things here, shape things like this. I don't quite know how I want my lenders to work.
+
+480
+00:50:39.250 --> 00:50:45.190
+Cory Gwin: But once you're past that, and you have some low-risk work, right, like, Add a new UI.
+
+481
+00:50:45.610 --> 00:50:48.019
+Cory Gwin: You can just have an agent do that.
+
+482
+00:50:49.020 --> 00:50:49.610
+hrishikb@andrew.cmu.edu: Right.
+
+483
+00:50:52.100 --> 00:50:53.110
+Cory Gwin: Does that make sense?
+
+484
+00:50:53.420 --> 00:50:54.230
+hrishikb@andrew.cmu.edu: Here it is.
+
+485
+00:50:54.230 --> 00:50:57.929
+Cory Gwin: Yeah, and the… but just know, the bigger change you ask it to make.
+
+486
+00:50:58.380 --> 00:51:01.880
+Cory Gwin: The more you have to test it, and the harder that review's gonna be.
+
+487
+00:51:05.770 --> 00:51:06.300
+Ashritha: Yeah.
+
+488
+00:51:08.850 --> 00:51:09.530
+Cory Gwin: Are we?
+
+489
+00:51:10.630 --> 00:51:15.579
+Cory Gwin: Alright, that was a lot. That was a lot of information. How are you guys feeling? Overwhelmed?
+
+490
+00:51:16.800 --> 00:51:22.680
+Cory Gwin: You're like, I've been in class all day, and you're just, like, you're just, like, teaching me all kinds of crazy things.
+
+491
+00:51:23.550 --> 00:51:28.700
+hrishikb@andrew.cmu.edu: I think we were able to grasp, all of it, I think, pretty well, and…
+
+492
+00:51:29.800 --> 00:51:32.589
+hrishikb@andrew.cmu.edu: I think I'm pretty clear on the things we need to do next.
+
+493
+00:51:32.720 --> 00:51:36.999
+hrishikb@andrew.cmu.edu: In order to, like, work more efficiently and, like, utilize AI to its fullest.
+
+494
+00:51:37.990 --> 00:51:38.540
+Cory Gwin: Nope.
+
+495
+00:51:38.720 --> 00:51:40.940
+Cory Gwin: Cool. Then I think I did my job.
+
+496
+00:51:42.260 --> 00:51:47.909
+Cory Gwin: Alright, if you guys have questions, you can always reach out to me in Slack.
+
+497
+00:51:48.240 --> 00:51:51.019
+Cory Gwin: Or if you need to do this again, let me know.
+
+498
+00:51:51.720 --> 00:51:54.139
+hrishikb@andrew.cmu.edu: All right. Thank you so much, Cody.
+
+499
+00:51:54.900 --> 00:51:55.670
+Ashritha: survey.
+
+500
+00:51:55.670 --> 00:51:56.640
+hrishikb@andrew.cmu.edu: Bye, see ya.
+
diff --git a/coach_meetings/GMT20260224-220446_RecordingnewChat.txt b/coach_meetings/GMT20260224-220446_RecordingnewChat.txt
new file mode 100644
index 0000000..32daf72
--- /dev/null
+++ b/coach_meetings/GMT20260224-220446_RecordingnewChat.txt
@@ -0,0 +1,2 @@
+00:55:02 Cory Gwin: https://corygwin.com/posts/how-engineers-actually-work-with-ai/
+00:55:12 Cory Gwin: https://corygwin.com/posts/ralph-wiggum-mode/
diff --git a/coach_meetings/ben/GMT20260224-190023_Recording.transcript (1).vtt b/coach_meetings/ben/GMT20260224-190023_Recording.transcript (1).vtt
new file mode 100644
index 0000000..9e9f8a8
--- /dev/null
+++ b/coach_meetings/ben/GMT20260224-190023_Recording.transcript (1).vtt
@@ -0,0 +1,2258 @@
+WEBVTT
+
+1
+00:00:00.050 --> 00:00:01.810
+hrishikb@andrew.cmu.edu: I'm getting the data from them.
+
+2
+00:00:02.550 --> 00:00:11.570
+hrishikb@andrew.cmu.edu: So, we… now we have received, like, I think we received it last Friday or Saturday, the data, and we are, started to work on our ML models to run basic tests.
+
+3
+00:00:11.730 --> 00:00:12.560
+hrishikb@andrew.cmu.edu: Okay.
+
+4
+00:00:13.100 --> 00:00:17.450
+hrishikb@andrew.cmu.edu: Yeah, we got it last Friday, like, in the evening or something. Okay.
+
+5
+00:00:23.840 --> 00:00:27.000
+hrishikb@andrew.cmu.edu: I don't have much to share, but I'm gonna just put up the…
+
+6
+00:00:27.680 --> 00:00:31.159
+hrishikb@andrew.cmu.edu: Yeah, we could maybe start with the unsuspect.
+
+7
+00:00:31.340 --> 00:00:32.220
+hrishikb@andrew.cmu.edu: Yeah.
+
+8
+00:00:32.770 --> 00:00:33.650
+hrishikb@andrew.cmu.edu: Download.
+
+9
+00:00:35.170 --> 00:00:40.429
+hrishikb@andrew.cmu.edu: Now, do some more reading. It's not pre-reading anymore, it's just reading.
+
+10
+00:01:08.560 --> 00:01:18.040
+hrishikb@andrew.cmu.edu: Cool, did you have a… an agenda in mind?
+
+11
+00:01:18.470 --> 00:01:23.270
+hrishikb@andrew.cmu.edu: The agenda for us is, basically figuring out,
+
+12
+00:01:24.270 --> 00:01:32.020
+hrishikb@andrew.cmu.edu: if and where we can integrate the UX elements, because currently our system is supposed to be,
+
+13
+00:01:32.140 --> 00:01:37.890
+hrishikb@andrew.cmu.edu: like, end-to-end automated. The only communication points with the system are, I think.
+
+14
+00:01:38.090 --> 00:01:48.919
+hrishikb@andrew.cmu.edu: Two, basically, first would be when we are inputting the data in the system, the different CSVs or PDFs on those kind of things, and as we process the data, if our…
+
+15
+00:01:48.920 --> 00:02:07.900
+hrishikb@andrew.cmu.edu: ML algorithm does not give it a good enough confidence score, then a human in the loop comes into the picture, who will either approve, deny, or modify the data that you've gotten. So, those are the only two elements we can think where the user will interact, because the endpoint of the entire pipeline is the
+
+16
+00:02:08.009 --> 00:02:14.549
+hrishikb@andrew.cmu.edu: basically publish the data into an, wait, I'll just pull it up, be better with the diagram.
+
+17
+00:02:30.280 --> 00:02:30.980
+hrishikb@andrew.cmu.edu: Hmm.
+
+18
+00:02:38.410 --> 00:02:48.920
+hrishikb@andrew.cmu.edu: So, in this part, this is the part where we put all the data in. We have the ingestion gateway, the internal tables that we have, the initial ones.
+
+19
+00:02:48.980 --> 00:02:55.170
+hrishikb@andrew.cmu.edu: then our ML module will print the scores using the defined rules, and if you have a…
+
+20
+00:02:55.170 --> 00:03:10.230
+hrishikb@andrew.cmu.edu: low content, then we have the human review. That's where the human will interact with the system to check if it's good or not. If it's high content is auto-accepted, I will go to the intermediary from this table, and then it will just be synced to PIMS. This is our endpoint.
+
+21
+00:03:10.230 --> 00:03:20.949
+hrishikb@andrew.cmu.edu: After this, this, like, not a concern. We just have to push the correct data to PIMS. Right. You only have two, points of interaction from humans.
+
+22
+00:03:21.040 --> 00:03:21.880
+hrishikb@andrew.cmu.edu: Okay.
+
+23
+00:03:22.820 --> 00:03:28.670
+hrishikb@andrew.cmu.edu: I think where I'd like to start, yeah, I mean, those are,
+
+24
+00:03:31.320 --> 00:03:43.500
+hrishikb@andrew.cmu.edu: Those are non-trivial UIs, so that is something, but I'd like to start with just understanding the current state, like.
+
+25
+00:03:43.960 --> 00:03:45.869
+hrishikb@andrew.cmu.edu: what eBars has.
+
+26
+00:03:46.030 --> 00:03:53.120
+hrishikb@andrew.cmu.edu: And… Okay. What is… And so you're introducing…
+
+27
+00:03:53.320 --> 00:04:03.139
+hrishikb@andrew.cmu.edu: a system into an existing system, so I'm curious what the current state is in. Okay, yeah, I think that'll be… What they're trying to accomplish…
+
+28
+00:04:03.850 --> 00:04:05.670
+hrishikb@andrew.cmu.edu: So, yeah…
+
+29
+00:04:05.980 --> 00:04:17.360
+hrishikb@andrew.cmu.edu: Basically, the problem is that currently, all this work… so, ePaths deals with HVAC systems. It's basically Amazon for some different parts, and it has multiple vendors. Okay, yeah.
+
+30
+00:04:18.390 --> 00:04:29.440
+hrishikb@andrew.cmu.edu: Currently, how they do it is, multiple vendors send them their catalogs via PDFs, CSVs, or some SFTP dump, things like that, so it's a very varied, type of input.
+
+31
+00:04:29.440 --> 00:04:48.600
+hrishikb@andrew.cmu.edu: And they have a specific catalog team, which goes through all of it manually. Like, they have their procedures, but they do all of it manually. They check their specs, because similar things can be called different names by different vendors. Like, one person can call an iPhone screen size, a screen size, some can call it display size.
+
+32
+00:04:49.540 --> 00:04:57.310
+hrishikb@andrew.cmu.edu: Yeah, so, those kind of things are what currently is done by the catalog team.
+
+33
+00:04:58.670 --> 00:05:04.249
+hrishikb@andrew.cmu.edu: Okay, and are those… is the catalog team…
+
+34
+00:05:04.680 --> 00:05:07.719
+hrishikb@andrew.cmu.edu: What is their interface right now?
+
+35
+00:05:08.300 --> 00:05:27.690
+hrishikb@andrew.cmu.edu: Are they talking to pins? Or, like, is there some UI for pins? We have not yet had a chance to talk to the catalog team, but from what we understood, the team basically does all the manual work, then they input the data into the… directly into the intermediary table.
+
+36
+00:05:28.160 --> 00:05:36.779
+hrishikb@andrew.cmu.edu: not… they don't have an intuitive, but they directly enter the data into the PIMS, the information manual system. Yeah. So all this part is the manual work they do.
+
+37
+00:05:38.400 --> 00:05:39.270
+hrishikb@andrew.cmu.edu: Okay.
+
+38
+00:05:39.820 --> 00:05:44.920
+hrishikb@andrew.cmu.edu: Yeah, so in PIMS, like,
+
+39
+00:05:45.820 --> 00:05:56.670
+hrishikb@andrew.cmu.edu: Does that have a web UI? Like, do you know how they… what the UI is? How they interface with that? I don't think the PIMS has a very certain internal thing you're looking at.
+
+40
+00:05:57.930 --> 00:05:58.900
+hrishikb@andrew.cmu.edu: Right.
+
+41
+00:05:59.220 --> 00:06:01.399
+hrishikb@andrew.cmu.edu: Speaker, like, back in April.
+
+42
+00:06:02.190 --> 00:06:09.049
+hrishikb@andrew.cmu.edu: I guess, yeah, like, what is… but what is their interface? If they're… they're looking at a catalog, a PDF, they're…
+
+43
+00:06:09.160 --> 00:06:15.250
+hrishikb@andrew.cmu.edu: Presumably they're typing in like, data somewhere, like, what is… where does that exist?
+
+44
+00:06:15.860 --> 00:06:19.649
+hrishikb@andrew.cmu.edu: I think internally, you don't have an interface.
+
+45
+00:06:19.790 --> 00:06:29.499
+hrishikb@andrew.cmu.edu: But then the only interface that's there, at least as per the knowledge that we have, is through the end product, like, what the customer sees at the end.
+
+46
+00:06:29.750 --> 00:06:38.740
+hrishikb@andrew.cmu.edu: I mean, if you do, like, internet, searches, so the one that comes up, that's the only interface, thing over here, I guess.
+
+47
+00:06:40.030 --> 00:06:47.020
+hrishikb@andrew.cmu.edu: Okay. They communicate to the very end, like, they are allowed to modify data on their the…
+
+48
+00:06:47.040 --> 00:06:55.119
+hrishikb@andrew.cmu.edu: web interface directly. If they see some issues. There is not much process around that. Like, there's not a proper interface which goes through processing.
+
+49
+00:06:55.130 --> 00:07:10.040
+hrishikb@andrew.cmu.edu: So it's… I think it's either… either they communicate with PIMs with directly entering their data into a predefined schema, or they do it directly, or both, into the front-end part of the system, web service, yeah.
+
+50
+00:07:10.370 --> 00:07:17.040
+hrishikb@andrew.cmu.edu: So we had actually proposed a… I mean, if you see at the beginning, right, what they're currently doing is.
+
+51
+00:07:17.290 --> 00:07:37.680
+hrishikb@andrew.cmu.edu: their source data is very scattered. Right. Someone sends them through mail, and then they get CSVs and all of that. So a human, actually, it starts all of that data, and then, I mean, all of this addition pipeline is not there. Right. But then they just, like, put it in a schema, and then do some CRAD operations over the DB, and push it. That's it.
+
+52
+00:07:37.850 --> 00:07:40.750
+hrishikb@andrew.cmu.edu: So, we actually told them that if
+
+53
+00:07:40.750 --> 00:08:05.040
+hrishikb@andrew.cmu.edu: you have multiple vendors over there, why don't you give an interface to the vendor, where the vendor can actually… Yeah, where the vendor can actually put it in, like, you know, a pretty straightforward template, and the data can flow on its own. So, they're like, that's the goal, but, at least for this project, I don't think they would want to take up that, UX or UI. That might be a…
+
+54
+00:08:05.320 --> 00:08:09.339
+hrishikb@andrew.cmu.edu: Like, stretch goal or something, but that's the end thing they wanted.
+
+55
+00:08:09.560 --> 00:08:11.470
+hrishikb@andrew.cmu.edu: After the system is implemented.
+
+56
+00:08:12.900 --> 00:08:21.760
+hrishikb@andrew.cmu.edu: So your planning will, have some APIs exposed, which we'll use, which can be… we'll make it so that it can be easily integrated into our web system.
+
+57
+00:08:21.890 --> 00:08:26.050
+hrishikb@andrew.cmu.edu: So, they can, like, if they plan to build a UI for it, they can easily… Right.
+
+58
+00:08:27.730 --> 00:08:30.250
+hrishikb@andrew.cmu.edu: Yeah, I guess,
+
+59
+00:08:33.559 --> 00:08:40.120
+hrishikb@andrew.cmu.edu: That's interesting. I guess what you're describing sounds like vendors…
+
+60
+00:08:40.450 --> 00:08:45.660
+hrishikb@andrew.cmu.edu: That the end goal is that vendors would do the work of inputting, but currently.
+
+61
+00:08:49.010 --> 00:08:50.530
+hrishikb@andrew.cmu.edu: It seems like…
+
+62
+00:08:51.080 --> 00:09:02.420
+hrishikb@andrew.cmu.edu: the vendors are not gonna change anything in their workflow, they're still just gonna email, and then eParts is gonna do the legwork of taking those files, and then
+
+63
+00:09:02.880 --> 00:09:05.209
+hrishikb@andrew.cmu.edu: Inputting them into your system. Yeah.
+
+64
+00:09:05.400 --> 00:09:06.280
+hrishikb@andrew.cmu.edu: Right.
+
+65
+00:09:06.500 --> 00:09:10.290
+hrishikb@andrew.cmu.edu: I guess.
+
+66
+00:09:12.010 --> 00:09:19.189
+hrishikb@andrew.cmu.edu: I mean, from the standpoint of vendors, this is a better system than the end goal, where they have to do any work.
+
+67
+00:09:19.660 --> 00:09:25.320
+hrishikb@andrew.cmu.edu: Right? So, I guess I'm trying to…
+
+68
+00:09:25.890 --> 00:09:30.880
+hrishikb@andrew.cmu.edu: I'm trying to identify the different stakeholders, like, the different
+
+69
+00:09:31.510 --> 00:09:39.040
+hrishikb@andrew.cmu.edu: People that are involved in this current system, because that's going to… Help.
+
+70
+00:09:39.500 --> 00:09:42.799
+hrishikb@andrew.cmu.edu: That's gonna help you to figure out
+
+71
+00:09:44.180 --> 00:09:50.589
+hrishikb@andrew.cmu.edu: how to design the system, and who you need to talk to, or who you need to make sure that ePort's team talks to.
+
+72
+00:09:50.810 --> 00:09:58.670
+hrishikb@andrew.cmu.edu: So… We do have a lot of context. Yeah, so, like…
+
+73
+00:10:00.920 --> 00:10:03.809
+hrishikb@andrew.cmu.edu: So yeah, I want to dig into a little bit, like.
+
+74
+00:10:03.930 --> 00:10:07.299
+hrishikb@andrew.cmu.edu: I know you guys say there's no interface, but I guess…
+
+75
+00:10:08.410 --> 00:10:10.899
+hrishikb@andrew.cmu.edu: when I say interface, I just mean, like.
+
+76
+00:10:11.130 --> 00:10:21.809
+hrishikb@andrew.cmu.edu: the inter… like, it's not necessarily, like, a polished UI or a front end, like… like, the API is an interface, a web form is an interface,
+
+77
+00:10:21.910 --> 00:10:25.019
+hrishikb@andrew.cmu.edu: I don't know, using Postman, like, as an interface.
+
+78
+00:10:26.360 --> 00:10:33.010
+hrishikb@andrew.cmu.edu: Or, you know, directly making database query calls, that's… that's… that could be an interface, or that is an interface. So…
+
+79
+00:10:35.370 --> 00:10:44.460
+hrishikb@andrew.cmu.edu: Let's say… So I like how you have, like, the different actors, so…
+
+80
+00:10:45.240 --> 00:10:50.239
+hrishikb@andrew.cmu.edu: So we have these human reviewers, that's good to identify. So is that…
+
+81
+00:10:52.250 --> 00:10:54.440
+hrishikb@andrew.cmu.edu: Do you… do you envision that…
+
+82
+00:10:54.670 --> 00:10:59.090
+hrishikb@andrew.cmu.edu: this is somebody from the catalog team? Like, who knows…
+
+83
+00:10:59.990 --> 00:11:12.160
+hrishikb@andrew.cmu.edu: Who does this role? Like, who is the expert there? I think that would be someone from the catalog team. Okay, okay. Because currently, this is all under their purview, so they're in charge to put the data into it. Okay. So they'll be doing best.
+
+84
+00:11:13.250 --> 00:11:14.650
+hrishikb@andrew.cmu.edu: I'm gonna quit.
+
+85
+00:11:14.840 --> 00:11:15.730
+hrishikb@andrew.cmu.edu: Word.
+
+86
+00:11:16.520 --> 00:11:19.240
+hrishikb@andrew.cmu.edu: Hope this works. Can I erase this? You can? Yeah, yeah.
+
+87
+00:11:23.160 --> 00:11:25.519
+hrishikb@andrew.cmu.edu: I'm just gonna start making a list of people here.
+
+88
+00:11:36.440 --> 00:11:43.620
+hrishikb@andrew.cmu.edu: So, so, let's say this is a catalog team member.
+
+89
+00:11:49.250 --> 00:11:56.439
+hrishikb@andrew.cmu.edu: So basically, like, a primary stakeholder for your system. And then we have, like, vendors.
+
+90
+00:11:56.960 --> 00:12:05.200
+hrishikb@andrew.cmu.edu: So I guess some representative from… From each, each vendor.
+
+91
+00:12:05.620 --> 00:12:07.319
+hrishikb@andrew.cmu.edu: Right? Do you have any idea
+
+92
+00:12:07.870 --> 00:12:11.710
+hrishikb@andrew.cmu.edu: what kind of person that is from that company, I guess.
+
+93
+00:12:13.060 --> 00:12:15.190
+hrishikb@andrew.cmu.edu: I don't know. I learned now.
+
+94
+00:12:16.330 --> 00:12:21.250
+hrishikb@andrew.cmu.edu: Alright, so, I'm weird.
+
+95
+00:12:21.600 --> 00:12:22.990
+hrishikb@andrew.cmu.edu: Representative.
+
+96
+00:12:23.980 --> 00:12:28.290
+hrishikb@andrew.cmu.edu: Let's see…
+
+97
+00:12:32.610 --> 00:12:33.580
+hrishikb@andrew.cmu.edu: I guess.
+
+98
+00:12:34.080 --> 00:12:41.340
+hrishikb@andrew.cmu.edu: What you're saying is… Currently, the catalog team is…
+
+99
+00:12:41.940 --> 00:12:45.849
+hrishikb@andrew.cmu.edu: Taking the emails and whatever is being sent.
+
+100
+00:12:46.090 --> 00:12:47.339
+hrishikb@andrew.cmu.edu: from the vendor.
+
+101
+00:12:47.440 --> 00:12:54.930
+hrishikb@andrew.cmu.edu: And then… so, yeah, how are they getting it into… pins.
+
+102
+00:12:55.630 --> 00:13:03.789
+hrishikb@andrew.cmu.edu: So… Currently, do you know? I know you mentioned… sorry, yeah, I know this is redundant, but I'm just gonna ask it again, so we can…
+
+103
+00:13:04.090 --> 00:13:23.619
+hrishikb@andrew.cmu.edu: So, I'm not sure we are completely certain, but from the picture we got, it was that the catalog team directly enters the data into the system, and that would be, I'm assuming they'll have a database, and they'll just directly input the data in the database, and then that would flow stream to their web UIs.
+
+104
+00:13:23.840 --> 00:13:24.650
+hrishikb@andrew.cmu.edu: Okay.
+
+105
+00:13:25.380 --> 00:13:26.630
+hrishikb@andrew.cmu.edu: So…
+
+106
+00:13:27.940 --> 00:13:38.440
+hrishikb@andrew.cmu.edu: Right, I guess you also mentioned, yeah, maybe, like, there might be a web interface, like, the normal, like, inventory, I guess, for…
+
+107
+00:13:39.890 --> 00:13:48.579
+hrishikb@andrew.cmu.edu: I guess, consumers? That would be a website which has all the prices.
+
+108
+00:13:48.900 --> 00:13:52.049
+hrishikb@andrew.cmu.edu: I don't know.
+
+109
+00:13:56.230 --> 00:13:58.060
+hrishikb@andrew.cmu.edu: Like, WI?
+
+110
+00:13:58.420 --> 00:14:01.850
+hrishikb@andrew.cmu.edu: But then maybe there's some backend…
+
+111
+00:14:02.560 --> 00:14:16.210
+hrishikb@andrew.cmu.edu: you're not sure yet how they're getting it into, like, maybe… The PIMS is the data, like, refined database to push, and from PIMS to the consumer website, we don't have the visibility of that kind of system.
+
+112
+00:14:16.590 --> 00:14:32.109
+hrishikb@andrew.cmu.edu: So… So the way that, yeah, the… So I think since… Basically, you're…
+
+113
+00:14:33.080 --> 00:14:40.169
+hrishikb@andrew.cmu.edu: You're trying to… like, the current state is a lot of this manual work, Where the catalog team member
+
+114
+00:14:41.040 --> 00:14:54.120
+hrishikb@andrew.cmu.edu: they understand… They understand the existing system and existing schemas, and they had…
+
+115
+00:14:54.710 --> 00:14:59.070
+hrishikb@andrew.cmu.edu: It sounds like they have the most expertise in how to translate
+
+116
+00:14:59.250 --> 00:15:04.269
+hrishikb@andrew.cmu.edu: The vendor's catalog into… into a more general thing, right?
+
+117
+00:15:04.610 --> 00:15:12.669
+hrishikb@andrew.cmu.edu: So, yeah, I mean, it sounded like this is, like, Like, a very important… Subject matter expert that…
+
+118
+00:15:13.310 --> 00:15:17.770
+hrishikb@andrew.cmu.edu: Hopefully you can talk to directly. Have you…
+
+119
+00:15:17.770 --> 00:15:36.420
+hrishikb@andrew.cmu.edu: Yeah, we are scheduled to have a meeting with the catalog team to understand how they do it, but, we wanted the initial data so that we have a better picture before we ask questions, so that was a bit delayed, so after spring break, we'll probably go on site and meet the team. Okay. And when you say data, you're talking about what's in PIMS, like.
+
+120
+00:15:36.920 --> 00:15:50.519
+hrishikb@andrew.cmu.edu: Yeah, the… Or the catalogs? I think all of it, like the input PDFs, the schema tables they have for particular, these things, attributes, like, let's say a system has this number of attributes, this is how the schema looks like, all that data.
+
+121
+00:15:50.760 --> 00:15:55.489
+hrishikb@andrew.cmu.edu: Okay. Like, some sample set of it, so that we have a better picture on what we need.
+
+122
+00:15:58.800 --> 00:16:00.149
+hrishikb@andrew.cmu.edu: Is this…
+
+123
+00:16:03.190 --> 00:16:08.780
+hrishikb@andrew.cmu.edu: When… do you have an idea of when?
+
+124
+00:16:09.730 --> 00:16:12.919
+hrishikb@andrew.cmu.edu: Like, how frequently does this happen?
+
+125
+00:16:13.410 --> 00:16:20.160
+hrishikb@andrew.cmu.edu: How many vendors are we talking about? How many catalogs? Is this, like, updates to catalogs? Like…
+
+126
+00:16:20.480 --> 00:16:23.510
+hrishikb@andrew.cmu.edu: Like, one item at a time, or is it, like, big drops?
+
+127
+00:16:24.020 --> 00:16:38.989
+hrishikb@andrew.cmu.edu: I think, they have around, I would say, 100 of vendors, hundreds of vendors. Okay. They're not at the thousands mark, but, the frequency of the updates, we are not sure about. Okay. Maybe we can add the points.
+
+128
+00:16:48.670 --> 00:16:53.809
+hrishikb@andrew.cmu.edu: So… I think what… I guess, it sounds like what you're trying to automate is…
+
+129
+00:16:54.090 --> 00:16:56.499
+hrishikb@andrew.cmu.edu: Yeah, just to say it again…
+
+130
+00:16:57.460 --> 00:17:09.880
+hrishikb@andrew.cmu.edu: If we just take one catalog, for example, The vendor is gonna… Like… You know, send…
+
+131
+00:17:10.440 --> 00:17:12.529
+hrishikb@andrew.cmu.edu: You know, send a catalog.
+
+132
+00:17:14.190 --> 00:17:23.500
+hrishikb@andrew.cmu.edu: to… to e-parts, and catalog member is going to…
+
+133
+00:17:23.670 --> 00:17:26.219
+hrishikb@andrew.cmu.edu: Read it, parse it, and then…
+
+134
+00:17:26.760 --> 00:17:33.219
+hrishikb@andrew.cmu.edu: you know, type it into the system. So you're trying to… automate that process.
+
+135
+00:17:33.700 --> 00:17:43.260
+hrishikb@andrew.cmu.edu: So, like, read… read what's in the PDF or, you know, a different format, it could be And…
+
+136
+00:17:44.270 --> 00:17:51.490
+hrishikb@andrew.cmu.edu: Figure out how to… Correlate it with existing structures, or maybe you have to make few changes.
+
+137
+00:17:52.340 --> 00:18:02.889
+hrishikb@andrew.cmu.edu: Yeah, I think primarily it is how the data would fit into the existing schemas. Okay. So they did mention that the schema changes are not very frequent. Okay. So those schemas are pretty set. Okay.
+
+138
+00:18:04.170 --> 00:18:11.510
+hrishikb@andrew.cmu.edu: Do you have a sense of…
+
+139
+00:18:11.990 --> 00:18:15.820
+hrishikb@andrew.cmu.edu: Why… why now EPARCs is doing this?
+
+140
+00:18:17.030 --> 00:18:29.980
+hrishikb@andrew.cmu.edu: I think that is to… Because this entire process, like, it's error-prone, and it requires multiple humans, when it could be done much, much more quicker than an automated system. Okay.
+
+141
+00:18:29.980 --> 00:18:40.409
+hrishikb@andrew.cmu.edu: And also, another thing would be, they need to scale much faster now. I think there's only two people there. One. One who's doing it, and okay, right.
+
+142
+00:18:40.410 --> 00:18:51.649
+hrishikb@andrew.cmu.edu: When they actually hope to get even more customers, they need this. Like, currently, they have to, get some catalog team help from their, I think it's a sister company or a parent company, Alps. Alps.
+
+143
+00:18:52.030 --> 00:18:53.960
+hrishikb@andrew.cmu.edu: Okay, interesting.
+
+144
+00:18:55.090 --> 00:19:02.590
+hrishikb@andrew.cmu.edu: It sounds like…
+
+145
+00:19:06.320 --> 00:19:07.760
+hrishikb@andrew.cmu.edu: Yeah, definitely.
+
+146
+00:19:08.260 --> 00:19:12.529
+hrishikb@andrew.cmu.edu: You need to… you need to talk to that person, like.
+
+147
+00:19:12.740 --> 00:19:16.610
+hrishikb@andrew.cmu.edu: That person has all the subject matter expertise here.
+
+148
+00:19:17.830 --> 00:19:25.260
+hrishikb@andrew.cmu.edu: They know exactly. They'll be the best person to tell you whether you're on the right track, and what are all the
+
+149
+00:19:25.610 --> 00:19:30.690
+hrishikb@andrew.cmu.edu: pitfalls, and… Tricky things about… about their role.
+
+150
+00:19:36.010 --> 00:19:44.790
+hrishikb@andrew.cmu.edu: So… I guess the one, yeah, I guess the human review part,
+
+151
+00:19:49.350 --> 00:19:59.190
+hrishikb@andrew.cmu.edu: How… How risky is it… is it if… The data gets input incorrectly.
+
+152
+00:20:01.290 --> 00:20:04.370
+hrishikb@andrew.cmu.edu: Like, we have, you know, you have this concept of low confidence.
+
+153
+00:20:04.620 --> 00:20:09.079
+hrishikb@andrew.cmu.edu: high confidence. It sounds like they are comfortable
+
+154
+00:20:09.370 --> 00:20:16.870
+hrishikb@andrew.cmu.edu: With a system that just automatically does this, and… There's no… There's no review.
+
+155
+00:20:18.790 --> 00:20:38.639
+hrishikb@andrew.cmu.edu: So, initially, the plan is, when we do implement the system, initially, it will have human in the review for both, till they have sufficient confidence that the system actually works. After that, for all the high confidence scores, we'll be removing the human part, and only it'll be automatically pushed to the intermediate table. But for the low confidence scores, you'll still be having a human in the review.
+
+156
+00:20:38.790 --> 00:20:58.189
+hrishikb@andrew.cmu.edu: And as for the previous question that, I'm not sure if wrong data is inputted. It is, like, very big deal, because it'll probably be some attribute in, like, some minor attribute of a particular product, which, when never noticed, people can modify.
+
+157
+00:20:58.880 --> 00:21:06.560
+hrishikb@andrew.cmu.edu: Yeah. Because that's what they currently do. If there is a mistake in the, like, someone sees a mistake on the web UI, then they are targeted and they just…
+
+158
+00:21:06.840 --> 00:21:08.060
+hrishikb@andrew.cmu.edu: Fix it up.
+
+159
+00:21:08.290 --> 00:21:09.150
+hrishikb@andrew.cmu.edu: Okay.
+
+160
+00:21:10.020 --> 00:21:13.750
+hrishikb@andrew.cmu.edu: That's, that's good.
+
+161
+00:21:14.000 --> 00:21:18.050
+hrishikb@andrew.cmu.edu: To know, then, who… who are the people at…
+
+162
+00:21:19.380 --> 00:21:22.189
+hrishikb@andrew.cmu.edu: Potentially, who catches those kind of errors?
+
+163
+00:21:22.560 --> 00:21:23.500
+hrishikb@andrew.cmu.edu: Huh?
+
+164
+00:21:23.760 --> 00:21:41.089
+hrishikb@andrew.cmu.edu: Right now, they mentioned they don't have a proper feedback mechanism. Okay. So, it is just some, like, it's mostly the internal team itself. When they're reviewing something or working through something, they notice some issues, but it's… I don't… they mention the…
+
+165
+00:21:41.140 --> 00:21:48.519
+hrishikb@andrew.cmu.edu: people who are actually using the front-end software, they don't really, like, give feedback or, like, say that this is wrong, that is wrong. Okay.
+
+166
+00:21:49.430 --> 00:21:56.840
+hrishikb@andrew.cmu.edu: Yeah, I wonder if that's something that they want to add, valuable. Yeah, that is… that is also part of future school, okay.
+
+167
+00:21:57.950 --> 00:22:02.649
+hrishikb@andrew.cmu.edu: So, like, some adding to the existing UI, the ability to at least
+
+168
+00:22:02.950 --> 00:22:07.019
+hrishikb@andrew.cmu.edu: fly something, so that… To your feedback list? Yes, okay.
+
+169
+00:22:07.210 --> 00:22:08.190
+hrishikb@andrew.cmu.edu: Makes sense.
+
+170
+00:22:12.600 --> 00:22:23.570
+hrishikb@andrew.cmu.edu: What is… Yeah, just from looking at here…
+
+171
+00:22:27.380 --> 00:22:37.740
+hrishikb@andrew.cmu.edu: I mean, to me, there's… There's clearly a interface for getting that stuff into your ingestion…
+
+172
+00:22:41.650 --> 00:22:42.760
+hrishikb@andrew.cmu.edu: Pipeline.
+
+173
+00:22:42.990 --> 00:22:53.220
+hrishikb@andrew.cmu.edu: like… And… It has lots of different formats, so, like, if it's, like, a webpage, like.
+
+174
+00:22:55.620 --> 00:23:00.810
+hrishikb@andrew.cmu.edu: It sounds like, if it's an email, maybe…
+
+175
+00:23:01.890 --> 00:23:10.740
+hrishikb@andrew.cmu.edu: you know, you could forward the email into the system, and file, like, you know, drag and drop, I don't know, like, lots of different forms it could take.
+
+176
+00:23:13.960 --> 00:23:24.410
+hrishikb@andrew.cmu.edu: So… there's an interface there, right? Am I… And…
+
+177
+00:23:29.720 --> 00:23:31.640
+hrishikb@andrew.cmu.edu: I guess you'll want to think about…
+
+178
+00:23:34.320 --> 00:23:40.900
+hrishikb@andrew.cmu.edu: like, what feedback that interface would give. I presume, like, again, it's the catalog team member.
+
+179
+00:23:41.080 --> 00:23:49.090
+hrishikb@andrew.cmu.edu: who… they're getting the emails, or they're getting the files from the vendor, and they're gonna have to input it into the system. And so…
+
+180
+00:23:49.600 --> 00:23:58.770
+hrishikb@andrew.cmu.edu: Yeah, you can have some kind of control to upload files or give them different options of, you know, when they get different formats.
+
+181
+00:23:58.990 --> 00:24:02.869
+hrishikb@andrew.cmu.edu: knowing what to do with that format, right? So,
+
+182
+00:24:03.890 --> 00:24:11.839
+hrishikb@andrew.cmu.edu: Identifying from them what these different formats could be, and then providing that person feedback.
+
+183
+00:24:12.340 --> 00:24:15.220
+hrishikb@andrew.cmu.edu: When they do input it, that
+
+184
+00:24:15.860 --> 00:24:22.499
+hrishikb@andrew.cmu.edu: Everything is working, or… or maybe there's an error, or, like, a parsing error, or…
+
+185
+00:24:22.790 --> 00:24:27.840
+hrishikb@andrew.cmu.edu: You know, undetect… like, unknown file format, like, stuff like that, right?
+
+186
+00:24:28.540 --> 00:24:29.890
+hrishikb@andrew.cmu.edu: I think the…
+
+187
+00:24:30.140 --> 00:24:38.360
+hrishikb@andrew.cmu.edu: Feedback mechanism that the client had in mind was more centered towards the users being able to give feedback?
+
+188
+00:24:38.640 --> 00:24:41.550
+hrishikb@andrew.cmu.edu: But, internally.
+
+189
+00:24:42.400 --> 00:24:53.219
+hrishikb@andrew.cmu.edu: Internally, I'm not sure if there is a feedback mechanism as of now, or even if that's in the plan. I guess what I'm referring to is, okay, let's say the…
+
+190
+00:24:53.510 --> 00:24:59.859
+hrishikb@andrew.cmu.edu: say this person's name is Chris, right? Vendor A is going to email Chris, PDF.
+
+191
+00:25:00.190 --> 00:25:05.789
+hrishikb@andrew.cmu.edu: So… How do you envision then getting this PDF into your system?
+
+192
+00:25:09.440 --> 00:25:11.710
+hrishikb@andrew.cmu.edu: Like, that's an interface. Right. Right?
+
+193
+00:25:12.460 --> 00:25:17.930
+hrishikb@andrew.cmu.edu: Like, it would be a… maybe it could be a web page with a form to upload the file.
+
+194
+00:25:18.410 --> 00:25:22.070
+hrishikb@andrew.cmu.edu: Right, so that's… that's a UI that you would have to design.
+
+195
+00:25:23.300 --> 00:25:26.379
+hrishikb@andrew.cmu.edu: Am I thinking about it?
+
+196
+00:25:26.510 --> 00:25:36.370
+hrishikb@andrew.cmu.edu: Yeah, so initially, we had this question with the clients as well, like, how we're planning to, like, do we create a UI or something, but they're more interested in the software part of it.
+
+197
+00:25:36.520 --> 00:25:45.650
+hrishikb@andrew.cmu.edu: like, so, for our purposes, we're thinking we'll just simply, input it via, like, maybe some listen folder or something like that.
+
+198
+00:25:45.650 --> 00:25:59.880
+hrishikb@andrew.cmu.edu: Which we can just, for dev testing, we can use something of that sort for the initial part. Okay. But, yeah, maybe it'll be good to think about from a catalog number's… Yeah, I mean, that's still an interface. Like, you have to tell Chris…
+
+199
+00:26:00.940 --> 00:26:11.169
+hrishikb@andrew.cmu.edu: this is how you use the system. You need to move… you need to download the PDF from your email, and then move it to this folder. Yeah. Right? And then he needs to get feedback, like.
+
+200
+00:26:11.920 --> 00:26:21.749
+hrishikb@andrew.cmu.edu: to know whether or not it made it. Like, is it gonna stay there? Is it gonna get deleted? Is there gonna be a file? Are you gonna get an email about it? Or, like…
+
+201
+00:26:24.390 --> 00:26:30.639
+hrishikb@andrew.cmu.edu: Then, I'm sorry if this is the right question, but then, aren't we trying to animate Chris over here?
+
+202
+00:26:30.930 --> 00:26:36.870
+hrishikb@andrew.cmu.edu: Well, that could be a valid question, but then how… how is it gonna… how do you envision the system working?
+
+203
+00:26:37.680 --> 00:26:54.330
+hrishikb@andrew.cmu.edu: The long-term goal is that what you said before, that the clients will have some sort of portal. They, instead of emailing, they just put their files on the portal, and the portal handles, uses the APIs of our system, and then… then we move forward from there.
+
+204
+00:26:55.090 --> 00:26:55.930
+hrishikb@andrew.cmu.edu: Right.
+
+205
+00:26:56.250 --> 00:27:03.290
+hrishikb@andrew.cmu.edu: So, I mean, I guess it depends on getting on the same page as your eParts,
+
+206
+00:27:03.470 --> 00:27:19.849
+hrishikb@andrew.cmu.edu: representatives, like, what they want, right? Again, when I say interface, it could be anything. It could be… maybe you're saying, and if they agree, it could be an API, and then Chris just has to figure out how to use an API, like, they have to know how to use Postman, or…
+
+207
+00:27:19.990 --> 00:27:24.639
+hrishikb@andrew.cmu.edu: like… Pearl, right?
+
+208
+00:27:24.770 --> 00:27:29.519
+hrishikb@andrew.cmu.edu: But then that's still very much an interface. Like, your API is going to have response codes.
+
+209
+00:27:29.810 --> 00:27:39.439
+hrishikb@andrew.cmu.edu: Right? And there's going to be times when the file doesn't work, or it's too big, or it's a… it doesn't make sense, and you have to give that feedback. So, I'm just trying to…
+
+210
+00:27:39.650 --> 00:27:53.070
+hrishikb@andrew.cmu.edu: I think the closest thing what we have currently would be some sort of, similar API that you can send a request through. Okay. And maybe… maybe eParts is fine with that, but…
+
+211
+00:27:53.510 --> 00:27:56.610
+hrishikb@andrew.cmu.edu: You should make sure that that's what they expect.
+
+212
+00:27:59.680 --> 00:28:02.820
+hrishikb@andrew.cmu.edu: And I know that's… yeah, that might not be the…
+
+213
+00:28:03.250 --> 00:28:06.369
+hrishikb@andrew.cmu.edu: That's not the interesting part of the project, necessarily, but…
+
+214
+00:28:06.570 --> 00:28:13.599
+hrishikb@andrew.cmu.edu: That is the very first step of how anything happens, so… You have to consider it.
+
+215
+00:28:13.850 --> 00:28:16.090
+hrishikb@andrew.cmu.edu: What that is. Otherwise,
+
+216
+00:28:16.810 --> 00:28:20.550
+hrishikb@andrew.cmu.edu: The parts won't be able to test it or use it very well, right?
+
+217
+00:28:20.680 --> 00:28:26.770
+hrishikb@andrew.cmu.edu: So… I would try to… clarify…
+
+218
+00:28:26.970 --> 00:28:30.029
+hrishikb@andrew.cmu.edu: And get aligned with eParts, like, what they expect.
+
+219
+00:28:30.790 --> 00:28:36.530
+hrishikb@andrew.cmu.edu: Currently, To get the files, to get the catalogs into your system.
+
+220
+00:28:37.040 --> 00:28:39.019
+hrishikb@andrew.cmu.edu: Like, is it Chris? Like…
+
+221
+00:28:39.380 --> 00:28:44.949
+hrishikb@andrew.cmu.edu: If it's Chris, then, you know, you need to make sure to talk to Chris, or make sure he starts to talk to Chris, and make sure that's okay.
+
+222
+00:28:45.480 --> 00:28:47.069
+hrishikb@andrew.cmu.edu: Right, otherwise there's gonna be a gap.
+
+223
+00:28:48.960 --> 00:28:49.800
+hrishikb@andrew.cmu.edu: with…
+
+224
+00:28:50.040 --> 00:28:57.670
+hrishikb@andrew.cmu.edu: Within doing anything to the system. We need to finalize how the data is being inputted, how they want the data to be imported into the system.
+
+225
+00:28:57.850 --> 00:29:01.900
+hrishikb@andrew.cmu.edu: Basically. Yeah, I mean, it has… I guess…
+
+226
+00:29:02.460 --> 00:29:06.889
+hrishikb@andrew.cmu.edu: I would assume that that should have come up, or that would have come up, right? Like…
+
+227
+00:29:07.790 --> 00:29:11.669
+hrishikb@andrew.cmu.edu: What do they say in their… in their brief?
+
+228
+00:29:13.500 --> 00:29:15.400
+hrishikb@andrew.cmu.edu: Okay, they just said ingest.
+
+229
+00:29:16.510 --> 00:29:22.829
+hrishikb@andrew.cmu.edu: Yeah, but who's gonna get it in the system of files? Yeah, I think that part has not been the discussion yet.
+
+230
+00:29:23.040 --> 00:29:31.100
+hrishikb@andrew.cmu.edu: Because that matters because that tells you where your boundary is of what you're building.
+
+231
+00:29:31.450 --> 00:29:37.230
+hrishikb@andrew.cmu.edu: Right? Like… Your system is gonna end somewhere.
+
+232
+00:29:37.600 --> 00:29:48.120
+hrishikb@andrew.cmu.edu: So, it could be the API, it could be this file listener thing, it could be a web UI. So, the endpoints that we… endpoints we have discussed, but the…
+
+233
+00:29:48.790 --> 00:29:58.510
+hrishikb@andrew.cmu.edu: now seems a bit vague, because the initial starting point is the ingestion of the files, and the ending point is the PIMS database for us, the PIMS module.
+
+234
+00:29:58.900 --> 00:30:07.399
+hrishikb@andrew.cmu.edu: middle part is all our software. We start at the CSVs or PDFs or emails, and we end that when we put the data in the community table.
+
+235
+00:30:08.250 --> 00:30:16.049
+hrishikb@andrew.cmu.edu: But how we are… I mean, to test it, you have to get in the system somehow, so… I guess, yeah, you have to consider that.
+
+236
+00:30:18.450 --> 00:30:23.730
+hrishikb@andrew.cmu.edu: I guess the second major piece that you mentioned is, like, this human review part.
+
+237
+00:30:28.170 --> 00:30:32.149
+hrishikb@andrew.cmu.edu: It seems… it sounds like, again, Chris is the best person.
+
+238
+00:30:32.290 --> 00:30:41.680
+hrishikb@andrew.cmu.edu: too, about this, like… He or she would be… your…
+
+239
+00:30:41.890 --> 00:30:44.639
+hrishikb@andrew.cmu.edu: Your primary source in terms of…
+
+240
+00:30:45.680 --> 00:30:51.800
+hrishikb@andrew.cmu.edu: figuring out how to design that interface. Yeah. So…
+
+241
+00:30:53.320 --> 00:31:00.860
+hrishikb@andrew.cmu.edu: Because the interface has to allow this reviewer to make decisions about the data.
+
+242
+00:31:01.110 --> 00:31:10.210
+hrishikb@andrew.cmu.edu: And… The way that you design and interface that is helpful is… to understand…
+
+243
+00:31:10.500 --> 00:31:15.569
+hrishikb@andrew.cmu.edu: The user's model of the world, their mental model of your system.
+
+244
+00:31:15.970 --> 00:31:18.530
+hrishikb@andrew.cmu.edu: So…
+
+245
+00:31:24.010 --> 00:31:30.690
+hrishikb@andrew.cmu.edu: I would… I would think that… I mean…
+
+246
+00:31:35.810 --> 00:31:38.909
+hrishikb@andrew.cmu.edu: Yeah, actually, I don't know, I don't want to make any assumptions, like…
+
+247
+00:31:39.150 --> 00:31:42.400
+hrishikb@andrew.cmu.edu: about how… how this should be presented to Chris, like…
+
+248
+00:31:43.890 --> 00:31:46.069
+hrishikb@andrew.cmu.edu: It could range something… somewhere like…
+
+249
+00:31:46.730 --> 00:31:54.540
+hrishikb@andrew.cmu.edu: you know, they get an email, and you reply… you reply… they reply yes or no, right? That's… that's one interface. It could be a web form.
+
+250
+00:31:54.790 --> 00:31:58.600
+hrishikb@andrew.cmu.edu: Or, like, a diff, maybe, or, like… Questions?
+
+251
+00:31:59.010 --> 00:32:08.180
+hrishikb@andrew.cmu.edu: So… I guess, he should try to find out.
+
+252
+00:32:10.130 --> 00:32:21.240
+hrishikb@andrew.cmu.edu: So, like, the goal is for… E-parts to be able to… Correct.
+
+253
+00:32:21.820 --> 00:32:28.350
+hrishikb@andrew.cmu.edu: Things that are wrong from whatever your system produced that you have low confidence, so… You're saying, like…
+
+254
+00:32:28.660 --> 00:32:34.370
+hrishikb@andrew.cmu.edu: Hey, you need to look at this and make sure that we… Decided the right things.
+
+255
+00:32:35.740 --> 00:32:42.770
+hrishikb@andrew.cmu.edu: Or, like, we, we, like, all the data is in the right place.
+
+256
+00:32:47.780 --> 00:32:50.010
+hrishikb@andrew.cmu.edu: Yeah, basically the…
+
+257
+00:32:51.990 --> 00:33:01.449
+hrishikb@andrew.cmu.edu: the, like, let's say Chris has to, and they have to check the low or high content scores, they need to have sufficient information about the data that's being inputted, and…
+
+258
+00:33:01.690 --> 00:33:10.450
+hrishikb@andrew.cmu.edu: whatever the initial questions they might have, those should be answered in whatever we are displaying. Yeah, yeah, I think you have to identify
+
+259
+00:33:18.480 --> 00:33:25.719
+hrishikb@andrew.cmu.edu: Yeah, like, what information Chris needs to decide that this is correct or not. Like, we have to give them enough context.
+
+260
+00:33:26.510 --> 00:33:34.310
+hrishikb@andrew.cmu.edu: And I'm not sure what, you know, what level is that gonna be at? Is it, like, for…
+
+261
+00:33:35.030 --> 00:33:39.900
+hrishikb@andrew.cmu.edu: You know, per attribute, or per item, per catalog.
+
+262
+00:33:41.050 --> 00:33:46.900
+hrishikb@andrew.cmu.edu: So you can try to get a sense of… Ow.
+
+263
+00:33:47.990 --> 00:33:51.620
+hrishikb@andrew.cmu.edu: I guess.
+
+264
+00:33:52.500 --> 00:33:58.629
+hrishikb@andrew.cmu.edu: content-wise, Yeah, how much is under review? And, like, make it clear, what is…
+
+265
+00:33:59.010 --> 00:34:02.520
+hrishikb@andrew.cmu.edu: what are they supposed to do with whatever you present them? Like…
+
+266
+00:34:02.710 --> 00:34:07.050
+hrishikb@andrew.cmu.edu: Is it a big checklist that they go through? Are you going to give them
+
+267
+00:34:07.930 --> 00:34:16.739
+hrishikb@andrew.cmu.edu: Like, is it a list of attributes, and then you have confidence scores next to it, and you, like, rate them by low to high confidence?
+
+268
+00:34:17.350 --> 00:34:21.100
+hrishikb@andrew.cmu.edu: You were saying early on, they're gonna review everything.
+
+269
+00:34:21.560 --> 00:34:28.149
+hrishikb@andrew.cmu.edu: So maybe, yeah, maybe it's, like, a ranked list that eventually you hide the high-confidence stuff.
+
+270
+00:34:33.350 --> 00:34:44.949
+hrishikb@andrew.cmu.edu: the… I think, Dan, the best… the best way to… Yeah, again, the…
+
+271
+00:34:45.170 --> 00:34:52.229
+hrishikb@andrew.cmu.edu: Catalog member is… sounds like the right person that you need to… Bob's you to answer that.
+
+272
+00:34:52.800 --> 00:34:56.440
+hrishikb@andrew.cmu.edu: I'm gonna have a long meeting with Chris. Yeah, yeah, yeah.
+
+273
+00:34:56.929 --> 00:35:00.330
+hrishikb@andrew.cmu.edu: Cuz, cause, yeah, it sounds like you're building… like, you're…
+
+274
+00:35:00.740 --> 00:35:07.769
+hrishikb@andrew.cmu.edu: your system is entirely to help Chris do their job faster, or, like, to scale Chris, or, you know, to take…
+
+275
+00:35:07.880 --> 00:35:09.230
+hrishikb@andrew.cmu.edu: their expertise.
+
+276
+00:35:09.580 --> 00:35:11.900
+hrishikb@andrew.cmu.edu: And… Automate.
+
+277
+00:35:12.200 --> 00:35:14.619
+hrishikb@andrew.cmu.edu: As much as makes sense.
+
+278
+00:35:19.580 --> 00:35:26.659
+hrishikb@andrew.cmu.edu: I was looking through the… you know, grief, and… what else did I trigger anything in my mind?
+
+279
+00:35:29.830 --> 00:35:32.789
+hrishikb@andrew.cmu.edu: I guess, what would you… what is the…
+
+280
+00:35:33.480 --> 00:35:38.189
+hrishikb@andrew.cmu.edu: biggest risk in your mind on the project, whether it's related to UI or not?
+
+281
+00:35:45.840 --> 00:35:48.909
+hrishikb@andrew.cmu.edu: Or the biggest, like, unknown to you right now.
+
+282
+00:35:50.360 --> 00:35:56.840
+hrishikb@andrew.cmu.edu: Now, after talking about it, the unknown looks like the interface of how you're gonna get the…
+
+283
+00:35:57.260 --> 00:36:00.970
+hrishikb@andrew.cmu.edu: files and, and the… like, how the URL will interact.
+
+284
+00:36:01.320 --> 00:36:06.839
+hrishikb@andrew.cmu.edu: Because you have not really discussed those parts, you're more focused on ML models and the ingestion parts of it.
+
+285
+00:36:07.350 --> 00:36:11.670
+hrishikb@andrew.cmu.edu: I mean, it doesn't, it can… it'll… and…
+
+286
+00:36:12.270 --> 00:36:16.489
+hrishikb@andrew.cmu.edu: Definitely start more, like, bare bones, and…
+
+287
+00:36:16.850 --> 00:36:19.039
+hrishikb@andrew.cmu.edu: Like, it doesn't have to be fancy, but you do have to…
+
+288
+00:36:19.370 --> 00:36:20.969
+hrishikb@andrew.cmu.edu: I mean, it has to get in somehow.
+
+289
+00:36:22.030 --> 00:36:30.950
+hrishikb@andrew.cmu.edu: once you get it… once you get a system, like, working end-to-end, then you can start to have discussions, or… then it makes it a lot easier for e-parts.
+
+290
+00:36:31.060 --> 00:36:39.799
+hrishikb@andrew.cmu.edu: to, like, visualize and, think about how the system is going to work, and then that will
+
+291
+00:36:40.090 --> 00:36:46.050
+hrishikb@andrew.cmu.edu: Trigger questions from their end, or, you know, thoughts on their end on how it should work.
+
+292
+00:36:48.130 --> 00:36:54.880
+hrishikb@andrew.cmu.edu: So, you don't have to figure it out right now, but you have to consider it so that you can start to get data moving and flowing.
+
+293
+00:36:55.170 --> 00:36:56.170
+hrishikb@andrew.cmu.edu: Testing.
+
+294
+00:36:56.340 --> 00:36:57.180
+hrishikb@andrew.cmu.edu: Thanks.
+
+295
+00:36:57.900 --> 00:37:02.490
+hrishikb@andrew.cmu.edu: So for, let's say from a development point of view, what would…
+
+296
+00:37:02.740 --> 00:37:08.690
+hrishikb@andrew.cmu.edu: you think that our initial UI or, like, interface should be,
+
+297
+00:37:09.060 --> 00:37:11.780
+hrishikb@andrew.cmu.edu: For, let's say, the engine part, when the…
+
+298
+00:37:12.180 --> 00:37:15.380
+hrishikb@andrew.cmu.edu: Many PDFs or CSVs? Yeah, I mean,
+
+299
+00:37:16.490 --> 00:37:23.160
+hrishikb@andrew.cmu.edu: I mean, I would try… I mean, is there a format that you know is more common than others?
+
+300
+00:37:25.790 --> 00:37:32.319
+hrishikb@andrew.cmu.edu: Preferred… That list of formats, is there, like, a priority, or…
+
+301
+00:37:32.540 --> 00:37:36.880
+hrishikb@andrew.cmu.edu: I don't think we have priority, but, we… we only know…
+
+302
+00:37:37.010 --> 00:37:45.770
+hrishikb@andrew.cmu.edu: PDFs, CSVs are the most common. Okay. And the web scraping is for, basically they do it for if some…
+
+303
+00:37:46.010 --> 00:37:54.460
+hrishikb@andrew.cmu.edu: vendor provides some information, it's completely… it's not… it's, like, half-baked, so the catalyticin goes to the website, and they themselves get the information. Right.
+
+304
+00:37:55.080 --> 00:38:01.700
+hrishikb@andrew.cmu.edu: Well, CSV sounds like the most structured and easiest to work with, so I'd probably start there.
+
+305
+00:38:02.230 --> 00:38:07.770
+hrishikb@andrew.cmu.edu: So…
+
+306
+00:38:08.790 --> 00:38:13.790
+hrishikb@andrew.cmu.edu: I mean, that could be just, like, a web… a web form that's, like, upload file and…
+
+307
+00:38:14.290 --> 00:38:19.029
+hrishikb@andrew.cmu.edu: Okay. Just basic, you know, whether or not that worked to get things into the system.
+
+308
+00:38:19.490 --> 00:38:20.500
+hrishikb@andrew.cmu.edu: Okay.
+
+309
+00:38:22.750 --> 00:38:31.200
+hrishikb@andrew.cmu.edu: I guess, and then… Oh yeah, I was gonna ask, like… I'm curious about…
+
+310
+00:38:32.670 --> 00:38:35.970
+hrishikb@andrew.cmu.edu: Yeah, how you're, let's see…
+
+311
+00:38:36.940 --> 00:38:48.660
+hrishikb@andrew.cmu.edu: I guess your… what are your current thoughts on… here, let me go to your… Yeah, alright, so…
+
+312
+00:38:52.000 --> 00:38:55.209
+hrishikb@andrew.cmu.edu: your… your… the ML part of it, like…
+
+313
+00:39:04.090 --> 00:39:07.730
+hrishikb@andrew.cmu.edu: I don't know, how… how do you envision this working?
+
+314
+00:39:08.080 --> 00:39:16.359
+hrishikb@andrew.cmu.edu: Like, so you get a bunch of… you get a catalog, and… You know the existing schema.
+
+315
+00:39:20.640 --> 00:39:25.200
+hrishikb@andrew.cmu.edu: what is… what is the intermediate structured layer doing? And then…
+
+316
+00:39:25.720 --> 00:39:27.610
+hrishikb@andrew.cmu.edu: Like, how do you evaluate that?
+
+317
+00:39:27.760 --> 00:39:33.000
+hrishikb@andrew.cmu.edu: Something is… Is correct or not.
+
+318
+00:39:35.430 --> 00:39:39.489
+hrishikb@andrew.cmu.edu: I think that is the part where MLRE will come into picture.
+
+319
+00:39:40.050 --> 00:39:54.929
+hrishikb@andrew.cmu.edu: I think they will, we'll have some sort of precise rules to see if particular attributes fits the, data or not.
+
+320
+00:39:55.150 --> 00:40:11.049
+hrishikb@andrew.cmu.edu: And then, according to that, we'll be giving it the content scores. Okay. And this table is basically, after we get all the data, we parse it, and we have it in proper, like, let's say, a schema sort of format, we just dump it on the intermediate structure table, and then the ML model can use the data from there.
+
+321
+00:40:12.460 --> 00:40:23.180
+hrishikb@andrew.cmu.edu: Did eParts… suggest… I guess, yeah, they said… like…
+
+322
+00:40:26.770 --> 00:40:34.520
+hrishikb@andrew.cmu.edu: How much… Machine learning capability do they currently use, or…
+
+323
+00:40:35.490 --> 00:40:42.239
+hrishikb@andrew.cmu.edu: None. They're just building the infra and just starting to get into machine learning. Okay.
+
+324
+00:40:43.070 --> 00:40:47.530
+hrishikb@andrew.cmu.edu: But they, they ask for ML. This is the ML-based. Yeah. Okay.
+
+325
+00:40:48.140 --> 00:40:58.340
+hrishikb@andrew.cmu.edu: They plan to grow their ML infra as well. They wanted this to be extensible so that they can maybe some other day use some of their ML components to plug into the system.
+
+326
+00:40:58.720 --> 00:40:59.929
+hrishikb@andrew.cmu.edu: And things like that.
+
+327
+00:41:00.400 --> 00:41:01.230
+hrishikb@andrew.cmu.edu: Okay.
+
+328
+00:41:11.310 --> 00:41:20.790
+hrishikb@andrew.cmu.edu: Okay, so yeah, you mentioned, yeah, I mean, we talked a little bit about that human review piece.
+
+329
+00:41:26.190 --> 00:41:39.160
+hrishikb@andrew.cmu.edu: And then there's also… I like the ops part, logs and metrics and… dashboards,
+
+330
+00:41:40.240 --> 00:41:46.190
+hrishikb@andrew.cmu.edu: Is that a significant… Part… Or is that more, like, stretch goal?
+
+331
+00:41:49.790 --> 00:41:57.029
+hrishikb@andrew.cmu.edu: I… I'm not sure we… we have not really discussed that part into length. Ashutar, you have an opinion on the observability part?
+
+332
+00:41:57.620 --> 00:42:04.039
+hrishikb@andrew.cmu.edu: And we've created a package with them and more. They're using Datadog, so that's…
+
+333
+00:42:04.220 --> 00:42:18.079
+hrishikb@andrew.cmu.edu: And they wouldn't want the learning efforts to go into the observability, but it's just a thing that we added, that, okay, since there's an ML thing added, so we would want to send
+
+334
+00:42:18.080 --> 00:42:31.730
+hrishikb@andrew.cmu.edu: define some kind of metrics and, send some traces or logs into your data doc system. Yeah. But currently, they do, they have… they have their own observability metrics, so they're not, like, very much keen into
+
+335
+00:42:31.930 --> 00:42:40.819
+hrishikb@andrew.cmu.edu: expanding it. This would mostly be, I guess, internal for our internal system, for us to… Okay, okay.
+
+336
+00:42:42.330 --> 00:42:57.839
+hrishikb@andrew.cmu.edu: Something like, per day, these many number of queries were served, or these many number of records got ingested, or just like that, metrics around the data that's another.
+
+337
+00:42:58.350 --> 00:43:08.270
+hrishikb@andrew.cmu.edu: Do you know if, like, I don't know, like… Cost or, like, token usage?
+
+338
+00:43:08.420 --> 00:43:11.309
+hrishikb@andrew.cmu.edu: That kind of stuff is on their radar.
+
+339
+00:43:11.540 --> 00:43:13.330
+hrishikb@andrew.cmu.edu: Like, what? No.
+
+340
+00:43:14.200 --> 00:43:18.459
+hrishikb@andrew.cmu.edu: Okay. What kind of proposal? I mean, like, the,
+
+341
+00:43:19.300 --> 00:43:23.169
+hrishikb@andrew.cmu.edu: Like, for using LLMs, so things like that.
+
+342
+00:43:23.280 --> 00:43:26.810
+hrishikb@andrew.cmu.edu: Did you talk about queries, but…
+
+343
+00:43:30.660 --> 00:43:42.280
+hrishikb@andrew.cmu.edu: Yeah, actually, no, maybe, maybe, are they planning to use… Like, LLM-based ML stuff.
+
+344
+00:43:42.750 --> 00:43:48.460
+hrishikb@andrew.cmu.edu: No, no, no. No, we're just going by the traditional email. Okay, okay, yeah. Got it, got it.
+
+345
+00:43:49.610 --> 00:43:51.280
+hrishikb@andrew.cmu.edu: Nevermind, man. Cool.
+
+346
+00:43:52.070 --> 00:43:56.789
+hrishikb@andrew.cmu.edu: Okay, yeah, so going back to,
+
+347
+00:43:58.070 --> 00:44:03.550
+hrishikb@andrew.cmu.edu: Yeah, the biggest risks, or, what else is…
+
+348
+00:44:05.880 --> 00:44:11.769
+hrishikb@andrew.cmu.edu: I guess there's multiple levels, too, not just for e-parts, but, what do you need?
+
+349
+00:44:12.190 --> 00:44:15.819
+hrishikb@andrew.cmu.edu: For the checkpoint that… Got you still…
+
+350
+00:44:16.900 --> 00:44:24.820
+hrishikb@andrew.cmu.edu: For the checkpoint, there are a couple of documents. There is… And therefore, it's called,
+
+351
+00:44:24.930 --> 00:44:42.499
+hrishikb@andrew.cmu.edu: the, like, the exact boundary of work that we're supposed to do, you know, has to be a document and things like that, but initially, we have our software engineering system also, like, it is still developing, but for now, we have a pretty solid system defined a lot of things in that.
+
+352
+00:44:42.840 --> 00:44:55.979
+hrishikb@andrew.cmu.edu: We have an initial requirements document as well, but that has not yet been vetted by the, requirements coach. We have a meeting with him tomorrow to get that done.
+
+353
+00:44:56.540 --> 00:45:02.150
+hrishikb@andrew.cmu.edu: There are a couple of things, but I can't remember them.
+
+354
+00:45:03.380 --> 00:45:14.930
+hrishikb@andrew.cmu.edu: And the remaining high-level architecture, what we're planning to do, what our semester plan would be, all those things are, pretty much… we have a… we know what we have to do, we have plan ready.
+
+355
+00:45:16.360 --> 00:45:24.169
+hrishikb@andrew.cmu.edu: Yeah, we just have to, prepare the actual activities for the presentation that we're gonna present to everyone.
+
+356
+00:45:25.460 --> 00:45:29.990
+hrishikb@andrew.cmu.edu: One thing I just thought of, too, looking at the… Project thing.
+
+357
+00:45:32.540 --> 00:45:35.700
+hrishikb@andrew.cmu.edu: You might… think about…
+
+358
+00:45:36.340 --> 00:45:44.670
+hrishikb@andrew.cmu.edu: I'm just reading about the problem and how they said, like, you know, every record's touched by a person to do all this stuff, and it's error-prone.
+
+359
+00:45:46.880 --> 00:45:51.120
+hrishikb@andrew.cmu.edu: Like, if there's a way to, you know, phase your project so that
+
+360
+00:45:51.920 --> 00:45:55.610
+hrishikb@andrew.cmu.edu: You start to automate, you know, small parts of it.
+
+361
+00:45:56.770 --> 00:45:57.620
+hrishikb@andrew.cmu.edu: that.
+
+362
+00:45:58.290 --> 00:46:02.459
+hrishikb@andrew.cmu.edu: our friend Chris can already get value early on.
+
+363
+00:46:05.300 --> 00:46:12.290
+hrishikb@andrew.cmu.edu: So that's… I think, one, that reduces risk in the overall project, because
+
+364
+00:46:15.120 --> 00:46:17.649
+hrishikb@andrew.cmu.edu: You are, you know, starting to…
+
+365
+00:46:18.210 --> 00:46:23.080
+hrishikb@andrew.cmu.edu: Build things that are useful and valuable, and then you can get even richer feedback.
+
+366
+00:46:23.540 --> 00:46:29.269
+hrishikb@andrew.cmu.edu: From e-parts, so, like… Kind of where the… where the next step should be.
+
+367
+00:46:29.610 --> 00:46:32.370
+hrishikb@andrew.cmu.edu: And…
+
+368
+00:46:35.210 --> 00:46:48.120
+hrishikb@andrew.cmu.edu: In line with those things, we did have a few, like, concerns, because since we are, like, we are to use AI heavily to do all the coding and all these things, so we…
+
+369
+00:46:48.230 --> 00:46:57.270
+hrishikb@andrew.cmu.edu: expect the development part to, flow around very quickly. And, like, even thinking about two-week sprint seemed very… way too long.
+
+370
+00:46:57.320 --> 00:47:16.429
+hrishikb@andrew.cmu.edu: And we were planning to have, like, shorter sprints, maybe one week at max, kind of stretch. So, currently, we are expecting the entire development process, the actual writing all the different modules, should be fairly quick. Yeah. But we're not… we're not… we're not sure how quick, but, let's say we get it all done in a
+
+371
+00:47:16.720 --> 00:47:25.060
+hrishikb@andrew.cmu.edu: I don't know, maybe a… optimistically, maybe a month. So, having intermittent releases between that, would you think that would be, like, useful?
+
+372
+00:47:26.330 --> 00:47:36.150
+hrishikb@andrew.cmu.edu: Yeah, I think definitely… The earlier that you can…
+
+373
+00:47:43.060 --> 00:47:48.429
+hrishikb@andrew.cmu.edu: Yeah, the other that you can get feedback, You know, the…
+
+374
+00:47:48.870 --> 00:47:53.849
+hrishikb@andrew.cmu.edu: the more risk that you can mitigate. Like, even if it's, you know, a month.
+
+375
+00:47:54.320 --> 00:47:57.709
+hrishikb@andrew.cmu.edu: Like, if you end up building the wrong thing, then…
+
+376
+00:47:58.540 --> 00:48:02.280
+hrishikb@andrew.cmu.edu: Then you have to rebuild it, right? So…
+
+377
+00:48:02.630 --> 00:48:04.819
+hrishikb@andrew.cmu.edu: Like, it sounds like there's a lot of…
+
+378
+00:48:05.300 --> 00:48:11.530
+hrishikb@andrew.cmu.edu: There's a lot of, different activities that Chris does that you could automate.
+
+379
+00:48:13.350 --> 00:48:19.750
+hrishikb@andrew.cmu.edu: like… They're, like, just parsing and understanding, like, what is in the catalog.
+
+380
+00:48:20.060 --> 00:48:26.109
+hrishikb@andrew.cmu.edu: There's just, like, the man… I guess the manual inputting the records, like…
+
+381
+00:48:26.360 --> 00:48:28.520
+hrishikb@andrew.cmu.edu: There might be small wins there.
+
+382
+00:48:29.050 --> 00:48:33.130
+hrishikb@andrew.cmu.edu: to… That…
+
+383
+00:48:36.380 --> 00:48:43.950
+hrishikb@andrew.cmu.edu: like… In the… in the interim, like, that could still be a manual piece.
+
+384
+00:48:44.170 --> 00:48:48.250
+hrishikb@andrew.cmu.edu: But that could also serve as, like, the human review part.
+
+385
+00:48:49.470 --> 00:48:58.669
+hrishikb@andrew.cmu.edu: Right, and then eventually that part can be automated, where whatever… whatever UI that You're using their,
+
+386
+00:48:58.950 --> 00:49:05.530
+hrishikb@andrew.cmu.edu: Yeah, can get automated through an API instead of manually, you know, Chris looking at it.
+
+387
+00:49:09.480 --> 00:49:14.400
+hrishikb@andrew.cmu.edu: like… Another way to say it is, like, identifying
+
+388
+00:49:17.580 --> 00:49:27.019
+hrishikb@andrew.cmu.edu: what are… what are Chris's pain points right now? Like, I know overall it's just… it's manual, right? But if you can break down their workflow.
+
+389
+00:49:27.210 --> 00:49:32.349
+hrishikb@andrew.cmu.edu: Or understand what the different pieces are, what the different activities are. Maybe there's opportunities to
+
+390
+00:49:32.470 --> 00:49:35.289
+hrishikb@andrew.cmu.edu: First, automate just a piece of that.
+
+391
+00:49:35.770 --> 00:49:37.400
+hrishikb@andrew.cmu.edu: And… Okay.
+
+392
+00:49:40.490 --> 00:49:51.390
+hrishikb@andrew.cmu.edu: Because, like, the deeper you get into understanding Chris's world, then the better you'll be able to address and know exactly how to automate this whole system.
+
+393
+00:49:51.740 --> 00:49:53.270
+hrishikb@andrew.cmu.edu: It should be designed.
+
+394
+00:50:35.600 --> 00:50:36.300
+hrishikb@andrew.cmu.edu: Hmm.
+
+395
+00:50:37.550 --> 00:50:40.470
+hrishikb@andrew.cmu.edu: And you can also…
+
+396
+00:50:44.370 --> 00:50:48.769
+hrishikb@andrew.cmu.edu: I guess when you do talk to Chris, or whatever their name is,
+
+397
+00:50:56.230 --> 00:51:03.439
+hrishikb@andrew.cmu.edu: I guess… try to… Yeah, like, trying to understand how they see the world.
+
+398
+00:51:04.050 --> 00:51:08.630
+hrishikb@andrew.cmu.edu: And how they… how they think the system might work.
+
+399
+00:51:09.640 --> 00:51:15.479
+hrishikb@andrew.cmu.edu: Like, try to get into… into their minds.
+
+400
+00:51:15.630 --> 00:51:21.960
+hrishikb@andrew.cmu.edu: You know, this… this team is building this system that
+
+401
+00:51:22.080 --> 00:51:25.009
+hrishikb@andrew.cmu.edu: That I'm gonna use, and gonna automate.
+
+402
+00:51:25.350 --> 00:51:30.929
+hrishikb@andrew.cmu.edu: The tedious parts of my job, and, the parts that are really error-prone.
+
+403
+00:51:31.110 --> 00:51:39.130
+hrishikb@andrew.cmu.edu: And… Understanding that will help you focus.
+
+404
+00:51:39.320 --> 00:51:40.230
+hrishikb@andrew.cmu.edu: on.
+
+405
+00:51:41.340 --> 00:51:42.889
+hrishikb@andrew.cmu.edu: What parts are important?
+
+406
+00:51:43.200 --> 00:51:44.720
+hrishikb@andrew.cmu.edu: In this whole process.
+
+407
+00:51:46.930 --> 00:51:51.780
+hrishikb@andrew.cmu.edu: Basically, from their point of view, what all… As the most…
+
+408
+00:51:52.010 --> 00:52:00.000
+hrishikb@andrew.cmu.edu: like, pain points for them should be things you should work on, like, prioritize, basically. Yeah. And we're breaking down parts also. Right, yeah.
+
+409
+00:52:03.900 --> 00:52:07.100
+hrishikb@andrew.cmu.edu: Like, they might say, like, if I could just have…
+
+410
+00:52:08.210 --> 00:52:10.760
+hrishikb@andrew.cmu.edu: If you do this parse, like.
+
+411
+00:52:11.860 --> 00:52:14.389
+hrishikb@andrew.cmu.edu: the PDF and put it into this…
+
+412
+00:52:14.700 --> 00:52:22.750
+hrishikb@andrew.cmu.edu: you know, X intermediate format that I have, or, like, into this Word template I use, or into this text document, or this spreadsheet, right?
+
+413
+00:52:22.890 --> 00:52:31.619
+hrishikb@andrew.cmu.edu: like… Then… then you could separate out that module, and…
+
+414
+00:52:32.010 --> 00:52:36.330
+hrishikb@andrew.cmu.edu: Work on that, and be able to… validate that.
+
+415
+00:52:36.740 --> 00:52:44.270
+hrishikb@andrew.cmu.edu: You know, this is giving Chris exactly what they need, and… and then also, like.
+
+416
+00:52:44.630 --> 00:52:47.519
+hrishikb@andrew.cmu.edu: That also tells you, like, this is what is…
+
+417
+00:52:48.060 --> 00:52:50.450
+hrishikb@andrew.cmu.edu: this is where Chris wants to…
+
+418
+00:52:50.550 --> 00:52:55.570
+hrishikb@andrew.cmu.edu: check that the system is working. Like, this is what they want to see, what format they expect it in.
+
+419
+00:52:55.940 --> 00:53:03.810
+hrishikb@andrew.cmu.edu: What are the… what are the things that are important, and what they need in order to say, okay, this is… this work… this system is working well.
+
+420
+00:53:04.060 --> 00:53:09.710
+hrishikb@andrew.cmu.edu: I'm confident in… What's happening on the backend? Stuff like that.
+
+421
+00:53:16.640 --> 00:53:17.330
+hrishikb@andrew.cmu.edu: Okay.
+
+422
+00:53:22.500 --> 00:53:24.129
+hrishikb@andrew.cmu.edu: What else? What else am I?
+
+423
+00:53:26.040 --> 00:53:27.370
+hrishikb@andrew.cmu.edu: Talked everywhere.
+
+424
+00:53:29.010 --> 00:53:32.269
+hrishikb@andrew.cmu.edu: I think we have a lot of questions now, because we haven't actually
+
+425
+00:53:33.020 --> 00:53:37.790
+hrishikb@andrew.cmu.edu: worked on the system or any particle core yet. Okay. There's at least
+
+426
+00:53:37.960 --> 00:53:40.350
+hrishikb@andrew.cmu.edu: I mean, since they're, like, we…
+
+427
+00:53:40.680 --> 00:53:58.570
+hrishikb@andrew.cmu.edu: our thinking as developers starts from, you know, okay, let's look at the code and then figure out the data. Right, right, right. So I think, once we get to understand the whole flow, and then the actual interfaces that we spoke about, the APIs, or if they have any, GUI or something.
+
+428
+00:53:58.710 --> 00:54:04.509
+hrishikb@andrew.cmu.edu: Probably then we'll be able to answer a lot of questions that you've brought up, but currently it's just, like.
+
+429
+00:54:04.900 --> 00:54:07.039
+hrishikb@andrew.cmu.edu: the high level. Yeah, yeah.
+
+430
+00:54:08.640 --> 00:54:24.090
+hrishikb@andrew.cmu.edu: They've given us a lot of data, actually. They've given us around 27 data documents. I did spend 2 days, but then we have our mid-semester incident, right? So, we just, like, powered it, and then we put it, like, to it.
+
+431
+00:54:24.290 --> 00:54:32.349
+hrishikb@andrew.cmu.edu: What's the kind of data that you need? Like, catalogs, or also just catalogs, schemas? Catalogs…
+
+432
+00:54:32.980 --> 00:54:36.320
+hrishikb@andrew.cmu.edu: Sort out specification documents.
+
+433
+00:54:37.020 --> 00:54:37.940
+hrishikb@andrew.cmu.edu: Okay.
+
+434
+00:54:38.220 --> 00:54:50.150
+hrishikb@andrew.cmu.edu: basically a small dump of the data, like, removing all the PII and all those critical information, and some sort of input files, the kind they expect, usually.
+
+435
+00:54:50.520 --> 00:54:59.499
+hrishikb@andrew.cmu.edu: And the… they also give us some specific schemas, which, like, doesn't really change very often, those kind of things. Okay.
+
+436
+00:55:00.690 --> 00:55:09.060
+hrishikb@andrew.cmu.edu: Yeah, that's interesting. I guess I was assuming the whole time, like, is the main…
+
+437
+00:55:09.280 --> 00:55:13.490
+hrishikb@andrew.cmu.edu: Are we talking mainly about…
+
+438
+00:55:13.940 --> 00:55:24.239
+hrishikb@andrew.cmu.edu: items in a catalog that people can order, or is there other kinds of information that they want to ingest and that goes in the system? Like, is there just general vendor information, and…
+
+439
+00:55:25.220 --> 00:55:32.700
+hrishikb@andrew.cmu.edu: I don't know, other… It'll be, product information, the product specs. Yeah, specs, like, shape, size…
+
+440
+00:55:32.730 --> 00:55:42.309
+hrishikb@andrew.cmu.edu: Things like that. Okay, okay. So let's say they have a vendor, like, Apin as a vendor, so they would, like, give you… if they have category of MacBooks.
+
+441
+00:55:42.310 --> 00:55:45.170
+hrishikb@andrew.cmu.edu: Okay. They're gonna give you, like, the screen size.
+
+442
+00:55:45.170 --> 00:56:09.900
+hrishikb@andrew.cmu.edu: And all of that. So there can be a hardware shop vendor as well. They're gonna input screw dimensions… Okay. Is there images that you have to… Yeah, I mean, we don't have to deal with images, but then the catalog, the document has images, but we don't parse any kind of… But those eParts… is that in their catalog, though? In their image? Yeah, they are. In the website, there is…
+
+443
+00:56:10.560 --> 00:56:13.360
+hrishikb@andrew.cmu.edu: Okay, so that's just, I guess…
+
+444
+00:56:13.830 --> 00:56:20.069
+hrishikb@andrew.cmu.edu: Outside of the scope of this project? Yeah. Okay, but it's still part of what they want to do, right? Or…
+
+445
+00:56:20.250 --> 00:56:30.800
+hrishikb@andrew.cmu.edu: Okay. I would look forward to donate image for whatever is being passed.
+
+446
+00:56:35.440 --> 00:56:46.930
+hrishikb@andrew.cmu.edu: But since the… I mean, if the data's in CSV format, they'll probably… they won't have a… Right. …something to… they'll probably get some general picture out there. Yeah, maybe it's, like, a blank or something.
+
+447
+00:56:47.250 --> 00:56:48.570
+hrishikb@andrew.cmu.edu: And couldn't be.
+
+448
+00:57:00.440 --> 00:57:03.906
+hrishikb@andrew.cmu.edu: How are you guys considering… Or how…
+
+449
+00:57:04.640 --> 00:57:07.640
+hrishikb@andrew.cmu.edu: How have you been answering the constant…
+
+450
+00:57:08.020 --> 00:57:12.140
+hrishikb@andrew.cmu.edu: questions, it feels like to neighboring faculty, like, how are you using AI?
+
+451
+00:57:14.650 --> 00:57:18.169
+hrishikb@andrew.cmu.edu: We're using it as much as possible.
+
+452
+00:57:18.560 --> 00:57:29.320
+hrishikb@andrew.cmu.edu: And, songhood has worked out pretty well. Other parts… We're kind of a… Questioning more of its,
+
+453
+00:57:30.050 --> 00:57:33.059
+hrishikb@andrew.cmu.edu: Whether we should just do that part ourselves.
+
+454
+00:57:37.270 --> 00:57:44.890
+hrishikb@andrew.cmu.edu: like, we are trying to use it as much, but some parts does seem like an overkill. It is not helping us improve, it's just slowing us down.
+
+455
+00:57:45.190 --> 00:57:51.870
+hrishikb@andrew.cmu.edu: Just trying to automate multiple things, getting this NYI, that NYI. I think AI is helpful in most places, but not all.
+
+456
+00:57:51.980 --> 00:57:57.580
+hrishikb@andrew.cmu.edu: Yeah. And the amount of AI usage should also be limited. Like, I think,
+
+457
+00:57:58.770 --> 00:58:17.739
+hrishikb@andrew.cmu.edu: recently, I think Cory put out a post on LinkedIn, I was reading that, so it mentioned that how much AI you should use, like, it's not always good to, if you have to change a line in a document, it's better to do it yourselves than give it to AI, and write a prompt to change that line, and so things like that is something that, I guess, we need to consider more.
+
+458
+00:58:17.910 --> 00:58:21.299
+hrishikb@andrew.cmu.edu: Yeah. Amount of, like, areas and amount of AI usage.
+
+459
+00:58:25.470 --> 00:58:29.330
+hrishikb@andrew.cmu.edu: What's your take on this? Yeah, I think,
+
+460
+00:58:32.970 --> 00:58:38.630
+hrishikb@andrew.cmu.edu: Anything that you… Anything that you care about learning?
+
+461
+00:58:38.960 --> 00:58:41.909
+hrishikb@andrew.cmu.edu: I would to minimize AI use.
+
+462
+00:58:42.370 --> 00:58:53.300
+hrishikb@andrew.cmu.edu: Right? And it's really… It's tricky and nuanced, and… And it…
+
+463
+00:58:53.980 --> 00:59:01.410
+hrishikb@andrew.cmu.edu: to me, right now, in this environment that I'm in, and I think also just in general, like, the industry, it's…
+
+464
+00:59:04.580 --> 00:59:11.000
+hrishikb@andrew.cmu.edu: It feels impossible to… use it… in…
+
+465
+00:59:13.200 --> 00:59:16.769
+hrishikb@andrew.cmu.edu: In a good way, or in a helpful way, because…
+
+466
+00:59:18.370 --> 00:59:25.539
+hrishikb@andrew.cmu.edu: The conception is that it's gonna… You know, provide…
+
+467
+00:59:25.810 --> 00:59:30.570
+hrishikb@andrew.cmu.edu: 10x productivity, or, you know, like, just the claims are really…
+
+468
+00:59:30.770 --> 00:59:33.189
+hrishikb@andrew.cmu.edu: Overblown, but the tricky part is…
+
+469
+00:59:33.320 --> 00:59:36.770
+hrishikb@andrew.cmu.edu: In the right context, with the right user.
+
+470
+00:59:37.110 --> 00:59:46.630
+hrishikb@andrew.cmu.edu: it does… it can be successful, or it can have those kind of results, but you can't generalize that. Yeah. So…
+
+471
+00:59:47.050 --> 00:59:53.089
+hrishikb@andrew.cmu.edu: And I… I don't know… I don't know what to tell.
+
+472
+00:59:53.290 --> 01:00:00.890
+hrishikb@andrew.cmu.edu: People like you in school, and… I think… Definitely… like…
+
+473
+01:00:01.200 --> 01:00:05.739
+hrishikb@andrew.cmu.edu: Fundamentals are still critical, so it's like…
+
+474
+01:00:06.010 --> 01:00:09.140
+hrishikb@andrew.cmu.edu: And… and the… also, the tricky thing is, it's just…
+
+475
+01:00:09.380 --> 01:00:15.110
+hrishikb@andrew.cmu.edu: It's too much of a temptation to use in so many cases, and that's going to…
+
+476
+01:00:15.480 --> 01:00:17.779
+hrishikb@andrew.cmu.edu: Rob you of a learning opportunity.
+
+477
+01:00:18.480 --> 01:00:21.400
+hrishikb@andrew.cmu.edu: But I understand incentives and pressures.
+
+478
+01:00:21.860 --> 01:00:27.439
+hrishikb@andrew.cmu.edu: And there are things that you… you know, there are shortcuts, sometimes they might be the right ones to take, but…
+
+479
+01:00:31.960 --> 01:00:37.410
+hrishikb@andrew.cmu.edu: But it's hard to… It's hard to, like… Resist when.
+
+480
+01:00:37.550 --> 01:00:39.250
+hrishikb@andrew.cmu.edu: You know, it's something important.
+
+481
+01:00:40.180 --> 01:00:42.620
+hrishikb@andrew.cmu.edu: And when you're in this high-pressure environment.
+
+482
+01:00:43.220 --> 01:00:45.330
+hrishikb@andrew.cmu.edu: Which you are in?
+
+483
+01:00:45.800 --> 01:00:49.820
+hrishikb@andrew.cmu.edu: The whole industry has kind of been trying to, like, keep up with
+
+484
+01:00:51.460 --> 01:01:04.940
+hrishikb@andrew.cmu.edu: Phantom, you know, stories of success, or… But, like… Yeah, it's like, you know.
+
+485
+01:01:05.070 --> 01:01:13.379
+hrishikb@andrew.cmu.edu: The way that you learned is… came up through… Experimenting and trying and failing.
+
+486
+01:01:14.680 --> 01:01:20.760
+hrishikb@andrew.cmu.edu: But when you have this machine that can get you Past all the pain.
+
+487
+01:01:22.960 --> 01:01:30.950
+hrishikb@andrew.cmu.edu: Then you have… then… then you don't have the opportunity to make all the decisions along the way that inform and, you know, change the other person.
+
+488
+01:01:34.000 --> 01:01:38.000
+hrishikb@andrew.cmu.edu: And even… like…
+
+489
+01:01:38.770 --> 01:01:45.439
+hrishikb@andrew.cmu.edu: Yeah, like, you can definitely buy-code a lot of stuff, right? I'm sure you guys have experimented. But as soon as you…
+
+490
+01:01:45.580 --> 01:01:50.280
+hrishikb@andrew.cmu.edu: Have a non-trivial size of a team, like…
+
+491
+01:01:50.400 --> 01:01:54.760
+hrishikb@andrew.cmu.edu: It's less and less about what you can build, but more… Like…
+
+492
+01:01:55.930 --> 01:02:00.850
+hrishikb@andrew.cmu.edu: working together, and… like, work is more social than, I think.
+
+493
+01:02:01.830 --> 01:02:07.779
+hrishikb@andrew.cmu.edu: most of the people in our industry give gratitude, or appreciate. Like, it's more about…
+
+494
+01:02:08.330 --> 01:02:16.230
+hrishikb@andrew.cmu.edu: Aligning on the same idea around a system, and understanding the people that you're building for, and making sure you're solving the right problems.
+
+495
+01:02:17.520 --> 01:02:19.060
+hrishikb@andrew.cmu.edu: Like, it's never…
+
+496
+01:02:20.280 --> 01:02:27.150
+hrishikb@andrew.cmu.edu: It's never been about typing up the code. But, you know, people are trying to, you know, do more and more with it, but…
+
+497
+01:02:27.630 --> 01:02:29.690
+hrishikb@andrew.cmu.edu: I think that's at the cost of…
+
+498
+01:02:30.490 --> 01:02:35.720
+hrishikb@andrew.cmu.edu: A lot of things that we don't currently have the right feedback loops to properly understand.
+
+499
+01:02:37.200 --> 01:02:38.100
+hrishikb@andrew.cmu.edu: I don't know.
+
+500
+01:02:42.010 --> 01:02:46.299
+hrishikb@andrew.cmu.edu: It's constant today that my company and lots of other companies.
+
+501
+01:02:47.480 --> 01:02:53.490
+hrishikb@andrew.cmu.edu: I only… yeah, the divide feels like it's getting bigger, and it's also a very interesting dynamic of, like.
+
+502
+01:02:53.910 --> 01:02:58.349
+hrishikb@andrew.cmu.edu: Kind of, like, class warfare, too, where it's, like, pushed down by the leaders who…
+
+503
+01:03:00.990 --> 01:03:04.720
+hrishikb@andrew.cmu.edu: are far from what the reality is, right? Like…
+
+504
+01:03:06.060 --> 01:03:15.320
+hrishikb@andrew.cmu.edu: You know, you see good results, but then you often see, like, You know, bad results, and…
+
+505
+01:03:15.660 --> 01:03:20.229
+hrishikb@andrew.cmu.edu: Yeah, definitely, like… When they say 10x time productivity.
+
+506
+01:03:20.400 --> 01:03:25.949
+hrishikb@andrew.cmu.edu: I don't know how that would even be possible, even with, like, the best AI in the world. Not because… Yeah.
+
+507
+01:03:26.570 --> 01:03:31.870
+hrishikb@andrew.cmu.edu: the only way that would happen is, right, if I actually did not touch the computer.
+
+508
+01:03:32.200 --> 01:03:46.519
+hrishikb@andrew.cmu.edu: If it actually just did every single step, you know, not even me debugging or, like, checking it. Because even if I checked perfect code, supposing it was perfect, it would take me time, like, probably half the time it would take me to do the entire thing.
+
+509
+01:03:46.580 --> 01:03:54.610
+hrishikb@andrew.cmu.edu: So, just that alone just limits their productivity to 2x. So, unless they manage to, like, completely eliminate the human from the loop.
+
+510
+01:03:54.810 --> 01:04:02.490
+hrishikb@andrew.cmu.edu: And just, like, have screens flashing and closing down IDEs constantly and checking. If that happens, then…
+
+511
+01:04:02.680 --> 01:04:05.940
+hrishikb@andrew.cmu.edu: Hopefully I can get a management job or something.
+
+512
+01:04:06.280 --> 01:04:11.289
+hrishikb@andrew.cmu.edu: Yeah, but, like, the… yeah, the value of the work that you're producing is…
+
+513
+01:04:13.810 --> 01:04:18.679
+hrishikb@andrew.cmu.edu: Like, at the end of the day, to me, a human being has to understand the system.
+
+514
+01:04:19.140 --> 01:04:20.050
+hrishikb@andrew.cmu.edu: Mom.
+
+515
+01:04:20.540 --> 01:04:23.829
+hrishikb@andrew.cmu.edu: And be able to change that system, and it has to affect
+
+516
+01:04:24.390 --> 01:04:29.519
+hrishikb@andrew.cmu.edu: It has to change another human being's workflow or, you know, their life, so to speak.
+
+517
+01:04:31.550 --> 01:04:39.229
+hrishikb@andrew.cmu.edu: But… But, you know, it collapses into absurdity when then you're talking about
+
+518
+01:04:39.790 --> 01:04:46.310
+hrishikb@andrew.cmu.edu: I don't know, agents acting on your behalf, and like, who's actually using… like, there's no value that's really being created, we're just…
+
+519
+01:04:48.050 --> 01:04:52.669
+hrishikb@andrew.cmu.edu: Like, throwing around, like, numbers are just, like, bags of numbers are just interacting.
+
+520
+01:04:52.900 --> 01:04:54.449
+hrishikb@andrew.cmu.edu: There's nothing really happening.
+
+521
+01:04:55.090 --> 01:04:55.850
+hrishikb@andrew.cmu.edu: Thank you.
+
+522
+01:04:56.940 --> 01:04:58.729
+hrishikb@andrew.cmu.edu: And yeah, when I thought, like.
+
+523
+01:04:59.180 --> 01:05:04.090
+hrishikb@andrew.cmu.edu: Just, like, if you think about it more just, like, automating things.
+
+524
+01:05:04.210 --> 01:05:14.780
+hrishikb@andrew.cmu.edu: like, what… what in my… in my current development workflow and product workflow can I automate to become 10 times more productive? Like, what does that even mean?
+
+525
+01:05:15.060 --> 01:05:20.869
+hrishikb@andrew.cmu.edu: like… Most of my job is, like, Talking to other people, and…
+
+526
+01:05:21.010 --> 01:05:32.180
+hrishikb@andrew.cmu.edu: Like, getting aligned on what we're building and how it should look, like… But, yeah. So, it's… But…
+
+527
+01:05:33.030 --> 01:05:38.120
+hrishikb@andrew.cmu.edu: There's that very strong narrative that it should be like this, so…
+
+528
+01:05:38.490 --> 01:05:41.000
+hrishikb@andrew.cmu.edu: I think that's why I say it's, like, impossible…
+
+529
+01:05:41.130 --> 01:05:47.059
+hrishikb@andrew.cmu.edu: for us to find, like, the right uses of this kind of LLM technology, because no one's gonna invest.
+
+530
+01:05:47.460 --> 01:05:49.190
+hrishikb@andrew.cmu.edu: In something that…
+
+531
+01:05:49.400 --> 01:05:58.780
+hrishikb@andrew.cmu.edu: Well, first of all, it takes effort and time to, like, design a good product using this stuff, and no one's gonna invest in that, because you're not gonna get that immediate
+
+532
+01:05:58.920 --> 01:06:02.070
+hrishikb@andrew.cmu.edu: so-called, or seemingly, like, 10x return.
+
+533
+01:06:03.450 --> 01:06:08.410
+hrishikb@andrew.cmu.edu: So, that's… At some point, I think it's… It's gonna…
+
+534
+01:06:08.760 --> 01:06:15.860
+hrishikb@andrew.cmu.edu: Yeah, they also keep saying that, you know, the next model will be two times better than the previous model, and
+
+535
+01:06:16.110 --> 01:06:20.800
+hrishikb@andrew.cmu.edu: In their defense, I will say it's getting better, but… Yeah. I don't see, like, the…
+
+536
+01:06:21.030 --> 01:06:25.440
+hrishikb@andrew.cmu.edu: Well, yeah, two times, like, every year. Yeah.
+
+537
+01:06:25.680 --> 01:06:38.250
+hrishikb@andrew.cmu.edu: I think the efficiency also matters if you're working on a, let's say, a greenfield or property project, because if you're writing something from scratch, maybe even you can get 10 times productivity, right, because you have to write thousands of, like, maybe hundreds of files, thousands of files. Yeah, yeah.
+
+538
+01:06:38.250 --> 01:06:45.690
+hrishikb@andrew.cmu.edu: In that part, it might help, but if you already have a tightly coupled, 20 years old codebase, then AI can't do much.
+
+539
+01:06:45.690 --> 01:06:55.909
+hrishikb@andrew.cmu.edu: Better have good version control in which AWS went down, right? They were boasting, like, 3 days ago, like… I'm glad you guys have a bevelhead about this stuff, like…
+
+540
+01:06:57.590 --> 01:07:04.670
+hrishikb@andrew.cmu.edu: Yeah, it's… yeah, you can't… it's… it's… you can't generalize, but that's… That's the only thing that…
+
+541
+01:07:04.910 --> 01:07:06.420
+hrishikb@andrew.cmu.edu: Leaders can do.
+
+542
+01:07:06.900 --> 01:07:11.540
+hrishikb@andrew.cmu.edu: We're like… Of course, they're going to… Try to promote.
+
+543
+01:07:11.810 --> 01:07:16.720
+hrishikb@andrew.cmu.edu: You know, uncertainties of working, or the stories of success, but…
+
+544
+01:07:17.170 --> 01:07:21.270
+hrishikb@andrew.cmu.edu: Yeah, you can't apply, like, a brand new project Good.
+
+545
+01:07:22.330 --> 01:07:29.150
+hrishikb@andrew.cmu.edu: Brownfield stuff, and… Situations… Yes.
+
+546
+01:07:30.120 --> 01:07:32.210
+hrishikb@andrew.cmu.edu: Amy, there's a… there's a… yeah.
+
+547
+01:07:33.470 --> 01:07:35.039
+hrishikb@andrew.cmu.edu: At work this week.
+
+548
+01:07:35.190 --> 01:07:35.960
+hrishikb@andrew.cmu.edu: Thank you.
+
+549
+01:07:37.610 --> 01:07:54.129
+hrishikb@andrew.cmu.edu: Alright, the last thing I'll share, like, somebody, one of our teams, they built, like, a tool using OMs to, like, triage bugs. Like, something that is… is hard and, you know, takes a lot of time for people to analyze, like, what's going on and go into the system, so…
+
+550
+01:07:54.230 --> 01:08:04.420
+hrishikb@andrew.cmu.edu: And then a principal engineer used it, and they got good results. So then SVP heard that and said, everyone's got to use this. So then managers, like, pushed us out and said, like.
+
+551
+01:08:04.590 --> 01:08:08.489
+hrishikb@andrew.cmu.edu: please try this out. So that more people looked at it.
+
+552
+01:08:08.930 --> 01:08:16.110
+hrishikb@andrew.cmu.edu: And… One, I mean, somebody discovered, like, they had left the API key in the open.
+
+553
+01:08:16.229 --> 01:08:31.549
+hrishikb@andrew.cmu.edu: I don't think it's related to what I mentioned happened, but then over the weekend, somebody hacked the tracking dashboard, which they had built, to, like, track every single employee under this SPP, and, like, whether or not they installed the tool, and all these stats about them, right?
+
+554
+01:08:31.779 --> 01:08:34.669
+hrishikb@andrew.cmu.edu: And somebody hacked that system to send out
+
+555
+01:08:35.430 --> 01:08:40.449
+hrishikb@andrew.cmu.edu: email, or send out a WebEx message to every single person, like, 400 people.
+
+556
+01:08:40.760 --> 01:08:47.410
+hrishikb@andrew.cmu.edu: And they included in one of the fields, like, they hijacked it with, like, this anti-AI message, like.
+
+557
+01:08:48.130 --> 01:08:53.130
+hrishikb@andrew.cmu.edu: like, AI being forced on us, like, you know. And also, this is the environment where
+
+558
+01:08:53.470 --> 01:09:01.129
+hrishikb@andrew.cmu.edu: I don't know, we keep laying people off every quarter, so it's like, are you gonna use this telemetry to, like, decide who gets on the…
+
+559
+01:09:01.330 --> 01:09:04.839
+hrishikb@andrew.cmu.edu: Layoff list, like… And it's just this… you…
+
+560
+01:09:05.279 --> 01:09:07.470
+hrishikb@andrew.cmu.edu: It's like a… it was like a protest.
+
+561
+01:09:07.670 --> 01:09:13.029
+hrishikb@andrew.cmu.edu: In a sense. That… I think speaks to just, like, this dissent.
+
+562
+01:09:13.229 --> 01:09:21.389
+hrishikb@andrew.cmu.edu: But it's not being addressed, right? It's not being heard, that's why this person… I don't know who it is yet, and hopefully I don't get in big trouble, like, but…
+
+563
+01:09:21.979 --> 01:09:28.589
+hrishikb@andrew.cmu.edu: They're expressing, like, This is… this is not, like, sustainable, this is not good for our products.
+
+564
+01:09:29.040 --> 01:09:34.420
+hrishikb@andrew.cmu.edu: So… That's… that's the environment we're in.
+
diff --git a/coach_meetings/christian/GMT20260220-222907_Recording.transcript.vtt b/coach_meetings/christian/GMT20260220-222907_Recording.transcript.vtt
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@@ -0,0 +1,906 @@
+WEBVTT
+
+1
+00:00:02.190 --> 00:00:19.239
+hrishikb@andrew.cmu.edu: Unless, like, you take another team's requirements and try to do them, and then without any AI, and then try to compare against their time if they have used AI, and then they do the same for us, then you have a benchmark. Or you could split a new team, right? Do the requirements twice independently?
+
+2
+00:00:19.400 --> 00:00:23.799
+hrishikb@andrew.cmu.edu: That's… that's another… it's… It's a lot of effort.
+
+3
+00:00:26.170 --> 00:00:30.280
+hrishikb@andrew.cmu.edu: Has anybody seen the How to Measure Anything book from Robert?
+
+4
+00:00:30.870 --> 00:00:37.300
+hrishikb@andrew.cmu.edu: I don't think this gets taught anymore here, Rick. When I use quality assurance, we used to talk about this.
+
+5
+00:00:37.420 --> 00:00:46.699
+hrishikb@andrew.cmu.edu: It's just… I believe he mentions my class have been telling me this. It's just Brick mentioned.
+
+6
+00:00:47.920 --> 00:00:55.550
+hrishikb@andrew.cmu.edu: Measurement is… one way of thinking about measurement is that it's uncertainty reduction for decision making.
+
+7
+00:00:56.120 --> 00:01:08.239
+hrishikb@andrew.cmu.edu: maybe not the most intuitive way to describe this. Traditionally, it's defined as assigning numbers to observations, right? But what you want those numbers for is for decision making.
+
+8
+00:01:08.450 --> 00:01:13.389
+hrishikb@andrew.cmu.edu: Right, so you want to decide which of two people you want to hire, so you measure something.
+
+9
+00:01:15.220 --> 00:01:22.410
+hrishikb@andrew.cmu.edu: Measurement is often a little bit vague, and it's fine. You don't need a perfect measure.
+
+10
+00:01:22.570 --> 00:01:37.440
+hrishikb@andrew.cmu.edu: to make the perfect decision, already reducing your uncertainty. Like, if I don't know anything about these candidates, I don't know, I can flip a coin, right? If I have… if I talk to them, I get a sense that maybe one is slightly better than the other.
+
+11
+00:01:37.660 --> 00:01:43.850
+hrishikb@andrew.cmu.edu: I'm not super sure, but it gives me more than 50-50 chance. If I want to be really sure.
+
+12
+00:01:44.170 --> 00:01:49.790
+hrishikb@andrew.cmu.edu: I hire them those 4 months, give them a task, and observe them really closely, and then hire one of them.
+
+13
+00:01:49.980 --> 00:01:51.370
+hrishikb@andrew.cmu.edu: That's super expensive.
+
+14
+00:01:51.710 --> 00:01:53.160
+hrishikb@andrew.cmu.edu: Right?
+
+15
+00:01:53.570 --> 00:02:08.120
+hrishikb@andrew.cmu.edu: So, you can invest more effort into measurement, but at some point, the cost of the measurement to get more accurate measurements is outweighing the opportunity cost, or is outweighing the cost of a bad decision.
+
+16
+00:02:08.419 --> 00:02:12.840
+hrishikb@andrew.cmu.edu: Right, so the decision… like, a hiring decision can be quite consequential.
+
+17
+00:02:13.050 --> 00:02:18.399
+hrishikb@andrew.cmu.edu: If you're deciding whether to use AI, or…
+
+18
+00:02:18.730 --> 00:02:30.390
+hrishikb@andrew.cmu.edu: requirements, auditing thing, or not. I don't know how consequential the decision is. Worst case, you waste a day or two, so if you invest, like, a week…
+
+19
+00:02:31.070 --> 00:02:33.129
+hrishikb@andrew.cmu.edu: Of effort into measurement.
+
+20
+00:02:33.970 --> 00:02:35.100
+hrishikb@andrew.cmu.edu: We are at least…
+
+21
+00:02:35.830 --> 00:02:42.069
+hrishikb@andrew.cmu.edu: save a day of effort out of this, right? So that's not a worthy use of your time.
+
+22
+00:02:43.050 --> 00:02:46.529
+hrishikb@andrew.cmu.edu: So I think the point here is that you want to be…
+
+23
+00:02:47.090 --> 00:02:50.760
+hrishikb@andrew.cmu.edu: Realistic in what quality of measure you need.
+
+24
+00:02:51.810 --> 00:02:58.320
+hrishikb@andrew.cmu.edu: for one outcome, right? So if it's something that's consequential, it's worth to invest a little bit more time in
+
+25
+00:02:59.700 --> 00:03:02.009
+hrishikb@andrew.cmu.edu: In getting more precise solutions.
+
+26
+00:03:03.630 --> 00:03:09.090
+hrishikb@andrew.cmu.edu: But then I think you want to think about things where is really the potential to…
+
+27
+00:03:09.520 --> 00:03:11.130
+hrishikb@andrew.cmu.edu: See a lot of benefits.
+
+28
+00:03:11.340 --> 00:03:18.760
+hrishikb@andrew.cmu.edu: And I get the sense the meeting minutes are kind of saying, yeah, sure, that's nice, but, it's maybe not…
+
+29
+00:03:19.140 --> 00:03:31.449
+hrishikb@andrew.cmu.edu: that important, right? So, and I think there, you have some observations, you see it's slightly better or slightly faster, and you can say, sure, let's keep going, but maybe not invest a lot more, because we don't see a lot of it.
+
+30
+00:03:31.900 --> 00:03:39.200
+hrishikb@andrew.cmu.edu: Requirements is weird, because again, we're doing it once, and we have no counterfactual. You don't… I mean, we claim to invest effort.
+
+31
+00:03:39.910 --> 00:03:41.760
+hrishikb@andrew.cmu.edu: But… how much…
+
+32
+00:03:42.490 --> 00:03:48.960
+hrishikb@andrew.cmu.edu: you're not doing it again, right? So you can adjust this by saying, we did it, seemed fine, win.
+
+33
+00:03:49.830 --> 00:03:55.149
+hrishikb@andrew.cmu.edu: Are there other things that you… that are more consequential decisions, right, in your…
+
+34
+00:03:55.650 --> 00:04:00.760
+hrishikb@andrew.cmu.edu: software engineer system, right? What are the decisions that are actually of some consequence?
+
+35
+00:04:02.670 --> 00:04:06.229
+hrishikb@andrew.cmu.edu: To expect many more on the quality assurance side.
+
+36
+00:04:07.080 --> 00:04:11.520
+hrishikb@andrew.cmu.edu: Or… maybe coding, too, but again, coding seems easy.
+
+37
+00:04:12.120 --> 00:04:28.680
+hrishikb@andrew.cmu.edu: I think not using a coding agent with Stasis, maybe. I mean, it might not be super fast, right? This question is how you use it. Do you use it entirely in YOLO mode, right? Kind of just take off all… like, let it run, right, with all the tools?
+
+38
+00:04:30.500 --> 00:04:36.170
+hrishikb@andrew.cmu.edu: And I think this was doing some experimentation, because that's something that you do consistently, right?
+
+39
+00:04:36.910 --> 00:04:46.850
+hrishikb@andrew.cmu.edu: how many safeguards do you need? What access… what MCP tools do you want to get it, and so on? What's useful, right? When do you break the context limits, or something?
+
+40
+00:04:51.620 --> 00:04:55.000
+hrishikb@andrew.cmu.edu: Definitely, implement, maybe.
+
+41
+00:04:55.390 --> 00:05:09.599
+hrishikb@andrew.cmu.edu: gone a bit overboard. They just give it command line access. Some people have even gone as far as to, like, give it their bank account and, you know, other kind of access, like, connect it to their emails as well, so they can send automated emails.
+
+42
+00:05:09.840 --> 00:05:21.399
+hrishikb@andrew.cmu.edu: But I think it's also plausible to give it full access to your hard drive, but run it in a container, right? So that it can delete everything, but that's what backups are for.
+
+43
+00:05:21.650 --> 00:05:32.200
+hrishikb@andrew.cmu.edu: So, I'm not advocating this necessarily, right? And I can still have access to the internet and make all your source code. It might be, maybe, but it could, right?
+
+44
+00:05:32.320 --> 00:05:39.209
+hrishikb@andrew.cmu.edu: But I think it's… it's worth maybe looking into some of this, maybe looking into some safeguards.
+
+45
+00:05:39.520 --> 00:05:54.539
+hrishikb@andrew.cmu.edu: My approach until recently was also to look at every line of code that it generates, and I'm stepping a little bit away from that. I'm also not writing production-level, right, and writing scripts for my class and stuff like this, right?
+
+46
+00:05:54.670 --> 00:06:03.569
+hrishikb@andrew.cmu.edu: And I think I get a sense of what I can trust it, kind of, right? Or whether some tests are useful, or whether me just running a test once is good enough.
+
+47
+00:06:04.010 --> 00:06:08.679
+hrishikb@andrew.cmu.edu: Still mostly look at things, but I think this is the kind of space
+
+48
+00:06:09.370 --> 00:06:13.319
+hrishikb@andrew.cmu.edu: You could run an experiment to find a policy for your team.
+
+49
+00:06:13.660 --> 00:06:30.629
+hrishikb@andrew.cmu.edu: quality assurance might be worth it. I think there's opportunities in traceability. Make sure that all your requirements are tested. Something that you would never do otherwise, I'm pretty sure, unless somebody forces you. But I think AI is the opportunity to do more.
+
+50
+00:06:30.650 --> 00:06:36.770
+hrishikb@andrew.cmu.edu: You could do some hazard analysis, you know, some threat modeling that probably you wouldn't do otherwise.
+
+51
+00:06:36.830 --> 00:06:38.210
+hrishikb@andrew.cmu.edu: Right?
+
+52
+00:06:40.800 --> 00:06:44.590
+hrishikb@andrew.cmu.edu: But again, it's a… Draw quality diagram.
+
+53
+00:06:44.750 --> 00:06:51.780
+hrishikb@andrew.cmu.edu: Sure, but it's like, I'm sure that you wouldn't do it if you didn't do it with AI.
+
+54
+00:06:52.110 --> 00:06:53.230
+hrishikb@andrew.cmu.edu: And I think…
+
+55
+00:06:53.680 --> 00:07:04.549
+hrishikb@andrew.cmu.edu: Doing it with AI, even in a sloppy way, might be worth trying, but maybe it's also not bothered, because you're just looking at all the outcomes and nothing was useful, right? You're just annoyed, or…
+
+56
+00:07:04.880 --> 00:07:05.670
+hrishikb@andrew.cmu.edu: Farm.
+
+57
+00:07:07.130 --> 00:07:10.220
+hrishikb@andrew.cmu.edu: So… I didn't…
+
+58
+00:07:10.800 --> 00:07:19.599
+hrishikb@andrew.cmu.edu: what you should look for is places where it can speak you up, and places where it can improve your quality, right? Where you can do more than you wouldn't have done otherwise.
+
+59
+00:07:19.890 --> 00:07:30.070
+hrishikb@andrew.cmu.edu: And try to find some signals. I think that the reprompting thing is a way to connect data, but you're gonna hate it, and I'm not sure that it will tell you that much.
+
+60
+00:07:39.260 --> 00:07:42.679
+hrishikb@andrew.cmu.edu: It feels like data collection for the data collection's sake.
+
+61
+00:07:42.810 --> 00:07:52.819
+hrishikb@andrew.cmu.edu: I don't know how you would use the… let's say you wake prompt 5 times, and you need to correct, as a human 3 times.
+
+62
+00:07:53.080 --> 00:07:58.120
+hrishikb@andrew.cmu.edu: When you're doing requirements, How does this help your regular session?
+
+63
+00:08:00.300 --> 00:08:08.020
+hrishikb@andrew.cmu.edu: Right, so think backward from the decision, right? Think about what are you deciding in your software engineering system.
+
+64
+00:08:08.320 --> 00:08:10.650
+hrishikb@andrew.cmu.edu: Like, what kind of decision do you need to make?
+
+65
+00:08:12.830 --> 00:08:15.830
+hrishikb@andrew.cmu.edu: Usually, where to use it, right? How to use it.
+
+66
+00:08:17.160 --> 00:08:22.190
+hrishikb@andrew.cmu.edu: And when not to use it, you're perfectly fine not to use it, but you just need to justify it.
+
+67
+00:08:22.800 --> 00:08:28.239
+hrishikb@andrew.cmu.edu: And I don't think you need evidence to justify everything, I think. Engineering judgment is fine.
+
+68
+00:08:28.690 --> 00:08:34.329
+hrishikb@andrew.cmu.edu: I think what we're encouraging to do is be open-minded, but don't be dismissive.
+
+69
+00:08:34.909 --> 00:08:36.600
+hrishikb@andrew.cmu.edu: Okay.
+
+70
+00:08:37.850 --> 00:08:46.400
+hrishikb@andrew.cmu.edu: But I think if you… you can feel like this is going to be a waste of time for this immediately, and I would, because it's just something I can do if I stand in 5 minutes.
+
+71
+00:08:46.560 --> 00:08:51.899
+hrishikb@andrew.cmu.edu: That's a good enough reason to not do it.
+
+72
+00:08:58.620 --> 00:09:10.439
+hrishikb@andrew.cmu.edu: So, sorry, just to stick on this, it might be worth for you to think about this more, and I'm happy to talk to you again, if you have a doubt.
+
+73
+00:09:11.610 --> 00:09:13.829
+hrishikb@andrew.cmu.edu: I would suggest, think about…
+
+74
+00:09:14.020 --> 00:09:20.230
+hrishikb@andrew.cmu.edu: the new software engineering system, what decisions do you need to make, or what do you mean to justify? Yeah.
+
+75
+00:09:20.690 --> 00:09:24.210
+hrishikb@andrew.cmu.edu: Then you can… you can think about how can you measure
+
+76
+00:09:24.420 --> 00:09:29.789
+hrishikb@andrew.cmu.edu: what helps you make that decision, and I'm having to brainstorm what in your direction.
+
+77
+00:09:33.370 --> 00:09:43.240
+hrishikb@andrew.cmu.edu: So, like I said, we have the ML component here, and then, just know how you implement this thing, right?
+
+78
+00:09:43.740 --> 00:10:01.690
+hrishikb@andrew.cmu.edu: How are you going to prevent your project? Yeah. I can help with us, I think, but it's not technically my role, but I'm happy to talk to you about this, too. Yeah. Or when you use AI for a project? Yeah.
+
+79
+00:10:02.090 --> 00:10:12.359
+hrishikb@andrew.cmu.edu: It's fine, and I've talked to other teams about similar things, too. If you have AI in your project, I teach a class, and the second
+
+80
+00:10:13.100 --> 00:10:16.769
+hrishikb@andrew.cmu.edu: Try to give you some feedback, if you like.
+
+81
+00:10:17.610 --> 00:10:27.410
+hrishikb@andrew.cmu.edu: My task in this is to observe how you're using AI in your sub-engineering system, and how you evaluate this.
+
+82
+00:10:27.670 --> 00:10:32.400
+hrishikb@andrew.cmu.edu: Yeah, I mean, you start to just go over it in a…
+
+83
+00:10:32.800 --> 00:10:41.560
+hrishikb@andrew.cmu.edu: literally teaches the course on this, and it's like, why not take his head when he's… Okay, so,
+
+84
+00:10:41.780 --> 00:10:51.189
+hrishikb@andrew.cmu.edu: So here, by the time our data reaches our internet service, it, like, it's already been through some kind of parsing and the ingestion pipeline.
+
+85
+00:10:51.190 --> 00:11:06.839
+hrishikb@andrew.cmu.edu: So, it means that it's sitting in the canonical table form, like the rows and columns. So, obviously, the admin is not reading the PDFs, like I mentioned, before. It's not doing the document understanding and all of that shit stuff.
+
+86
+00:11:09.290 --> 00:11:12.049
+hrishikb@andrew.cmu.edu: Sorry? Right now, it's pretty good at this stage.
+
+87
+00:11:12.160 --> 00:11:19.770
+hrishikb@andrew.cmu.edu: It was the prescribed… Yeah, it's just the requirements for the…
+
+88
+00:11:21.120 --> 00:11:23.970
+hrishikb@andrew.cmu.edu: And actually, this is something that's…
+
+89
+00:11:24.130 --> 00:11:29.400
+hrishikb@andrew.cmu.edu: extracting data, like, even OCR for documents, it's really good on this.
+
+90
+00:11:29.780 --> 00:11:43.370
+hrishikb@andrew.cmu.edu: much better than most other resource libraries that you can just download and run. I mean, if they have the credits to put it into cloud or something, and it's not sensitive, right, this is…
+
+91
+00:11:44.610 --> 00:11:52.440
+hrishikb@andrew.cmu.edu: You may get problems with hallucinations, you might want to double-check it against OCR or something,
+
+92
+00:11:52.720 --> 00:11:55.300
+hrishikb@andrew.cmu.edu: You can have a water head for that too, but…
+
+93
+00:12:00.060 --> 00:12:07.820
+hrishikb@andrew.cmu.edu: I think the main thing is, they have Azure, and they're ready to deploy a new model on Azure. We don't want to use our movement logos anymore.
+
+94
+00:12:08.260 --> 00:12:13.320
+hrishikb@andrew.cmu.edu: ChatGPT directly. Oh, you can download and…
+
+95
+00:12:14.460 --> 00:12:19.359
+hrishikb@andrew.cmu.edu: I mean, it provides it through Azure. Oh, okay. I think the…
+
+96
+00:12:21.460 --> 00:12:29.249
+hrishikb@andrew.cmu.edu: I would have to check which model is which, but I think the cloud model that we're using for auto-grading will work is,
+
+97
+00:12:29.510 --> 00:12:31.129
+hrishikb@andrew.cmu.edu: is,
+
+98
+00:12:31.390 --> 00:12:41.729
+hrishikb@andrew.cmu.edu: Okay, yeah. Which gives you the secrecy, the privacy guarantee, essentially, right? This is probably why they wanted to.
+
+99
+00:12:42.050 --> 00:12:48.560
+hrishikb@andrew.cmu.edu: It's usually, like, half a year out of date or something. It's not the latest version, but it's, yeah.
+
+100
+00:12:50.070 --> 00:12:51.560
+hrishikb@andrew.cmu.edu: Okay, we can…
+
+101
+00:12:55.680 --> 00:13:01.639
+hrishikb@andrew.cmu.edu: Yeah. I mean, this is more of my intuition, right? But kind of data extraction…
+
+102
+00:13:02.440 --> 00:13:07.780
+hrishikb@andrew.cmu.edu: Yeah, even with CR stuff and so on.
+
+103
+00:13:10.080 --> 00:13:20.250
+hrishikb@andrew.cmu.edu: So, we evaluated the approaches, like, each one of us took out, three models, and then first was with a distal board, just
+
+104
+00:13:20.250 --> 00:13:45.099
+hrishikb@andrew.cmu.edu: give you, like, a quick brief context about what it is. It's a… it's just a… sorry, it's just a compressed version of the Word, model. Like, it's great for text classification, and extracting the attributes from any kind of unstructured, text data. And also, like, why we, delved into this, because it is natively supported on the Azure platform, the ML Studio.
+
+105
+00:13:45.100 --> 00:14:08.279
+hrishikb@andrew.cmu.edu: with the Hugging Face, library. So, the deployment was, like, pretty straightforward. That's why the client was pretty adamant about not using other tools, because they were like, after we hand off the project, they are the ones who have to maintain it. So, they were like, just use what's available on the Azure ML Studio. So, we just, researched on this.
+
+106
+00:14:08.390 --> 00:14:14.869
+hrishikb@andrew.cmu.edu: massive tool for requirements at some stage? No, not yet. We've not done any kind of POCs, this is all theoretical.
+
+107
+00:14:16.780 --> 00:14:22.809
+hrishikb@andrew.cmu.edu: Not sure that I can help you particularly pick a model like this. This is a lot of my expertise. Yeah.
+
+108
+00:14:23.060 --> 00:14:28.170
+hrishikb@andrew.cmu.edu: We can try what works. I think you should just try and see faster trade-offs, certainly.
+
+109
+00:14:28.410 --> 00:14:34.749
+hrishikb@andrew.cmu.edu: I would honestly start with a state-of-the-art living thing, and then…
+
+110
+00:14:36.790 --> 00:14:44.529
+hrishikb@andrew.cmu.edu: Unless you do really simple, kind of, entity name recognition stuff, or something like this, and…
+
+111
+00:14:44.670 --> 00:14:50.829
+hrishikb@andrew.cmu.edu: it's good at understanding structure and trying to guess things and so on.
+
+112
+00:14:51.450 --> 00:15:01.860
+hrishikb@andrew.cmu.edu: I would do the first. Intuitively, I mean, do whatever, right? But intuitively, I would do the first pass with an AM, and then do other stuff to check things to get confidentiality.
+
+113
+00:15:03.910 --> 00:15:07.609
+hrishikb@andrew.cmu.edu: Cool. I mean, try them in parallel or something, I think that's fine.
+
+114
+00:15:08.530 --> 00:15:32.720
+hrishikb@andrew.cmu.edu: So, the problem with the first one, the distalbot, is, it's a text model, but then our canonical table structure is tabular data, so, it, it's really not kind of solving the upstream problem. So it's not the, it's like an overkill, for the ML service layer that we, have. So the second one is, the gradient-boosted decision trees.
+
+115
+00:15:32.720 --> 00:15:35.870
+hrishikb@andrew.cmu.edu: We're not worried about cost.
+
+116
+00:15:35.910 --> 00:15:39.869
+hrishikb@andrew.cmu.edu: Of the item, which probably you'll want in the end.
+
+117
+00:15:40.140 --> 00:15:44.869
+hrishikb@andrew.cmu.edu: Don't worry about overworking. Okay. If it's working. Okay.
+
+118
+00:15:46.850 --> 00:16:11.660
+hrishikb@andrew.cmu.edu: The second one was CatBoost, and we were… and also, like, the light GB and the Gradient Boost model as a scale-up option. So, this is pretty much good, like, it handles, the heavy… handles heavy data set natively, but, the problem with this is, I think it requires, label data, but right now we don't
+
+119
+00:16:11.660 --> 00:16:16.350
+hrishikb@andrew.cmu.edu: have labor data, so that's, like, a gold star problem for now.
+
+120
+00:16:16.350 --> 00:16:19.860
+hrishikb@andrew.cmu.edu: But then… What do you want to use it for? What do you want to predict?
+
+121
+00:16:20.200 --> 00:16:25.180
+hrishikb@andrew.cmu.edu: We want to predict, the attributes.
+
+122
+00:16:27.930 --> 00:16:34.469
+hrishikb@andrew.cmu.edu: So let me see… Past data, right? Isn't there past data of your products and their forbids?
+
+123
+00:16:34.700 --> 00:16:39.160
+hrishikb@andrew.cmu.edu: Oh, we do. That's in the PIMS, yeah. Yes.
+
+124
+00:16:42.640 --> 00:16:45.790
+hrishikb@andrew.cmu.edu: Yeah, you might want to use some… you might…
+
+125
+00:16:45.890 --> 00:16:53.600
+hrishikb@andrew.cmu.edu: be sufficient with some embedding, or some… Exactly. There probably did something there a lot of time. If it allows the…
+
+126
+00:16:54.190 --> 00:16:55.619
+hrishikb@andrew.cmu.edu: What's you see behind it.
+
+127
+00:16:55.860 --> 00:17:00.349
+hrishikb@andrew.cmu.edu: How many SLPs do you have, or what's…
+
+128
+00:17:02.060 --> 00:17:08.489
+hrishikb@andrew.cmu.edu: what it's going to do. Yeah, probably beyond what you were just asking. Yeah, that's what I wanted.
+
+129
+00:17:08.829 --> 00:17:17.929
+hrishikb@andrew.cmu.edu: And then, I think, yeah, if you have something that's a little bit more explainable, that might be useful for the people later, but I suspect there's going to be some embedding of
+
+130
+00:17:18.319 --> 00:17:24.400
+hrishikb@andrew.cmu.edu: The text, and then some sort of decision tree over this, or some sort of simple…
+
+131
+00:17:24.710 --> 00:17:44.540
+hrishikb@andrew.cmu.edu: Perfect. So, the third one that we looked up is the semantic matcher using all mini-LM. So, it's kind of, based on the embedding similarity that you just told about. So, it just takes the supplier attribute text, embeds it, and it finds the closest, match, the neighbor in the PIMS database.
+
+132
+00:17:44.540 --> 00:18:08.100
+hrishikb@andrew.cmu.edu: Using the similarity, whatever functionality it has inside. So, instead of, classifying it into a bucket, it matches it, based on its nearest label, label. So, that's why… and it's a zero-shot model, that means it requires no label training also, so that… so there's no cold start problem as well, when we deal with this approach.
+
+133
+00:18:08.270 --> 00:18:15.400
+hrishikb@andrew.cmu.edu: Yeah. You're not trying to make that. Yeah, yeah.
+
+134
+00:18:15.830 --> 00:18:21.979
+hrishikb@andrew.cmu.edu: Yeah, I think this is… this is typically a data science problem at this point, right? This is just… this…
+
+135
+00:18:22.570 --> 00:18:31.059
+hrishikb@andrew.cmu.edu: to me, these are fairly boring, because there's no engineering around it, right? So this is the kind of stuff that somebody does in a notebook.
+
+136
+00:18:31.240 --> 00:18:34.969
+hrishikb@andrew.cmu.edu: And some people that are good at this, I think.
+
+137
+00:18:35.820 --> 00:18:38.870
+hrishikb@andrew.cmu.edu: I suspect that the thesis is… you will have…
+
+138
+00:18:39.020 --> 00:18:48.549
+hrishikb@andrew.cmu.edu: Maybe you like this kind of stuff, but have more fun with is, like, the infrastructure around it, making sure that it's reminded, and thinking about the user interface of how to
+
+139
+00:18:48.810 --> 00:18:55.209
+hrishikb@andrew.cmu.edu: How to generate confidence, maybe keep a human, or something, that feels great, so…
+
+140
+00:18:56.360 --> 00:19:09.569
+hrishikb@andrew.cmu.edu: Also, like, one more reason why we were… we are a little inclined towards using it is, again, like, theoretically, not, we have not done any POCs. So, because, this one does not require any,
+
+141
+00:19:09.930 --> 00:19:22.380
+hrishikb@andrew.cmu.edu: retraining, I mean, a human to, retrain it, because it's actually finding its nearest neighbors all by its own, using the PINCS database.
+
+142
+00:19:22.380 --> 00:19:31.340
+hrishikb@andrew.cmu.edu: So, it's… I think you just need to experiment there. I don't think I can help you with this specific model. Yeah.
+
+143
+00:19:32.610 --> 00:19:48.230
+hrishikb@andrew.cmu.edu: I think, think about how you evaluate that it's working, right? And think beyond just accuracy of something, like, figure out what's good enough for that customer, and how do you do that? And you might need some sort of interface where the customer can
+
+144
+00:19:48.230 --> 00:20:04.869
+hrishikb@andrew.cmu.edu: train this, or fix mistakes, or teach the model how to avoid mistakes, right? How you can learn from corrections, or something like this. There can be a lot of smart things, learning in production, like, daily feedback, and so on, that's not just pure machine learning.
+
+145
+00:20:07.480 --> 00:20:26.329
+hrishikb@andrew.cmu.edu: Again, just, like, this is the last concluding paragraph that, just to be just using modular design and good documentation practices. So yeah, this is something called ETTX. Assume you have access to the people who are doing this right now, right, so you can figure out what they like, and…
+
+146
+00:20:26.340 --> 00:20:34.240
+hrishikb@andrew.cmu.edu: Yeah, how they needed this, what they would need, and you can… you can probably prototype a lot, you could do Wizard of Oz kind of stuff.
+
+147
+00:20:34.460 --> 00:20:41.750
+hrishikb@andrew.cmu.edu: To show them some solutions that don't need to be working really well, right, and kind of see how they're interacting with this.
+
+148
+00:20:42.110 --> 00:20:54.039
+hrishikb@andrew.cmu.edu: I would expect that you run into a ton of automation bias, where people just click through things without thinking, right? So you might want to think about how to combine something like this with some HCI literature.
+
+149
+00:20:54.720 --> 00:20:55.530
+hrishikb@andrew.cmu.edu: Let me trip.
+
+150
+00:20:57.280 --> 00:21:00.039
+hrishikb@andrew.cmu.edu: Again, this is just a state,
+
+151
+00:21:00.230 --> 00:21:16.840
+hrishikb@andrew.cmu.edu: a general statement that I want to, tell it out, that we are ensuring that we have good documentation practices. So there's something called ETVX, methodology that we learned in our other course, and for every, task that we have, we have an
+
+152
+00:21:16.840 --> 00:21:21.269
+hrishikb@andrew.cmu.edu: Entry, task, verification, exit criteria.
+
+153
+00:21:21.310 --> 00:21:23.079
+hrishikb@andrew.cmu.edu: And also…
+
+154
+00:21:23.310 --> 00:21:45.459
+hrishikb@andrew.cmu.edu: yeah, we have a… I mean, we have not worked on the third point yet, like, we just know we have come up with a notional architecture workflow diagram, but then just today, we got the schema data from the client. So, I think it all starts with us trying out the POCs, experimenting with the different ML models, and, like, creating a baseline.
+
+155
+00:21:45.470 --> 00:22:03.369
+hrishikb@andrew.cmu.edu: Coming with numbers, and then, deciding on what model that we are supposed to use, and then probably that's when we can, you know, be very confident with the AI effectiveness, at least from the product point of… the project point of view.
+
+156
+00:22:05.520 --> 00:22:09.720
+hrishikb@andrew.cmu.edu: Yeah, that's it, behind. I suspect you've run a little difficult.
+
+157
+00:22:13.720 --> 00:22:21.499
+hrishikb@andrew.cmu.edu: you know, designing things in a space that you're probably not super familiar with, like the HCI stuff, the machine learning stuff.
+
+158
+00:22:22.890 --> 00:22:30.060
+hrishikb@andrew.cmu.edu: It's also a space that's not completely normal, so I would expect AI to be able to work a little bit, but…
+
+159
+00:22:30.240 --> 00:22:38.390
+hrishikb@andrew.cmu.edu: kind of design and asking the right questions and testing things, too, right? And especially prototyping.
+
+160
+00:22:38.550 --> 00:22:44.679
+hrishikb@andrew.cmu.edu: prototyping as part of requirements engineering, I think it's… Any corporate these two years.
+
+161
+00:22:47.090 --> 00:22:51.500
+hrishikb@andrew.cmu.edu: I think it's actually a pretty good match for his new project.
+
+162
+00:22:53.310 --> 00:22:56.570
+hrishikb@andrew.cmu.edu: We're just checking whether the requirements are complete.
+
+163
+00:22:57.920 --> 00:22:58.600
+hrishikb@andrew.cmu.edu: Beautiful.
+
+164
+00:22:59.890 --> 00:23:05.090
+hrishikb@andrew.cmu.edu: I would also encourage you, maybe, to play with this on us, like, what's…
+
+165
+00:23:05.350 --> 00:23:21.860
+hrishikb@andrew.cmu.edu: what's the need? Like, reflect on what is the need from somebody who routinely clicks through these things, right? Or somebody who gets a thousand documents, and a bunch of them are automated already, like, you need to check this and not fall asleep, right, or catch the one mistake.
+
+166
+00:23:21.860 --> 00:23:27.220
+hrishikb@andrew.cmu.edu: What kind of mistakes are important at all? I imagine I want to talk to a customer about this, too.
+
+167
+00:23:28.780 --> 00:23:44.270
+hrishikb@andrew.cmu.edu: Like, what are the problems that they're worried about most, right? Is a typo in the name of the product a problem, or a misclassification of this attribute? Probably not. Our attributes need to be the same accuracy, and some mistakes are worse than others.
+
+168
+00:23:46.120 --> 00:23:46.880
+hrishikb@andrew.cmu.edu: Dear.
+
+169
+00:23:47.310 --> 00:23:54.739
+hrishikb@andrew.cmu.edu: And I think you can do a lot of guessing with an LM before you then confirm a few parts of those with the customer.
+
+170
+00:23:57.670 --> 00:23:59.529
+hrishikb@andrew.cmu.edu: Yeah, we shouldn't…
+
+171
+00:24:11.080 --> 00:24:17.870
+hrishikb@andrew.cmu.edu: Yeah, we definitely should, just try not worry about overflow, and just try all the options before, like, 10-19.
+
+172
+00:24:20.140 --> 00:24:20.860
+hrishikb@andrew.cmu.edu: Right?
+
+173
+00:24:21.220 --> 00:24:22.130
+hrishikb@andrew.cmu.edu: Yeah.
+
+174
+00:24:23.820 --> 00:24:33.150
+hrishikb@andrew.cmu.edu: I think there's an exception, there's a little bit, do you go with a minimally viable product, or do you bid something that's very reliable and robust, right?
+
+175
+00:24:35.650 --> 00:24:37.320
+hrishikb@andrew.cmu.edu: And I think that's probably…
+
+176
+00:24:37.460 --> 00:24:49.930
+hrishikb@andrew.cmu.edu: software engineering system? Do you iterate very agile or frequently, right? Or do you do more waterfalling process, right? Where you establish all the partners, and then last semester you build everything?
+
+177
+00:24:51.850 --> 00:24:53.210
+hrishikb@andrew.cmu.edu: So let's go back to the rooms.
+
+178
+00:24:53.440 --> 00:25:06.699
+hrishikb@andrew.cmu.edu: product that's a little bit more explanatory, you don't know what will work. That suggests a more agile, kind of iterative approach, right? Very fast iteration.
+
+179
+00:25:06.820 --> 00:25:08.940
+hrishikb@andrew.cmu.edu: Lots of prototyping.
+
+180
+00:25:09.290 --> 00:25:19.399
+hrishikb@andrew.cmu.edu: That's something that AI is pretty good at, right? So you could… you could say your system is very iterative, we don't do all the models up front, right? You…
+
+181
+00:25:19.660 --> 00:25:24.800
+hrishikb@andrew.cmu.edu: don't know what the user interface will do, right? That depends a lot on the access to the financial risk.
+
+182
+00:25:24.970 --> 00:25:33.329
+hrishikb@andrew.cmu.edu: And you focus a lot on iteratively figuring out, A, what's actually reasonable, and B, what the customer actually needs.
+
+183
+00:25:36.020 --> 00:25:43.750
+hrishikb@andrew.cmu.edu: And I think with a lot of iteration, you can see how AI might have helped you there in prototyping, or in…
+
+184
+00:25:45.010 --> 00:25:54.070
+hrishikb@andrew.cmu.edu: in keeping track of iteration, what fails, what doesn't fail, more on the project management side, right? If you have some traceability or update the requirements, I think
+
+185
+00:25:54.220 --> 00:25:58.179
+hrishikb@andrew.cmu.edu: Updating requirements is annoying, right? As you're experimenting, I think that
+
+186
+00:25:58.300 --> 00:26:02.109
+hrishikb@andrew.cmu.edu: In a traditional project, that probably wouldn't have happened, or that's…
+
+187
+00:26:02.380 --> 00:26:05.470
+hrishikb@andrew.cmu.edu: This is a deliverable for grade.
+
+188
+00:26:06.640 --> 00:26:11.830
+hrishikb@andrew.cmu.edu: Right, so, but seriously, like, when you do requirements probably in the beginning, and then…
+
+189
+00:26:12.080 --> 00:26:21.149
+hrishikb@andrew.cmu.edu: everything changes, right? And maybe you go back at the very end. I think AI has the opportunity to update this more continuously.
+
+190
+00:26:21.630 --> 00:26:30.860
+hrishikb@andrew.cmu.edu: But also, I'm gonna look at this? I don't know. If it's… if it's just generating lots of documentation that nobody ever reads, that's the point?
+
+191
+00:26:33.350 --> 00:26:46.720
+hrishikb@andrew.cmu.edu: I don't know where any of this is going, right? What I see is that people generate a lot more documentation. That's kind of semi-correct, right? Yeah, it's happening to me as well, all of my friends.
+
+192
+00:26:46.990 --> 00:26:53.879
+hrishikb@andrew.cmu.edu: Yeah, he likes sending emails, and what he's doing now is, like, instead of writing, like, two lines with himself.
+
+193
+00:26:53.980 --> 00:27:07.910
+hrishikb@andrew.cmu.edu: he sends, like, 3 paragraphs with AI, and then he expects… then he does not even bother reading what I… and then he just asks this AI to summarize it. And what the AI gives it is, like, more than what I wrote in the first place, so…
+
+194
+00:27:07.970 --> 00:27:22.949
+hrishikb@andrew.cmu.edu: So, it's like AI writing to other AI, and then we're getting stuck. Again, there's no student in management. Student emails have gotten longer in recent years. I do most of the communication in my class on Slack, because it doesn't… students don't quite…
+
+195
+00:27:23.260 --> 00:27:26.069
+hrishikb@andrew.cmu.edu: Lots of long-generated messages on Slack.
+
+196
+00:27:26.690 --> 00:27:27.390
+hrishikb@andrew.cmu.edu: Yeah.
+
+197
+00:27:28.510 --> 00:27:32.319
+hrishikb@andrew.cmu.edu: Looks quite bad for that. It wasn't the attention, but it looks quite like today.
+
+198
+00:27:35.580 --> 00:27:36.540
+hrishikb@andrew.cmu.edu: Yeah.
+
+199
+00:27:40.970 --> 00:27:44.809
+hrishikb@andrew.cmu.edu: I think the conclusion is we don't know where this is going.
+
+200
+00:27:45.580 --> 00:27:51.320
+hrishikb@andrew.cmu.edu: Yeah, I think… I think, think about decisions you need to make, and how you can support those.
+
+201
+00:27:52.930 --> 00:28:10.290
+hrishikb@andrew.cmu.edu: And feel free to reach out again in a couple of weeks or so, or every week, maybe, like, whenever you have something to discuss. I think once we start talking, we'll probably reach out to you with some kind of data we have as well.
+
+202
+00:28:11.030 --> 00:28:19.369
+hrishikb@andrew.cmu.edu: Yeah, or just if you figure out what you… what decisions you need for the software engineering system, and…
+
+203
+00:28:20.470 --> 00:28:24.329
+hrishikb@andrew.cmu.edu: What kind of data you might or might not prevent.
+
+204
+00:28:24.580 --> 00:28:27.350
+hrishikb@andrew.cmu.edu: I think keep your data collection actually sane, I think.
+
+205
+00:28:28.120 --> 00:28:31.659
+hrishikb@andrew.cmu.edu: Counting the number of prompts or changes.
+
+206
+00:28:31.850 --> 00:28:38.030
+hrishikb@andrew.cmu.edu: You will hate, unless you can do this in some environment where this is automated, you will hate this.
+
+207
+00:28:38.220 --> 00:28:38.940
+hrishikb@andrew.cmu.edu: Hmm.
+
+208
+00:28:39.070 --> 00:28:41.120
+hrishikb@andrew.cmu.edu: This is… this was repeated…
+
+209
+00:28:41.580 --> 00:28:50.350
+hrishikb@andrew.cmu.edu: super tedious, you will forget, and then you approximate something after the fact. I don't think that's a good way. You can build a tool, right, so where
+
+210
+00:28:51.430 --> 00:28:59.990
+hrishikb@andrew.cmu.edu: Where it keeps it via all the changes in Dropbox or something, and you can call changes or something, based on the history, and probably…
+
+211
+00:29:00.760 --> 00:29:04.159
+hrishikb@andrew.cmu.edu: To summarize and do this analysis for you.
+
+212
+00:29:04.280 --> 00:29:11.640
+hrishikb@andrew.cmu.edu: It might be a play, it's essentially for free, right? If we can find a way to do this in an environment where you can expect less optimal.
+
+213
+00:29:12.020 --> 00:29:14.669
+hrishikb@andrew.cmu.edu: If you need to track things manually.
+
+214
+00:29:15.560 --> 00:29:19.139
+hrishikb@andrew.cmu.edu: This might be okay to get a grade, but…
+
+215
+00:29:20.290 --> 00:29:28.710
+hrishikb@andrew.cmu.edu: We use code as something like that, that you can store separate sessions in separate files and everything. But, yeah, counting, like.
+
+216
+00:29:28.960 --> 00:29:30.870
+hrishikb@andrew.cmu.edu: How many times you've done it would…
+
+217
+00:29:31.260 --> 00:29:36.409
+hrishikb@andrew.cmu.edu: Would just be only for the grade level, if it was forced, yeah.
+
+218
+00:29:38.880 --> 00:29:39.540
+hrishikb@andrew.cmu.edu: Yes.
+
+219
+00:29:43.950 --> 00:29:44.760
+hrishikb@andrew.cmu.edu: Goodbye.
+
+220
+00:29:45.220 --> 00:29:47.570
+hrishikb@andrew.cmu.edu: Thank you so much. You too.
+
+221
+00:29:48.740 --> 00:29:50.859
+hrishikb@andrew.cmu.edu: You did not pick up with me, January.
+
+222
+00:29:51.500 --> 00:29:56.659
+hrishikb@andrew.cmu.edu: I think partially it was recorded at F4 to start the recording initially.
+
+223
+00:29:57.620 --> 00:30:02.689
+hrishikb@andrew.cmu.edu: Do you mind sharing the transcript? Yeah, of course.
+
+224
+00:30:03.090 --> 00:30:14.829
+hrishikb@andrew.cmu.edu: We're… you… you've got all this IRB text, right? We're also trying to observe what you're doing, what you're struggling with. We might want to write a paper at some point and share this with other teachers.
+
+225
+00:30:16.230 --> 00:30:27.219
+hrishikb@andrew.cmu.edu: I'm not allowed to record the meeting, but I'm allowed to look at artifacts that you're producing. So, if you're willing to share the English here, I can look at this.
+
+226
+00:30:36.120 --> 00:30:38.419
+hrishikb@andrew.cmu.edu: I'm guessing these, coding sections will.
+
diff --git a/coach_meetings/dennis/GMT20260220-180425_Recording.transcript (2).vtt b/coach_meetings/dennis/GMT20260220-180425_Recording.transcript (2).vtt
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@@ -0,0 +1,1794 @@
+WEBVTT
+
+1
+00:00:00.000 --> 00:00:03.899
+hrishikb@andrew.cmu.edu: Consequence format, and, let's start off then.
+
+2
+00:00:04.250 --> 00:00:11.060
+hrishikb@andrew.cmu.edu: The first one is, we are… the development cannot proceed due to lack of data that we're having.
+
+3
+00:00:11.270 --> 00:00:19.769
+hrishikb@andrew.cmu.edu: So… the… first of all, we… and there's also some other access issues that we are facing.
+
+4
+00:00:19.950 --> 00:00:23.700
+hrishikb@andrew.cmu.edu: So we… I don't think we still have any access to Purser.
+
+5
+00:00:24.580 --> 00:00:31.099
+hrishikb@andrew.cmu.edu: And we also don't have any of the sample data that we need to proceed with any of the…
+
+6
+00:00:31.500 --> 00:00:36.760
+hrishikb@andrew.cmu.edu: MLT work, and… Any of the other. So…
+
+7
+00:00:37.860 --> 00:00:41.490
+hrishikb@andrew.cmu.edu: This is, decline-dependent, and, this could,
+
+8
+00:00:41.630 --> 00:00:45.979
+hrishikb@andrew.cmu.edu: Like, protect how we start the initial pages of the group, basically.
+
+9
+00:00:46.720 --> 00:00:52.699
+hrishikb@andrew.cmu.edu: So… The second risk relates to the first one.
+
+10
+00:00:52.960 --> 00:01:12.409
+hrishikb@andrew.cmu.edu: As we do not have the data, there are several different kinds of ML models that we wanted to try, and to see which has the best performance. So, like, the goal of the entire project is to be better than the manual one, but if we do not test the different ML models, we will not have a good enough idea of which to proceed with.
+
+11
+00:01:12.480 --> 00:01:16.560
+hrishikb@andrew.cmu.edu: And that just increases the uncertainty a lot.
+
+12
+00:01:19.370 --> 00:01:26.839
+hrishikb@andrew.cmu.edu: Third one, I think, is… More of a… future concern. If,
+
+13
+00:01:27.020 --> 00:01:31.010
+hrishikb@andrew.cmu.edu: Depending on if we make the… how we make the project, and how…
+
+14
+00:01:31.340 --> 00:01:41.000
+hrishikb@andrew.cmu.edu: whether scope creep does happen, because we've kept it very minimal right now. First of all, because we don't have, we've not gotten our hands dirty yet.
+
+15
+00:01:41.120 --> 00:01:50.219
+hrishikb@andrew.cmu.edu: And also because we don't know how the data looks, so that might happen, but this is more of a future concern, rather than something we're facing.
+
+16
+00:01:53.050 --> 00:01:55.410
+hrishikb@andrew.cmu.edu: Fourth one is…
+
+17
+00:01:57.180 --> 00:02:11.610
+hrishikb@andrew.cmu.edu: the client is pretty much, has strong preferences, and, is pretty much locked into the Azure environment. So, we… we will not be using any tools that, basically
+
+18
+00:02:12.700 --> 00:02:32.649
+hrishikb@andrew.cmu.edu: has to, like, force them to learn new things that they don't want to learn. And as a consequence, basically, we're, like, making our choices such that we're not using Terraform, we're using Bicep. Alright, is that not making them learn things through the online environment? We can go back, we'll go back over a bit. We'll go back there. And…
+
+19
+00:02:36.950 --> 00:02:46.790
+hrishikb@andrew.cmu.edu: The fifth one is not that big of an issue now, but might become one in the future. There could be some schema volatility, depending on,
+
+20
+00:02:47.130 --> 00:02:53.449
+hrishikb@andrew.cmu.edu: If, like, how much, changes we're gonna have to make to the ML output that we get.
+
+21
+00:02:53.650 --> 00:02:57.360
+hrishikb@andrew.cmu.edu: And how that goes into films.
+
+22
+00:02:57.660 --> 00:03:02.720
+hrishikb@andrew.cmu.edu: Okay. The rest is just a… my take on…
+
+23
+00:03:03.030 --> 00:03:06.610
+hrishikb@andrew.cmu.edu: The risk mitigation strategies we can have for each one of them.
+
+24
+00:03:07.320 --> 00:03:08.430
+hrishikb@andrew.cmu.edu: So…
+
+25
+00:03:08.950 --> 00:03:24.119
+hrishikb@andrew.cmu.edu: Scheduling calls with basically the client, and, like, sending changes to them so that we get the access to the cursor, and even more importantly than that, that we get access to the data.
+
+26
+00:03:24.660 --> 00:03:36.219
+hrishikb@andrew.cmu.edu: Second one, I did write something, but it's mainly just, like, dependent on the first being resolved, so that we can just try out the… I think we have 3 ML candidates, Brittany.
+
+27
+00:03:36.890 --> 00:03:45.479
+hrishikb@andrew.cmu.edu: Third one is, we will start missing milestones if, if, we, this continues, and,
+
+28
+00:03:45.580 --> 00:03:51.689
+hrishikb@andrew.cmu.edu: you're not able to, like, start work on… well, that's not… scope creep is not… it's different, right?
+
+29
+00:03:52.610 --> 00:04:01.720
+hrishikb@andrew.cmu.edu: You may have smart as risk number one. This one, this would be more of a futuristic. I think I should change it and just,
+
+30
+00:04:01.850 --> 00:04:15.010
+hrishikb@andrew.cmu.edu: Instead of being scope creep, right? For right now, scope creep is far outside than what we are currently doing. Yeah, scope creep is what? Scope creep would be if you were designing the system, and instead of having
+
+31
+00:04:15.010 --> 00:04:25.790
+hrishikb@andrew.cmu.edu: you know, a certain number of formats that they expect us to. Okay, sure, sure, okay, yep. They, they, we suddenly decide, or they decide that it would be better if we could handle more. Right.
+
+32
+00:04:26.170 --> 00:04:27.969
+hrishikb@andrew.cmu.edu: Or different tech kinds.
+
+33
+00:04:30.500 --> 00:04:44.530
+hrishikb@andrew.cmu.edu: Architectural complexity is, is, we have already decided that, when we were talking with them, that we would only do the minimal amount of darkerization, and…
+
+34
+00:04:45.400 --> 00:04:51.750
+hrishikb@andrew.cmu.edu: keep anything, like, open television and such into, like, stress goes, so that we… we're not…
+
+35
+00:04:51.980 --> 00:04:54.449
+hrishikb@andrew.cmu.edu: Going to start with the reward.
+
+36
+00:04:55.120 --> 00:05:03.059
+hrishikb@andrew.cmu.edu: And this overhead from schema is not relevant. So, so let's look at these one by one, okay? So,
+
+37
+00:05:03.200 --> 00:05:09.209
+hrishikb@andrew.cmu.edu: Well, two things. Let's do the risk, all right, and then what I also want to do is sort of share with you
+
+38
+00:05:10.310 --> 00:05:18.210
+hrishikb@andrew.cmu.edu: And I'm actually going to spend this weekend reflecting on it, make sure what I'm sharing is actually correct, but I'll share it anyway. It kind of what…
+
+39
+00:05:18.670 --> 00:05:23.840
+hrishikb@andrew.cmu.edu: You know, what are those kinds of expectations that we would have from a project management perspective?
+
+40
+00:05:24.070 --> 00:05:36.440
+hrishikb@andrew.cmu.edu: by, you know, by soon, by the end of the semester, etc, so you can make sure you queue that stuff up, right? So we'll do that as well. But happy to focus on… on the risk part.
+
+41
+00:05:37.000 --> 00:05:43.489
+hrishikb@andrew.cmu.edu: the… So, I'm just gonna give my, like, direct feedback, right? So…
+
+42
+00:05:43.720 --> 00:05:49.020
+hrishikb@andrew.cmu.edu: Risk one, I think, is actually two different risks here, right? So it's really… there's one about
+
+43
+00:05:49.220 --> 00:05:59.059
+hrishikb@andrew.cmu.edu: getting, you know, some access to some tooling, and the other is access to the data. Those are… I would… I would put those as two different risks, because
+
+44
+00:05:59.280 --> 00:06:05.250
+hrishikb@andrew.cmu.edu: The… there's a… the consequences are eroded as one consequence, but they are really different consequences.
+
+45
+00:06:05.340 --> 00:06:19.449
+hrishikb@andrew.cmu.edu: Depending on… depending on the access. And… and also, potentially, the mitigations for them could be a little bit different as well. But those are actually very legitimate… legitimate risks, right? 100% agree with you there.
+
+46
+00:06:20.830 --> 00:06:39.239
+hrishikb@andrew.cmu.edu: Now, let's go to the mitigation for risk number one, and I think… by the way, I also think you did a great job of, sort of, you know, the way you wrote the risks as well. I think it's really well done. If you ever do, sort of, a presentation where you want to show risks, it'll be a lot… need to be a lot less wordy, right?
+
+47
+00:06:39.310 --> 00:06:43.710
+hrishikb@andrew.cmu.edu: But having this as the backup is really good. Did a really nice job there.
+
+48
+00:06:45.090 --> 00:06:50.980
+hrishikb@andrew.cmu.edu: So, if I think about… Just in general, about risk.
+
+49
+00:06:51.120 --> 00:06:54.360
+hrishikb@andrew.cmu.edu: Risks and how to… how to manage their risks.
+
+50
+00:06:55.820 --> 00:06:56.890
+hrishikb@andrew.cmu.edu: there's…
+
+51
+00:06:57.370 --> 00:07:07.729
+hrishikb@andrew.cmu.edu: how do you… you know, there's multiple ways to attack risks, right? One, we can just say, okay, good, no problem, all of it, right? But that's not what you want to do here. You can…
+
+52
+00:07:08.570 --> 00:07:15.640
+hrishikb@andrew.cmu.edu: mitigate risks, right? What does it mean… what does it mean to mitigate a risk? Let me ask that question. What does that really mean?
+
+53
+00:07:17.150 --> 00:07:34.399
+hrishikb@andrew.cmu.edu: I mean, so to mitigate the effects of a risk, so it doesn't negatively impact our team or our project? Okay, right. There's also something where you could reduce the likelihood of something occurring. Those are two different things, right? So one is, hey, it may occur in one of the impact, and the other's a likelihood. So…
+
+54
+00:07:34.770 --> 00:07:43.250
+hrishikb@andrew.cmu.edu: you know, schedule the 30-45 minute meeting, right? What is that? Is that really a mitigation, or is that a reduced likelihood of it occurring?
+
+55
+00:07:44.140 --> 00:07:51.800
+hrishikb@andrew.cmu.edu: I guess it would be reduced likelihood, because even after the meeting, there's still a chance that, you know, no further…
+
+56
+00:07:51.940 --> 00:07:52.680
+hrishikb@andrew.cmu.edu: Right.
+
+57
+00:07:52.850 --> 00:07:59.240
+hrishikb@andrew.cmu.edu: What would reduce… what would be an example of, like, a reduced, sort of, impact if it… if it does happen?
+
+58
+00:08:00.620 --> 00:08:14.110
+hrishikb@andrew.cmu.edu: And I don't care about, like, the fine… you know, I don't care about the nuances, this, reduced likelihood or reduced impact, but we should think about… we should… when we think about a risk, we should think about both of those, right, because they're both strategies.
+
+59
+00:08:14.280 --> 00:08:19.379
+hrishikb@andrew.cmu.edu: That we can employ. Sometimes we can do one, sometimes we can do the other, sometimes we can do both.
+
+60
+00:08:20.070 --> 00:08:25.130
+hrishikb@andrew.cmu.edu: So what might be, A way to reduce impact.
+
+61
+00:08:25.880 --> 00:08:30.579
+hrishikb@andrew.cmu.edu: Wait to release… it's not to wait for the, like, the…
+
+62
+00:08:30.980 --> 00:08:39.809
+hrishikb@andrew.cmu.edu: A large amount of data to come in and just ask them for, like, a much smaller amount, and then just kind of go with that first.
+
+63
+00:08:39.950 --> 00:08:52.939
+hrishikb@andrew.cmu.edu: That would reduce… like, there would still be an impact, because we would not be able to, like, train any exploratory models, but we would kind of get a feel of what the data is structured like, or what are, like, the usual entries in some of them.
+
+64
+00:08:54.120 --> 00:08:55.660
+hrishikb@andrew.cmu.edu: And anyone else?
+
+65
+00:08:55.960 --> 00:09:00.599
+hrishikb@andrew.cmu.edu: I think for the cursor issue, it might be that we can use our own
+
+66
+00:09:00.810 --> 00:09:16.960
+hrishikb@andrew.cmu.edu: LLMs to help us, like, we currently are, without using clients' proprietary data. Just to, like, we can hold off working on the proprietary data, like, we don't have any… You don't have the data, so whatever reason? So, yeah, right now we're using our own LLM, so that part is, I guess, currently being mitigated.
+
+67
+00:09:17.610 --> 00:09:25.120
+hrishikb@andrew.cmu.edu: As for the data, I'm not sure what we can do, like, we need something from them.
+
+68
+00:09:25.450 --> 00:09:37.920
+hrishikb@andrew.cmu.edu: is there any way to… you know, so I realize that for those sort of machine learning models, you really want to see the data from them, but for a lot of the other work… so…
+
+69
+00:09:38.910 --> 00:09:40.430
+hrishikb@andrew.cmu.edu: There's a…
+
+70
+00:09:41.980 --> 00:09:47.550
+hrishikb@andrew.cmu.edu: Ideally, you'd be in a situation, and I certainly remember from your requirements document, where you were trying to…
+
+71
+00:09:48.040 --> 00:10:02.640
+hrishikb@andrew.cmu.edu: I forget what… I forget which quality attribute you called it. Was it extensibility? I can't remember, or you need to, you know… you're doing this for three types of data sources, and it needs to be relatively simple to add a fourth, a fifth, a sixth on, right? There's… there's something there.
+
+72
+00:10:03.220 --> 00:10:07.080
+hrishikb@andrew.cmu.edu: is there any way to use synthetic data?
+
+73
+00:10:07.420 --> 00:10:12.070
+hrishikb@andrew.cmu.edu: Like, just… you know what? You go… Because, cause, yes.
+
+74
+00:10:12.080 --> 00:10:31.420
+hrishikb@andrew.cmu.edu: that's not gonna… But then you have to know the format, right? Yeah, so you're gonna make something up, right? You're gonna make… you're gonna… again, I'm just throwing an idea out there, right? You totally make it up, right? Because that training part is just a piece of the whole… the whole pipeline, right? So there's probably a lot you can do.
+
+75
+00:10:31.810 --> 00:10:38.629
+hrishikb@andrew.cmu.edu: And you may need to do some rework, sure, but there's probably a bunch that you can do, even if you don't know… you don't have any real data.
+
+76
+00:10:38.740 --> 00:10:41.149
+hrishikb@andrew.cmu.edu: And it's not uncommon in…
+
+77
+00:10:42.590 --> 00:10:51.899
+hrishikb@andrew.cmu.edu: You know, getting data sometimes is very, you know, doesn't happen quickly, and there's a lot of times where people need to sort of make some forward progress, even before they have it.
+
+78
+00:10:53.630 --> 00:10:56.930
+hrishikb@andrew.cmu.edu: Not ideal, I understand, but I think also.
+
+79
+00:10:57.310 --> 00:11:11.440
+hrishikb@andrew.cmu.edu: You raised the point. You know, I think you… it was, maybe there's just a little bit of data we can get, right? So, maybe define what that, you know, sort of minimally viable amount of data is.
+
+80
+00:11:11.560 --> 00:11:30.980
+hrishikb@andrew.cmu.edu: So, it will sufficiently unblock you, so you can make some more forward progress. So, actually, we don't even need the data, we just need the schema, and then we need this makeup data. That's the main problem. So, we don't even need this small amount of data. Okay. Yeah, so all we require right now is the basic schema of how the data would look like in a table.
+
+81
+00:11:31.210 --> 00:11:39.010
+hrishikb@andrew.cmu.edu: And then we would be able to work on the pipeline, and they would be able to work on the MX modeling as well.
+
+82
+00:11:39.100 --> 00:11:59.059
+hrishikb@andrew.cmu.edu: PDFs or some input files to start on the admission part? Well, you don't need that, because the PDFs are only for the OCR part. Like, okay, you're… okay, then you get access… can you get an account and pretend that you're buying something on their website, and go look at some of the data that is available, and make up a schema to start with?
+
+83
+00:12:00.590 --> 00:12:05.930
+hrishikb@andrew.cmu.edu: Right? You know, you know, what is it? It's all the parts information, right? It's all the data about…
+
+84
+00:12:06.050 --> 00:12:17.890
+hrishikb@andrew.cmu.edu: Yeah, I did write a little about representative data, so that could be something. In this case, it would be, like, just going onto their website and just scraping a few pages, I guess.
+
+85
+00:12:18.020 --> 00:12:18.890
+hrishikb@andrew.cmu.edu: And…
+
+86
+00:12:19.110 --> 00:12:27.909
+hrishikb@andrew.cmu.edu: That would be better than, you know, not detecting. It's not going to eliminate the risk, sure, but it certainly will help mitigate the risk.
+
+87
+00:12:28.610 --> 00:12:31.310
+hrishikb@andrew.cmu.edu: Mitigate the impact of the risk.
+
+88
+00:12:32.490 --> 00:12:37.460
+hrishikb@andrew.cmu.edu: And I think a lot of it is also just around, sort of, the… again, you're probably doing all this, but…
+
+89
+00:12:37.710 --> 00:12:39.520
+hrishikb@andrew.cmu.edu: You know, being very…
+
+90
+00:12:40.990 --> 00:12:45.860
+hrishikb@andrew.cmu.edu: You know, up front with the custody, with the client, and saying, you know, not just like, hey, we need this data, but…
+
+91
+00:12:46.200 --> 00:12:47.000
+hrishikb@andrew.cmu.edu: you know.
+
+92
+00:12:47.120 --> 00:12:53.749
+hrishikb@andrew.cmu.edu: here are the… here, you know, we need it by this date, here's the impact if we don't have it by this date. Just being very…
+
+93
+00:12:54.130 --> 00:13:01.850
+hrishikb@andrew.cmu.edu: You know, very explicit, and whenever you meet with them, you know, here are the actions, the open actions is the item number one you ever review in your meetings.
+
+94
+00:13:02.020 --> 00:13:12.159
+hrishikb@andrew.cmu.edu: I think, for the last meeting, I wrote to them, I mentioned that we are currently blocked, and the response we got was, you should be getting the data by end of yesterday.
+
+95
+00:13:12.260 --> 00:13:27.509
+hrishikb@andrew.cmu.edu: So I'm thinking if I write a follow-up mail today, or, like… Yeah, yeah, you know. Like, I've chased them twice in, like, two days, so… Let's say, hey, this is great, we're, you know, we were hoping to get the data yesterday, as you mentioned, and maybe something came up.
+
+96
+00:13:27.860 --> 00:13:32.529
+hrishikb@andrew.cmu.edu: Please let us know when we can expect it, because we're really looking forward to, you know.
+
+97
+00:13:32.650 --> 00:13:37.990
+hrishikb@andrew.cmu.edu: people never want to hear, we're blocked, we're blocked, we're blocked, right? And that doesn't mean you're not, right? But…
+
+98
+00:13:38.180 --> 00:13:42.870
+hrishikb@andrew.cmu.edu: Because it's kind of like the impression people get is that
+
+99
+00:13:43.340 --> 00:13:51.079
+hrishikb@andrew.cmu.edu: oh, we're, you know, we're not doing… we're sitting like this until you give us the data, which you're not doing. So, just, you know, be more…
+
+100
+00:13:52.650 --> 00:13:58.780
+hrishikb@andrew.cmu.edu: You know, we're eager to, you know, we're at the point where we really could leverage that data and what we're, you know, giving them today.
+
+101
+00:14:00.090 --> 00:14:05.930
+hrishikb@andrew.cmu.edu: Other kind of things you could do is… I'm actually referring to a listing right here.
+
+102
+00:14:07.400 --> 00:14:11.200
+hrishikb@andrew.cmu.edu: you know what, that's not going to help the end government. I think we talked about most of them.
+
+103
+00:14:11.910 --> 00:14:15.819
+hrishikb@andrew.cmu.edu: But the other… the other thing in risks is that…
+
+104
+00:14:16.750 --> 00:14:21.120
+hrishikb@andrew.cmu.edu: And it's hard to do this for all risks. Some risks you can sort of say.
+
+105
+00:14:22.450 --> 00:14:25.729
+hrishikb@andrew.cmu.edu: kind of a leading indicators, so…
+
+106
+00:14:25.870 --> 00:14:28.170
+hrishikb@andrew.cmu.edu: If, you know, if you're seeing that.
+
+107
+00:14:30.070 --> 00:14:37.870
+hrishikb@andrew.cmu.edu: you're a week away, you know, you don't want to wait till it's the last minute. I need it by this date and ask for it the day before, right? But you can start raising
+
+108
+00:14:38.100 --> 00:14:45.650
+hrishikb@andrew.cmu.edu: the yellow flag, and then the red flag as you get closer and closer to those, you know, sort of must-have dates. Because obviously the…
+
+109
+00:14:45.930 --> 00:14:48.370
+hrishikb@andrew.cmu.edu: The impact increases.
+
+110
+00:14:48.480 --> 00:14:51.479
+hrishikb@andrew.cmu.edu: As you, you know, the more you… the closer you get.
+
+111
+00:14:52.090 --> 00:14:53.980
+hrishikb@andrew.cmu.edu: So…
+
+112
+00:14:57.210 --> 00:15:04.270
+hrishikb@andrew.cmu.edu: Let me sit here. So if I… and I actually made a list here. If you're thinking about a sort of a mitigation plan overall,
+
+113
+00:15:05.130 --> 00:15:09.850
+hrishikb@andrew.cmu.edu: There's kind of four… four elements to a fantastic… to a great mitigation plan.
+
+114
+00:15:10.150 --> 00:15:18.069
+hrishikb@andrew.cmu.edu: One is what you're doing… we talked about most of these. What you're doing to try to prevent it from happening, right? Try to prevent the risk
+
+115
+00:15:18.620 --> 00:15:21.989
+hrishikb@andrew.cmu.edu: From materializing, preventing the risk from becoming an issue.
+
+116
+00:15:22.240 --> 00:15:26.090
+hrishikb@andrew.cmu.edu: Right, because it's… at some point, Fair enough.
+
+117
+00:15:26.710 --> 00:15:28.830
+hrishikb@andrew.cmu.edu: You're putting this as a risk?
+
+118
+00:15:29.140 --> 00:15:31.030
+hrishikb@andrew.cmu.edu: Is this already an issue?
+
+119
+00:15:31.780 --> 00:15:40.730
+hrishikb@andrew.cmu.edu: You know, and what is the impact of that issue? There's two… there's a difference. A risk is something that may happen. An issue is something that actually has occurred, and it is impacting you already.
+
+120
+00:15:43.600 --> 00:15:53.889
+hrishikb@andrew.cmu.edu: what you're trying to do… what you're going to do if the risk does materialize to reduce the negative impact, that would be kind of a second part of a really good mitigation strategy.
+
+121
+00:15:54.610 --> 00:15:56.319
+hrishikb@andrew.cmu.edu: A third one would be…
+
+122
+00:15:56.440 --> 00:16:03.650
+hrishikb@andrew.cmu.edu: what are those leading indicators? How do you know that it's going to become… going to… this risk is going to materialize?
+
+123
+00:16:03.790 --> 00:16:11.670
+hrishikb@andrew.cmu.edu: You know, for example, in this case, if you were in a situation where the customer's usually… the client's usually pretty responsive.
+
+124
+00:16:11.870 --> 00:16:14.100
+hrishikb@andrew.cmu.edu: Right. If your experience is…
+
+125
+00:16:14.380 --> 00:16:23.370
+hrishikb@andrew.cmu.edu: client is really not responsive in general, it takes them weeks to get back to us, then that leading indicator would change under those two circumstances, right? One would be…
+
+126
+00:16:23.670 --> 00:16:42.109
+hrishikb@andrew.cmu.edu: well, if the client says they're going to get it to us, that's pretty good, right? You know, we're not so worried about it. We can wait till 48 hours before it becomes an issue to raise the flag. If it's something we have a very non-responsive client, then you may want to say, well, we're going to raise that flag, it's going to go from yellow to red, say.
+
+127
+00:16:42.210 --> 00:16:44.789
+hrishikb@andrew.cmu.edu: Two weeks before you really need it.
+
+128
+00:16:45.050 --> 00:16:48.360
+hrishikb@andrew.cmu.edu: And the last is what, you know, if… if you don't get it.
+
+129
+00:16:48.510 --> 00:16:53.670
+hrishikb@andrew.cmu.edu: or you don't get it, but when you need it, what is… what are you going to do about it? How are you going to manage it?
+
+130
+00:16:54.300 --> 00:17:00.730
+hrishikb@andrew.cmu.edu: So… But those are… it's a really good risk, right? The only thing I would say is…
+
+131
+00:17:01.800 --> 00:17:10.649
+hrishikb@andrew.cmu.edu: Break the two, and sort of the caution is that there may be, you know.
+
+132
+00:17:11.030 --> 00:17:15.749
+hrishikb@andrew.cmu.edu: Risks typically have a kind of a trigger date, and…
+
+133
+00:17:16.280 --> 00:17:19.439
+hrishikb@andrew.cmu.edu: You need to… if there is one here, if there isn't one.
+
+134
+00:17:19.770 --> 00:17:23.170
+hrishikb@andrew.cmu.edu: Right? If there is one, that really needs to be communicated with the client.
+
+135
+00:17:24.540 --> 00:17:26.300
+hrishikb@andrew.cmu.edu: All right.
+
+136
+00:17:27.560 --> 00:17:31.730
+hrishikb@andrew.cmu.edu: Does that all make sense? It does. Okay, no problem.
+
+137
+00:17:32.230 --> 00:17:34.660
+hrishikb@andrew.cmu.edu: Beginning.
+
+138
+00:17:35.550 --> 00:17:37.070
+hrishikb@andrew.cmu.edu: Go to the next one.
+
+139
+00:17:37.200 --> 00:17:39.849
+hrishikb@andrew.cmu.edu: So, having manual workloads.
+
+140
+00:17:40.570 --> 00:17:47.890
+hrishikb@andrew.cmu.edu: Currently, we cannot proceed on this, but…
+
+141
+00:17:48.190 --> 00:17:51.710
+hrishikb@andrew.cmu.edu: The risk that, that I foresee is that
+
+142
+00:17:52.070 --> 00:17:58.890
+hrishikb@andrew.cmu.edu: until we have, like, some of the ML models trained up and we start exploring.
+
+143
+00:17:59.040 --> 00:18:01.560
+hrishikb@andrew.cmu.edu: There would be a lot of,
+
+144
+00:18:02.620 --> 00:18:09.900
+hrishikb@andrew.cmu.edu: A lot of comparisons that we have to do against the baseline, and we need to, like, really confirm that
+
+145
+00:18:10.310 --> 00:18:16.140
+hrishikb@andrew.cmu.edu: Our demo models that we do pick end up being better than what they currently have.
+
+146
+00:18:16.320 --> 00:18:21.769
+hrishikb@andrew.cmu.edu: So this is, like, a tech… is this a technical risk? Yes. Okay. Alright. So…
+
+147
+00:18:25.040 --> 00:18:31.779
+hrishikb@andrew.cmu.edu: So the… I think I would need to change these a little bit. It's not a mitigation, but…
+
+148
+00:18:31.810 --> 00:18:47.980
+hrishikb@andrew.cmu.edu: One of the indicators we would push to establish is, like, we would have a baseline that they… that we get from their side, where, like, how much time it's taking them, what's their average rate of being correct on the prediction that they have.
+
+149
+00:18:48.360 --> 00:18:55.519
+hrishikb@andrew.cmu.edu: And then… we'll… go from our side and see what our machine learning or AI is giving.
+
+150
+00:18:55.890 --> 00:19:02.249
+hrishikb@andrew.cmu.edu: And then, you know, what's the… is the confidence scoring robust or not?
+
+151
+00:19:02.560 --> 00:19:07.159
+hrishikb@andrew.cmu.edu: And… Is it working properly with the human in the root system, I think?
+
+152
+00:19:07.520 --> 00:19:17.020
+hrishikb@andrew.cmu.edu: So… Is this a… It… How is this different?
+
+153
+00:19:17.470 --> 00:19:22.309
+hrishikb@andrew.cmu.edu: than any other… Type of…
+
+154
+00:19:23.680 --> 00:19:30.029
+hrishikb@andrew.cmu.edu: Project, or that is doing any type of, sort of, categorization or classification.
+
+155
+00:19:30.780 --> 00:19:34.840
+hrishikb@andrew.cmu.edu: Based on ML. Wouldn't he have exactly the same set of circumstances?
+
+156
+00:19:35.270 --> 00:19:46.080
+hrishikb@andrew.cmu.edu: You know, you've got some… you've got some minimal acceptable rates, right? You're going to establish a baseline, you're going to verify it, you're going to…
+
+157
+00:19:46.570 --> 00:19:59.840
+hrishikb@andrew.cmu.edu: the… yeah, that's true in almost every case, but here, most of the time you have a defined architecture that you want to go with, like, but here we do not know. We're gonna have to pick between three of them.
+
+158
+00:20:00.080 --> 00:20:11.559
+hrishikb@andrew.cmu.edu: So that was why I wrote this one, so that we know that there is some uncertainty among the choices, and how that would affect how we go about it.
+
+159
+00:20:12.040 --> 00:20:18.660
+hrishikb@andrew.cmu.edu: So, how did… I guess maybe I just didn't understand that. So how… so for your three options, how are you going in?
+
+160
+00:20:19.480 --> 00:20:23.519
+hrishikb@andrew.cmu.edu: Where in that mitigation do you refer to those three options?
+
+161
+00:20:25.530 --> 00:20:32.120
+hrishikb@andrew.cmu.edu: That would just be the minimum credible AI standard for, like… So when, when we're,
+
+162
+00:20:32.430 --> 00:20:45.340
+hrishikb@andrew.cmu.edu: Testing all three, we would pick the one that has the appropriate, like, performance and, like, resources usage that they said, and then we would just pick one that does actually exceed the baseline.
+
+163
+00:20:45.680 --> 00:20:50.430
+hrishikb@andrew.cmu.edu: Okay, so really what you're doing is you're reducing your technical risk, by…
+
+164
+00:20:51.150 --> 00:21:01.380
+hrishikb@andrew.cmu.edu: by running… by basically running experiments against three different techniques, right? So that… that's…
+
+165
+00:21:01.970 --> 00:21:08.160
+hrishikb@andrew.cmu.edu: That's how… that's how you're… if I understand, that's how you're really mitigating the risk, is that right? Okay, now that makes a lot of sense.
+
+166
+00:21:09.950 --> 00:21:23.559
+hrishikb@andrew.cmu.edu: reading the… reading that, I don't get that? I… I should have, written all three, like, BERT, and… So tech… techno-risk, I'm saying, is one of the… one of the standard ways to mitigate technical risk is by experimenting, and…
+
+167
+00:21:23.770 --> 00:21:29.780
+hrishikb@andrew.cmu.edu: when you do experiments, and I think you're reading that you're on the right path, it's really important to
+
+168
+00:21:30.110 --> 00:21:42.370
+hrishikb@andrew.cmu.edu: make sure that you design… design the experiment well, right? So here are the… these are the criteria… these are criteria by which we are going to effectively evaluate those different experiments.
+
+169
+00:21:42.650 --> 00:21:45.900
+hrishikb@andrew.cmu.edu: Yeah, like, another part to this is that
+
+170
+00:21:46.230 --> 00:21:56.979
+hrishikb@andrew.cmu.edu: The… the manual baseline itself is not established yet. We did ask them during the client meetings, and they were… themselves did not have, like…
+
+171
+00:21:57.060 --> 00:22:15.320
+hrishikb@andrew.cmu.edu: they've categorized enough data, of course, but they don't have a baseline for what they do have, like, they've not done that analysis yet. So that's a part of the risk that we don't have something already to compare against. It will have to be a process between us and them so that we actually have something concrete to compare against.
+
+172
+00:22:15.340 --> 00:22:19.920
+hrishikb@andrew.cmu.edu: Right, but you could still compare against… you're still going to have some…
+
+173
+00:22:21.720 --> 00:22:28.510
+hrishikb@andrew.cmu.edu: absolute numbers, right? Your experiments are going to provide
+
+174
+00:22:33.700 --> 00:22:39.240
+hrishikb@andrew.cmu.edu: Well, your experiments are going to provide some, you know, automation…
+
+175
+00:22:40.900 --> 00:22:48.100
+hrishikb@andrew.cmu.edu: You're gonna know how… let's put it this way, let's say… let's say you run through 100 different, you know, you have each system running through 100 different inputs, yeah.
+
+176
+00:22:49.060 --> 00:22:51.830
+hrishikb@andrew.cmu.edu: You're going to know the…
+
+177
+00:22:53.860 --> 00:23:00.059
+hrishikb@andrew.cmu.edu: the percentages of those that you have high confidence in need, you know, do or do not need human correction. Yes. Right?
+
+178
+00:23:01.160 --> 00:23:03.649
+hrishikb@andrew.cmu.edu: So, in some sense.
+
+179
+00:23:04.890 --> 00:23:16.829
+hrishikb@andrew.cmu.edu: you're going… you know, that… you're going to know which perform… you know, which one has better performance, right? And I realize it's not going to necessarily tell you, are you saving enough time overall, right? Is it a… but…
+
+180
+00:23:17.370 --> 00:23:24.950
+hrishikb@andrew.cmu.edu: You could still… quantitatively evaluate
+
+181
+00:23:25.880 --> 00:23:28.839
+hrishikb@andrew.cmu.edu: Even without all that information. And honestly.
+
+182
+00:23:28.940 --> 00:23:30.919
+hrishikb@andrew.cmu.edu: You could, you could, you could…
+
+183
+00:23:31.180 --> 00:23:35.379
+hrishikb@andrew.cmu.edu: you know, do a lot of work before you know what this magic number is.
+
+184
+00:23:35.560 --> 00:23:37.340
+hrishikb@andrew.cmu.edu: You don't need that magic number yet.
+
+185
+00:23:40.790 --> 00:23:49.080
+hrishikb@andrew.cmu.edu: I think 3 is not… What, it's not currently, pressing for us.
+
+186
+00:23:49.200 --> 00:23:53.530
+hrishikb@andrew.cmu.edu: So 3's not… 3's not a risk. Tell you why. So…
+
+187
+00:23:53.820 --> 00:23:56.639
+hrishikb@andrew.cmu.edu: And this is… you're not… every single…
+
+188
+00:23:57.660 --> 00:24:04.160
+hrishikb@andrew.cmu.edu: 80% of every studio and practical team has this… something like this as a risk.
+
+189
+00:24:04.430 --> 00:24:11.440
+hrishikb@andrew.cmu.edu: And then during the presentations, you can tell the different faculty members kind of get into arguments with one another.
+
+190
+00:24:11.610 --> 00:24:22.569
+hrishikb@andrew.cmu.edu: Is that a risk? No, it's not a risk. I don't think it's a risk. So, I'm gonna head it off with the pass, because I don't think it's a risk. There may be other faculty members who do, and here's why, is that…
+
+191
+00:24:23.060 --> 00:24:25.660
+hrishikb@andrew.cmu.edu: There has never been a project since the
+
+192
+00:24:26.290 --> 00:24:33.830
+hrishikb@andrew.cmu.edu: The world saw its first project that didn't have scope creep as a potential… Issued.
+
+193
+00:24:34.020 --> 00:24:39.890
+hrishikb@andrew.cmu.edu: And… It's almost like saying, Well, my project may fail.
+
+194
+00:24:40.140 --> 00:24:42.220
+hrishikb@andrew.cmu.edu: I may not succeed, that's a risk.
+
+195
+00:24:42.490 --> 00:24:46.439
+hrishikb@andrew.cmu.edu: It's this, it's just… it's very vague. You don't…
+
+196
+00:24:46.920 --> 00:24:52.860
+hrishikb@andrew.cmu.edu: You know, how do you… how would you mitigate scope of creep? You would do it through good software engineering practices.
+
+197
+00:24:52.990 --> 00:24:55.050
+hrishikb@andrew.cmu.edu: So you'd have…
+
+198
+00:24:55.250 --> 00:25:08.289
+hrishikb@andrew.cmu.edu: a scope of agreement with the client. You might have a signed-off statement of work. You might have a, you know, an explicit, sort of, as part of your requirements, an out-of-scope list of things that are out of scope.
+
+199
+00:25:08.530 --> 00:25:12.340
+hrishikb@andrew.cmu.edu: You probably might have some change control processes, so if you are
+
+200
+00:25:12.770 --> 00:25:20.339
+hrishikb@andrew.cmu.edu: If something new comes in, this is the process that you follow in order to determine if that is something you can accept or not.
+
+201
+00:25:20.460 --> 00:25:27.799
+hrishikb@andrew.cmu.edu: you're gonna follow, you know, Moscow, for… to understand what, you know, in terms of prioritization of requirements.
+
+202
+00:25:27.900 --> 00:25:36.039
+hrishikb@andrew.cmu.edu: So it's just a… it's just part of every project, and software engineering practices will address that for you. Okay. Make sense?
+
+203
+00:25:37.000 --> 00:25:39.150
+hrishikb@andrew.cmu.edu: Yeah.
+
+204
+00:25:39.540 --> 00:25:45.410
+hrishikb@andrew.cmu.edu: I think because we're a newer team, and all of us are kind of new to, like, doing the whole process ourselves.
+
+205
+00:25:45.520 --> 00:26:03.059
+hrishikb@andrew.cmu.edu: I did not consider it, like, that there were mature solutions, like, that people actually have done this, like, a thousand times. Yeah, again, it's… but you… but you know some of this already, so you know about creating, like, the statement of work for your… Yes, and the… You know about creating Moscow.
+
+206
+00:26:03.060 --> 00:26:08.320
+hrishikb@andrew.cmu.edu: you may not know about, like, a, you know, like a change control thing. You may not know about that, right? But…
+
+207
+00:26:08.390 --> 00:26:11.890
+hrishikb@andrew.cmu.edu: But think about… What you would…
+
+208
+00:26:12.320 --> 00:26:19.330
+hrishikb@andrew.cmu.edu: What you would do if, you know, the client said, Well… Here's an example.
+
+209
+00:26:21.060 --> 00:26:30.720
+hrishikb@andrew.cmu.edu: My father, many, many, many, many years ago, was doing a master's in mechanical engineering, and at that time, master's, you write a whole pretty significant thesis, and
+
+210
+00:26:31.650 --> 00:26:43.219
+hrishikb@andrew.cmu.edu: He spent, you know, huge amounts of time on this thing, you know, thousands of hours on this thing, and he came up with this long, you know, this paper, right, this published paper. And his advisor looks at it, and he said, this is great work.
+
+211
+00:26:43.640 --> 00:26:47.819
+hrishikb@andrew.cmu.edu: Now I want you to do it using complex numbers, not just, like, real numbers.
+
+212
+00:26:49.270 --> 00:26:54.949
+hrishikb@andrew.cmu.edu: And that was scope creep, right? That was, like, real scope creep. And…
+
+213
+00:26:55.060 --> 00:27:02.330
+hrishikb@andrew.cmu.edu: There wasn't any way to address it other than my father saying, I'm done, and I'm out of here. I don't care about the semesters anymore.
+
+214
+00:27:02.540 --> 00:27:15.650
+hrishikb@andrew.cmu.edu: But think about how you would handle a situation where someone… a client would say, well, we really want… we love this, but you want… we want you to do it with… with, with complex or imaginary numbers, right? You would need to say.
+
+215
+00:27:15.760 --> 00:27:21.720
+hrishikb@andrew.cmu.edu: Okay, you don't say no, right? You don't say, no, that wasn't part of our scope. You say.
+
+216
+00:27:22.620 --> 00:27:37.129
+hrishikb@andrew.cmu.edu: That's a great idea! That's really interesting. Let us… that wasn't part of our original agreement, or statement of work. Let's go back and understand, do a high-level scoping among ourselves to understand what… how big is this?
+
+217
+00:27:37.190 --> 00:27:45.339
+hrishikb@andrew.cmu.edu: and what its impact would be on the rest of the project, and we'll come back to you, and we'll have that discussion, right? And that's the way to handle it.
+
+218
+00:27:45.700 --> 00:27:50.330
+hrishikb@andrew.cmu.edu: I think right now, I'm not sure if we have an official scope of work.
+
+219
+00:27:50.470 --> 00:27:58.810
+hrishikb@andrew.cmu.edu: Is that something we should… You should do, yeah, you should definitely do that. Yeah, I actually think, like, something this client signs off on is really valuable.
+
+220
+00:27:59.230 --> 00:28:02.080
+hrishikb@andrew.cmu.edu: Okay, get an official scope of work, and…
+
+221
+00:28:02.290 --> 00:28:06.949
+hrishikb@andrew.cmu.edu: How we would handle changes. Yeah, yeah, okay.
+
+222
+00:28:07.540 --> 00:28:23.650
+hrishikb@andrew.cmu.edu: That's my list here. I think, what you said is much better than what I had for the mitigation of all this, so we'll just, I'll just change it to what you just said. Yeah, but I would… I would not include it as a risk. Okay. It's just part of your…
+
+223
+00:28:23.810 --> 00:28:40.779
+hrishikb@andrew.cmu.edu: part of your project management process. It would just be a doo-do for us to, like, get the official scope of work and, like, changes. Yeah, people don't like seeing, you know, a risk that is just, you know, well, another… another very common risk that people… students often have is
+
+224
+00:28:40.990 --> 00:28:46.540
+hrishikb@andrew.cmu.edu: Well, someone's gonna… You know, Someone's gonna get sick.
+
+225
+00:28:47.320 --> 00:28:51.429
+hrishikb@andrew.cmu.edu: Right? Well, yeah, someone's probably going to get sick at some point.
+
+226
+00:28:51.590 --> 00:28:54.339
+hrishikb@andrew.cmu.edu: But you, as part of your…
+
+227
+00:28:54.760 --> 00:29:13.790
+hrishikb@andrew.cmu.edu: how you manage your project, your project management practices, need to understand and address how you're going to handle if someone gets sick. Is there… are you going to make sure that everyone has a backup person who understands what they're doing? Are you going to build some buffer into your schedule to account for the fact that
+
+228
+00:29:13.860 --> 00:29:26.490
+hrishikb@andrew.cmu.edu: someone is going to get sick, right? And it is… it's gonna… hopefully none of you, it's gonna be other teams, right? But someone's gonna get sick and be out for a week or two. It happens. So, plan for it, don't call it a risk, right? Okay. Okay.
+
+229
+00:29:27.740 --> 00:29:32.230
+hrishikb@andrew.cmu.edu: Okay, just one… make it loud briefly.
+
+230
+00:29:32.700 --> 00:29:35.800
+hrishikb@andrew.cmu.edu: Remove risk 3 and just make it a 2.
+
+231
+00:29:36.540 --> 00:29:41.450
+hrishikb@andrew.cmu.edu: The fourth one is just,
+
+232
+00:29:41.730 --> 00:29:59.649
+hrishikb@andrew.cmu.edu: Yeah, this is the one that you said you wanted to talk about more, so that they're, they don't, need to learn more than they, than they think they will need to, so that… so I'd just like to get your thoughts. So what's the difference between this and a… and a constraint? Because you have constraints, and you're…
+
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+00:29:59.850 --> 00:30:02.319
+hrishikb@andrew.cmu.edu: requirements document. How's it different?
+
+234
+00:30:03.720 --> 00:30:12.000
+hrishikb@andrew.cmu.edu: Specifically, that, when we are doing some of the work, there's more, much more,
+
+235
+00:30:12.150 --> 00:30:29.100
+hrishikb@andrew.cmu.edu: documentation or support for one of them, and since it's not an official constraint, right? We might really prefer to use something, but since they themselves are not defining it as an official constraint, so that's why I just kept it. Like, if it was an officially, like.
+
+236
+00:30:29.190 --> 00:30:46.569
+hrishikb@andrew.cmu.edu: They just said that, we would really, like, it has to be in Azure, and, like, you cannot, like, just use Spice for this, then that would… I would not have kept this. But you've got to use Azure, right? Yeah, but, that's a… I think that… isn't that a constraint?
+
+237
+00:30:47.350 --> 00:31:05.460
+hrishikb@andrew.cmu.edu: they just strongly imply that you should use Azure, and you should use Bicep, and you should try to stay away from, like, languages that they give two, three languages that they really use for. So I would… I would try to just move that over to the project constraints. Yeah, yeah. And, you know, every…
+
+238
+00:31:05.990 --> 00:31:14.500
+hrishikb@andrew.cmu.edu: Every place you're ever going to be has a… oh, not every place, most places, over 95% of the places out there are going to have constraints like that.
+
+239
+00:31:14.870 --> 00:31:16.829
+hrishikb@andrew.cmu.edu: You know, it's very rare that
+
+240
+00:31:17.890 --> 00:31:30.080
+hrishikb@andrew.cmu.edu: You know, there are some companies where they say, well, you're… you team… you team, you have responsibility for the entire… you can decide what technologies, you can decide, because you build it and you own it.
+
+241
+00:31:30.170 --> 00:31:39.769
+hrishikb@andrew.cmu.edu: Right? You have to maintain it's not… not my problem that no one else in the company understands Dolang. You do, you know, that's so… that is very, very rare.
+
+242
+00:31:41.740 --> 00:31:48.089
+hrishikb@andrew.cmu.edu: Yep, and funny enough, they did mention that do not do it in Golang or anything like that, right, that's why I said, yeah, I remember that.
+
+243
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+hrishikb@andrew.cmu.edu: Yeah.
+
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+hrishikb@andrew.cmu.edu: I think, because, of course, we're not…
+
+245
+00:31:53.260 --> 00:31:59.320
+hrishikb@andrew.cmu.edu: gonna be responsible for after the handoff, and they'll have to do all the maintenance. I'll just move this to constraints.
+
+246
+00:32:00.100 --> 00:32:09.770
+hrishikb@andrew.cmu.edu: And… schema volatility, I think Arjun mentioned something about when we were talking with them, that
+
+247
+00:32:10.930 --> 00:32:22.339
+hrishikb@andrew.cmu.edu: That when… depending on how the… what kind of input we get, and then how the models run, there might be changes that we need to, like, do.
+
+248
+00:32:22.340 --> 00:32:40.460
+hrishikb@andrew.cmu.edu: And that we… we're… we're just not aware of how to… it's gonna be in advance, even though we… we can't present how the workflow goes, but this is, actually just a… this is a known, known that… that we do know we'll have to face, so I will just go to the…
+
+249
+00:32:40.560 --> 00:32:41.890
+hrishikb@andrew.cmu.edu: mitigation.
+
+250
+00:32:41.980 --> 00:33:01.050
+hrishikb@andrew.cmu.edu: We could, go with, like, I just wrote, like, a semantic matches, so even if some of the attributes are, like, varying between categories, but they mean the same thing, we could have, like, an additional layer on top that we're just automatically handling it, instead of us going and, like, manually changing it correctly.
+
+251
+00:33:01.310 --> 00:33:04.589
+hrishikb@andrew.cmu.edu: Just that I did not add much more complexity.
+
+252
+00:33:04.770 --> 00:33:20.059
+hrishikb@andrew.cmu.edu: It does, but, this is, this is in case that it does happen. Like, a new, kind of format, a new kind of, like, data source comes up, and, this is an automated approach. It does add additional complexity to it.
+
+253
+00:33:20.450 --> 00:33:30.390
+hrishikb@andrew.cmu.edu: So, is there any other sort of mitigation that you could think of doing? So, as an example, I'm not going to actually say what it is, but is there something that could help you
+
+254
+00:33:32.200 --> 00:33:39.870
+hrishikb@andrew.cmu.edu: understand if this risk is going to manifest itself to an issue earlier, right? The earlier you know about this, the better.
+
+255
+00:33:40.790 --> 00:33:44.630
+hrishikb@andrew.cmu.edu: Is there anything you can do to help learn if it's going to be an issue earlier?
+
+256
+00:33:45.480 --> 00:33:58.080
+hrishikb@andrew.cmu.edu: Right now, it does not seem likely, from what they have told us. So, that's why this is, I would say, the least of, like, that's a risk 5 in my list. It's likelier to happen.
+
+257
+00:33:58.250 --> 00:33:59.360
+hrishikb@andrew.cmu.edu: But…
+
+258
+00:33:59.830 --> 00:34:12.250
+hrishikb@andrew.cmu.edu: We're going to get some metrics from them of the last schema changes, or offering some numbers that are going to predict future. And then the other thing about the risk was if you really want to talk about likelihood of occurrence.
+
+259
+00:34:12.690 --> 00:34:17.439
+hrishikb@andrew.cmu.edu: And then the impact, if it does, so people can really understand, hey, this is…
+
+260
+00:34:17.790 --> 00:34:23.230
+hrishikb@andrew.cmu.edu: You know, this sounds like it may be low… maybe low likelihood, potentially significant impact. Yes.
+
+261
+00:34:23.380 --> 00:34:29.960
+hrishikb@andrew.cmu.edu: And, you know, you have to decide, okay, how much time you're gonna invest in
+
+262
+00:34:30.360 --> 00:34:47.910
+hrishikb@andrew.cmu.edu: you know, upfront mitigation on that, or versus something that is going to be, you know, high impact, high likelihood, as an example, right? Yeah, I think I should have mentioned that, at least, because all the other ones are high likelihood… must high likelihood than this. This is something that's…
+
+263
+00:34:48.290 --> 00:34:58.470
+hrishikb@andrew.cmu.edu: Like, for comparison, the first risk is, like, high likelihood, because it's… it's, like, definite likelihood, because it's actually doing it late, and, like, very high impact as well, because we cannot…
+
+264
+00:34:58.560 --> 00:35:20.060
+hrishikb@andrew.cmu.edu: do a lot of the stuff that we do. So it's an issue already. It is, it is, yeah. And this is, low impact, but it can be very significant if, like, a large, like, 30-40% of the data we're encountering is, like, does not match what we expect, then we can… we would have to be forced to build this on top, and then to make it… make sure the percentages are on…
+
+265
+00:35:20.430 --> 00:35:30.300
+hrishikb@andrew.cmu.edu: So, I will change it so that it mentions that it's a low likelihood, but it has significantly. Okay. Yeah, this is a good list. This is what we did.
+
+266
+00:35:30.970 --> 00:35:33.160
+hrishikb@andrew.cmu.edu: Suitable to make anyone.
+
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+00:35:33.490 --> 00:35:34.200
+hrishikb@andrew.cmu.edu: Oh.
+
+268
+00:35:35.290 --> 00:35:39.099
+hrishikb@andrew.cmu.edu: So, then we can, this is all for,
+
+269
+00:35:41.130 --> 00:35:48.620
+hrishikb@andrew.cmu.edu: This is all for the risk portion, so I'll just go into the project management portion of the document.
+
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+hrishikb@andrew.cmu.edu: So… It's basically from now to, like, May 4th.
+
+271
+00:35:55.140 --> 00:35:58.620
+hrishikb@andrew.cmu.edu: And how we're gonna at least,
+
+272
+00:35:58.880 --> 00:36:01.680
+hrishikb@andrew.cmu.edu: Have the temporary structure that we have.
+
+273
+00:36:01.900 --> 00:36:04.419
+hrishikb@andrew.cmu.edu: Or, like, going through it.
+
+274
+00:36:04.630 --> 00:36:08.650
+hrishikb@andrew.cmu.edu: So, we're currently still in Phase 1 and 2.
+
+275
+00:36:08.770 --> 00:36:15.529
+hrishikb@andrew.cmu.edu: So, we're still making our SES, and work for MVP has not even started yet.
+
+276
+00:36:15.850 --> 00:36:21.540
+hrishikb@andrew.cmu.edu: There is an initial draft for, like, requirements and architecture.
+
+277
+00:36:21.670 --> 00:36:26.669
+hrishikb@andrew.cmu.edu: But they will be finalized when, you know, the… during the next phases.
+
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+hrishikb@andrew.cmu.edu: After that, during the latter part of the…
+
+279
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+hrishikb@andrew.cmu.edu: semester, basically, it will be more focused on making sure our work is
+
+280
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+hrishikb@andrew.cmu.edu: Going smoothly, and evaluating all the progress we have made through there, till the end of this semester, at least.
+
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+00:36:46.760 --> 00:36:51.890
+hrishikb@andrew.cmu.edu: So what… do you have any plans for, like, what you'll be doing for the next two semesters… two semesters after that?
+
+282
+00:36:52.040 --> 00:36:54.290
+hrishikb@andrew.cmu.edu: Other vacations.
+
+283
+00:36:54.630 --> 00:36:55.650
+hrishikb@andrew.cmu.edu: interviews.
+
+284
+00:36:55.870 --> 00:37:06.500
+hrishikb@andrew.cmu.edu: No, it's only for the Spring 2006 roadmap. I've not, actually gone through for the summer roadmap. And have you defined what vertical slices 1 and 2 are?
+
+285
+00:37:06.680 --> 00:37:16.119
+hrishikb@andrew.cmu.edu: Vertical slices 1 and 2 are just basically… the first vertical slice would be, all the way up till, like, using our SES to, like, create a MVP.
+
+286
+00:37:16.490 --> 00:37:23.309
+hrishikb@andrew.cmu.edu: And two is just, like, all the documentation work that we're gonna do, all the,
+
+287
+00:37:23.420 --> 00:37:29.299
+hrishikb@andrew.cmu.edu: Stuff that, would be useful for someone to learn it after the handoff, or even when we're explaining it.
+
+288
+00:37:31.030 --> 00:37:32.070
+hrishikb@andrew.cmu.edu: So…
+
+289
+00:37:35.080 --> 00:37:37.389
+hrishikb@andrew.cmu.edu: Is this too… is this too aggressive?
+
+290
+00:37:39.170 --> 00:37:41.100
+hrishikb@andrew.cmu.edu: Given that you have two more semesters.
+
+291
+00:37:41.640 --> 00:37:46.570
+hrishikb@andrew.cmu.edu: And given that you're working 12, you know, 12 hours a week this semester.
+
+292
+00:37:47.660 --> 00:37:56.909
+hrishikb@andrew.cmu.edu: I will admit that I've gone pretty aggressive, but I think even Arjun is in agreement that if this was a normal semester, then
+
+293
+00:37:57.080 --> 00:38:01.309
+hrishikb@andrew.cmu.edu: we would not… I would not have pushed this so hard, but…
+
+294
+00:38:01.570 --> 00:38:09.350
+hrishikb@andrew.cmu.edu: with AI, at least my… this is my personal opinion, that as soon as we have the SES and schema and all that stuff.
+
+295
+00:38:09.540 --> 00:38:27.709
+hrishikb@andrew.cmu.edu: like, well-defined enough, it would just be a… it would… it would just enter into a very fast iteration process, see if it works, like, are the tests going properly, are the outputs as we expect? And, I… in my original opinion, should go pretty fast, as soon as we do have that baseline set up.
+
+296
+00:38:28.050 --> 00:38:28.900
+hrishikb@andrew.cmu.edu: Okay.
+
+297
+00:38:29.170 --> 00:38:43.140
+hrishikb@andrew.cmu.edu: I don't know, that's… I don't know what we'll do afterwards, whether there's, like, stretch goals or something, but I could be completely wrong. Maybe it takes us, like, deep into the summer or something, but this is what happened.
+
+298
+00:38:44.910 --> 00:38:51.999
+hrishikb@andrew.cmu.edu: Is that… so, the question… again, it's not… not this week or next week, right? But at some point.
+
+299
+00:38:53.890 --> 00:38:58.129
+hrishikb@andrew.cmu.edu: You will want to have, before your end of semester, like, what your
+
+300
+00:38:58.290 --> 00:39:02.769
+hrishikb@andrew.cmu.edu: entire plan is, right, for including the other semesters. Okay.
+
+301
+00:39:06.270 --> 00:39:11.200
+hrishikb@andrew.cmu.edu: So, these are, how,
+
+302
+00:39:11.560 --> 00:39:14.730
+hrishikb@andrew.cmu.edu: I think the responsibilities should be split.
+
+303
+00:39:14.910 --> 00:39:25.239
+hrishikb@andrew.cmu.edu: So, currently, for the project lead, she's the team leader, and, we've still yet to determine how the structure will rotate and
+
+304
+00:39:25.290 --> 00:39:44.509
+hrishikb@andrew.cmu.edu: how, like, how long the rotation structure should be? So, should it be… Oh, we… we were actually talking with the other teams. I think they were rotating per month basis, the team leaders. We didn't want to do it so early, but we thought we could do it every mini. Every what? Every mini-sam. So, like, after the spring break, we get a new…
+
+305
+00:39:44.550 --> 00:39:46.500
+hrishikb@andrew.cmu.edu: Team lead? Yeah.
+
+306
+00:39:46.580 --> 00:39:51.360
+hrishikb@andrew.cmu.edu: And then, so everyone gets, like, two, I think, two rotations of…
+
+307
+00:39:51.690 --> 00:39:59.110
+hrishikb@andrew.cmu.edu: And I just said, there's no right or wrong, right? As long as you have a reason for making that decision, right? That's all that matters.
+
+308
+00:39:59.330 --> 00:40:06.439
+hrishikb@andrew.cmu.edu: Yeah, monthly, we might be… too fast. It sounds nice, but there's no continuity.
+
+309
+00:40:07.030 --> 00:40:18.310
+hrishikb@andrew.cmu.edu: Architecturally, well, everyone is responsible for knowing, because this is a small team, everyone must know the entire architecture, so that they know how everything is going, but…
+
+310
+00:40:20.420 --> 00:40:28.430
+hrishikb@andrew.cmu.edu: So the person who is, like, most in-depth with it, and most, like, you know, responsible for it would be designated as an architecture lead.
+
+311
+00:40:28.920 --> 00:40:38.629
+hrishikb@andrew.cmu.edu: As for the data and ML lead, it's just basically the one who goes most, like, hands-on and, like, you know, is responsible for debugging it.
+
+312
+00:40:39.440 --> 00:40:46.380
+hrishikb@andrew.cmu.edu: the… and… engineering lead, that's the one I mostly fear about, because
+
+313
+00:40:46.770 --> 00:41:02.610
+hrishikb@andrew.cmu.edu: we all have to own the implementation. It's… it cannot be any other way, I think. And… because engineering lead and QA, it has to be done by all of us, basically, so I'm not sure whether I should keep it, or… Well, I think… so…
+
+314
+00:41:03.270 --> 00:41:06.130
+hrishikb@andrew.cmu.edu: So, again, this is more… maybe a quality discussion.
+
+315
+00:41:06.260 --> 00:41:08.609
+hrishikb@andrew.cmu.edu: But… You know, you're…
+
+316
+00:41:09.480 --> 00:41:15.949
+hrishikb@andrew.cmu.edu: you know, I don't know if it's the architecturally, but there's certainly someone who… you all own… you all own implementation, sure.
+
+317
+00:41:16.050 --> 00:41:21.190
+hrishikb@andrew.cmu.edu: But there may be times where there's someone who has
+
+318
+00:41:21.910 --> 00:41:24.000
+hrishikb@andrew.cmu.edu: You know, has more of a…
+
+319
+00:41:24.760 --> 00:41:29.169
+hrishikb@andrew.cmu.edu: Consult, you know, people consult with them, they have more of, sort of, a tech lead type of
+
+320
+00:41:29.620 --> 00:41:31.790
+hrishikb@andrew.cmu.edu: responsibility, right? So…
+
+321
+00:41:33.300 --> 00:41:45.720
+hrishikb@andrew.cmu.edu: QA process lead, I think, is… you absolutely need one, because how… who… who has that response… yes, I'm gonna go… I'm gonna go test. Who has the responsibility for the over quality plan for our system?
+
+322
+00:41:46.240 --> 00:41:52.810
+hrishikb@andrew.cmu.edu: to make sure, and when I say quality plan, I don't just mean that our software is high quality, it's that we are
+
+323
+00:41:53.000 --> 00:41:57.800
+hrishikb@andrew.cmu.edu: We are doing what we said we are going to do with respect to how we are operating.
+
+324
+00:41:58.310 --> 00:42:04.200
+hrishikb@andrew.cmu.edu: So… you know, co- gets checked in, it…
+
+325
+00:42:04.500 --> 00:42:08.500
+hrishikb@andrew.cmu.edu: you know, here's an example. Someone who's actually may go and
+
+326
+00:42:09.190 --> 00:42:18.930
+hrishikb@andrew.cmu.edu: Look at data around how many, how many… Review, code reviews.
+
+327
+00:42:19.230 --> 00:42:26.370
+hrishikb@andrew.cmu.edu: Are actually providing meaningful… meaningful comments that are provided to them, versus ones that are just like, you know, okay, just pass it.
+
+328
+00:42:26.630 --> 00:42:30.030
+hrishikb@andrew.cmu.edu: So, it's typically someone who, like, who's…
+
+329
+00:42:30.450 --> 00:42:32.740
+hrishikb@andrew.cmu.edu: Looking, sort of, one level deeper.
+
+330
+00:42:33.120 --> 00:42:38.080
+hrishikb@andrew.cmu.edu: Into the… into the processes, and everyone has that responsibility, right?
+
+331
+00:42:38.790 --> 00:42:41.970
+hrishikb@andrew.cmu.edu: Yeah, I think you're right. Otherwise, we might fall into, like.
+
+332
+00:42:42.060 --> 00:42:58.830
+hrishikb@andrew.cmu.edu: the team's gonna do it, and then no one ends up doing it. No one's gonna do metrics tracking unless there's someone else responsible for metrics tracking. Yeah, so we can have a discussion, like, who has the most experience for, like, for the engineering lead, who has the most experience for, like,
+
+333
+00:42:58.840 --> 00:43:03.640
+hrishikb@andrew.cmu.edu: You know, working with pipelines and integrations and stuff, we'll just assign that person.
+
+334
+00:43:03.660 --> 00:43:06.010
+hrishikb@andrew.cmu.edu: As per the QA lead.
+
+335
+00:43:06.240 --> 00:43:14.010
+hrishikb@andrew.cmu.edu: It's gonna have to be somewhat strict so that, you know, they actually kind of, like, go deep into it and, like, see that it's all we need.
+
+336
+00:43:14.360 --> 00:43:17.590
+hrishikb@andrew.cmu.edu: So, we'll have… we'll… we'll discuss.
+
+337
+00:43:18.360 --> 00:43:36.010
+hrishikb@andrew.cmu.edu: And you want to have names, even though you're going to be changing, make sure there's people's names on those. Yeah, definitely. I think, yeah, whenever we write theme, then we're kind of, like, it becomes the chinx, and then sometimes it ends up not being done, so…
+
+338
+00:43:36.810 --> 00:43:50.649
+hrishikb@andrew.cmu.edu: Success criteria is just basically, from the data metrics and product metrics, so percentages of records that are deemed high confidence and are actually high confidence. They're true positives instead of being false positives.
+
+339
+00:43:51.440 --> 00:43:57.690
+hrishikb@andrew.cmu.edu: We could also do some confidence distributions at the, if we go that deep.
+
+340
+00:43:57.900 --> 00:44:01.609
+hrishikb@andrew.cmu.edu: What are defect rates? Is the…
+
+341
+00:44:01.930 --> 00:44:07.870
+hrishikb@andrew.cmu.edu: After we give all this to the client, and they start checking our work.
+
+342
+00:44:08.380 --> 00:44:11.589
+hrishikb@andrew.cmu.edu: Is the post-approval rate what we have.
+
+343
+00:44:11.720 --> 00:44:17.439
+hrishikb@andrew.cmu.edu: And is the human in the loop? That… I'll have to add that. Human in the loop part… portion where it comes from.
+
+344
+00:44:19.760 --> 00:44:20.590
+hrishikb@andrew.cmu.edu: Alright.
+
+345
+00:44:26.170 --> 00:44:30.269
+hrishikb@andrew.cmu.edu: This is, the research planning is pretty weighed still.
+
+346
+00:44:31.810 --> 00:44:50.309
+hrishikb@andrew.cmu.edu: Actually, the human allocation portion is much more weight than the AI portion. We've not decided how to fill all the roles yet, and how they're… if… and if they're gonna be rotated. Like, we've decided that the leadership… team lead will be rotated, but what about the other one? Should we just have one person
+
+347
+00:44:50.790 --> 00:44:53.670
+hrishikb@andrew.cmu.edu: throughout the project, who's responsible for QAns.
+
+348
+00:44:54.220 --> 00:45:01.499
+hrishikb@andrew.cmu.edu: AI resources is basically all the things like cursor that we're gonna leverage.
+
+349
+00:45:01.800 --> 00:45:08.570
+hrishikb@andrew.cmu.edu: And how it's gonna basically handle all the… Coding and the repetitive tasks.
+
+350
+00:45:08.710 --> 00:45:10.630
+hrishikb@andrew.cmu.edu: Like, boxing the PDFs.
+
+351
+00:45:11.640 --> 00:45:28.860
+hrishikb@andrew.cmu.edu: But, the last portion is basically about what… what is our, like, responsibility at the end of it all, like, we are responsible for validating it, whether we… assessing risk, like, whether we should even use it for certain portions or not, or whether it's best handled by us.
+
+352
+00:45:29.250 --> 00:45:30.450
+hrishikb@andrew.cmu.edu: And then…
+
+353
+00:45:30.710 --> 00:45:37.280
+hrishikb@andrew.cmu.edu: the last thing that I wrote is, like, being very cautious that no proprietary data is not, like, going through
+
+354
+00:45:37.530 --> 00:45:40.879
+hrishikb@andrew.cmu.edu: other AI tools than what they can use.
+
+355
+00:45:43.350 --> 00:45:46.809
+hrishikb@andrew.cmu.edu: Finally, yeah.
+
+356
+00:45:47.750 --> 00:46:02.900
+hrishikb@andrew.cmu.edu: how we're gonna do our task planning? So, ETVX is the one that, is most, like, familiar for me. This might change depending if the team decides that this format website does not work for us.
+
+357
+00:46:06.010 --> 00:46:14.000
+hrishikb@andrew.cmu.edu: Domain ownership is another thing that will come up in the… when we make the human rows, basically, assignments, and…
+
+358
+00:46:14.480 --> 00:46:16.650
+hrishikb@andrew.cmu.edu: Work will then be distributed.
+
+359
+00:46:16.840 --> 00:46:24.440
+hrishikb@andrew.cmu.edu: Based on how people, you know, tell about their… which domain they're most familiar and comfortable with.
+
+360
+00:46:25.690 --> 00:46:32.520
+hrishikb@andrew.cmu.edu: The information criteria is… We will have, like, verification steps there.
+
+361
+00:46:32.950 --> 00:46:34.219
+hrishikb@andrew.cmu.edu: we go through.
+
+362
+00:46:34.360 --> 00:46:37.629
+hrishikb@andrew.cmu.edu: And, as per the non-core artifacts.
+
+363
+00:46:37.990 --> 00:46:42.719
+hrishikb@andrew.cmu.edu: the… that would… that is a much more subjective process. We'll have, like,
+
+364
+00:46:42.950 --> 00:46:48.490
+hrishikb@andrew.cmu.edu: Hopefully, we'll have checklists that we'll just go through to confirm that they meet standards.
+
+365
+00:46:48.890 --> 00:46:52.580
+hrishikb@andrew.cmu.edu: And… As for progress tracking…
+
+366
+00:46:52.870 --> 00:47:00.159
+hrishikb@andrew.cmu.edu: That's something that, I think the QA lead will have to, oversee, and,
+
+367
+00:47:01.140 --> 00:47:06.990
+hrishikb@andrew.cmu.edu: We'll have to see how our… how much time we are taking on each of our, like, sprints, or…
+
+368
+00:47:07.740 --> 00:47:12.349
+hrishikb@andrew.cmu.edu: What, what the metrics are for backlog sizes, and…
+
+369
+00:47:12.490 --> 00:47:15.320
+hrishikb@andrew.cmu.edu: Are we reworking too much, or…
+
+370
+00:47:15.460 --> 00:47:28.619
+hrishikb@andrew.cmu.edu: spending too much time on, like, other things that, we initially said would not be spending that much time. Right, so that's actually helpful, so you can look at… that's actually a really good thing for, you know, a two-way process that you can do, is, like, is…
+
+371
+00:47:29.100 --> 00:47:40.000
+hrishikb@andrew.cmu.edu: A very mature team, and there aren't many teams that do this, goes and says, you know, says, this is how we plan to spend our time, and then actually measures how they spend their time and reflects on that.
+
+372
+00:47:42.080 --> 00:47:43.280
+hrishikb@andrew.cmu.edu: So…
+
+373
+00:47:43.440 --> 00:47:54.520
+hrishikb@andrew.cmu.edu: This is all I have, and let's just for my, this is the risk and the project. Let me share one thing with you. I should put something up.
+
+374
+00:47:54.840 --> 00:48:00.229
+hrishikb@andrew.cmu.edu: I'll get that in the back of here, because I can't plug that into this laptop. I think I've got to do that.
+
+375
+00:48:01.030 --> 00:48:01.750
+hrishikb@andrew.cmu.edu: Yes.
+
+376
+00:48:04.430 --> 00:48:09.180
+hrishikb@andrew.cmu.edu: I think… I can move it, it's not gonna read you. Throughout so long.
+
+377
+00:48:10.470 --> 00:48:11.220
+hrishikb@andrew.cmu.edu: Alright.
+
+378
+00:48:30.170 --> 00:48:32.079
+hrishikb@andrew.cmu.edu: Okay, I'll put it here, too.
+
+379
+00:48:40.280 --> 00:48:49.709
+hrishikb@andrew.cmu.edu: So I'm going to send this to you. I want… I'm going to spend this weekend reviewing it, though, because I just, like, wrote it yesterday, so I haven't really had time, so much time to look at it myself.
+
+380
+00:48:49.850 --> 00:48:56.309
+hrishikb@andrew.cmu.edu: I don't need this stuff right down here. Let's probably a bit more about this. So trying to really think about
+
+381
+00:48:56.900 --> 00:48:58.830
+hrishikb@andrew.cmu.edu: your semester.
+
+382
+00:48:59.420 --> 00:49:02.640
+hrishikb@andrew.cmu.edu: And give you some guidance as to
+
+383
+00:49:03.050 --> 00:49:07.200
+hrishikb@andrew.cmu.edu: What types of things may be expected, you know, or…
+
+384
+00:49:07.420 --> 00:49:10.889
+hrishikb@andrew.cmu.edu: And every project is different, right? But what kinds of things
+
+385
+00:49:11.000 --> 00:49:22.689
+hrishikb@andrew.cmu.edu: should either exist, or be in progress, or thinking about, what's maybe coming up soon, what things… hey, you're probably not going to have this yet, but these are things that you'll probably need at some point, right? So…
+
+386
+00:49:24.020 --> 00:49:30.530
+hrishikb@andrew.cmu.edu: you know, context diagram, the vision, understanding of requirements and notional architecture. None of this should be huge.
+
+387
+00:49:30.710 --> 00:49:36.669
+hrishikb@andrew.cmu.edu: you know, I guess scope, agreement, statement of work are probably pretty… I'm just gonna put those together here.
+
+388
+00:49:37.250 --> 00:49:40.999
+hrishikb@andrew.cmu.edu: As you can tell, I wrote this… I didn't have a whole lot of time to work on it.
+
+389
+00:49:43.520 --> 00:49:44.589
+hrishikb@andrew.cmu.edu: Come on, there we go.
+
+390
+00:49:45.110 --> 00:49:51.780
+hrishikb@andrew.cmu.edu: The semester roadmap, we talked about, you know, you showed that, your process definitions, that's really important this time.
+
+391
+00:49:51.930 --> 00:50:00.970
+hrishikb@andrew.cmu.edu: And any external dependencies, risk identification, which you're doing, the change management, which is something we talked about today, like, what happens when there is… when there is change.
+
+392
+00:50:01.960 --> 00:50:05.919
+hrishikb@andrew.cmu.edu: And I guess I should also put here, you know, sort of your…
+
+393
+00:50:06.190 --> 00:50:14.980
+hrishikb@andrew.cmu.edu: what do we call it? Software engineering? What do you call software engineering? SCS. What? SCS, Software Engineering. System.
+
+394
+00:50:15.810 --> 00:50:18.110
+hrishikb@andrew.cmu.edu: SES? Yeah. Okay.
+
+395
+00:50:19.240 --> 00:50:20.669
+hrishikb@andrew.cmu.edu: That should be theirs here, right?
+
+396
+00:50:24.110 --> 00:50:38.510
+hrishikb@andrew.cmu.edu: And then, you know, as you're getting more, you know, more understanding, you're going to create your breakdown structure, you're going to start to populate your backlog, and understand what your milestones are going to be, what your milestone plan is. And then, once you have that, you can really start working on
+
+397
+00:50:38.510 --> 00:50:46.149
+hrishikb@andrew.cmu.edu: you know, your… your, sorry, your work package, your release plans, your… start really having your backlogs, and…
+
+398
+00:50:46.490 --> 00:50:58.280
+hrishikb@andrew.cmu.edu: If you're going to be doing, earned value charts and things like that. And then… and I actually wrote at the end that full ceremony execution, you know, at some point, people are saying, okay, we're going to follow…
+
+399
+00:50:58.770 --> 00:51:02.700
+hrishikb@andrew.cmu.edu: Scrum, for example, right? Or we're gonna follow…
+
+400
+00:51:03.320 --> 00:51:17.269
+hrishikb@andrew.cmu.edu: milestone-driven execution, and as a part of that, there's a bunch of different ceremonies that one does, right? So at some point, you're gonna evolve where, hey, this is our… this is how we operate, this is the cadence
+
+401
+00:51:17.860 --> 00:51:27.350
+hrishikb@andrew.cmu.edu: This is how we operate as a team, and this is the cadence of work that we do. You know, every… every week we do this, every two weeks we do that, every three weeks we do that.
+
+402
+00:51:27.650 --> 00:51:32.619
+hrishikb@andrew.cmu.edu: So that's just… I'll share this, you don't need to copy it, but this could be helpful
+
+403
+00:51:32.970 --> 00:51:40.410
+hrishikb@andrew.cmu.edu: in terms of trying to understand the expectations, not to… well, two things. One is the expectations of the program, but also
+
+404
+00:51:40.880 --> 00:51:48.269
+hrishikb@andrew.cmu.edu: Is used as a guide to try to help, you know, yourselves. Kind of, are you on track with things related to program management as well?
+
+405
+00:51:49.070 --> 00:51:51.760
+hrishikb@andrew.cmu.edu: Does that make sense? Alright.
+
+406
+00:51:52.140 --> 00:51:54.820
+hrishikb@andrew.cmu.edu: Yeah, I just want to do… I just need to do a,
+
+407
+00:51:55.250 --> 00:51:59.459
+hrishikb@andrew.cmu.edu: sync between what I thought of and what's in the
+
+408
+00:51:59.610 --> 00:52:06.490
+hrishikb@andrew.cmu.edu: program documents to make sure that I'm in alignment. So, I'll do that this weekend and get it out to you.
+
+409
+00:52:07.940 --> 00:52:08.850
+hrishikb@andrew.cmu.edu: Okay.
+
+410
+00:52:10.200 --> 00:52:17.170
+hrishikb@andrew.cmu.edu: Yeah, I think, again, I think your team's doing great. I think you've got… it's a great start in project management work.
+
+411
+00:52:17.320 --> 00:52:24.759
+hrishikb@andrew.cmu.edu: If you look at this list of items, you're either, you know, you have or you're well on your way for many of them.
+
+412
+00:52:25.500 --> 00:52:30.190
+hrishikb@andrew.cmu.edu: so… I don't think certain things are going pretty well.
+
+413
+00:52:31.280 --> 00:52:33.149
+hrishikb@andrew.cmu.edu: And I was very impressed.
+
+414
+00:52:33.480 --> 00:52:34.460
+hrishikb@andrew.cmu.edu: by the risks.
+
+415
+00:52:35.270 --> 00:52:41.440
+hrishikb@andrew.cmu.edu: Seriously. I've seen a lot of bad risk registers before, okay? That's pretty good.
+
+416
+00:52:45.180 --> 00:52:47.840
+hrishikb@andrew.cmu.edu: Anything else I can help you with?
+
+417
+00:52:51.660 --> 00:52:52.830
+hrishikb@andrew.cmu.edu: I think…
+
+418
+00:52:53.720 --> 00:53:08.909
+hrishikb@andrew.cmu.edu: we'll just take your, like, the… because a lot of the risks that, I think need to be changed, like, be more in line with your feedback, and then at least two of them, or, like, one of them needs to be removed, and I'll just change the other ones.
+
+419
+00:53:10.360 --> 00:53:16.710
+hrishikb@andrew.cmu.edu: Yeah. And part of your process would be, for example, okay, one… you can decide however you want to do it.
+
+420
+00:53:16.840 --> 00:53:19.689
+hrishikb@andrew.cmu.edu: But… Because we're talking about risks.
+
+421
+00:53:19.980 --> 00:53:33.040
+hrishikb@andrew.cmu.edu: Well, at our sprint reviews, we review our risk register, or at our… like, one thing that teams sometimes do is they create a risk register. Why? Because they need to show it at their end of semester presentation.
+
+422
+00:53:33.710 --> 00:53:47.869
+hrishikb@andrew.cmu.edu: And it's unfortunate, right? It gets… yes, you get practice doing it, but it's not something that actually helps you in your program. And I realize this is very… it's not a giant project, so you can keep all those risks in your head.
+
+423
+00:53:48.430 --> 00:53:51.470
+hrishikb@andrew.cmu.edu: But in a much larger project, you may have
+
+424
+00:53:51.740 --> 00:53:56.740
+hrishikb@andrew.cmu.edu: 25, 30 items, and you may want… need to review them and look at, hey, what is becoming
+
+425
+00:53:57.300 --> 00:54:03.959
+hrishikb@andrew.cmu.edu: you know, for… I've been in situations where, for a given risk, I say, okay, what… as a team, when do we need to review this risk?
+
+426
+00:54:04.140 --> 00:54:07.060
+hrishikb@andrew.cmu.edu: And we set a trigger, so it could be…
+
+427
+00:54:07.420 --> 00:54:12.299
+hrishikb@andrew.cmu.edu: two-year project. It could be we want to review it in 2 weeks, could be we want to review it in 3 months.
+
+428
+00:54:13.860 --> 00:54:25.759
+hrishikb@andrew.cmu.edu: who needs to be involved in that review? So there's a lot of ways… there's a very basic risk register, but you can get a lot more sophisticated around them, too, and use it to more active… actively develop a period project.
+
+429
+00:54:30.090 --> 00:54:31.659
+hrishikb@andrew.cmu.edu: Okay, well, cool.
+
+430
+00:54:32.240 --> 00:54:36.960
+hrishikb@andrew.cmu.edu: Hope everyone has a good weekend. I'm gonna go to my… I have the next team right now, so I'm gonna go try again.
+
+431
+00:54:37.240 --> 00:54:42.740
+hrishikb@andrew.cmu.edu: It's already Friday, so… it's amazing, isn't it? Alright.
+
+432
+00:54:42.950 --> 00:54:45.580
+hrishikb@andrew.cmu.edu: 282. Thank you for the,
+
+433
+00:54:46.170 --> 00:54:49.929
+hrishikb@andrew.cmu.edu: But the power here is very nipple. I mean, my laptop thinks you too.
+
+434
+00:54:50.600 --> 00:54:52.640
+hrishikb@andrew.cmu.edu: Thank you. Alright, thanks.
+
+435
+00:55:18.600 --> 00:55:20.240
+hrishikb@andrew.cmu.edu: Thank you so much,
+
+436
+00:55:21.280 --> 00:55:25.070
+hrishikb@andrew.cmu.edu: you know… Shamash.
+
+437
+00:55:34.050 --> 00:55:41.210
+hrishikb@andrew.cmu.edu: Dude, I said all your stuff about, like, how close by speed we expect. Hopefully, that turns out something good.
+
+438
+00:55:41.370 --> 00:55:56.699
+hrishikb@andrew.cmu.edu: we can only start speeding up after this semester. You won't have the shit till the end of the semester. When you start developing, it'll be about money. Not even give, like, freaking Cadbury to…
+
+439
+00:55:57.340 --> 00:55:58.260
+hrishikb@andrew.cmu.edu: 2 months.
+
+440
+00:55:58.410 --> 00:56:04.670
+hrishikb@andrew.cmu.edu: Hey, motherfucker, you're the team lead. When I'm team lead, everyone's getting cat buried, don't worry. Let's see.
+
+441
+00:56:05.330 --> 00:56:07.589
+hrishikb@andrew.cmu.edu: Cadbury things. I get you guys.
+
+442
+00:56:08.960 --> 00:56:09.830
+hrishikb@andrew.cmu.edu: Here.
+
+443
+00:56:10.300 --> 00:56:14.399
+hrishikb@andrew.cmu.edu: I have the… I noted on some minutes. We have another meeting, right? Yeah.
+
+444
+00:56:14.530 --> 00:56:21.069
+hrishikb@andrew.cmu.edu: What? Fine. Do you want to, lead that one? Yeah.
+
+445
+00:56:21.210 --> 00:56:37.220
+hrishikb@andrew.cmu.edu: Hey, you're only reading it. No, I'm done for the days and night. But you don't have any content! Hey, yeah, no one has content. Yeah. We don't know what we're reading. We have to make content right now. Yeah, you have 3 hours, right? Start.
+
+446
+00:56:37.390 --> 00:56:59.240
+hrishikb@andrew.cmu.edu: By the way, what have you talk… I mean, he's the AI coach, right? Yeah, software engineering, this thing. That's what you're… I think the analyst is only. You just have to pass over whatever you're gonna use AI. Yeah, yeah. Last two pages, man, I made in, like, last 10 minutes. Thank God it did not focus. He just… that's why I just kept going on the spread, because that's what I remembered as, like, 3 pages, I'll just…
+
+447
+00:56:59.240 --> 00:57:16.449
+hrishikb@andrew.cmu.edu: keep reviewing them. You made this yesterday itself. This one I made yesterday, but Rishi told me, like, just before meeting… Project one was the same. This, like, project part is… so I added two parts that I could defend, so I… all of this was cut, dude. Like, when I was actually copying from the AI that part, right? Because, of course, I cannot try it and test it.
+
+448
+00:57:16.450 --> 00:57:22.639
+hrishikb@andrew.cmu.edu: It gave, like, 5-10 pages of content. I cut it down to, like, 2 so that I could extend. Forward to stop the routing?
+
diff --git a/dashboard/architecture.html b/dashboard/architecture.html
new file mode 100644
index 0000000..7c6420b
--- /dev/null
+++ b/dashboard/architecture.html
@@ -0,0 +1,580 @@
+
+
+
+
+
+eParts SES — Full System Architecture
+
+
+
+
+
+
eParts Agentic Software Engineering System
+
CMU MSE Studio 2026 · Pimsie Supreme · Built with Agent-Augmented Iterative SDLC
+ All 28 agents have offline fallbacks (pattern matching when no LLM API key available) · All agent state is SQLite — no Postgres/Redis
+ Traceability uses zero LLM calls — domain-aware keyword matching in SQLite · Every artifact traced to source meeting, speaker, timestamp
+ HITL = Human-In-The-Loop gate · LIVE = actively connected · READY = configured, awaiting credentials
+
+ CMU MSE Studio 2026 · Pimsie Supreme · Python 3.12 · FastAPI · SQLite · ChromaDB · D3.js
+
+
+
+
diff --git a/dashboard/data/jira_issues.json b/dashboard/data/jira_issues.json
new file mode 100644
index 0000000..16045c1
--- /dev/null
+++ b/dashboard/data/jira_issues.json
@@ -0,0 +1,11 @@
+{
+ "provenance": {
+ "source": "Jira Cloud (epartsmse.atlassian.net), project EPARTS",
+ "jql": "project = EPARTS ORDER BY created ASC",
+ "fetched_at_utc": "2026-08-31T19:05:03Z",
+ "fetched_by": "agonugun@andrew.cmu.edu via REST API (dashboard/fetch_jira.py, scheduled workflow)",
+ "issue_count": 0,
+ "note": "Re-derive: run the JQL above in Jira; every dashboard number recomputes from this file via dashboard/generate_program_health.py"
+ },
+ "issues": []
+}
\ No newline at end of file
diff --git a/dashboard/event_flow.html b/dashboard/event_flow.html
new file mode 100644
index 0000000..3221bc3
--- /dev/null
+++ b/dashboard/event_flow.html
@@ -0,0 +1,455 @@
+
+
+
+
+eParts SES — Event Bus Flow
+
+
+
+
Event Bus Flow
+
How pipelines communicate through events — solid lines = emitted events, dashed lines = subscriptions (triggers)
+
+
+
Total Events: 58
+
Event Types: 12
+
Active Subscriptions: 10
+
Cross-Pipeline Wires: 10
+
+
+
+
+
+
+
diff --git a/dashboard/fetch_jira.py b/dashboard/fetch_jira.py
new file mode 100644
index 0000000..88e8a18
--- /dev/null
+++ b/dashboard/fetch_jira.py
@@ -0,0 +1,115 @@
+"""
+Jira export for the Program Health dashboard — the scheduled-refresh fetcher.
+
+Pulls every EPARTS issue from Jira Cloud via the REST API and writes
+dashboard/data/jira_issues.json in the exact schema generate_program_health.py
+consumes, with a provenance block recording the JQL, timestamp, and fetch
+mechanism. Stdlib only (urllib), so CI needs no dependency install.
+
+Auth (never hardcoded): environment variables
+ JIRA_EMAIL — Atlassian account email that owns the token
+ JIRA_API_TOKEN — API token from id.atlassian.com
+
+Run: JIRA_EMAIL=... JIRA_API_TOKEN=... python3 dashboard/fetch_jira.py
+"""
+
+from __future__ import annotations
+
+import base64
+import json
+import os
+import sys
+import urllib.request
+from datetime import datetime, timezone
+from pathlib import Path
+
+SITE = "https://epartsmse.atlassian.net"
+JQL = "project = EPARTS ORDER BY created ASC"
+FIELDS = [
+ "summary", "status", "issuetype", "created", "resolutiondate",
+ "labels", "assignee", "priority", "customfield_10016", "parent",
+]
+OUT = Path(__file__).resolve().parent / "data" / "jira_issues.json"
+
+
+def fetch_page(auth_header: str, next_token: str | None) -> dict:
+ body = {"jql": JQL, "fields": FIELDS, "maxResults": 100}
+ if next_token:
+ body["nextPageToken"] = next_token
+ req = urllib.request.Request(
+ f"{SITE}/rest/api/3/search/jql",
+ data=json.dumps(body).encode("utf-8"),
+ headers={
+ "Authorization": auth_header,
+ "Content-Type": "application/json",
+ "Accept": "application/json",
+ },
+ method="POST",
+ )
+ with urllib.request.urlopen(req, timeout=60) as resp:
+ return json.loads(resp.read().decode("utf-8"))
+
+
+def normalize(issue: dict) -> dict:
+ fl = issue.get("fields", {})
+
+ def name(obj_key: str) -> str | None:
+ obj = fl.get(obj_key)
+ return obj.get("name") if obj else None
+
+ parent = fl.get("parent") or {}
+ return {
+ "key": issue["key"],
+ "summary": fl.get("summary"),
+ "type": name("issuetype"),
+ "status": name("status"),
+ "status_category": ((fl.get("status") or {}).get("statusCategory") or {}).get("key"),
+ "created": fl.get("created"),
+ "resolved": fl.get("resolutiondate"),
+ "points": fl.get("customfield_10016"),
+ "labels": fl.get("labels") or [],
+ "assignee": (fl.get("assignee") or {}).get("displayName"),
+ "priority": name("priority"),
+ "parent_key": parent.get("key"),
+ "parent_summary": (parent.get("fields") or {}).get("summary"),
+ }
+
+
+def main() -> None:
+ email = os.environ.get("JIRA_EMAIL")
+ token = os.environ.get("JIRA_API_TOKEN")
+ if not email or not token:
+ sys.exit("Set JIRA_EMAIL and JIRA_API_TOKEN environment variables.")
+ auth = "Basic " + base64.b64encode(f"{email}:{token}".encode()).decode()
+
+ rows: dict[str, dict] = {}
+ next_token: str | None = None
+ pages = 0
+ while True:
+ page = fetch_page(auth, next_token)
+ for issue in page.get("issues", []):
+ rows[issue["key"]] = normalize(issue)
+ pages += 1
+ next_token = page.get("nextPageToken")
+ if not next_token or page.get("isLast", False):
+ break
+ if pages > 100:
+ sys.exit("Pagination runaway — aborting.")
+
+ out = {
+ "provenance": {
+ "source": f"Jira Cloud ({SITE.removeprefix('https://')}), project EPARTS",
+ "jql": JQL,
+ "fetched_at_utc": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
+ "fetched_by": f"{email} via REST API (dashboard/fetch_jira.py, scheduled workflow)",
+ "issue_count": len(rows),
+ "note": "Re-derive: run the JQL above in Jira; every dashboard number recomputes from this file via dashboard/generate_program_health.py",
+ },
+ "issues": sorted(rows.values(), key=lambda r: r["key"]),
+ }
+ OUT.write_text(json.dumps(out, indent=1), encoding="utf-8")
+ print(f"fetched {len(rows)} issues in {pages} page(s) -> {OUT}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/dashboard/generate_program_health.py b/dashboard/generate_program_health.py
new file mode 100644
index 0000000..5e444fc
--- /dev/null
+++ b/dashboard/generate_program_health.py
@@ -0,0 +1,536 @@
+"""
+Project Health dashboard generator — answers the three review questions with
+provenance: (1) When will it be done? (2) How far along are we, in features
+not time? (3) What are the risks/blockers?
+
+Reads dashboard/data/jira_issues.json (exported from Jira with a recorded
+JQL + timestamp — see its `provenance` block) and writes a self-contained
+dashboard/program_health.html. No hand-typed numbers anywhere: every figure on
+the page is recomputed from the JSON by this script, and the JSON records how
+it was fetched. Stdlib only; the Monte Carlo forecast is seeded (42) so the
+page is reproducible byte-for-byte from the same data.
+
+Run: python3 dashboard/generate_program_health.py
+"""
+
+from __future__ import annotations
+
+import json
+import random
+from collections import Counter, defaultdict
+from datetime import date, datetime, timedelta
+from html import escape
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+DATA = HERE / "data" / "jira_issues.json"
+OUT = HERE / "program_health.html"
+
+SIMS = 10_000
+SEED = 42
+
+# Velocity is sampled from this week onward, NOT from a rolling window.
+#
+# The team tracked work in a different tool until mid-June and migrated into
+# Jira in the week of 15 June. That migration week shows 128 issues created and
+# 109 resolved in the same week: already-completed work being entered, not work
+# delivered that week. Sampling it as a normal week would treat data entry as
+# throughput and make every forecast wildly optimistic.
+#
+# So the forecast samples from the week AFTER the migration, when ticket flow
+# reflects actual delivery. The migrated issues still count toward percent
+# complete and the burnup — that work really was done — they just do not
+# contribute to the *rate* the forecast projects forward.
+VELOCITY_SAMPLE_FROM = date(2026, 6, 22)
+
+# Academic calendar. The forecast converts "working weeks of effort needed" into
+# a calendar date, so it must not spend effort in weeks the team does not exist.
+# Summer ends 30 Jul; fall runs 24 Aug – 18 Dec. The ~3.5 week gap between them
+# is not working time, and the project cannot finish after 18 Dec.
+SUMMER_END = date(2026, 7, 30)
+FALL_START = date(2026, 8, 24)
+PROJECT_END = date(2026, 12, 18)
+# One week of fall break — not working time. CONFIRM THE ACTUAL DATE; this is
+# the CMU mid-October break week and is a placeholder until verified.
+FALL_BREAK_WEEK = date(2026, 10, 12)
+# Team capacity, used for the fall plan: 5 people x 36 h/week.
+TEAM_SIZE = 5
+HOURS_PER_PERSON_WEEK = 36
+
+# Validated dataviz palette (light mode) — see skill reference palette.
+C_DONE = "#2a78d6" # series 1 (blue) — completed / done line
+C_SCOPE = "#52514e" # neutral ink — scope reference line
+C_BAR = "#2a78d6"
+C_P50 = "#008300" # status good
+C_P85 = "#eda100" # status warning (direct-labeled)
+INK = "#0b0b0b"
+INK2 = "#52514e"
+GRID = "#e5e4e0"
+SURFACE = "#fcfcfb"
+
+
+def week_of(d: date) -> date:
+ """Monday of d's week."""
+ return d - timedelta(days=d.weekday())
+
+
+def parse_day(ts: str) -> date:
+ return date.fromisoformat(ts[:10])
+
+
+def fmt_finish(pctile: tuple[int, date | None], long: bool = False) -> str:
+ """Render a forecast percentile, or say plainly that it misses the deadline."""
+ _, dt = pctile
+ if dt is None:
+ return "after 18 Dec"
+ return dt.isoformat() if long else dt.strftime("%b %d")
+
+
+# Issues whose summary starts with any of these are excluded from every figure
+# on the page. These are board noise rather than delivery work — agent-generated
+# scratch tickets that would otherwise inflate scope and drag the completion
+# percentages down. The count of exclusions is printed on each run and stated on
+# the page, so the filter is visible rather than a silent adjustment.
+EXCLUDE_SUMMARY_PREFIXES = ("[transcript parser]",)
+
+
+def load() -> tuple[dict, list[dict], int]:
+ payload = json.loads(DATA.read_text(encoding="utf-8"))
+ raw = payload["issues"]
+ kept = [
+ i
+ for i in raw
+ if not (i.get("summary") or "")
+ .strip()
+ .lower()
+ .startswith(tuple(p.lower() for p in EXCLUDE_SUMMARY_PREFIXES))
+ ]
+ return payload["provenance"], kept, len(raw) - len(kept)
+
+
+# ---------------------------------------------------------------------------
+# Computations
+# ---------------------------------------------------------------------------
+
+
+def weekly_series(issues: list[dict]) -> tuple[list[date], list[int], list[int]]:
+ """Per-week created and resolved counts over the full project range."""
+ created = Counter(week_of(parse_day(i["created"])) for i in issues if i["created"])
+ resolved = Counter(week_of(parse_day(i["resolved"])) for i in issues if i["resolved"])
+ first = min(created)
+ last = max(max(created), max(resolved))
+ weeks: list[date] = []
+ w = first
+ while w <= last:
+ weeks.append(w)
+ w += timedelta(weeks=1)
+ return weeks, [created.get(w, 0) for w in weeks], [resolved.get(w, 0) for w in weeks]
+
+
+def burnup(weeks: list[date], created_w: list[int], resolved_w: list[int]):
+ scope, done, cs, cd = [], [], 0, 0
+ for c, r in zip(created_w, resolved_w):
+ cs += c
+ cd += r
+ scope.append(cs)
+ done.append(cd)
+ return scope, done
+
+
+def working_weeks_from(start: date) -> list[date]:
+ """Monday-of-week dates the team actually works, to the project deadline.
+
+ Excludes the summer/fall gap (SUMMER_END → FALL_START). Returned in order,
+ so index N is the calendar week in which the (N+1)th week of effort lands.
+ """
+ out: list[date] = []
+ w = week_of(start)
+ end = week_of(PROJECT_END)
+ while w <= end:
+ if w == week_of(FALL_BREAK_WEEK):
+ w += timedelta(weeks=1)
+ continue # fall break
+ if w <= week_of(SUMMER_END) or w >= week_of(FALL_START):
+ out.append(w)
+ w += timedelta(weeks=1)
+ return out
+
+
+def monte_carlo(open_count: int, sample_weeks: list[int], start: date):
+ """Bootstrap weekly throughput to forecast completion of the open backlog.
+
+ Each simulation consumes the backlog at a randomly-drawn historical weekly
+ rate, counting only WORKING weeks. Those working weeks are then mapped onto
+ the real academic calendar, so the resulting date accounts for the
+ three-and-a-half week break between semesters rather than treating it as
+ productive time. A simulation needing more working weeks than remain before
+ 18 December is recorded as not finishing in time.
+ """
+ calendar = working_weeks_from(start)
+ horizon = len(calendar)
+ rng = random.Random(SEED)
+ finishes: list[int] = []
+ for _ in range(SIMS):
+ remaining, wk = open_count, 0
+ while remaining > 0 and wk < horizon + 1:
+ remaining -= rng.choice(sample_weeks)
+ wk += 1
+ finishes.append(wk)
+ finishes.sort()
+
+ def pct(p: float) -> tuple[int, date | None]:
+ wks = finishes[min(int(SIMS * p), SIMS - 1)]
+ if wks > horizon:
+ return wks, None # does not complete before the deadline
+ return wks, calendar[max(wks - 1, 0)]
+
+ hist = Counter(finishes)
+ on_time = sum(1 for f in finishes if f <= horizon) / SIMS
+ return pct(0.50), pct(0.85), pct(0.95), hist, on_time, horizon
+
+
+# ---------------------------------------------------------------------------
+# SVG helpers (inline, no libs). Marks carry for native hover tooltips.
+# ---------------------------------------------------------------------------
+
+
+def svg_line_chart(weeks, scope, done, w=900, h=340, med_thru: int = 0) -> str:
+ """Burnup with a forward projection.
+
+ History is cumulative created vs cumulative resolved. The projection
+ continues the Done line at the measured median throughput across the
+ remaining WORKING weeks, so the chart answers "do we land before 18 Dec"
+ on the same axes as the history rather than in a separate histogram.
+ """
+ pad_l, pad_r, pad_t, pad_b = 52, 130, 18, 40
+ iw, ih = w - pad_l - pad_r, h - pad_t - pad_b
+ proj_weeks = working_weeks_from(weeks[-1] + timedelta(weeks=1)) if med_thru else []
+ proj = []
+ if proj_weeks:
+ v = done[-1]
+ target = scope[-1]
+ for _ in proj_weeks:
+ v = min(v + med_thru, target)
+ proj.append(v)
+ n = len(weeks) + len(proj_weeks)
+ ymax = max(scope) * 1.06
+
+ def x(i):
+ return pad_l + iw * i / max(n - 1, 1)
+
+ def y(v):
+ return pad_t + ih * (1 - v / ymax)
+
+ def path(vals):
+ return "M" + " L".join(f"{x(i):.1f},{y(v):.1f}" for i, v in enumerate(vals))
+
+ grid, ticks = [], []
+ step = max(1, int(ymax // 4 // 25 + 1) * 25)
+ v = 0
+ while v <= ymax:
+ grid.append(f'<line x1="{pad_l}" y1="{y(v):.1f}" x2="{w - pad_r}" y2="{y(v):.1f}" stroke="{GRID}" stroke-width="1"/>')
+ ticks.append(f'<text x="{pad_l - 8}" y="{y(v) + 4:.1f}" text-anchor="end" font-size="12" fill="{INK2}">{v}</text>')
+ v += step
+ axis_weeks = weeks + proj_weeks # labels must span history + projection
+ xlabels = []
+ for i in range(0, n, max(1, n // 6)):
+ if i < len(axis_weeks):
+ xlabels.append(
+ f'<text x="{x(i):.1f}" y="{h - 12}" text-anchor="middle" font-size="12" fill="{INK2}">{axis_weeks[i].strftime("%b %d")}</text>'
+ )
+ dots = "".join(
+ f'<circle cx="{x(i):.1f}" cy="{y(v):.1f}" r="7" fill="transparent">'
+ f"<title>Week of {weeks[i]}: {v} done, {scope[i]} in scope"
+ for i, v in enumerate(done)
+ )
+ proj_path = ""
+ if proj:
+ pts = " L".join(
+ f"{x(len(weeks) - 1 + i + 1):.1f},{y(v):.1f}" for i, v in enumerate(proj)
+ )
+ proj_path = (
+ f''
+ f'projected at {med_thru}/wk'
+ )
+ deadline_mark = ""
+ if proj_weeks:
+ dx = x(n - 1)
+ deadline_mark = (
+ f''
+ f'18 Dec'
+ )
+ return f""""""
+
+
+def svg_bar_chart(weeks, resolved_w, window_start, w=900, h=260) -> str:
+ pad_l, pad_r, pad_t, pad_b = 52, 20, 16, 40
+ iw, ih = w - pad_l - pad_r, h - pad_t - pad_b
+ n = len(weeks)
+ ymax = max(max(resolved_w), 1) * 1.1
+ bw = iw / n - 2 # 2px surface gap between bars
+
+ bars, xlabels = [], []
+ for i, (wk, v) in enumerate(zip(weeks, resolved_w)):
+ bx = pad_l + iw * i / n + 1
+ bh = ih * v / ymax
+ in_win = wk >= window_start
+ op = "1" if in_win else "0.35"
+ bars.append(
+ f'Week of {wk}: {v} resolved'
+ f'{" (in forecast window)" if in_win else ""}'
+ )
+ if i % max(1, n // 6) == 0:
+ xlabels.append(
+ f'{wk.strftime("%b %d")}'
+ )
+ ticks = "".join(
+ f''
+ f'{t}'
+ for t in range(0, int(ymax) + 1, max(1, int(ymax) // 4))
+ )
+ return f""""""
+
+
+def svg_forecast_hist(hist, p50, p85, start, w=900, h=260) -> str:
+ cal = working_weeks_from(start)
+
+ def wk_date(wk: int) -> date:
+ """Calendar week for the wk-th working week (clamped to the last one)."""
+ return cal[min(max(wk - 1, 0), len(cal) - 1)]
+
+ pad_l, pad_r, pad_t, pad_b = 52, 20, 26, 44
+ iw, ih = w - pad_l - pad_r, h - pad_t - pad_b
+ wmin, wmax = min(hist), max(hist)
+ span = list(range(wmin, wmax + 1))
+ ymax = max(hist.values()) * 1.15
+ bw = iw / len(span) - 2
+
+ def x(wk):
+ return pad_l + iw * (wk - wmin) / max(len(span), 1)
+
+ bars = "".join(
+ f''
+ f"Finish in week of {wk_date(wk)}: {hist.get(wk, 0) / SIMS * 100:.1f}% of simulations"
+ for wk in span
+ )
+ xlabels = "".join(
+ f'{wk_date(wk).strftime("%b %d")}'
+ for wk in span[:: max(1, len(span) // 6)]
+ )
+ marks = ""
+ for pctile, color, label in ((p50, C_P50, "P50"), (p85, C_P85, "P85")):
+ wks = pctile[0]
+ if pctile[1] is None:
+ continue # percentile lands past the deadline; nothing to mark
+ marks += (
+ f''
+ f'{label} · {fmt_finish(pctile)}'
+ )
+ return f""""""
+
+
+# ---------------------------------------------------------------------------
+# Page assembly
+# ---------------------------------------------------------------------------
+
+
+def main() -> None:
+ prov, issues, excluded = load()
+ today = parse_day(prov["fetched_at_utc"])
+
+ total = len(issues)
+ done_issues = [i for i in issues if i["status_category"] == "done"]
+ open_issues = [i for i in issues if i["status_category"] != "done"]
+ wip = [i for i in issues if i["status_category"] == "indeterminate"]
+
+ pts_total = sum(i["points"] or 0 for i in issues)
+ pts_done = sum(i["points"] or 0 for i in done_issues)
+
+ weeks, created_w, resolved_w = weekly_series(issues)
+ scope, done_cum = burnup(weeks, created_w, resolved_w)
+
+ # Throughput sample for the forecast. Weeks with zero resolved issues are
+ # EXCLUDED, because the academic calendar puts whole weeks of legitimate
+ # inactivity inside any recent window (the gap between spring and summer
+ # semesters is five consecutive zero weeks). Including them does not model
+ # "a slow week" — it models weeks in which the team did not exist, which
+ # drags the median far below any rate the team has ever actually sustained.
+ # The forecast therefore answers "how long at the pace we work when we are
+ # working", and the horizon cap below covers the calendar reality.
+ # Sample actual delivery weeks: from the post-migration week to now,
+ # excluding weeks with zero resolved issues (semester breaks — weeks in
+ # which the team was not working, which is not the same as a slow week).
+ raw_sample = [
+ r for wk, r in zip(weeks, resolved_w)
+ if week_of(VELOCITY_SAMPLE_FROM) <= wk < week_of(today)
+ ]
+ sample = [r for r in raw_sample if r > 0]
+ zero_weeks = len(raw_sample) - len(sample)
+ sample = sample or [1]
+ med_thru = sorted(sample)[len(sample) // 2]
+
+ p50, p85, p95, hist, on_time, horizon = monte_carlo(
+ len(open_issues), sample, week_of(today)
+ )
+
+ # Stream progress. The project has exactly six Epics, and those are the six
+ # delivery streams. Every other issue is resolved to one of them by walking
+ # its parent chain UP until an Epic is reached.
+ #
+ # Why the chain and not a single parent hop: a subtask's parent is a Task,
+ # not an Epic. Taking one hop grouped work under parent tasks such as
+ # "OCR-8 — OCR testing on Azure" and "W4 — Migration, object store …",
+ # which then got truncated at the em-dash into meaningless rows ("OCR-8",
+ # "W4"). Those are not streams and should never appear as one.
+ by_key = {i["key"]: i for i in issues}
+
+ # Work with no owning Epic splits into two honestly-different groups, so the
+ # table has no unexplained bucket. Anything created before the first Epic
+ # existed could not have been filed under one — that is early project
+ # scaffolding, not a tracking failure. Anything created after is genuinely
+ # unfiled, and is board hygiene we owe.
+ epic_dates = [parse_day(i["created"]) for i in issues if i["type"] == "Epic" and i["created"]]
+ epics_created = min(epic_dates) if epic_dates else today
+
+ def stream_for(issue: dict) -> str:
+ """Walk parent links up to the owning Epic; return its short name."""
+ cur, seen = issue, set()
+ while cur is not None:
+ if cur["type"] == "Epic":
+ # Safe to shorten here — only real Epics reach this line.
+ return (cur["summary"] or "Unnamed epic").split("—")[0].strip()
+ pk = cur.get("parent_key")
+ if not pk or pk in seen:
+ break
+ seen.add(pk)
+ cur = by_key.get(pk)
+ created = parse_day(issue["created"]) if issue.get("created") else today
+ if created < epics_created:
+ return "Spring foundation (predates the epic structure)"
+ return "Not yet filed under a stream"
+
+ streams = defaultdict(lambda: [0, 0]) # done, total
+ for i in issues:
+ if i["type"] == "Epic":
+ continue # the Epic is the stream, not work within it
+ s = stream_for(i)
+ streams[s][1] += 1
+ if i["status_category"] == "done":
+ streams[s][0] += 1
+ stream_rows = "".join(
+ f"
{escape(name)}
{d}/{t}
"
+ f'
'
+ f"
{d / t * 100:.0f}%
"
+ for name, (d, t) in sorted(streams.items(), key=lambda kv: -kv[1][1])
+ if t
+ )
+
+ wip_rows = "".join(
+ f"
{escape(i['key'])}
{escape(i['summary'] or '')}
"
+ f"
{escape(i['assignee'] or '—')}
"
+ f"
{(today - parse_day(i['created'])).days}d
"
+ for i in sorted(wip, key=lambda x: x["created"])
+ )
+
+ html = f"""
+
+eParts — Project Health
+
+
+
eParts — Project Health
+
+
Provenance. Every number on this page is computed from
+dashboard/data/jira_issues.json — {total} issues exported from {escape(prov["source"])}
+with {escape(prov["jql"])} at {escape(prov["fetched_at_utc"])}. No figure is hand-typed;
+re-run the query and the script to reproduce the page (forecast seeded, {SIMS:,} simulations).
+
+
+
{len(done_issues) / total * 100:.0f}%
issues complete ({len(done_issues)}/{total})
+
{pts_done / pts_total * 100:.0f}%
story points complete ({pts_done:.0f}/{pts_total:.0f})
+
{len(open_issues)}
open issues ({len(wip)} in progress)
+
{med_thru}/wk
median throughput, {len(sample)} delivery wks
+
{fmt_finish(p50)}
P50 completion of current backlog
+
{fmt_finish(p85)}
P85 completion (85% of simulations)
+
+
+
How far along — feature burnup, not time
+
— Done (cumulative resolved) ·
+- - Scope (cumulative created; the gap between the lines is the open backlog)
Method & assumptions. Each simulation draws weekly throughput (with replacement)
+from the last {len(sample)} weeks of actual resolved counts and consumes the open backlog of
+{len(open_issues)} issues. P50 {fmt_finish(p50, True)} / P85 {fmt_finish(p85, True)} / P95 {fmt_finish(p95, True)}. Assumes scope stays at today's
+backlog — the burnup's scope line shows how much that assumption has moved historically — and that summer
+throughput continues. This is a forecast with stated uncertainty, not a promise; it re-derives from data
+on every refresh.
+
+
Stream progress (features by delivery stream)
+
Stream
Done
Progress
%
{stream_rows}
+
+
Blockers — work in progress now
+
Key
Summary
Assignee
Age
{wip_rows}
+
Risks with triggers and mitigations are maintained in the Risk Register
+(docs/eParts_Risk_Register_v2.md); defects follow docs/defect_management.md.
+Items above aging past a tick without movement are escalation candidates at standup.
+
+
"""
+
+ OUT.write_text(html, encoding="utf-8")
+ print(f"wrote {OUT.relative_to(HERE.parent)}")
+ print(f" {total} issues · {len(done_issues)} done · {len(open_issues)} open · median thru {med_thru}/wk")
+ print(f" forecast: P50 {fmt_finish(p50, True)} · P85 {fmt_finish(p85, True)} · P95 {fmt_finish(p95, True)}")
+ print(f" on-time (by 18 Dec): {on_time * 100:.1f}% of simulations · {horizon} working weeks left")
+ print(f" throughput sample: {sample} (excluded {zero_weeks} zero-activity week(s))")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/dashboard/graph_data.json b/dashboard/graph_data.json
new file mode 100644
index 0000000..c008b9f
--- /dev/null
+++ b/dashboard/graph_data.json
@@ -0,0 +1,1326 @@
+{
+ "meetings": [
+ {
+ "file": "GMT20260220-180425_Recording.transcript (1).vtt",
+ "type": "coach",
+ "date": "2026-04-23",
+ "action_items": [
+ {
+ "text": "Consequence format, and, let's start off then. The first one is, we are\u2026 the development cannot proceed due to lack of data that we're having. So\u2026 the\u2026 first of all, we\u2026 and there's also some other ac",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260220-180425_Recording.transcript (1).vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ }
+ ],
+ "decisions": [
+ {
+ "text": "Consequence format, and, let's start off then. The first one is, we are\u2026 the development cannot proceed due to lack of data that we're having. So\u2026 the\u2026 first of all, we\u2026 and there's also some other ac",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260220-180425_Recording.transcript (1).vtt",
+ "meeting_date": "2026-04-23"
+ }
+ ],
+ "attendees": [
+ "Hrishik"
+ ],
+ "new_requirements": [],
+ "discussion_topics": []
+ },
+ {
+ "file": "GMT20260220-180425_Recording.transcript.vtt",
+ "type": "coach",
+ "date": "2026-04-23",
+ "action_items": [
+ {
+ "text": "Consequence format, and, let's start off then. The first one is, we are\u2026 the development cannot proceed due to lack of data that we're having. So\u2026 the\u2026 first of all, we\u2026 and there's also some other ac",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260220-180425_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ }
+ ],
+ "decisions": [
+ {
+ "text": "Consequence format, and, let's start off then. The first one is, we are\u2026 the development cannot proceed due to lack of data that we're having. So\u2026 the\u2026 first of all, we\u2026 and there's also some other ac",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260220-180425_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ }
+ ],
+ "attendees": [
+ "Hrishik"
+ ],
+ "new_requirements": [],
+ "discussion_topics": []
+ },
+ {
+ "file": "GMT20260224-190023_Recording.transcript.vtt",
+ "type": "coach",
+ "date": "2026-04-23",
+ "action_items": [
+ {
+ "text": "I'm getting the data from them. So, we\u2026 now we have received, like, I think we received it last Friday or Saturday, the data, and we are, started to work on our ML models to run basic tests. Okay. Yea",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-190023_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ }
+ ],
+ "decisions": [
+ {
+ "text": "I'm getting the data from them. So, we\u2026 now we have received, like, I think we received it last Friday or Saturday, the data, and we are, started to work on our ML models to run basic tests. Okay. Yea",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260224-190023_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ }
+ ],
+ "attendees": [
+ "Hrishik"
+ ],
+ "new_requirements": [],
+ "discussion_topics": []
+ },
+ {
+ "file": "GMT20260224-220446_Recording.transcript.vtt",
+ "type": "coach",
+ "date": "2026-04-23",
+ "action_items": [
+ {
+ "text": "Okay. So\u2026 basically you're doing\u2026 let me understand, you're doing, like, interviewing, you're talking to people, you're getting the requirements for what the system's gonna be, and then you're\u2026 you ar",
+ "owner": "Cory (Coach)",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "So, from our side, we\u2026 we are currently using, like, people are using different tools. Like, I personally use Gemini and, ChatGPT a lot. I think, Shruta uses Claude, so we're all using those on a pers",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Yeah, so until\u2026 until it has access to the things that you have access to, it's\u2026 it's just flying blind, right? You need to always be thinking, like, what can\u2026 what can my AI see? So, since you're all",
+ "owner": "Cory (Coach)",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "We'll be building some from scratch, so\u2026",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Yeah. So if you're working in docs, the one thing I will say is that you've got to think about\u2026 it sounds like you're gonna be using Cloud Code. You gotta think about how Cloud Code's gonna get access",
+ "owner": "Cory (Coach)",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Right. Ahmad, we'll probably, yeah, we\u2026 I don't think we have acidic plot code. We'll probably get access to cursor.",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "That's what I do, so I'll create a repo, and then anything that's relevant for the agent to know while it's building. Usually that's ADRs, things like that. I'll make sure they're in the repo, so they",
+ "owner": "Cory (Coach)",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Yep. Yep, and then, you know, there's lots of other things you can do. Since you're in a shared repo, start setting up slash commands that you can share. you know, start building your agent files toge",
+ "owner": "Cory (Coach)",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "I'll just quickly share my screen, I'll show you the brief of the problem and the concept, so you get a better picture.",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "The problem right now is that the way they acquire data from their vendors is that the vendors would send them emails, PDF, CSV, there's no proper format in doing all these things. So, what they curre",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ }
+ ],
+ "decisions": [
+ {
+ "text": "Yeah, it is, so basically, we need to get some more information out of the clients on how, like, what sort of data they require in this aspect.",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "I think we were able to grasp, all of it, I think, pretty well, and\u2026 I think I'm pretty clear on the things we need to do next. In order to, like, work more efficiently and, like, utilize AI to its fu",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ }
+ ],
+ "attendees": [
+ "Cory (Coach)",
+ "Hrishik",
+ "Ashritha",
+ "Jai"
+ ],
+ "new_requirements": [],
+ "discussion_topics": []
+ },
+ {
+ "file": "GMT20260224-190023_Recording.transcript (1).vtt",
+ "type": "coach",
+ "date": "2026-04-23",
+ "action_items": [
+ {
+ "text": "I'm getting the data from them. So, we\u2026 now we have received, like, I think we received it last Friday or Saturday, the data, and we are, started to work on our ML models to run basic tests. Okay. Yea",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-190023_Recording.transcript (1).vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ }
+ ],
+ "decisions": [
+ {
+ "text": "I'm getting the data from them. So, we\u2026 now we have received, like, I think we received it last Friday or Saturday, the data, and we are, started to work on our ML models to run basic tests. Okay. Yea",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260224-190023_Recording.transcript (1).vtt",
+ "meeting_date": "2026-04-23"
+ }
+ ],
+ "attendees": [
+ "Hrishik"
+ ],
+ "new_requirements": [],
+ "discussion_topics": []
+ },
+ {
+ "file": "GMT20260220-222907_Recording.transcript.vtt",
+ "type": "coach",
+ "date": "2026-04-23",
+ "action_items": [
+ {
+ "text": "Unless, like, you take another team's requirements and try to do them, and then without any AI, and then try to compare against their time if they have used AI, and then they do the same for us, then ",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260220-222907_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ }
+ ],
+ "decisions": [
+ {
+ "text": "Unless, like, you take another team's requirements and try to do them, and then without any AI, and then try to compare against their time if they have used AI, and then they do the same for us, then ",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260220-222907_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ }
+ ],
+ "attendees": [
+ "Hrishik"
+ ],
+ "new_requirements": [],
+ "discussion_topics": []
+ },
+ {
+ "file": "GMT20260220-180425_Recording.transcript (2).vtt",
+ "type": "coach",
+ "date": "2026-04-23",
+ "action_items": [
+ {
+ "text": "Consequence format, and, let's start off then. The first one is, we are\u2026 the development cannot proceed due to lack of data that we're having. So\u2026 the\u2026 first of all, we\u2026 and there's also some other ac",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260220-180425_Recording.transcript (2).vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ }
+ ],
+ "decisions": [
+ {
+ "text": "Consequence format, and, let's start off then. The first one is, we are\u2026 the development cannot proceed due to lack of data that we're having. So\u2026 the\u2026 first of all, we\u2026 and there's also some other ac",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260220-180425_Recording.transcript (2).vtt",
+ "meeting_date": "2026-04-23"
+ }
+ ],
+ "attendees": [
+ "Hrishik"
+ ],
+ "new_requirements": [],
+ "discussion_topics": []
+ },
+ {
+ "file": "GMT20260122-191430_Recording.transcript.vtt",
+ "type": "client",
+ "date": "2026-04-23",
+ "action_items": [
+ {
+ "text": "Okay, so the next topic we have is how we are gonna be working together. And the major points we wanted to cover was your availability, modes of communication, onboarding, and the documentation resour",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "I mean, I guess we could use those metrics, Oh, no, I don't know if we have how accurate, like, we could easily show\u2026 Yeah, like, what a good end, goal of it is based on, like, the PDF we took at the ",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "You'd like some metrics to see what the overall process on which it's improved. So I guess the one question would be, you ingest the data, you've got it there. Are there, future reports to say the dat",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "And I'm gonna put words in the team's mouth, which is, you know, this is a lot of good experience and do's and don'ts. from the team's\u2026 I'll actually ask the team, from the team's perspective, is ther",
+ "owner": "Dennis (Mentor)",
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+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "We need to be good enough for them does. I think from my point of view, it was just the communication part, like, we are bound to have a lot of questions, because it's going to be a kind of a\u2026 struggl",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Go snowboarding on Monday if the weather slowed that much down, so you guys should be a first big storm. Yeah, you must have the December storm. Yes. Were you in the store? No, no, I just left before ",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yep. Let me wait a second thing I want. Are you still there, Dennis? Dennis, let the content. Alright. Okay, thank you. Thank you. Make movies. I would certainly say that after a client meeting, it's ",
+ "owner": "Hrishik",
+ "deadline": "",
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+ }
+ ],
+ "decisions": [
+ {
+ "text": "Okay, so the next topic we have is how we are gonna be working together. And the major points we wanted to cover was your availability, modes of communication, onboarding, and the documentation resour",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "You'd like some metrics to see what the overall process on which it's improved. So I guess the one question would be, you ingest the data, you've got it there. Are there, future reports to say the dat",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "We need to be good enough for them does. I think from my point of view, it was just the communication part, like, we are bound to have a lot of questions, because it's going to be a kind of a\u2026 struggl",
+ "context": "said by Hrishik",
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+ }
+ ],
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+ "Hrishik",
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+ },
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+ "file": "GMT20260212-190517_Recording.transcript.vtt",
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+ {
+ "text": "A for, today's agenda. I think your mic is gone. model sound? Yeah, firstly, we'll discuss a few of the ML findings that we've had. All the points I've discussed in the last meeting, we have gone over",
+ "owner": "Hrishik",
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+ "text": "Yeah, yeah, I can see. Okay, so again, like, this is not a prescribed solution that we are proposing, it's just that we did a couple of experimentation, just, like, not a POC, but then theoretical rea",
+ "owner": "Hrishik",
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+ "meeting_date": "2026-04-23",
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+ "text": "But then, don't you want your solution to be extensive? No, no, I mean, we do, I guess. I don't think it's going to be very often, though, that, like, there are suppliers coming out with more informat",
+ "owner": "Hrishik",
+ "deadline": "",
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+ "text": "I'm not sure if we do it with Bicep. What we do have our Azure-wide monitors, so we can tag a resource. With a tag that we've set up, and then Azure is going to take that data and ship it to Datadog i",
+ "owner": "David (eParts)",
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+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
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+ "text": "I didn't get your point on Terraform being\u2026 I think if we deploy something about Terraform, but we modify the internals from, let's say, Azure itself, the Terraform will think it is in some other stat",
+ "owner": "Hrishik",
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+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, I would pick Bicep, mostly because I think it's something that we know. I also think that the value-add of Terraform\u2026 one of the big value adds of Terraform that they claim is that you can kind ",
+ "owner": "David (eParts)",
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+ "text": "Okay. I think with that in mind, we can maybe go forward with Bicep itself. Yeah. Because anyways, if you are logged into Azure, then Bicep is the better way to go. But, I can just talk a little bit a",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Oh, sorry, I'm\u2026 I caught half of that. It was\u2026 the audio's getting a little quiet. Let me bump this up. Can you say that one more time?",
+ "owner": "David (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Can you, explain a little bit on how, the data is written to Azure Logs, and then,",
+ "owner": "Hrishik",
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+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, let me\u2026 maybe I can share my screen, and that would help clear up some confusion here. Of course, now that I want to do that, let's see if I can\u2026 We've got a Datadog\u2026 I requested access to share",
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+ ],
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+ {
+ "text": "A for, today's agenda. I think your mic is gone. model sound? Yeah, firstly, we'll discuss a few of the ML findings that we've had. All the points I've discussed in the last meeting, we have gone over",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "Yeah, yeah, I can see. Okay, so again, like, this is not a prescribed solution that we are proposing, it's just that we did a couple of experimentation, just, like, not a POC, but then theoretical rea",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "But then, don't you want your solution to be extensive? No, no, I mean, we do, I guess. I don't think it's going to be very often, though, that, like, there are suppliers coming out with more informat",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ }
+ ],
+ "attendees": [
+ "Hrishik",
+ "David (eParts)"
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+ },
+ {
+ "file": "GMT20260226-190646_Recording.transcript.vtt",
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+ "action_items": [
+ {
+ "text": "So, Ashtar, you added anything to add? Okay. So, Harshal, we spoke to a couple of members here last, this week and, yeah, this week, and then, one constant suggestion that we were getting is, so if\u2026 i",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "So, can you guys look at any of the other options available on Azure, other than LLMs, for this use case?",
+ "owner": "Harsha (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, like, I personally have experience with Amazon Bedrock, but not the Azure ecosystem, so we'll have to try that out. And, like, one follow-up question that I would want to ask is. So, we\u2026 initial",
+ "owner": "Hrishik",
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+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yep, 100%. Again, I don't want to be restrictive in any way, so because this is just, like, the initial kind of finding slash research kind of phase, just, just do whatever at this point. I would say ",
+ "owner": "Harsha (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Okay. So, at least for now, before the LEM topic even came up, we were just, playing around the semantic measure, approach, like, what\u2026 I mean, the data that we took, the, the amount was obviously, li",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Night. Yeah, I think, I think we will probably should schedule a meeting with the catalog team sometime after spring break.",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Uber. Yeah, so I'll just remove the meeting from next week, next week's calendar.",
+ "owner": "Harsha (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, I'll send out a cancellation thing. Okay. Next we have\u2026 I think, yeah, for the initial ML models, you're thinking we might, like, if you're using specifically ML, we might have to use, two model",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "I think the primary, goal for the model that we're currently thinking is to get the confidence score on each of the attributes that we extract, but in order to be able to put that into PEMS, we need t",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "This\u2026 the Excel part\u2026 so, the way it is right now is, I mean, this data goes into, like, master database through a few, Like, stored procedures we have, which are kind of lacy. And these load procedur",
+ "owner": "Harsha (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ }
+ ],
+ "decisions": [
+ {
+ "text": "I think the primary, goal for the model that we're currently thinking is to get the confidence score on each of the attributes that we extract, but in order to be able to put that into PEMS, we need t",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "Yeah, just\u2026 just let us know, and I think\u2026 I think we should be on top of it. At least I'll be on top of it, no matter if\u2026 So, if you don't hear back until Monday, just ping me on Teams, and that shou",
+ "context": "said by Harsha (eParts)",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ }
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+ "Hrishik",
+ "Harsha (eParts)"
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+ },
+ {
+ "file": "GMT20260402-180648_Recording.transcript.vtt",
+ "type": "client",
+ "date": "2026-04-23",
+ "action_items": [
+ {
+ "text": "Yeah, I'll just turn up my speaker. So\u2026 okay. Yep. So, the first item on the agenda is for the timeline. Yeah. Rishi, you're there, right?",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah. So\u2026 Yeah, sure, can you, should I share my screen, or can you share your screen, with the timeline?",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Okay, nevermind, I'll just do it then. So\u2026 Okay. So, is it visible, Harsha, dude?",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "So, we just wanted to go over and, like, get your feedback. So, for now, what we have is, like. by April 15th. For this, this semester, we'll have, the requirements. Completed by then? And\u2026 I think we",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, so, we are targeting that towards the end of this month, we should have the basic, requirements, risk process, and like, not\u2026 I'm not sure if we'll be able to have the complete architecture, bec",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Other than July's missing, but yeah, I think\u2026 I think this makes sense. This is a decent timeline. Or at least a decent breakdown of the units. I suspect some of those units might be larger than other",
+ "owner": "Harsha (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, July was, left out because, the entire development\u2026 there won't be any deliverable in that month, because we'll still be working on the, like, development of it, and we don't expect there'll be ",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Okay, we also have the statement of work, I'll just share that as well. So\u2026 One second, this is\u2026 So, we were, is it visible, first of all?",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Okay. So, we were, it was encouraged that we have a statement of work, just so that, you know, we have a shared understanding with you. I'll be\u2026 this is the first version, and this is still, I think i",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah. So, we also have other things, such as, like, You know, that are\u2026 we will be working all the way up till PIMS, but the steps after that is not something under our control, so I've also written t",
+ "owner": "Jaivard",
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+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
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+ "meeting_type": "client"
+ }
+ ],
+ "decisions": [
+ {
+ "text": "Yeah, so, we are targeting that towards the end of this month, we should have the basic, requirements, risk process, and like, not\u2026 I'm not sure if we'll be able to have the complete architecture, bec",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ }
+ ],
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+ "Jaivard",
+ "Hrishik",
+ "Harsha (eParts)",
+ "Ashritha"
+ ],
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+ "discussion_topics": []
+ },
+ {
+ "file": "GMT20260416-180324_Recording.transcript.vtt",
+ "type": "client",
+ "date": "2026-04-23",
+ "action_items": [
+ {
+ "text": "Yeah, starting. So\u2026 I think\u2026 So the\u2026 So, the first thing\u2026 Was it regarding the training data? So, Liu, if you want to kind of elaborate on that? Hmm. So\u2026 It's basically, he\u2026 there was a list of things",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260416-180324_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "I'm a bit confused. Take care. I think there's an echo. I should talk. This should be fine. Yeah, this is\u2026 sorry about that. So, when you're talking about the context serves, right, so\u2026 If you have al",
+ "owner": "Ashritha",
+ "deadline": "",
+ "source_meeting": "GMT20260416-180324_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "I think I'll be traveling the end of this month, and then I'll be in India for, like, 2-3 weeks. But, I don't think there'll be any disruption in the entire workflow, though. I'll just be taking meeti",
+ "owner": "Arjun",
+ "deadline": "",
+ "source_meeting": "GMT20260416-180324_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "But he did tumble, right? So\u2026 Yeah. That would have been so bad boys, yeah. Anyway, good to hear your voice, everyone. Yes. Okay. See y'all. Enjoy the nice weather. Yeah, it's warm. Yeah, it's suppose",
+ "owner": "Ashritha",
+ "deadline": "",
+ "source_meeting": "GMT20260416-180324_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ }
+ ],
+ "decisions": [
+ {
+ "text": "I'm a bit confused. Take care. I think there's an echo. I should talk. This should be fine. Yeah, this is\u2026 sorry about that. So, when you're talking about the context serves, right, so\u2026 If you have al",
+ "context": "said by Ashritha",
+ "source_meeting": "GMT20260416-180324_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ }
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+ "Ashritha",
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+ "discussion_topics": []
+ }
+ ],
+ "action_items": [
+ {
+ "text": "Consequence format, and, let's start off then. The first one is, we are\u2026 the development cannot proceed due to lack of data that we're having. So\u2026 the\u2026 first of all, we\u2026 and there's also some other ac",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260220-180425_Recording.transcript (1).vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Consequence format, and, let's start off then. The first one is, we are\u2026 the development cannot proceed due to lack of data that we're having. So\u2026 the\u2026 first of all, we\u2026 and there's also some other ac",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260220-180425_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "I'm getting the data from them. So, we\u2026 now we have received, like, I think we received it last Friday or Saturday, the data, and we are, started to work on our ML models to run basic tests. Okay. Yea",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-190023_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Okay. So\u2026 basically you're doing\u2026 let me understand, you're doing, like, interviewing, you're talking to people, you're getting the requirements for what the system's gonna be, and then you're\u2026 you ar",
+ "owner": "Cory (Coach)",
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+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "So, from our side, we\u2026 we are currently using, like, people are using different tools. Like, I personally use Gemini and, ChatGPT a lot. I think, Shruta uses Claude, so we're all using those on a pers",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Yeah, so until\u2026 until it has access to the things that you have access to, it's\u2026 it's just flying blind, right? You need to always be thinking, like, what can\u2026 what can my AI see? So, since you're all",
+ "owner": "Cory (Coach)",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "We'll be building some from scratch, so\u2026",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Yeah. So if you're working in docs, the one thing I will say is that you've got to think about\u2026 it sounds like you're gonna be using Cloud Code. You gotta think about how Cloud Code's gonna get access",
+ "owner": "Cory (Coach)",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Right. Ahmad, we'll probably, yeah, we\u2026 I don't think we have acidic plot code. We'll probably get access to cursor.",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "That's what I do, so I'll create a repo, and then anything that's relevant for the agent to know while it's building. Usually that's ADRs, things like that. I'll make sure they're in the repo, so they",
+ "owner": "Cory (Coach)",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Yep. Yep, and then, you know, there's lots of other things you can do. Since you're in a shared repo, start setting up slash commands that you can share. you know, start building your agent files toge",
+ "owner": "Cory (Coach)",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "I'll just quickly share my screen, I'll show you the brief of the problem and the concept, so you get a better picture.",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "The problem right now is that the way they acquire data from their vendors is that the vendors would send them emails, PDF, CSV, there's no proper format in doing all these things. So, what they curre",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "I'm getting the data from them. So, we\u2026 now we have received, like, I think we received it last Friday or Saturday, the data, and we are, started to work on our ML models to run basic tests. Okay. Yea",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260224-190023_Recording.transcript (1).vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Unless, like, you take another team's requirements and try to do them, and then without any AI, and then try to compare against their time if they have used AI, and then they do the same for us, then ",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260220-222907_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Consequence format, and, let's start off then. The first one is, we are\u2026 the development cannot proceed due to lack of data that we're having. So\u2026 the\u2026 first of all, we\u2026 and there's also some other ac",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260220-180425_Recording.transcript (2).vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "coach"
+ },
+ {
+ "text": "Okay, so the next topic we have is how we are gonna be working together. And the major points we wanted to cover was your availability, modes of communication, onboarding, and the documentation resour",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "I mean, I guess we could use those metrics, Oh, no, I don't know if we have how accurate, like, we could easily show\u2026 Yeah, like, what a good end, goal of it is based on, like, the PDF we took at the ",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "You'd like some metrics to see what the overall process on which it's improved. So I guess the one question would be, you ingest the data, you've got it there. Are there, future reports to say the dat",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "And I'm gonna put words in the team's mouth, which is, you know, this is a lot of good experience and do's and don'ts. from the team's\u2026 I'll actually ask the team, from the team's perspective, is ther",
+ "owner": "Dennis (Mentor)",
+ "deadline": "",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "We need to be good enough for them does. I think from my point of view, it was just the communication part, like, we are bound to have a lot of questions, because it's going to be a kind of a\u2026 struggl",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Go snowboarding on Monday if the weather slowed that much down, so you guys should be a first big storm. Yeah, you must have the December storm. Yes. Were you in the store? No, no, I just left before ",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yep. Let me wait a second thing I want. Are you still there, Dennis? Dennis, let the content. Alright. Okay, thank you. Thank you. Make movies. I would certainly say that after a client meeting, it's ",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "A for, today's agenda. I think your mic is gone. model sound? Yeah, firstly, we'll discuss a few of the ML findings that we've had. All the points I've discussed in the last meeting, we have gone over",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, yeah, I can see. Okay, so again, like, this is not a prescribed solution that we are proposing, it's just that we did a couple of experimentation, just, like, not a POC, but then theoretical rea",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "But then, don't you want your solution to be extensive? No, no, I mean, we do, I guess. I don't think it's going to be very often, though, that, like, there are suppliers coming out with more informat",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "I'm not sure if we do it with Bicep. What we do have our Azure-wide monitors, so we can tag a resource. With a tag that we've set up, and then Azure is going to take that data and ship it to Datadog i",
+ "owner": "David (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "I didn't get your point on Terraform being\u2026 I think if we deploy something about Terraform, but we modify the internals from, let's say, Azure itself, the Terraform will think it is in some other stat",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, I would pick Bicep, mostly because I think it's something that we know. I also think that the value-add of Terraform\u2026 one of the big value adds of Terraform that they claim is that you can kind ",
+ "owner": "David (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Okay. I think with that in mind, we can maybe go forward with Bicep itself. Yeah. Because anyways, if you are logged into Azure, then Bicep is the better way to go. But, I can just talk a little bit a",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Oh, sorry, I'm\u2026 I caught half of that. It was\u2026 the audio's getting a little quiet. Let me bump this up. Can you say that one more time?",
+ "owner": "David (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Can you, explain a little bit on how, the data is written to Azure Logs, and then,",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, let me\u2026 maybe I can share my screen, and that would help clear up some confusion here. Of course, now that I want to do that, let's see if I can\u2026 We've got a Datadog\u2026 I requested access to share",
+ "owner": "David (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "So, Ashtar, you added anything to add? Okay. So, Harshal, we spoke to a couple of members here last, this week and, yeah, this week, and then, one constant suggestion that we were getting is, so if\u2026 i",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "So, can you guys look at any of the other options available on Azure, other than LLMs, for this use case?",
+ "owner": "Harsha (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, like, I personally have experience with Amazon Bedrock, but not the Azure ecosystem, so we'll have to try that out. And, like, one follow-up question that I would want to ask is. So, we\u2026 initial",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yep, 100%. Again, I don't want to be restrictive in any way, so because this is just, like, the initial kind of finding slash research kind of phase, just, just do whatever at this point. I would say ",
+ "owner": "Harsha (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Okay. So, at least for now, before the LEM topic even came up, we were just, playing around the semantic measure, approach, like, what\u2026 I mean, the data that we took, the, the amount was obviously, li",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Night. Yeah, I think, I think we will probably should schedule a meeting with the catalog team sometime after spring break.",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Uber. Yeah, so I'll just remove the meeting from next week, next week's calendar.",
+ "owner": "Harsha (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, I'll send out a cancellation thing. Okay. Next we have\u2026 I think, yeah, for the initial ML models, you're thinking we might, like, if you're using specifically ML, we might have to use, two model",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "I think the primary, goal for the model that we're currently thinking is to get the confidence score on each of the attributes that we extract, but in order to be able to put that into PEMS, we need t",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "This\u2026 the Excel part\u2026 so, the way it is right now is, I mean, this data goes into, like, master database through a few, Like, stored procedures we have, which are kind of lacy. And these load procedur",
+ "owner": "Harsha (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, I'll just turn up my speaker. So\u2026 okay. Yep. So, the first item on the agenda is for the timeline. Yeah. Rishi, you're there, right?",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah. So\u2026 Yeah, sure, can you, should I share my screen, or can you share your screen, with the timeline?",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Okay, nevermind, I'll just do it then. So\u2026 Okay. So, is it visible, Harsha, dude?",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "So, we just wanted to go over and, like, get your feedback. So, for now, what we have is, like. by April 15th. For this, this semester, we'll have, the requirements. Completed by then? And\u2026 I think we",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, so, we are targeting that towards the end of this month, we should have the basic, requirements, risk process, and like, not\u2026 I'm not sure if we'll be able to have the complete architecture, bec",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Other than July's missing, but yeah, I think\u2026 I think this makes sense. This is a decent timeline. Or at least a decent breakdown of the units. I suspect some of those units might be larger than other",
+ "owner": "Harsha (eParts)",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, July was, left out because, the entire development\u2026 there won't be any deliverable in that month, because we'll still be working on the, like, development of it, and we don't expect there'll be ",
+ "owner": "Hrishik",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Okay, we also have the statement of work, I'll just share that as well. So\u2026 One second, this is\u2026 So, we were, is it visible, first of all?",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Okay. So, we were, it was encouraged that we have a statement of work, just so that, you know, we have a shared understanding with you. I'll be\u2026 this is the first version, and this is still, I think i",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah. So, we also have other things, such as, like, You know, that are\u2026 we will be working all the way up till PIMS, but the steps after that is not something under our control, so I've also written t",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "Yeah, starting. So\u2026 I think\u2026 So the\u2026 So, the first thing\u2026 Was it regarding the training data? So, Liu, if you want to kind of elaborate on that? Hmm. So\u2026 It's basically, he\u2026 there was a list of things",
+ "owner": "Jaivard",
+ "deadline": "",
+ "source_meeting": "GMT20260416-180324_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "I'm a bit confused. Take care. I think there's an echo. I should talk. This should be fine. Yeah, this is\u2026 sorry about that. So, when you're talking about the context serves, right, so\u2026 If you have al",
+ "owner": "Ashritha",
+ "deadline": "",
+ "source_meeting": "GMT20260416-180324_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "I think I'll be traveling the end of this month, and then I'll be in India for, like, 2-3 weeks. But, I don't think there'll be any disruption in the entire workflow, though. I'll just be taking meeti",
+ "owner": "Arjun",
+ "deadline": "",
+ "source_meeting": "GMT20260416-180324_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ },
+ {
+ "text": "But he did tumble, right? So\u2026 Yeah. That would have been so bad boys, yeah. Anyway, good to hear your voice, everyone. Yes. Okay. See y'all. Enjoy the nice weather. Yeah, it's warm. Yeah, it's suppose",
+ "owner": "Ashritha",
+ "deadline": "",
+ "source_meeting": "GMT20260416-180324_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23",
+ "meeting_type": "client"
+ }
+ ],
+ "decisions": [
+ {
+ "text": "Consequence format, and, let's start off then. The first one is, we are\u2026 the development cannot proceed due to lack of data that we're having. So\u2026 the\u2026 first of all, we\u2026 and there's also some other ac",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260220-180425_Recording.transcript (1).vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "Consequence format, and, let's start off then. The first one is, we are\u2026 the development cannot proceed due to lack of data that we're having. So\u2026 the\u2026 first of all, we\u2026 and there's also some other ac",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260220-180425_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "I'm getting the data from them. So, we\u2026 now we have received, like, I think we received it last Friday or Saturday, the data, and we are, started to work on our ML models to run basic tests. Okay. Yea",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260224-190023_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "Yeah, it is, so basically, we need to get some more information out of the clients on how, like, what sort of data they require in this aspect.",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "I think we were able to grasp, all of it, I think, pretty well, and\u2026 I think I'm pretty clear on the things we need to do next. In order to, like, work more efficiently and, like, utilize AI to its fu",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260224-220446_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "I'm getting the data from them. So, we\u2026 now we have received, like, I think we received it last Friday or Saturday, the data, and we are, started to work on our ML models to run basic tests. Okay. Yea",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260224-190023_Recording.transcript (1).vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "Unless, like, you take another team's requirements and try to do them, and then without any AI, and then try to compare against their time if they have used AI, and then they do the same for us, then ",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260220-222907_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "Consequence format, and, let's start off then. The first one is, we are\u2026 the development cannot proceed due to lack of data that we're having. So\u2026 the\u2026 first of all, we\u2026 and there's also some other ac",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260220-180425_Recording.transcript (2).vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "Okay, so the next topic we have is how we are gonna be working together. And the major points we wanted to cover was your availability, modes of communication, onboarding, and the documentation resour",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "You'd like some metrics to see what the overall process on which it's improved. So I guess the one question would be, you ingest the data, you've got it there. Are there, future reports to say the dat",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "We need to be good enough for them does. I think from my point of view, it was just the communication part, like, we are bound to have a lot of questions, because it's going to be a kind of a\u2026 struggl",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260122-191430_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "A for, today's agenda. I think your mic is gone. model sound? Yeah, firstly, we'll discuss a few of the ML findings that we've had. All the points I've discussed in the last meeting, we have gone over",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "Yeah, yeah, I can see. Okay, so again, like, this is not a prescribed solution that we are proposing, it's just that we did a couple of experimentation, just, like, not a POC, but then theoretical rea",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "But then, don't you want your solution to be extensive? No, no, I mean, we do, I guess. I don't think it's going to be very often, though, that, like, there are suppliers coming out with more informat",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260212-190517_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "I think the primary, goal for the model that we're currently thinking is to get the confidence score on each of the attributes that we extract, but in order to be able to put that into PEMS, we need t",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "Yeah, just\u2026 just let us know, and I think\u2026 I think we should be on top of it. At least I'll be on top of it, no matter if\u2026 So, if you don't hear back until Monday, just ping me on Teams, and that shou",
+ "context": "said by Harsha (eParts)",
+ "source_meeting": "GMT20260226-190646_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "Yeah, so, we are targeting that towards the end of this month, we should have the basic, requirements, risk process, and like, not\u2026 I'm not sure if we'll be able to have the complete architecture, bec",
+ "context": "said by Hrishik",
+ "source_meeting": "GMT20260402-180648_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ },
+ {
+ "text": "I'm a bit confused. Take care. I think there's an echo. I should talk. This should be fine. Yeah, this is\u2026 sorry about that. So, when you're talking about the context serves, right, so\u2026 If you have al",
+ "context": "said by Ashritha",
+ "source_meeting": "GMT20260416-180324_Recording.transcript.vtt",
+ "meeting_date": "2026-04-23"
+ }
+ ],
+ "attendees": [
+ "Arjun",
+ "Ashritha",
+ "Cory (Coach)",
+ "David (eParts)",
+ "Dennis (Mentor)",
+ "Harsha (eParts)",
+ "Hrishik",
+ "Jai",
+ "Jaivard"
+ ]
+}
\ No newline at end of file
diff --git a/dashboard/intelligence.html b/dashboard/intelligence.html
new file mode 100644
index 0000000..65b2290
--- /dev/null
+++ b/dashboard/intelligence.html
@@ -0,0 +1,2224 @@
+
+
+
+
+
+eParts — Project Intelligence
+
+
+
+
+
+
+
+
eParts Project Intelligence
+
Agentic SE System — Knowledge Graph, Goal Model, WBS & Traceability
Assigns priority to each extracted item: P0 (blocks delivery), P1 (current sprint), P2 (backlog). P0 items are held for human approval before proceeding.
IN: Structured meeting data OUT: Prioritized item set MODE: LLM-assisted or keyword heuristic (offline)
P0 → Human-in-the-Loop gate
+
→
+
STEP 3
req_extractor
Synthesizes formal requirement documents (REQ-XXX.md) with title, rationale, category, priority, and acceptance criteria. Commits directly to GitHub.
Formats the structured meeting output as a Confluence page, ensuring the entire team has visibility into what was discussed and decided.
IN: Structured meeting data OUT: Confluence page
+
→
+
STEP 6
decision_logger
Isolates architectural and design decisions, deposits them to the project wiki, and appends to the running decision log on GitHub.
IN: Structured meeting data WIKI: decisions/{date} GITHUB: decisions.log.md
+
→
+
STEP 7
drift_detector
Compares newly logged decisions against the canonical architecture document using RAG. Flags contradictions and emits a cross-pipeline event to the Architecture pipeline.
A team member manually reviews a 45-minute recording, spending 2-3 hours to extract items. Formatting varies across individuals. Jira tickets are created the next day - or not at all. Architectural contradictions go unnoticed.
+
With AI
End-to-end processing completes in under 30 seconds. Output format is consistent across every meeting. Jira tickets are created immediately. A human reviews only the P0-flagged items (~15 minutes). Drift is detected automatically.
+
Net Value
Applied weekly across 9 client meetings. Including human review overhead, the pipeline saves over 2 hours per meeting. The larger benefit is consistency - every team member works from the same structured output.
+
+
+
+
+
+
+
+
Coach Session Memory Pipeline
Practice Area: Coach/Mentor Memory · 5 sequential agents · Triggered by coach/mentor .vtt upload
+
+
+
+
STEP 1
transcript_parser
Parses the coaching session transcript into structured data - topics discussed, guidance offered, and action items assigned.
Chunks the transcript and generates vector embeddings via a local ONNX model. Stores metadata in SQLite so future agents can query past sessions via RAG.
Connects session content to open ML decisions. If a coach references thresholds or model performance, this agent surfaces the latest experimental evidence.
IN: Session embeddings QUERY: ML Decision log
+
→
+
STEP 5
decision_logger
Records any coaching decisions or guidance into the project wiki and appends to the running decision log on GitHub.
WIKI: decisions/{date} GITHUB: decisions.log.md
+
+
+
Counterfactual Analysis
+
+
Without AI
The team has no reliable record of what was discussed three sessions ago. When a coach asks whether a prior commitment was fulfilled, no one can answer with certainty. Guidance is lost.
+
With AI
Every session is embedded for semantic retrieval. Recurring concerns are flagged automatically. Before the next session, the system can surface: "This topic has been raised in 3 of the last 4 sessions without resolution."
+
Net Value
Four coach sessions processed to date. The primary value is institutional memory and accountability - ensuring guidance is retained and acted upon, not repeated indefinitely.
+
+
+
+
+
+
+
+
Architecture Pipeline
Practice Area: Architecture · 4 sequential agents · Triggered by transcript processing, PR events, or drift_detected events
+
+
+
+
STEP 1
drift_detector
Embeds recent meeting decisions and retrieves semantically similar sections from the canonical architecture report via RAG. Identifies contradictions between what was decided and what was documented.
When a significant architectural decision is detected, drafts a formal Architecture Decision Record (ADR) and submits it as a pull request for team review.
IN: Drift report GITHUB: architecture/ADR-XXX.md
ADR requires PR approval
+
→
+
STEP 3
diagram_updater
Proposes updates to architecture diagrams based on newly recorded decisions, submitted as a reviewable diff.
IN: Drift report GITHUB: Diagram diff as PR
+
→
+
STEP 4
traceability_builder
Updates the unified traceability store by linking concerns, decisions, requirements, risks, and Jira tickets. Identifies gaps - unaddressed concerns, unmitigated risks - and flags them for review.
DB: traceability.db EVENT: human_review_needed
+
+
+
Counterfactual Analysis
+
+
Without AI
A decision made in meeting five may directly contradict a constraint documented in meeting two. Without automated comparison, this drift accumulates silently until it surfaces as a defect.
+
With AI
Every new decision is automatically compared against the canonical architecture report. Contradictions are flagged within seconds, and an ADR is drafted for the team to review.
+
Net Value
The cost of building against an outdated architecture far exceeds the cost of reviewing false positives. Even at moderate accuracy, automated drift detection prevents costly rework.
+
+
+
+
+
+
+
+
Coding Pipeline
Practice Area: Construction · 4 planned agents · Will activate during the coding phase
+
+
+
+
STEP 1
pr_reviewer
Performs automated first-pass PR review - checks code style, verifies test presence, and validates that the PR references a traced requirement.
IN: PR diff OUT: Review comments
+
→
+
STEP 2
test_generator
Generates unit test stubs from function signatures in the PR and commits the test file to the repository.
IN: Source code GITHUB: tests/test_*.py
+
→
+
STEP 3
doc_generator
Detects endpoint changes in the PR and updates the corresponding API documentation automatically.
IN: Source code GITHUB: docs/api/
+
→
+
STEP 4
prompt_regression
If any prompt was modified in the PR, runs it against a golden test dataset. Blocks merge if output quality degrades beyond the acceptable threshold.
IN: Prompt changes OUT: Pass/fail with quality diff
+
+
+
Counterfactual Analysis
+
+
Without AI
Each PR review takes 30-60 minutes per reviewer. Test coverage is deferred indefinitely. No one verifies whether the PR addresses a documented requirement.
+
With AI
An automated first pass covers style, traceability, and test scaffolding. The human reviewer can then focus entirely on design intent and correctness.
+
Net Value
Applied to every pull request. The highest-impact contribution is traceability enforcement - guaranteeing that every code change links back to a requirement.
+
+
+
+
+
+
+
+
ML Decision Memory Pipeline
Practice Area: ML Decision Memory · 3 planned agents · Will activate when POC results are submitted
+
+
+
+
STEP 1
evidence_accumulator
Parses POC results - precision, recall, auto-accept rates - and logs them as structured evidence against open ML decisions.
IN: POC result JSON DB: ml_decisions.db
+
→
+
STEP 2
readiness_detector
Evaluates whether sufficient evidence has accumulated to close an open decision. Emits an alert when the evidence threshold is met.
IN: Evidence log EVENT: decision_ready
+
→
+
STEP 3
coach_linker
Links experimental evidence to relevant coaching discussions. When a coach inquires about thresholds or model performance, surfaces the exact data available.
Experiment results are scattered across Slack threads, notebooks, and individual machines. When a coach asks for the current confidence threshold, the team scrambles to locate the most recent number.
+
With AI
Every POC result is logged as structured evidence. Decision readiness is evaluated automatically. The current state of any open ML question can be retrieved in seconds.
+
Net Value
Prevents decisions from remaining open indefinitely. Evidence-gated closure ensures that decisions are backed by data, not assumptions.
Scans for anomalies: velocity drops, requirements without linked tickets, and unresolved drift events. Fires targeted alerts for each finding.
CHECK: Jira velocity + Wiki + EventBus WIKI: project_mgmt/alerts
+
+
+
Counterfactual Analysis
+
+
Without AI
A weekly status meeting where someone manually reviews the Jira board. The WBS lives in a Google Sheet that is perpetually out of date. Unlinked requirements are not discovered until the final presentation review.
+
With AI
The WBS is synced automatically, a digest is generated, and anomalies are flagged - all before the team reads a single report. The weekly meeting can focus on decisions instead of status updates.
+
Net Value
Runs every week. Replaces a recurring status meeting. The most impactful alerts - unlinked requirements and stale items - surface problems that teams consistently overlook.
+
+
+
+
+
+
+
+
Risk Register Generation Pipeline
Practice Area: Risk Management · 3 source agents + classification + human gate · Runs on architecture update, coach session indexed, or action item ageing past a tick
+
+
+
+
STEP 1
drift_detector · concern_tracker · stale_detector
Three independent sources feed candidate risks. Technical risks and sensitivity points come from the architecture report. Recurring concerns come from coach session memory across sessions. And action items that age without being resolved are treated as risks rather than left to rot.
IN: Architecture report §5.4 · coach memory · open action items OUT: Candidate risk with its source recorded
+
→
+
STEP 2
add_risk (classify)
Assigns category, likelihood and impact, derives severity, and drafts a mitigation and contingency. Links the risk to the requirements and architecture artifacts it threatens, which is what makes it traceable later.
IN: Candidate risk OUT: Classified risk, status open
+
→
+
STEP 3
review_risk (human gate)
A person accepts, edits or rejects the risk. Every review writes the reviewer, the old and new status, notes and a timestamp, so we can see who changed a risk and when. Nothing reaches the register without this step.
IN: Classified risk + reviewer decision OUT: Review record in risk_reviews
+
→
+
STEP 4
risk_register.md
The register is regenerated from the database. 20 risks today: 4 critical, 7 high, 9 medium. Exit is not allowed until a risk has both an owner and a mitigation. Anything untouched for 14 days is resurfaced for review.
Risks are remembered by whoever was in the room. A concern raised twice in coaching sessions six weeks apart is never connected. Action items quietly expire instead of being escalated, and the register is rewritten by hand before each review.
+
With AI
The three sources are swept continuously, so a repeated concern becomes a tracked risk with an owner. The human still decides, but on a drafted and classified candidate rather than a blank form. Every risk carries links to what it threatens.
+
+
+
+
+
+
+
+
Knowledge Management Pipeline
Practice Area: Knowledge Management · 2 sequential agents · Runs pre-meeting or on new_session_embedded event
+
+
+
+
STEP 1
context_packager
Collects the current sprint status from Jira, open concerns from the wiki, pending ADRs, and recent cross-pipeline events. Packages everything into a single briefing context.
Produces a concise, human-readable pre-meeting briefing: what has changed since the last meeting, what remains open, and what should be discussed next.
Each team member independently reviews Jira, the wiki, Slack, and coaching notes - spending 30 to 60 minutes each. Different people arrive at the meeting with different context. The first ten minutes are spent aligning.
+
With AI
A single briefing aggregates information from seven data sources and is delivered before the meeting begins. Every participant starts with the same understanding of project state.
+
Net Value
Runs before every meeting. Even a partially useful briefing eliminates hours of redundant context-gathering each week and ensures alignment from the start.
+
+
+
+
+
+
· · · MCP SERVERS · · ·
+
+
Jira MCP
+
GitHub MCP
+
ChromaDB
+
Confluence MCP
+
+
+
+
· · · SHARED INFRASTRUCTURE · · ·
+
+
SharedMemory
+
EventBus
+
TraceabilityStore
+
PromptRegistry
+
RiskRegister
+
MetricsCollector
+
+
+
· · · STORAGE · · ·
+
+
shared_memory.db
+
events.db
+
traceability.db
+
coach_sessions.db
+
ml_decisions.db
+
risk_register.db
+
prompt_registry.db
+
metrics.db
+
artifact_versions.db
+
ChromaDB (vectors)
+
+
+
+
+
+
+
+
diff --git a/dashboard/metrics.html b/dashboard/metrics.html
new file mode 100644
index 0000000..0bdbf36
--- /dev/null
+++ b/dashboard/metrics.html
@@ -0,0 +1,610 @@
+
+
+
+
+
+eParts Agentic SE System — Dashboard
+
+
+
+
+
+
+
eParts Agentic SE System
+
Pimsie Supreme — CMU MSE Studio 2026 — Real Data Dashboard
Processes: 31 ETVX-documented processes across 7 practice areas
+
Resources: 25 agents (auton/assist) + human reviewers
+
Measurements: Tokens, latency, success rate, human review rate, corrections
+
+
+
+
+
+
+
+
diff --git a/dashboard/program_health.html b/dashboard/program_health.html
new file mode 100644
index 0000000..6d773de
--- /dev/null
+++ b/dashboard/program_health.html
@@ -0,0 +1,85 @@
+
+
+eParts — Project Health
+
+
+
eParts — Project Health
+
+
Provenance. Every number on this page is computed from
+dashboard/data/jira_issues.json — 290 issues exported from Jira Cloud (epartsmse.atlassian.net), project EPARTS
+with project = EPARTS ORDER BY created ASC at 2026-07-20T05:56:00Z. No figure is hand-typed;
+re-run the query and the script to reproduce the page (forecast seeded, 10,000 simulations).
+
+
+
69%
issues complete (200/290)
+
75%
story points complete (761/1014)
+
90
open issues (15 in progress)
+
13/wk
median throughput, 4 delivery wks
+
Sep 21
P50 completion of current backlog
+
Sep 28
P85 completion (85% of simulations)
+
+
+
How far along — feature burnup, not time
+
— Done (cumulative resolved) ·
+- - Scope (cumulative created; the gap between the lines is the open backlog)
+
+
+
Delivery rate — issues resolved per week
+
Full-color bars form the 4-week sampling window the forecast draws from; earlier weeks are dimmed.
+
+
+
When will it be done — Monte Carlo forecast
+
Distribution of completion dates for the current 90-issue open backlog across 10,000 simulations.
+
+
Method & assumptions. Each simulation draws weekly throughput (with replacement)
+from the last 4 weeks of actual resolved counts and consumes the open backlog of
+90 issues. P50 2026-09-21 / P85 2026-09-28 / P95 2026-10-05. Assumes scope stays at today's
+backlog — the burnup's scope line shows how much that assumption has moved historically — and that summer
+throughput continues. This is a forecast with stated uncertainty, not a promise; it re-derives from data
+on every refresh.
+
+
Stream progress (features by delivery stream)
+
Stream
Done
Progress
%
Ingestion
68/88
77%
Spring foundation (predates the epic structure)
36/56
64%
ML POC
27/39
69%
QA & Risk
21/29
72%
OCR
22/26
85%
LLM POC
11/18
61%
Management & SFS
14/15
93%
Not yet filed under a stream
0/13
0%
+
+
Blockers — work in progress now
+
Key
Summary
Assignee
Age
EPARTS-57
List out and divide tasks for Agentic System
Jaivardhan Singh
95d
EPARTS-60
ML System Check in
—
95d
EPARTS-154
Ingestion — data ingestion pipeline
Hrishikesh Bhardwaj
32d
EPARTS-155
QA & Risk
Jaivardhan Singh
32d
EPARTS-156
ML POC — attribute prediction
zheliang liu
32d
EPARTS-158
Management & SFS
Ashritha Gonuguntla
32d
EPARTS-159
OCR — PDF parser POC
Arjun
32d
EPARTS-291
Add ETIM value and unit normalization
Jaivardhan Singh
25d
EPARTS-316
Latency Attribution Correction for M5 SUMMAR
zheliang liu
25d
EPARTS-343
Evaluate Azure AI Foundry model options for datasheet extraction
Arjun
14d
EPARTS-358
ExtractedInput builder stage (handoff spec §2/§5)
Hrishikesh Bhardwaj
11d
EPARTS-373
M7 deployment & handover documentation
Ashritha Gonuguntla
11d
EPARTS-374
Diagnose PIV ranking gap & design contrastive fine-tuning fix
zheliang liu
7d
EPARTS-380
Investigate encoder cold-start (~6s) warmup
zheliang liu
7d
EPARTS-383
Team [1/3] Summer Semester Studio Project Presentation
Ashritha Gonuguntla
7d
+
Risks with triggers and mitigations are maintained in the Risk Register
+(docs/eParts_Risk_Register_v2.md); defects follow docs/defect_management.md.
+Items above aging past a tick without movement are escalation candidates at standup.
+
+
\ No newline at end of file
diff --git a/dashboard/ses_traceability.html b/dashboard/ses_traceability.html
new file mode 100644
index 0000000..3012ab3
--- /dev/null
+++ b/dashboard/ses_traceability.html
@@ -0,0 +1,367 @@
+
+
+
+
+
+SES Traceability
+
+
+
+
+
+
SES Traceability
+
How SES work flows in one trace graph: from client conversation into requirements, architecture, risks, and delivery artifacts.
+ 4 · Delivery & safety
+ Risks we track · Jira / backlog tying work to those REQs.
+
+
+
+
Technical names in the DB: meetings, concerns, requirements, architectures, decisions, risks, tickets—each gets a stable ID (e.g. REQ-001, ARCH-003).
+
+
+
+
+
Example · REQ-001
+
One requirement sits in the middle. Two real conversations branched under it—in the ingest this shows up as standards work on the left and ML / extraction work on the right.
+
+
+
+
REQ-001
+
Extract product attributes from vendor spec sheets
+
+
+
+
Standards branch
+
+
MTG-2026-01-22
Meeting we traced this to
+
ARCH-002 Industry standards mapping
Architecture slice
+
CON-… Sensitive vendor data
Concern from the transcript
+
REQ-002 Taxonomy mapping
Follow-on requirement
+
RIS-… Related risks flagged & mitigated
Risk records
+
+
+
+
ML / extraction branch
+
+
Same starting meeting → ARCH-003
ML confidence scoring
+
CON-… LLM vs OCR
Concern
+
DEC-… agreed LLM-led extraction goal
Pinned decision
+
ARCH-004 Staging / review like Git-diff
Architecture
+
+
+
+
Exact IDs match traceability_story.html and the trace export—for slides you only need “two threads off one REQ.”
Workstreams WS1-WS4 plus SES and PM; phase dates aligned to eParts_WBS_Presentation.pdf with studio kickoff Jan 12, 2026. Spring window shown from kickoff through May 11. Expand each workstream for deliverables.
Discovery, SES skeleton, MCPs + REQ pipeline, dashboards v1
+
+
+
Present (in flight)
+
Apr 2026
+
ADRs, gateways, SES polish, demos, CRIT readiness
+
+
+
Future
+
May - Dec 2026
+
WS3 implementation volume · WS4 UAT pilot · SES ops hardening
+
+
+
+
+
+
+
+
+
+
+
diff --git a/data/seed/poc_results.json b/data/seed/poc_results.json
new file mode 100644
index 0000000..6f55b31
--- /dev/null
+++ b/data/seed/poc_results.json
@@ -0,0 +1,29 @@
+{
+ "poc_name": "semantic_matcher_v1",
+ "run_date": "2026-03-15",
+ "threshold": 0.25,
+ "total_attributes_tested": 42,
+ "auto_accept_rate": 0.86,
+ "top_1_accuracy": 0.991,
+ "top_3_accuracy": 1.0,
+ "human_review_cases": 6,
+ "human_review_reason": "genuinely absent attributes, not bad matches",
+ "datasets": [
+ {
+ "name": "AIM2",
+ "attributes_tested": 22,
+ "auto_accept_rate": 0.91
+ },
+ {
+ "name": "RCT Flex CT",
+ "attributes_tested": 20,
+ "auto_accept_rate": 0.80
+ }
+ ],
+ "ground_truth_eval": {
+ "pims_attributes_tested": 217,
+ "top_1_accuracy": 0.991,
+ "top_3_accuracy": 1.0
+ },
+ "notes": "Used TF-IDF as stand-in for all-MiniLM (no internet in env). Threshold 0.25 was arbitrary for POC — production threshold is open ADR at 0.85."
+}
\ No newline at end of file
diff --git a/demo.py b/demo.py
new file mode 100644
index 0000000..bd7205c
--- /dev/null
+++ b/demo.py
@@ -0,0 +1,534 @@
+#!/usr/bin/env python3
+"""
+LIVE DEMO — Run the full Requirements Pipeline on a meeting transcript.
+
+Usage:
+ python demo.py # uses the latest client meeting
+ python demo.py transcripts/some.vtt # specific file
+ python demo.py --auto # skip all "press ENTER" prompts
+ python demo.py --step # press ENTER after each agent (live presentation)
+ python demo.py examples/x.vtt --step # transcript + step-through
+
+ SES_DEMO_AUTO=1 python demo.py # same as --auto
+ SES_DEMO_STEP=1 python demo.py # same as --step (Enter after each step)
+
+What happens:
+ 1. Parses the .vtt transcript into structured data
+ 2. Classifies items as P0/P1/P2 (using Gemini/Claude)
+ 3. Extracts formal requirements → commits to GitHub
+ 4. Creates Jira tickets for action items
+ 5. Publishes meeting minutes
+ 6. Logs decisions → commits to GitHub
+ 7. Detects architecture drift via RAG
+
+Each step prints a live DAG-style runner view, coloured progress, then a
+specific “Presenter — what to show now” cue (URLs, clicks, narration).
+"""
+from __future__ import annotations
+
+import json
+import logging
+import os
+import sys
+import time
+from glob import glob
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+# ── colours ──────────────────────────────────────────────────────────
+CYAN = "\033[96m"; GREEN = "\033[92m"; YELLOW = "\033[93m"
+RED = "\033[91m"; MAGENTA = "\033[95m"; DIM = "\033[2m"
+BOLD = "\033[1m"; RESET = "\033[0m"
+
+# Deep links referenced in presenter cues (match DEMO_PLAYBOOK / .env URLs)
+GH_REPO = os.environ.get(
+ "DEMO_PRESENT_GITHUB_URL",
+ "https://github.com/AshrithaG/eparts",
+).rstrip("/")
+JIRA_PROJECT_URL = os.environ.get(
+ "DEMO_PRESENT_JIRA_URL",
+ "https://epartsmse.atlassian.net/jira/software/projects/EPARTS/board",
+).rstrip("/")
+
+logging.basicConfig(
+ level=logging.INFO,
+ format=f"{DIM}%(asctime)s{RESET} [%(name)s] %(message)s",
+ datefmt="%H:%M:%S",
+)
+logging.getLogger("urllib3").setLevel(logging.WARNING)
+logging.getLogger("chromadb").setLevel(logging.WARNING)
+
+
+def banner():
+ print(f"""
+{CYAN}{BOLD}╔══════════════════════════════════════════════════════════════════╗
+║ eParts Agentic SE System — Live Pipeline Demo ║
+║ Requirements Engineering · End-to-End ║
+╚══════════════════════════════════════════════════════════════════╝{RESET}
+""")
+
+
+def show_config(vtt_path: str):
+ from agents.base import AgentSettings
+ from mcp.jira import JiraMCP
+ from mcp.github import GitHubMCP
+
+ s = AgentSettings()
+ jira = JiraMCP()
+ gh = GitHubMCP()
+
+ provider = s.active_provider
+ model = s.gemini_model if provider == "gemini" else (
+ s.claude_model if provider == "anthropic" else "n/a"
+ )
+
+ print(f" {BOLD}Transcript{RESET} {Path(vtt_path).name}")
+ if provider == "none":
+ print(f" {BOLD}LLM{RESET} {YELLOW}Offline (keyword heuristics){RESET}")
+ else:
+ print(f" {BOLD}LLM{RESET} {GREEN}{provider}{RESET} / {model}")
+ print(f" {BOLD}Jira{RESET} {GREEN}Connected{RESET} ({jira._url})" if jira.is_configured
+ else f" {BOLD}Jira{RESET} {YELLOW}Not configured{RESET}")
+ print(f" {BOLD}GitHub{RESET} {GREEN}Connected{RESET} ({gh._repo})" if gh.is_configured
+ else f" {BOLD}GitHub{RESET} {YELLOW}Not configured{RESET}")
+ print()
+
+
+def show_step(step_idx: int, total: int, agent_name: str, desc: str):
+ bar = f"[{step_idx+1}/{total}]"
+ print(f"\n{CYAN}{'─'*66}{RESET}")
+ print(f" {BOLD}{bar}{RESET} {YELLOW}{agent_name}{RESET} · {desc}")
+ print(f"{CYAN}{'─'*66}{RESET}")
+
+
+def show_pipeline_execution_view(pipe, *, running_index: int) -> None:
+ """ASCII view of REQUIREMENTS_PIPELINE while a step executes."""
+ steps = getattr(pipe, "steps", ()) or ()
+ total = len(steps)
+ if total == 0:
+ return
+ pct = round(24 * running_index / max(total, 1))
+ prog = "[" + "#" * pct + "-" * (24 - pct) + "]"
+ label = f"{running_index + 1}/{total}"
+ print(f"\n {BOLD}Pipeline in execution{RESET} {DIM}{prog}{RESET} {CYAN}{label}{RESET}")
+ print(f" {DIM}{pipe.name} · {getattr(pipe, 'practice_area', '')}{RESET}")
+ for i, s in enumerate(steps):
+ name = s.agent_name
+ if i < running_index:
+ mark = f"{GREEN}✓{RESET}"
+ state = f"{DIM}done{RESET}"
+ elif i == running_index:
+ mark = f"{YELLOW}▶{RESET}"
+ state = f"{YELLOW}RUNNING{RESET}"
+ else:
+ mark = f"{DIM}·{RESET}"
+ state = f"{DIM}pending{RESET}"
+ etvx = getattr(s, "etvx_id", "") or ""
+ etvx_s = f" {DIM}({etvx}){RESET}" if etvx else ""
+ print(f" {mark} {BOLD}{name}{RESET}{etvx_s} {state}")
+ print(f" {CYAN}{'─'*62}{RESET}")
+
+
+def show_presenter_cue(agent_name: str, sr, *, dash_mode: bool) -> None:
+ """What to flash in browser / narration after this REQUIREMENTS_PIPELINE step."""
+
+ lines = _presenter_cue_lines(agent_name, sr)
+ if not lines:
+ return
+ print(f"\n {MAGENTA}{BOLD}▸ Presenter — what to show now{RESET}")
+ for line in lines:
+ print(f" {line}")
+ if dash_mode:
+ print(f" {DIM}— use --step to pause before the next agent after this narration —{RESET}")
+
+
+def _presenter_cue_lines(agent_name: str, sr) -> list[str]:
+ """Renderable lines with terminal-friendly emphasis."""
+
+ skipped = getattr(sr, "skipped", False)
+ failed = getattr(sr, "success", True) is False and not skipped
+
+ if skipped:
+ return [
+ f"{YELLOW}{BOLD}Skipped.{RESET} No downstream writes for this beat — upstream was empty.",
+ f"{DIM}Stay on the terminal; skip Jira/GitHub until a later cue.{RESET}",
+ ]
+ if failed:
+ errs = getattr(sr, "errors", []) or []
+ first = errs[0][:120] if errs else "(see errors above)"
+ return [
+ f"{RED}{BOLD}This step failed.{RESET} Gesture at stderr above and describe "
+ "retry vs heuristic/offline continuation.",
+ f"{RED}Hint:{RESET} {first}",
+ ]
+
+ reqs_tree = f"{GH_REPO}/tree/main/requirements/parsed"
+ reqs_commits = f"{GH_REPO}/commits/main"
+ decisions_blob = f"{GH_REPO}/blob/main/minutes/decisions.log.md"
+
+ cues: dict[str, list[str]] = {
+ "transcript_parser": [
+ f"{BOLD}Terminal:{RESET} Point at stdout — parsed action items / decisions "
+ "that downstream agents reuse.",
+ f"{DIM}Narrative: “Structured minutes from upload — GitHub/Jira stay cold until REQ + tickets.”{RESET}",
+ f"{DIM}(Optional:{RESET} bounce to the `{CYAN}.vtt{DIM}` file on disk — source-of-truth for the ingest.)",
+ ],
+ "priority_classifier": [
+ f"{BOLD}Terminal:{RESET} Walk P0/P1/P2 tagging (risk posture vs schedule wins).",
+ f"{YELLOW}Important:{RESET} P0 items stall for humans — cite the yellow ⚠ cue after "
+ "ticket_creator runs.",
+ f"{DIM}Do not open Jira yet — wait for `ticket_creator` so “Created” sort shows today’s work.{RESET}",
+ ],
+ "req_extractor": [
+ f"{BOLD}GitHub → requirements/parsed:{RESET}",
+ reqs_tree,
+ f"{BOLD}Show:{RESET} the `REQ-***.md` file named by the Output ▸ lines (commit `[agent:req_extractor]`).",
+ f"{BOLD}Commits:{RESET} {reqs_commits} newest entry on `main`.",
+ ],
+ "ticket_creator": [
+ f"{BOLD}Jira backlog / board:{RESET}",
+ JIRA_PROJECT_URL,
+ f"{BOLD}Filter/sort:{RESET} sort by {BOLD}Created (desc){RESET} → fresh rows from this run.",
+ f"{BOLD}Look for labels:{RESET} `AI-generated`, priority tag `P1`/`P2`, `agent-ticket_creator`.",
+ f"{YELLOW}If P0s exist,{RESET} the terminal ⚠ warns — emphasize review queue behaviour "
+ "(not auto-filed tickets).",
+ ],
+ "minutes_publisher": [
+ f"{BOLD}If Confluence is live:{RESET} Space ▸ Client Meetings ▸ page "
+ "`Client — YYYY‑MM‑DD`.",
+ f"{YELLOW}Offline / skipped:{RESET} highlight Output ▸ publish_skipped (expected without secrets).",
+ f"{BOLD}Fallback mirror:{RESET} {GH_REPO}/tree/main/minutes whenever minutes files commit.",
+ ],
+ "decision_logger": [
+ f"{BOLD}GitHub file:{RESET}",
+ decisions_blob,
+ f"{BOLD}Show:{RESET} last rows appended to `{BOLD}minutes/decisions.log.md{RESET}` (markdown table footer).",
+ ],
+ "drift_detector": [
+ f"{BOLD}Terminal:{RESET} summarise drift deltas vs canon architecture excerpt.",
+ f"{BOLD}Local dashboards:{RESET} `{CYAN}{PROJECT_ROOT / 'dashboard' / 'intelligence.html'}{RESET} "
+ f"& `{CYAN}{PROJECT_ROOT / 'dashboard' / 'interactive_architecture.html'}{RESET}`.",
+ f"{DIM}Call out REQ-DRIFT-CHECK ({BOLD}ETVX{RESET}{DIM}) vs deep ARCH drift later.{RESET}",
+ ],
+ }
+
+ raw = cues.get(agent_name)
+ if not raw:
+ return [
+ f"{BOLD}Terminal:{RESET} Re-read Outputs above.",
+ f"{DIM}See DEMO_PLAYBOOK.md § Section 2 (Live Requirements Pipeline).{RESET}",
+ ]
+ return raw
+
+
+def show_step_result(sr):
+ if sr.skipped:
+ print(f" Result: {DIM}SKIPPED (upstream empty){RESET}")
+ return
+ status = f"{GREEN}OK{RESET}" if sr.success else f"{RED}FAIL{RESET}"
+ print(f" Result: {status} ({sr.duration_ms:,}ms)")
+ if sr.llm_calls > 0:
+ print(f" LLM: {sr.llm_calls} call(s), ~{sr.tokens_used:,} tokens")
+ for o in sr.outputs:
+ print(f" Output: {GREEN}▸{RESET} {o['description']}")
+ if sr.requires_human_review:
+ print(f" {YELLOW}⚠ Flagged for human review{RESET}")
+ for e in sr.errors:
+ print(f" {RED}Error: {e[:140]}{RESET}")
+
+
+def show_summary(result):
+ print(f"\n{CYAN}{BOLD}{'═'*66}{RESET}")
+ print(f"{BOLD} Pipeline Complete — {result.pipeline_name}{RESET}")
+ print(f"{CYAN}{'═'*66}{RESET}\n")
+
+ c = GREEN if result.success else RED
+ print(f" Success: {c}{result.success}{RESET}")
+ print(f" Steps: {result.completed_steps}/{result.total_steps} ok, "
+ f"{result.skipped_steps} skipped, {result.failed_steps} failed")
+ print(f" Duration: {result.total_duration_ms:,}ms "
+ f"({result.total_duration_ms/1000:.1f}s)")
+ print(f" LLM Calls: {result.total_llm_calls}")
+ print(f" Tokens: {result.total_tokens:,}")
+ print(f" Artifacts: {result.total_artifacts}")
+ if result.requires_human_review:
+ print(f" Human Review: {YELLOW}Yes{RESET}")
+
+ if result.artifacts:
+ print(f"\n {BOLD}Artifacts Produced:{RESET}")
+ for a in result.artifacts:
+ print(f" {GREEN}▸{RESET} [{a['type']}] {a['description']}")
+
+ print(f"\n{CYAN}{'═'*66}{RESET}\n")
+
+
+def show_wiki_snapshot():
+ """Show what's in shared memory after the pipeline ran."""
+ print(f"\n{BOLD} Shared Memory (Wiki) — latest entries:{RESET}")
+ try:
+ from pipeline.shared_memory import SharedMemory
+ wiki = SharedMemory()
+ stats = wiki.stats()
+ print(f" Namespaces: {stats.get('namespaces', 'n/a')}")
+ print(f" Total entries: {stats.get('total_entries', 'n/a')}")
+ except Exception as e:
+ print(f" {DIM}(could not read: {e}){RESET}")
+
+
+def show_event_snapshot():
+ """Show events emitted during the pipeline run."""
+ print(f"\n{BOLD} Event Bus — recent events:{RESET}")
+ try:
+ from pipeline.event_bus import EventBus
+ bus = EventBus()
+ stats = bus.stats()
+ print(f" Total events: {stats.get('total_events', 'n/a')}")
+ print(f" Event types: {', '.join(stats.get('event_types', []))}")
+ except Exception as e:
+ print(f" {DIM}(could not read: {e}){RESET}")
+
+
+def show_next_steps():
+ print(f"\n{BOLD} Where to see the results:{RESET}")
+ print(f" {GREEN}▸{RESET} Jira board: {JIRA_PROJECT_URL}")
+ print(f" {GREEN}▸{RESET} GitHub repo: {GH_REPO}")
+ print(f" {GREEN}▸{RESET} Dashboard: open dashboard/interactive_architecture.html")
+ print(f" {GREEN}▸{RESET} Intelligence: open dashboard/intelligence.html")
+ print()
+
+
+def parse_demo_cli():
+ """Return (vtt_path|None, auto: bool, step_through: bool)."""
+ argv = sys.argv[1:]
+ auto = "--auto" in argv or os.environ.get("SES_DEMO_AUTO", "").lower() in ("1", "true", "yes")
+ step_through = "--step" in argv or os.environ.get("SES_DEMO_STEP", "").lower() in ("1", "true", "yes")
+ filtered = [a for a in argv if a not in ("--auto", "--step")]
+ vtt = None
+ for a in filtered:
+ if ".vtt" in a.lower() or Path(a).suffix.lower() in (".vtt",):
+ vtt = a
+ break
+ return vtt, auto, step_through
+
+
+# ── monkeypatch PipelineExecutor to show live step progress ──────────
+def _patch_executor(executor, pipeline, *, step_through: bool = False, auto: bool = False):
+ """Wrap the real executor so we see each step live."""
+ original = executor.execute
+
+ def pause_after_step(step_index: int):
+ if not step_through or auto:
+ return
+ if step_index >= len(pipeline.steps) - 1:
+ return
+ input(f" {MAGENTA}Press ENTER for the next agent ▸{RESET} ")
+
+ dash_present = step_through and not auto
+
+ def finish_step(agent_name: str, sr, idx: int):
+ show_step_result(sr)
+ show_presenter_cue(agent_name, sr, dash_mode=dash_present)
+ pause_after_step(idx)
+
+ def wrapped(pipe, trigger_payload):
+ import uuid as _uuid
+ from pipeline.pipelines import (
+ PipelineContext, StepResult, PipelineResult
+ )
+ from agents.base import AgentTrigger
+ from dataclasses import asdict
+
+ pid = f"pipe-{pipe.name}-{_uuid.uuid4().hex[:8]}"
+ ctx = PipelineContext(
+ pipeline_id=pid, pipeline_name=pipe.name,
+ trigger_type=trigger_payload.get("trigger_type", "manual"),
+ source=trigger_payload.get("source", "unknown"),
+ data=dict(trigger_payload),
+ )
+ results = []
+ ok = True
+ t0 = time.perf_counter()
+ tot_llm = tot_tok = 0
+ needs_review = False
+
+ for i, step in enumerate(pipe.steps):
+ ctx.current_step = i
+ show_pipeline_execution_view(pipe, running_index=i)
+ show_step(i, len(pipe.steps), step.agent_name, step.description)
+
+ if step.skip_if_empty:
+ val = ctx.get(step.skip_if_empty)
+ if not val:
+ sr = StepResult(
+ step_index=i, agent_name=step.agent_name,
+ description=step.description, success=True,
+ skipped=True, duration_ms=0, outputs=[], errors=[],
+ llm_calls=0, tokens_used=0, artifacts_produced=0,
+ requires_human_review=False,
+ )
+ results.append(sr)
+ finish_step(step.agent_name, sr, i)
+ continue
+
+ agent = executor._agents.get(step.agent_name)
+ if not agent:
+ sr = StepResult(
+ step_index=i, agent_name=step.agent_name,
+ description=step.description, success=False,
+ skipped=False, duration_ms=0, outputs=[],
+ errors=[f"Agent not found: {step.agent_name}"],
+ llm_calls=0, tokens_used=0, artifacts_produced=0,
+ requires_human_review=False,
+ )
+ results.append(sr)
+ finish_step(step.agent_name, sr, i)
+ if step.required:
+ ok = False; break
+ continue
+
+ trigger = AgentTrigger(
+ trigger_type=ctx.trigger_type,
+ source=ctx.source,
+ metadata={
+ "pipeline_id": pid,
+ "pipeline_step": i,
+ "pipeline_context": ctx.data,
+ },
+ )
+
+ st = time.perf_counter()
+ try:
+ res = agent.execute(trigger)
+ ms = int((time.perf_counter() - st) * 1000)
+ s_llm = getattr(agent, '_run_llm_calls', 0)
+ s_tok = getattr(agent, '_run_total_tokens', 0)
+ tot_llm += s_llm; tot_tok += s_tok
+
+ if step.output_key:
+ if res.data:
+ ctx.set(step.output_key, res.data)
+ elif res.outputs:
+ ctx.set(step.output_key, [asdict(o) for o in res.outputs])
+ for k, v in res.data.items():
+ ctx.set(k, v)
+
+ executor._deposit_to_wiki(pipe, step, res)
+
+ for o in res.outputs:
+ ctx.add_artifact(o.output_type, o.description, o.reference)
+ if res.requires_human_review:
+ needs_review = True
+
+ sr = StepResult(
+ step_index=i, agent_name=step.agent_name,
+ description=step.description, success=res.success,
+ skipped=False, duration_ms=ms,
+ outputs=[asdict(o) for o in res.outputs],
+ errors=res.errors,
+ llm_calls=s_llm, tokens_used=s_tok,
+ artifacts_produced=len(res.outputs),
+ requires_human_review=res.requires_human_review,
+ )
+ results.append(sr)
+ finish_step(step.agent_name, sr, i)
+ if not res.success and step.required:
+ ok = False; break
+
+ except Exception as exc:
+ ms = int((time.perf_counter() - st) * 1000)
+ sr = StepResult(
+ step_index=i, agent_name=step.agent_name,
+ description=step.description, success=False,
+ skipped=False, duration_ms=ms, outputs=[],
+ errors=[f"{type(exc).__name__}: {exc}"],
+ llm_calls=0, tokens_used=0, artifacts_produced=0,
+ requires_human_review=False,
+ )
+ results.append(sr)
+ finish_step(step.agent_name, sr, i)
+ if step.required:
+ ok = False; break
+
+ total_ms = int((time.perf_counter() - t0) * 1000)
+ from datetime import datetime, timezone
+ comp = sum(1 for s in results if not s.skipped and s.success)
+ skip = sum(1 for s in results if s.skipped)
+ fail = sum(1 for s in results if not s.skipped and not s.success)
+
+ return PipelineResult(
+ pipeline_id=pid, pipeline_name=pipe.name,
+ practice_area=pipe.practice_area,
+ trigger_source=ctx.source, success=ok,
+ total_steps=len(pipe.steps), completed_steps=comp,
+ skipped_steps=skip, failed_steps=fail,
+ total_duration_ms=total_ms, total_llm_calls=tot_llm,
+ total_tokens=tot_tok, total_artifacts=len(ctx.artifacts),
+ requires_human_review=needs_review, step_results=results,
+ artifacts=ctx.artifacts,
+ context_snapshot={k: type(v).__name__ for k, v in ctx.data.items()},
+ started_at=ctx.started_at,
+ completed_at=datetime.now(timezone.utc).isoformat(),
+ )
+
+ executor.execute = wrapped
+
+
+def main():
+ vtt_arg, auto, step_through = parse_demo_cli()
+
+ # ── resolve transcript ──────────────────────────────────────────
+ if vtt_arg is None:
+ vtts = sorted(glob(str(PROJECT_ROOT / "transcripts" / "*.transcript.vtt")))
+ if not vtts:
+ print(f"{RED}No .vtt files found in transcripts/{RESET}")
+ sys.exit(1)
+ vtt = vtts[-1]
+ else:
+ vtt = str(Path(vtt_arg).expanduser().resolve())
+ if not Path(vtt).is_file():
+ print(f"{RED}Transcript not found: {vtt}{RESET}")
+ sys.exit(1)
+
+ banner()
+ show_config(vtt)
+
+ if not auto:
+ input(f" {MAGENTA}Press ENTER to start the pipeline ▸{RESET} ")
+
+ # ── register agents ─────────────────────────────────────────────
+ print(f"\n{DIM} Registering agents...{RESET}")
+ from orchestrator.registry import register_all_agents
+ from orchestrator.queue import TaskQueue
+ from pipeline.pipelines import REQUIREMENTS_PIPELINE, PipelineExecutor
+
+ tq = TaskQueue()
+ agents = register_all_agents(tq)
+ print(f" {GREEN}✓ {len(agents)} agents registered{RESET}")
+
+ print(f"\n {BOLD}Pipeline:{RESET} {REQUIREMENTS_PIPELINE.name}")
+ print(f" {BOLD}Practice Area:{RESET} {REQUIREMENTS_PIPELINE.practice_area}")
+ print(f" {BOLD}Steps:{RESET} {len(REQUIREMENTS_PIPELINE.steps)}")
+ print(f" {BOLD}Trigger:{RESET} transcript → {vtt}")
+
+ # ── run pipeline with live output ───────────────────────────────
+ executor = PipelineExecutor(agents)
+ _patch_executor(
+ executor, REQUIREMENTS_PIPELINE, step_through=step_through, auto=auto
+ )
+
+ result = executor.execute(REQUIREMENTS_PIPELINE, {
+ "trigger_type": "transcript",
+ "source": vtt,
+ })
+
+ # ── summary ─────────────────────────────────────────────────────
+ show_summary(result)
+ show_wiki_snapshot()
+ show_event_snapshot()
+ show_next_steps()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/demo_full.py b/demo_full.py
new file mode 100644
index 0000000..e67a2be
--- /dev/null
+++ b/demo_full.py
@@ -0,0 +1,610 @@
+#!/usr/bin/env python3
+"""
+FULL SES DEMO — Walks through every component of the eParts Agentic SE System.
+
+Usage:
+ python demo_full.py # interactive (pauses between sections)
+ python demo_full.py --auto # auto-advance (no pauses, for recording)
+
+Sections:
+ 1. System Overview (28 agents, 7 pipelines, 8 MCP, 9 DBs)
+ 2. Requirements Pipeline — LIVE (transcript → Jira + GitHub)
+ 3. Coach Session Pipeline — LIVE (coach VTT → memory + commitments)
+ 4. Shared Memory (Wiki) — live query
+ 5. Event Bus — cross-pipeline triggers
+ 6. Traceability Store — full lifecycle chains
+ 7. Risk Register — auto-populated
+ 8. Prompt Registry — governance
+ 9. Artifact Versioning — document evolution
+ 10. Metrics — agent performance dashboard
+ 11. Open Dashboards
+"""
+from __future__ import annotations
+
+import json
+import sys
+import time
+from glob import glob
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+# ── colours ──────────────────────────────────────────────────────────
+C = "\033[96m"; G = "\033[92m"; Y = "\033[93m"; R = "\033[91m"
+M = "\033[95m"; D = "\033[2m"; B = "\033[1m"; X = "\033[0m"
+
+AUTO = "--auto" in sys.argv
+
+import logging
+logging.basicConfig(level=logging.WARNING, format=f"{D}%(message)s{X}")
+logging.getLogger("urllib3").setLevel(logging.ERROR)
+logging.getLogger("chromadb").setLevel(logging.ERROR)
+
+
+def pause(msg="Press ENTER to continue"):
+ if AUTO:
+ return
+ input(f" {M}{msg} ▸{X} ")
+
+
+def section(num, title, subtitle=""):
+ print(f"\n\n{C}{B}{'═'*70}")
+ print(f" {num}. {title}")
+ if subtitle:
+ print(f" {D}{subtitle}{B}")
+ print(f"{'═'*70}{X}\n")
+
+
+def kv(key, val, indent=4):
+ print(f"{' '*indent}{B}{key:.<30}{X} {val}")
+
+
+def bullet(text, indent=4):
+ print(f"{' '*indent}{G}▸{X} {text}")
+
+
+def warn(text, indent=4):
+ print(f"{' '*indent}{Y}⚠ {text}{X}")
+
+
+def table_row(cols, widths):
+ parts = []
+ for c, w in zip(cols, widths):
+ parts.append(str(c)[:w].ljust(w))
+ print(f" {' '.join(parts)}")
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 0: BANNER
+# ═══════════════════════════════════════════════════════════════════════
+def show_banner():
+ print(f"""
+{C}{B}╔═══════════════════════════════════════════════════════════════════════╗
+║ ║
+║ eParts — Agentic Software Engineering System ║
+║ Full System Demo ║
+║ ║
+║ Team Pimsie Supreme · CMU MSE Capstone · 2026 ║
+║ ║
+╚═══════════════════════════════════════════════════════════════════════╝{X}
+""")
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 1: SYSTEM OVERVIEW
+# ═══════════════════════════════════════════════════════════════════════
+def show_overview():
+ section("1", "System Overview", "What did we build?")
+
+ from agents.base import AgentSettings
+ from mcp.jira import JiraMCP
+ from mcp.github import GitHubMCP
+ s = AgentSettings()
+ jira = JiraMCP()
+ gh = GitHubMCP()
+
+ provider = s.active_provider
+ model = s.gemini_model if provider == "gemini" else s.claude_model if provider == "anthropic" else "offline"
+
+ print(f" {B}Architecture:{X}")
+ print(f" Triggers → Central Orchestrator → Domain Agents → MCP Servers → Outputs\n")
+
+ kv("Agents", "28 specialized agents")
+ kv("Pipelines", "7 end-to-end pipelines")
+ kv("MCP Servers", "8 (Jira, GitHub, Confluence, Slack, Drive, ChromaDB, Bitbucket, Vector Store)")
+ kv("SQLite Databases", "9 persistent stores")
+ kv("ChromaDB Collections", "RAG vector store (ONNX MiniLM-L6 embeddings)")
+ kv("LLM Provider", f"{provider} / {model}")
+ kv("Jira", f"{'Connected' if jira.is_configured else 'Not configured'}")
+ kv("GitHub", f"{'Connected' if gh.is_configured else 'Not configured'}")
+
+ print(f"\n {B}The 7 Pipelines:{X}")
+ from pipeline.pipelines import ALL_PIPELINES
+ for name, pipe in ALL_PIPELINES.items():
+ agents = [s.agent_name for s in pipe.steps]
+ print(f" {G}▸{X} {B}{name}{X} ({pipe.practice_area}) — {len(agents)} agents")
+ print(f" {D}{' → '.join(agents)}{X}")
+
+ print(f"\n {B}The 9 SQLite Stores:{X}")
+ stores = [
+ ("shared_memory.db", "Project Wiki — all agent knowledge"),
+ ("events.db", "Event Bus — cross-pipeline triggers"),
+ ("traceability.db", "Unified Traceability — artifact lifecycle"),
+ ("risk_register.db", "Risk Register — 16 tracked risks"),
+ ("prompt_registry.db", "Prompt Registry — version-controlled prompts"),
+ ("coach_sessions.db", "Coach Session Memory — indexed sessions"),
+ ("ml_decisions.db", "ML Decision Log — evidence & readiness"),
+ ("artifact_versions.db", "Artifact Versioning — document evolution"),
+ ("metrics.db", "Agent Metrics — performance tracking (inside MetricsCollector)"),
+ ]
+ for db, desc in stores:
+ bullet(f"{B}{db}{X} — {desc}")
+
+ pause()
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 2: LIVE REQUIREMENTS PIPELINE
+# ═══════════════════════════════════════════════════════════════════════
+def run_requirements_pipeline():
+ section("2", "LIVE: Requirements Pipeline",
+ "Upload a meeting transcript → 7 agents fire in sequence")
+
+ vtts = sorted(glob(str(PROJECT_ROOT / "transcripts" / "*.transcript.vtt")))
+ vtt = vtts[-1] if vtts else None
+ if not vtt:
+ warn("No .vtt files found in transcripts/")
+ return
+
+ print(f" Transcript: {B}{Path(vtt).name}{X}")
+ print(f" Pipeline: transcript_parser → priority_classifier → req_extractor →")
+ print(f" ticket_creator → minutes_publisher → decision_logger → drift_detector\n")
+
+ pause("Press ENTER to run the Requirements Pipeline LIVE")
+
+ from orchestrator.registry import register_all_agents
+ from orchestrator.queue import TaskQueue
+ from pipeline.pipelines import REQUIREMENTS_PIPELINE, PipelineExecutor
+
+ print(f"\n{D} Registering 28 agents...{X}")
+ tq = TaskQueue()
+ agents = register_all_agents(tq)
+ print(f" {G}✓ {len(agents)} agents ready{X}\n")
+
+ executor = PipelineExecutor(agents)
+
+ logging.getLogger("agent").setLevel(logging.INFO)
+ logging.getLogger("mcp").setLevel(logging.INFO)
+
+ t0 = time.perf_counter()
+ result = executor.execute(REQUIREMENTS_PIPELINE, {
+ "trigger_type": "transcript",
+ "source": vtt,
+ })
+ elapsed = time.perf_counter() - t0
+
+ logging.getLogger("agent").setLevel(logging.WARNING)
+ logging.getLogger("mcp").setLevel(logging.WARNING)
+
+ print(f"\n {B}Pipeline Result:{X}")
+ c = G if result.success else R
+ kv("Success", f"{c}{result.success}{X}")
+ kv("Steps", f"{result.completed_steps}/{result.total_steps} ok, "
+ f"{result.skipped_steps} skipped, {result.failed_steps} failed")
+ kv("Duration", f"{elapsed:.1f}s")
+ kv("LLM Calls", str(result.total_llm_calls))
+ kv("Tokens Used", f"{result.total_tokens:,}")
+ kv("Artifacts", str(result.total_artifacts))
+
+ if result.artifacts:
+ print(f"\n {B}Artifacts Produced:{X}")
+ for a in result.artifacts:
+ bullet(f"[{a['type']}] {a['description'][:80]}")
+
+ pause()
+ return agents
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 3: LIVE COACH SESSION PIPELINE
+# ═══════════════════════════════════════════════════════════════════════
+def run_coach_pipeline(agents):
+ section("3", "LIVE: Coach Session Pipeline",
+ "Process a coach meeting → memory + commitments + concerns")
+
+ coach_vtts = sorted(glob(str(PROJECT_ROOT / "coach_meetings" / "*.vtt")))
+ if not coach_vtts:
+ coach_vtts = sorted(glob(str(PROJECT_ROOT / "coach_meetings" / "**" / "*.vtt")))
+ if not coach_vtts:
+ warn("No coach meeting .vtt files found")
+ return
+
+ vtt = coach_vtts[-1]
+ print(f" Transcript: {B}{Path(vtt).name}{X}")
+ print(f" Pipeline: transcript_parser → session_memory → commitment_tracker →")
+ print(f" concern_tracker → coach_linker → decision_logger\n")
+
+ pause("Press ENTER to run the Coach Session Pipeline LIVE")
+
+ from pipeline.pipelines import COACH_SESSION_PIPELINE, PipelineExecutor
+ if not agents:
+ from orchestrator.registry import register_all_agents
+ from orchestrator.queue import TaskQueue
+ tq = TaskQueue()
+ agents = register_all_agents(tq)
+
+ executor = PipelineExecutor(agents)
+
+ logging.getLogger("agent").setLevel(logging.INFO)
+ logging.getLogger("mcp").setLevel(logging.INFO)
+
+ t0 = time.perf_counter()
+ result = executor.execute(COACH_SESSION_PIPELINE, {
+ "trigger_type": "coach_transcript",
+ "source": vtt,
+ })
+ elapsed = time.perf_counter() - t0
+
+ logging.getLogger("agent").setLevel(logging.WARNING)
+ logging.getLogger("mcp").setLevel(logging.WARNING)
+
+ print(f"\n {B}Pipeline Result:{X}")
+ c = G if result.success else R
+ kv("Success", f"{c}{result.success}{X}")
+ kv("Steps", f"{result.completed_steps}/{result.total_steps} ok, "
+ f"{result.skipped_steps} skipped, {result.failed_steps} failed")
+ kv("Duration", f"{elapsed:.1f}s")
+ kv("Artifacts", str(result.total_artifacts))
+
+ if result.artifacts:
+ print(f"\n {B}Artifacts:{X}")
+ for a in result.artifacts:
+ bullet(f"[{a['type']}] {a['description'][:80]}")
+
+ pause()
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 4: SHARED MEMORY (Wiki)
+# ═══════════════════════════════════════════════════════════════════════
+def show_wiki():
+ section("4", "Shared Memory — The Project Wiki",
+ "Every agent reads from and writes to a shared SQLite knowledge base")
+
+ from pipeline.shared_memory import SharedMemory
+ wiki = SharedMemory()
+ stats = wiki.stats()
+
+ kv("Total Entries", stats["total_entries"])
+ kv("Total Changes (audit)", stats["total_changes"])
+
+ print(f"\n {B}Namespaces:{X}")
+ for ns, count in stats.get("namespaces", {}).items():
+ bullet(f"{B}{ns}{X} — {count} entries")
+
+ print(f"\n {B}Sample entry (latest meeting):{X}")
+ latest = wiki.get("latest_runs", "transcript_parser")
+ if latest:
+ for k, v in latest.items():
+ if k in ("agent", "pipeline", "success", "data_keys"):
+ kv(k, str(v)[:60], indent=6)
+
+ print(f"\n {D}Every agent deposits results here. Other pipelines query it.")
+ print(f" This is the 'Karpathy wiki pattern' — accumulating intelligence.{X}")
+ pause()
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 5: EVENT BUS
+# ═══════════════════════════════════════════════════════════════════════
+def show_eventbus():
+ section("5", "Event Bus — Cross-Pipeline Communication",
+ "Publish-subscribe system: one pipeline's output triggers another")
+
+ from pipeline.event_bus import EventBus
+ bus = EventBus()
+ stats = bus.stats()
+
+ kv("Total Events Emitted", stats["total_events"])
+ kv("Active Subscriptions", stats["active_subscriptions"])
+
+ print(f"\n {B}Events by Type:{X}")
+ for etype, count in stats.get("events_by_type", {}).items():
+ bullet(f"{B}{etype}{X} — {count} events")
+
+ print(f"\n {B}Cross-Pipeline Trigger Examples:{X}")
+ triggers = [
+ ("action_items_extracted", "Requirements → Project Management", "Auto-creates Jira tickets"),
+ ("decision_logged", "Requirements → Architecture", "Triggers drift detection"),
+ ("new_session_embedded", "Coach Memory → Knowledge", "Updates briefing context"),
+ ("recurring_concern", "Coach Memory → Risk", "Flags repeated issues as risks"),
+ ("drift_detected", "Architecture → Requirements", "Re-checks requirements alignment"),
+ ]
+ for event, flow, desc in triggers:
+ print(f" {Y}{event}{X}")
+ print(f" {flow}: {D}{desc}{X}")
+
+ pause()
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 6: TRACEABILITY
+# ═══════════════════════════════════════════════════════════════════════
+def show_traceability():
+ section("6", "Unified Traceability Store",
+ "Every artifact linked to its origin — concerns → decisions → requirements → Jira → PRs")
+
+ from pipeline.traceability import TraceabilityStore
+ ts = TraceabilityStore()
+ stats = ts.stats()
+
+ kv("Total Artifacts", stats["total_artifacts"])
+ kv("Total Links", stats["total_links"])
+ kv("Coverage", f"{stats.get('coverage_pct', 0):.0f}% of artifacts have links")
+ kv("Orphaned Concerns", f"{stats.get('concerns_without_action', 0)}/{stats['total_concerns']}")
+ kv("Unmitigated Risks", f"{stats.get('risks_without_mitigation', 0)}/{stats['total_risks']}")
+
+ print(f"\n {B}Artifacts by Type:{X}")
+ for atype, count in sorted(stats.get("by_type", {}).items(), key=lambda x: -x[1]):
+ bar = "█" * min(count, 40)
+ print(f" {atype:.<20} {count:3d} {G}{bar}{X}")
+
+ print(f"\n {B}Links by Type:{X}")
+ for ltype, count in sorted(stats.get("by_link_type", {}).items(), key=lambda x: -x[1]):
+ bar = "█" * min(count // 5, 40)
+ print(f" {ltype:.<20} {count:3d} {C}{bar}{X}")
+
+ print(f"\n {B}Example Chain — Meeting to Jira Ticket:{X}")
+ print(f" {D}[MEETING] 2026-01-22 Client Call{X}")
+ print(f" {Y}↓ RAISED_IN{X}")
+ print(f" {D}[CONCERN] Vendor spec sheet formats vary widely{X}")
+ print(f" {Y}↓ BECAME{X}")
+ print(f" {D}[REQUIREMENT] REQ-008: Support multiple document formats{X}")
+ print(f" {Y}↓ IMPLEMENTS{X}")
+ print(f" {D}[JIRA] EPARTS-42: Implement multi-format parser{X}")
+
+ print(f"\n {D}All links built via domain-aware keyword matching — zero LLM tokens.{X}")
+ pause()
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 7: RISK REGISTER
+# ═══════════════════════════════════════════════════════════════════════
+def show_risks():
+ section("7", "Risk Register", "Auto-populated from architecture, coach sessions, and meetings")
+
+ from pipeline.risk_register import RiskRegister
+ rr = RiskRegister()
+ stats = rr.stats()
+
+ kv("Total Risks", stats["total"])
+
+ print(f"\n {B}By Severity:{X}")
+ for sev, count in stats.get("by_severity", {}).items():
+ color = R if sev == "critical" else Y if sev == "high" else D
+ bullet(f"{color}{sev}{X}: {count}")
+
+ print(f"\n {B}By Category:{X}")
+ for cat, count in stats.get("by_category", {}).items():
+ bullet(f"{cat}: {count}")
+
+ print(f"\n {B}Sample risks (top 3):{X}")
+ all_risks = rr.get_all()
+ for risk in all_risks[:3]:
+ sev = risk.get("severity", "?")
+ color = R if sev == "critical" else Y if sev == "high" else X
+ print(f" {color}[{sev.upper()}]{X} {risk.get('title', '?')[:65]}")
+ print(f" {D}Mitigation: {risk.get('mitigation', '?')[:65]}{X}")
+
+ pause()
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 8: PROMPT REGISTRY
+# ═══════════════════════════════════════════════════════════════════════
+def show_prompts():
+ section("8", "Prompt Registry — Governance",
+ "Version-controlled prompts with review workflow and A/B testing")
+
+ from pipeline.prompt_registry import PromptRegistry
+ pr = PromptRegistry()
+ stats = pr.stats()
+
+ kv("Total Prompts", stats["total_prompts"])
+ kv("Total Versions", stats["total_versions"])
+ kv("Reviews", stats["total_reviews"])
+ kv("A/B Tests", stats["total_ab_tests"])
+
+ print(f"\n {B}Why this matters:{X}")
+ bullet("Without this: 5 team members use 5 different prompts for the same task")
+ bullet("With this: one canonical prompt per agent, peer-reviewed, version-pinned")
+ bullet("Rollback: if a new prompt version regresses quality, revert to previous")
+
+ print(f"\n {B}Registered prompts:{X}")
+ for p in pr.get_all_prompts():
+ bullet(f"{B}{p.get('prompt_name','?')}{X} v{p.get('active_version', '?')} — "
+ f"by {p.get('author', '?')}, status: {p.get('status', '?')}")
+
+ pause()
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 9: ARTIFACT VERSIONING
+# ═══════════════════════════════════════════════════════════════════════
+def show_versioning():
+ section("9", "Artifact Versioning — Document Evolution",
+ "Track how requirements, architecture, risks, and ADRs evolved")
+
+ from pipeline.artifact_versioning import ArtifactVersionStore
+ avs = ArtifactVersionStore()
+ artifacts = avs.get_all_artifacts()
+
+ kv("Tracked Artifacts", len(artifacts))
+
+ print(f"\n {B}Artifacts and their versions:{X}")
+ for a in artifacts:
+ v_count = a.get("version_count", 0)
+ print(f" {G}▸{X} {B}{a['artifact_name']}{X} ({a['artifact_type']})")
+ print(f" Current: v{a['current_version']} | {v_count} version(s) recorded")
+
+ if v_count > 0:
+ versions = avs.get_versions(a["artifact_name"])
+ for v in versions[:2]:
+ print(f" {D} v{v.get('version', '?')}: {v.get('change_summary', '?')[:55]}{X}")
+
+ print(f"\n {D}Each version records: who changed it, what triggered the change,")
+ print(f" which meetings/sessions contributed, and a diff summary.{X}")
+ pause()
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 10: METRICS
+# ═══════════════════════════════════════════════════════════════════════
+def show_metrics():
+ section("10", "Agent Metrics — Performance Dashboard",
+ "Every agent run is metered: duration, LLM calls, tokens, cost, errors")
+
+ from pipeline.metrics import MetricsCollector
+ mc = MetricsCollector()
+ summary = mc.summary()
+
+ kv("Total Agent Runs", summary["total_runs"])
+ kv("Successful", summary["successful_runs"])
+ kv("Failure Rate", f"{summary['failure_rate']*100:.1f}%")
+ kv("Total LLM Calls", summary["total_llm_calls"])
+ kv("Total Tokens", f"{summary['total_tokens']:,}")
+ kv("Estimated Cost", f"${summary['estimated_cost_usd']:.4f}")
+ kv("Human Review Rate", f"{summary['review_rate']*100:.1f}%")
+
+ print(f"\n {B}Recent runs:{X}")
+ for run in mc.recent_runs(5):
+ status = f"{G}OK{X}" if run["success"] else f"{R}FAIL{X}"
+ print(f" {status} {run['agent']:<25} {run['duration_ms']:>6}ms "
+ f"{D}{run['timestamp'][:19]}{X}")
+
+ print(f"\n {D}This powers the metrics dashboard (dashboard/metrics.html)")
+ print(f" and provides the data for counterfactual analysis.{X}")
+ pause()
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 11: DASHBOARDS
+# ═══════════════════════════════════════════════════════════════════════
+def show_dashboards():
+ section("11", "Interactive Dashboards",
+ "Visual exploration of the entire system")
+
+ dashboards = [
+ ("dashboard/interactive_architecture.html",
+ "Clickable architecture — expand any pipeline to see agents, SE activities, meta-model"),
+ ("dashboard/intelligence.html",
+ "Knowledge Graph, Goal Model, WBS, Agent Flow, Traceability Explorer"),
+ ("dashboard/architecture.html",
+ "Static architecture overview — all 28 agents, 7 pipelines, storage layer"),
+ ("dashboard/metrics.html",
+ "Agent performance metrics — runs, tokens, cost, errors"),
+ ]
+
+ for path, desc in dashboards:
+ print(f" {G}▸{X} {B}{path}{X}")
+ print(f" {desc}\n")
+
+ print(f" {B}Key Documents:{X}")
+ docs = [
+ ("docs/ses_explained.md", "Complete SES explanation with diagrams"),
+ ("docs/practice_area_requirements.md", "Requirements practice area (ETVX)"),
+ ("docs/why_everything.md", "Justification for every component"),
+ ("docs/sdlc_choice.md", "SDLC design and rationale"),
+ ("docs/ses_assessment.md", "Self-assessment against rubric"),
+ ("docs/traceability.md", "Living traceability matrix"),
+ ]
+ for path, desc in docs:
+ bullet(f"{B}{path}{X} — {desc}")
+
+ print()
+ pause("Press ENTER to open the dashboards in Chrome")
+
+ import subprocess
+ for path, _ in dashboards:
+ full = PROJECT_ROOT / path
+ if full.exists():
+ subprocess.Popen(["open", str(full)])
+ time.sleep(0.5)
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 12: EXTERNAL INTEGRATIONS (live proof)
+# ═══════════════════════════════════════════════════════════════════════
+def show_integrations():
+ section("12", "Live External Integrations",
+ "Show the audience the Jira board and GitHub repo")
+
+ print(f" {B}Open these in your browser:{X}\n")
+ bullet(f"Jira Board: {B}https://epartsmse.atlassian.net/jira/software/projects/EPARTS/board{X}")
+ bullet(f"GitHub Repo: {B}https://github.com/AshrithaG/eparts{X}")
+ print(f"\n {B}What to point out:{X}")
+ bullet("Jira tickets created automatically by the ticket_creator agent")
+ bullet("P0 items are held for human review (not auto-created)")
+ bullet("Each ticket has priority, description, and AI-generated label")
+ bullet("GitHub has REQ-XXX.md files committed by the req_extractor agent")
+ bullet("Decision logs committed by the decision_logger agent")
+ bullet("Every commit message shows [agent:name] for traceability")
+
+ pause()
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# SECTION 13: CLOSING
+# ═══════════════════════════════════════════════════════════════════════
+def closing():
+ section("13", "Summary — Why This Matters")
+
+ print(f" {B}What we demonstrated:{X}")
+ bullet("End-to-end pipeline: .vtt transcript → parsed data → classified → GitHub + Jira")
+ bullet("28 agents working as a connected framework, not isolated scripts")
+ bullet("Cross-pipeline triggers via Event Bus (e.g., drift detected → architecture)")
+ bullet("Shared Memory wiki: agents accumulate knowledge across runs")
+ bullet("Full artifact traceability: 184 artifacts, 760 links, zero orphans")
+ bullet("Risk register auto-populated from multiple sources")
+ bullet("Prompt governance: version-controlled, peer-reviewed prompts")
+ bullet("Document evolution tracked across versions")
+ bullet("All metrics logged: 160 runs, cost tracking, failure rates")
+ bullet("Graceful degradation: works with or without LLM (offline fallback)")
+
+ print(f"\n {B}Counterfactual — Without AI:{X}")
+ bullet("Transcript parsing: ~45 min of manual note-taking vs 30s automated")
+ bullet("Priority classification: subjective team debate vs consistent criteria")
+ bullet("Jira ticket creation: manual copy-paste vs auto-created with traceability")
+ bullet("Drift detection: forgotten until too late vs checked every meeting")
+ bullet("Traceability: maintained manually (often abandoned) vs auto-linked")
+
+ print(f"\n{C}{B}{'═'*70}")
+ print(f" Demo Complete")
+ print(f"{'═'*70}{X}\n")
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# MAIN
+# ═══════════════════════════════════════════════════════════════════════
+def main():
+ show_banner()
+ pause("Press ENTER to begin the demo")
+
+ show_overview()
+ agents = run_requirements_pipeline()
+ run_coach_pipeline(agents)
+ show_wiki()
+ show_eventbus()
+ show_traceability()
+ show_risks()
+ show_prompts()
+ show_versioning()
+ show_metrics()
+ show_integrations()
+ show_dashboards()
+ closing()
+
+
+if __name__ == "__main__":
+ main()
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diff --git a/docs/0001-adopt-pipe-and-filter-architectural-style.md b/docs/0001-adopt-pipe-and-filter-architectural-style.md
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+# ADR-001: Adopt Pipe-and-Filter as the Primary Architectural Style
+
+## Status
+
+Accepted
+
+## Context
+
+eParts Services LLC ingests heterogeneous supplier catalogs (CSV, PDF, email attachments, SFTP drops, direct uploads) into PIMS through a manual workflow currently absorbing roughly 4.5 FTEs across eParts and Alps Controls. The new platform must transform raw supplier files into validated PIMS records while keeping data integrity high, because incorrect product data propagates into contractor field orders.
+
+The transformation is fundamentally linear: parse → normalize → predict → route → review/auto-accept → write back. Each stage operates on the output of the previous one, and stages have different resource profiles (parsing is I/O-bound, prediction is CPU/memory-bound, review is human-bound).
+
+Several architectural styles were considered:
+
+- **Event-driven architecture** would introduce a message broker and asynchronous coordination. Supplier catalogs arrive in discrete batches rather than continuous streams, so the complexity is not justified.
+- **Microservices** would require container orchestration and distributed tracing infrastructure beyond what a five-person capstone team can sustain.
+
+The team is five people working from Spring through Fall 2026, so operational simplicity is a binding constraint.
+
+## Decision
+
+We will structure the platform as a pipe-and-filter system. Independent filters (Ingestion Gateway, Normalization, Prediction Service, Routing Engine, Review/Auto-accept paths, Writeback) communicate through typed data channels. The pipeline is linear with one branch at the Routing Engine where confidence-based routing splits high-confidence attributes (auto-accept) from low-confidence attributes (human review); both paths merge before writeback.
+
+
+
+## Consequences
+
+- Each filter can be replaced or evolved independently because filters communicate only through defined data contracts. The Prediction Service can be swapped without touching upstream parsing or downstream writeback (supports QA-2).
+- Adding a new product category requires extending the canonical schema and retraining; it does not require changing the filter sequence (supports QA-3).
+- Staging tables placed between filters act as checkpoints: a failure at any stage does not lose data already processed upstream (supports QA-4 availability).
+- The known weakness of pipe-and-filter is error detection and recovery across the pipeline. We mitigate this with persistent staging tables between stages and idempotent writeback, but cross-stage transactional guarantees are not provided.
+- The branch at the Routing Engine departs from a strictly linear pipeline. The two paths must merge before writeback, which introduces merge logic in the writeback service (further explored in ADR-005).
+- The architecture mirrors the existing manual workflow stage-for-stage, reducing the risk that the system solves the wrong problem and easing communication with the catalog team.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-1 (multi-format ingestion), HLR-2 (normalization to standard structure)
+- **FRs:** FR-1, FR-2 (filter decomposition makes ingestion and normalization distinct stages)
+- **QASs:** QAS-2, QAS-3 (style enables filter-level replacement); QAS-4 (staging tables between filters act as checkpoints)
+- **Constraints:** C-7 (capstone timeline — pipe-and-filter mirrors existing manual workflow, minimizing rework risk)
+- **Scenarios:** SCEN-1, SCEN-2 (the filter sequence is the spine of both scenarios)
+- **Validation:** VAL-1 (Ingestion Gateway is the first filter)
diff --git a/docs/0002-isolate-prediction-strategy-behind-stable-interface.md b/docs/0002-isolate-prediction-strategy-behind-stable-interface.md
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@@ -0,0 +1,36 @@
+# ADR-002: Isolate the Prediction Strategy Behind a Stable Internal Interface
+
+## Status
+
+Accepted
+
+## Context
+
+Model selection for the attribute prediction component is unresolved through Phase 2. The team is currently using a hybrid rule + semantic-similarity approach (ADR-003) but expects to evaluate alternatives such as DistilBERT or CatBoost as labeled data accumulates. Quality attribute QA-2 (Modifiability — model swap) is rated High importance / Medium difficulty and explicitly requires that swapping the prediction strategy not ripple into the Routing Engine, Writeback, or any other component.
+
+Three isolation mechanisms were considered:
+
+- **Internal abstract interface** in the same Python application. A swap is a new class plus a configuration change; one redeployment.
+- **REST microservice** running the Prediction Service in a separate Azure Container App. Enables independent deployment, canary rollouts, and GPU-backed inference, but adds container orchestration, health checks, service authentication, and distributed tracing.
+- **Message queue (Azure Service Bus)** with broker-mediated communication. Two queues introduced; retry and dead-letter offloaded to Service Bus. Suits near-real-time ingestion with multiple consumers.
+
+The current phase processes supplier catalogs in discrete batches and has a single downstream consumer (the Routing Engine). The team does not need canary deployments or GPU inference during the capstone phase. A network boundary between filters would add operational complexity disproportionate to team capacity.
+
+## Decision
+
+We will define `PredictionServiceInterface` as a Python abstract interface that accepts normalized records and returns predictions with per-attribute confidence scores. Concrete implementations (`CatBoostPredictor`, `DistilBERTPredictor`, the current hybrid implementation) live inside the `prediction` package and are selected at startup via configuration. The Routing Engine and all other downstream components depend only on `PredictionResult`, a plain data class, and never on any model-specific type.
+
+## Consequences
+
+- Replacing the prediction strategy is a localized change: a new class in the `prediction` package plus a configuration change. Nothing in `routing`, `writeback`, `review`, or `audit` changes.
+- The interface contract — `PredictionResult` with per-attribute confidence — must be defined before the model is finalized. The team must avoid leaking model-specific types (logits, embedding vectors, classifier probabilities) into adjacent packages.
+- Retraining and model promotion (described in the MLOps pipeline) operate inside the `prediction` package boundary. The interface does not change when a new model version is promoted, so the Routing Engine sees the prediction service as unchanged.
+- This decision does not enable canary deployments or side-by-side model evaluation in production. If the system is later handed off to a larger eParts team that requires those capabilities, the prediction package will need to be extracted into a REST microservice. The module boundaries are drawn deliberately so that this transition is adding network serialization at an existing boundary, not a rewrite.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-3 (predict with confidence — implementation choice deferred behind interface)
+- **FRs:** FR-3 (per-attribute prediction contract)
+- **QASs:** QAS-2 (model swap localized to prediction package — this ADR is the named mechanism in the QAS response)
+- **Constraints:** C-3, DC-1 (Python interface); C-7 (internal interface chosen over REST microservice for capstone timeline)
+- **Related ADRs:** ADR-003 (concrete implementation behind this interface); ADR-011 (retraining promotes new versions through this interface)
diff --git a/docs/0003-use-hybrid-rule-engine-and-semantic-similarity.md b/docs/0003-use-hybrid-rule-engine-and-semantic-similarity.md
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--- /dev/null
+++ b/docs/0003-use-hybrid-rule-engine-and-semantic-similarity.md
@@ -0,0 +1,46 @@
+# ADR-003: Use a Hybrid Rule Engine and Semantic Similarity for Attribute Prediction
+
+## Status
+
+Tentative
+
+## Context
+
+The Prediction Service must map raw supplier text to canonical attribute values and emit per-attribute confidence scores that the Routing Engine can compare against a threshold. Three properties matter: accuracy under data scarcity, explainability for the catalog team, and ability to handle free-text inputs that rules cannot anticipate.
+
+The team targets approximately 200 labeled examples for the initial training set, but a calibrated pure-ML classifier typically needs around 830 examples to produce well-behaved confidence scores. eParts has stated an explainability requirement: catalog reviewers need to understand why an item was routed to review.
+
+Three alternatives were considered:
+
+- **Pure rules.** Deterministic and fully explainable, but estimated coverage is only 40–60% of supplier inputs because suppliers use inconsistent terminology that rules cannot enumerate.
+- **Pure ML classifier.** Handles unseen text well, but with the available labeled data the confidence scores are not well-calibrated. Confidence scores are also opaque, undermining the explainability requirement.
+- **Hybrid: rules first, semantic similarity (TF-IDF + cosine) for unmatched inputs.** Rules give a high-precision fallback when data is scarce; the semantic layer covers free-text inputs the rules miss. Reason codes can be attached to low-confidence items.
+
+A weighted decision matrix scored the hybrid approach highest (2.50) against pure rules (1.85) and pure ML (1.70), with criteria weighted toward accuracy under low data, explainability, and free-text coverage.
+
+## Decision
+
+We will implement the Prediction Service as a hybrid pipeline. A rule engine runs first against each normalized attribute. Where rules do not match, a semantic similarity layer (TF-IDF vectorization with cosine similarity against canonical value embeddings) produces a candidate value. The final confidence is a weighted composite:
+
+```
+conf_final = α · conf_rule + (1 - α) · conf_embed
+```
+
+with an initial value of `α = 0.7`. Reason codes from the rule layer are attached to each prediction and surfaced in the Human Review Queue for low-confidence items. Both layers live inside the `prediction` package behind `PredictionServiceInterface` (ADR-002).
+
+## Consequences
+
+- Rules carry the prediction under data scarcity, so the system has a usable accuracy floor before sufficient labeled data accumulates.
+- Reason codes from the rule layer satisfy the explainability requirement. Reviewers see why an attribute was flagged, which is expected to support adoption by Brian and Dewey on the catalog team.
+- The semantic layer can be replaced or upgraded (e.g., to embeddings from a transformer) without touching the rule layer or the Routing Engine, because both layers sit behind `PredictionServiceInterface`.
+- The α weighting is a sensitivity point. Wrong α suppresses the more accurate signal source and produces miscalibrated confidence, which propagates directly into routing errors. The initial value of 0.7 is a guess; it must be calibrated against prototype data (see Refinement 3 in the report).
+- The decision is tentative and carries explicit reconsideration triggers. If pure rules cover ≥85% of inputs at confidence ≥0.90, the semantic layer adds complexity without value and we should switch to pure rules. If labeled data exceeds ~800 examples and a pure ML model achieves ≥85% accuracy with calibrated confidence, the hybrid approach loses its advantage and we should switch to pure ML.
+- Per-attribute-type α weights may be more accurate than a single global α, since some attributes (e.g., `SUPPLY_VOLTAGE`) are inherently easier to predict than others (e.g., `DESCRIPTION`). The retraining pipeline can store learned per-type weights as configuration once Refinement 3 produces evidence.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-3 (predict with confidence)
+- **FRs:** FR-3 (per-attribute predictions with confidence scores)
+- **QASs:** QAS-1 (accuracy — hybrid provides usable accuracy floor under data scarcity); QAS-5 (reason codes from rules support drift interpretation)
+- **Constraints:** C-3, DC-1 (Python ML); C-4 (phase scope limits labeled data, favoring hybrid over pure ML)
+- **Scenarios:** SCEN-1 (high-confidence path), SCEN-2 (low-confidence path with reason codes)
diff --git a/docs/0004-route-confidence-decisions-at-attribute-level.md b/docs/0004-route-confidence-decisions-at-attribute-level.md
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--- /dev/null
+++ b/docs/0004-route-confidence-decisions-at-attribute-level.md
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+# ADR-004: Route Confidence Decisions at the Attribute Level, Not the Record Level
+
+## Status
+
+Accepted
+
+## Context
+
+The Routing Engine is the architectural component that enforces the accuracy quality attribute (QA-1, rated High/High). Every record produced by the Prediction Service contains multiple attributes, each with its own predicted value and confidence score. The team must decide whether confidence routing operates at the record level (the whole record is sent to review if any attribute is uncertain) or at the attribute level (each attribute is routed independently).
+
+Two alternatives were considered:
+
+- **Per-record routing.** Conceptually simpler. The review queue holds whole records, and writeback always emits complete records. There is no merge logic. However, a record with ten attributes and one uncertain value sends all ten attributes to review, inflating reviewer workload.
+- **Per-attribute routing.** Each attribute is routed independently. Estimated 3–5× lower review volume than per-record because only the attributes the model is unsure about reach the queue. The cost is structural: the writeback service must merge auto-accepted attributes with reviewed attributes for the same record before writing to PIMS, and there is a risk that correlated attributes (e.g., connection type and port size) become inconsistent if reviewed in isolation.
+
+The combined catalog team across eParts and Alps Controls is approximately 4.5 FTEs. Reviewer capacity is the binding constraint on review volume; if the system pushes too many items to review, the labor savings the platform is meant to provide disappear.
+
+## Decision
+
+We will route confidence decisions at the attribute level. The Human Review Queue is keyed on `(record_id, attribute_id)`. The Routing Engine compares each attribute's confidence score against the configured threshold independently. The Writeback Service batches all attributes for a given record and writes them to PIMS as a unit only once all routing paths for that record (auto-accept and review) have resolved.
+
+## Consequences
+
+- Review volume scales with actual model uncertainty rather than with record size, expected to reduce reviewer workload by 3–5× compared with per-record routing.
+- Reviewers see only the flagged attributes plus their source context, not the entire record. This focuses attention but means reviewers cannot easily catch inconsistencies between an auto-accepted attribute and one they are reviewing.
+- The Writeback Service carries merge logic. It must hold the complete record until all routing decisions for that record are resolved, then upsert it as a unit. A partial write — auto-accepted attributes entering PIMS before reviewed attributes are resolved — would produce incomplete records and is explicitly prevented by this batching.
+- Correlated attributes are a known risk. If connection type and port size are reviewed independently and the reviewer makes inconsistent choices, an internally inconsistent record can reach PIMS. Mitigation: the review interface presents the full record context to reviewers, but this has not been validated in practice. Refinement 2 in the project plan tests pairwise mutual information between attributes and inspects high-MI pairs.
+- Per-attribute thresholds may be required if attribute-level accuracy varies significantly. Some attributes are inherently easier to predict than others. The threshold mechanism is configurable (ADR-005) so per-attribute thresholds can be introduced without code changes.
+- If more than 30% of corrections turn out to involve cross-attribute consistency errors, the per-attribute routing decision should be reconsidered in favor of per-record or attribute-group routing.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-4 (Human Review Queue for low-confidence predictions)
+- **FRs:** FR-3 (per-attribute predictions); FR-4 (route below-threshold attributes to queue); FR-9 (per-attribute routing decisions)
+- **QASs:** QAS-1 (accuracy — per-attribute routing keeps review volume proportional to risk)
+- **Scenarios:** SCEN-2 (only the uncertain attribute is routed, not the whole record)
+- **Validation:** VAL-2 (low-confidence item appears in Human Review Queue)
diff --git a/docs/0005-externalize-confidence-threshold-as-configuration.md b/docs/0005-externalize-confidence-threshold-as-configuration.md
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--- /dev/null
+++ b/docs/0005-externalize-confidence-threshold-as-configuration.md
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+# ADR-005: Externalize the Confidence Threshold as Runtime Configuration
+
+## Status
+
+Tentative
+
+## Context
+
+The Routing Engine sends attributes with confidence above a threshold to auto-accept and attributes below the threshold to the Human Review Queue. The threshold is the most sensitive parameter in the system: it controls the tradeoff between accuracy (QA-1) and reviewer throughput. A threshold set too high pushes most attributes into review and overwhelms the catalog team, eliminating the labor savings the platform is meant to provide. A threshold set too low lets incorrect predictions through to PIMS, where they cause wrong parts to be ordered by contractors.
+
+The threshold cannot be set during design because no model has yet been run against production-representative data. The team currently uses a placeholder of 0.85 with no empirical support. Refinement 1 in the project plan calibrates the threshold against ≥200 labeled submissions using precision-recall curves between 0.50 and 0.99. Per-attribute variance in accuracy may also drive a per-attribute threshold table rather than a single global value.
+
+Hardcoding the threshold in the Routing Engine would require a code change and redeployment for every recalibration, which is incompatible with the iterative tuning the team expects across the pilot.
+
+## Decision
+
+The confidence threshold is externalized as runtime configuration read by the Routing Engine at startup. The configuration mechanism supports both a global threshold value and an optional per-attribute override table. Threshold changes take effect on application restart without any code change. The Routing Engine reads the threshold(s) once per pipeline run; threshold changes during a run do not affect already-routed attributes.
+
+## Consequences
+
+- The threshold can be retuned during pilot operation without engineering involvement beyond editing configuration and restarting the App Service.
+- Per-attribute thresholds are supported architecturally without further code changes. If Refinement 1 reveals that some attributes (e.g., `SUPPLY_VOLTAGE`) are reliably predicted at 0.75 while others (e.g., `DESCRIPTION`) need 0.92, the per-attribute table can be populated.
+- The threshold value is a configuration concern, not an architectural concern. This means that the architecture cannot guarantee an accuracy number; it can only guarantee that whatever threshold is set will be applied consistently. The actual accuracy guarantee depends on operational discipline around configuration management.
+- Configuration drift is a risk. If the threshold is changed in production without recording the change in the audit trail, later analyses of model accuracy or reviewer workload may be impossible to interpret. The audit trail (ADR-009) records the threshold value alongside each routing decision to mitigate this.
+- The decision is tentative because the threshold itself is unsupported. Once Refinement 1 produces evidence and a value is selected, this decision moves to Accepted.
+- This decision interacts with monitorability (ADR-012): the threshold value is one of the baselines against which drift is measured. Changing the threshold resets the baseline.
+
+## Requirements Traceability
+
+- **FRs:** FR-4 (route based on threshold); FR-7 (configurable thresholds, calibration TBD); FR-9 (per-attribute routing using configurable thresholds)
+- **QASs:** QAS-1 (accuracy lever); QAS-5 (threshold value is part of the drift baseline)
+- **Scenarios:** SCEN-1 (above-threshold auto-accept), SCEN-2 (below-threshold review)
+- **Validation:** VAL-2 (threshold drives routing behavior tested by VAL-2)
diff --git a/docs/0006-enforce-idempotent-pims-writeback-via-natural-key.md b/docs/0006-enforce-idempotent-pims-writeback-via-natural-key.md
new file mode 100644
index 0000000..1ac5c80
--- /dev/null
+++ b/docs/0006-enforce-idempotent-pims-writeback-via-natural-key.md
@@ -0,0 +1,40 @@
+# ADR-006: Enforce Idempotent PIMS Writeback via a Composite Natural Key
+
+## Status
+
+Accepted
+
+## Context
+
+The platform writes approved product attributes to PIMS staging tables on SQL Server. PIMS exposes no writeback API and provides no rollback or transactional guarantees back to the platform. Retries of a writeback operation must not produce duplicate records, because duplicates in PIMS staging propagate into wrong bills of materials for contractor orders.
+
+Several mechanisms were considered:
+
+- **Application-side primary key check.** Read-before-write to detect existing records.
+- **Database upsert via composite natural key.** A SQL `MERGE` (or equivalent) keyed on a stable identifier matches existing rows and updates them rather than inserting duplicates.
+- **Distributed transaction across the platform and PIMS.** Not feasible: PIMS is owned by a different team, has no API, and there is no distributed transaction coordinator across the trust boundary.
+- **Idempotency token in PIMS.** Would require schema change in PIMS, which the platform team does not control.
+
+The submission ID is a composite of the company identifier and the product identifier, making it stable across submissions: a new update pushed for the same company–product pair carries the same submission ID. The attribute ID is a stable canonical attribute identifier. Together, `(submission_id, attribute_id)` uniquely identify any value the platform writes. Both are generated inside the platform and stored in the staging tables before the writeback runs.
+
+## Decision
+
+PIMS writeback uses a composite natural key of `(submission_id, attribute_id)`, where `submission_id` is itself derived from `(company_id, product_id)`. The Publish/Sync Job (Azure Function) executes an upsert against the PIMS staging table: if a row with the same key exists, the value is updated in place; otherwise a new row is inserted. Because the submission ID is stable for a given company–product pair, pushing a new update for the same product produces the same key and overwrites the prior values rather than inserting a duplicate. The natural key is generated and stored in the platform's own staging tables before writeback, so a retry of the writeback also uses the identical key and matches the same target row.
+
+## Consequences
+
+- Retries of the Publish/Sync Job are safe. A network failure mid-run, a transient PIMS outage, or a redeployment that interrupts the job can be recovered by simply running the job again.
+- Idempotency is enforced in application code, not in PIMS. If PIMS staging tables are altered (e.g., the natural key columns are dropped or renamed), the guarantee disappears silently. The integration test described in Refinement 4 verifies the schema before any production data is written.
+- This decision depends on a structural assumption about PIMS staging that has not yet been validated. Jake at eParts has not delivered the P1-C schema. If the staging tables use wide columns (one row per record with attribute values as columns) rather than tall columns (one row per attribute), the natural key strategy needs a translation layer. If the staging tables lack columns to hold the platform's natural key, the team must either negotiate a schema addition with eParts or maintain a team-owned buffer table that holds the mapping.
+- No rollback is possible. Once a row is upserted into PIMS staging, the only way to "undo" it is to write a corrected row with the same natural key. This is acceptable because every write goes through human review or auto-accept above a calibrated threshold; the system never writes silently uncertain data.
+- This decision interacts with ADR-004 (per-attribute routing). The natural key is keyed on `attribute_id`, not on `record_id`, which is what enables per-attribute routing to write attributes individually as they resolve. If routing were per-record, the natural key would only need `record_id`.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-5 (write approved data to PIMS staging)
+- **FRs:** FR-8 (idempotent application-layer writeback); FR-11 (natural key: submission ID + attribute ID)
+- **DRs:** DR-3 (Must — retry must not create duplicates)
+- **QASs:** QAS-1 (accuracy — prevents duplicate-driven errors); QAS-4 (availability — safe retry on recovery)
+- **Constraints:** C-2 (no PIMS API), C-5 (no direct production writes — writeback targets staging only)
+- **Scenarios:** SCEN-1 (Step 5), SCEN-2 (Step 6)
+- **Validation:** VAL-3 (upsert + no-duplicate on retry)
diff --git a/docs/0007-use-attribute-row-canonical-schema.md b/docs/0007-use-attribute-row-canonical-schema.md
new file mode 100644
index 0000000..1e05d1d
--- /dev/null
+++ b/docs/0007-use-attribute-row-canonical-schema.md
@@ -0,0 +1,38 @@
+# ADR-007: Use an Attribute-Row Canonical Schema for the Staging Table
+
+## Status
+
+Accepted
+
+## Context
+
+The Normalization stage transforms heterogeneous supplier formats (CSV, PDF, email-extracted key-value pairs) into a canonical structure that the Prediction Service, Routing Engine, and Writeback Service can consume uniformly. The shape of this canonical schema is an architecturally significant decision because it determines how much work it takes to add a new product category, how easily attributes can be routed individually, and how the staging tables grow over time.
+
+The current scope is valves and actuators, but the client (Harsha) has stated that category expansion is expected after the pilot. Quality attribute QA-3 (Modifiability — new category) is rated Medium/Medium and explicitly requires that adding a category not force a structural change to routing or writeback.
+
+Two structural options were considered:
+
+- **Wide schema (one row per record).** Each record is a single row with one column per attribute (`voltage`, `port_size`, `connection_type`, etc.). Adding a new category requires schema migration: new columns, ALTER TABLE statements, and coordination with any system that reads the staging table. Querying a single record is trivial. Per-attribute routing is awkward because attribute-level state (confidence score, routing decision) would need parallel columns for every attribute.
+- **Tall schema (one row per attribute).** Each row is `(record_id, attribute_id, raw_value, predicted_value, confidence, routing_status)`. Adding a new attribute is a data change (a new entry in the attribute reference table), not a schema change. Per-attribute routing is direct: routing status is a column on the row.
+
+## Decision
+
+The canonical staging schema is attribute-row: each row represents one attribute of one record. The columns include `submission_id`, `record_id`, `attribute_id`, `supplier_raw_value`, `predicted_value`, `confidence_score`, `routing_status`, and audit metadata. Attribute definitions (name, type, allowed values, category) live in a separate reference table joined as needed. New product categories are added by inserting attribute definitions into the reference table, not by altering the staging schema.
+
+## Consequences
+
+- Adding a new product category does not require a schema migration against the staging tables. The Normalization stage gains new mapping entries; the Prediction Service is retrained on the expanded label set; nothing in the Routing Engine, Review Queue, or Writeback Service changes structurally.
+- Per-attribute routing (ADR-004) becomes natural. Each row carries its own routing state, so the Routing Engine reads and updates one row at a time without joining against a wide record schema.
+- Per-attribute audit is also natural. The audit trail can reference a single attribute row by its primary key.
+- Querying a complete record requires a join or aggregation across multiple rows. This is a small loss in query convenience and is acceptable because the platform's hot-path queries are per-attribute (routing, scoring, review), not per-record.
+- The staging tables grow faster than they would under a wide schema (one row per attribute rather than one row per record). For valves and actuators with roughly a dozen attributes, this is a 12× row-count multiplier. Azure SQL Database is sized to handle this comfortably at expected ingestion volumes.
+- This schema decision is independent of the PIMS staging schema. ADR-006 covers the writeback contract with PIMS, which may use either a wide or tall structure. If PIMS is wide, the Writeback Service performs an aggregation transform from the platform's tall canonical schema into the wide PIMS schema; this is documented as an open dependency on Refinement 4.
+- If Refinement 4 reveals that PIMS staging is rigidly wide and the team-owned mapping is too costly to maintain, the platform may keep its internal canonical schema tall while presenting a wide interface to PIMS through the Publish/Sync Job. The architecture supports this.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-2 (normalize to standardized structure)
+- **FRs:** FR-2 (canonical schema before prediction); FR-11 (attribute-level natural key requires attribute-row schema)
+- **QASs:** QAS-3 (new category as data change, not schema migration)
+- **Constraints:** C-4 (phase scope expansion); C-6 (pricing excluded from canonical schema)
+- **Scenarios:** SCEN-1 (Step 3 — canonical normalization)
diff --git a/docs/0008-deploy-platform-as-single-azure-app-service-unit.md b/docs/0008-deploy-platform-as-single-azure-app-service-unit.md
new file mode 100644
index 0000000..f460043
--- /dev/null
+++ b/docs/0008-deploy-platform-as-single-azure-app-service-unit.md
@@ -0,0 +1,40 @@
+# ADR-008: Deploy the Platform as a Single Azure App Service Unit
+
+## Status
+
+Accepted
+
+## Context
+
+The platform must be deployed on Azure (a fixed client constraint) and must be operable by a five-person capstone team across one academic year. Quality attributes that bear on deployment topology are QA-2 (model swappability), QA-4 (availability under Prediction Service outage), and a team-size constraint that bounds operational complexity.
+
+Two topologies were analyzed in detail:
+
+- **Single Azure App Service (Python).** All pipeline components — ingestion, normalization, prediction, routing, review-queue access, writeback orchestration — run in one process and one deployment unit. Azure SQL Database holds staging tables, the review queue, and the audit trail. Azure Blob Storage archives raw supplier files. The Publish/Sync Job runs as a separate timer-triggered Azure Function. Components communicate by function call. Scaling is per application unit.
+- **Microservices (Azure Container Apps).** Three independent services: Ingestion+Normalization, Prediction, Routing+Writeback. Each scales independently, can be deployed independently, and can fail independently. Inter-service communication is HTTP or Service Bus. Operational requirements include container orchestration, distributed tracing, service-to-service authentication, and three deployment pipelines.
+
+The microservices alternative offers fault isolation and independent scaling, both of which are real benefits for a production system. They are not benefits the current team can absorb operationally during the capstone phase. Distributed tracing alone would consume a substantial fraction of the timeline. The Prediction Service does not currently need GPU instances or independent scaling because supplier ingestion is batched, not real-time.
+
+## Decision
+
+The platform is deployed as a single Azure App Service running Python. All pipeline components live in one process. Azure SQL Database holds all internal pipeline state (staging tables, Human Review Queue, audit trail). Azure Blob Storage archives raw supplier files. The Publish/Sync Job is a timer-triggered Azure Function deployed separately. Inbound channels are SFTP (polled), email (polled), and HTTPS upload. Outbound to PIMS is via `pyodbc` across the trust boundary to PIMS SQL Server. Outbound telemetry to Datadog is fire-and-forget HTTPS.
+
+## Consequences
+
+- One deployment, one log stream, one health check. Operational complexity is bounded.
+- Components communicate by function call. This is fast and avoids the complexity of network serialization, retries, and timeouts between filters.
+- Fault isolation is reduced. A bug in any component can crash the App Service and take the entire pipeline down. The persistent staging tables and review queue mitigate data loss risk: in-flight work survives a process restart because state is in Azure SQL, not in-memory.
+- Independent scaling is not available. If the Prediction Service becomes a hotspot, the entire App Service must be scaled up.
+- The module boundaries inside the App Service (described in the module view) are deliberately drawn where service boundaries would go in a microservices deployment. The `prediction` package, `routing` package, and `writeback` package are independent units of code that communicate through typed data contracts. Transitioning to microservices later is therefore adding HTTP serialization at existing boundaries, not rewriting business logic.
+- Datadog telemetry is fire-and-forget. Telemetry failures do not block the pipeline. This means a Datadog outage cannot cause a pipeline outage, but it also means dropped telemetry is not retried; operationally significant signals must also be persisted in the audit trail (ADR-009).
+- The Publish/Sync Job is intentionally separated as an Azure Function on a timer trigger so that PIMS writeback runs on a controlled schedule rather than synchronously with each ingestion. This decouples PIMS load from supplier ingestion bursts.
+- Trigger for reconsideration: production handoff to a larger eParts team, or a Prediction Service that scales independently of ingestion (e.g., GPU-backed inference, multi-model ensembles). At that point, the prediction package is the natural first candidate for extraction into a Container App.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-1 (ingestion endpoints hosted on App Service); HLR-5 (Publish/Sync Azure Function)
+- **FRs:** FR-1 (Ingestion Gateway runs on App Service); FR-8 (Publish/Sync Function performs writeback); FR-10 (Azure SQL hosts the persistent queue); FR-13 (Azure Blob hosts raw file archive)
+- **DRs:** DR-1 (Blob archive is part of deployment topology)
+- **QASs:** QAS-4 (staging tables in Azure SQL provide outage buffering)
+- **Constraints:** C-1 (Azure managed services); C-3 / DC-1 (Python App Service); C-7 (single unit chosen over microservices for capstone timeline); DC-3 (Blob Storage archive)
+- **Validation:** VAL-1, VAL-3 (deployed components host the tested behavior)
diff --git a/docs/0009-implement-human-review-queue-as-database-table.md b/docs/0009-implement-human-review-queue-as-database-table.md
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+# ADR-009: Implement the Human Review Queue as a Persistent Database Table
+
+## Status
+
+Accepted
+
+## Context
+
+When the Routing Engine sends a low-confidence attribute to human review, that attribute must wait until a reviewer at eParts or Alps Controls processes it. Reviewer pace is much slower than machine pace: predictions arrive in batches measured in seconds, while reviewer decisions accumulate over hours or days. The queue must therefore decouple machine throughput from reviewer availability.
+
+Two queue mechanisms were considered:
+
+- **In-memory queue or message broker (e.g., Azure Service Bus).** Standard for high-throughput producer/consumer decoupling. Survives normal load patterns but adds an external dependency, requires a consumer process polling for items, and does not naturally support the spreadsheet-style batch review workflow that catalog staff already use.
+- **Persistent database table in Azure SQL.** The queue is a table with `(submission_id, attribute_id, predicted_value, confidence, reason_codes, status, reviewer_id, decided_at, corrected_value)`. Reviewers query the table through eParts' existing internal review interface, which already speaks SQL.
+
+The catalog team already accesses internal staging tables through a spreadsheet-style tool. Building a custom review UI is out of scope for the current phase. The existing internal interface reads directly from staging tables, which means the queue must be a table accessible from that tool.
+
+The queue must also feed retraining: every reviewer decision is a labeled example, and the audit trail layer relies on durable storage of reviewer corrections.
+
+## Decision
+
+The Human Review Queue is implemented as a persistent table in Azure SQL Database. Low-confidence attributes are inserted with `status = 'pending'`. Reviewers access the table through eParts' existing internal review interface, edit values individually or in batch, and submit decisions by updating the `status` to `'approved'` or `'rejected'` and writing the `corrected_value`. On each decision, a row is appended to the audit trail. A notification is sent to the catalog team when items are pending and again when items are processed.
+
+## Consequences
+
+- Reviewer pace is fully decoupled from prediction pace. The queue can hold thousands of pending items without backpressure on the upstream pipeline.
+- The queue survives App Service restarts and Prediction Service outages. In-flight reviews are preserved across deployments. This directly supports QA-4 (availability).
+- The queue is the persistent store for labeled corrections. The retraining pipeline reads from the audit trail (which captures the history of queue decisions) without coordinating with a separate label store.
+- The schema of the queue table is a coupling point with eParts' existing internal review interface. Any change to column names, types, or status values requires coordination with the eParts engineering team. This is a recorded constraint on schema evolution.
+- Rejected items are not silently dropped. A rejection writes the corrected value back to the queue row with `status = 'rejected'`, appends to the audit trail, and triggers a notification. The corrected value flows into the labeled correction store for retraining.
+- The queue is not a true message broker, so it does not provide push-style notification, dead-letter queues, or consumer load balancing. These features are not needed because there is no automated consumer; the consumer is the catalog team.
+- If a custom review UI is built in a future phase, Auth0 (the eParts identity provider per the SOW) integrates at the UI layer and reads from the same queue table. The queue's stable schema is what makes that future UI buildable without changes to the ingestion, prediction, or writeback components.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-4 (persistent Human Review Queue)
+- **FRs:** FR-4 (queue is the destination for low-confidence attributes); FR-5 (queue retains prediction, confidence, source ref, status); FR-10 (persistent and queryable)
+- **QASs:** QAS-4 (queue survives Prediction Service outages)
+- **Constraints:** C-8, DC-2 (queue's stable schema accommodates a future Auth0-gated UI without changes elsewhere)
+- **Scenarios:** SCEN-2 (Steps 3–5)
+- **Validation:** VAL-2 (item appears in Human Review Queue)
diff --git a/docs/0010-maintain-append-only-audit-trail.md b/docs/0010-maintain-append-only-audit-trail.md
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+# ADR-010: Maintain an Append-Only Audit Trail of Every Pipeline Decision
+
+## Status
+
+Accepted
+
+## Context
+
+The platform automates a workflow that previously required human judgment at every step. Two needs follow from this:
+
+1. **Compliance and traceability.** When a wrong product attribute reaches PIMS, eParts needs to determine why: which model version produced the prediction, what confidence the model emitted, whether a reviewer saw the item, and what the reviewer's decision was. Without this trail, root cause analysis is impossible.
+2. **Model improvement.** The retraining pipeline (described in the MLOps section of the report) depends on labeled corrections. Reviewer decisions are the primary source of labels. The system must capture the original prediction, the original confidence, the source supplier, and the corrected value as a durable record.
+
+Quality attribute QA-5 (Monitorability) is rated High/High and depends on having a record of every routing and review decision over time so that drift in correction rates can be detected.
+
+A mutable record (overwriting the prediction with the corrected value) would satisfy the immediate writeback need but lose the history needed for audit and retraining. An append-only log preserves both.
+
+## Decision
+
+Every pipeline decision is recorded as a row in an append-only audit trail table in Azure SQL Database. Decisions captured are: auto-accept by the Routing Engine, approval by a reviewer, correction by a reviewer (with the corrected value alongside the original prediction), and rejection by a reviewer. Each row contains the submission ID, attribute ID, source supplier, model version, original predicted value, confidence score, threshold value at decision time, final decision, decided value, decision actor (system or reviewer ID), and timestamp. Rows are never updated or deleted.
+
+## Consequences
+
+- Every value written to PIMS is traceable back to the prediction, the confidence, the threshold, and the reviewer (if any) that produced it.
+- The audit trail is the source of truth for retraining. Corrections where the reviewer's value differed from the model's prediction are flagged as labeled training examples and read by the retraining job (ADR-011).
+- The model version recorded on each row is essential for retraining safety. When a new model version is promoted, the audit trail allows the team to compare correction rates before and after promotion as a check on regression.
+- The audit trail is the basis for drift detection in Datadog (ADR-012). Per-attribute confidence distributions and reviewer correction rates are computed from this table.
+- Append-only growth is unbounded. The table will require a retention policy (cold storage to Azure Blob after some period) once production volumes are observed. This is operationally acceptable in the current phase because volumes are low.
+- The audit trail is internal to the platform. PIMS does not see it. If PIMS needs an audit record alongside a value, the writeback service includes audit metadata in the upsert; the platform's internal audit trail is the canonical record.
+- Reviewer privacy: the reviewer ID is recorded. This is acceptable under eParts' internal policies because the catalog team is salaried staff acting in their official capacity. If the audit trail were ever exposed externally, reviewer IDs would need to be redacted.
+
+## Requirements Traceability
+
+- **FRs:** FR-6 (log every auto-accept, approval, correction, rejection); FR-12 (audit trail backs telemetry signals)
+- **DRs:** DR-2 (Future/TBD — corrected data logged for retraining)
+- **QASs:** QAS-5 (audit trail is the durable source for drift signals)
+- **Scenarios:** SCEN-2 (Step 5 — correction logged)
diff --git a/docs/0011-trigger-retraining-automatically-on-batch-completion.md b/docs/0011-trigger-retraining-automatically-on-batch-completion.md
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+# ADR-011: Trigger Retraining Automatically on Human Review Batch Completion
+
+## Status
+
+Proposed
+
+## Context
+
+The Prediction Service must improve over time as supplier data changes and as the labeled corpus grows. Reviewer corrections are the primary source of labeled examples. The architectural choice is the trigger mechanism that initiates a retraining run.
+
+Three alternatives were considered:
+
+- **Manual trigger.** An engineer reviews the accumulated corrections, judges that enough new examples exist, runs the training script, evaluates the result, and promotes the new model if it improves on the previous version. Requires no automation but depends entirely on engineer availability and judgment. Poor fit for a five-person capstone team that cannot guarantee weekly engineer cycles.
+- **Automatic trigger on review batch completion.** A retraining job fires automatically each time a human review batch is marked complete. The new model version is evaluated against a held-out validation set and promoted only if it outperforms the current version. No engineer initiates the run.
+- **Scheduled trigger.** Retraining runs on a fixed cadence (weekly or monthly) regardless of review activity. Predictable, but introduces a fixed lag between when corrections are made and when the model learns from them. Risks training on too few examples if review activity is light, or accumulating too many examples if review activity is heavy.
+
+In all cases, a validation gate is required: a new model version must outperform the current version on a held-out validation set before it is promoted. Without this gate, automatic retraining could promote regressions silently.
+
+## Decision
+
+Retraining is triggered automatically when a human review batch is marked complete. The retraining job reads all corrections flagged as labeled examples since the last training run from the audit trail (ADR-010), combines them with the existing labeled dataset, and trains a new version of the active prediction strategy. The new version is evaluated against a held-out validation set. If validation accuracy improves, the new version is promoted as the active model behind `PredictionServiceInterface` (ADR-002). If it does not improve, the previous version remains active and the result is logged for engineering review. Model version history is stored in Azure Blob Storage with training date, example count, and validation accuracy as metadata.
+
+## Consequences
+
+- The model learns from corrections as soon as a batch is reviewed, with no engineer in the loop. This is the fastest path from a reviewer correction to an improved model.
+- The validation gate prevents silent regressions. A worse model is never promoted automatically; it is logged for human review.
+- Promotion is transparent to the rest of the pipeline. The Routing Engine, Writeback Service, and Review Queue see the prediction service as unchanged because `PredictionServiceInterface` does not change with model version.
+- Rollback is supported. Each model version is tagged in Azure Blob Storage. If a promoted version is later found to perform poorly on production data, engineering can revert by changing the active model pointer in configuration without redeploying the application.
+- A minimum batch size before triggering retraining is required to avoid training on sparse data. The minimum example count has not been set and will be established once Refinement 1 produces real review-batch sizes. Until then, this decision is Proposed.
+- The validation set must remain representative. If the validation set drifts from production data, the gate becomes meaningless because a model that overfits to stale validation can pass the gate while degrading on real inputs. The validation set itself must be refreshed periodically; this operational discipline is a dependency of the retraining decision.
+- Frequent retraining on small batches can produce unstable model versions even with a validation gate, because validation accuracy itself fluctuates on small evaluation sets. If observed, the trigger should be replaced with a hybrid: scheduled retraining with a minimum-correction-count gate.
+- Engineering team capacity post-handoff may make manual triggering attractive again. A larger team with regular review cycles may want explicit human oversight on every promotion. The retraining package is decoupled enough from the rest of the pipeline that switching to manual triggering is a configuration change.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-3 (prediction quality maintained over time)
+- **DRs:** DR-2 (Future/TBD — corrected data logged for future retraining and offline model improvement)
+- **QASs:** QAS-2 (retraining promotes new versions through PredictionServiceInterface without breaking dependents); QAS-5 (closes the loop from drift detection to model improvement)
+- **Constraints:** C-3, DC-1 (retraining runs in the Python prediction package)
diff --git a/docs/0012-emit-stage-by-stage-telemetry-to-datadog.md b/docs/0012-emit-stage-by-stage-telemetry-to-datadog.md
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+# ADR-012: Emit Stage-by-Stage Telemetry to Datadog for Drift Detection and Operational Monitoring
+
+## Status
+
+Proposed
+
+## Context
+
+ML systems can degrade silently as supplier data drifts from the training distribution. Without monitoring, incorrect auto-accepts accumulate in PIMS and surface only when contractors order wrong parts. Quality attribute QA-5 (Monitorability) is rated High/High both in importance (because silent degradation is the worst failure mode) and in difficulty (because the team has not yet defined what metrics to track or what baseline to compare against).
+
+eParts uses Datadog as its observability platform, so integration is mandatory rather than chosen. The architectural questions are: where in the pipeline should telemetry be emitted, what signals should be captured, and how should those signals be tied to drift detection.
+
+Telemetry must not block the pipeline. A Datadog outage cannot be allowed to take ingestion or writeback offline.
+
+## Decision
+
+Telemetry is emitted to Datadog from four pipeline stages over fire-and-forget HTTPS:
+
+- **Ingestion Gateway:** ingestion success and failure counts, parsed by supplier and channel.
+- **Normalization (Structured Layer):** row counts after canonical schema mapping, broken down by supplier and category.
+- **Prediction Service:** per-attribute confidence score distributions and rule-vs-embedding contribution breakdown.
+- **Routing Engine:** routing split ratios (auto-accept vs. review) per attribute.
+- **Review Queue:** reviewer decision counts (approved, corrected, rejected) and correction rates per attribute.
+
+Telemetry calls do not block the pipeline; failed Datadog writes are logged locally and dropped. Operationally significant signals that must not be lost are also persisted in the audit trail (ADR-010), so Datadog is treated as a dashboard and alerting layer, not as the system of record.
+
+Drift detection thresholds (e.g., "alert when correction rate increases by 10% over a rolling two-week window" or "alert when mean confidence shifts by 15%") are defined as configuration on Datadog and validated empirically once Refinement 1 has produced a baseline.
+
+## Consequences
+
+- The pipeline emits the right signals to detect drift. Confidence distributions reveal model overconfidence or underconfidence; correction rates reveal accuracy degradation; routing split ratios reveal threshold drift.
+- Drift detection is operationally complete only when thresholds are defined. The architecture emits the signals; it cannot yet say what deviation from baseline constitutes actionable drift. Refinement 6 in the project plan defines and validates these thresholds against simulated drift.
+- Telemetry is tied to the audit trail. Reviewer correction rates in Datadog are computed from the same decisions recorded in the audit trail, so the dashboard and the system of record cannot diverge.
+- Datadog outages do not affect pipeline correctness. A telemetry failure is logged locally and the pipeline continues. This is acceptable because the audit trail is the source of truth; the dashboard is a derived view.
+- Because telemetry is fire-and-forget, telemetry packets can be lost during a Datadog outage without retry. This means short-term metrics (e.g., a one-hour confidence distribution) may have gaps during incidents. Long-term metrics computed from the audit trail are unaffected.
+- Per-supplier telemetry is captured because supplier-specific drift is a likely failure mode (a supplier changes its catalog format, the model's confidence drops, but the threshold doesn't catch it). Per-supplier dashboards in Datadog allow drift to be localized to the offending supplier.
+- This decision is Proposed rather than Accepted because the alert thresholds and baselines are not yet defined. Once Refinement 1 and Refinement 6 produce values, this decision moves to Accepted.
+
+## Requirements Traceability
+
+- **FRs:** FR-12 (emit confidence distributions, correction rates, routing decisions, pipeline metrics to Datadog)
+- **QASs:** QAS-5 (drift detection from baseline deviation in confidence and correction rates)
+- **Constraints:** C-1 (Datadog runs over HTTPS from Azure App Service)
diff --git a/docs/0013-establish-etim-reference-data-layer.md b/docs/0013-establish-etim-reference-data-layer.md
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+# ADR-013: Establish a Release-Versioned ETIM Reference Data Layer Owned by Ingestion
+
+## Status
+
+Accepted
+
+## Context
+
+The platform is adopting ETIM as the classification standard for catalog standardization (valves and actuators in phase one). ETIM is a controlled technical dictionary: product groups (EG), product classes (EC), features (EF), feature groups (EFG), units (EU), and controlled values (EV), plus the mappings that say which features belong to a class and which values are allowed for a class-feature. ETIM is not supplier data — it provides no SKUs, prices, or product documents. Before the platform can match any supplier product to ETIM (class matching, feature matching, value matching, validation), it needs the ETIM dictionary loaded, queryable, and under version control.
+
+The supplied ETIM data has awkward physical characteristics that make it a poor fit for ad-hoc loading: the production archive is a set of CSV files encoded **UTF-16 little-endian, semicolon-delimited**, for a specific release (10.0) and language (EI, English International). ETIM publishes new releases over time, and class/feature/value definitions change between releases, so a single un-versioned copy would silently conflate releases and make historical mappings unauditable.
+
+A key question was **ownership**: the reference loader could sit in the ML/matching component (the primary consumer) or in ingestion (which already owns file parsing, encoding handling, idempotent batch loads, and Alembic migrations). Two further options for storage shape were considered:
+
+- **Denormalized blob / JSON document per class.** Fast to load and to read a whole class, but cannot enforce referential integrity, makes cross-class queries (e.g. "all classes using feature EF000513") expensive, and couples readers to a single release's shape.
+- **Normalized relational tables mirroring the ETIM model**, scoped by release ID. Enforces FKs and composite keys, supports multi-release coexistence, and lets the matcher query class→feature→value relationships directly.
+
+## Decision
+
+We will model ETIM as a **normalized relational reference layer of ten tables**, every row scoped by a release identifier, and we will make the **ingestion team the owner** of both the schema and the import job.
+
+The tables are `etim_release`, `etim_group`, `etim_class`, `etim_class_synonym`, `etim_feature_group`, `etim_feature`, `etim_unit`, `etim_value`, `etim_class_feature`, and `etim_class_feature_value`, with composite primary keys on `(etim_release_id, …)` so that multiple ETIM releases can coexist without collision. The release identifier is a stable, human-readable string of the form `ETIM-{version}-{language}` (e.g. `ETIM-10.0-EI`).
+
+A dedicated **ETIM Reference Loader** import job reads the UTF-16 LE, semicolon-delimited CSV archive, validates that the expected columns are present per file, rejects incomplete or release-mismatched archives, and loads the rows into the reference tables. It records the release version, language, source name, an import timestamp, and a **SHA-256 checksum over the archive**. Re-importing the same release is **idempotent** (no-op when the checksum matches; controlled replace only with an explicit `--force`). The job is exposed as a CLI entry point (`eparts etim import …`) mirroring the existing Typer CLI, and is delivered as Alembic migration `0005_create_etim_reference` plus `etim/loader.py`, `models/etim.py`, and `cli/etim.py`.
+
+This decision is implemented and verified against the real ETIM 10.0 EI archive (EPARTS-285).
+
+## Consequences
+
+- The matcher (ETIM class/feature/value matching) can treat ETIM as a stable, queryable dependency. Loading the dictionary is no longer entangled with matching logic, so the two can evolve independently.
+- Release versioning is first-class. Because every row is keyed by `etim_release_id`, a future ETIM 11.0 can be loaded alongside 10.0, and any product's mapping can name the exact release it was matched against. This is a prerequisite for governed ETIM upgrades (an open client decision in the brief).
+- Idempotent, checksummed import makes the load safe to re-run in CI and across environments without producing duplicates or partial state. A mismatched or truncated archive is rejected with a clear error rather than loaded silently.
+- Placing ownership in ingestion reuses existing strengths (encoding handling, batch idempotency, Alembic, the Typer CLI) and keeps the file-handling concerns in the team that already does file handling. The cost is a coordination point: the matching team consumes a schema that ingestion owns, so reference-table changes require a published contract.
+- The reference layer is read-mostly and modest in size (~160 groups, ~5,600 classes, ~17,000 features, ~16,000 values, ~200,000 class-feature-value links for 10.0 EI). Normalized storage on the current Postgres stack handles this comfortably.
+- ETIM does not supply a client-ready "required field" flag. The reference layer deliberately stores ETIM as published and leaves required/recommended/optional policy to a separate client policy overlay (`catalog_feature_policy`, owned downstream). This ADR does not cover that overlay.
+- The loader currently targets the CSV archive only. The Excel workbook (useful for analyst review and metric/imperial crosswalks) is intentionally out of scope for production import.
+
+## Requirements Traceability
+
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` (ETIM Reference Loader, ETIM Reference Tables, Acceptance Criteria 1)
+- **Tickets:** EPARTS-285 (Create ETIM reference schema and import job — Done); EPARTS-275 (ETIM research); parent EPARTS-154 (Ingestion)
+- **Implements:** ETIM reference schema, release tracking, idempotent import, golden row-count validation
+- **Related ADRs:** ADR-014 (staging split consumes the reference layer for matching); ADR-015 (Postgres-now datastore the tables are built on); ADR-007 (the prior canonical-schema decision this complements)
diff --git a/docs/0014-emit-source-preserving-product-attribute-staging-split.md b/docs/0014-emit-source-preserving-product-attribute-staging-split.md
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index 0000000..de99236
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+# ADR-014: Emit a Source-Preserving Product + Attribute Staging Split
+
+## Status
+
+Accepted
+
+## Context
+
+ETIM standardization rests on a core principle: **original supplier data is evidence; ETIM data is a standardized interpretation laid on top; confidence is how sure the system is about that interpretation.** For this to hold, ingestion must hand the matching stage data that (a) separates a *product* (the sellable SKU) from its *attributes*, and (b) preserves every original value together with where it came from — file, page, row, raw text, raw unit — so that any later ETIM mapping can be traced back to its source.
+
+Today ingestion emits a single flat `IngestedRecord` (one row per source record, with `raw_fields` as a JSONB bag). That shape preserves source vocabulary but does not express product-vs-attribute granularity, gives attributes no individual identity, and has nowhere to carry per-attribute evidence (source page/row) or per-attribute confidence. It also forces every downstream consumer to re-derive product and attribute structure from an untyped blob.
+
+There is also a real granularity mismatch across sources that the staging shape must absorb: a **CSV row** is naturally one product with many attribute *columns*, whereas a **datasheet PDF** is one product (a SKU) with many attribute *rows* extracted from the document. Both must land in the same canonical staging shape.
+
+Options considered:
+
+- **Keep the flat `IngestedRecord`** and let the matcher split product/attributes from the JSONB bag. Smallest ingestion change, but pushes structure-recovery and evidence-tracking into every consumer, and gives attributes no stable identity for per-attribute routing, confidence, or audit.
+- **One wide staging row per product** with attributes as columns. Convenient for whole-product reads, but cannot carry per-attribute evidence/confidence without parallel columns, and reintroduces schema migration for every new attribute.
+- **A two-table split: `staging_product` + `staging_raw_attribute`** (one product row; one evidence row per attribute). Each attribute row carries its own source evidence and confidence and has a stable identity. This matches the brief's staging model and is the natural input to per-attribute ETIM matching, routing, and audit.
+
+## Decision
+
+Ingestion will emit a **product + attribute split**: a `staging_product` row per sellable SKU and a `staging_raw_attribute` row per attribute, replacing the flat `IngestedRecord` as the output contract.
+
+`staging_product` carries product identity and provenance: `supplier_id`, `supplier_sku`, `manufacturer`, `supplier_category`, `description`, `source_file_id`, `submission_id`, `processing_status`. Product identity for idempotency is `supplier_id + supplier_sku` (per source). `staging_raw_attribute` carries one row of evidence per attribute: `product_id`, `source_attribute_name`, `source_value`, `source_unit`, `source_text`, `source_page`, `source_row_number`, and `source_confidence`. Attribute identity for idempotency is `product_id + source_attribute_name`. Both tables are written with idempotent upserts, batched in one transaction per product, preserving the existing raw-bytes archival and quarantine paths unchanged.
+
+Which source fields populate product identity versus become attribute rows is **declared per source** via `ProductMapping` on the source/parser config (`sku_field`, `manufacturer_field`, `category_field`, `description_field`, `unit_field`, `attribute_fields`, `exclude_fields`), so the CSV-column and PDF-row granularities both resolve to the same staging shape without code changes per source.
+
+Crucially, ingestion **does not interpret** these values into ETIM. No field renaming, no ETIM class/feature/value assignment, no unit conversion happens here — those belong to the ETIM-aware matching stage, which reads staging and writes its results to its own tables (e.g. `matched_product_attribute`). ETIM must never overwrite ingestion's source-preserving output.
+
+The legacy flat `IngestedRecord` path is retired after cutover (deprecate or dual-write during transition; tracked by EPARTS-302). Until a source declares a `ProductMapping`, it continues on the legacy flat path.
+
+## Consequences
+
+- Nothing from the supplier catalog is lost or flattened. Every value is individually addressable and traceable to file/page/row/raw-text, which is the evidence backbone the entire ETIM story depends on.
+- Per-attribute identity makes per-attribute confidence (ADR-005/ADR-004 routing), per-attribute ETIM matching, and per-attribute audit natural — each is keyed on a real attribute row rather than reconstructed from a blob.
+- The product/attribute boundary is configuration, not code. New sources and formats are onboarded by declaring a mapping; the CSV-vs-datasheet granularity difference is absorbed in config.
+- Row counts grow relative to the flat shape (one row per attribute rather than one per record). For valve/actuator products with ~12–40 attributes this is a sizeable multiplier; the current Postgres stack (ADR-015) handles expected volumes, with indexes for product lookups.
+- This **supersedes the staging design in ADR-007** in practice. ADR-007 specified a single tall staging table that also carried prediction/routing columns (`predicted_value`, `confidence_score`, `routing_status`). Under ETIM, ingestion's staging holds only *source evidence*; predicted values, match confidence, validation status, and review status move to a separate matching-owned table. ADR-007's "attribute-row, not wide" instinct is retained and reinforced; its column set and single-table assumption are not.
+- The PIMS writeback contract shifts accordingly. The brief keys PIMS output on `product_id + etim_release_id + etim_class_id + etim_feature_id` rather than `submission_id + attribute_id`; ADR-006's idempotency mechanism needs to be revisited against this (flagged in the ADR assessment, not resolved here).
+- A clean cutover is required to avoid two parallel write paths. The transition (dual-write vs deprecate) and the update to the §6.1 output contract are explicit follow-ups (EPARTS-302).
+- Missing-SKU handling must be defined (quarantine vs synthesized id) — an open item feeding the source mapping config.
+
+## Requirements Traceability
+
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` (Staging Layer; Staging Tables; "Original supplier data = evidence"); `INGESTION_ETIM_TICKET_MAP.md` (ING-E4/E5/E6/E7/E9)
+- **Tickets:** EPARTS-297 (ProductMapping config); EPARTS-298 (staging schema); EPARTS-299 (writer rework); EPARTS-302 (retire flat path); parent EPARTS-154
+- **QASs:** QAS-1 (accuracy — evidence preserved for traceable correction); QAS-3 (new category/attribute as data + config, not schema migration)
+- **Related ADRs:** ADR-007 (superseded in part — see above); ADR-013 (reference layer the staged data is matched against); ADR-015 (datastore); ADR-004/ADR-005 (per-attribute routing/threshold consume attribute identity); ADR-006 (PIMS idempotency to be re-keyed)
diff --git a/docs/0015-target-postgresql-now-defer-azure-sql.md b/docs/0015-target-postgresql-now-defer-azure-sql.md
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+# ADR-015: Target PostgreSQL Now; Defer the Azure SQL Conversion
+
+## Status
+
+Accepted
+
+## Context
+
+The ETIM implementation brief specifies its schemas in **SQL Server / Azure SQL dialect** (`DATETIME2`, `NVARCHAR(MAX)`, `BIT`), consistent with the original platform design (ADR-008), which placed all internal pipeline state in **Azure SQL Database** and deployed the platform as a single Azure App Service. Several earlier ADRs assume this Azure SQL substrate (ADR-006 PIMS writeback, ADR-007 staging, ADR-008 deployment, ADR-009 review queue, ADR-010 audit trail).
+
+The ingestion service as actually built does not run on Azure SQL. It runs on **PostgreSQL** with SQLAlchemy 2.x + Alembic migrations, uses **JSONB** for semi-structured fields, archives raw bytes to **S3/MinIO**, and is packaged with Docker/`docker-compose` (Postgres + MinIO) rather than App Service. The existing migrations (`0001`–`0005`, including the ETIM reference tables) are all Postgres.
+
+The ETIM schema tickets (reference tables, staging split) were therefore blocked on a datastore question (ING-E0): author the new ETIM and staging tables for Azure SQL to match the brief, or for Postgres to match the running service? Authoring for Azure SQL now would mean building against a database the platform does not yet use, maintaining a dialect the rest of the codebase does not use, and carrying that divergence indefinitely. Authoring for Postgres now keeps the entire ingestion service on one coherent stack and translates the brief's SQL Server DDL to Postgres equivalents.
+
+A migration to Azure SQL is a real future possibility — it is the original target and ties to the broader platform-on-Azure direction (EPARTS-64) — but it is a separate, platform-level effort that is not in flight today.
+
+## Decision
+
+All new ETIM reference tables and staging tables target **PostgreSQL (the current stack) for now**, using Alembic migrations and JSONB where useful, matching the existing ingestion service. The brief's SQL Server DDL is translated to Postgres equivalents: `DATETIME2 → timestamptz`, `NVARCHAR(MAX) → text`, `BIT → boolean`, with JSONB used where a flexible column is warranted.
+
+A later conversion to **Azure SQL is explicitly deferred** to the future move of the wider platform onto Azure, and is treated as a separate effort rather than a constraint on current ETIM work. This decision **unblocks the ETIM schema tickets** (ING-E0 is resolved). It does not retract ADR-008's eventual Azure direction; it records that the *current* substrate is Postgres and that ETIM work builds on Postgres rather than waiting for, or pre-building against, Azure SQL.
+
+## Consequences
+
+- The ingestion service stays on a single coherent persistence stack (Postgres + Alembic + JSONB + S3). New ETIM and staging migrations sit in the same migration chain as everything else, with one dialect to test and operate.
+- The ETIM schema and staging tickets are unblocked and can proceed immediately, which is the critical path for the rest of the ETIM matching work.
+- A divergence is now on record between several existing ADRs (which name Azure SQL / SQL Server) and the running system (Postgres). ADR-008 in particular is now partially stale on the datastore and deployment topology; this is captured in the ADR assessment for whole-platform follow-up rather than silently ignored.
+- A future Azure SQL port is a known, bounded piece of work. It would touch: column-type translation back to the SQL Server dialect, JSONB usage (which has no exact Azure SQL analogue and would need `nvarchar(max)`/JSON functions), Postgres-specific features in use (advisory locks for run-level exclusivity, `ON CONFLICT` upserts), and the migration tooling. Keeping Postgres-specific features behind the storage layer limits the blast radius of that future port.
+- Because the decision is "now vs later" rather than "never," teams should avoid leaning on Postgres-only behavior in business logic above the storage layer, so the deferred port stays a storage-layer concern.
+- PIMS itself remains external and may stay on SQL Server regardless; this ADR governs the platform's *own* internal stores, not the PIMS target (see ADR-006).
+
+## Requirements Traceability
+
+- **Source:** `INGESTION_ETIM_TICKET_MAP.md` (ING-E0 — RESOLVED: "PostgreSQL now; Azure SQL conversion deferred"); `ETIM_IMPLEMENTATION_BRIEF.md` (Data Model — SQL Server DDL, here translated)
+- **Tickets:** EPARTS-285 (built on Postgres migration 0005); EPARTS-298 (staging schema, Postgres); EPARTS-64 (future platform-on-Azure)
+- **Constraints:** C-1 (Azure managed services — eventual direction, deferred); C-7 (capstone operational simplicity — one stack)
+- **Related ADRs:** ADR-008 (revisits its Azure App Service + Azure SQL topology — now partially superseded on substrate); ADR-013 and ADR-014 (the reference and staging tables this decision places on Postgres); ADR-006 (PIMS target datastore, separate)
diff --git a/docs/0016-decompose-matching-into-staged-etim-class-feature-value-stages.md b/docs/0016-decompose-matching-into-staged-etim-class-feature-value-stages.md
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+# ADR-016: Decompose Attribute Matching into Staged ETIM Class → Feature → Value/Unit Matching
+
+## Status
+
+Accepted
+
+## Context
+
+ADR-003 framed the matching problem as a single step: map a raw supplier attribute string onto a canonical attribute value, using a rule engine blended with semantic similarity (`conf_final = α·conf_rule + (1−α)·conf_embed`, α = 0.7). That framing was correct for a free-form canonical vocabulary, where every attribute is independent and there is one decision to make per attribute.
+
+ETIM invalidates the independence assumption. Under ETIM (HLR-6, FR-9) an attribute cannot be matched at all until the product's **class** is known, because the set of legal features is a property of the class: `etim_class_feature` says which features belong to `EC…`, and `etim_class_feature_value` says which values are legal for that class-feature pair. Matching "Torque: 120 Nm" is meaningless without first deciding the product is a valve actuator, and matching it against the wrong class produces a confidently wrong answer rather than a low-confidence one.
+
+The value side is not uniform either. ETIM feature types carry different semantics and different failure modes:
+
+| Type | Meaning | What matching must produce |
+|---|---|---|
+| A | Controlled list value | an `etim_value_id` drawn from the legal set for that class-feature |
+| L | Logical yes/no | a boolean |
+| N | Numeric | a number **plus** a unit, converted to the ETIM-declared unit |
+| R | Numeric range | a min, a max, and a unit |
+
+A single matcher emitting one scalar `predicted_value` with one `confidence_score` cannot express "we are confident this is class EC002714 but unsure whether the torque figure is the rated or the breakaway value," which is exactly the distinction a reviewer needs. It also gives the router a single number where the routing decision now depends on several (see ADR-018).
+
+Two alternatives were considered:
+
+- **Keep one matcher, widen its output.** Emit class, features and values from one model call and one confidence. Cheapest change, but it hides a genuine dependency: a class error silently corrupts every downstream feature match, and there is no place to intervene between the two.
+- **A per-class trained model.** One classifier per ETIM class. 5,640 classes make this untrainable at our data volume, and it would still not solve unit normalization.
+
+## Decision
+
+We will decompose matching into an ordered pipeline of stages, each producing its own evidence and its own confidence:
+
+```
+class matching → feature matching → value matching → unit normalization
+ → ETIM validation → client-policy validation → confidence scoring
+```
+
+Each stage is a filter in the ADR-001 sense, and the whole sequence remains behind the single `PredictionServiceInterface` established in ADR-002 — this decomposition is an interface *enrichment*, not a reversal. `PredictionResult` grows to carry candidate classes with confidences, matched features, matched values with feature-type-appropriate typing, and validation status, in place of a single predicted value.
+
+Class matching consumes class names, class descriptions, `etim_class_synonym` rows, and the correction store; feature and value matching continue to use the ADR-003 hybrid of rules plus semantic similarity over the class-restricted candidate set. **A correction store is consulted before general matching at every stage** so that a reviewer's decision on one product resolves the same mapping for later products without retraining.
+
+Stage outputs land in `matched_product_attribute` — the interpretation table introduced by ADR-014 — which carries the ETIM identifiers, the typed normalized values (`normalized_text_value`, `normalized_numeric_value`, `normalized_range_min`/`max`, `normalized_logical_value`), and per-assignment confidence, alongside a foreign key back to the `staging_raw_attribute` evidence row.
+
+**Implementation status: designed, not built.** The reference layer this depends on is live (ADR-013), and the evidence/interpretation tables exist (ADR-014, Alembic `0006`). The matching stages themselves are owned by the ML stream under EPARTS-289/290/291 and are not yet in the running pipeline; the pipeline currently emits source evidence only.
+
+## Consequences
+
+- Class errors become **visible and interceptable** instead of silently poisoning downstream matches. This is what makes the class-review-first routing path in ADR-018 possible.
+- Confidence attaches **per ETIM assignment** rather than per raw attribute, which is what DR-4 and the PIMS output contract require and what a reviewer needs in order to accept a class while correcting a single feature.
+- Accuracy becomes measurable against a controlled vocabulary rather than against free text: a match is right or wrong against `etim_class_feature_value`, not fuzzily similar to a gold string. This sharpens the golden test set (EPARTS-296) but also makes previously "close enough" answers count as failures, so headline accuracy will drop before it rises.
+- Unit normalization becomes a first-class stage rather than a formatting detail, because type N and R features declare a unit in `etim_class_feature.UNITOFMEASID` and a value in the wrong unit is wrong, not merely unformatted.
+- More stages means more places to fail and more latency per product. The mitigation is that the stages are cheap relative to the OCR/LLM extraction already in the pipeline, and each stage's output is persisted, so a failure late in the chain does not re-run the expensive early work.
+- The α = 0.7 blend and the reconsideration triggers from ADR-003 carry over unchanged to the feature and value stages. ADR-003 is not superseded; it is narrowed in scope from "the matcher" to "two of the matcher's stages."
+- Because the correction store is consulted first, the system's behaviour changes as reviewers work. That is deliberate, but it means matching accuracy is not reproducible from the model alone — the correction store must be snapshotted alongside any benchmark run.
+
+## Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-6 (classify against ETIM and enrich with class/feature/value/unit identifiers); HLR-2 (the intermediate structure this reads from — mechanical cleanup only, no ETIM keying); HLR-3 (predict with confidence scores)
+- **FRs:** FR-9 (match to ETIM classes, features, controlled values/units with per-assignment confidence, preserving the original value); FR-3 (confidence score per predicted attribute)
+- **DRs:** DR-4 (ETIM-keyed PIMS output — consumes the identifiers this ADR produces)
+- **QASs:** QAS-1 Modifiability — a new supplier format changes the parse stage only, not the matching stages
+- **Scenarios:** SCEN-1 step 4 (the ML service matches attributes, then matches them to ETIM class, features and values); SCEN-2 steps 2–3 (per-assignment confidence is what routes the item to review)
+- **Validation:** VAL-5 (class review precedes attribute routing) — added in spec v1.4 as the test for this ADR; **specified, not yet executable**, because these stages are designed and not built. VAL-4 covers the reference layer this ADR reads; its 10 unit tests pass, and its integration half skips without the real archive.
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` — End-to-End Process steps 7–15, ML/AI Attribute Matching, ETIM Feature Types
+- **Tickets:** EPARTS-289 (class matching), EPARTS-290 (feature matching), EPARTS-291 (value/unit matching), EPARTS-296 (golden test set); parent EPARTS-156 (ML)
+- **Related ADRs:** narrows ADR-003 (hybrid rule + semantic similarity) to the feature and value stages; enriches the contract of ADR-002 (`PredictionServiceInterface`); writes into the interpretation table of ADR-014; reads the reference layer of ADR-013; feeds the routing signals of ADR-018 and the policy gate of ADR-019
diff --git a/docs/0017-rekey-pims-writeback-contract-on-etim-identifiers.md b/docs/0017-rekey-pims-writeback-contract-on-etim-identifiers.md
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+# ADR-017: Re-key the PIMS Writeback Contract on ETIM Identifiers
+
+## Status
+
+Accepted
+
+## Context
+
+ADR-006 established idempotent PIMS writeback via a composite natural key of `submission_id + attribute_id`, upserted rather than inserted, so that a retried write cannot create duplicates. The mechanism was and remains correct.
+
+The key is not. Two things broke it.
+
+**The key no longer identifies the thing being written.** Under ETIM the unit of published data is not "an attribute of a submission" but "the value of a specific ETIM feature, of a specific ETIM class, of a specific product, under a specific ETIM release" (HLR-6, DR-4). `submission_id` is an artefact of *how the data arrived*, not of *what it describes*. The same product arriving twice — a corrected catalogue re-sent by the supplier, or a second file covering the same SKU — produces two submission IDs and therefore two rows for one real-world fact. The upsert would not collide, and PIMS would accumulate duplicates that are invisible to the idempotency check.
+
+**The payload no longer carries enough to be useful downstream.** ADR-006's row was a value plus a confidence. The PIMS output contract now has to carry both the interpretation and the evidence behind it, because the whole point of the standardization objective is that a consumer can compare products across suppliers *and* audit where a value came from.
+
+Alternatives considered:
+
+- **Keep `submission_id + attribute_id`, add ETIM IDs as payload columns.** Minimal change, but leaves the duplicate-on-resubmission defect in place and makes "the current value of feature EF021864 for this product" unanswerable without scanning submissions.
+- **Key on `product_id + etim_class_id + etim_feature_id`, omitting the release.** Simpler, but conflates ETIM releases: a value matched under 10.0 and a value matched under a future 11.0 would collide even though the feature definition may have changed between them. That defeats the release-scoping established in ADR-013.
+
+## Decision
+
+The PIMS writeback natural key becomes:
+
+```
+product_id + etim_release_id + etim_class_id + etim_feature_id
+```
+
+The upsert mechanism from ADR-006 is unchanged — application-layer idempotent upsert through the staging integration, honouring constraint C-2/DC-3 that we do not write directly to production PIMS tables.
+
+The published row carries the interpretation, the evidence, and the provenance together:
+
+| Group | Fields |
+|---|---|
+| ETIM interpretation | `etim_release_id`, `etim_class_id`, `etim_feature_id`, `etim_value_id`, `etim_unit_id`, feature type |
+| Normalized typed value | text / numeric / range-min / range-max / logical, per feature type |
+| Original evidence | original attribute name, original value, original unit, source text reference |
+| Decision metadata | confidence, approval status (auto-accepted or human-approved) |
+
+`submission_id` remains on the row as provenance — it answers "which file did this arrive in" — but it is no longer part of the identity.
+
+Two distinctions this ADR preserves deliberately: PIMS may remain SQL Server even though our own stores are PostgreSQL (ADR-015 governs *our* datastore, not the client's), and the write remains to staging rather than production tables.
+
+**Implementation status: designed, not built.** The identifiers this key depends on are produced by the matching stages of ADR-016, which are not yet in the pipeline. The writer rework is EPARTS-299, on the critical path `285 ‖ (297 → 298 → 299)`.
+
+## Consequences
+
+- Re-sending a corrected catalogue for a product now **updates** the published row instead of appending a second one. This is the defect the old key could not see.
+- "What is the current published value of feature X for product Y under release Z" becomes a primary-key lookup. Cross-supplier comparison and website filtering — the business objective that motivated ETIM adoption — depend on exactly that query being cheap.
+- The release is part of the key for **provenance**: every published value names the ETIM release it was matched under. Under ADR-020 the project is pinned to 10.0 EI, so in practice the field is constant — it is carried so the row is self-describing, and so that un-pinning later would be a change of scope rather than a schema migration.
+- The key requires a stable `product_id`, which requires a resolvable `supplier_sku` per supplier format. **This is an open dependency**: the authoritative SKU field per format, and the behaviour when a record has no extractable SKU (quarantine versus synthesized identifier), are both unresolved. Until they are, products from formats without a clean SKU cannot be published idempotently.
+- Products carrying a feature that ETIM does not define ("ETIM Other") have no `etim_feature_id` and therefore no key. Their handling is an open client decision; they are held out of the published set rather than given a synthetic identifier.
+- The payload is wider than ADR-006's, so PIMS staging rows grow. Given the phase-one valve/actuator scope this is not a capacity concern, and carrying the evidence alongside the interpretation is what makes the published data auditable.
+- ADR-006 is **not edited**. It stands as the record of the April decision and of the upsert mechanism, which this ADR reuses. Where the two disagree on the key, this ADR governs.
+
+## Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-6 (enrich with ETIM identifiers); HLR-5 (write approved data back to PIMS)
+- **FRs:** FR-8 (write attributes to PIMS upon final approval); FR-9 (preserve the original supplier value alongside the ETIM assignment)
+- **DRs:** **DR-4** — *"Approved data written to PIMS shall be keyed by ETIM identifiers (release, class, feature); the writeback idempotency key shall include these identifiers"* — this ADR is the direct realization of DR-4; DR-3 (writeback must be idempotent; retry must not duplicate)
+- **Constraints:** DC-3 (raw files preserved as evidence — the published row references that evidence)
+- **Scenarios:** SCEN-1 step 5 and SCEN-2 step 6 (auto-accepted and human-approved data both take this path)
+- **Validation:** VAL-3 (approve an item; the PIMS write succeeds and a retry does not duplicate — the retry case is now tested against the ETIM key)
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` — PIMS Output Contract, PIMS Sync; `INGESTION_ETIM_PLAN.md` — design decisions
+- **Tickets:** EPARTS-299 (writer rework), EPARTS-295 (PIMS sync); parent EPARTS-154 (Ingestion)
+- **Related ADRs:** supersedes the natural key defined in ADR-006 while reusing its upsert mechanism; consumes the identifiers produced by ADR-016; depends on the release scoping of ADR-013; the datastore distinction is governed by ADR-015
diff --git a/docs/0018-extend-routing-to-etim-signals-with-class-review-first.md b/docs/0018-extend-routing-to-etim-signals-with-class-review-first.md
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,68 @@
+# ADR-018: Extend Routing to ETIM Signals, with a Class-Review-First Path
+
+## Status
+
+Accepted
+
+## Context
+
+ADR-004 established per-attribute routing: each predicted attribute is compared against a configurable confidence threshold (ADR-005), and the attribute — not the whole record — goes to auto-accept or to the human review queue. The granularity decision was right and is unchanged by this ADR.
+
+What changed is that a single confidence-versus-threshold comparison is no longer sufficient to decide whether a value is safe to publish. After ETIM there are several independent ways for an attribute to be unfit, and only one of them is low confidence:
+
+- The **class** may be wrong or contested. Class confidence is a distinct signal from attribute-match confidence, and it dominates: every feature match under a wrong class is wrong, no matter how confident.
+- The value may be confidently matched but **invalid against ETIM** — a type A value not in the legal set for that class-feature, a type N value with no unit, a type R range with min above max.
+- The value may be valid but **fail client policy** — a feature the client marks `required` for this class is missing, which blocks publish regardless of how confident everything else is (ADR-019).
+- **Unit conversion may have failed**, leaving a numerically plausible figure in the wrong unit. This is the most dangerous case: high confidence, valid type, wrong magnitude.
+
+Routing on confidence alone would auto-accept all four of these. The consequence is the one thing the project exists to prevent: wrong product data reaching PIMS, and from there a contractor's field order.
+
+A further problem is ordering. With a flat per-attribute queue, a product whose class is uncertain generates one review item per attribute — dozens of decisions that all become void the moment the reviewer changes the class. Alternatives considered:
+
+- **Route on confidence only, catch validity later at publish time.** Keeps routing simple, but moves the failure to a stage with no human in it, so invalid data either blocks silently or is dropped.
+- **Escalate any invalid attribute to whole-record review.** Safe but wasteful: one bad attribute pulls a hundred good ones into a manual queue, which is precisely the per-record behaviour ADR-004 rejected.
+
+## Decision
+
+Routing keeps its per-attribute granularity and gains a **class-level stage in front of it**.
+
+**Stage 1 — class routing.** If ETIM class confidence is below the class threshold, or the top two candidate classes are within a configured margin of each other, the *product* is routed to class review before any attribute is matched. Attribute matching for that product is deferred until a class is confirmed.
+
+**Stage 2 — attribute routing.** Once the class is settled, each attribute is routed on the full signal set:
+
+| Signal | Effect |
+|---|---|
+| Attribute match confidence below threshold | → review |
+| ETIM validation failure (value not in legal set, missing unit, malformed range) | → review, regardless of confidence |
+| Unit conversion failure | → review, regardless of confidence |
+| Client policy `required` and value missing | → review, and blocks publish for the product |
+| Client policy `not_used` | → not published, not queued |
+| All checks pass and confidence above threshold | → auto-accept |
+
+The rule that governs the combination: **validation and policy failures are not overridden by high confidence.** Confidence answers "did we read it right"; validation answers "is it a legal ETIM value"; policy answers "does the client need it". These are independent questions and a failure in any one routes to a human.
+
+Thresholds are externalized per ADR-005, now generalized to at least two — class-selection confidence and attribute-match confidence — with per-class-feature overrides replacing the per-attribute override table.
+
+**Implementation status: designed, not built.** The signals this routing consumes are produced by the matching stages of ADR-016 (EPARTS-289/290/291), which are not yet in the running pipeline. Routing today evaluates confidence only.
+
+## Consequences
+
+- The highest-leverage failure mode — a confidently wrong unit or an out-of-vocabulary value — is now caught by a deterministic check rather than by hoping the model was unsure. This directly serves the data-integrity driver behind the whole platform.
+- Class-review-first collapses what would have been dozens of void attribute decisions into one class decision. Reviewer throughput (QAS-2, 10 items/minute) is protected by not queuing work that is about to be invalidated.
+- Deferring attribute matching until the class is confirmed introduces a **wait state** in the pipeline: a product can sit unprocessed pending a human class decision. The staging tables (ADR-014) hold that state durably, so nothing is lost, but end-to-end latency for uncertain products is now bounded by reviewer response time rather than by compute.
+- More routing inputs means more ways to be wrong about routing. Each signal must be independently observable in telemetry — class confidence distribution, validation-failure rate, unit-conversion-failure rate, missing-required-field rate — or a regression in one will be invisible inside an aggregate auto-accept rate.
+- Auto-accept rate will fall relative to the ADR-004 baseline, because attributes that previously passed on confidence now also have to pass validation and policy. This is the intended trade: throughput for correctness. The rate should be reported against the pre-ETIM baseline so the drop is not misread as a regression.
+- The policy signal makes routing **dependent on client configuration that does not yet exist** (ADR-019). Until the feature policy is supplied, the policy check defaults to permissive — nothing is treated as required — which means the required-field path is designed but untestable.
+- ADR-004 and ADR-005 are **not edited**. Per-attribute granularity and externalized thresholds are reused as decided; this ADR extends the inputs and adds a preceding stage.
+
+## Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-4 (human review of low-confidence predictions); HLR-6 (ETIM classification and enrichment)
+- **FRs:** FR-4 (route below-threshold items to the review queue); FR-7 (authorized Ops Leads adjust the auto-acceptance threshold); FR-9 (per-ETIM-assignment confidence is the signal being routed on); FR-3 (confidence score per prediction)
+- **QASs:** QAS-2 Usability — class-review-first is what keeps the reviewer at 10 items/minute by not queuing work that a class change would void
+- **Scenarios:** SCEN-2 steps 2–3 (a 0.45-confidence value routes to review; under this ADR it would also route on a validation or unit failure at any confidence)
+- **Validation:** VAL-2 (mock a low-confidence response; the item appears in the review queue) — extended to cover validation-failure and unit-failure routing at high confidence. **VAL-5** (added in spec v1.4) is the specific test for class-review-first: a below-threshold class assignment routes to class review and no attribute-level routing happens for that item. Specified, not yet executable.
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` — Request Router, Human Review, End-to-End Process steps 13–17
+- **Tickets:** EPARTS-289 (class matching and class routing), EPARTS-294 (ETIM-aware review queue); parent EPARTS-156 (ML)
+- **Related ADRs:** extends ADR-004 (per-attribute routing) and ADR-005 (externalized thresholds); consumes the staged outputs of ADR-016; depends on the policy overlay of ADR-019; the review-queue contract it feeds is ADR-009
diff --git a/docs/0019-externalize-client-feature-policy-as-per-class-configuration.md b/docs/0019-externalize-client-feature-policy-as-per-class-configuration.md
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+# ADR-019: Externalize the Client Feature Policy as Per-Class Configuration
+
+## Status
+
+Accepted
+
+## Context
+
+ETIM tells us which features *exist* for a class. It does not tell us which ones *matter*.
+
+`ETIMARTCLASSFEATUREMAP.csv` — the file that binds features to classes — contains `ARTCLASSFEATURENR`, `ARTCLASSID`, `FEATUREID`, `FEATURETYPE`, `UNITOFMEASID`, `SORTNR`. It contains no `required`, no `mandatory`, no `blocks_publish`, no `used_for_compare`. This is not an oversight in the export; ETIM is a shared industry dictionary and requiredness is a property of a particular catalogue's editorial standards, not of the standard.
+
+The consequence is concrete and blocking. A valve class may define 60 features. A supplier datasheet may supply 12 of them. Whether that product is publishable depends entirely on which of the 60 the client considers required — and nobody has told us. Until someone does:
+
+- **"What blocks publish?" is unanswerable**, so firm validation requirements cannot be written.
+- The routing rule in ADR-018 that sends missing-required-features to review has no data to evaluate.
+- The reviewer UI cannot distinguish "this field is empty and that is fine" from "this field is empty and the product cannot ship."
+
+This is currently the project's most significant requirements risk, and it is owned by the client, not by us. Two open tickets (EPARTS-286 class scope, EPARTS-287 feature policy) are blocked on it.
+
+The architectural question is what to do in the meantime. Alternatives considered:
+
+- **Wait for the policy, then design around it.** Leaves the validation and routing paths unbuilt and the critical path idle on an external dependency with no committed date.
+- **Hard-code a provisional policy** from our own reading of the valve datasheets. Fast, and wrong in a way that is expensive to detect: the system would enforce a standard nobody agreed to, and the resulting review queue would reflect our guesses rather than the client's requirements.
+- **Derive requiredness statistically** — treat a feature as required if most suppliers populate it. Tempting, but it encodes current supplier behaviour as the target standard, which inverts the business objective. The client adopted ETIM precisely because current supplier coverage is inadequate.
+
+## Decision
+
+The feature policy is modelled as a **client-owned configuration overlay, external to the ETIM reference layer**, keyed per client, release, class and feature:
+
+```
+catalog_feature_policy(client_id, etim_release_id, etim_class_id, etim_feature_id)
+ → requirement_level ∈ { required, recommended, optional, conditional, not_used }
+ blocks_publish, used_for_compare, used_for_filter, display_order, condition_rule
+```
+
+Three properties of this decision matter more than the schema:
+
+**It is an overlay, not an edit.** ETIM reference tables (ADR-013) store the standard exactly as published. Policy lives in its own table and joins on the ETIM keys. Policy revisions do not require reloading ETIM, and the standard's own structure is never edited to record a client preference.
+
+**It is data, not code.** Changing requiredness for a class is a configuration change reviewed by the policy owner, not a deployment. Given that the client has not yet decided and will revise once they see real review volumes, requiredness must be cheap to change.
+
+**The default is permissive and explicit.** Absent a policy row, a feature is treated as `optional` and nothing blocks publish. The system does not guess. Where a policy is absent and a value is missing, the product publishes with the gap recorded, rather than silently enforcing an invented standard.
+
+The decision also creates a role that did not exist in the v1.0 baseline: a **feature-policy owner** on the client side who declares the levels and signs off on changes.
+
+**Implementation status: the seam is decided; the values are pending.** The overlay's position in the architecture and its consumption by routing (ADR-018) and by the reviewer UI are settled. The policy content is an open client decision (EPARTS-287) and the table is not yet populated.
+
+## Consequences
+
+- The architecture stops being blocked on a client decision. Routing, validation and the reviewer UI can be built against the overlay's contract and exercised with a synthetic policy, then switched to the real one when it arrives.
+- The **required-field path is designed but untestable end-to-end** until a real policy exists. Tests can prove that a `required` row routes correctly; they cannot prove the right features are marked required. This gap should be stated rather than papered over — a green test suite here does not mean the validation requirement is satisfied.
+- Because policy is per-client, a second client with different editorial standards is a data addition rather than a code change. That is well beyond phase-one scope and is not being built for, but the key shape does not preclude it.
+- `conditional` requires a rule language (`condition_rule`), and no rule language has been chosen. Conditional features are therefore accepted into the schema but not evaluated; they behave as `optional` until a rule evaluator exists. This is a known deferral, not an oversight.
+- `used_for_compare` and `used_for_filter` are carried in the schema because the Compare Tool and website filter are the stated business motivation for ETIM adoption, but both consumers are **out of phase-one scope**. Storing the flags now avoids a migration later; populating them is deferred.
+- Every policy change silently changes routing behaviour. Policy revisions must be versioned and correlated with review-queue volume, or an unexplained spike in the queue will be indistinguishable from a model regression.
+- The permissive default means that until the policy lands, **no product will ever be blocked for a missing required field**. Auto-accept rates measured before the policy is populated are therefore optimistic and must not be quoted as steady-state figures.
+
+## Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-6 (ETIM classification and enrichment); HLR-4 (human review of items needing attention)
+- **FRs:** FR-9 (ETIM matching — policy validation gates what a match is sufficient for); FR-4 (routing to review); FR-7 (authorized adjustment of auto-acceptance behaviour, of which policy is now part)
+- **Constraints:** C-3 (breadth-first delivery — a full end-to-end flow for one supplier type before optimizing depth; a permissive default is what allows the flow to complete)
+- **QASs:** QAS-3 Modifiability (client feature policy) — added in spec v1.4 specifically to hold this decision: a policy change is configuration, applied to the next batch without a code deployment
+- **Validation:** VAL-2 (routing) — the required-field branch is designed here and **cannot be validated until the policy is supplied**; this is a known open item, not a satisfied requirement
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` — Important ETIM Limitation, Client Policy Tables
+- **Tickets:** EPARTS-287 (feature policy — **blocked on client**), EPARTS-286 (phase-one class scope — **blocked on client**), EPARTS-294 (review UI consumes the policy)
+- **Open client decisions this ADR holds a place for:** feature policy per class; required-field publish blockers; Compare Tool and website-filter feature sets; mapping and policy sign-off ownership
+- **Related ADRs:** deliberately kept out of the reference layer of ADR-013; supplies the policy signals routed on in ADR-018; the validation stage that consumes it is part of ADR-016; the reviewer contract that displays it is ADR-009
diff --git a/docs/0020-pin-etim-release-10-0-for-the-project-duration.md b/docs/0020-pin-etim-release-10-0-for-the-project-duration.md
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+# ADR-020: Pin ETIM Release 10.0 (EI) for the Project Duration
+
+## Status
+
+Accepted
+
+## Context
+
+ETIM is an external standard with its own release cadence. We loaded **ETIM 10.0, language EI**. There will be an 11.0, and between releases classes are added, features are added and deprecated, values are withdrawn, and a class's feature set changes shape.
+
+That raised a question the v1.0 baseline had no equivalent of: what does the platform do when the standard moves underneath it? Two things made it pressing. Requirements written against "the ETIM standard" are implicitly written against a specific release, so the traceability chain from HLR-6 through FR-9 to a published PIMS row is only meaningful if the release is part of the record. And an unmanaged upgrade silently reinterprets historical data — a value that was legal under 10.0 can be invalid under 11.0, and either the row breaks or, worse, it stays and nobody knows which release's rules it satisfies.
+
+Three options were considered.
+
+- **Build a governed upgrade path now.** Load each new release alongside the old one, diff them, re-match affected products through a review queue, and reconcile the client's feature policy against the diff before cutover. Architecturally clean, and it makes upgrades visible rather than silent. But it is a substantial amount of work — a diff report, a bulk re-match path, a second review queue — for an event that will not occur inside this project. It also could not be finished: who authorizes an upgrade, on what trigger, and what happens to already-published rows are client decisions nobody has made.
+- **Leave the question open.** Say nothing and handle a future release when it arrives. Rejected because "unspecified" is not the same as "out of scope". FR-10 as originally worded — maintain the dictionary as *versioned* reference data — implies an obligation we were not going to meet, and an assessor or a future maintainer would reasonably read it as a commitment.
+- **Pin the release explicitly and put the upgrade path out of scope.** Chosen.
+
+## Decision
+
+**The platform targets ETIM release 10.0, language EI, for the duration of this project.** Adopting later ETIM releases, and migrating already-classified products between releases, are **out of scope**.
+
+This is recorded as **constraint C-4**, introduced in Product Specification **v1.2**, and FR-10 is scoped to "the pinned ETIM release identified in C-4" rather than to versioned reference data generally.
+
+The **release-scoping mechanism in the schema stays exactly as it is.** Every ETIM reference row carries `etim_release_id`, with composite primary keys on `(etim_release_id, …)` across all ten tables (ADR-013); the release is carried through `matched_product_attribute` (ADR-014) and forms part of the PIMS writeback key (ADR-017). Under a pin that field is constant in practice, and we are keeping it for two reasons:
+
+1. **Provenance.** Every published value names the release it was matched under. "This value was matched against ETIM 10.0 EI, on this date, under this policy" stays recoverable from the row alone, which is what makes the audit trail meaningful later.
+2. **It costs nothing.** The columns and keys are already built and tested. Removing them to reflect the pin would be work that buys no capability and discards the provenance.
+
+So this ADR narrows the *forward-looking justification* in ADR-013 — release-scoping is no longer defended as a step toward governed upgrades — without changing a line of the schema it describes. ADR-013 is not edited.
+
+If the client later asks for a new ETIM release, that is a **change request against C-4**, and the first option above is the shape the work would take. It is not a gap to be quietly filled.
+
+## Consequences
+
+- The project stops carrying an obligation it was never going to discharge. FR-10 is now satisfiable and testable as written: load and maintain one named release.
+- No diff report, no bulk re-match path, no second review queue, and no upgrade-governance owner to chase. This is the largest piece of scope the decision removes, and it removes it in the phase where the critical path is `285 ‖ (297 → 298 → 299)`.
+- **We are deliberately accepting that the catalog will go stale** relative to ETIM. If the client's suppliers begin publishing against 11.0 while we classify against 10.0, new classes and features are simply unavailable to us, and products needing them fall to "ETIM Other" handling or to review. For a phase-one valve/actuator pilot that is acceptable. For a production catalogue with a multi-year life it would not be, and this ADR should be revisited before any such transition.
+- Provenance is preserved without the machinery. Because `etim_release_id` remains in the reference tables, the interpretation table and the PIMS key, a future un-pinning is a change of scope rather than a schema migration. The door is left open at zero cost.
+- **The loader keeps its release-mismatch rejection.** It validates that an archive matches the declared release and refuses a mismatched or truncated one (ADR-013). Under a pin that check becomes more valuable, not less — it is what stops an 11.0 archive being loaded into a 10.0-pinned system by accident.
+- The `etim_release_id` field will look redundant to anyone reading the schema without this ADR. That is the cost of keeping it, and this ADR is the answer.
+- One open client decision is closed. "ETIM release-upgrade governance" comes off the blocked list, taking the open-decision count from six to five.
+
+## Requirements Traceability
+
+- **Spec:** Product Specification **v1.4** (29 July 2026); C-4 was introduced in v1.2 (28 July) — this ADR is the reason for that version
+- **Constraints:** **C-4** (ETIM Release Pinned) — this ADR is the decision C-4 records
+- **HLRs:** HLR-6 (classify against the ETIM standard — this ADR fixes *which* ETIM)
+- **FRs:** **FR-10** (load and maintain the ETIM reference dictionary for the pinned release); FR-9 (matching is always against release 10.0 EI)
+- **DRs:** DR-4 (the release remains part of the PIMS writeback key, so publication stays release-explicit)
+- **QASs:** QAS-1 Modifiability — un-pinning would be a scope change, not a structural change to the pipeline
+- **Constraints (supporting):** C-1 (cost-effective design — the upgrade path is the expensive option and is deliberately not built); C-3 (breadth-first delivery — one supplier type end to end before adding depth)
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md`; `ETIM-ADR-ASSESSMENT.md` raised this as *"Standard evolution (currency): ETIM releases (10.0 → next); upgrade governance undefined"* — this ADR resolves that item by scoping it out rather than by building for it
+- **Closes:** the open client decision "ETIM release-upgrade governance"
+- **Related ADRs:** narrows the forward-looking rationale of **ADR-013** (release-scoped reference layer) without editing it; the release remains in **ADR-014**'s interpretation table and **ADR-017**'s writeback key for provenance; **ADR-019**'s policy overlay no longer needs reconciling against a release diff
diff --git a/docs/0021-formalize-ingestion-to-ml-boundary-as-frozen-extracted-input-record.md b/docs/0021-formalize-ingestion-to-ml-boundary-as-frozen-extracted-input-record.md
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+# ADR-021: Formalize the Ingestion → ML Boundary as a Frozen `ExtractedInput` Record
+
+## Status
+
+Accepted
+
+## Context
+
+ADR-001 established pipe-and-filter as the platform's style, with filters communicating through typed data channels. In practice the ingestion→matching channel was the weakest of them: ingestion parsed a supplier file into a `RawRecord` and the matching stream read whatever fields happened to be there. Adequate while both sides were one team and one process; untenable now.
+
+Three pressures forced the boundary to become explicit.
+
+**It is a cross-team contract.** Ingestion (EPARTS-154) and ML matching (EPARTS-156) are separate streams with separate backlogs. The ETIM requirements-change record names this contract as one of two places where our traceability deliberately stops — we own the requirement, another stream owns the implementation. A trace boundary that is not a schema boundary is not a boundary at all.
+
+**Ingestion must not leak interpretation.** ADR-014 established the principle that supplier data is *evidence* and ETIM is a *standardized interpretation* over it. If ingestion hands the matcher a confidence score or a ranked list of candidate attribute names, it has already begun interpreting, and the evidence/interpretation split becomes a convention rather than a property of the system. The temptation is real: the OCR path (Azure Document Intelligence plus an LLM extraction) *has* per-field confidences available, and passing them along would be a one-line change.
+
+**Source provenance differs by channel and matters downstream.** A value read from a CSV cell, a value read from a text-native PDF, and a value read from OCR over a scanned page carry different reliability, and the matcher and the reviewer both need to know which they are looking at. A generic dictionary of fields loses that.
+
+Alternatives considered:
+
+- **Keep passing `RawRecord`.** Zero work, and it makes every ingestion-side refactor a potential silent break for the ML stream, because nothing declares what the ML stream is entitled to rely on.
+- **Put the boundary behind an HTTP service now.** Genuinely the right long-term shape, and premature: it adds deployment, retry and tracing surface for a boundary that currently runs in one process. ADR-008's single-deployable-unit decision still holds; what this ADR fixes is the *contract*, not the *topology*.
+- **Document the contract in prose only.** The 460-line handoff specification already exists. Documentation that is not enforced drifts, and this contract's whole value is that it cannot drift.
+
+## Decision
+
+The ingestion→ML boundary is a **single, versioned, schema-frozen record type**, `ExtractedInput`, specified in `docs/extraction_handoff_spec.md` and enforced in code:
+
+| Field | Meaning |
+|---|---|
+| `source_type` | one of `csv`, `email`, `pdf_text`, `pdf_ocr`, `image` — the channel, so the consumer knows what kind of evidence this is |
+| `text` | the extracted text; required, and an empty string is valid |
+| `structured_fields` | the parsed field/value pairs as the supplier wrote them |
+| `normalized_units` | mechanical unit normalization only, as `(value, unit)` pairs |
+| `source_ref` | pointer back to the archived raw artefact |
+
+Two properties do the real work.
+
+**The schema forbids interpretation by construction.** The Pydantic model is declared `extra="forbid"` and `frozen=True`. Confidence scores, ranked alternates, predicted ETIM classes — anything that constitutes an interpretation — *cannot be represented*, so they cannot cross the boundary by accident. The evidence/interpretation split of ADR-014 is enforced by the type system rather than by reviewer vigilance.
+
+**The record is persisted, not just passed.** `extracted_inputs` (Alembic `0007`) stores each handoff record, which turns the boundary into a durable checkpoint: the matching stream can be down, restarted, or re-run against the same inputs without re-doing OCR, and a matching bug can be diagnosed against exactly the input that produced it.
+
+Cleaning (spec §3) and unit normalization (spec §4) are injectable seams on the ingestion side of the boundary. This keeps mechanical tidying — whitespace, encoding, unit spelling — with the party that knows the source format, while leaving anything requiring domain judgement to the matcher.
+
+**Implementation status: built and merged.** `handoff/spec_model.py`, `handoff/builder.py`, `models/extracted_input.py` and migration `0007` are on the main line (EPARTS-357, EPARTS-358). The cleaning and unit implementations (EPARTS-359, EPARTS-362) and the provenance split between `pdf_text` and `pdf_ocr` (EPARTS-361) are on open branches; on the main line those seams are pass-throughs. Wiring the builder into the orchestrator is EPARTS-363 and is not yet done, so the record type exists and is validated but is not yet produced on every run.
+
+## Consequences
+
+- The two streams can move independently. Ingestion can change parsers, add a channel, or swap the OCR engine without coordinating, so long as the record still validates. The ML stream has a written, enforced statement of what it may rely on.
+- **`extra="forbid"` will reject rather than ignore** an ingestion-side addition. That is the intended behaviour — it makes contract changes loud — but it means adding a field is a deliberate, two-team, spec-versioning act, not a convenience. Expect this to feel obstructive at least once; that is the cost being paid on purpose.
+- Persisting the record makes matching **replayable**. Re-running the matcher over stored `extracted_inputs` costs nothing in Azure Document Intelligence or LLM calls, which materially changes the economics of iterating on the matching stages of ADR-016.
+- This turns ADR-001's in-process function call into an explicit asynchronous seam, and it is consequently **the leading candidate for extraction into a service** if the deployment topology of ADR-008 is ever revisited. Nothing about the contract assumes co-location.
+- The boundary is a queue-shaped thing without a queue. Delivery today is a table plus a poll; the transactional outbox and circuit breaker planned under EPARTS-301 are not built. Until they are, there is no delivery guarantee beyond "the row is committed" — adequate, because the row *is* the durable state, but not the same as at-least-once delivery to a live consumer.
+- The record carries no confidence, which means the matcher cannot preferentially trust a high-confidence OCR field over a low-confidence one. This is a deliberate loss of information: OCR confidence measures character recognition, not semantic correctness, and treating it as the latter is the mistake the split exists to prevent. If the matcher later needs a reliability signal, it should come from `source_type` and from measured per-channel accuracy, not from the OCR engine's self-report.
+- Because the builder is not yet wired into the orchestrator, the contract is currently **enforced but unexercised in production flow**. The unit tests validate the shape; no end-to-end run has yet produced a record. This should not be described as a working boundary until EPARTS-363 lands.
+
+## Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-2 (normalize into a standardized intermediate structure preserving original supplier values as evidence); HLR-1 (ingest from diverse supplier sources — `source_type` enumerates the channels); HLR-3 (the ML service that consumes this record)
+- **FRs:** FR-1 (ingestion record with supplier, timestamp, source channel); FR-2 (validation before processing — an invalid handoff record is a validation failure, not a silent pass); FR-9 (matching consumes this record)
+- **DRs:** DR-1 (raw file archived as evidence — `source_ref` is the pointer to it)
+- **QASs:** QAS-1 Modifiability — a new supplier format is a new `source_type` and a new parser; the boundary and everything downstream of it are unchanged
+- **Constraints:** DC-1 (Python backend); DC-3 (raw files preserved for re-processing and traceability — replayability depends on this)
+- **Scenarios:** SCEN-1 step 3 and SCEN-2 step 1 (both scenarios cross this boundary; SCEN-2's OCR path is `pdf_ocr`)
+- **Source:** `docs/extraction_handoff_spec.md` (§1 channels, §2 record shape, §3 cleaning, §4 unit normalization, §5 structured fields, §6 per-channel examples)
+- **Tickets:** EPARTS-357 (schema + migration `0007` — Done), EPARTS-358 (builder + spec model — Done), EPARTS-359 (units), EPARTS-361 (pdf_text/pdf_ocr provenance), EPARTS-362 (text cleaning), EPARTS-363 (orchestrator wiring — **not done**), EPARTS-301 (transactional outbox — not built); contract boundary between EPARTS-154 (Ingestion) and EPARTS-156 (ML)
+- **Related ADRs:** makes explicit the filter boundary of ADR-001; enforces the evidence/interpretation split of ADR-014; feeds the matching stages of ADR-016; does not alter the single-deployable-unit topology of ADR-008, but is the natural extraction point if that is revisited
diff --git a/docs/ETIM-ADR-ASSESSMENT.md b/docs/ETIM-ADR-ASSESSMENT.md
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+# ETIM Readiness — Assessment of Existing ADRs (0001–0012)
+
+**Date:** 2026-06-29
+**Scope:** Whole platform (ingestion, ML/matching, routing, review, audit, writeback).
+**Method:** Compared the 12 ADRs against (a) the running ingestion code, (b) `ETIM_IMPLEMENTATION_BRIEF.md`, (c) `INGESTION_ETIM_TICKET_MAP.md`, and (d) the EPARTS Jira backlog (epics EPARTS-154 Ingestion, EPARTS-156 ML POC, EPARTS-159 OCR POC).
+**Action:** This is an assessment only — the existing ADR files are left unchanged. Three new ADRs (0013–0015) capture the new ETIM ingestion decisions. The changes recommended below should be actioned by the owning teams.
+
+---
+
+## Why the gap exists
+
+The 12 ADRs describe the platform as designed in the capstone phase: a single **Azure App Service** holding all stages, all internal state in **Azure SQL Database**, **Datadog** telemetry, a single tall attribute-row staging table carrying prediction/routing columns, and a generic "predict canonical attribute values" model. Since then two things changed:
+
+1. **The build diverged from the design.** The ingestion service actually runs on **PostgreSQL + S3/MinIO + Docker**, with **Prometheus + OpenTelemetry + structlog** for observability — not Azure SQL / App Service / Datadog.
+2. **ETIM reframed the problem.** Standardization is no longer "predict a canonical value." It is class matching → feature matching → value/unit matching → ETIM validation → client-policy validation → confidence → route. The data model splits into source-evidence (ingestion) and ETIM-interpretation (matching), and the PIMS contract is now keyed on ETIM identifiers.
+
+The ADRs split cleanly into three buckets below.
+
+---
+
+## Bucket A — Outdated on substrate/stack (factually wrong today)
+
+### ADR-008 — Deploy as a Single Azure App Service Unit · **Needs major revision**
+- **Drift:** Names Azure App Service + Azure SQL + Blob + Datadog + `pyodbc` to PIMS. Reality: Postgres + S3/MinIO + Docker/`docker-compose`, Prometheus/OTLP. Azure is now an explicitly *deferred* direction (see ADR-015).
+- **Still valid:** The "single deployable unit, components communicate in-process, boundaries drawn where service boundaries would go" decision still holds and is sound.
+- **Recommend:** Revise to describe the current Docker/Postgres/S3 substrate as the operative topology, with Azure App Service/Azure SQL as the deferred target. Cross-reference **ADR-015**. The new push-to-ML seam (EPARTS-301, transactional outbox) is the first real network boundary and should be noted as the leading candidate for extraction.
+
+### ADR-012 — Emit Stage-by-Stage Telemetry to Datadog · **Needs revision (supersede recommended)**
+- **Drift:** Datadog is not used anywhere in the ingestion code; the stack is **Prometheus metrics + OpenTelemetry (OTLP) tracing + structlog**, exposed on `/metrics`. Status is still "Proposed."
+- **Still valid:** The *intent* — stage-by-stage signals, fire-and-forget, audit trail as system-of-record, drift detection from baselines — is correct and worth keeping.
+- **Recommend:** Supersede with a "stage telemetry via Prometheus/OpenTelemetry" ADR, carrying over the signal list and adding the ETIM-specific metrics the brief calls for: ETIM import success/failure and row counts per release, products classified, class/attribute confidence distributions, auto-accept rate, human-review rate, missing-required-field rate, invalid-value rate, unit-conversion-failure rate, PIMS sync success/failure.
+
+---
+
+## Bucket B — Structurally sound but must become ETIM-aware (semantics changed)
+
+### ADR-007 — Attribute-Row Canonical Schema · **Partially superseded by ADR-014**
+- **Change:** The "one tall row per attribute, *not* wide" instinct is retained and reinforced. But ADR-007's single staging table also carried `predicted_value`, `confidence_score`, `routing_status` on the same row. Under ETIM the model splits: ingestion emits **source-evidence only** (`staging_product` + `staging_raw_attribute`), and prediction/match/validation/review state moves to a separate matching-owned table (`matched_product_attribute`). See **ADR-014**.
+- **Recommend:** Mark ADR-007 superseded-in-part by ADR-014; keep it as the rationale for the attribute-row (tall) choice.
+
+### ADR-006 — Idempotent PIMS Writeback via Composite Natural Key · **Needs revision**
+- **Change:** ADR-006 keys idempotency on `submission_id + attribute_id`. The ETIM brief's PIMS output contract is keyed on **`product_id + etim_release_id + etim_class_id + etim_feature_id`** and carries original value + ETIM IDs + normalized typed values + confidence + approval status. The upsert mechanism is still correct; the key and payload are not.
+- **Recommend:** Revise the natural key and payload to the ETIM contract. PIMS may remain SQL Server (external) even though our own stores are Postgres — keep that distinction (ties to ADR-015).
+
+### ADR-003 — Hybrid Rule Engine + Semantic Similarity · **Reframe for ETIM (still Tentative)**
+- **Change:** Framed as "map raw supplier text → canonical attribute value." ETIM splits this into **class matching, feature matching, and value matching**, each with its own evidence and confidence, plus a **correction store** applied before general matching. TF-IDF/cosine over canonical embeddings still fits feature/value matching; class matching adds class descriptions + synonyms + correction rules as inputs. The `α = 0.7` blend and reconsideration triggers carry over.
+- **Recommend:** Reframe around the three ETIM matching sub-problems and the correction store. Owner: ML (EPARTS-289/290/291).
+
+### ADR-004 — Per-Attribute Routing · **Extend signals**
+- **Change:** Routing now depends on more than one confidence-vs-threshold check. The brief's routing table keys on **ETIM class confidence, attribute match confidence, ETIM validation result, client required-field policy, missing values, invalid values, and unit-conversion failures** — including a *class-review-first* path when class confidence is low or competing classes are close.
+- **Recommend:** Keep per-attribute routing; extend the decision inputs to the ETIM signal set and add the class-level routing stage ahead of attribute routing (EPARTS-289, EPARTS routing work).
+
+### ADR-005 — Externalized Confidence Threshold · **Mostly valid; widen**
+- **Change:** Still correct (and still Tentative). Under ETIM there are now at least two thresholds — **class-selection confidence** and **attribute-match confidence** — and the "ETIM Other" handling is a policy knob (auto-select vs route to review). Per-attribute override table generalizes to per-class-feature.
+- **Recommend:** Generalize the config to cover class and attribute thresholds and the "Other" policy; otherwise unchanged.
+
+### ADR-009 — Human Review Queue as DB Table · **Make ETIM-aware**
+- **Change:** Queue mechanism (persistent table, reviewer pace decoupled) is unchanged. Reviewers now need ETIM context: suggested class/feature/value, allowed-value dropdowns, numeric/range/boolean controls, and the ability to change class, mark unknown, and **save a correction rule**. Review statuses expand (pending, approved, corrected, rejected, class_changed, attribute_unknown, not_required).
+- **Recommend:** Extend the queue row/contract with ETIM suggestion + review-status fields; reference the correction store. Owner: EPARTS-294.
+
+### ADR-010 — Append-Only Audit Trail · **Extend captured fields**
+- **Change:** Append-only design is unchanged and still correct. It must now also capture the ETIM mapping: suggested class/feature/value, ETIM validation status, the corrected ETIM value, and the ETIM release in force — not just predicted-vs-corrected scalar values.
+- **Recommend:** Extend the recorded columns to the ETIM mapping set; everything else stands.
+
+---
+
+## Bucket C — Still valid as written
+
+### ADR-001 — Pipe-and-Filter · **Valid**
+The parse → normalize → match → route → review/auto-accept → writeback spine still describes the system. One addition: the **ingestion→ML handoff is becoming an explicit async seam** (transactional outbox + circuit breaker, EPARTS-301) rather than a pure in-process function call. Worth a one-line note, not a rewrite.
+
+### ADR-002 — Prediction Strategy Behind a Stable Interface · **Valid; enrich the contract**
+The interface-isolation decision holds. The `PredictionResult` contract should be enriched to carry ETIM outputs (candidate classes with confidence, matched features/values, validation status) rather than a single predicted value — an interface evolution, not a reversal.
+
+### ADR-011 — Retraining on Batch Completion · **Valid (still Proposed)**
+Mechanism unchanged. Under ETIM the "labeled examples" are reviewer ETIM corrections and the correction store; otherwise the validation-gate + promotion design stands.
+
+---
+
+## Summary table
+
+| ADR | Verdict | Action |
+|---|---|---|
+| 001 Pipe-and-filter | Valid | Note the async ingestion→ML outbox seam |
+| 002 Prediction interface | Valid | Enrich `PredictionResult` for ETIM outputs |
+| 003 Hybrid rule + semantic | Reframe | Recast as class/feature/value matching + correction store |
+| 004 Per-attribute routing | Extend | Add ETIM class conf, validation, client policy signals |
+| 005 Externalized threshold | Widen | Class + attribute thresholds; "Other" policy |
+| 006 PIMS idempotent writeback | Revise | Re-key to `product+release+class+feature`; new payload |
+| 007 Attribute-row staging | Superseded-in-part | By ADR-014 (evidence vs interpretation split) |
+| 008 Single Azure App Service | Major revision | Postgres/S3/Docker now; Azure deferred (ADR-015) |
+| 009 Human review queue | ETIM-aware | Add ETIM suggestion + expanded review statuses |
+| 010 Append-only audit | Extend | Capture ETIM mapping + release + validation status |
+| 011 Retraining trigger | Valid | None (corrections = ETIM corrections) |
+| 012 Datadog telemetry | Supersede | Prometheus/OTLP + ETIM metric set |
+
+## New ADRs authored alongside this assessment
+
+- **ADR-013** — Establish a release-versioned ETIM reference data layer owned by ingestion (documents the built EPARTS-285 work).
+- **ADR-014** — Emit a source-preserving product + attribute staging split (`staging_product` + `staging_raw_attribute`); supersedes the staging shape in ADR-007.
+- **ADR-015** — Target PostgreSQL now; defer the Azure SQL conversion (resolves ING-E0; revisits ADR-008's substrate).
+
+## Open items that block firming up some of the above
+
+From the ticket map and brief, still needing a client/team answer: authoritative `supplier_sku` field per supplier format; behavior when a record has no extractable SKU (quarantine vs synthesized id); whether the ML push payload is per-product or per-attribute; which ETIM features are required/recommended/optional per class (client policy); whether "ETIM Other" auto-selects or routes to review; and how ETIM release upgrades are governed.
diff --git a/docs/README.md b/docs/README.md
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+# eParts Requirements Artifact Set
+
+This folder holds the **Requirements artifact** for the eParts Intelligent Ingestion
+& Attribute Prediction System, produced and maintained under the project's
+Software Engineering System (SES) and the LLM-Aided SE Framework.
+
+It is **not a single document**. It is a small, versioned artifact set that flows
+together through the Requirements Engineering process described below.
+
+---
+
+## 1. Artifacts in this folder
+
+| File | Purpose | Format | Owner | Lifecycle |
+|---|---|---|---|---|
+| `eparts_requirements.md` | Human-readable body of the requirements specification (Introduction, HLRs, FRs in EARS, DRs, QAS, Operational Scenarios, Design Constraints, Validation). | Markdown | Requirements Lead | Draft → Under Review → Approved → Baselined |
+| `eparts_requirements.yaml` | Structured front matter and machine-readable mirror of the requirements tables (HLR / FR / DR / QAS / DC / VAL). Source of truth for downstream agents. | YAML | Requirements Lead | Draft → Under Review → Approved → Baselined |
+| `user_stories.yaml` | One story per FR/DR with acceptance criteria. Auto-maintained by an agent that watches `eparts_requirements.yaml`. | YAML | Product Owner (approves) / Agent (drafts) | Draft → Approved |
+| `traceability.csv` | HLR ↔ FR ↔ DR ↔ QAS ↔ VAL ↔ Test traceability matrix. Regenerated whenever the YAML changes. | CSV | Requirements Lead | Regenerated artifact |
+| `requirements.metrics.yaml` | Process measurements for the Requirements Engineering process itself (time, defects, reviewer, prompt effectiveness, tokens, story deltas, run history). | YAML | QA / Process Lead | Append-only log |
+| `../adrs/` | Architecture Decision Records linked from requirements when a requirement locks in an architectural choice. | Markdown | Architecture Lead | Draft → Approved |
+| `README.md` | This file. Describes the ETVX of the Requirements Engineering process. | Markdown | Requirements Lead | Living document |
+
+All artifacts (except `requirements.metrics.yaml`, which is an append-only log)
+follow the SES lifecycle: **Draft → Under Review → Approved → Baselined**.
+The current state is recorded in the front matter of each file.
+
+---
+
+## 2. Source inputs (out-of-folder)
+
+These inputs feed the Requirements Engineering process and are referenced from
+`eparts_requirements.yaml.source_inputs`:
+
+- `../Revised eParts Project Statement of Work.docx`
+- `../eParts General Info.pdf`
+- `../Context Diagram V3.pdf` / `Context Diagram V3.png`
+- `../Notional Software Workflow Diagram_version2 (02_11).pdf`
+- `../Software Engineering System (SES).pdf`
+- `../LLM Aided SE Framework - Studio Orientatiojn Day Presentation.pdf`
+- Meeting notes and design discussion summaries (linked per requirement where applicable)
+
+Per SES §4.2.1, **meeting notes and discovery discussions feed into requirements
+artifacts**; they are not optional inputs.
+
+---
+
+## 3. Requirements Engineering process (ETVX)
+
+This is the L1 process that produces and maintains the artifacts above. It is an
+instantiation of SES §4.2.1 and the LLM-Aided SE Framework's L1 Requirements
+diagram (slide 16).
+
+### Entry criteria
+
+The process may begin a new revision cycle when **any** of the following hold:
+
+1. A new or revised source input is available (SOW change, new meeting notes,
+ updated product plan, new context diagram).
+2. A stakeholder change request is filed against an Approved or Baselined
+ requirement.
+3. A defect found downstream (architecture, implementation, test) traces back
+ to a missing, ambiguous, or incorrect requirement.
+4. A scheduled review interval elapses (default: end of each iteration).
+
+Pre-conditions:
+- Source inputs are accessible and version-pinned.
+- The current `eparts_requirements.yaml` state is known (Draft / Under Review /
+ Approved / Baselined).
+- The Requirements Lead and at least one Stakeholder Reviewer are identified.
+
+### Tasks
+
+1. **Requirements Extraction (AI-assisted).**
+ The Requirements Lead, with LLM assistance, extracts or revises:
+ - HLRs (high-level requirements)
+ - FRs in EARS form
+ - DRs (derived requirements) with priority and HLR trace
+ - QAS using the SEI 6-part template
+ - Operational scenarios with per-step requirement refs
+ - Design constraints with source attribution
+ - Validation requirements
+ The LLM contributes: template generation, completeness checking, ambiguity
+ detection, and cross-reference checks. **The human owns scope, meaning, and
+ approval (SES §4.2.1).**
+2. **Structured Mirror Update.**
+ Update `eparts_requirements.yaml` so that the structured tables match the
+ Markdown body. The YAML is the source of truth for downstream agents.
+3. **User Story Sync (Agentic).**
+ An agent watches `eparts_requirements.yaml` and updates `user_stories.yaml`
+ so that every FR and "Must"/"Should" DR has a corresponding story with
+ acceptance criteria. Story deltas are logged.
+4. **Traceability Refresh.**
+ Regenerate `traceability.csv` so HLR ↔ FR ↔ DR ↔ QAS ↔ VAL ↔ Test links are
+ complete.
+5. **Metrics Capture.**
+ Append a run record to `requirements.metrics.yaml` (see §5).
+
+### Verification
+
+A revision is verified before it can advance state.
+
+- **Completeness check (AI-assisted):** every HLR has at least one FR; every
+ FR has at least one VAL; every DR traces to an HLR; every QAS has a Measure.
+- **Ambiguity check (AI-assisted):** flag EARS violations, vague modal verbs,
+ unmeasurable QAS, and missing acceptance criteria in user stories.
+- **Stakeholder review (human):** Ops Reviewer, Architecture Lead, and Data/ML
+ Lead read the diff. Comments are resolved or recorded as deferred.
+- **Feasibility assessment (human):** Engineering Lead confirms each FR/DR is
+ buildable within the iteration, or the requirement is moved out of scope.
+- **Traceability check:** `traceability.csv` has no orphan rows.
+
+A revision fails verification if any of the above is open. Failures return the
+artifact to **Draft** state with review comments captured in the metrics log.
+
+### Exit criteria
+
+A revision exits the process when **all** of the following are true:
+
+1. All verification checks pass.
+2. All review comments are resolved or explicitly deferred with a tracked
+ follow-up.
+3. The state field in `eparts_requirements.yaml` is advanced:
+ - **Draft** while authoring.
+ - **Under Review** when verification is in progress.
+ - **Approved** when the Requirements Lead and required reviewers sign off.
+ - **Baselined** when the iteration's scope is locked; further changes
+ require a new revision cycle.
+4. `user_stories.yaml`, `traceability.csv`, and `requirements.metrics.yaml`
+ are updated and committed in the same change.
+5. The new version is recorded in the Version History block of
+ `eparts_requirements.md`.
+
+---
+
+## 4. Resources (who does what)
+
+Per SES §5.1 / §5.2:
+
+| Role | Responsibility in this process |
+|---|---|
+| Requirements Lead (Team/Project Lead) | Owns scope, runs extraction, drives reviews, advances state. |
+| Architecture Lead | Reviews requirements for architectural feasibility; raises ADRs when a requirement implies a decision. |
+| Data / ML Lead | Validates ML-related FRs (confidence scoring, routing thresholds, retraining). |
+| Engineering Lead | Feasibility and buildability sign-off. |
+| QA / Process Lead | Owns `requirements.metrics.yaml`, validation requirements, and quality gates. |
+| Ops Reviewer (stakeholder) | Validates personas, operational scenarios, and review-UI requirements. |
+| **AI / LLM** | Drafts, completeness-checks, detects ambiguity, generates user-story templates and acceptance criteria, regenerates traceability. **Does not approve.** No proprietary data is uploaded to external AI tools (SES §5.2). |
+
+---
+
+## 5. Measurements
+
+Captured per run in `requirements.metrics.yaml`. Aligned with the LLM-Aided SE
+Framework slide 19 (L1 Partial Measurement Design - Requirements Detail) and
+SES §6.
+
+Per-run fields:
+
+- `run_id`, `timestamp`, `triggered_by`
+- `time_spent_minutes` (total, and broken down by extraction / review / sync)
+- `defects_found` (count, by type, by reviewer)
+- `prompt_effectiveness` (re-prompt rate, useful-output ratio)
+- `tokens_used` (input / output)
+- `example_quality` (subjective 1-5 from author + reviewer)
+- `story_deltas` (count of stories added / changed / removed)
+- `run_history_ref` (link to the conversation or commit that produced the change)
+
+Aggregate metrics reviewed at each iteration boundary:
+
+- Documentation churn (changes per requirement per iteration)
+- Number of times a requirement is revisited after Baselined
+- Review latency (Under Review → Approved time)
+- Human correction volume vs LLM draft size
+
+---
+
+## 6. Conventions
+
+- **IDs are stable.** Once an HLR/FR/DR/QAS/DC/VAL ID is published in an
+ Approved revision, it is never reused or renumbered. Deprecated requirements
+ are marked `state: deprecated` rather than deleted.
+- **EARS form** for all FRs: *"When/While/If \, the \ shall
+ \."* Ambient requirements use *"The \ shall \."*
+- **Traceability is mandatory.** Every DR cites its parent HLR. Every VAL cites
+ the requirement(s) it verifies.
+- **One requirement per row / list item.** No compound requirements joined by
+ "and".
+- **No proprietary supplier data** in any file in this folder, including
+ examples (SES §5.2).
+
+---
+
+## 7. Quick start for a new revision
+
+1. Create a working branch.
+2. Set `state: Draft` in `eparts_requirements.yaml` front matter and bump the
+ version (`X.Y` minor for content changes, `X.0` major for scope changes).
+3. Run extraction; edit `eparts_requirements.md` and mirror into
+ `eparts_requirements.yaml`.
+4. Let the user-story agent regenerate `user_stories.yaml` and review the diff.
+5. Regenerate `traceability.csv`.
+6. Append a run record to `requirements.metrics.yaml`.
+7. Move state to `Under Review`, request reviewers.
+8. Resolve comments → `Approved` → at iteration lock → `Baselined`.
diff --git a/docs/REQUIREMENTS-TO-ADR-MAPPING.md b/docs/REQUIREMENTS-TO-ADR-MAPPING.md
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+# Requirements-to-ADR Traceability Matrix
+
+This document maps every requirement, constraint, quality attribute scenario, operational scenario, and validation test from the **Product Specification v2.0 (April 24, 2026)** to the Architecture Decision Records that satisfy or constrain it.
+
+Each entry identifies which ADRs are *primary* (the decision that directly addresses the requirement) and which are *supporting* (decisions that the requirement also depends on).
+
+---
+
+## 1. High-Level Requirements (HLRs)
+
+| ID | Requirement | Primary ADRs | Supporting ADRs |
+|---|---|---|---|
+| HLR-1 | Ingest from diverse supplier formats (Email, SFTP, CSV, PDF) | ADR-001 (pipe-and-filter establishes Ingestion Gateway as a distinct filter) | ADR-008 (App Service hosts the polled SFTP/email/HTTPS inbound channels) |
+| HLR-2 | Normalize ingested data into a standardized intermediate structure | ADR-007 (attribute-row canonical schema is the standardized structure) | ADR-001 (Normalization is a separate filter between ingestion and prediction) |
+| HLR-3 | Predict attributes and assign confidence scores using ML | ADR-003 (hybrid prediction produces values + confidence) | ADR-002 (PredictionServiceInterface is the contract); ADR-011 (retraining keeps the model current) |
+| HLR-4 | Maintain a persistent Human Review Queue for low-confidence predictions | ADR-009 (review queue as persistent DB table) | ADR-004 (per-attribute routing determines what enters the queue); ADR-005 (configurable threshold determines what counts as low-confidence) |
+| HLR-5 | Write approved data to PIMS staging tables for downstream reconciliation | ADR-006 (idempotent writeback via composite natural key) | ADR-008 (Publish/Sync Job runs as a timer-triggered Azure Function) |
+
+---
+
+## 2. Functional Requirements (FRs)
+
+| ID | Requirement | Primary ADRs | Supporting ADRs |
+|---|---|---|---|
+| FR-1 | Ingestion Gateway shall create ingestion record (supplier ID, timestamp, source) on SFTP/Email receipt | ADR-001 (Ingestion Gateway as a distinct filter) | ADR-007 (ingestion record stored in canonical schema); ADR-008 (App Service hosts the gateway) |
+| FR-2 | Normalize heterogeneous supplier inputs into canonical schema before prediction | ADR-007 (attribute-row canonical schema) | ADR-001 (normalization filter precedes prediction filter) |
+| FR-3 | Generate predictions per-attribute with confidence scores | ADR-003 (hybrid prediction emits per-attribute confidence); ADR-004 (per-attribute routing requires per-attribute scoring) | ADR-002 (PredictionServiceInterface defines the per-attribute contract) |
+| FR-4 | Route below-threshold attributes to the persistent Human Review Queue | ADR-004 (per-attribute routing); ADR-005 (configurable threshold) | ADR-009 (queue is the destination) |
+| FR-5 | Queue retains prediction, confidence, source file reference, review status | ADR-009 (review queue schema) | ADR-010 (audit trail captures the same fields for history) |
+| FR-6 | Log every auto-accept, approval, correction, rejection in append-only audit trail | ADR-010 (append-only audit trail) | — |
+| FR-7 | Configurable confidence thresholds (calibration TBD) | ADR-005 (externalized threshold as configuration) | ADR-004 (per-attribute routing makes per-attribute thresholds possible) |
+| FR-8 | Write approved attributes to PIMS staging using idempotent application-layer writeback | ADR-006 (idempotent writeback via composite natural key) | ADR-008 (Publish/Sync Job is the writeback runtime); ADR-007 (canonical schema feeds the upsert) |
+| FR-9 | Routing Engine makes per-attribute decisions using configurable thresholds | ADR-004 (per-attribute routing); ADR-005 (configurable threshold) | — |
+| FR-10 | Human Review Queue shall be persistent and queryable | ADR-009 (queue as persistent DB table is queryable via SQL) | ADR-008 (Azure SQL Database hosts the queue) |
+| FR-11 | Writeback idempotent via natural key (submission ID + attribute ID) | ADR-006 (composite natural key) | ADR-007 (attribute-row schema makes attribute-level natural key meaningful) |
+| FR-12 | Emit confidence distributions, correction rates, routing decisions, pipeline metrics to Datadog | ADR-012 (stage-by-stage Datadog telemetry) | ADR-010 (audit trail is the durable backing source for these metrics) |
+| FR-13 | Archive raw supplier files in Azure Blob Storage for traceability | ADR-008 (deployment topology specifies Blob Storage for raw file archive) | ADR-010 (audit trail references the archived file) |
+
+---
+
+## 3. Derived Requirements (DRs)
+
+| ID | Requirement | Primary ADRs | Supporting ADRs |
+|---|---|---|---|
+| DR-1 (Must) | Archive raw supplier files in Azure Blob Storage for traceability | ADR-008 (Blob Storage is part of the deployment topology) | ADR-010 (audit trail entries reference archived files) |
+| DR-2 (Future / TBD) | Corrected data from review queue logged for future retraining and offline model improvement | ADR-010 (audit trail captures corrections as labeled examples); ADR-011 (retraining pipeline consumes them) | ADR-009 (queue is where corrections originate); ADR-002 (interface contract is preserved across retraining) |
+| DR-3 (Must) | Writeback must be idempotent; retry must not create duplicates | ADR-006 (idempotent writeback via composite natural key) | — |
+
+---
+
+## 4. Quality Attribute Scenarios (QASs)
+
+| ID | Attribute | Primary ADRs | Supporting ADRs |
+|---|---|---|---|
+| QAS-1 | Accuracy — ≥95% auto-accepted attributes correct; no incorrect records reach PIMS without review | ADR-004 (per-attribute routing keeps review volume proportional to risk); ADR-005 (configurable threshold is the accuracy lever); ADR-006 (idempotency prevents mechanical duplication errors) | ADR-003 (hybrid prediction provides the confidence signal); ADR-009 (review queue is the gate before PIMS); ADR-012 (telemetry detects accuracy drift) |
+| QAS-2 | Modifiability — model swap localized to prediction package | ADR-002 (PredictionServiceInterface) | ADR-001 (pipe-and-filter style enables filter replacement); ADR-011 (retraining swaps model versions through the same interface) |
+| QAS-3 | Modifiability — new product category supported without changing pipeline structure | ADR-007 (attribute-row schema; new categories are data, not schema migrations) | ADR-001 (filter sequence unchanged across categories); ADR-002 (prediction strategy retrained behind stable interface) |
+| QAS-4 | Availability — zero data loss during Prediction Service outage | ADR-009 (persistent review queue survives outages); ADR-008 (Azure SQL holds staging tables that buffer in-flight data) | ADR-001 (staging tables between filters act as checkpoints); ADR-006 (idempotent writeback enables safe retry on recovery) |
+| QAS-5 | Monitorability — drift detected before accuracy drops below QAS-1 | ADR-012 (Datadog telemetry from each stage); ADR-010 (audit trail is the durable source for drift signals) | ADR-011 (correction rates feed retraining as well as drift detection) |
+
+---
+
+## 5. System Constraints (C-1 to C-8)
+
+| ID | Constraint | Primary ADRs | Supporting ADRs |
+|---|---|---|---|
+| C-1 | Azure deployment using managed services | ADR-008 (single Azure App Service unit; Azure SQL; Azure Blob; Azure Function) | — |
+| C-2 | No PIMS API; integrate via staging tables | ADR-006 (idempotent writeback via natural key, application-layer enforced because no API/rollback) | ADR-008 (Publish/Sync Job uses pyodbc across the trust boundary) |
+| C-3 | Python backend for ML | ADR-008 (single Python Azure App Service); ADR-002 (Python `PredictionServiceInterface`) | ADR-003 (hybrid implementation in Python); ADR-011 (retraining job in the Python prediction package) |
+| C-4 | Phase scope: valves and actuators only initially | ADR-007 (attribute-row schema designed so category expansion is a data change, not a structural one) | ADR-003 (hybrid approach tolerates the small initial labeled set typical of phase scope) |
+| C-5 | No direct production writes; use staging | ADR-006 (writeback targets staging tables only) | ADR-009 (review queue mediates before any approval); ADR-010 (audit trail records every staging write) |
+| C-6 | No pricing in ML pipeline | (System-wide scope rule; not specifically addressed by any single ADR — ADR-007's canonical schema simply excludes pricing attributes) | ADR-007 (canonical schema scope) |
+| C-7 | Capstone timeline | ADR-008 (single deployment unit chosen specifically because microservices exceed team capacity); ADR-002 (internal interface chosen over REST microservice for the same reason) | ADR-001 (pipe-and-filter mirrors existing manual workflow, reducing risk and rework) |
+| C-8 | Auth0 stretch goal for RBAC | ADR-009 (review queue is a DB table with stable schema; Auth0-gated UI would read from it without changes elsewhere) | — |
+
+---
+
+## 6. Design Constraints (DC-1 to DC-3)
+
+| ID | Constraint | Primary ADRs | Supporting ADRs |
+|---|---|---|---|
+| DC-1 | Python-based backend | ADR-008 (Python on Azure App Service); ADR-002 (Python interface) | ADR-003, ADR-011 (Python prediction and retraining) |
+| DC-2 | Auth0 negotiable, stretch goal | ADR-009 (queue schema stable enough to plug an Auth0-gated UI on top later) | — |
+| DC-3 | Raw supplier files archived in Azure Blob Storage | ADR-008 (Blob Storage in deployment topology) | ADR-010 (audit trail references archived files) |
+
+---
+
+## 7. Operational Scenarios (SCEN-1, SCEN-2)
+
+| ID | Scenario | ADRs Exercised |
+|---|---|---|
+| SCEN-1 | End-to-end ingestion happy path: supplier upload → ingestion record → normalization → high-confidence prediction → auto-accept → PIMS staging | ADR-001 (filter sequence); ADR-007 (canonical schema for normalization); ADR-003 (prediction with confidence); ADR-004 (per-attribute routing decision); ADR-005 (threshold comparison); ADR-006 (idempotent writeback); ADR-008 (App Service + Publish/Sync Function); ADR-010 (audit trail entries at each stage); ADR-012 (telemetry at each stage) |
+| SCEN-2 | Low-confidence human-in-the-loop: messy PDF → low-confidence prediction → review queue → reviewer correction → idempotent writeback | ADR-001 (filter sequence); ADR-003 (hybrid prediction emits low confidence with reason codes); ADR-004 (per-attribute routing of the low-confidence attribute only); ADR-005 (threshold comparison); ADR-009 (review queue holds the item); ADR-010 (correction recorded in audit trail and flagged as labeled example); ADR-011 (correction feeds next retraining cycle); ADR-006 (idempotent writeback of corrected value) |
+
+---
+
+## 8. Validation Requirements (VAL-1 to VAL-3)
+
+| ID | Validation Test | ADRs Validated |
+|---|---|---|
+| VAL-1 | Ingestion trigger: file upload → ingestion record in DB within 30s | ADR-001 (Ingestion Gateway as filter); ADR-008 (App Service receiving inbound); ADR-007 (canonical schema persists ingestion record) |
+| VAL-2 | Routing logic: mock ML response with Conf=0.2 → item appears in Human Review Queue | ADR-004 (per-attribute routing); ADR-005 (configurable threshold); ADR-009 (persistent review queue) |
+| VAL-3 | PIMS integration: approve item → upsert into PIMS staging; retry does not duplicate | ADR-006 (idempotent writeback via composite natural key); ADR-008 (Publish/Sync Function performs the upsert) |
+
+---
+
+## 9. Coverage Check
+
+Every requirement, constraint, scenario, and validation test in the spec maps to at least one ADR.
+
+ADRs that are not directly traced from any single FR/HLR but support the spec's overall architecture:
+
+- **ADR-001 (pipe-and-filter)** is the structural premise of Section 2 ("System Architecture") in the spec ("The system follows a pipe-and-filter pipeline architecture..."). It is the architectural decision that enables every functional requirement that decomposes the pipeline into stages.
+- **ADR-002 (PredictionServiceInterface)** is the formal mechanism behind QAS-2's response ("...added behind the existing PredictionServiceInterface..."), which is the only place the spec names the interface.
+- **ADR-011 (auto-retraining)** addresses DR-2, which the spec marks Future/TBD. The ADR is `Proposed` rather than `Accepted` because the requirement itself is future-scoped.
+- **ADR-012 (Datadog telemetry)** addresses FR-12 and QAS-5 directly; thresholds remain unspecified pending Refinement 6.
+
+ADRs derived from the architecture report that the spec does *not* mention explicitly:
+
+- **ADR-007 (attribute-row canonical schema)** — the spec mentions "canonical schema" (FR-2, glossary) but does not specify its shape. The ADR records the structural decision behind that schema.
+- **ADR-008 (single Azure App Service)** — the spec calls out hosting on Azure App Service (Section 3.1) but does not justify the single-unit topology. The ADR records the alternatives considered and why microservices were rejected.
+- **ADR-010 (append-only audit trail)** — FR-6 requires the audit trail; the ADR records the append-only design and the rationale for keeping it as the system of record behind ADR-012's dashboards.
+
+---
+
+# 10. ETIM change — Product Specification v1.4 (29 July 2026) → ADRs 0013–0021
+
+Sections 1–9 above map the **v2.0 (24 April 2026)** requirement set to ADRs 0001–0012 and are left intact. This section maps the **v1.3 (29 July 2026)** requirement set — a different, leaner document lineage — to the ETIM ADRs. See [`product-spec-changelog.md`](product-spec-changelog.md) for the two-lineage caveat and [`etim-requirements-change.md`](etim-requirements-change.md) for how the change was managed.
+
+## 10.1 High-Level Requirements (v1.4)
+
+| ID | Requirement | Primary ADRs | Supporting ADRs |
+|---|---|---|---|
+| HLR-1 | Ingest from diverse supplier sources (SFTP and direct upload today; Email and web planned), CSV and PDF | ADR-021 (`source_type` enumerates the channels) | ADR-001 |
+| HLR-2 | Normalize into a standardized intermediate structure by **mechanical cleanup only**, preserving original supplier values as evidence; ETIM assignment is downstream *(v1.3)* | **ADR-014** (evidence/interpretation split), **ADR-021** (nothing interpretive crosses the ingestion boundary) | ADR-013 |
+| HLR-3 | Predict attributes and assign confidence scores using ML | **ADR-016** (ML owns both phases — attribute matching, then ETIM matching) | ADR-002, ADR-003 |
+| HLR-4 | Provide a UI for human review of low-confidence predictions | ADR-018 (what reaches the queue and in what order) | ADR-009, ADR-019 |
+| HLR-5 | Write approved data back to PIMS | **ADR-017** | ADR-006 (mechanism) |
+| **HLR-6** | **Classify products against ETIM and enrich attributes with ETIM identifiers (class, feature, value, unit), keeping original values as evidence** | **ADR-013** (the dictionary), **ADR-016** (the classification), **ADR-014** (keeping originals as evidence) | ADR-017, ADR-019, ADR-020 |
+
+## 10.2 Functional Requirements (v1.4)
+
+| ID | Requirement | Primary ADRs | Supporting ADRs |
+|---|---|---|---|
+| FR-1 | Ingestion record with supplier ID, timestamp, source channel | ADR-021 | ADR-001 |
+| FR-2 | Validate file integrity before processing; failures to quarantine, not silently dropped | ADR-021 (an invalid handoff record is a validation failure) | — |
+| FR-3 | Confidence score (0.0–1.0) for every predicted attribute | ADR-016 (confidence per ETIM assignment) | ADR-018 |
+| FR-4 | Route below-threshold items to the Human Review Queue | **ADR-018** | ADR-004, ADR-005, ADR-009 |
+| FR-5 | Review UI shows predicted value alongside the original source snippet | ADR-014 (the evidence columns that make this possible) | ADR-009, ADR-019 |
+| FR-6 | Log every human review action to an immutable audit trail | ADR-010 | ADR-016 (the ETIM mapping is what gets logged) |
+| FR-7 | Authorized Ops Leads adjust the auto-acceptance threshold | ADR-018 (class + attribute thresholds) | ADR-005, ADR-019 |
+| FR-8 | Write attributes to PIMS upon final approval | **ADR-017** | ADR-006 |
+| **FR-9** | **After attribute matching, the ML service matches attributes to ETIM classes, features, controlled values/units, attaching confidence per ETIM assignment and preserving the original supplier value** *(attributed to ML in v1.3)* | **ADR-016** | ADR-013, ADR-014, ADR-018, ADR-019 |
+| **FR-10** | **Load and maintain the ETIM reference dictionary for the pinned ETIM release identified in C-4** | **ADR-013** (load), **ADR-020** (which release, and why only one) | ADR-015 |
+
+## 10.3 Derived Requirements (v1.4)
+
+| ID | Requirement | Trace | Primary ADRs |
+|---|---|---|---|
+| DR-1 | Archive the original raw file in Azure Blob Storage as evidence | HLR-1 | ADR-021 (`source_ref`) |
+| DR-2 | Support manual triggering of retraining using corrected review data | HLR-3 | ADR-011 |
+| DR-3 | Writeback must be idempotent; retry must not duplicate | HLR-5 | ADR-017 (mechanism from ADR-006) |
+| **DR-4** | **Approved PIMS data keyed by ETIM identifiers (release, class, feature); the idempotency key shall include them** | HLR-6 | **ADR-017** — direct realization |
+
+## 10.4 Quality Attribute Scenarios (v1.4)
+
+v1.4 carries three QAS. The five-QAS set (QAS-1 Accuracy … QAS-5 Monitorability) cited by ADRs 0001–0015 belongs to the v2.0 lineage and is mapped in section 4 above.
+
+⚠️ **ID collision, owned not hidden.** This lineage's **QAS-3 (Modifiability — client feature policy)** is a different scenario from the v2.0 lineage's **QAS-3 (Accuracy)**. QAS-3 is the next free number in *this* document, and skipping it to dodge a foreign document's numbering would be worse. Reconciling the two ID spaces is the same open work already recorded in `product-spec-changelog.md`.
+
+| ID | Scenario | Measure | Primary ADRs | Supporting ADRs |
+|---|---|---|---|---|
+| QAS-1 | **Modifiability** — a new supplier format is added to the pipeline | Integrated and deployed within 4 engineering hours | ADR-021 (a new format is a new `source_type` + parser; nothing downstream changes) | ADR-001, ADR-016 |
+| QAS-2 | **Usability** — reviewer faces 100 low-confidence items | 10 items/minute for simple accept/reject | **ADR-018** (class-review-first stops the queue filling with decisions a class change would void) | ADR-009, ADR-019 |
+| **QAS-3** | **Modifiability** — the client changes which ETIM features are mandatory for a class | Takes effect for the next batch with no code deployment | **ADR-019** — added in v1.4 to hold this decision | ADR-016, ADR-018 |
+
+## 10.5 System Constraints (v1.4: C-1 … C-4)
+
+| ID | Constraint | ADRs |
+|---|---|---|
+| C-1 | Cost-effective design; avoid cost growing linearly per tenant | ADR-015 (Postgres now rather than Azure SQL); ADR-020 (the upgrade path is the expensive option and is not built) |
+| C-2 | Privacy compliance — support deletion requests for specific supplier submissions | ADR-014 (submission-scoped evidence rows are what make targeted deletion possible) |
+| C-3 | Breadth-first delivery — full end-to-end flow for one supplier type before optimizing depth | ADR-019 (permissive policy default is what lets the flow complete); ADR-020 (no upgrade path built); phase-one valve/actuator scope |
+| **C-4** | **ETIM release pinned to 10.0 (EI); later releases and cross-release migration out of scope** | **ADR-020** — this ADR is the decision C-4 records |
+
+## 10.6 Design Constraints (v1.4)
+
+| ID | Constraint | ADRs |
+|---|---|---|
+| DC-1 | Python backend | ADR-021, ADR-013 (loader and CLI are Python) |
+| DC-2 | Auth0 for identity and RBAC | Not addressed by any ETIM ADR — see gaps below |
+| DC-3 | Raw files preserved in Azure Blob for re-processing and traceability | ADR-021 (`source_ref`), ADR-014 (evidence columns), ADR-017 (evidence carried into the published row) |
+
+## 10.7 Operational Scenarios (v1.4)
+
+| Scenario | Step | ADRs |
+|---|---|---|
+| SCEN-1 | 3 — normalize into the Canonical Table, retaining original values; **no ETIM assignment here** *(v1.3)* | ADR-014, ADR-021 |
+| SCEN-1 | 4 — ML matches attributes, then matches them to ETIM class/features/values *(v1.3)* | ADR-016 |
+| SCEN-1 | 5 — auto-accept writes to PIMS | ADR-017, ADR-018 |
+| SCEN-2 | 1 — datasheet PDF via Azure Document Intelligence + LLM extraction | ADR-021 (`pdf_ocr`) |
+| SCEN-2 | 2–3 — 0.45 confidence falls below threshold, routed to review | ADR-018 |
+| SCEN-2 | 4–5 — reviewer sees source evidence, corrects, action logged | ADR-014, ADR-010 |
+| SCEN-2 | 6 — validated value written to PIMS | ADR-017 |
+
+## 10.8 Validation Requirements (v1.4)
+
+| ID | Test | ADRs | Status |
+|---|---|---|---|
+| VAL-1 | Upload to SFTP → ingestion record within 30s | ADR-021, ADR-001 | Ingestion path built |
+| VAL-2 | Mock ML response Conf=0.2 → item in Review Queue | ADR-018 | Confidence branch designed; **validation-failure, unit-failure and required-field branches not testable until ADR-016 lands and the ADR-019 policy is supplied** |
+| VAL-3 | Approve an item → PIMS write succeeds, retry does not duplicate | ADR-017 | Designed; retry case must be re-tested against the ETIM key, not the ADR-006 key |
+| **VAL-4** | **Load ETIM 10.0, then load it again → all 159/5,640/17,377/201,284 rows present, second run is a no-op** | **ADR-013**, ADR-020 | **10 unit tests passing** (`tests/unit/test_etim_loader.py`). `tests/integration/test_etim_real_files.py` covers the same load against the real archive but **skips unless `.tmp_etim_csv` is present**, and that archive is not committed. Neither runs in CI. |
+| **VAL-5** | **Below-threshold class assignment → item routes to class review, no attribute-level routing** | **ADR-018**, ADR-016 | **Specified, not executable** — the matching stages are designed and not built. Recorded rather than deferred silently. |
+
+## 10.9 Coverage check
+
+**Forward trace (completeness).** Every v1.3 requirement maps to at least one ADR, except **DC-2 (Auth0)**, which no ETIM ADR addresses. Auth0 is listed in v1.3 §3.1 as mandatory and in v2.0 as a negotiable stretch goal (C-8/DC-2) — the two lineages disagree, and no ADR resolves it. **This is an open gap.**
+
+**Backward trace (currency).** Every ADR 0013–0021 traces to at least one v1.3 requirement:
+
+| ADR | Anchor requirement |
+|---|---|
+| 0013 | FR-10, HLR-6 |
+| 0014 | HLR-2, HLR-6, FR-5 |
+| 0015 | C-1, FR-10 |
+| 0016 | FR-9, HLR-3, HLR-6 |
+| 0017 | DR-4, FR-8, HLR-5 |
+| 0018 | FR-4, QAS-2 |
+| 0019 | FR-9, C-3, VAL-2 |
+| 0020 | **C-4**, FR-10 |
+| 0021 | HLR-2, FR-1, FR-2, QAS-1 |
+
+No ADR in this set is orphaned, and no ADR was written for work with no requirement behind it (no gold plating).
+
+## 10.10 Known gaps
+
+1. **DC-2 (Auth0)** — mandatory in v1.3, stretch goal in v2.0, no ADR either way.
+2. **The two ID spaces are not reconciled.** ADRs 0001–0015 cite QAS-3/4/5, C-7 and FR-11/12/13, which do not exist in v1.3. Reconciliation is open work; the spring ADRs are deliberately unedited.
+3. **VAL-2's ETIM branches are untestable** until the ADR-016 matching stages land and the ADR-019 client policy is supplied. A passing test suite here does not mean the validation requirement is satisfied.
+4. **Cross-team boundary.** FR-9's implementation sits with the ML stream behind the EPARTS-156 contract, and the OCR path behind EPARTS-159. Our traceability stops at those contracts by design; ADR-021 is the ingestion-side half made explicit.
diff --git a/docs/adr-index.md b/docs/adr-index.md
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+# Architecture Decision Records — eParts Intelligent Ingestion & Attribute Prediction Platform
+
+Project: Pimsie Supreme (CMU MSE Studio Capstone)
+Format: Michael Nygard ADR template (Title, Status, Context, Decision, Consequences), plus a Requirements Traceability section.
+
+**This `docs/00NN-*.md` series is the authoritative ADR set.** See "Known defect" at the foot of this page.
+
+## Index
+
+### Spring baseline — ADRs 0001–0012 (April 2026)
+
+Written against **Product Specification v2.0 (24 April 2026)**. Several are now partly outdated; they are **deliberately left unedited** as the record of what the team decided in April. What changed and why is in [`ETIM-ADR-ASSESSMENT.md`](ETIM-ADR-ASSESSMENT.md).
+
+| # | Title | Status | ETIM verdict |
+|---|-------|--------|--------------|
+| 0001 | Adopt Pipe-and-Filter as the Primary Architectural Style | Accepted | Valid; the ingestion→ML seam is now explicit (ADR-021) |
+| 0002 | Isolate the Prediction Strategy Behind a Stable Internal Interface | Accepted | Valid; both ML phases sit behind it, contract enriched by ADR-016 |
+| 0003 | Use a Hybrid Rule Engine and Semantic Similarity for Attribute Prediction | Tentative | Still holds as ML phase 1; reused inside ADR-016 phase 2 |
+| 0004 | Route Confidence Decisions at the Attribute Level, Not the Record Level | Accepted | Extended by ADR-018 |
+| 0005 | Externalize the Confidence Threshold as Runtime Configuration | Tentative | Widened by ADR-018 to class + attribute thresholds |
+| 0006 | Enforce Idempotent PIMS Writeback via a Composite Natural Key | Accepted | Key superseded by ADR-017; mechanism reused |
+| 0007 | Use an Attribute-Row Canonical Schema for the Staging Table | Accepted | Superseded in part by ADR-014 |
+| 0008 | Deploy the Platform as a Single Azure App Service Unit | Accepted | Topology holds; substrate revisited by ADR-015 |
+| 0009 | Implement the Human Review Queue as a Persistent Database Table | Accepted | Mechanism holds; ETIM context added via ADR-018/0019 |
+| 0010 | Maintain an Append-Only Audit Trail of Every Pipeline Decision | Accepted | Valid; captured fields extend to the ETIM mapping |
+| 0011 | Trigger Retraining Automatically on Human Review Batch Completion | Proposed | Valid; labels are now reviewer ETIM corrections |
+| 0012 | Emit Stage-by-Stage Telemetry to Datadog for Drift Detection | Proposed | Valid. Datadog is the production target; Prometheus + OpenTelemetry + structlog is the local development substrate. `ETIM-ADR-ASSESSMENT.md` reads the code as a contradiction; it is a two-environment choice. |
+
+### ETIM change — ADRs 0013–0021 (June–July 2026)
+
+Written against **Product Specification v1.4 (29 July 2026)** — see [`product-spec-changelog.md`](product-spec-changelog.md) and [`etim-requirements-change.md`](etim-requirements-change.md).
+
+| # | Title | Status | Built? |
+|---|-------|--------|--------|
+| 0013 | Establish a Release-Versioned ETIM Reference Data Layer Owned by Ingestion | Accepted | **Yes** — `models/etim.py`, `etim/loader.py`, `cli/etim.py`, Alembic `0005`; verified against the real ETIM 10.0 EI archive |
+| 0014 | Emit a Source-Preserving Product + Attribute Staging Split | Accepted | **Yes** — `models/staging.py`, Alembic `0006` |
+| 0015 | Target PostgreSQL Now; Defer the Azure SQL Conversion | Accepted | **Yes** — running substrate |
+| 0016 | Add ETIM Matching as a Second ML Phase After Attribute Matching | Accepted | Phase 1 exists; **phase 2 designed** — ML stream, EPARTS-289/290/291 |
+| 0017 | Re-key the PIMS Writeback Contract on ETIM Identifiers | Accepted | No — designed; writer rework EPARTS-299 |
+| 0018 | Extend Routing to ETIM Signals, with a Class-Review-First Path | Accepted | No — designed; depends on 0016 |
+| 0019 | Externalize the Client Feature Policy as Per-Class Configuration | Accepted | Seam decided; **policy values blocked on client** (EPARTS-287) |
+| 0020 | Pin ETIM Release 10.0 (EI) for the Project Duration | Accepted | **Yes** — C-4 introduced in spec v1.2; release-scoping kept in the schema for provenance |
+| 0021 | Formalize the Ingestion → ML Boundary as a Frozen `ExtractedInput` Record | Accepted | **Partly** — spec model, builder, Alembic `0007` merged; orchestrator wiring EPARTS-363 outstanding |
+
+## Status legend
+
+- **Accepted** — Decision made and reflected in the architecture. Does *not* imply the code is written; see the "Built?" column.
+- **Tentative** — Decision made provisionally; specific parameters await empirical evidence.
+- **Proposed** — Decision shape is set; key sub-parameters or ownership are not yet defined. (No ADR currently carries this status.)
+
+## Requirements traceability
+
+Each ADR ends with a **Requirements Traceability** section. ADRs 0001–0012 cite Product Specification v2.0 (24 April 2026); ADRs 0016–0021 cite Product Specification v1.4 (29 July 2026).
+
+A consolidated bidirectional view is in [`REQUIREMENTS-TO-ADR-MAPPING.md`](REQUIREMENTS-TO-ADR-MAPPING.md) — sections 1–9 cover the v2.0 requirement set, section 10 covers the ETIM change.
+
+## Cross-cutting traceability
+
+- **ADR-001** (pipe-and-filter) is the structural premise the rest build on. **ADR-021** makes its most important filter boundary explicit and schema-enforced.
+- **ADR-002** (`PredictionServiceInterface`) is the boundary that protects ADR-003 and ADR-011 from leaking model details. **ADR-016** decomposes what sits behind that interface without changing the interface's role.
+- **ADR-004**, **ADR-005** and **ADR-007** are mutually reinforcing: per-attribute routing needs the attribute-row schema and a tunable threshold. **ADR-018** extends the routing inputs; **ADR-014** splits the schema.
+- **ADR-013 → 0014 → 0016 → 0017** is the ETIM spine: load the dictionary, split evidence from interpretation, match in ML (attribute first, then ETIM), publish keyed on the result.
+- **ADR-019** (client policy) is the only decision gated on an external party, and it gates the validation half of **ADR-018**.
+- **ADR-020** pins the standard to ETIM 10.0 EI and puts upgrades out of scope. `etim_release_id` stays in 0013, 0014 and 0017 for provenance, not for coexistence.
+- **ADR-010** (audit trail) and **ADR-012** (telemetry) provide the observability layer that ADR-011 and drift detection depend on.
+
+## Known defect: a colliding ADR series
+
+`docs/adr/ADR-001-threshold-calibration.md`, `ADR-002-staging-tables.md`, `ADR-003-human-in-loop.md` and `ADR-004-per-attribute-routing.md` are an **agent-generated second series** whose numbers collide with this one while describing different decisions. `docs/adr_adr_threshold_calibration.md` is a third, thinner duplicate of the same decision.
+
+Only `docs/00NN-*.md` is authoritative. Do not add to `docs/adr/`. Note that `.github/workflows/requirements-extraction.yml` writes generated ADRs into `docs/adr/**`, so the collision will grow until that workflow is repointed or its output is triaged into this series. This is recorded as a known artifact-hygiene defect rather than silently tolerated.
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diff --git a/docs/adr/ADR-001-threshold-calibration.md b/docs/adr/ADR-001-threshold-calibration.md
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+# ADR-001: ML Confidence Threshold Calibration
+
+**Status:** Accepted
+**Date:** 2026-03-05
+**Deciders:** Architecture Lead, Data/ML Lead
+**Traced from:** REQ-003 (Confidence Scoring), REQ-005 (Human-in-the-Loop for Low Confidence)
+**Contributing meetings:** Meeting 2026-02-05, Meeting 2026-03-05
+**Contributing sessions:** Christian 2026-02-20
+
+---
+
+## Context
+
+The eParts ML pipeline extracts product attributes from vendor spec sheets. Every prediction
+must carry a confidence score that determines whether it is auto-promoted to the production
+catalog or routed to human review.
+
+During Meeting 2 (Feb 05), the client emphasized that incorrect data in PIMS is worse than
+missing data. POC results from Meeting 4 (Mar 05) revealed that a single global threshold
+fails: product names achieve >0.92 confidence on average while technical specifications hover
+around 0.65. A flat 0.80 cutoff rejects most spec predictions while rubber-stamping name
+predictions that still contain errors.
+
+## Decision
+
+We adopt **per-attribute configurable thresholds** calibrated using **Expected Calibration
+Error (ECE)** on a held-out validation set, combined with **alpha-weighted hybrid scoring**.
+
+**Threshold Calibration.** Each attribute type maintains its own threshold in a versioned YAML
+configuration file. Thresholds are recalibrated when a new model is deployed or when monitoring
+ECE exceeds 0.05. Calibration uses isotonic regression to map raw outputs to well-calibrated
+probabilities.
+
+**Hybrid Scoring.** The final confidence score is:
+
+ score = α × fuzzy_match_score + (1 − α) × ml_model_score
+
+For structured fields like category codes, `α` is low (ML-dominant). For free-text fields like
+product names, `α` is higher to leverage lexical similarity with existing catalog entries. This
+provides a fallback signal when the ML model is uncertain.
+
+**Operational Controls.** Thresholds are exposed in the review dashboard. Every change is logged
+in `artifact_versions.db` for traceability. A safety floor prevents thresholds below 0.50
+without ML Lead approval.
+
+## Consequences
+
+**Positive:**
+- Attribute types with high natural accuracy are not penalized by thresholds set for harder
+ attributes, reducing unnecessary review volume.
+- ECE calibration ensures a 0.85 score genuinely means 85% correctness likelihood.
+- Hybrid scoring provides graceful degradation if the ML model drifts.
+
+**Negative:**
+- Requires a labeled validation set per attribute type (minimum 200 examples each).
+- Per-attribute thresholds add configuration complexity; misconfigured low-volume attributes
+ could go undetected without monitoring alerts.
+- Alpha weighting introduces a second hyperparameter per attribute to tune and document.
+
+## Alternatives Considered
+
+**Single Global Threshold.** One cutoff (e.g., 0.80) across all attributes. Rejected — POC
+data showed a 27-point spread in mean confidence across attribute types.
+
+**Fixed Percentile Cutoff.** Route the bottom N% to review regardless of absolute confidence.
+Rejected — provides no quality guarantee; wastes reviewer time in high-accuracy periods and
+leaks bad predictions in degradation periods.
+
+**No Thresholds (Review Everything).** Rejected — at ~50,000 attributes per batch, full review
+requires more hours than the current manual process, providing negative ROI.
diff --git a/docs/adr/ADR-002-staging-tables.md b/docs/adr/ADR-002-staging-tables.md
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+# ADR-002: Staging Tables Before Production
+
+**Status:** Accepted
+**Date:** 2026-02-19
+**Deciders:** Architecture Lead, Engineering Lead
+**Traced from:** REQ-010 (PIMS Integration), ARCH-004
+**Contributing meetings:** Meeting 2026-02-05, Meeting 2026-02-19, Meeting 2026-03-05
+**Contributing sessions:** Ben 2026-03-10
+
+---
+
+## Context
+
+The eParts ML pipeline produces attribute predictions that must be written to the production
+PIMS catalog. PIMS is the system of record feeding downstream procurement, e-commerce, and
+compliance processes.
+
+Writing predictions directly to PIMS risks corrupting production data — a miscalibrated model
+or malformed vendor spec sheet could affect thousands of SKUs. The client stated in Meeting 3
+(Feb 19) that incorrect catalog data has caused procurement errors before, and automation must
+not increase that risk. The PIMS schema is also subject to infrequent but high-impact changes
+(Risk 4). All infrastructure must be Azure-native, deployed via Bicep.
+
+## Decision
+
+All ML output is written to **staging tables** in Azure SQL Database. No prediction is written
+directly to production PIMS. Promotion occurs only after human approval or an automated holding
+period for above-threshold predictions (see ADR-003).
+
+**Staging Schema.** Mirrors the PIMS schema with additional columns: `prediction_confidence`,
+`model_version`, `source_document_id`, `review_status` (pending/approved/rejected/auto_promoted),
+`reviewed_by`, `staged_at`, and `promoted_at`.
+
+**Promotion Workflow:**
+1. ML pipeline writes predictions with `review_status = pending`.
+2. Above-threshold predictions enter a 24-hour hold, then auto-promote unless flagged.
+3. Below-threshold predictions remain pending in the review queue.
+4. Reviewers approve, reject, or edit via the dashboard.
+5. A scheduled job copies approved/auto-promoted rows to PIMS via its API.
+
+**Retention.** Staged records are retained 90 days post-promotion for rollback. Rejected records
+are kept indefinitely as negative training examples.
+
+## Consequences
+
+**Positive:**
+- Production PIMS is never exposed to unreviewed ML output.
+- Staging provides a full audit trail from source document through model version to review
+ decision to production write.
+- PIMS schema changes can be absorbed by updating the promotion job without modifying the
+ ML pipeline.
+- Rejected predictions become labeled data for model retraining.
+
+**Negative:**
+- Introduces minimum 24-hour latency between prediction and production availability.
+- Doubles storage during the retention window.
+- The promotion job adds operational complexity — failures and partial promotions must be
+ handled transactionally and monitored.
+
+## Alternatives Considered
+
+**Direct Production Write with Rollback.** Write to PIMS and maintain a rollback log. Rejected
+because PIMS serves downstream consumers in near-real-time; by the time a bad batch is detected,
+procurement systems may have acted on incorrect data. Rollback cannot undo downstream effects.
+
+**Shadow Mode (Read-Only Comparison).** Run ML in parallel with manual entry and compare.
+Rejected as permanent architecture — it requires maintaining the full manual workflow. The team
+will use shadow mode during Iteration 1 as a validation technique, not as production design.
+
+**Dual-Write (Staging + Production Simultaneously).** Write to both in a single transaction.
+Rejected because it eliminates the review gate and complicates cross-system transaction management.
diff --git a/docs/adr/ADR-003-human-in-loop.md b/docs/adr/ADR-003-human-in-loop.md
new file mode 100644
index 0000000..894a9df
--- /dev/null
+++ b/docs/adr/ADR-003-human-in-loop.md
@@ -0,0 +1,75 @@
+# ADR-003: Human-in-the-Loop Review Workflow
+
+**Status:** Accepted
+**Date:** 2026-03-05
+**Deciders:** Architecture Lead, Data/ML Lead, QA/Process Lead
+**Traced from:** REQ-005 (Human-in-the-Loop), REQ-003 (Confidence Scoring), ARCH-005
+**Contributing meetings:** Meeting 2026-02-19, Meeting 2026-03-05, Meeting 2026-03-19
+**Contributing sessions:** Christian 2026-02-20, Dennis 2026-03-20
+
+---
+
+## Context
+
+The eParts ML pipeline automates attribute extraction, but full automation is unacceptable for
+the production catalog — incorrect data causes procurement errors and compliance violations.
+However, routing every prediction to review defeats ML efficiency gains. At ~50,000 attributes
+per batch with reviewers Brian and Dewey, full review would exceed current manual process time.
+
+Coach Dennis (Mar 20) flagged reviewer fatigue as a real risk: if reviewers see mostly correct
+predictions, they stop paying attention. The workflow must concentrate human effort where it
+has highest impact while keeping the queue challenging enough to maintain engagement.
+
+## Decision
+
+We implement a **confidence-calibrated review workflow** with three routing tiers.
+
+| Tier | Condition | Action |
+|------|-----------|--------|
+| **Auto-promote** | Confidence ≥ threshold AND not P0 | 24-hour hold, then promoted |
+| **Review queue** | Confidence < threshold OR drift-flagged | Routed to human reviewer |
+| **Mandatory review** | P0 item (safety-critical, high-value, client-flagged) | Always requires human approval |
+
+P0 classification follows business rules in the dashboard configuration — not model confidence.
+Examples: safety-critical parts (brakes, electrical), items above a dollar-value threshold.
+
+**Queue Ordering.** Ordered by expected impact, not simply lowest confidence:
+
+ review_priority = (1 − confidence) × attribute_business_weight × batch_volume_factor
+
+Reviewers always work the highest-impact items first.
+
+**Reviewer Actions.** Approve (accept as-is), Edit (correct and promote — logged as training
+data), Reject (discard prediction), or Escalate (flag for team discussion).
+
+**Feedback Loop.** Every action feeds back into the ML pipeline: approvals confirm calibration,
+edits become gold-labeled retraining data, and rejection patterns identify systematic failures
+by attribute type, vendor, or document format.
+
+## Consequences
+
+**Positive:**
+- Human effort focuses on predictions most likely wrong or most costly if wrong.
+- P0 mandatory review provides a hard safety net regardless of model confidence.
+- The feedback loop means review effort directly improves future accuracy, shrinking the
+ review queue over time.
+- Priority-weighted ordering mitigates reviewer fatigue.
+
+**Negative:**
+- Auto-promote with 24-hour hold adds latency for high-confidence predictions.
+- Business weight configuration requires ongoing maintenance as catalog priorities evolve.
+- Inconsistent reviewer behavior (liberal vs. conservative) creates noisy feedback. Inter-rater
+ reliability must be monitored by QA/Process Lead.
+
+## Alternatives Considered
+
+**Review Everything.** Rejected — does not scale. At 50,000 attributes and ~30 seconds per
+review, full review requires ~417 reviewer-hours per batch. Also causes severe fatigue.
+
+**Review Nothing (Fully Automated).** Rejected — business risk too high. Client trust in the
+ML pipeline is not yet established, and PIMS errors are downstream and difficult to reverse.
+May become viable after sustained production accuracy is demonstrated.
+
+**Random Sampling.** Review a fixed random sample (e.g., 10%) for quality monitoring. Rejected
+as primary workflow because it provides no protection for individual high-risk predictions.
+The team will use sampling as a supplementary monitoring metric alongside this workflow.
diff --git a/docs/adr/ADR-004-per-attribute-routing.md b/docs/adr/ADR-004-per-attribute-routing.md
new file mode 100644
index 0000000..8cedf55
--- /dev/null
+++ b/docs/adr/ADR-004-per-attribute-routing.md
@@ -0,0 +1,76 @@
+# ADR-004: Per-Attribute ML Routing
+
+**Status:** Accepted
+**Date:** 2026-03-19
+**Deciders:** Data/ML Lead, Architecture Lead
+**Traced from:** REQ-008 (Multi-Vendor Format Support), ARCH-003, Risk 3 (ML Uncertainty)
+**Contributing meetings:** Meeting 2026-02-19, Meeting 2026-03-05, Meeting 2026-03-19
+**Contributing sessions:** Jim 2026-03-15
+
+---
+
+## Context
+
+The eParts catalog contains diverse attribute types with fundamentally different characteristics:
+product names are semi-structured free text, category codes come from a controlled vocabulary,
+technical specifications involve numeric values with units from PDF tables, and unit of measure
+is a constrained enumeration.
+
+POC experiments (Meeting 4, Mar 05) confirmed that a single model trained on all attribute types
+produces mediocre results. The BERT-based model achieved 0.91 F1 on category codes but only 0.68
+on technical specifications. The all-MiniLM semantic matcher scored 0.89 on product names but
+failed on numeric specs where semantic similarity is meaningless.
+
+The team has three candidate models (BERT, all-MiniLM, fine-tuned LLM extractor) with different
+strengths. Risk 3 identifies this model selection uncertainty as a key risk. Rather than forcing
+a single-model decision, the architecture should let each attribute type use its best model.
+
+## Decision
+
+We implement **per-attribute routing** where each attribute type flows through its own model
+and threshold configuration, managed via a YAML routing table.
+
+**Routing Configuration** is maintained in `config/attribute_routing.yaml`. Each attribute entry
+specifies: primary model, calibrated threshold (per ADR-001), alpha weight for hybrid scoring,
+and a fallback strategy when confidence drops below the 0.50 safety floor. Current mappings:
+`product_name` → all-MiniLM (α=0.6, threshold 0.88), `category_code` → BERT classifier
+(α=0.2, threshold 0.82), `technical_spec` → LLM extractor (α=0.3, threshold 0.75),
+`unit_of_measure` → enum-matcher (α=0.1, threshold 0.90).
+
+**Pipeline Flow:** Ingestion parses vendor documents and identifies attribute fields → each
+attribute dispatches to its configured model → hybrid scoring (ADR-001) computes calibrated
+confidence → predictions write to staging (ADR-002) and route through review (ADR-003).
+
+**Model Lifecycle.** Each attribute's model is independently versioned, trained, and evaluated.
+A category code model upgrade does not require revalidating product name. New attribute types
+are onboarded by adding a routing entry and deploying a model — no pipeline code changes.
+
+## Consequences
+
+**Positive:**
+- Each attribute uses the architecture best suited to its data, maximizing per-attribute
+ accuracy over single-model average-case optimization.
+- Independent versioning enables targeted retraining on the worst-performing attribute without
+ disrupting well-performing ones.
+- The routing table is a single, auditable configuration surface for the entire ML pipeline.
+- New attributes are a config change, supporting the client's goal of expanding coverage.
+
+**Negative:**
+- Multiple models to train, evaluate, and maintain. With 4 attributes and 3 candidates, the
+ experimentation matrix is 12 combinations.
+- Pipeline complexity increases — the dispatcher must handle model failures, timeouts, and
+ version mismatches independently per attribute.
+- Cross-attribute consistency is not guaranteed. A SKU may have high-confidence name but
+ low-confidence category, creating a fragmented review experience.
+
+## Alternatives Considered
+
+**Single Model for All Attributes.** One fine-tuned LLM extracts everything in a single pass.
+Rejected — POC showed a 23-point F1 spread across attribute types with every unified model
+tested. A single model optimizes for average performance, underserving hard attributes while
+wasting capacity on easy ones, and creates a single point of failure.
+
+**Manual Rules for Some + ML for Others.** Deterministic rules (regex, lookup tables) for
+structured attributes, ML for unstructured. Rejected as permanent architecture because rules
+require manual updates when categories or units change. The `enum-matcher` for unit of measure
+borrows from this idea while staying within the ML routing framework.
diff --git a/docs/adr_adr_threshold_calibration.md b/docs/adr_adr_threshold_calibration.md
new file mode 100644
index 0000000..71a2567
--- /dev/null
+++ b/docs/adr_adr_threshold_calibration.md
@@ -0,0 +1,31 @@
+# ADR: Adr Threshold Calibration
+
+**Current Version:** v1.0
+**Versions:** 2
+
+## Version History
+
+### v0.1
+**Changed by:** adr_generator
+**Trigger:** Client Meeting 2 (Feb 05)
+**Date:** ?
+**Contributing meetings:** ['Meeting 2026-02-05']
+
+Decision opened after client emphasized accuracy concerns in Meeting 2.
+
+
+
+
+### v1.0
+**Changed by:** adr_generator
+**Trigger:** POC results + Client Meeting 4
+**Date:** ?
+**Contributing meetings:** ['Meeting 2026-03-05']
+
+After POC results showed wide variance across attribute types, team decided per-attribute calibration.
+
+
+
+
+---
+_Auto-generated from artifact_versions.db_
\ No newline at end of file
diff --git a/docs/agentic-augmented-scrum.pdf b/docs/agentic-augmented-scrum.pdf
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diff --git a/docs/ai_measurements.md b/docs/ai_measurements.md
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+++ b/docs/ai_measurements.md
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+# AI Effectiveness Measurements — Real Numbers
+
+This document answers: "Is using AI worth it? What's the evidence?"
+
+---
+
+## Hard Numbers From Our System
+
+| Metric | Value |
+|--------|-------|
+| Total agent runs | 183 |
+| Successful | 173 (94.5%) |
+| Failed | 10 (5.5%) |
+| LLM API calls | 7 |
+| Total tokens consumed | 18,817 |
+| Total cost | $0.069 |
+| Average run duration | 1,799 ms |
+| Runs needing human review | 1 (0.55%) |
+| Human corrections applied | 0 |
+
+---
+
+## Counterfactual Analysis: AI vs. No AI
+
+Christian's framework: compare total cost WITH AI (including review, correction, rework) vs. WITHOUT AI.
+
+### Task 1: Meeting Transcript Parsing
+
+| | Without AI | With AI |
+|---|-----------|---------|
+| Human time | 5 meetings × 45 min = **225 min** | 5 × 30 sec = **2.5 min** |
+| LLM cost | — | **$0.02** (3 calls, ~5K tokens) |
+| Review time | — | 5 min spot-check |
+| Rework time | — | 15 min fixing 1 bad parse |
+| **Total cost** | **225 min** | **22.5 min + $0.02** |
+| **Net savings** | — | **202 min (90%)** |
+| **Repeatable?** | Yes, every meeting | Yes, every meeting |
+
+### Task 2: Priority Classification (P0/P1/P2)
+
+| | Without AI | With AI |
+|---|-----------|---------|
+| Human time | ~100 items × 2 min team discussion = **200 min** | 100 × 2 sec = **3 min** |
+| LLM cost | — | **$0.01** (2 calls) |
+| Review time | — | 10 min review P0 items |
+| Rework time | — | 5 min adjusting 3 mis-classified items |
+| **Total cost** | **200 min** | **18 min + $0.01** |
+| **Net savings** | — | **182 min (91%)** |
+| Accuracy | Human: ~95% (consensus) | AI: ~87% (matches human on 87/100) |
+
+### Task 3: Jira Ticket Creation
+
+| | Without AI | With AI |
+|---|-----------|---------|
+| Human time | 50 tickets × 3 min = **150 min** | 50 × 2 sec = **1.7 min** |
+| LLM cost | — | **$0.00** (offline, keyword-based) |
+| Review time | — | 15 min scanning tickets for correctness |
+| Rework time | — | 10 min fixing 4 bad descriptions |
+| **Total cost** | **150 min** | **26.7 min** |
+| **Net savings** | — | **123 min (82%)** |
+
+### Task 4: Risk Register Population
+
+| | Without AI | With AI |
+|---|-----------|---------|
+| Human time | Identify + document 16 risks = **120 min** | One seed run = **5 min** |
+| Benefit | Risks you thought of | Risks from 3 sources (arch doc, coach, meetings) — catches more |
+| Review time | — | 20 min reviewing risk statements |
+| **Total cost** | **120 min** | **25 min** |
+| **Net savings** | — | **95 min (79%)** |
+
+### Task 5: Traceability Linking
+
+| | Without AI | With AI |
+|---|-----------|---------|
+| Human time | 764 links across 189 artifacts = **gets abandoned** | Auto-linked = **~10 sec** |
+| LLM cost | — | **$0.00** (keyword matching) |
+| Quality | Humans link 15-20 items then give up | 764 links, ~85% semantically correct |
+| False positives | — | ~15% false positive rate (keyword matching) |
+| **Verdict** | **Doesn't happen** | **Imperfect but exists** |
+
+### Task 6: Requirements Extraction
+
+| | Without AI | With AI |
+|---|-----------|---------|
+| Human time | Read meeting notes, write formal REQs = **180 min** | LLM synthesis = **2 min** |
+| LLM cost | — | **$0.03** (2 calls, ~8K tokens) |
+| Review time | — | 30 min review for accuracy |
+| Rework time | — | 20 min fixing 2 bad requirements |
+| Quality | Human: precise but slow | AI: broader coverage, sometimes imprecise |
+| **Total cost** | **180 min** | **52 min + $0.03** |
+| **Net savings** | — | **128 min (71%)** |
+
+---
+
+## Summary: Where AI Helps vs. Doesn't
+
+### Where AI Genuinely Helps
+
+| Area | Why |
+|------|-----|
+| **Transcript → Structure** | Humans hate transcribing 45-min meetings. LLM does it in 30 sec. |
+| **Bulk Jira creation** | Repetitive task. Perfect for automation. |
+| **Traceability** | Impossible to maintain manually at 760+ links. AI makes it possible. |
+| **Risk surfacing** | AI catches risks from multiple sources that humans miss when reading one doc at a time. |
+| **Coach session memory** | RAG makes coach advice searchable. Without it, advice is locked in recordings. |
+| **Drift detection** | Compares current decisions against architecture doc. Human would need to re-read the doc each time. |
+
+### Where AI Does NOT Help (Honest Assessment)
+
+| Area | Why | What We Did About It |
+|------|-----|---------------------|
+| **Offline transcript parsing** | Without LLM, regex extracts speech fragments, not real action items. Quality drops from ~87% to ~40%. | We designed graceful degradation — system works but flags low-confidence outputs for human review. |
+| **False positive traceability** | Keyword matching links "data access" risk to "data pipeline" requirement even when semantically unrelated. ~15% false positive rate. | Accepted trade-off: 85% correct links > 0% links. LLM integration would improve to ~95% but at token cost. |
+| **Short/casual meetings** | 24-min casual meeting produces 4 thin action items regardless of AI. | Not an AI problem — garbage in, garbage out. We document meeting quality as a metadata field. |
+| **Prompt sensitivity** | Same meeting + different prompt version = different output. | Prompt registry pins versions. But fundamentally, probabilistic models are probabilistic. |
+| **Requirement categorization** | AI sometimes misclassifies CONSTRAINT as NON_FUNCTIONAL. Boundary between categories is fuzzy even for humans. | Prompt engineering helps (~87% accuracy). Human review catches remaining 13%. |
+
+---
+
+## Cost Breakdown
+
+| Component | Cost | Notes |
+|-----------|------|-------|
+| LLM API tokens | $0.069 | 18,817 tokens across 7 calls |
+| ChromaDB embeddings | $0.00 | Local ONNX model — no API cost |
+| Keyword matching | $0.00 | Pattern matching, no LLM |
+| SQLite storage | $0.00 | Local files |
+| **Total infrastructure** | **$0.069** | For 183 agent runs |
+
+### Is It Worth the Tokens?
+
+Christian's test: *"Is the improvement worth more than the measurement cost?"*
+
+- **Repeated tasks (meetings every 2 weeks):** YES. $0.01/meeting × 8 meetings = $0.08 total. Saves 200+ minutes per meeting cycle.
+- **One-time tasks (risk register seeding):** YES, but barely. $0 token cost (offline). Saved 95 min. No ongoing benefit.
+- **Measurement itself:** Our metrics system adds ~50ms per agent run. Zero token cost. The measurement is essentially free.
+
+---
+
+## GQIM Framework Application
+
+| Goal | Question | Indicator | Metric |
+|------|----------|-----------|--------|
+| Reduce manual effort | How much time does AI save per meeting cycle? | Time comparison | **202 min saved per meeting (90%)** |
+| Ensure quality | Are AI outputs usable without rework? | Rework rate | **5.5% failure, 0.55% needs review** |
+| Control cost | Is AI cost justified vs. human cost? | $/minute-saved | **$0.0003/min saved** |
+| Maintain traceability | Can we trace artifacts to origins? | Coverage | **0 orphaned artifacts out of 184** |
+| Improve decisions | Are risks identified proactively? | Risk coverage | **16 risks from 3 sources vs ~5 from manual** |
diff --git a/docs/arch.png b/docs/arch.png
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diff --git a/docs/artifact_catalog.md b/docs/artifact_catalog.md
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+# eParts SES — Artifact Catalog
+
+> Every concrete artifact the system produces, organized by pipeline.
+> For each artifact: name, storage location, producing agent, format, and example content.
+
+---
+
+## Table of Contents
+
+1. [Requirements Pipeline](#1-requirements-pipeline)
+2. [Coach Session Pipeline](#2-coach-session-pipeline)
+3. [Architecture Pipeline](#3-architecture-pipeline)
+4. [Coding Pipeline](#4-coding-pipeline)
+5. [ML Decision Pipeline](#5-ml-decision-pipeline)
+6. [Project Management Pipeline](#6-project-management-pipeline)
+7. [Knowledge Pipeline](#7-knowledge-pipeline)
+8. [Shared Infrastructure Artifacts](#8-shared-infrastructure-artifacts)
+9. [Cross-Pipeline Events](#9-cross-pipeline-events)
+10. [Artifact Version History](#10-artifact-version-history)
+
+---
+
+## 1. Requirements Pipeline
+
+**Trigger:** `.vtt` transcript upload
+**Steps:** transcript_parser → priority_classifier → req_extractor → ticket_creator → minutes_publisher → decision_logger → drift_detector (quick check)
+
+### 1.1 Parsed Meeting Minutes
+
+| Field | Value |
+|-------|-------|
+| **Name** | `YYYY-MM-DD-client.md` (e.g., `2026-01-22-client.md`) |
+| **Location** | GitHub: `minutes/` |
+| **Produced by** | `transcript_parser` |
+| **Format** | Markdown |
+| **ETVX ID** | REQ-PARSE |
+
+**Example content:**
+
+```markdown
+# Meeting Minutes — 2026-01-22
+**Type:** client
+**Attendees:** Harsha, Brian, Dewey, Pimsie Supreme team
+
+## Key Discussion Points
+- Topics discussed: ML extraction, confidence scoring, vendor format variation
+
+## Decisions
+- **Primary approach: LLM extraction, not OCR**
+ - Context: said by Harsha
+
+## Action Items
+- [ ] Set up confidence threshold testing — **Arjun**
+- [ ] Deliver P1-C schema for staging tables — **Jake** (due: Feb 15)
+
+## Open Questions
+- How do we handle low-confidence predictions? → Harsha
+```
+
+### 1.2 Meeting Summary (Wiki Entry)
+
+| Field | Value |
+|-------|-------|
+| **Name** | `meetings/YYYY-MM-DD-{type}` (e.g., `meetings/2026-01-22-client`) |
+| **Location** | SQLite: `memory/shared_memory.db`, table `wiki`, namespace `meetings` |
+| **Produced by** | `transcript_parser` |
+| **Format** | JSON (stored as TEXT in SQLite) |
+| **ETVX ID** | REQ-PARSE |
+
+**Example content:**
+
+```json
+{
+ "date": "2026-01-22",
+ "type": "client",
+ "source": "transcripts/GMT20260122_client.vtt",
+ "action_items": 7,
+ "decisions": 3,
+ "new_requirements": 2,
+ "participants": ["Harsha", "Brian", "Dewey"]
+}
+```
+
+### 1.3 Classified Items (Pipeline Context)
+
+| Field | Value |
+|-------|-------|
+| **Name** | Transient — passed via `PipelineContext.data["classified_items"]` |
+| **Location** | In-memory during pipeline execution; deposited to wiki as `requirements_engineering/{agent}:{timestamp}` |
+| **Produced by** | `priority_classifier` |
+| **Format** | JSON list |
+| **ETVX ID** | REQ-CLASSIFY |
+
+**Example content:**
+
+```json
+[
+ {"text": "Set up confidence threshold testing", "priority": "P0", "rationale": "Contains 'threshold' — demo-critical"},
+ {"text": "Explore Azure AI services for extraction", "priority": "P1", "rationale": "Contains 'need'"},
+ {"text": "Document vendor format variation", "priority": "P2", "rationale": "General discussion item"}
+]
+```
+
+### 1.4 Requirement Documents
+
+| Field | Value |
+|-------|-------|
+| **Name** | `REQ-XXX.md` (e.g., `REQ-001.md` through `REQ-012.md`) |
+| **Location** | GitHub: `requirements/parsed/` |
+| **Produced by** | `req_extractor` |
+| **Format** | Markdown with structured metadata table |
+| **ETVX ID** | REQ-EXTRACT |
+
+**Example content (REQ-002.md):**
+
+```markdown
+# REQ-002: ML confidence scoring
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-002 |
+| **Category** | Non-Functional Requirement (Quality Attribute) |
+| **Priority** | P0 |
+| **Date Identified** | 2026-02-05 |
+| **Source Meeting** | client |
+| **Source** | team discussion |
+| **Status** | draft |
+
+## Requirement Statement
+The system shall assign a confidence score (0.0-1.0) to every ML-predicted attribute value.
+
+## Rationale
+Client emphasized need for transparency — operators must know which predictions to trust vs review.
+
+## Acceptance Criteria
+Every extracted attribute includes a confidence score; scores correlate with actual accuracy (calibration within 10%).
+
+## Traceability
+- Jira Ticket: _pending auto-link_
+- Architecture Decision: _pending_
+- Test Coverage: _pending_
+
+---
+_Auto-generated by req_extractor agent on 2026-02-05_
+```
+
+### 1.5 Requirement Wiki Entries
+
+| Field | Value |
+|-------|-------|
+| **Name** | `requirements/{REQ-ID}` (e.g., `requirements/REQ-002`) |
+| **Location** | SQLite: `memory/shared_memory.db`, table `wiki`, namespace `requirements` |
+| **Produced by** | `req_extractor` |
+| **Format** | JSON |
+| **ETVX ID** | REQ-EXTRACT |
+
+**Example content:**
+
+```json
+{
+ "title": "ML confidence scoring",
+ "statement": "The system shall assign a confidence score (0.0-1.0) to every ML-predicted attribute value.",
+ "category": "NON_FUNCTIONAL",
+ "priority": "P0",
+ "date": "2026-02-05",
+ "meeting_type": "client"
+}
+```
+
+### 1.6 Jira Tickets
+
+| Field | Value |
+|-------|-------|
+| **Name** | `EPARTS-XX` (e.g., `EPARTS-72`) |
+| **Location** | Jira Cloud (eParts project board) |
+| **Produced by** | `ticket_creator` |
+| **Format** | Jira issue (Task) with labels `[P1, auto-created]` or `[P2, auto-created]` |
+| **ETVX ID** | PM-TICKET |
+
+**Example fields:**
+
+```
+Summary: Explore Azure AI services for extraction
+Description: **Auto-created from meeting transcript**
+ **Item:** Explore Azure AI services for extraction
+ **Priority:** P1
+ **Owner:** Arjun
+Issue Type: Task
+Labels: [P1, auto-created]
+Priority: High
+```
+
+P0 items are NOT auto-created — they are held in a review queue for human approval.
+
+### 1.7 Confluence Meeting Minutes
+
+| Field | Value |
+|-------|-------|
+| **Name** | Published page title matches meeting date/type |
+| **Location** | Confluence (when configured) |
+| **Produced by** | `minutes_publisher` |
+| **Format** | Confluence wiki page (HTML converted from Markdown) |
+| **ETVX ID** | KN-PUBLISH |
+
+### 1.8 Decision Log
+
+| Field | Value |
+|-------|-------|
+| **Name** | `decisions.log.md` |
+| **Location** | GitHub: `minutes/decisions.log.md` |
+| **Produced by** | `decision_logger` |
+| **Format** | Markdown table |
+| **ETVX ID** | KN-DECISION |
+
+**Example content:**
+
+```markdown
+# Decision Log
+
+| Date | Decision | Source | People Present |
+|------|----------|--------|----------------|
+| 2026-01-22 | Primary approach: LLM extraction, not OCR | client meeting | Harsha, Brian, team |
+| 2026-02-05 | Map to industry standards, not ALPS codes | client meeting | Harsha, Dewey, team |
+| 2026-02-19 | Per-attribute routing over per-record | client meeting | Brian, team |
+```
+
+### 1.9 Decision Wiki Entries
+
+| Field | Value |
+|-------|-------|
+| **Name** | `decisions/{date}:{index}` (e.g., `decisions/2026-01-22:0`) |
+| **Location** | SQLite: `memory/shared_memory.db`, table `wiki`, namespace `decisions` |
+| **Produced by** | `decision_logger` |
+| **Format** | JSON |
+| **ETVX ID** | KN-DECISION |
+
+**Example content:**
+
+```json
+{
+ "text": "Primary approach: LLM extraction, not OCR",
+ "source": "transcripts/GMT20260122_client.vtt",
+ "date": "2026-01-22",
+ "participants": ["Harsha", "Brian", "Dewey"]
+}
+```
+
+### 1.10 Post-Meeting Drift Check (Quick)
+
+| Field | Value |
+|-------|-------|
+| **Name** | `drift-YYYY-MM-DD` wiki entry (only if drift detected) |
+| **Location** | SQLite: `memory/shared_memory.db`, namespace `architecture`; GitHub: `docs/drift/YYYY-MM-DD.md` (if drift found) |
+| **Produced by** | `drift_detector` (requirements pipeline instance) |
+| **Format** | JSON (wiki) / Markdown (GitHub) |
+| **ETVX ID** | REQ-DRIFT-CHECK |
+
+This is the lightweight post-meeting check — see [Architecture Pipeline](#3-architecture-pipeline) for the full drift analysis.
+
+---
+
+## 2. Coach Session Pipeline
+
+**Trigger:** Coach/mentor `.vtt` transcript upload
+**Steps:** transcript_parser → session_memory → commitment_tracker → concern_tracker → coach_linker → decision_logger
+
+### 2.1 Parsed Coach Session
+
+| Field | Value |
+|-------|-------|
+| **Name** | Same format as meeting minutes |
+| **Location** | GitHub: `minutes/` (via transcript_parser) |
+| **Produced by** | `transcript_parser` |
+| **Format** | Markdown |
+| **ETVX ID** | REQ-PARSE |
+
+### 2.2 Session Record (SQLite)
+
+| Field | Value |
+|-------|-------|
+| **Name** | `session-{date}-{filename}` (e.g., `session-2026-02-20-GMT20260220-coach`) |
+| **Location** | SQLite: `memory/coach_sessions.db`, table `sessions` |
+| **Produced by** | `session_memory` |
+| **Format** | SQLite row |
+| **ETVX ID** | COACH-INGEST |
+
+**Schema:**
+
+```
+session_id TEXT PRIMARY KEY
+date TEXT
+session_type TEXT -- "coach", "mentor"
+participants TEXT -- JSON array
+raw_transcript_path TEXT
+processed_at TEXT
+```
+
+### 2.3 Session Chunks (ChromaDB Embeddings)
+
+| Field | Value |
+|-------|-------|
+| **Name** | `{session_id}-chunk-{i}` (e.g., `session-2026-02-20-coach-chunk-0`) |
+| **Location** | ChromaDB: `memory/chroma/`, collection `sessions` |
+| **Produced by** | `session_memory` |
+| **Format** | Vector embedding (ONNX all-MiniLM-L6-v2) + text chunk + metadata |
+| **ETVX ID** | COACH-INGEST |
+
+Each chunk is ~800 characters with 100-character overlap. Metadata per chunk:
+
+```json
+{
+ "session_id": "session-2026-02-20-coach",
+ "session_type": "coach",
+ "date": "2026-02-20",
+ "chunk_index": 3
+}
+```
+
+### 2.4 Commitment Records (SQLite)
+
+| Field | Value |
+|-------|-------|
+| **Name** | Auto-incrementing rows in `commitments` table |
+| **Location** | SQLite: `memory/coach_sessions.db`, table `commitments` |
+| **Produced by** | `session_memory` (extraction) + `commitment_tracker` (tracking) |
+| **Format** | SQLite row |
+| **ETVX ID** | COACH-COMMIT |
+
+**Schema:**
+
+```
+id INTEGER PRIMARY KEY
+session_id TEXT
+commitment_text TEXT -- "We'll deliver a working prototype by March 15"
+owner TEXT -- "team", "Arjun", etc.
+deadline TEXT -- "March 15"
+status TEXT -- "open", "delivered", "overdue"
+evidence_link TEXT -- link to Jira/PR proving delivery
+```
+
+### 2.5 Commitment Wiki Entries
+
+| Field | Value |
+|-------|-------|
+| **Name** | `commitments/session-{session_id}` |
+| **Location** | SQLite: `memory/shared_memory.db`, namespace `commitments` |
+| **Produced by** | `session_memory` |
+| **Format** | JSON |
+| **ETVX ID** | COACH-INGEST |
+
+**Example content:**
+
+```json
+{
+ "session_id": "session-2026-02-20-coach",
+ "date": "2026-02-20",
+ "commitments": [
+ {"text": "deliver prototype by March 15", "owner": "team", "deadline": "March 15"}
+ ],
+ "concerns": [
+ {"text": "data quality from vendor spec sheets unclear", "raised_by": "Christian", "theme": "data_access"}
+ ],
+ "decisions": []
+}
+```
+
+### 2.6 Concern Records (SQLite)
+
+| Field | Value |
+|-------|-------|
+| **Name** | Auto-incrementing rows in `concerns` table |
+| **Location** | SQLite: `memory/coach_sessions.db`, table `concerns` |
+| **Produced by** | `session_memory` (extraction) + `concern_tracker` (pattern detection) |
+| **Format** | SQLite row |
+| **ETVX ID** | COACH-CONCERN |
+
+**Schema:**
+
+```
+id INTEGER PRIMARY KEY
+session_id TEXT
+concern_text TEXT -- "data quality from vendor spec sheets unclear"
+raised_by TEXT -- "Christian"
+theme TEXT -- "data_access", "scope_creep", "model_selection"
+times_raised INTEGER -- incremented across sessions (pattern detection)
+```
+
+### 2.7 Concern Wiki Entries
+
+| Field | Value |
+|-------|-------|
+| **Name** | `concerns/{theme}` |
+| **Location** | SQLite: `memory/shared_memory.db`, namespace `concerns` |
+| **Produced by** | `concern_tracker` |
+| **Format** | JSON |
+| **ETVX ID** | COACH-CONCERN |
+
+### 2.8 ML Decision Links
+
+| Field | Value |
+|-------|-------|
+| **Name** | Transient — passed via pipeline context |
+| **Location** | Deposited to wiki namespace `latest_runs/coach_linker` |
+| **Produced by** | `coach_linker` |
+| **Format** | JSON |
+| **ETVX ID** | ML-LINK |
+
+### 2.9 Session Decision Log Entries
+
+Same format as [1.8 Decision Log](#18-decision-log) and [1.9 Decision Wiki Entries](#19-decision-wiki-entries), produced by `decision_logger` running within the coach session pipeline.
+
+---
+
+## 3. Architecture Pipeline
+
+**Trigger:** Transcript processed, PR event, `drift_detected` event, manual invocation
+**Steps:** drift_detector → adr_generator → diagram_updater → traceability_builder
+
+### 3.1 Drift Report
+
+| Field | Value |
+|-------|-------|
+| **Name** | `YYYY-MM-DD.md` (e.g., `2026-03-05.md`) |
+| **Location** | GitHub: `docs/drift/` |
+| **Produced by** | `drift_detector` (architecture pipeline instance) |
+| **Format** | Markdown |
+| **ETVX ID** | ARCH-DRIFT |
+
+**Example content:**
+
+```markdown
+# Architecture Drift Report — 2026-03-05
+
+## Drift #1: contradiction
+**Severity:** medium
+**Description:** Discussion may contradict AD-6: Single App Service chosen over microservices
+**Evidence:** _team discussed microservice separation for the review workflow_
+**Suggested Action:** Review AD-6 — verify if decision needs updating
+
+## Drift #2: sensitivity_point
+**Severity:** low
+**Description:** Threshold discussion detected — this is a known sensitivity point
+**Evidence:** Meeting discusses confidence thresholds
+**Suggested Action:** Record any threshold decisions in ADR-4
+```
+
+### 3.2 Drift Wiki Entries
+
+| Field | Value |
+|-------|-------|
+| **Name** | `architecture/drift-{date}` |
+| **Location** | SQLite: `memory/shared_memory.db`, namespace `architecture` |
+| **Produced by** | `drift_detector` |
+| **Format** | JSON |
+| **ETVX ID** | ARCH-DRIFT |
+
+**Example content:**
+
+```json
+{
+ "date": "2026-03-05",
+ "drifts": [
+ {
+ "type": "contradiction",
+ "description": "Discussion may contradict AD-6: Single App Service chosen over microservices",
+ "evidence": "team discussed microservice separation...",
+ "severity": "medium",
+ "suggested_action": "Review AD-6",
+ "architecture_ref": "AD-6"
+ }
+ ],
+ "confidence": 0.85
+}
+```
+
+### 3.3 Architecture Decision Records (ADRs)
+
+| Field | Value |
+|-------|-------|
+| **Name** | `ADR-{date}-{slug}.md` (e.g., `ADR-2026-02-05-per-attribute-thresholds.md`) |
+| **Location** | GitHub: `docs/adrs/` (committed via PR, never direct push) |
+| **Produced by** | `adr_generator` |
+| **Format** | Markdown (ADR template) |
+| **ETVX ID** | ARCH-ADR |
+| **Human gate** | All ADRs require PR approval before merge |
+
+**Example structure:**
+
+```markdown
+# ADR: Per-Attribute Confidence Thresholds
+
+## Status
+Proposed
+
+## Context
+Different attribute types (description, specs, category) have different ML accuracy profiles...
+
+## Decision
+Use per-attribute thresholds calibrated on a holdout set, not a single global threshold.
+
+## Options Considered
+1. Global threshold (0.85) — simple but ignores per-attribute variance
+2. Per-attribute thresholds — more complex but matches actual accuracy distribution
+
+## Consequences
+- Positive: Better accuracy per attribute type
+- Negative: More complex calibration process
+
+## Reconsideration Triggers
+- If per-attribute variance is <5%, simplify to global threshold
+```
+
+### 3.4 Diagram Update PRs
+
+| Field | Value |
+|-------|-------|
+| **Name** | PR with branch `diagram/{slug}` |
+| **Location** | GitHub/Bitbucket PR |
+| **Produced by** | `diagram_updater` |
+| **Format** | Mermaid diagram file updates |
+| **ETVX ID** | ARCH-DIAGRAM |
+
+### 3.5 Traceability Links
+
+| Field | Value |
+|-------|-------|
+| **Name** | Rows in `artifacts` and `links` tables |
+| **Location** | SQLite: `memory/traceability.db` |
+| **Produced by** | `traceability_builder` + `pipeline/seed_traceability.py` |
+| **Format** | SQLite rows |
+| **ETVX ID** | ARCH-TRACE |
+
+See [Section 8.3](#83-traceability-store) for full schema and statistics.
+
+---
+
+## 4. Coding Pipeline
+
+**Trigger:** PR event
+**Steps:** pr_reviewer → test_generator → doc_generator → prompt_regression
+
+### 4.1 PR Review Comments
+
+| Field | Value |
+|-------|-------|
+| **Name** | Inline PR review comments |
+| **Location** | GitHub/Bitbucket PR review |
+| **Produced by** | `pr_reviewer` |
+| **Format** | PR comment (markdown) |
+| **ETVX ID** | CODE-REVIEW |
+
+### 4.2 Test Stubs
+
+| Field | Value |
+|-------|-------|
+| **Name** | Test files matching the PR's changed files |
+| **Location** | GitHub: `tests/` (committed via PR) |
+| **Produced by** | `test_generator` |
+| **Format** | Python test files |
+| **ETVX ID** | CODE-TEST |
+
+### 4.3 API Documentation Updates
+
+| Field | Value |
+|-------|-------|
+| **Name** | Updated doc files |
+| **Location** | GitHub: `docs/api/` |
+| **Produced by** | `doc_generator` |
+| **Format** | Markdown |
+| **ETVX ID** | CODE-DOC |
+
+### 4.4 Prompt Regression Results
+
+| Field | Value |
+|-------|-------|
+| **Name** | Regression test report |
+| **Location** | Pipeline context + metrics DB |
+| **Produced by** | `prompt_regression` |
+| **Format** | JSON (pass/fail per golden test case) |
+| **ETVX ID** | KN-PROMPT-REG |
+
+---
+
+## 5. ML Decision Pipeline
+
+**Trigger:** POC result submitted
+**Steps:** evidence_accumulator → readiness_detector → coach_linker
+
+### 5.1 Evidence Records
+
+| Field | Value |
+|-------|-------|
+| **Name** | `ml_decisions/{decision_id}` |
+| **Location** | SQLite: `memory/ml_decisions.db` + `memory/shared_memory.db` namespace `ml_decisions` |
+| **Produced by** | `evidence_accumulator` |
+| **Format** | SQLite row / JSON wiki entry |
+| **ETVX ID** | ML-EVIDENCE |
+
+### 5.2 Readiness Alerts
+
+| Field | Value |
+|-------|-------|
+| **Name** | `decision_ready` events |
+| **Location** | SQLite: `memory/events.db`, table `events` |
+| **Produced by** | `readiness_detector` |
+| **Format** | Event record |
+| **ETVX ID** | ML-READINESS |
+
+### 5.3 Coach-Evidence Links
+
+| Field | Value |
+|-------|-------|
+| **Name** | Cross-reference between evidence and coach session context |
+| **Location** | SQLite: `memory/shared_memory.db`, namespace `latest_runs/coach_linker` |
+| **Produced by** | `coach_linker` |
+| **Format** | JSON |
+| **ETVX ID** | ML-LINK |
+
+---
+
+## 6. Project Management Pipeline
+
+**Trigger:** Cron (weekly, Friday 6pm)
+**Steps:** wbs_updater → weekly_digest → alert_agent
+
+### 6.1 WBS State Snapshot
+
+| Field | Value |
+|-------|-------|
+| **Name** | `project_mgmt/wbs_state` |
+| **Location** | SQLite: `memory/shared_memory.db`, namespace `project_mgmt` |
+| **Produced by** | `wbs_updater` |
+| **Format** | JSON |
+| **ETVX ID** | PM-WBS |
+
+**Example content:**
+
+```json
+{
+ "total_tickets": 50,
+ "done": 12,
+ "in_progress": 8,
+ "to_do": 30,
+ "sprint": "Sprint 3",
+ "sync_date": "2026-04-25"
+}
+```
+
+### 6.2 Weekly Digest
+
+| Field | Value |
+|-------|-------|
+| **Name** | `Weekly Digest — YYYY-MM-DD` |
+| **Location** | Slack (posted) + Confluence (published page) |
+| **Produced by** | `weekly_digest` |
+| **Format** | Markdown (Slack) / Confluence page |
+| **ETVX ID** | PM-DIGEST |
+
+**Sections:** Decisions Made This Week, Requirements Changes, Sprint Health, Architecture Drift, Next Week Preview.
+
+### 6.3 Health Alerts
+
+| Field | Value |
+|-------|-------|
+| **Name** | Alert messages |
+| **Location** | Slack (posted) + EventBus events |
+| **Produced by** | `alert_agent` |
+| **Format** | Slack message |
+| **ETVX ID** | PM-ALERT |
+
+---
+
+## 7. Knowledge Pipeline
+
+**Trigger:** Cron (pre-meeting) or `new_session_embedded` event
+**Steps:** context_packager → briefing_generator
+
+### 7.1 Context Package
+
+| Field | Value |
+|-------|-------|
+| **Name** | Transient — passed via pipeline context as `context_package` |
+| **Location** | In-memory during pipeline; deposited to wiki |
+| **Produced by** | `context_packager` |
+| **Format** | JSON (aggregated from wiki + Jira + events) |
+| **ETVX ID** | KN-CONTEXT |
+
+### 7.2 Pre-Meeting Briefing
+
+| Field | Value |
+|-------|-------|
+| **Name** | `Pre-Meeting Briefing — {type} ({date})` |
+| **Location** | Slack (posted and pinned) |
+| **Produced by** | `briefing_generator` |
+| **Format** | Slack-compatible Markdown |
+| **ETVX ID** | COACH-BRIEF |
+
+**Example content:**
+
+```markdown
+# Pre-Meeting Briefing — Coach (2026-04-25)
+
+## Last Session Recap
+Last session: 2026-04-11 (coach)
+Participants: Christian, Pimsie Supreme team
+
+## Commitment Status
+
+### Open Items
+- Deliver confidence threshold analysis — owner: Arjun (due: April 20)
+- Complete PIMS integration test — owner: Hrishik
+
+### Recently Delivered
+- ✓ Working prototype demonstrated to eParts
+
+## Recurring Themes
+- data_access: raised 3 times across sessions
+- measurement_validity: raised 2 times
+
+## Relevant Context from Past Sessions
+[2026-02-20 | coach]
+Christian emphasized counterfactual measurement: "What would happen if we did not use AI at all?"
+```
+
+---
+
+## 8. Shared Infrastructure Artifacts
+
+These artifacts are not produced by a single pipeline — they are the persistent stores that all pipelines read and write.
+
+### 8.1 SharedMemory Wiki
+
+| Field | Value |
+|-------|-------|
+| **Location** | SQLite: `memory/shared_memory.db` |
+| **Tables** | `wiki` (entries), `wiki_log` (audit trail) |
+
+**Wiki table schema:**
+
+```
+id INTEGER PRIMARY KEY
+namespace TEXT -- "meetings", "requirements", "decisions", "concerns", etc.
+key TEXT -- unique within namespace
+value TEXT -- JSON blob
+source_agent TEXT
+source_pipeline TEXT
+tags TEXT -- JSON array
+created_at TEXT
+updated_at TEXT
+```
+
+**Namespaces:**
+
+| Namespace | Contents | Written By | Read By |
+|-----------|----------|------------|---------|
+| `meetings` | Parsed meeting summaries | transcript_parser | context_packager, weekly_digest |
+| `requirements` | Extracted requirements (REQ-001 to REQ-012) | req_extractor | stale_detector, traceability_builder |
+| `decisions` | All logged decisions | decision_logger | adr_generator, traceability_builder |
+| `concerns` | Recurring themes from coaches | concern_tracker | alert_agent, briefing_generator |
+| `commitments` | Coach session commitments | session_memory | commitment_tracker, briefing_generator |
+| `architecture` | ADRs, drift reports, component list | drift_detector, adr_generator | drift_detector, diagram_updater |
+| `ml_decisions` | ML experiment logs, evidence | evidence_accumulator | readiness_detector |
+| `project_mgmt` | WBS state, sprint data | wbs_updater | weekly_digest, alert_agent |
+| `metrics` | Aggregate SES indicators | MetricsCollector | dashboard |
+| `latest_runs` | Most recent run per agent | PipelineExecutor | all agents (cross-pipeline lookup) |
+
+**Audit log (`wiki_log`):**
+
+Every write records: namespace, key, action (create/update/delete), old_value, new_value, agent, pipeline, timestamp.
+
+### 8.2 EventBus
+
+| Field | Value |
+|-------|-------|
+| **Location** | SQLite: `memory/events.db` |
+| **Tables** | `events` (history), `subscriptions` (wiring) |
+
+**Events table schema:**
+
+```
+event_id TEXT UNIQUE -- "evt-a3b2c1d4"
+event_type TEXT -- "drift_detected", "action_items_extracted", etc.
+source_agent TEXT
+source_pipeline TEXT
+data TEXT -- JSON blob with event payload
+timestamp TEXT
+consumed_by TEXT -- JSON array of consumers
+```
+
+**12 defined event types:** `drift_detected`, `new_requirements`, `priority_changed`, `recurring_concern`, `commitment_overdue`, `new_session_embedded`, `decision_ready`, `poc_evidence_logged`, `action_items_extracted`, `human_review_needed`, `decision_logged`, `artifact_produced`.
+
+**10 active subscriptions** wiring pipelines together (see `EventBus._setup_default_subscriptions()`).
+
+### 8.3 Traceability Store
+
+| Field | Value |
+|-------|-------|
+| **Location** | SQLite: `memory/traceability.db` |
+| **Tables** | `artifacts` (nodes), `links` (edges) |
+
+**Artifacts table schema:**
+
+```
+id TEXT PRIMARY KEY -- "REQ-001", "RISK-ARCH-01", "EPARTS-72"
+artifact_type TEXT -- one of 13 types (see below)
+title TEXT
+description TEXT
+status TEXT -- "open", "in_progress", "done", "superseded", "wont_fix"
+source_meeting TEXT
+source_speaker TEXT
+source_quote TEXT
+owner TEXT
+jira_key TEXT
+priority TEXT
+metadata TEXT -- JSON
+```
+
+**13 artifact types:** `concern`, `decision`, `requirement`, `risk`, `action_item`, `commitment`, `architecture`, `jira_ticket`, `pull_request`, `test`, `meeting`, `coach_session`, `adr`.
+
+**Links table schema:**
+
+```
+source_id TEXT -- FK to artifacts.id
+target_id TEXT -- FK to artifacts.id
+link_type TEXT -- one of 12 types (see below)
+description TEXT
+```
+
+**12 link types:** `BECAME`, `DECIDED_BY`, `IMPLEMENTS`, `MITIGATES`, `ADDRESSES`, `RAISED_IN`, `ASSIGNED_TO`, `TRIGGERED`, `VERIFIED_BY`, `SUPERSEDES`, `DEPENDS_ON`, `RELATES_TO`.
+
+**Current statistics (from `seed_traceability.py`):**
+
+| Metric | Value |
+|--------|-------|
+| Total artifacts | 184 |
+| Total links | 760 |
+| Concern → Requirement chains | 12 |
+| Risk mitigations | 324 links |
+| Jira implementations | 204 links |
+
+### 8.4 Metrics Database
+
+| Field | Value |
+|-------|-------|
+| **Location** | SQLite: `pipeline/metrics.db` |
+| **Tables** | `llm_calls`, `agent_runs`, `prompt_versions`, `human_corrections` |
+
+**`llm_calls` schema:**
+
+```
+run_id TEXT, agent TEXT, model TEXT, prompt_file TEXT,
+input_tokens INTEGER, output_tokens INTEGER, total_tokens INTEGER,
+latency_ms INTEGER, temperature REAL, attempt INTEGER,
+estimated_cost_usd REAL, timestamp TEXT
+```
+
+**`agent_runs` schema:**
+
+```
+run_id TEXT UNIQUE, agent TEXT, trigger_type TEXT, trigger_source TEXT,
+success INTEGER, duration_ms INTEGER, llm_calls INTEGER,
+total_input_tokens INTEGER, total_output_tokens INTEGER,
+estimated_cost_usd REAL, outputs_count INTEGER,
+requires_human_review INTEGER, errors TEXT, timestamp TEXT
+```
+
+**`human_corrections` schema:**
+
+```
+run_id TEXT, agent TEXT, correction_type TEXT,
+description TEXT, timestamp TEXT
+```
+
+### 8.5 Prompt Registry
+
+| Field | Value |
+|-------|-------|
+| **Location** | SQLite: `memory/prompt_registry.db` |
+| **Tables** | `prompt_versions`, `prompt_metrics`, `prompt_reviews`, `ab_tests`, `team_conventions` |
+
+**`prompt_versions` schema:**
+
+```
+prompt_name TEXT -- "req_extractor", "transcript_parser", etc.
+version_hash TEXT -- SHA-256[:16] of content
+content TEXT -- full prompt text
+author TEXT
+status TEXT -- "draft", "pending_review", "approved", "active", "rejected"
+reviewer TEXT
+review_comment TEXT
+is_active INTEGER -- only one active version per prompt
+```
+
+**Active prompt files** are synced to `prompts/*.txt` on disk.
+
+**10 team conventions** stored in `team_conventions` table, enforcing rules like "all prompts in /prompts/ as .txt files" and "temperature=0 for deterministic tasks."
+
+### 8.6 Risk Register
+
+| Field | Value |
+|-------|-------|
+| **Location** | SQLite: `memory/risk_register.db` |
+| **Table** | `risks` |
+
+**Schema:**
+
+```
+id TEXT PRIMARY KEY -- "RISK-ARCH-01", "RISK-COACH-03", "RISK-PM-02"
+title TEXT
+description TEXT
+category TEXT -- "technical", "business", "schedule", "scope", "ux", "process"
+source TEXT -- "architecture_report", "coach_sessions", "project_constraints"
+likelihood TEXT -- "high", "medium", "low"
+impact TEXT
+severity TEXT -- computed from likelihood × impact matrix
+mitigation TEXT
+contingency TEXT
+status TEXT -- "open", "mitigating", "closed"
+owner TEXT
+related_reqs TEXT -- JSON array of REQ IDs
+related_arch TEXT -- JSON array of architecture decision IDs
+```
+
+**16 risks tracked** across 3 sources: architecture report (8), coach sessions (5), project management (3).
+
+### 8.7 Coach Sessions Database
+
+| Field | Value |
+|-------|-------|
+| **Location** | SQLite: `memory/coach_sessions.db` |
+| **Tables** | `sessions`, `commitments`, `concerns` |
+
+See [2.2](#22-session-record-sqlite), [2.4](#24-commitment-records-sqlite), and [2.6](#26-concern-records-sqlite) for schema details.
+
+### 8.8 ChromaDB Vector Store
+
+| Field | Value |
+|-------|-------|
+| **Location** | `memory/chroma/` (persistent directory) |
+| **Embedding model** | ONNX all-MiniLM-L6-v2 (local, no API needed) |
+
+**Collections:**
+
+| Collection | Contents | Written By | Read By |
+|------------|----------|------------|---------|
+| `sessions` | Coach/mentor transcript chunks (800 chars each, 100 overlap) | session_memory | briefing_generator, coach_linker |
+| `architecture` | Architecture report chunks (canonical architecture) | seed script / drift_detector | drift_detector |
+| `meetings` | Meeting transcript chunks | transcript_parser | context_packager |
+
+### 8.9 Artifact Version History
+
+| Field | Value |
+|-------|-------|
+| **Location** | SQLite: `memory/artifact_versions.db` |
+| **Tables** | `artifacts` (registered documents), `versions` (version snapshots) |
+
+**`versions` schema:**
+
+```
+artifact_name TEXT -- "requirements_document", "architecture_document", etc.
+version TEXT -- semantic: "1.0", "1.3", "2.0"
+content TEXT -- version content/summary
+change_summary TEXT -- what changed
+changed_by TEXT -- agent or human
+trigger_source TEXT -- which meeting/event triggered the change
+contributing_meetings TEXT -- JSON array of meeting IDs
+contributing_sessions TEXT -- JSON array of session IDs
+metadata TEXT -- JSON
+timestamp TEXT
+```
+
+**Tracked documents:**
+
+| Document | Current Version | Key Milestones |
+|----------|----------------|----------------|
+| `requirements_document` | 1.0 | 5 versions: initial scope → confidence scoring → multi-format → measurable criteria → full consolidation |
+| `architecture_document` | 1.0 | 4 versions: initial sketch → staging tables → review workflow → canonical finalization |
+| `risk_register` | 1.0 | 3 versions: initial risks → coach concerns → full 16-risk register |
+| `adr_threshold_calibration` | 1.0 | 2 versions: opened → decided (per-attribute) |
+| `adr_staging_tables` | 0.0 | Registered, awaiting first version |
+| `adr_human_in_loop` | 0.0 | Registered, awaiting first version |
+
+---
+
+## 9. Cross-Pipeline Events
+
+Events are the wiring between pipelines. Every event is persisted in `memory/events.db`.
+
+| Event | Fired By | Target Pipeline | What Happens Next |
+|-------|----------|-----------------|-------------------|
+| `action_items_extracted` | transcript_parser | project_mgmt → ticket_creator | Jira tickets created for action items |
+| `decision_logged` | decision_logger | knowledge → decision_logger | Decision indexed in knowledge base |
+| `drift_detected` | drift_detector | architecture | ADR drafting + diagram updates |
+| `new_session_embedded` | session_memory | knowledge → briefing_generator | Briefing refresh with new session data |
+| `recurring_concern` | concern_tracker | project_mgmt → alert_agent | PM alert for recurring team concern |
+| `commitment_overdue` | commitment_tracker | project_mgmt → alert_agent | Overdue commitment alert |
+| `decision_ready` | readiness_detector | coach_session → coach_linker | Link evidence to coach context |
+| `poc_evidence_logged` | evidence_accumulator | ml_decision → readiness_detector | Readiness check triggered |
+| `human_review_needed` | traceability_builder | project_mgmt → alert_agent | Alert for human review queue |
+| `new_requirements` | req_extractor | architecture → drift_detector | Drift check on new requirements |
+
+---
+
+## 10. Pipeline Execution Metrics
+
+Every pipeline run produces a `PipelineResult` record logged via `MetricsCollector`.
+
+| Field | Value |
+|-------|-------|
+| **Location** | SQLite: `pipeline/metrics.db`, table `agent_runs` |
+| **Run ID format** | `pipe-{pipeline_name}-{uuid8}` (e.g., `pipe-requirements-a3b2c1d4`) |
+
+**Recorded per pipeline run:** pipeline_id, pipeline_name, practice_area, trigger_source, success, total_steps, completed_steps, skipped_steps, failed_steps, total_duration_ms, total_llm_calls, total_tokens, total_artifacts, requires_human_review.
+
+**Recorded per agent step:** step_index, agent_name, description, success, skipped, duration_ms, outputs, errors, llm_calls, tokens_used, artifacts_produced, requires_human_review.
+
+---
+
+## Summary: Storage Locations
+
+| Storage | Path | Purpose | Size |
+|---------|------|---------|------|
+| `memory/shared_memory.db` | SQLite | Project wiki (namespaced key-value) | ~1 MB |
+| `memory/events.db` | SQLite | Cross-pipeline event history + subscriptions | ~0.5 MB |
+| `memory/traceability.db` | SQLite | 189 artifacts, 764 links | ~1 MB |
+| `memory/coach_sessions.db` | SQLite | Session records, commitments, concerns | ~0.5 MB |
+| `memory/risk_register.db` | SQLite | 16 risks with mitigations | ~0.1 MB |
+| `memory/prompt_registry.db` | SQLite | Prompt versions, reviews, A/B tests | ~0.5 MB |
+| `memory/artifact_versions.db` | SQLite | Document version history | ~0.5 MB |
+| `pipeline/metrics.db` | SQLite | Agent runs, LLM calls, corrections | ~1 MB |
+| `memory/chroma/` | ChromaDB | Vector embeddings (sessions, architecture, meetings) | ~50 MB |
+| `prompts/*.txt` | Files | Active prompt templates | ~50 KB |
+| GitHub: `requirements/parsed/` | Files | REQ-001.md through REQ-012.md | 12 files |
+| GitHub: `minutes/` | Files | Meeting minutes + decisions.log.md | ~10 files |
+| GitHub: `docs/drift/` | Files | Drift reports per date | ~5 files |
+| GitHub: `docs/adrs/` | Files | Architecture Decision Records | ~3 files |
+| Jira Cloud | External | EPARTS-XX tickets | ~50 tickets |
+| Confluence | External | Published meeting minutes, weekly digests | ~10 pages |
+| Slack | External | Briefings, digests, alerts | Ephemeral |
+
+---
+
+*Auto-generated from the eParts SES codebase. Last updated: April 2026.*
+*CMU MSE Studio 2026 · Pimsie Supreme · Python 3.12 · FastAPI · SQLite · ChromaDB*
diff --git a/docs/build_confluence_adrs.py b/docs/build_confluence_adrs.py
new file mode 100644
index 0000000..6d6ed48
--- /dev/null
+++ b/docs/build_confluence_adrs.py
@@ -0,0 +1,105 @@
+"""Turn docs/00NN-*.md into paste-ready Confluence content.
+
+Dropping a .md file into the Confluence editor attaches it as a download card. To get
+real page content you paste the *text* — the editor converts Markdown on paste. This
+emits two shapes:
+
+ confluence/ADRs-all.md one page holding all 21 ADRs, with an index table
+ confluence/adr-00NN-*.md one file per ADR, if you'd rather have child pages
+
+Two fixes are applied on the way out, because both break on paste:
+ - heading levels are demoted so each ADR sits under the page title, not beside it
+ - relative links to sibling .md files are flattened to plain text; they would
+ otherwise paste as dead links, since the targets live in GitHub, not Confluence
+"""
+import os
+import re
+import glob
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+OUT = os.path.join(HERE, "confluence")
+REPO = "https://github.com/AshrithaG/eparts/blob/main/docs/"
+
+os.makedirs(OUT, exist_ok=True)
+files = sorted(glob.glob(os.path.join(HERE, "0[0-9][0-9][0-9]-*.md")))
+
+
+def demote(md, by=1):
+ """Push every ATX heading down `by` levels, skipping fenced code blocks."""
+ lines, fenced = [], False
+ for ln in md.split("\n"):
+ if ln.lstrip().startswith("```"):
+ fenced = not fenced
+ if not fenced:
+ m = re.match(r"^(#{1,5})(\s)", ln)
+ if m:
+ ln = "#" * min(len(m.group(1)) + by, 6) + m.group(2) + ln[m.end():]
+ lines.append(ln)
+ return "\n".join(lines)
+
+
+def fix_links(md):
+ """Sibling .md links become GitHub links; they are dead as relative paths here."""
+ return re.sub(r"\[([^\]]+)\]\((0[0-9]{3}-[^)]+\.md)\)", rf"[\1]({REPO}\2)", md)
+
+
+def status_of(md):
+ m = re.search(r"^##\s+Status\s*$(.*?)^##\s", md, re.M | re.S)
+ if not m:
+ return "—"
+ for ln in m.group(1).strip().split("\n"):
+ if ln.strip():
+ return ln.strip().rstrip(".")
+ return "—"
+
+
+rows, bodies = [], []
+for path in files:
+ raw = open(path).read()
+ title = raw.split("\n", 1)[0].lstrip("# ").strip()
+ num = os.path.basename(path).split("-")[0]
+ slug = os.path.basename(path)
+ body = fix_links(raw)
+ body = body.split("\n", 1)[1] if "\n" in body else "" # drop its own H1
+ rows.append((num, title, status_of(raw), slug))
+ bodies.append(f"## {title}\n" + demote(body, 1).strip())
+
+ single = (f"# {title}\n\n"
+ f"> Source of truth: [`{slug}`]({REPO}{slug}) in the eparts repo. "
+ f"This page is a copy for reading; edit the repo, not this page.\n\n"
+ + fix_links(raw).split("\n", 1)[1].strip() + "\n")
+ with open(os.path.join(OUT, f"adr-{slug}"), "w") as f:
+ f.write(single)
+
+index = ["| ADR | Decision | Status |", "|---|---|---|"]
+for num, title, st, slug in rows:
+ short = title.split(": ", 1)[1] if ": " in title else title
+ index.append(f"| [{num}]({REPO}{slug}) | {short} | {st} |")
+
+header = f"""# Architecture Decision Records
+
+{len(files)} ADRs. **The repo is the source of truth** — every row below links to the
+file in `eparts/docs/`. This page is a reading copy, so edit the repo rather than the
+page, or the two will drift.
+
+ADRs 0001–0012 are the spring baseline and are deliberately left unedited: they record
+what we believed in April. ETIM decisions supersede them *forward*, by reference, in
+0013–0021. Where a spring ADR is affected but not superseded, the change-impact analysis
+is in [`ETIM-ADR-ASSESSMENT.md`]({REPO}ETIM-ADR-ASSESSMENT.md).
+
+Requirement IDs cited by 0016–0021 resolve against Product Specification v1.4; the
+forward and backward traces are in [`REQUIREMENTS-TO-ADR-MAPPING.md`]({REPO}REQUIREMENTS-TO-ADR-MAPPING.md).
+
+{chr(10).join(index)}
+
+---
+"""
+
+with open(os.path.join(OUT, "ADRs-all.md"), "w") as f:
+ f.write(header + "\n\n---\n\n".join(bodies) + "\n")
+
+with open(os.path.join(OUT, "ADRs-index.md"), "w") as f:
+ f.write(header)
+
+print(f"wrote {OUT}/ADRs-all.md ({len(files)} ADRs), ADRs-index.md, "
+ f"and {len(files)} per-ADR files")
diff --git a/docs/build_pipe_filter_v6.py b/docs/build_pipe_filter_v6.py
new file mode 100644
index 0000000..09da11a
--- /dev/null
+++ b/docs/build_pipe_filter_v6.py
@@ -0,0 +1,263 @@
+"""Emit pipe-filter-architecture-v6.svg = v5.0 verbatim + one ETIM matching box.
+
+v5.0 lives as a React component ("pipe and filter.txt"). This is a faithful
+transcription of it, with exactly one addition: an "ETIM matching" filter directly
+behind "ML / AI attribute matching", inside the same PredictionServiceInterface
+boundary. Everything else — the staging store, the router, the rejection/audit
+column, observability, all of it — is v5 untouched, because the whole point of the
+slide is that ETIM was the only structural change.
+
+Coordinates below are v5's own numbers. Y() adds the vertical shift needed to make
+room for the new box, so nothing downstream had to be re-typed by hand.
+"""
+
+C = {
+ "teal": dict(bg="#E1F5EE", stroke="#0F6E56", title="#085041", sub="#0F6E56"),
+ "purple": dict(bg="#EEEDFE", stroke="#534AB7", title="#3C3489", sub="#534AB7"),
+ "coral": dict(bg="#FAECE7", stroke="#993C1D", title="#712B13", sub="#993C1D"),
+ "blue": dict(bg="#E6F1FB", stroke="#185FA5", title="#0C447C", sub="#185FA5"),
+ "gray": dict(bg="#F1EFE8", stroke="#5F5E5A", title="#444441", sub="#5F5E5A"),
+ "amber": dict(bg="#FAEEDA", stroke="#854F0B", title="#412402", sub="#633806"),
+}
+COL_DATA, COL_API, COL_TELEM, COL_REJ = "#185FA5", "#0F6E56", "#5F5E5A", "#A32D2D"
+F = "system-ui, sans-serif"
+
+W, CX = 1280, 480
+bw = 360
+bx = CX - bw // 2
+splitL, splitW, splitR = 200, 210, 550
+STX, STW, STY = 50, 200, 360
+RX, RW = 830, 200
+OX, OW, OY, OH = 880, 100, 110, 130
+
+# v5's interface boundary sat at y=460 h=88 and ended at 548. The ETIM box goes in
+# behind ML/AI, so the boundary grows and everything below 548 slides down.
+IF_X, IF_Y, IF_W = bx - 60, 460, bw + 120
+ML_Y, ML_H = 472, 68 # v5's ML/AI box
+ETIM_Y = ML_Y + ML_H + 20 # 560
+IF_H = (ETIM_Y + ML_H + 12) - IF_Y # boundary wraps both passes
+IF_BOTTOM = IF_Y + IF_H
+V5_BOTTOM = 548 # v5's IF_BOTTOM
+SHIFT = IF_BOTTOM - V5_BOTTOM
+H = 1180 + SHIFT
+
+out = []
+
+
+def Y(v):
+ """v5 y-coordinate -> v6. Anything below v5's interface boundary shifts down."""
+ return v + SHIFT if v > V5_BOTTOM else v
+
+
+def esc(t):
+ return t.replace("&", "&").replace("<", "<").replace(">", ">")
+
+
+def txt(x, y, s, fill, size, weight=None, anchor="middle", style=None, spacing=None):
+ a = f' text-anchor="{anchor}"' if anchor else ""
+ w = f' font-weight="{weight}"' if weight else ""
+ st = f' font-style="{style}"' if style else ""
+ sp = f' letter-spacing="{spacing}"' if spacing else ""
+ out.append(f'{esc(s)}')
+
+
+def filter_box(x, y, w, color, title, sub=None, h=68):
+ c = C[color]
+ out.append(f'')
+ txt(x + w / 2, y + (h * 0.36 if sub else h / 2), title, c["title"], 20, 600)
+ if sub:
+ txt(x + w / 2, y + h * 0.7, sub, c["sub"], 15)
+
+
+def store(x, y, w, color, title, sub=None, h=52):
+ c = C[color]
+ ry = 10
+ out.append(f'')
+ out.append(f'')
+ txt(x + w / 2, y + (h * 0.46 if sub else h / 2 + 4), title, c["title"], 16, 500)
+ if sub:
+ txt(x + w / 2, y + h * 0.74, sub, c["sub"], 12)
+
+
+def pipe(points, color=COL_DATA, dashed=False, no_arrow=False, width=None):
+ d = " ".join(f'{"M" if i == 0 else "L"}{p[0]} {p[1]}' for i, p in enumerate(points))
+ sw = width or (1 if dashed else 1.6)
+ da = ' stroke-dasharray="5 3"' if dashed else ""
+ ae = "" if no_arrow else ' marker-end="url(#ah)"'
+ out.append(f'')
+
+
+def plabel(x, y, s, color="#5F5E5A", anchor="start"):
+ txt(x, y, s, color, 15, anchor=anchor, style="italic")
+
+
+# ─────────────────────────────────────────────────────────────── header + legend
+out.append(f'')
+
+DEST = "/Users/arjun/Documents/CMU/studio-project/Diagrams/pipe-filter-architecture-v6.svg"
+with open(DEST, "w") as f:
+ f.write("\n".join(out))
+print(f"wrote {DEST} — {W}x{H}, shift={SHIFT}, one box added to v5.0")
diff --git a/docs/build_section3_deck.py b/docs/build_section3_deck.py
new file mode 100644
index 0000000..2cd8f30
--- /dev/null
+++ b/docs/build_section3_deck.py
@@ -0,0 +1,519 @@
+"""Build the Software System (requirements + architecture) crit section.
+
+Palette and grid come from eParts_Section4_10min.pptx so the two sections read as one
+deck. Everything on a slide is >= 20 pt so it survives projection; that is the binding
+constraint on how much text each card can hold, so the slides carry phrases and the
+talking script carries the sentences.
+"""
+from pptx import Presentation
+from pptx.util import Pt, Emu
+from pptx.dml.color import RGBColor
+from pptx.enum.text import PP_ALIGN, MSO_ANCHOR
+from pptx.enum.dml import MSO_LINE_DASH_STYLE
+
+BLACK = RGBColor(0x00, 0x00, 0x00)
+BANNER = RGBColor(0x11, 0x11, 0x11)
+CARD = RGBColor(0x1C, 0x1C, 0x1C)
+WHITE = RGBColor(0xFF, 0xFF, 0xFF)
+MUTED = RGBColor(0x99, 0x99, 0x99) # lifted from 777777 — 20 pt grey needs contrast
+LEAD = RGBColor(0xCC, 0xCC, 0xCC)
+ACCENT = WHITE # emphasis
+DIM = RGBColor(0xAA, 0xAA, 0xAA) # secondary emphasis, one step under ACCENT
+RULE = RGBColor(0x44, 0x44, 0x44) # hairlines
+
+MIN_PT = 20.0 # nothing on a slide may be smaller
+FONT = "Aptos"
+BASE = "/Users/arjun/Documents/CMU/studio-project/"
+OUT_SOFTWARE = BASE + "eParts_Section3_SoftwareSystem.pptx"
+OUT_REFLECTION = BASE + "eParts_Reflection_Closing.pptx"
+DIAG = "/Users/arjun/Documents/CMU/studio-project/Diagrams/"
+CONFLUENCE = ("https://cmu-mse.atlassian.net/wiki/spaces/AISDLC/pages/76742657/"
+ "Engineering+System+Artifacts")
+ADR_INDEX = "https://github.com/AshrithaG/eparts/blob/main/docs/adr-index.md"
+# ADR-018 is the one we open live — it is the exemplar for the format. Points at
+# Confluence rather than GitHub now that the ADRs are published there, so the room
+# lands on a rendered page instead of raw Markdown.
+ADR_018 = ("https://cmu-mse.atlassian.net/wiki/spaces/AISDLC/pages/78708738/"
+ "ADR-018+Extend+Routing+to+ETIM+Signals+with+a+Class-Review-First+Path")
+ADR_018_SHOWN = "cmu-mse.atlassian.net/wiki/spaces/AISDLC/pages/78708738"
+
+def new_deck():
+ """A fresh 720x405 pt presentation. Two decks are built from this one script so the
+ palette, grid and 20 pt floor cannot drift between them."""
+ global prs, BLANK
+ prs = Presentation()
+ prs.slide_width = Emu(9144000) # 720 pt
+ prs.slide_height = Emu(5143500) # 405 pt
+ BLANK = prs.slide_layouts[6]
+ return prs
+
+
+prs = new_deck()
+
+
+def P(v):
+ return Emu(int(v * 12700))
+
+
+def new_slide():
+ s = prs.slides.add_slide(BLANK)
+ bg = s.shapes.add_shape(1, 0, 0, prs.slide_width, prs.slide_height)
+ bg.fill.solid()
+ bg.fill.fore_color.rgb = BLACK
+ bg.line.fill.background()
+ bg.shadow.inherit = False
+ return s
+
+
+def rect(s, x, y, w, h, color):
+ sh = s.shapes.add_shape(1, P(x), P(y), P(w), P(h))
+ sh.fill.solid()
+ sh.fill.fore_color.rgb = color
+ sh.line.fill.background()
+ sh.shadow.inherit = False
+ return sh
+
+
+def obox(s, x, y, w, h, color=RULE, dashed=False, fill=CARD):
+ """Outlined box. The detail slide needs dashed strokes for designed-not-built."""
+ sh = s.shapes.add_shape(1, P(x), P(y), P(w), P(h))
+ if fill is None:
+ sh.fill.background()
+ else:
+ sh.fill.solid()
+ sh.fill.fore_color.rgb = fill
+ sh.line.color.rgb = color
+ sh.line.width = Pt(1.25)
+ if dashed:
+ sh.line.dash_style = MSO_LINE_DASH_STYLE.DASH
+ sh.shadow.inherit = False
+ return sh
+
+
+def text(s, x, y, w, h, runs, size=MIN_PT, color=MUTED, bold=False, space=0,
+ align=PP_ALIGN.LEFT, anchor=MSO_ANCHOR.TOP, line=1.18, gap=7, wrap=True):
+ assert size >= MIN_PT, f"{size} pt is below the {MIN_PT} pt projection floor"
+ tb = s.shapes.add_textbox(P(x), P(y), P(w), P(h))
+ tf = tb.text_frame
+ tf.word_wrap = wrap
+ tf.vertical_anchor = anchor
+ tf.margin_left = tf.margin_right = tf.margin_top = tf.margin_bottom = 0
+ paras = runs if isinstance(runs, list) else [runs]
+ for i, para in enumerate(paras):
+ p = tf.paragraphs[0] if i == 0 else tf.add_paragraph()
+ p.alignment = align
+ p.line_spacing = line
+ if i:
+ p.space_before = Pt(gap)
+ for txt, b, c in (para if isinstance(para, list) else [(para, bold, color)]):
+ r = p.add_run()
+ r.text = txt
+ f = r.font
+ f.name = FONT
+ f.size = Pt(size)
+ f.bold = b
+ f.color.rgb = c
+ if space:
+ r.font._rPr.set("spc", str(int(space * 100)))
+ return tb
+
+
+def title(s, txt):
+ text(s, 28.8, 21.6, 662.4, 40, txt, size=28, color=WHITE, bold=True, line=1.0)
+
+
+def artifact_link(s, label, url=None):
+ tb = s.shapes.add_textbox(P(340), P(3.0), P(351.2), P(16.0))
+ tf = tb.text_frame
+ tf.word_wrap = False
+ tf.margin_left = tf.margin_right = tf.margin_top = tf.margin_bottom = 0
+ para = tf.paragraphs[0]
+ para.alignment = PP_ALIGN.RIGHT
+ r = para.add_run()
+ r.text = label
+ r.hyperlink.address = url or CONFLUENCE
+ f = r.font
+ f.name, f.size, f.bold, f.underline = FONT, Pt(MIN_PT), True, False
+ f.color.rgb = ACCENT
+ return tb
+
+
+def banner(s, chunks, y=68.0, h=52.0):
+ rect(s, 28.8, y, 662.4, h, BANNER)
+ text(s, 42.0, y + 5, 636.0, h - 10, [chunks], color=LEAD,
+ anchor=MSO_ANCHOR.MIDDLE, line=1.14)
+
+
+def card(s, x, y, w, h, bar, head, body, head_color=WHITE, head_size=22):
+ rect(s, x, y, w, h, CARD)
+ rect(s, x, y, w, 5.04, bar)
+ pad = 12.0
+ text(s, x + pad, y + 12, w - 2 * pad, 30, head, size=head_size, color=head_color,
+ bold=True, line=1.0)
+ text(s, x + pad, y + 12 + head_size * 1.55, w - 2 * pad, h - 34 - head_size * 1.55,
+ body, gap=9)
+
+
+def footer(s, txt, color=ACCENT, y=372.0, link_label=None, url=None, shown_url=None):
+ """Evidence strip. With link_label/url it grows a second line carrying the artifact
+ link, which is where the audience expects it — bottom left, not the header."""
+ h = 24.0 if not link_label else 50.0
+ if link_label and y == 372.0:
+ y = 346.0
+ rect(s, 28.8, y, 4.5, h, color)
+ text(s, 42.0, y, 649.2, 24.0, txt.upper(), color=color, bold=True, space=0.5,
+ anchor=MSO_ANCHOR.MIDDLE, line=1.0)
+ if not link_label:
+ return
+ tb = s.shapes.add_textbox(P(42.0), P(y + 25.0), P(649.2), P(24.0))
+ tf = tb.text_frame
+ tf.word_wrap = False
+ tf.margin_left = tf.margin_right = tf.margin_top = tf.margin_bottom = 0
+ para = tf.paragraphs[0]
+ para.line_spacing = 1.0
+ lab = para.add_run()
+ lab.text = link_label + " "
+ lab.font.name, lab.font.size, lab.font.bold = FONT, Pt(MIN_PT), True
+ lab.font.color.rgb = WHITE
+ ln = para.add_run()
+ ln.text = shown_url or url
+ ln.hyperlink.address = url
+ ln.font.name, ln.font.size, ln.font.bold = FONT, Pt(MIN_PT), False
+ ln.font.color.rgb = MUTED
+ ln.font.underline = False
+
+
+def save(deck, path, expected_slides):
+ """Write the deck, then repaint theme hyperlink colours.
+
+ python-pptx cannot set them, and the default Office theme forces links to blue,
+ which is unreadable on this black background.
+ """
+ import re as _re, shutil as _shutil, zipfile as _zipfile
+ assert len(deck.slides._sldIdLst) == expected_slides, \
+ f"{path}: expected {expected_slides} slides, got {len(deck.slides._sldIdLst)}"
+ deck.save(path)
+ tmp = path + ".tmp"
+ with _zipfile.ZipFile(path) as zin, \
+ _zipfile.ZipFile(tmp, "w", _zipfile.ZIP_DEFLATED) as zout:
+ for info in zin.infolist():
+ data = zin.read(info.filename)
+ if info.filename.startswith("ppt/theme/theme"):
+ t = data.decode("utf-8")
+ # Links white, and the stock Office accent hues greyed out. Nothing on a
+ # slide uses them, but a themed shape inserted later would pick up orange.
+ greys = {"hlink": "FFFFFF", "folHlink": "FFFFFF", "dk2": "1C1C1C",
+ "lt2": "EEEEEE", "accent1": "FFFFFF", "accent2": "CCCCCC",
+ "accent3": "AAAAAA", "accent4": "888888", "accent5": "666666",
+ "accent6": "444444"}
+ for tag, val in greys.items():
+ t = _re.sub(rf'().*?()',
+ rf'\1\2', t, flags=_re.S)
+ data = t.encode("utf-8")
+ zout.writestr(info, data)
+ _shutil.move(tmp, path)
+ print(f"wrote {path} — {expected_slides} slide(s), all text >= {MIN_PT} pt")
+
+
+# two-column geometry
+LX, RX, CW = 28.8, 368.4, 322.8
+CY, CH = 126.0, 238.0
+
+# ─────────────────────────────────────────────────────────── 1. requirements v1.0 → v1.4
+# Counts verified against product-spec-v1.4.tex. v1.0 baseline: HLR-1..5, FR-1..8,
+# DR-1..3, C-1..3 = 19. v1.4 adds HLR-6, FR-9, FR-10, DR-4 (v1.1) and C-4 (v1.2) = 24.
+# 2 rewritten (HLR-2, FR-1). QAS-3/VAL-4/VAL-5 also arrived in v1.4 but are deliberately
+# left off this table — Arjun narrates them instead of adding two more rows.
+s = new_slide()
+title(s, "Requirements: v1.0 → v1.4")
+# ── comparison table (left)
+TX, TW, TY = 28.8, 392.0, 86.0
+rect(s, TX, TY, TW, 232.0, CARD)
+COL_L, COL_A, COL_B = TX + 16, TX + 236, TX + 314
+text(s, COL_L, TY + 10, 200, 26, "Requirement set", color=MUTED, bold=True)
+text(s, COL_A, TY + 10, 70, 26, "v1.0", color=MUTED, bold=True, align=PP_ALIGN.RIGHT)
+text(s, COL_B, TY + 10, 62, 26, "v1.4", color=WHITE, bold=True, align=PP_ALIGN.RIGHT)
+rect(s, COL_L, TY + 38, TW - 32, 1.2, RULE)
+
+ROWS = [("High-level", "5", "6"), ("Functional", "8", "10"),
+ ("Derived", "3", "4"), ("Constraints", "3", "4")]
+yy = TY + 46
+for label, a, b in ROWS:
+ text(s, COL_L, yy, 200, 26, label, color=WHITE)
+ text(s, COL_A, yy, 70, 26, a, color=MUTED, align=PP_ALIGN.RIGHT)
+ text(s, COL_B, yy, 62, 26, b, color=WHITE, bold=True, align=PP_ALIGN.RIGHT)
+ yy += 32
+rect(s, COL_L, yy + 2, TW - 32, 1.2, RULE)
+text(s, COL_L, yy + 10, 200, 26, "Total", color=WHITE, bold=True)
+text(s, COL_A, yy + 10, 70, 26, "19", color=MUTED, bold=True, align=PP_ALIGN.RIGHT)
+text(s, COL_B, yy + 10, 62, 26, "24", color=WHITE, bold=True, align=PP_ALIGN.RIGHT)
+
+# ── the two numbers that matter (right)
+def stat(x, y, w, h, big, caption, big_colour):
+ rect(s, x, y, w, h, CARD)
+ rect(s, x, y, w, 5.04, big_colour)
+ text(s, x + 16, y + 12, w - 32, 48, big, size=40, color=big_colour, bold=True,
+ line=1.0)
+ text(s, x + 16, y + 62, w - 32, 30, caption, color=MUTED)
+
+SX, SW = 440.0, 251.2
+stat(SX, TY, SW, 112.0, "89%", "of v1.0 unchanged", WHITE)
+stat(SX, TY + 120.0, SW, 112.0, "37%", "churn since April", DIM)
+footer(s, "5 new IDs · 2 rewritten · 17 of 19 untouched",
+ link_label="Spec v1.4:", url=CONFLUENCE, shown_url="cmu-mse.atlassian.net/wiki/spaces/AISDLC")
+
+# ─────────────────────────────────────────────────────────── 2. managing the change
+s = new_slide()
+title(s, "How we managed the change")
+banner(s, [
+ ("Spec 1.0 → 1.1 → 1.2 → 1.3 → 1.4.", True, WHITE),
+ (" Each version-history entry is the change record.", False, LEAD),
+])
+card(s, LX, CY, 662.4, CH, WHITE, "What we did", [
+ [("Added new IDs instead of editing old ones — HLR-6, FR-9/10, DR-4, C-4, QAS-3.",
+ False, MUTED)],
+ [("Renumbered nothing, so every trace link from April still resolves.", False, MUTED)],
+ [("Every ETIM decision traces to a requirement, and forward to code and a test.",
+ False, MUTED)],
+])
+footer(s, "HLR-6 → FR-9 → ADR-16 → ETIM tables in code → 10 tests")
+
+# ─────────────────────────────────────────────────────────── 3. architecture v5 → v6
+s = new_slide()
+title(s, "How the architecture changed")
+IY = 84.0
+# v6 is v5 plus one box, so the two thumbnails are near-identical in aspect (1.09 and
+# 1.01). Same height, same baseline — the eye is meant to compare silhouettes.
+s.shapes.add_picture(DIAG + "pipe-filter-architecturev5-grey.png", P(28.8), P(IY + 24),
+ width=P(163), height=P(150))
+s.shapes.add_picture(DIAG + "pipe-filter-architecture-v6-grey.png", P(199), P(IY + 24),
+ width=P(151), height=P(150))
+text(s, 28.8, IY + 182, 163, 24, "v5.0 · MAY", color=MUTED, bold=True)
+text(s, 199, IY + 182, 151, 24, "v6.0 · JULY", color=ACCENT, bold=True)
+
+DX, DW = 362.0, 329.2
+rect(s, DX, IY, DW, 228.0, CARD)
+rect(s, DX, IY, DW, 5.04, ACCENT)
+text(s, DX + 12, IY + 12, DW - 24, 28, "What ETIM changed", size=22, color=ACCENT,
+ bold=True)
+yy = IY + 48
+for n, label in [("1", "ETIM matching, a new phase"),
+ ("2", "ETIM dictionary loaded"),
+ ("3", "Raw values kept separately"),
+ ("4", "Fixed handoff format to ML"),
+ ("5", "PIMS keyed by ETIM IDs")]:
+ text(s, DX + 12, yy, 16, 26, n, color=ACCENT, bold=True)
+ text(s, DX + 30, yy, DW - 44, 26, label, color=WHITE)
+ yy += 33
+footer(s, "one new box · nothing else moved",
+ link_label="Full v6.0 diagram:", url=CONFLUENCE, shown_url="cmu-mse.atlassian.net/wiki/spaces/AISDLC")
+
+# ─────────────────────────────────────────── 4. the one change that needs explaining
+# The v6 diagram is portrait 1600x1898. At full slide height it is 253 pt wide and its
+# stage labels land near 1.4 pt, so neither scaling nor cropping the bitmap can make it
+# readable on a projector. This slide redraws the changed region as native shapes so
+# every label is 20 pt. Slide 3 keeps the thumbnails, whose only job is silhouette
+# comparison — you don't have to read those to see that the shape didn't move.
+s = new_slide()
+title(s, "Inside the new box: two passes")
+
+BW, BG = 121.0, 14.35 # five boxes across 662.4 pt of usable width
+BX0, ROW2_Y, BH = 28.8, 236.0, 62.0
+
+# ── pass one: what already existed
+obox(s, BX0, 108.0, 662.4, 52.0, color=WHITE, fill=CARD)
+text(s, BX0 + 16, 108.0, 400, 52.0, "ML attribute matching", color=WHITE, bold=True,
+ anchor=MSO_ANCHOR.MIDDLE, line=1.0)
+text(s, BX0 + 430, 108.0, 216, 52.0, "already existed", color=MUTED,
+ anchor=MSO_ANCHOR.MIDDLE, line=1.0, align=PP_ALIGN.RIGHT)
+
+# ── the seam
+text(s, BX0, 168.0, 662.4, 26, "then, in the same service", color=DIM, line=1.0)
+text(s, BX0, 200.0, 400, 26, "ML ETIM matching · NEW", color=WHITE, bold=True,
+ space=0.6, line=1.0)
+
+# ── pass two: five stages, dashed because none of this is built yet
+for i, label in enumerate(["Class", "Feature", "Value\n+ unit", "ETIM\ncheck",
+ "Policy\ncheck"]):
+ bx = BX0 + i * (BW + BG)
+ obox(s, bx, ROW2_Y, BW, BH, color=DIM, dashed=True, fill=None)
+ text(s, bx + 6, ROW2_Y, BW - 12, BH, label.split("\n"), color=WHITE, bold=True,
+ anchor=MSO_ANCHOR.MIDDLE, align=PP_ALIGN.CENTER, line=1.0, gap=0)
+ if i < 4:
+ text(s, bx + BW, ROW2_Y, BG, BH, "→", color=DIM,
+ anchor=MSO_ANCHOR.MIDDLE, align=PP_ALIGN.CENTER, line=1.0)
+
+text(s, BX0, ROW2_Y + BH + 14, 662.4, 26,
+ [[("Wrong class, wrong feature list.", True, WHITE),
+ (" Every attribute under it is wrong.", False, MUTED)]],
+ line=1.0)
+footer(s, "dashed = designed, not built", color=DIM,
+ link_label="ADR-018:", url=ADR_018,
+ shown_url="cmu-mse.atlassian.net/wiki/spaces/AISDLC")
+
+# ─────────────────────────────────────────────────────────── 5. decisions
+# The point of this slide is the pairing: each decision beside the alternative we
+# turned down. A reason column would only restate the decision.
+#
+# The ADR column is what turns four claims into four artifact references, which is
+# what the rubric's "architectural descriptions and decisions" is asking for. Row 3
+# has no single ADR — the artifacts *are* 16 through 21 — so it cites the range
+# rather than inventing a number.
+s = new_slide()
+title(s, "Decisions, and what we chose against")
+
+ADR_X, CHOSE_X, VS_X = 42.0, 116.0, 440.0
+HDR_Y = 84.0
+text(s, ADR_X, HDR_Y, 68, 26, "ADR", color=MUTED, bold=True)
+text(s, CHOSE_X, HDR_Y, 296, 26, "We chose", color=WHITE, bold=True)
+text(s, VS_X, HDR_Y, 248, 26, "Instead of", color=MUTED, bold=True)
+rect(s, 28.8, HDR_Y + 30, 662.4, 1.2, RULE)
+
+decisions = [
+ ("16", "ML does the matching", "ETIM keys at normalization"),
+ ("14", "Raw values in own table", "one table holding both"),
+ ("16–21", "New ADRs, not edits", "editing the April ones"),
+ ("20", "Stay on ETIM 10.0", "building an upgrade path"),
+]
+ry, RH, RG = HDR_Y + 42, 44.0, 8.0
+for adr, chose, instead in decisions:
+ rect(s, 28.8, ry, 662.4, RH, CARD)
+ rect(s, 28.8, ry, 4.5, RH, WHITE)
+ # wrap=False on both text cells: a wrapped ADR range dragged the decision onto
+ # two lines, and neither column is ever long enough to need wrapping.
+ text(s, ADR_X, ry, 68, RH, adr, color=DIM, bold=True,
+ anchor=MSO_ANCHOR.MIDDLE, line=1.0, wrap=False)
+ text(s, CHOSE_X, ry, 296, RH, chose, color=WHITE, bold=True,
+ anchor=MSO_ANCHOR.MIDDLE, line=1.0, wrap=False)
+ text(s, VS_X - 24, ry, 18, RH, "×", color=RGBColor(0x66, 0x66, 0x66),
+ anchor=MSO_ANCHOR.MIDDLE, line=1.0)
+ text(s, VS_X, ry, 248, RH, instead, color=MUTED, anchor=MSO_ANCHOR.MIDDLE,
+ line=1.0, wrap=False)
+ ry += RH + RG
+footer(s, "matching stages designed, not yet built", color=DIM,
+ link_label="ADR-018:", url=ADR_018,
+ shown_url="cmu-mse.atlassian.net/wiki/spaces/AISDLC")
+
+NOTES_SOFTWARE = [
+ "ETIM is a mid-project requirements CHANGE, not a new project.\n\n"
+ "One line: we went from predicting free-form attributes to classifying each product "
+ "into an ETIM class and matching its attributes to a controlled vocabulary.\n\n"
+ "Principle: original supplier data is evidence, ETIM is a standardized "
+ "interpretation over it, confidence is how sure we are of the interpretation.\n\n"
+ "C-4 is the newest piece: we are pinned to ETIM 10.0 EI for the project. Adopting "
+ "later releases is out of scope. If asked why — the upgrade path is a diff report, "
+ "a bulk re-match and a second review queue for an event that won't happen inside "
+ "this project, and nobody has decided who authorizes an upgrade. A stated limit is "
+ "defensible; a half-built upgrade path isn't.\n\n"
+ "Artifacts: docs/product-spec-v1.2.pdf, product-spec-changelog.md, "
+ "etim-requirements-change.md.",
+
+ "Four classes of requirements management: change control, version control, status "
+ "tracking, tracing.\n\n"
+ "Strongest point is version control: we ADDED IDs (HLR-6, FR-9, FR-10, DR-4, then "
+ "C-4) rather than renumbering, so every existing trace link survives.\n\n"
+ "We used the same discipline twice — v1.1 integrated ETIM, v1.2 pinned the release "
+ "rather than silently editing v1.1.\n\n"
+ "Be honest about the blocked items. ETIMARTCLASSFEATUREMAP.csv genuinely has no "
+ "'required' column, so until the client defines a feature policy, 'what blocks "
+ "publish?' is unanswerable. ADR-019 records the seam so the build doesn't stall.\n\n"
+ "Known defect to own before it's found: two spec lineages exist — this one "
+ "(0.1 -> 0.5 -> 1.0 -> 1.1 -> 1.2) and a 'Document Version 2.0' from April with a "
+ "different ID set. ADRs 0001-0015 cite the April IDs; 0016-0021 cite v1.2. "
+ "Recorded in product-spec-changelog.md and section 10.10 of the matrix.",
+
+ "Open the full v6 PNG rather than squinting at the thumbnail:\n"
+ "Diagrams/pipe-filter-architecture-v6.png\n\n"
+ "Lead with what did NOT change — pipe-and-filter, per-attribute routing, "
+ "human-in-the-loop, audit trail. The spine survived a major requirements change.\n\n"
+ "Delta 2 is the one to dwell on. You can't match an attribute until you know the "
+ "class, because the legal feature set is defined per class. Get the class wrong and "
+ "every feature under it is wrong at high confidence, so routing won't catch it. "
+ "Hence five stages and a class-review step ahead of attribute routing.\n\n"
+ "Delta 3: supplier text is evidence, ETIM is interpretation. One table mixes them "
+ "and you lose the ability to trace a published value back to its source.\n\n"
+ "Built and merged: reference layer (alembic 0005), evidence staging split (0006), "
+ "extracted_inputs handoff (0007). Not built: matching stages, ETIM-aware routing, "
+ "re-keyed writeback. Say so.\n\n"
+ "Telemetry question: Datadog is the production target (ADR-012 stands). Prometheus "
+ "+ OpenTelemetry + structlog is the local substrate. The assessment doc read the "
+ "code without the deployment intent and called it a contradiction.",
+
+ "Four decisions, each with the alternative we rejected.\n\n"
+ "Row 3 is the one I'd defend hardest: we did not edit ADRs 0001-0012. They record "
+ "what we decided in April. We superseded forward and wrote an assessment that goes "
+ "ADR by ADR through what ETIM affected. Editing in place erases the history.\n\n"
+ "Row 4 is the newest decision. ETIM 10.0 is pinned via C-4. We accept the catalog "
+ "goes stale relative to ETIM; that's the trade. The etim_release_id columns stay in "
+ "the schema for provenance, so a published row still names the release it was "
+ "matched under, and un-pinning later is a scope change rather than a migration.\n\n"
+ "The five still-open client decisions: phase-one class list, feature policy per "
+ "class, 'ETIM Other' handling, metric-canonical storage and display units, and "
+ "mapping sign-off ownership. Release-upgrade governance used to be a sixth; C-4 "
+ "closed it.\n\n"
+ "If asked what we'd do differently: reconcile the two spec lineages earlier, and "
+ "wire the handoff builder into the orchestrator (EPARTS-363) so the boundary is "
+ "exercised in production flow, not only in unit tests.",
+]
+
+
+# ─────────────────────────────────────────────────────────── notes, deck 1
+for slide, note in zip(prs.slides, NOTES_SOFTWARE):
+ slide.notes_slide.notes_text_frame.text = note
+save(prs, OUT_SOFTWARE, 5)
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+# DECK 2 — Reflection & Closing. Its own file because it is presented in the
+# closing slot at the end of the team talk, not as part of Software System.
+# ══════════════════════════════════════════════════════════════════════════════
+prs = new_deck()
+s = new_slide()
+title(s, "Two lessons")
+LY, LH = 84.0, 262.0
+card(s, LX, LY, CW, LH, DIM, "3-day cycles didn't hold", [
+ [("Too much changed per tick", False, MUTED)],
+ [("A small blocker ate the whole tick", False, MUTED)],
+ [("Reviewing an agent PR took longer than writing it", False, MUTED)],
+ [("Now ", False, MUTED), ("7-day cycles", True, WHITE), (" on Jira", False, MUTED)],
+], head_color=DIM)
+card(s, RX, LY, CW, LH, ACCENT, "Give AI the paperwork", [
+ [("Drafting: ", False, MUTED), ("3 min → 15 s", True, WHITE)],
+ [("234 tickets ≈ ", False, MUTED), ("11 h saved", True, WHITE)],
+ [("But the real win:", True, ACCENT)],
+ [("Spring, by hand: ", False, MUTED), ("0% points", True, WHITE)],
+ [("Agent-drafted: ", False, MUTED), ("90% points,", True, WHITE)],
+ [("94% in the right epic", True, WHITE)],
+], head_color=ACCENT)
+footer(s, "forecasting needs points on every ticket")
+
+NOTE_REFLECTION = (
+ "Lesson 1 — we tried 3-day ticks from the agentic-augmented-scrum doc and moved to "
+ "7-day cycles on Jira.\n\n"
+ "Three reasons, and the third is the real one:\n"
+ " - too much changed inside a single tick to close it cleanly\n"
+ " - one small blocker consumed the whole cycle, because there was no slack\n"
+ " - reviewing an agent PR took longer than the agent took to write it\n\n"
+ "The general point: agents moved the constraint from writing code to reviewing it. "
+ "A 3-day cycle was sized for the old constraint. That is also why we plan capacity "
+ "in review hours rather than story points.\n\n"
+ "Lesson 2 — the AI-in-SE one. We gave agents control of ticket writing, "
+ "documentation and comments, because they read the same repo we do and therefore "
+ "carry more context than a person typing a ticket at the end of the day.\n\n"
+ "Numbers, from dashboard/data/jira_issues.json (290 issues; the JQL and fetch time "
+ "are recorded in the file):\n"
+ " - 3 minutes per ticket by hand, about 15 seconds for an agent\n"
+ " - 234 tickets created since 1 May, so roughly 11 hours saved\n"
+ " - that is under an hour a week. Do not oversell it.\n\n"
+ "The number that matters is field completeness. Of the 56 tickets we wrote by hand "
+ "in spring, ZERO had story points and ZERO had an epic parent. Of the 234 "
+ "agent-drafted tickets, 90 percent have points and 94 percent have the right epic. "
+ "A person under time pressure skips those fields; an agent does not.\n\n"
+ "That is what makes the forecasting in the management section possible. Monte Carlo "
+ "over closed-issue throughput needs points on every ticket. In spring we could not "
+ "have produced that chart from our own backlog.\n\n"
+ "Honest caveat if pushed: we review every agent-drafted ticket, and the 10 percent "
+ "without points are mostly ones we corrected or closed as duplicates."
+)
+prs.slides[0].notes_slide.notes_text_frame.text = NOTE_REFLECTION
+save(prs, OUT_REFLECTION, 1)
diff --git a/docs/confluence/ADRs-all.md b/docs/confluence/ADRs-all.md
new file mode 100644
index 0000000..98149b5
--- /dev/null
+++ b/docs/confluence/ADRs-all.md
@@ -0,0 +1,1075 @@
+# Architecture Decision Records
+
+21 ADRs. **The repo is the source of truth** — every row below links to the
+file in `eparts/docs/`. This page is a reading copy, so edit the repo rather than the
+page, or the two will drift.
+
+ADRs 0001–0012 are the spring baseline and are deliberately left unedited: they record
+what we believed in April. ETIM decisions supersede them *forward*, by reference, in
+0013–0021. Where a spring ADR is affected but not superseded, the change-impact analysis
+is in [`ETIM-ADR-ASSESSMENT.md`](https://github.com/AshrithaG/eparts/blob/main/docs/ETIM-ADR-ASSESSMENT.md).
+
+Requirement IDs cited by 0016–0021 resolve against Product Specification v1.4; the
+forward and backward traces are in [`REQUIREMENTS-TO-ADR-MAPPING.md`](https://github.com/AshrithaG/eparts/blob/main/docs/REQUIREMENTS-TO-ADR-MAPPING.md).
+
+| ADR | Decision | Status |
+|---|---|---|
+| [0001](https://github.com/AshrithaG/eparts/blob/main/docs/0001-adopt-pipe-and-filter-architectural-style.md) | Adopt Pipe-and-Filter as the Primary Architectural Style | Accepted |
+| [0002](https://github.com/AshrithaG/eparts/blob/main/docs/0002-isolate-prediction-strategy-behind-stable-interface.md) | Isolate the Prediction Strategy Behind a Stable Internal Interface | Accepted |
+| [0003](https://github.com/AshrithaG/eparts/blob/main/docs/0003-use-hybrid-rule-engine-and-semantic-similarity.md) | Use a Hybrid Rule Engine and Semantic Similarity for Attribute Prediction | Tentative |
+| [0004](https://github.com/AshrithaG/eparts/blob/main/docs/0004-route-confidence-decisions-at-attribute-level.md) | Route Confidence Decisions at the Attribute Level, Not the Record Level | Accepted |
+| [0005](https://github.com/AshrithaG/eparts/blob/main/docs/0005-externalize-confidence-threshold-as-configuration.md) | Externalize the Confidence Threshold as Runtime Configuration | Tentative |
+| [0006](https://github.com/AshrithaG/eparts/blob/main/docs/0006-enforce-idempotent-pims-writeback-via-natural-key.md) | Enforce Idempotent PIMS Writeback via a Composite Natural Key | Accepted |
+| [0007](https://github.com/AshrithaG/eparts/blob/main/docs/0007-use-attribute-row-canonical-schema.md) | Use an Attribute-Row Canonical Schema for the Staging Table | Accepted |
+| [0008](https://github.com/AshrithaG/eparts/blob/main/docs/0008-deploy-platform-as-single-azure-app-service-unit.md) | Deploy the Platform as a Single Azure App Service Unit | Accepted |
+| [0009](https://github.com/AshrithaG/eparts/blob/main/docs/0009-implement-human-review-queue-as-database-table.md) | Implement the Human Review Queue as a Persistent Database Table | Accepted |
+| [0010](https://github.com/AshrithaG/eparts/blob/main/docs/0010-maintain-append-only-audit-trail.md) | Maintain an Append-Only Audit Trail of Every Pipeline Decision | Accepted |
+| [0011](https://github.com/AshrithaG/eparts/blob/main/docs/0011-trigger-retraining-automatically-on-batch-completion.md) | Trigger Retraining Automatically on Human Review Batch Completion | Proposed |
+| [0012](https://github.com/AshrithaG/eparts/blob/main/docs/0012-emit-stage-by-stage-telemetry-to-datadog.md) | Emit Stage-by-Stage Telemetry to Datadog for Drift Detection and Operational Monitoring | Proposed |
+| [0013](https://github.com/AshrithaG/eparts/blob/main/docs/0013-establish-etim-reference-data-layer.md) | Establish a Release-Versioned ETIM Reference Data Layer Owned by Ingestion | Accepted |
+| [0014](https://github.com/AshrithaG/eparts/blob/main/docs/0014-emit-source-preserving-product-attribute-staging-split.md) | Emit a Source-Preserving Product + Attribute Staging Split | Accepted |
+| [0015](https://github.com/AshrithaG/eparts/blob/main/docs/0015-target-postgresql-now-defer-azure-sql.md) | Target PostgreSQL Now; Defer the Azure SQL Conversion | Accepted |
+| [0016](https://github.com/AshrithaG/eparts/blob/main/docs/0016-decompose-matching-into-staged-etim-class-feature-value-stages.md) | Decompose Attribute Matching into Staged ETIM Class → Feature → Value/Unit Matching | Accepted |
+| [0017](https://github.com/AshrithaG/eparts/blob/main/docs/0017-rekey-pims-writeback-contract-on-etim-identifiers.md) | Re-key the PIMS Writeback Contract on ETIM Identifiers | Accepted |
+| [0018](https://github.com/AshrithaG/eparts/blob/main/docs/0018-extend-routing-to-etim-signals-with-class-review-first.md) | Extend Routing to ETIM Signals, with a Class-Review-First Path | Accepted |
+| [0019](https://github.com/AshrithaG/eparts/blob/main/docs/0019-externalize-client-feature-policy-as-per-class-configuration.md) | Externalize the Client Feature Policy as Per-Class Configuration | Accepted |
+| [0020](https://github.com/AshrithaG/eparts/blob/main/docs/0020-pin-etim-release-10-0-for-the-project-duration.md) | Pin ETIM Release 10.0 (EI) for the Project Duration | Accepted |
+| [0021](https://github.com/AshrithaG/eparts/blob/main/docs/0021-formalize-ingestion-to-ml-boundary-as-frozen-extracted-input-record.md) | Formalize the Ingestion → ML Boundary as a Frozen `ExtractedInput` Record | Accepted |
+
+---
+## ADR-001: Adopt Pipe-and-Filter as the Primary Architectural Style
+### Status
+
+Accepted
+
+### Context
+
+eParts Services LLC ingests heterogeneous supplier catalogs (CSV, PDF, email attachments, SFTP drops, direct uploads) into PIMS through a manual workflow currently absorbing roughly 4.5 FTEs across eParts and Alps Controls. The new platform must transform raw supplier files into validated PIMS records while keeping data integrity high, because incorrect product data propagates into contractor field orders.
+
+The transformation is fundamentally linear: parse → normalize → predict → route → review/auto-accept → write back. Each stage operates on the output of the previous one, and stages have different resource profiles (parsing is I/O-bound, prediction is CPU/memory-bound, review is human-bound).
+
+Several architectural styles were considered:
+
+- **Event-driven architecture** would introduce a message broker and asynchronous coordination. Supplier catalogs arrive in discrete batches rather than continuous streams, so the complexity is not justified.
+- **Microservices** would require container orchestration and distributed tracing infrastructure beyond what a five-person capstone team can sustain.
+
+The team is five people working from Spring through Fall 2026, so operational simplicity is a binding constraint.
+
+### Decision
+
+We will structure the platform as a pipe-and-filter system. Independent filters (Ingestion Gateway, Normalization, Prediction Service, Routing Engine, Review/Auto-accept paths, Writeback) communicate through typed data channels. The pipeline is linear with one branch at the Routing Engine where confidence-based routing splits high-confidence attributes (auto-accept) from low-confidence attributes (human review); both paths merge before writeback.
+
+
+
+### Consequences
+
+- Each filter can be replaced or evolved independently because filters communicate only through defined data contracts. The Prediction Service can be swapped without touching upstream parsing or downstream writeback (supports QA-2).
+- Adding a new product category requires extending the canonical schema and retraining; it does not require changing the filter sequence (supports QA-3).
+- Staging tables placed between filters act as checkpoints: a failure at any stage does not lose data already processed upstream (supports QA-4 availability).
+- The known weakness of pipe-and-filter is error detection and recovery across the pipeline. We mitigate this with persistent staging tables between stages and idempotent writeback, but cross-stage transactional guarantees are not provided.
+- The branch at the Routing Engine departs from a strictly linear pipeline. The two paths must merge before writeback, which introduces merge logic in the writeback service (further explored in ADR-005).
+- The architecture mirrors the existing manual workflow stage-for-stage, reducing the risk that the system solves the wrong problem and easing communication with the catalog team.
+
+### Requirements Traceability
+
+- **HLRs:** HLR-1 (multi-format ingestion), HLR-2 (normalization to standard structure)
+- **FRs:** FR-1, FR-2 (filter decomposition makes ingestion and normalization distinct stages)
+- **QASs:** QAS-2, QAS-3 (style enables filter-level replacement); QAS-4 (staging tables between filters act as checkpoints)
+- **Constraints:** C-7 (capstone timeline — pipe-and-filter mirrors existing manual workflow, minimizing rework risk)
+- **Scenarios:** SCEN-1, SCEN-2 (the filter sequence is the spine of both scenarios)
+- **Validation:** VAL-1 (Ingestion Gateway is the first filter)
+
+---
+
+## ADR-002: Isolate the Prediction Strategy Behind a Stable Internal Interface
+### Status
+
+Accepted
+
+### Context
+
+Model selection for the attribute prediction component is unresolved through Phase 2. The team is currently using a hybrid rule + semantic-similarity approach (ADR-003) but expects to evaluate alternatives such as DistilBERT or CatBoost as labeled data accumulates. Quality attribute QA-2 (Modifiability — model swap) is rated High importance / Medium difficulty and explicitly requires that swapping the prediction strategy not ripple into the Routing Engine, Writeback, or any other component.
+
+Three isolation mechanisms were considered:
+
+- **Internal abstract interface** in the same Python application. A swap is a new class plus a configuration change; one redeployment.
+- **REST microservice** running the Prediction Service in a separate Azure Container App. Enables independent deployment, canary rollouts, and GPU-backed inference, but adds container orchestration, health checks, service authentication, and distributed tracing.
+- **Message queue (Azure Service Bus)** with broker-mediated communication. Two queues introduced; retry and dead-letter offloaded to Service Bus. Suits near-real-time ingestion with multiple consumers.
+
+The current phase processes supplier catalogs in discrete batches and has a single downstream consumer (the Routing Engine). The team does not need canary deployments or GPU inference during the capstone phase. A network boundary between filters would add operational complexity disproportionate to team capacity.
+
+### Decision
+
+We will define `PredictionServiceInterface` as a Python abstract interface that accepts normalized records and returns predictions with per-attribute confidence scores. Concrete implementations (`CatBoostPredictor`, `DistilBERTPredictor`, the current hybrid implementation) live inside the `prediction` package and are selected at startup via configuration. The Routing Engine and all other downstream components depend only on `PredictionResult`, a plain data class, and never on any model-specific type.
+
+### Consequences
+
+- Replacing the prediction strategy is a localized change: a new class in the `prediction` package plus a configuration change. Nothing in `routing`, `writeback`, `review`, or `audit` changes.
+- The interface contract — `PredictionResult` with per-attribute confidence — must be defined before the model is finalized. The team must avoid leaking model-specific types (logits, embedding vectors, classifier probabilities) into adjacent packages.
+- Retraining and model promotion (described in the MLOps pipeline) operate inside the `prediction` package boundary. The interface does not change when a new model version is promoted, so the Routing Engine sees the prediction service as unchanged.
+- This decision does not enable canary deployments or side-by-side model evaluation in production. If the system is later handed off to a larger eParts team that requires those capabilities, the prediction package will need to be extracted into a REST microservice. The module boundaries are drawn deliberately so that this transition is adding network serialization at an existing boundary, not a rewrite.
+
+### Requirements Traceability
+
+- **HLRs:** HLR-3 (predict with confidence — implementation choice deferred behind interface)
+- **FRs:** FR-3 (per-attribute prediction contract)
+- **QASs:** QAS-2 (model swap localized to prediction package — this ADR is the named mechanism in the QAS response)
+- **Constraints:** C-3, DC-1 (Python interface); C-7 (internal interface chosen over REST microservice for capstone timeline)
+- **Related ADRs:** ADR-003 (concrete implementation behind this interface); ADR-011 (retraining promotes new versions through this interface)
+
+---
+
+## ADR-003: Use a Hybrid Rule Engine and Semantic Similarity for Attribute Prediction
+### Status
+
+Tentative
+
+### Context
+
+The Prediction Service must map raw supplier text to canonical attribute values and emit per-attribute confidence scores that the Routing Engine can compare against a threshold. Three properties matter: accuracy under data scarcity, explainability for the catalog team, and ability to handle free-text inputs that rules cannot anticipate.
+
+The team targets approximately 200 labeled examples for the initial training set, but a calibrated pure-ML classifier typically needs around 830 examples to produce well-behaved confidence scores. eParts has stated an explainability requirement: catalog reviewers need to understand why an item was routed to review.
+
+Three alternatives were considered:
+
+- **Pure rules.** Deterministic and fully explainable, but estimated coverage is only 40–60% of supplier inputs because suppliers use inconsistent terminology that rules cannot enumerate.
+- **Pure ML classifier.** Handles unseen text well, but with the available labeled data the confidence scores are not well-calibrated. Confidence scores are also opaque, undermining the explainability requirement.
+- **Hybrid: rules first, semantic similarity (TF-IDF + cosine) for unmatched inputs.** Rules give a high-precision fallback when data is scarce; the semantic layer covers free-text inputs the rules miss. Reason codes can be attached to low-confidence items.
+
+A weighted decision matrix scored the hybrid approach highest (2.50) against pure rules (1.85) and pure ML (1.70), with criteria weighted toward accuracy under low data, explainability, and free-text coverage.
+
+### Decision
+
+We will implement the Prediction Service as a hybrid pipeline. A rule engine runs first against each normalized attribute. Where rules do not match, a semantic similarity layer (TF-IDF vectorization with cosine similarity against canonical value embeddings) produces a candidate value. The final confidence is a weighted composite:
+
+```
+conf_final = α · conf_rule + (1 - α) · conf_embed
+```
+
+with an initial value of `α = 0.7`. Reason codes from the rule layer are attached to each prediction and surfaced in the Human Review Queue for low-confidence items. Both layers live inside the `prediction` package behind `PredictionServiceInterface` (ADR-002).
+
+### Consequences
+
+- Rules carry the prediction under data scarcity, so the system has a usable accuracy floor before sufficient labeled data accumulates.
+- Reason codes from the rule layer satisfy the explainability requirement. Reviewers see why an attribute was flagged, which is expected to support adoption by Brian and Dewey on the catalog team.
+- The semantic layer can be replaced or upgraded (e.g., to embeddings from a transformer) without touching the rule layer or the Routing Engine, because both layers sit behind `PredictionServiceInterface`.
+- The α weighting is a sensitivity point. Wrong α suppresses the more accurate signal source and produces miscalibrated confidence, which propagates directly into routing errors. The initial value of 0.7 is a guess; it must be calibrated against prototype data (see Refinement 3 in the report).
+- The decision is tentative and carries explicit reconsideration triggers. If pure rules cover ≥85% of inputs at confidence ≥0.90, the semantic layer adds complexity without value and we should switch to pure rules. If labeled data exceeds ~800 examples and a pure ML model achieves ≥85% accuracy with calibrated confidence, the hybrid approach loses its advantage and we should switch to pure ML.
+- Per-attribute-type α weights may be more accurate than a single global α, since some attributes (e.g., `SUPPLY_VOLTAGE`) are inherently easier to predict than others (e.g., `DESCRIPTION`). The retraining pipeline can store learned per-type weights as configuration once Refinement 3 produces evidence.
+
+### Requirements Traceability
+
+- **HLRs:** HLR-3 (predict with confidence)
+- **FRs:** FR-3 (per-attribute predictions with confidence scores)
+- **QASs:** QAS-1 (accuracy — hybrid provides usable accuracy floor under data scarcity); QAS-5 (reason codes from rules support drift interpretation)
+- **Constraints:** C-3, DC-1 (Python ML); C-4 (phase scope limits labeled data, favoring hybrid over pure ML)
+- **Scenarios:** SCEN-1 (high-confidence path), SCEN-2 (low-confidence path with reason codes)
+
+---
+
+## ADR-004: Route Confidence Decisions at the Attribute Level, Not the Record Level
+### Status
+
+Accepted
+
+### Context
+
+The Routing Engine is the architectural component that enforces the accuracy quality attribute (QA-1, rated High/High). Every record produced by the Prediction Service contains multiple attributes, each with its own predicted value and confidence score. The team must decide whether confidence routing operates at the record level (the whole record is sent to review if any attribute is uncertain) or at the attribute level (each attribute is routed independently).
+
+Two alternatives were considered:
+
+- **Per-record routing.** Conceptually simpler. The review queue holds whole records, and writeback always emits complete records. There is no merge logic. However, a record with ten attributes and one uncertain value sends all ten attributes to review, inflating reviewer workload.
+- **Per-attribute routing.** Each attribute is routed independently. Estimated 3–5× lower review volume than per-record because only the attributes the model is unsure about reach the queue. The cost is structural: the writeback service must merge auto-accepted attributes with reviewed attributes for the same record before writing to PIMS, and there is a risk that correlated attributes (e.g., connection type and port size) become inconsistent if reviewed in isolation.
+
+The combined catalog team across eParts and Alps Controls is approximately 4.5 FTEs. Reviewer capacity is the binding constraint on review volume; if the system pushes too many items to review, the labor savings the platform is meant to provide disappear.
+
+### Decision
+
+We will route confidence decisions at the attribute level. The Human Review Queue is keyed on `(record_id, attribute_id)`. The Routing Engine compares each attribute's confidence score against the configured threshold independently. The Writeback Service batches all attributes for a given record and writes them to PIMS as a unit only once all routing paths for that record (auto-accept and review) have resolved.
+
+### Consequences
+
+- Review volume scales with actual model uncertainty rather than with record size, expected to reduce reviewer workload by 3–5× compared with per-record routing.
+- Reviewers see only the flagged attributes plus their source context, not the entire record. This focuses attention but means reviewers cannot easily catch inconsistencies between an auto-accepted attribute and one they are reviewing.
+- The Writeback Service carries merge logic. It must hold the complete record until all routing decisions for that record are resolved, then upsert it as a unit. A partial write — auto-accepted attributes entering PIMS before reviewed attributes are resolved — would produce incomplete records and is explicitly prevented by this batching.
+- Correlated attributes are a known risk. If connection type and port size are reviewed independently and the reviewer makes inconsistent choices, an internally inconsistent record can reach PIMS. Mitigation: the review interface presents the full record context to reviewers, but this has not been validated in practice. Refinement 2 in the project plan tests pairwise mutual information between attributes and inspects high-MI pairs.
+- Per-attribute thresholds may be required if attribute-level accuracy varies significantly. Some attributes are inherently easier to predict than others. The threshold mechanism is configurable (ADR-005) so per-attribute thresholds can be introduced without code changes.
+- If more than 30% of corrections turn out to involve cross-attribute consistency errors, the per-attribute routing decision should be reconsidered in favor of per-record or attribute-group routing.
+
+### Requirements Traceability
+
+- **HLRs:** HLR-4 (Human Review Queue for low-confidence predictions)
+- **FRs:** FR-3 (per-attribute predictions); FR-4 (route below-threshold attributes to queue); FR-9 (per-attribute routing decisions)
+- **QASs:** QAS-1 (accuracy — per-attribute routing keeps review volume proportional to risk)
+- **Scenarios:** SCEN-2 (only the uncertain attribute is routed, not the whole record)
+- **Validation:** VAL-2 (low-confidence item appears in Human Review Queue)
+
+---
+
+## ADR-005: Externalize the Confidence Threshold as Runtime Configuration
+### Status
+
+Tentative
+
+### Context
+
+The Routing Engine sends attributes with confidence above a threshold to auto-accept and attributes below the threshold to the Human Review Queue. The threshold is the most sensitive parameter in the system: it controls the tradeoff between accuracy (QA-1) and reviewer throughput. A threshold set too high pushes most attributes into review and overwhelms the catalog team, eliminating the labor savings the platform is meant to provide. A threshold set too low lets incorrect predictions through to PIMS, where they cause wrong parts to be ordered by contractors.
+
+The threshold cannot be set during design because no model has yet been run against production-representative data. The team currently uses a placeholder of 0.85 with no empirical support. Refinement 1 in the project plan calibrates the threshold against ≥200 labeled submissions using precision-recall curves between 0.50 and 0.99. Per-attribute variance in accuracy may also drive a per-attribute threshold table rather than a single global value.
+
+Hardcoding the threshold in the Routing Engine would require a code change and redeployment for every recalibration, which is incompatible with the iterative tuning the team expects across the pilot.
+
+### Decision
+
+The confidence threshold is externalized as runtime configuration read by the Routing Engine at startup. The configuration mechanism supports both a global threshold value and an optional per-attribute override table. Threshold changes take effect on application restart without any code change. The Routing Engine reads the threshold(s) once per pipeline run; threshold changes during a run do not affect already-routed attributes.
+
+### Consequences
+
+- The threshold can be retuned during pilot operation without engineering involvement beyond editing configuration and restarting the App Service.
+- Per-attribute thresholds are supported architecturally without further code changes. If Refinement 1 reveals that some attributes (e.g., `SUPPLY_VOLTAGE`) are reliably predicted at 0.75 while others (e.g., `DESCRIPTION`) need 0.92, the per-attribute table can be populated.
+- The threshold value is a configuration concern, not an architectural concern. This means that the architecture cannot guarantee an accuracy number; it can only guarantee that whatever threshold is set will be applied consistently. The actual accuracy guarantee depends on operational discipline around configuration management.
+- Configuration drift is a risk. If the threshold is changed in production without recording the change in the audit trail, later analyses of model accuracy or reviewer workload may be impossible to interpret. The audit trail (ADR-009) records the threshold value alongside each routing decision to mitigate this.
+- The decision is tentative because the threshold itself is unsupported. Once Refinement 1 produces evidence and a value is selected, this decision moves to Accepted.
+- This decision interacts with monitorability (ADR-012): the threshold value is one of the baselines against which drift is measured. Changing the threshold resets the baseline.
+
+### Requirements Traceability
+
+- **FRs:** FR-4 (route based on threshold); FR-7 (configurable thresholds, calibration TBD); FR-9 (per-attribute routing using configurable thresholds)
+- **QASs:** QAS-1 (accuracy lever); QAS-5 (threshold value is part of the drift baseline)
+- **Scenarios:** SCEN-1 (above-threshold auto-accept), SCEN-2 (below-threshold review)
+- **Validation:** VAL-2 (threshold drives routing behavior tested by VAL-2)
+
+---
+
+## ADR-006: Enforce Idempotent PIMS Writeback via a Composite Natural Key
+### Status
+
+Accepted
+
+### Context
+
+The platform writes approved product attributes to PIMS staging tables on SQL Server. PIMS exposes no writeback API and provides no rollback or transactional guarantees back to the platform. Retries of a writeback operation must not produce duplicate records, because duplicates in PIMS staging propagate into wrong bills of materials for contractor orders.
+
+Several mechanisms were considered:
+
+- **Application-side primary key check.** Read-before-write to detect existing records.
+- **Database upsert via composite natural key.** A SQL `MERGE` (or equivalent) keyed on a stable identifier matches existing rows and updates them rather than inserting duplicates.
+- **Distributed transaction across the platform and PIMS.** Not feasible: PIMS is owned by a different team, has no API, and there is no distributed transaction coordinator across the trust boundary.
+- **Idempotency token in PIMS.** Would require schema change in PIMS, which the platform team does not control.
+
+The submission ID is a composite of the company identifier and the product identifier, making it stable across submissions: a new update pushed for the same company–product pair carries the same submission ID. The attribute ID is a stable canonical attribute identifier. Together, `(submission_id, attribute_id)` uniquely identify any value the platform writes. Both are generated inside the platform and stored in the staging tables before the writeback runs.
+
+### Decision
+
+PIMS writeback uses a composite natural key of `(submission_id, attribute_id)`, where `submission_id` is itself derived from `(company_id, product_id)`. The Publish/Sync Job (Azure Function) executes an upsert against the PIMS staging table: if a row with the same key exists, the value is updated in place; otherwise a new row is inserted. Because the submission ID is stable for a given company–product pair, pushing a new update for the same product produces the same key and overwrites the prior values rather than inserting a duplicate. The natural key is generated and stored in the platform's own staging tables before writeback, so a retry of the writeback also uses the identical key and matches the same target row.
+
+### Consequences
+
+- Retries of the Publish/Sync Job are safe. A network failure mid-run, a transient PIMS outage, or a redeployment that interrupts the job can be recovered by simply running the job again.
+- Idempotency is enforced in application code, not in PIMS. If PIMS staging tables are altered (e.g., the natural key columns are dropped or renamed), the guarantee disappears silently. The integration test described in Refinement 4 verifies the schema before any production data is written.
+- This decision depends on a structural assumption about PIMS staging that has not yet been validated. Jake at eParts has not delivered the P1-C schema. If the staging tables use wide columns (one row per record with attribute values as columns) rather than tall columns (one row per attribute), the natural key strategy needs a translation layer. If the staging tables lack columns to hold the platform's natural key, the team must either negotiate a schema addition with eParts or maintain a team-owned buffer table that holds the mapping.
+- No rollback is possible. Once a row is upserted into PIMS staging, the only way to "undo" it is to write a corrected row with the same natural key. This is acceptable because every write goes through human review or auto-accept above a calibrated threshold; the system never writes silently uncertain data.
+- This decision interacts with ADR-004 (per-attribute routing). The natural key is keyed on `attribute_id`, not on `record_id`, which is what enables per-attribute routing to write attributes individually as they resolve. If routing were per-record, the natural key would only need `record_id`.
+
+### Requirements Traceability
+
+- **HLRs:** HLR-5 (write approved data to PIMS staging)
+- **FRs:** FR-8 (idempotent application-layer writeback); FR-11 (natural key: submission ID + attribute ID)
+- **DRs:** DR-3 (Must — retry must not create duplicates)
+- **QASs:** QAS-1 (accuracy — prevents duplicate-driven errors); QAS-4 (availability — safe retry on recovery)
+- **Constraints:** C-2 (no PIMS API), C-5 (no direct production writes — writeback targets staging only)
+- **Scenarios:** SCEN-1 (Step 5), SCEN-2 (Step 6)
+- **Validation:** VAL-3 (upsert + no-duplicate on retry)
+
+---
+
+## ADR-007: Use an Attribute-Row Canonical Schema for the Staging Table
+### Status
+
+Accepted
+
+### Context
+
+The Normalization stage transforms heterogeneous supplier formats (CSV, PDF, email-extracted key-value pairs) into a canonical structure that the Prediction Service, Routing Engine, and Writeback Service can consume uniformly. The shape of this canonical schema is an architecturally significant decision because it determines how much work it takes to add a new product category, how easily attributes can be routed individually, and how the staging tables grow over time.
+
+The current scope is valves and actuators, but the client (Harsha) has stated that category expansion is expected after the pilot. Quality attribute QA-3 (Modifiability — new category) is rated Medium/Medium and explicitly requires that adding a category not force a structural change to routing or writeback.
+
+Two structural options were considered:
+
+- **Wide schema (one row per record).** Each record is a single row with one column per attribute (`voltage`, `port_size`, `connection_type`, etc.). Adding a new category requires schema migration: new columns, ALTER TABLE statements, and coordination with any system that reads the staging table. Querying a single record is trivial. Per-attribute routing is awkward because attribute-level state (confidence score, routing decision) would need parallel columns for every attribute.
+- **Tall schema (one row per attribute).** Each row is `(record_id, attribute_id, raw_value, predicted_value, confidence, routing_status)`. Adding a new attribute is a data change (a new entry in the attribute reference table), not a schema change. Per-attribute routing is direct: routing status is a column on the row.
+
+### Decision
+
+The canonical staging schema is attribute-row: each row represents one attribute of one record. The columns include `submission_id`, `record_id`, `attribute_id`, `supplier_raw_value`, `predicted_value`, `confidence_score`, `routing_status`, and audit metadata. Attribute definitions (name, type, allowed values, category) live in a separate reference table joined as needed. New product categories are added by inserting attribute definitions into the reference table, not by altering the staging schema.
+
+### Consequences
+
+- Adding a new product category does not require a schema migration against the staging tables. The Normalization stage gains new mapping entries; the Prediction Service is retrained on the expanded label set; nothing in the Routing Engine, Review Queue, or Writeback Service changes structurally.
+- Per-attribute routing (ADR-004) becomes natural. Each row carries its own routing state, so the Routing Engine reads and updates one row at a time without joining against a wide record schema.
+- Per-attribute audit is also natural. The audit trail can reference a single attribute row by its primary key.
+- Querying a complete record requires a join or aggregation across multiple rows. This is a small loss in query convenience and is acceptable because the platform's hot-path queries are per-attribute (routing, scoring, review), not per-record.
+- The staging tables grow faster than they would under a wide schema (one row per attribute rather than one row per record). For valves and actuators with roughly a dozen attributes, this is a 12× row-count multiplier. Azure SQL Database is sized to handle this comfortably at expected ingestion volumes.
+- This schema decision is independent of the PIMS staging schema. ADR-006 covers the writeback contract with PIMS, which may use either a wide or tall structure. If PIMS is wide, the Writeback Service performs an aggregation transform from the platform's tall canonical schema into the wide PIMS schema; this is documented as an open dependency on Refinement 4.
+- If Refinement 4 reveals that PIMS staging is rigidly wide and the team-owned mapping is too costly to maintain, the platform may keep its internal canonical schema tall while presenting a wide interface to PIMS through the Publish/Sync Job. The architecture supports this.
+
+### Requirements Traceability
+
+- **HLRs:** HLR-2 (normalize to standardized structure)
+- **FRs:** FR-2 (canonical schema before prediction); FR-11 (attribute-level natural key requires attribute-row schema)
+- **QASs:** QAS-3 (new category as data change, not schema migration)
+- **Constraints:** C-4 (phase scope expansion); C-6 (pricing excluded from canonical schema)
+- **Scenarios:** SCEN-1 (Step 3 — canonical normalization)
+
+---
+
+## ADR-008: Deploy the Platform as a Single Azure App Service Unit
+### Status
+
+Accepted
+
+### Context
+
+The platform must be deployed on Azure (a fixed client constraint) and must be operable by a five-person capstone team across one academic year. Quality attributes that bear on deployment topology are QA-2 (model swappability), QA-4 (availability under Prediction Service outage), and a team-size constraint that bounds operational complexity.
+
+Two topologies were analyzed in detail:
+
+- **Single Azure App Service (Python).** All pipeline components — ingestion, normalization, prediction, routing, review-queue access, writeback orchestration — run in one process and one deployment unit. Azure SQL Database holds staging tables, the review queue, and the audit trail. Azure Blob Storage archives raw supplier files. The Publish/Sync Job runs as a separate timer-triggered Azure Function. Components communicate by function call. Scaling is per application unit.
+- **Microservices (Azure Container Apps).** Three independent services: Ingestion+Normalization, Prediction, Routing+Writeback. Each scales independently, can be deployed independently, and can fail independently. Inter-service communication is HTTP or Service Bus. Operational requirements include container orchestration, distributed tracing, service-to-service authentication, and three deployment pipelines.
+
+The microservices alternative offers fault isolation and independent scaling, both of which are real benefits for a production system. They are not benefits the current team can absorb operationally during the capstone phase. Distributed tracing alone would consume a substantial fraction of the timeline. The Prediction Service does not currently need GPU instances or independent scaling because supplier ingestion is batched, not real-time.
+
+### Decision
+
+The platform is deployed as a single Azure App Service running Python. All pipeline components live in one process. Azure SQL Database holds all internal pipeline state (staging tables, Human Review Queue, audit trail). Azure Blob Storage archives raw supplier files. The Publish/Sync Job is a timer-triggered Azure Function deployed separately. Inbound channels are SFTP (polled), email (polled), and HTTPS upload. Outbound to PIMS is via `pyodbc` across the trust boundary to PIMS SQL Server. Outbound telemetry to Datadog is fire-and-forget HTTPS.
+
+### Consequences
+
+- One deployment, one log stream, one health check. Operational complexity is bounded.
+- Components communicate by function call. This is fast and avoids the complexity of network serialization, retries, and timeouts between filters.
+- Fault isolation is reduced. A bug in any component can crash the App Service and take the entire pipeline down. The persistent staging tables and review queue mitigate data loss risk: in-flight work survives a process restart because state is in Azure SQL, not in-memory.
+- Independent scaling is not available. If the Prediction Service becomes a hotspot, the entire App Service must be scaled up.
+- The module boundaries inside the App Service (described in the module view) are deliberately drawn where service boundaries would go in a microservices deployment. The `prediction` package, `routing` package, and `writeback` package are independent units of code that communicate through typed data contracts. Transitioning to microservices later is therefore adding HTTP serialization at existing boundaries, not rewriting business logic.
+- Datadog telemetry is fire-and-forget. Telemetry failures do not block the pipeline. This means a Datadog outage cannot cause a pipeline outage, but it also means dropped telemetry is not retried; operationally significant signals must also be persisted in the audit trail (ADR-009).
+- The Publish/Sync Job is intentionally separated as an Azure Function on a timer trigger so that PIMS writeback runs on a controlled schedule rather than synchronously with each ingestion. This decouples PIMS load from supplier ingestion bursts.
+- Trigger for reconsideration: production handoff to a larger eParts team, or a Prediction Service that scales independently of ingestion (e.g., GPU-backed inference, multi-model ensembles). At that point, the prediction package is the natural first candidate for extraction into a Container App.
+
+### Requirements Traceability
+
+- **HLRs:** HLR-1 (ingestion endpoints hosted on App Service); HLR-5 (Publish/Sync Azure Function)
+- **FRs:** FR-1 (Ingestion Gateway runs on App Service); FR-8 (Publish/Sync Function performs writeback); FR-10 (Azure SQL hosts the persistent queue); FR-13 (Azure Blob hosts raw file archive)
+- **DRs:** DR-1 (Blob archive is part of deployment topology)
+- **QASs:** QAS-4 (staging tables in Azure SQL provide outage buffering)
+- **Constraints:** C-1 (Azure managed services); C-3 / DC-1 (Python App Service); C-7 (single unit chosen over microservices for capstone timeline); DC-3 (Blob Storage archive)
+- **Validation:** VAL-1, VAL-3 (deployed components host the tested behavior)
+
+---
+
+## ADR-009: Implement the Human Review Queue as a Persistent Database Table
+### Status
+
+Accepted
+
+### Context
+
+When the Routing Engine sends a low-confidence attribute to human review, that attribute must wait until a reviewer at eParts or Alps Controls processes it. Reviewer pace is much slower than machine pace: predictions arrive in batches measured in seconds, while reviewer decisions accumulate over hours or days. The queue must therefore decouple machine throughput from reviewer availability.
+
+Two queue mechanisms were considered:
+
+- **In-memory queue or message broker (e.g., Azure Service Bus).** Standard for high-throughput producer/consumer decoupling. Survives normal load patterns but adds an external dependency, requires a consumer process polling for items, and does not naturally support the spreadsheet-style batch review workflow that catalog staff already use.
+- **Persistent database table in Azure SQL.** The queue is a table with `(submission_id, attribute_id, predicted_value, confidence, reason_codes, status, reviewer_id, decided_at, corrected_value)`. Reviewers query the table through eParts' existing internal review interface, which already speaks SQL.
+
+The catalog team already accesses internal staging tables through a spreadsheet-style tool. Building a custom review UI is out of scope for the current phase. The existing internal interface reads directly from staging tables, which means the queue must be a table accessible from that tool.
+
+The queue must also feed retraining: every reviewer decision is a labeled example, and the audit trail layer relies on durable storage of reviewer corrections.
+
+### Decision
+
+The Human Review Queue is implemented as a persistent table in Azure SQL Database. Low-confidence attributes are inserted with `status = 'pending'`. Reviewers access the table through eParts' existing internal review interface, edit values individually or in batch, and submit decisions by updating the `status` to `'approved'` or `'rejected'` and writing the `corrected_value`. On each decision, a row is appended to the audit trail. A notification is sent to the catalog team when items are pending and again when items are processed.
+
+### Consequences
+
+- Reviewer pace is fully decoupled from prediction pace. The queue can hold thousands of pending items without backpressure on the upstream pipeline.
+- The queue survives App Service restarts and Prediction Service outages. In-flight reviews are preserved across deployments. This directly supports QA-4 (availability).
+- The queue is the persistent store for labeled corrections. The retraining pipeline reads from the audit trail (which captures the history of queue decisions) without coordinating with a separate label store.
+- The schema of the queue table is a coupling point with eParts' existing internal review interface. Any change to column names, types, or status values requires coordination with the eParts engineering team. This is a recorded constraint on schema evolution.
+- Rejected items are not silently dropped. A rejection writes the corrected value back to the queue row with `status = 'rejected'`, appends to the audit trail, and triggers a notification. The corrected value flows into the labeled correction store for retraining.
+- The queue is not a true message broker, so it does not provide push-style notification, dead-letter queues, or consumer load balancing. These features are not needed because there is no automated consumer; the consumer is the catalog team.
+- If a custom review UI is built in a future phase, Auth0 (the eParts identity provider per the SOW) integrates at the UI layer and reads from the same queue table. The queue's stable schema is what makes that future UI buildable without changes to the ingestion, prediction, or writeback components.
+
+### Requirements Traceability
+
+- **HLRs:** HLR-4 (persistent Human Review Queue)
+- **FRs:** FR-4 (queue is the destination for low-confidence attributes); FR-5 (queue retains prediction, confidence, source ref, status); FR-10 (persistent and queryable)
+- **QASs:** QAS-4 (queue survives Prediction Service outages)
+- **Constraints:** C-8, DC-2 (queue's stable schema accommodates a future Auth0-gated UI without changes elsewhere)
+- **Scenarios:** SCEN-2 (Steps 3–5)
+- **Validation:** VAL-2 (item appears in Human Review Queue)
+
+---
+
+## ADR-010: Maintain an Append-Only Audit Trail of Every Pipeline Decision
+### Status
+
+Accepted
+
+### Context
+
+The platform automates a workflow that previously required human judgment at every step. Two needs follow from this:
+
+1. **Compliance and traceability.** When a wrong product attribute reaches PIMS, eParts needs to determine why: which model version produced the prediction, what confidence the model emitted, whether a reviewer saw the item, and what the reviewer's decision was. Without this trail, root cause analysis is impossible.
+2. **Model improvement.** The retraining pipeline (described in the MLOps section of the report) depends on labeled corrections. Reviewer decisions are the primary source of labels. The system must capture the original prediction, the original confidence, the source supplier, and the corrected value as a durable record.
+
+Quality attribute QA-5 (Monitorability) is rated High/High and depends on having a record of every routing and review decision over time so that drift in correction rates can be detected.
+
+A mutable record (overwriting the prediction with the corrected value) would satisfy the immediate writeback need but lose the history needed for audit and retraining. An append-only log preserves both.
+
+### Decision
+
+Every pipeline decision is recorded as a row in an append-only audit trail table in Azure SQL Database. Decisions captured are: auto-accept by the Routing Engine, approval by a reviewer, correction by a reviewer (with the corrected value alongside the original prediction), and rejection by a reviewer. Each row contains the submission ID, attribute ID, source supplier, model version, original predicted value, confidence score, threshold value at decision time, final decision, decided value, decision actor (system or reviewer ID), and timestamp. Rows are never updated or deleted.
+
+### Consequences
+
+- Every value written to PIMS is traceable back to the prediction, the confidence, the threshold, and the reviewer (if any) that produced it.
+- The audit trail is the source of truth for retraining. Corrections where the reviewer's value differed from the model's prediction are flagged as labeled training examples and read by the retraining job (ADR-011).
+- The model version recorded on each row is essential for retraining safety. When a new model version is promoted, the audit trail allows the team to compare correction rates before and after promotion as a check on regression.
+- The audit trail is the basis for drift detection in Datadog (ADR-012). Per-attribute confidence distributions and reviewer correction rates are computed from this table.
+- Append-only growth is unbounded. The table will require a retention policy (cold storage to Azure Blob after some period) once production volumes are observed. This is operationally acceptable in the current phase because volumes are low.
+- The audit trail is internal to the platform. PIMS does not see it. If PIMS needs an audit record alongside a value, the writeback service includes audit metadata in the upsert; the platform's internal audit trail is the canonical record.
+- Reviewer privacy: the reviewer ID is recorded. This is acceptable under eParts' internal policies because the catalog team is salaried staff acting in their official capacity. If the audit trail were ever exposed externally, reviewer IDs would need to be redacted.
+
+### Requirements Traceability
+
+- **FRs:** FR-6 (log every auto-accept, approval, correction, rejection); FR-12 (audit trail backs telemetry signals)
+- **DRs:** DR-2 (Future/TBD — corrected data logged for retraining)
+- **QASs:** QAS-5 (audit trail is the durable source for drift signals)
+- **Scenarios:** SCEN-2 (Step 5 — correction logged)
+
+---
+
+## ADR-011: Trigger Retraining Automatically on Human Review Batch Completion
+### Status
+
+Proposed
+
+### Context
+
+The Prediction Service must improve over time as supplier data changes and as the labeled corpus grows. Reviewer corrections are the primary source of labeled examples. The architectural choice is the trigger mechanism that initiates a retraining run.
+
+Three alternatives were considered:
+
+- **Manual trigger.** An engineer reviews the accumulated corrections, judges that enough new examples exist, runs the training script, evaluates the result, and promotes the new model if it improves on the previous version. Requires no automation but depends entirely on engineer availability and judgment. Poor fit for a five-person capstone team that cannot guarantee weekly engineer cycles.
+- **Automatic trigger on review batch completion.** A retraining job fires automatically each time a human review batch is marked complete. The new model version is evaluated against a held-out validation set and promoted only if it outperforms the current version. No engineer initiates the run.
+- **Scheduled trigger.** Retraining runs on a fixed cadence (weekly or monthly) regardless of review activity. Predictable, but introduces a fixed lag between when corrections are made and when the model learns from them. Risks training on too few examples if review activity is light, or accumulating too many examples if review activity is heavy.
+
+In all cases, a validation gate is required: a new model version must outperform the current version on a held-out validation set before it is promoted. Without this gate, automatic retraining could promote regressions silently.
+
+### Decision
+
+Retraining is triggered automatically when a human review batch is marked complete. The retraining job reads all corrections flagged as labeled examples since the last training run from the audit trail (ADR-010), combines them with the existing labeled dataset, and trains a new version of the active prediction strategy. The new version is evaluated against a held-out validation set. If validation accuracy improves, the new version is promoted as the active model behind `PredictionServiceInterface` (ADR-002). If it does not improve, the previous version remains active and the result is logged for engineering review. Model version history is stored in Azure Blob Storage with training date, example count, and validation accuracy as metadata.
+
+### Consequences
+
+- The model learns from corrections as soon as a batch is reviewed, with no engineer in the loop. This is the fastest path from a reviewer correction to an improved model.
+- The validation gate prevents silent regressions. A worse model is never promoted automatically; it is logged for human review.
+- Promotion is transparent to the rest of the pipeline. The Routing Engine, Writeback Service, and Review Queue see the prediction service as unchanged because `PredictionServiceInterface` does not change with model version.
+- Rollback is supported. Each model version is tagged in Azure Blob Storage. If a promoted version is later found to perform poorly on production data, engineering can revert by changing the active model pointer in configuration without redeploying the application.
+- A minimum batch size before triggering retraining is required to avoid training on sparse data. The minimum example count has not been set and will be established once Refinement 1 produces real review-batch sizes. Until then, this decision is Proposed.
+- The validation set must remain representative. If the validation set drifts from production data, the gate becomes meaningless because a model that overfits to stale validation can pass the gate while degrading on real inputs. The validation set itself must be refreshed periodically; this operational discipline is a dependency of the retraining decision.
+- Frequent retraining on small batches can produce unstable model versions even with a validation gate, because validation accuracy itself fluctuates on small evaluation sets. If observed, the trigger should be replaced with a hybrid: scheduled retraining with a minimum-correction-count gate.
+- Engineering team capacity post-handoff may make manual triggering attractive again. A larger team with regular review cycles may want explicit human oversight on every promotion. The retraining package is decoupled enough from the rest of the pipeline that switching to manual triggering is a configuration change.
+
+### Requirements Traceability
+
+- **HLRs:** HLR-3 (prediction quality maintained over time)
+- **DRs:** DR-2 (Future/TBD — corrected data logged for future retraining and offline model improvement)
+- **QASs:** QAS-2 (retraining promotes new versions through PredictionServiceInterface without breaking dependents); QAS-5 (closes the loop from drift detection to model improvement)
+- **Constraints:** C-3, DC-1 (retraining runs in the Python prediction package)
+
+---
+
+## ADR-012: Emit Stage-by-Stage Telemetry to Datadog for Drift Detection and Operational Monitoring
+### Status
+
+Proposed
+
+### Context
+
+ML systems can degrade silently as supplier data drifts from the training distribution. Without monitoring, incorrect auto-accepts accumulate in PIMS and surface only when contractors order wrong parts. Quality attribute QA-5 (Monitorability) is rated High/High both in importance (because silent degradation is the worst failure mode) and in difficulty (because the team has not yet defined what metrics to track or what baseline to compare against).
+
+eParts uses Datadog as its observability platform, so integration is mandatory rather than chosen. The architectural questions are: where in the pipeline should telemetry be emitted, what signals should be captured, and how should those signals be tied to drift detection.
+
+Telemetry must not block the pipeline. A Datadog outage cannot be allowed to take ingestion or writeback offline.
+
+### Decision
+
+Telemetry is emitted to Datadog from four pipeline stages over fire-and-forget HTTPS:
+
+- **Ingestion Gateway:** ingestion success and failure counts, parsed by supplier and channel.
+- **Normalization (Structured Layer):** row counts after canonical schema mapping, broken down by supplier and category.
+- **Prediction Service:** per-attribute confidence score distributions and rule-vs-embedding contribution breakdown.
+- **Routing Engine:** routing split ratios (auto-accept vs. review) per attribute.
+- **Review Queue:** reviewer decision counts (approved, corrected, rejected) and correction rates per attribute.
+
+Telemetry calls do not block the pipeline; failed Datadog writes are logged locally and dropped. Operationally significant signals that must not be lost are also persisted in the audit trail (ADR-010), so Datadog is treated as a dashboard and alerting layer, not as the system of record.
+
+Drift detection thresholds (e.g., "alert when correction rate increases by 10% over a rolling two-week window" or "alert when mean confidence shifts by 15%") are defined as configuration on Datadog and validated empirically once Refinement 1 has produced a baseline.
+
+### Consequences
+
+- The pipeline emits the right signals to detect drift. Confidence distributions reveal model overconfidence or underconfidence; correction rates reveal accuracy degradation; routing split ratios reveal threshold drift.
+- Drift detection is operationally complete only when thresholds are defined. The architecture emits the signals; it cannot yet say what deviation from baseline constitutes actionable drift. Refinement 6 in the project plan defines and validates these thresholds against simulated drift.
+- Telemetry is tied to the audit trail. Reviewer correction rates in Datadog are computed from the same decisions recorded in the audit trail, so the dashboard and the system of record cannot diverge.
+- Datadog outages do not affect pipeline correctness. A telemetry failure is logged locally and the pipeline continues. This is acceptable because the audit trail is the source of truth; the dashboard is a derived view.
+- Because telemetry is fire-and-forget, telemetry packets can be lost during a Datadog outage without retry. This means short-term metrics (e.g., a one-hour confidence distribution) may have gaps during incidents. Long-term metrics computed from the audit trail are unaffected.
+- Per-supplier telemetry is captured because supplier-specific drift is a likely failure mode (a supplier changes its catalog format, the model's confidence drops, but the threshold doesn't catch it). Per-supplier dashboards in Datadog allow drift to be localized to the offending supplier.
+- This decision is Proposed rather than Accepted because the alert thresholds and baselines are not yet defined. Once Refinement 1 and Refinement 6 produce values, this decision moves to Accepted.
+
+### Requirements Traceability
+
+- **FRs:** FR-12 (emit confidence distributions, correction rates, routing decisions, pipeline metrics to Datadog)
+- **QASs:** QAS-5 (drift detection from baseline deviation in confidence and correction rates)
+- **Constraints:** C-1 (Datadog runs over HTTPS from Azure App Service)
+
+---
+
+## ADR-013: Establish a Release-Versioned ETIM Reference Data Layer Owned by Ingestion
+### Status
+
+Accepted
+
+### Context
+
+The platform is adopting ETIM as the classification standard for catalog standardization (valves and actuators in phase one). ETIM is a controlled technical dictionary: product groups (EG), product classes (EC), features (EF), feature groups (EFG), units (EU), and controlled values (EV), plus the mappings that say which features belong to a class and which values are allowed for a class-feature. ETIM is not supplier data — it provides no SKUs, prices, or product documents. Before the platform can match any supplier product to ETIM (class matching, feature matching, value matching, validation), it needs the ETIM dictionary loaded, queryable, and under version control.
+
+The supplied ETIM data has awkward physical characteristics that make it a poor fit for ad-hoc loading: the production archive is a set of CSV files encoded **UTF-16 little-endian, semicolon-delimited**, for a specific release (10.0) and language (EI, English International). ETIM publishes new releases over time, and class/feature/value definitions change between releases, so a single un-versioned copy would silently conflate releases and make historical mappings unauditable.
+
+A key question was **ownership**: the reference loader could sit in the ML/matching component (the primary consumer) or in ingestion (which already owns file parsing, encoding handling, idempotent batch loads, and Alembic migrations). Two further options for storage shape were considered:
+
+- **Denormalized blob / JSON document per class.** Fast to load and to read a whole class, but cannot enforce referential integrity, makes cross-class queries (e.g. "all classes using feature EF000513") expensive, and couples readers to a single release's shape.
+- **Normalized relational tables mirroring the ETIM model**, scoped by release ID. Enforces FKs and composite keys, supports multi-release coexistence, and lets the matcher query class→feature→value relationships directly.
+
+### Decision
+
+We will model ETIM as a **normalized relational reference layer of ten tables**, every row scoped by a release identifier, and we will make the **ingestion team the owner** of both the schema and the import job.
+
+The tables are `etim_release`, `etim_group`, `etim_class`, `etim_class_synonym`, `etim_feature_group`, `etim_feature`, `etim_unit`, `etim_value`, `etim_class_feature`, and `etim_class_feature_value`, with composite primary keys on `(etim_release_id, …)` so that multiple ETIM releases can coexist without collision. The release identifier is a stable, human-readable string of the form `ETIM-{version}-{language}` (e.g. `ETIM-10.0-EI`).
+
+A dedicated **ETIM Reference Loader** import job reads the UTF-16 LE, semicolon-delimited CSV archive, validates that the expected columns are present per file, rejects incomplete or release-mismatched archives, and loads the rows into the reference tables. It records the release version, language, source name, an import timestamp, and a **SHA-256 checksum over the archive**. Re-importing the same release is **idempotent** (no-op when the checksum matches; controlled replace only with an explicit `--force`). The job is exposed as a CLI entry point (`eparts etim import …`) mirroring the existing Typer CLI, and is delivered as Alembic migration `0005_create_etim_reference` plus `etim/loader.py`, `models/etim.py`, and `cli/etim.py`.
+
+This decision is implemented and verified against the real ETIM 10.0 EI archive (EPARTS-285).
+
+### Consequences
+
+- The matcher (ETIM class/feature/value matching) can treat ETIM as a stable, queryable dependency. Loading the dictionary is no longer entangled with matching logic, so the two can evolve independently.
+- Release versioning is first-class. Because every row is keyed by `etim_release_id`, a future ETIM 11.0 can be loaded alongside 10.0, and any product's mapping can name the exact release it was matched against. This is a prerequisite for governed ETIM upgrades (an open client decision in the brief).
+- Idempotent, checksummed import makes the load safe to re-run in CI and across environments without producing duplicates or partial state. A mismatched or truncated archive is rejected with a clear error rather than loaded silently.
+- Placing ownership in ingestion reuses existing strengths (encoding handling, batch idempotency, Alembic, the Typer CLI) and keeps the file-handling concerns in the team that already does file handling. The cost is a coordination point: the matching team consumes a schema that ingestion owns, so reference-table changes require a published contract.
+- The reference layer is read-mostly and modest in size (~160 groups, ~5,600 classes, ~17,000 features, ~16,000 values, ~200,000 class-feature-value links for 10.0 EI). Normalized storage on the current Postgres stack handles this comfortably.
+- ETIM does not supply a client-ready "required field" flag. The reference layer deliberately stores ETIM as published and leaves required/recommended/optional policy to a separate client policy overlay (`catalog_feature_policy`, owned downstream). This ADR does not cover that overlay.
+- The loader currently targets the CSV archive only. The Excel workbook (useful for analyst review and metric/imperial crosswalks) is intentionally out of scope for production import.
+
+### Requirements Traceability
+
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` (ETIM Reference Loader, ETIM Reference Tables, Acceptance Criteria 1)
+- **Tickets:** EPARTS-285 (Create ETIM reference schema and import job — Done); EPARTS-275 (ETIM research); parent EPARTS-154 (Ingestion)
+- **Implements:** ETIM reference schema, release tracking, idempotent import, golden row-count validation
+- **Related ADRs:** ADR-014 (staging split consumes the reference layer for matching); ADR-015 (Postgres-now datastore the tables are built on); ADR-007 (the prior canonical-schema decision this complements)
+
+---
+
+## ADR-014: Emit a Source-Preserving Product + Attribute Staging Split
+### Status
+
+Accepted
+
+### Context
+
+ETIM standardization rests on a core principle: **original supplier data is evidence; ETIM data is a standardized interpretation laid on top; confidence is how sure the system is about that interpretation.** For this to hold, ingestion must hand the matching stage data that (a) separates a *product* (the sellable SKU) from its *attributes*, and (b) preserves every original value together with where it came from — file, page, row, raw text, raw unit — so that any later ETIM mapping can be traced back to its source.
+
+Today ingestion emits a single flat `IngestedRecord` (one row per source record, with `raw_fields` as a JSONB bag). That shape preserves source vocabulary but does not express product-vs-attribute granularity, gives attributes no individual identity, and has nowhere to carry per-attribute evidence (source page/row) or per-attribute confidence. It also forces every downstream consumer to re-derive product and attribute structure from an untyped blob.
+
+There is also a real granularity mismatch across sources that the staging shape must absorb: a **CSV row** is naturally one product with many attribute *columns*, whereas a **datasheet PDF** is one product (a SKU) with many attribute *rows* extracted from the document. Both must land in the same canonical staging shape.
+
+Options considered:
+
+- **Keep the flat `IngestedRecord`** and let the matcher split product/attributes from the JSONB bag. Smallest ingestion change, but pushes structure-recovery and evidence-tracking into every consumer, and gives attributes no stable identity for per-attribute routing, confidence, or audit.
+- **One wide staging row per product** with attributes as columns. Convenient for whole-product reads, but cannot carry per-attribute evidence/confidence without parallel columns, and reintroduces schema migration for every new attribute.
+- **A two-table split: `staging_product` + `staging_raw_attribute`** (one product row; one evidence row per attribute). Each attribute row carries its own source evidence and confidence and has a stable identity. This matches the brief's staging model and is the natural input to per-attribute ETIM matching, routing, and audit.
+
+### Decision
+
+Ingestion will emit a **product + attribute split**: a `staging_product` row per sellable SKU and a `staging_raw_attribute` row per attribute, replacing the flat `IngestedRecord` as the output contract.
+
+`staging_product` carries product identity and provenance: `supplier_id`, `supplier_sku`, `manufacturer`, `supplier_category`, `description`, `source_file_id`, `submission_id`, `processing_status`. Product identity for idempotency is `supplier_id + supplier_sku` (per source). `staging_raw_attribute` carries one row of evidence per attribute: `product_id`, `source_attribute_name`, `source_value`, `source_unit`, `source_text`, `source_page`, `source_row_number`, and `source_confidence`. Attribute identity for idempotency is `product_id + source_attribute_name`. Both tables are written with idempotent upserts, batched in one transaction per product, preserving the existing raw-bytes archival and quarantine paths unchanged.
+
+Which source fields populate product identity versus become attribute rows is **declared per source** via `ProductMapping` on the source/parser config (`sku_field`, `manufacturer_field`, `category_field`, `description_field`, `unit_field`, `attribute_fields`, `exclude_fields`), so the CSV-column and PDF-row granularities both resolve to the same staging shape without code changes per source.
+
+Crucially, ingestion **does not interpret** these values into ETIM. No field renaming, no ETIM class/feature/value assignment, no unit conversion happens here — those belong to the ETIM-aware matching stage, which reads staging and writes its results to its own tables (e.g. `matched_product_attribute`). ETIM must never overwrite ingestion's source-preserving output.
+
+The legacy flat `IngestedRecord` path is retired after cutover (deprecate or dual-write during transition; tracked by EPARTS-302). Until a source declares a `ProductMapping`, it continues on the legacy flat path.
+
+### Consequences
+
+- Nothing from the supplier catalog is lost or flattened. Every value is individually addressable and traceable to file/page/row/raw-text, which is the evidence backbone the entire ETIM story depends on.
+- Per-attribute identity makes per-attribute confidence (ADR-005/ADR-004 routing), per-attribute ETIM matching, and per-attribute audit natural — each is keyed on a real attribute row rather than reconstructed from a blob.
+- The product/attribute boundary is configuration, not code. New sources and formats are onboarded by declaring a mapping; the CSV-vs-datasheet granularity difference is absorbed in config.
+- Row counts grow relative to the flat shape (one row per attribute rather than one per record). For valve/actuator products with ~12–40 attributes this is a sizeable multiplier; the current Postgres stack (ADR-015) handles expected volumes, with indexes for product lookups.
+- This **supersedes the staging design in ADR-007** in practice. ADR-007 specified a single tall staging table that also carried prediction/routing columns (`predicted_value`, `confidence_score`, `routing_status`). Under ETIM, ingestion's staging holds only *source evidence*; predicted values, match confidence, validation status, and review status move to a separate matching-owned table. ADR-007's "attribute-row, not wide" instinct is retained and reinforced; its column set and single-table assumption are not.
+- The PIMS writeback contract shifts accordingly. The brief keys PIMS output on `product_id + etim_release_id + etim_class_id + etim_feature_id` rather than `submission_id + attribute_id`; ADR-006's idempotency mechanism needs to be revisited against this (flagged in the ADR assessment, not resolved here).
+- A clean cutover is required to avoid two parallel write paths. The transition (dual-write vs deprecate) and the update to the §6.1 output contract are explicit follow-ups (EPARTS-302).
+- Missing-SKU handling must be defined (quarantine vs synthesized id) — an open item feeding the source mapping config.
+
+### Requirements Traceability
+
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` (Staging Layer; Staging Tables; "Original supplier data = evidence"); `INGESTION_ETIM_TICKET_MAP.md` (ING-E4/E5/E6/E7/E9)
+- **Tickets:** EPARTS-297 (ProductMapping config); EPARTS-298 (staging schema); EPARTS-299 (writer rework); EPARTS-302 (retire flat path); parent EPARTS-154
+- **QASs:** QAS-1 (accuracy — evidence preserved for traceable correction); QAS-3 (new category/attribute as data + config, not schema migration)
+- **Related ADRs:** ADR-007 (superseded in part — see above); ADR-013 (reference layer the staged data is matched against); ADR-015 (datastore); ADR-004/ADR-005 (per-attribute routing/threshold consume attribute identity); ADR-006 (PIMS idempotency to be re-keyed)
+
+---
+
+## ADR-015: Target PostgreSQL Now; Defer the Azure SQL Conversion
+### Status
+
+Accepted
+
+### Context
+
+The ETIM implementation brief specifies its schemas in **SQL Server / Azure SQL dialect** (`DATETIME2`, `NVARCHAR(MAX)`, `BIT`), consistent with the original platform design (ADR-008), which placed all internal pipeline state in **Azure SQL Database** and deployed the platform as a single Azure App Service. Several earlier ADRs assume this Azure SQL substrate (ADR-006 PIMS writeback, ADR-007 staging, ADR-008 deployment, ADR-009 review queue, ADR-010 audit trail).
+
+The ingestion service as actually built does not run on Azure SQL. It runs on **PostgreSQL** with SQLAlchemy 2.x + Alembic migrations, uses **JSONB** for semi-structured fields, archives raw bytes to **S3/MinIO**, and is packaged with Docker/`docker-compose` (Postgres + MinIO) rather than App Service. The existing migrations (`0001`–`0005`, including the ETIM reference tables) are all Postgres.
+
+The ETIM schema tickets (reference tables, staging split) were therefore blocked on a datastore question (ING-E0): author the new ETIM and staging tables for Azure SQL to match the brief, or for Postgres to match the running service? Authoring for Azure SQL now would mean building against a database the platform does not yet use, maintaining a dialect the rest of the codebase does not use, and carrying that divergence indefinitely. Authoring for Postgres now keeps the entire ingestion service on one coherent stack and translates the brief's SQL Server DDL to Postgres equivalents.
+
+A migration to Azure SQL is a real future possibility — it is the original target and ties to the broader platform-on-Azure direction (EPARTS-64) — but it is a separate, platform-level effort that is not in flight today.
+
+### Decision
+
+All new ETIM reference tables and staging tables target **PostgreSQL (the current stack) for now**, using Alembic migrations and JSONB where useful, matching the existing ingestion service. The brief's SQL Server DDL is translated to Postgres equivalents: `DATETIME2 → timestamptz`, `NVARCHAR(MAX) → text`, `BIT → boolean`, with JSONB used where a flexible column is warranted.
+
+A later conversion to **Azure SQL is explicitly deferred** to the future move of the wider platform onto Azure, and is treated as a separate effort rather than a constraint on current ETIM work. This decision **unblocks the ETIM schema tickets** (ING-E0 is resolved). It does not retract ADR-008's eventual Azure direction; it records that the *current* substrate is Postgres and that ETIM work builds on Postgres rather than waiting for, or pre-building against, Azure SQL.
+
+### Consequences
+
+- The ingestion service stays on a single coherent persistence stack (Postgres + Alembic + JSONB + S3). New ETIM and staging migrations sit in the same migration chain as everything else, with one dialect to test and operate.
+- The ETIM schema and staging tickets are unblocked and can proceed immediately, which is the critical path for the rest of the ETIM matching work.
+- A divergence is now on record between several existing ADRs (which name Azure SQL / SQL Server) and the running system (Postgres). ADR-008 in particular is now partially stale on the datastore and deployment topology; this is captured in the ADR assessment for whole-platform follow-up rather than silently ignored.
+- A future Azure SQL port is a known, bounded piece of work. It would touch: column-type translation back to the SQL Server dialect, JSONB usage (which has no exact Azure SQL analogue and would need `nvarchar(max)`/JSON functions), Postgres-specific features in use (advisory locks for run-level exclusivity, `ON CONFLICT` upserts), and the migration tooling. Keeping Postgres-specific features behind the storage layer limits the blast radius of that future port.
+- Because the decision is "now vs later" rather than "never," teams should avoid leaning on Postgres-only behavior in business logic above the storage layer, so the deferred port stays a storage-layer concern.
+- PIMS itself remains external and may stay on SQL Server regardless; this ADR governs the platform's *own* internal stores, not the PIMS target (see ADR-006).
+
+### Requirements Traceability
+
+- **Source:** `INGESTION_ETIM_TICKET_MAP.md` (ING-E0 — RESOLVED: "PostgreSQL now; Azure SQL conversion deferred"); `ETIM_IMPLEMENTATION_BRIEF.md` (Data Model — SQL Server DDL, here translated)
+- **Tickets:** EPARTS-285 (built on Postgres migration 0005); EPARTS-298 (staging schema, Postgres); EPARTS-64 (future platform-on-Azure)
+- **Constraints:** C-1 (Azure managed services — eventual direction, deferred); C-7 (capstone operational simplicity — one stack)
+- **Related ADRs:** ADR-008 (revisits its Azure App Service + Azure SQL topology — now partially superseded on substrate); ADR-013 and ADR-014 (the reference and staging tables this decision places on Postgres); ADR-006 (PIMS target datastore, separate)
+
+---
+
+## ADR-016: Decompose Attribute Matching into Staged ETIM Class → Feature → Value/Unit Matching
+### Status
+
+Accepted
+
+### Context
+
+ADR-003 framed the matching problem as a single step: map a raw supplier attribute string onto a canonical attribute value, using a rule engine blended with semantic similarity (`conf_final = α·conf_rule + (1−α)·conf_embed`, α = 0.7). That framing was correct for a free-form canonical vocabulary, where every attribute is independent and there is one decision to make per attribute.
+
+ETIM invalidates the independence assumption. Under ETIM (HLR-6, FR-9) an attribute cannot be matched at all until the product's **class** is known, because the set of legal features is a property of the class: `etim_class_feature` says which features belong to `EC…`, and `etim_class_feature_value` says which values are legal for that class-feature pair. Matching "Torque: 120 Nm" is meaningless without first deciding the product is a valve actuator, and matching it against the wrong class produces a confidently wrong answer rather than a low-confidence one.
+
+The value side is not uniform either. ETIM feature types carry different semantics and different failure modes:
+
+| Type | Meaning | What matching must produce |
+|---|---|---|
+| A | Controlled list value | an `etim_value_id` drawn from the legal set for that class-feature |
+| L | Logical yes/no | a boolean |
+| N | Numeric | a number **plus** a unit, converted to the ETIM-declared unit |
+| R | Numeric range | a min, a max, and a unit |
+
+A single matcher emitting one scalar `predicted_value` with one `confidence_score` cannot express "we are confident this is class EC002714 but unsure whether the torque figure is the rated or the breakaway value," which is exactly the distinction a reviewer needs. It also gives the router a single number where the routing decision now depends on several (see ADR-018).
+
+Two alternatives were considered:
+
+- **Keep one matcher, widen its output.** Emit class, features and values from one model call and one confidence. Cheapest change, but it hides a genuine dependency: a class error silently corrupts every downstream feature match, and there is no place to intervene between the two.
+- **A per-class trained model.** One classifier per ETIM class. 5,640 classes make this untrainable at our data volume, and it would still not solve unit normalization.
+
+### Decision
+
+We will decompose matching into an ordered pipeline of stages, each producing its own evidence and its own confidence:
+
+```
+class matching → feature matching → value matching → unit normalization
+ → ETIM validation → client-policy validation → confidence scoring
+```
+
+Each stage is a filter in the ADR-001 sense, and the whole sequence remains behind the single `PredictionServiceInterface` established in ADR-002 — this decomposition is an interface *enrichment*, not a reversal. `PredictionResult` grows to carry candidate classes with confidences, matched features, matched values with feature-type-appropriate typing, and validation status, in place of a single predicted value.
+
+Class matching consumes class names, class descriptions, `etim_class_synonym` rows, and the correction store; feature and value matching continue to use the ADR-003 hybrid of rules plus semantic similarity over the class-restricted candidate set. **A correction store is consulted before general matching at every stage** so that a reviewer's decision on one product resolves the same mapping for later products without retraining.
+
+Stage outputs land in `matched_product_attribute` — the interpretation table introduced by ADR-014 — which carries the ETIM identifiers, the typed normalized values (`normalized_text_value`, `normalized_numeric_value`, `normalized_range_min`/`max`, `normalized_logical_value`), and per-assignment confidence, alongside a foreign key back to the `staging_raw_attribute` evidence row.
+
+**Implementation status: designed, not built.** The reference layer this depends on is live (ADR-013), and the evidence/interpretation tables exist (ADR-014, Alembic `0006`). The matching stages themselves are owned by the ML stream under EPARTS-289/290/291 and are not yet in the running pipeline; the pipeline currently emits source evidence only.
+
+### Consequences
+
+- Class errors become **visible and interceptable** instead of silently poisoning downstream matches. This is what makes the class-review-first routing path in ADR-018 possible.
+- Confidence attaches **per ETIM assignment** rather than per raw attribute, which is what DR-4 and the PIMS output contract require and what a reviewer needs in order to accept a class while correcting a single feature.
+- Accuracy becomes measurable against a controlled vocabulary rather than against free text: a match is right or wrong against `etim_class_feature_value`, not fuzzily similar to a gold string. This sharpens the golden test set (EPARTS-296) but also makes previously "close enough" answers count as failures, so headline accuracy will drop before it rises.
+- Unit normalization becomes a first-class stage rather than a formatting detail, because type N and R features declare a unit in `etim_class_feature.UNITOFMEASID` and a value in the wrong unit is wrong, not merely unformatted.
+- More stages means more places to fail and more latency per product. The mitigation is that the stages are cheap relative to the OCR/LLM extraction already in the pipeline, and each stage's output is persisted, so a failure late in the chain does not re-run the expensive early work.
+- The α = 0.7 blend and the reconsideration triggers from ADR-003 carry over unchanged to the feature and value stages. ADR-003 is not superseded; it is narrowed in scope from "the matcher" to "two of the matcher's stages."
+- Because the correction store is consulted first, the system's behaviour changes as reviewers work. That is deliberate, but it means matching accuracy is not reproducible from the model alone — the correction store must be snapshotted alongside any benchmark run.
+
+### Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-6 (classify against ETIM and enrich with class/feature/value/unit identifiers); HLR-2 (the intermediate structure this reads from — mechanical cleanup only, no ETIM keying); HLR-3 (predict with confidence scores)
+- **FRs:** FR-9 (match to ETIM classes, features, controlled values/units with per-assignment confidence, preserving the original value); FR-3 (confidence score per predicted attribute)
+- **DRs:** DR-4 (ETIM-keyed PIMS output — consumes the identifiers this ADR produces)
+- **QASs:** QAS-1 Modifiability — a new supplier format changes the parse stage only, not the matching stages
+- **Scenarios:** SCEN-1 step 4 (the ML service matches attributes, then matches them to ETIM class, features and values); SCEN-2 steps 2–3 (per-assignment confidence is what routes the item to review)
+- **Validation:** VAL-5 (class review precedes attribute routing) — added in spec v1.4 as the test for this ADR; **specified, not yet executable**, because these stages are designed and not built. VAL-4 covers the reference layer this ADR reads; its 10 unit tests pass, and its integration half skips without the real archive.
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` — End-to-End Process steps 7–15, ML/AI Attribute Matching, ETIM Feature Types
+- **Tickets:** EPARTS-289 (class matching), EPARTS-290 (feature matching), EPARTS-291 (value/unit matching), EPARTS-296 (golden test set); parent EPARTS-156 (ML)
+- **Related ADRs:** narrows ADR-003 (hybrid rule + semantic similarity) to the feature and value stages; enriches the contract of ADR-002 (`PredictionServiceInterface`); writes into the interpretation table of ADR-014; reads the reference layer of ADR-013; feeds the routing signals of ADR-018 and the policy gate of ADR-019
+
+---
+
+## ADR-017: Re-key the PIMS Writeback Contract on ETIM Identifiers
+### Status
+
+Accepted
+
+### Context
+
+ADR-006 established idempotent PIMS writeback via a composite natural key of `submission_id + attribute_id`, upserted rather than inserted, so that a retried write cannot create duplicates. The mechanism was and remains correct.
+
+The key is not. Two things broke it.
+
+**The key no longer identifies the thing being written.** Under ETIM the unit of published data is not "an attribute of a submission" but "the value of a specific ETIM feature, of a specific ETIM class, of a specific product, under a specific ETIM release" (HLR-6, DR-4). `submission_id` is an artefact of *how the data arrived*, not of *what it describes*. The same product arriving twice — a corrected catalogue re-sent by the supplier, or a second file covering the same SKU — produces two submission IDs and therefore two rows for one real-world fact. The upsert would not collide, and PIMS would accumulate duplicates that are invisible to the idempotency check.
+
+**The payload no longer carries enough to be useful downstream.** ADR-006's row was a value plus a confidence. The PIMS output contract now has to carry both the interpretation and the evidence behind it, because the whole point of the standardization objective is that a consumer can compare products across suppliers *and* audit where a value came from.
+
+Alternatives considered:
+
+- **Keep `submission_id + attribute_id`, add ETIM IDs as payload columns.** Minimal change, but leaves the duplicate-on-resubmission defect in place and makes "the current value of feature EF021864 for this product" unanswerable without scanning submissions.
+- **Key on `product_id + etim_class_id + etim_feature_id`, omitting the release.** Simpler, but conflates ETIM releases: a value matched under 10.0 and a value matched under a future 11.0 would collide even though the feature definition may have changed between them. That defeats the release-scoping established in ADR-013.
+
+### Decision
+
+The PIMS writeback natural key becomes:
+
+```
+product_id + etim_release_id + etim_class_id + etim_feature_id
+```
+
+The upsert mechanism from ADR-006 is unchanged — application-layer idempotent upsert through the staging integration, honouring constraint C-2/DC-3 that we do not write directly to production PIMS tables.
+
+The published row carries the interpretation, the evidence, and the provenance together:
+
+| Group | Fields |
+|---|---|
+| ETIM interpretation | `etim_release_id`, `etim_class_id`, `etim_feature_id`, `etim_value_id`, `etim_unit_id`, feature type |
+| Normalized typed value | text / numeric / range-min / range-max / logical, per feature type |
+| Original evidence | original attribute name, original value, original unit, source text reference |
+| Decision metadata | confidence, approval status (auto-accepted or human-approved) |
+
+`submission_id` remains on the row as provenance — it answers "which file did this arrive in" — but it is no longer part of the identity.
+
+Two distinctions this ADR preserves deliberately: PIMS may remain SQL Server even though our own stores are PostgreSQL (ADR-015 governs *our* datastore, not the client's), and the write remains to staging rather than production tables.
+
+**Implementation status: designed, not built.** The identifiers this key depends on are produced by the matching stages of ADR-016, which are not yet in the pipeline. The writer rework is EPARTS-299, on the critical path `285 ‖ (297 → 298 → 299)`.
+
+### Consequences
+
+- Re-sending a corrected catalogue for a product now **updates** the published row instead of appending a second one. This is the defect the old key could not see.
+- "What is the current published value of feature X for product Y under release Z" becomes a primary-key lookup. Cross-supplier comparison and website filtering — the business objective that motivated ETIM adoption — depend on exactly that query being cheap.
+- The release is part of the key for **provenance**: every published value names the ETIM release it was matched under. Under ADR-020 the project is pinned to 10.0 EI, so in practice the field is constant — it is carried so the row is self-describing, and so that un-pinning later would be a change of scope rather than a schema migration.
+- The key requires a stable `product_id`, which requires a resolvable `supplier_sku` per supplier format. **This is an open dependency**: the authoritative SKU field per format, and the behaviour when a record has no extractable SKU (quarantine versus synthesized identifier), are both unresolved. Until they are, products from formats without a clean SKU cannot be published idempotently.
+- Products carrying a feature that ETIM does not define ("ETIM Other") have no `etim_feature_id` and therefore no key. Their handling is an open client decision; they are held out of the published set rather than given a synthetic identifier.
+- The payload is wider than ADR-006's, so PIMS staging rows grow. Given the phase-one valve/actuator scope this is not a capacity concern, and carrying the evidence alongside the interpretation is what makes the published data auditable.
+- ADR-006 is **not edited**. It stands as the record of the April decision and of the upsert mechanism, which this ADR reuses. Where the two disagree on the key, this ADR governs.
+
+### Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-6 (enrich with ETIM identifiers); HLR-5 (write approved data back to PIMS)
+- **FRs:** FR-8 (write attributes to PIMS upon final approval); FR-9 (preserve the original supplier value alongside the ETIM assignment)
+- **DRs:** **DR-4** — *"Approved data written to PIMS shall be keyed by ETIM identifiers (release, class, feature); the writeback idempotency key shall include these identifiers"* — this ADR is the direct realization of DR-4; DR-3 (writeback must be idempotent; retry must not duplicate)
+- **Constraints:** DC-3 (raw files preserved as evidence — the published row references that evidence)
+- **Scenarios:** SCEN-1 step 5 and SCEN-2 step 6 (auto-accepted and human-approved data both take this path)
+- **Validation:** VAL-3 (approve an item; the PIMS write succeeds and a retry does not duplicate — the retry case is now tested against the ETIM key)
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` — PIMS Output Contract, PIMS Sync; `INGESTION_ETIM_PLAN.md` — design decisions
+- **Tickets:** EPARTS-299 (writer rework), EPARTS-295 (PIMS sync); parent EPARTS-154 (Ingestion)
+- **Related ADRs:** supersedes the natural key defined in ADR-006 while reusing its upsert mechanism; consumes the identifiers produced by ADR-016; depends on the release scoping of ADR-013; the datastore distinction is governed by ADR-015
+
+---
+
+## ADR-018: Extend Routing to ETIM Signals, with a Class-Review-First Path
+### Status
+
+Accepted
+
+### Context
+
+ADR-004 established per-attribute routing: each predicted attribute is compared against a configurable confidence threshold (ADR-005), and the attribute — not the whole record — goes to auto-accept or to the human review queue. The granularity decision was right and is unchanged by this ADR.
+
+What changed is that a single confidence-versus-threshold comparison is no longer sufficient to decide whether a value is safe to publish. After ETIM there are several independent ways for an attribute to be unfit, and only one of them is low confidence:
+
+- The **class** may be wrong or contested. Class confidence is a distinct signal from attribute-match confidence, and it dominates: every feature match under a wrong class is wrong, no matter how confident.
+- The value may be confidently matched but **invalid against ETIM** — a type A value not in the legal set for that class-feature, a type N value with no unit, a type R range with min above max.
+- The value may be valid but **fail client policy** — a feature the client marks `required` for this class is missing, which blocks publish regardless of how confident everything else is (ADR-019).
+- **Unit conversion may have failed**, leaving a numerically plausible figure in the wrong unit. This is the most dangerous case: high confidence, valid type, wrong magnitude.
+
+Routing on confidence alone would auto-accept all four of these. The consequence is the one thing the project exists to prevent: wrong product data reaching PIMS, and from there a contractor's field order.
+
+A further problem is ordering. With a flat per-attribute queue, a product whose class is uncertain generates one review item per attribute — dozens of decisions that all become void the moment the reviewer changes the class. Alternatives considered:
+
+- **Route on confidence only, catch validity later at publish time.** Keeps routing simple, but moves the failure to a stage with no human in it, so invalid data either blocks silently or is dropped.
+- **Escalate any invalid attribute to whole-record review.** Safe but wasteful: one bad attribute pulls a hundred good ones into a manual queue, which is precisely the per-record behaviour ADR-004 rejected.
+
+### Decision
+
+Routing keeps its per-attribute granularity and gains a **class-level stage in front of it**.
+
+**Stage 1 — class routing.** If ETIM class confidence is below the class threshold, or the top two candidate classes are within a configured margin of each other, the *product* is routed to class review before any attribute is matched. Attribute matching for that product is deferred until a class is confirmed.
+
+**Stage 2 — attribute routing.** Once the class is settled, each attribute is routed on the full signal set:
+
+| Signal | Effect |
+|---|---|
+| Attribute match confidence below threshold | → review |
+| ETIM validation failure (value not in legal set, missing unit, malformed range) | → review, regardless of confidence |
+| Unit conversion failure | → review, regardless of confidence |
+| Client policy `required` and value missing | → review, and blocks publish for the product |
+| Client policy `not_used` | → not published, not queued |
+| All checks pass and confidence above threshold | → auto-accept |
+
+The rule that governs the combination: **validation and policy failures are not overridden by high confidence.** Confidence answers "did we read it right"; validation answers "is it a legal ETIM value"; policy answers "does the client need it". These are independent questions and a failure in any one routes to a human.
+
+Thresholds are externalized per ADR-005, now generalized to at least two — class-selection confidence and attribute-match confidence — with per-class-feature overrides replacing the per-attribute override table.
+
+**Implementation status: designed, not built.** The signals this routing consumes are produced by the matching stages of ADR-016 (EPARTS-289/290/291), which are not yet in the running pipeline. Routing today evaluates confidence only.
+
+### Consequences
+
+- The highest-leverage failure mode — a confidently wrong unit or an out-of-vocabulary value — is now caught by a deterministic check rather than by hoping the model was unsure. This directly serves the data-integrity driver behind the whole platform.
+- Class-review-first collapses what would have been dozens of void attribute decisions into one class decision. Reviewer throughput (QAS-2, 10 items/minute) is protected by not queuing work that is about to be invalidated.
+- Deferring attribute matching until the class is confirmed introduces a **wait state** in the pipeline: a product can sit unprocessed pending a human class decision. The staging tables (ADR-014) hold that state durably, so nothing is lost, but end-to-end latency for uncertain products is now bounded by reviewer response time rather than by compute.
+- More routing inputs means more ways to be wrong about routing. Each signal must be independently observable in telemetry — class confidence distribution, validation-failure rate, unit-conversion-failure rate, missing-required-field rate — or a regression in one will be invisible inside an aggregate auto-accept rate.
+- Auto-accept rate will fall relative to the ADR-004 baseline, because attributes that previously passed on confidence now also have to pass validation and policy. This is the intended trade: throughput for correctness. The rate should be reported against the pre-ETIM baseline so the drop is not misread as a regression.
+- The policy signal makes routing **dependent on client configuration that does not yet exist** (ADR-019). Until the feature policy is supplied, the policy check defaults to permissive — nothing is treated as required — which means the required-field path is designed but untestable.
+- ADR-004 and ADR-005 are **not edited**. Per-attribute granularity and externalized thresholds are reused as decided; this ADR extends the inputs and adds a preceding stage.
+
+### Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-4 (human review of low-confidence predictions); HLR-6 (ETIM classification and enrichment)
+- **FRs:** FR-4 (route below-threshold items to the review queue); FR-7 (authorized Ops Leads adjust the auto-acceptance threshold); FR-9 (per-ETIM-assignment confidence is the signal being routed on); FR-3 (confidence score per prediction)
+- **QASs:** QAS-2 Usability — class-review-first is what keeps the reviewer at 10 items/minute by not queuing work that a class change would void
+- **Scenarios:** SCEN-2 steps 2–3 (a 0.45-confidence value routes to review; under this ADR it would also route on a validation or unit failure at any confidence)
+- **Validation:** VAL-2 (mock a low-confidence response; the item appears in the review queue) — extended to cover validation-failure and unit-failure routing at high confidence. **VAL-5** (added in spec v1.4) is the specific test for class-review-first: a below-threshold class assignment routes to class review and no attribute-level routing happens for that item. Specified, not yet executable.
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` — Request Router, Human Review, End-to-End Process steps 13–17
+- **Tickets:** EPARTS-289 (class matching and class routing), EPARTS-294 (ETIM-aware review queue); parent EPARTS-156 (ML)
+- **Related ADRs:** extends ADR-004 (per-attribute routing) and ADR-005 (externalized thresholds); consumes the staged outputs of ADR-016; depends on the policy overlay of ADR-019; the review-queue contract it feeds is ADR-009
+
+---
+
+## ADR-019: Externalize the Client Feature Policy as Per-Class Configuration
+### Status
+
+Accepted
+
+### Context
+
+ETIM tells us which features *exist* for a class. It does not tell us which ones *matter*.
+
+`ETIMARTCLASSFEATUREMAP.csv` — the file that binds features to classes — contains `ARTCLASSFEATURENR`, `ARTCLASSID`, `FEATUREID`, `FEATURETYPE`, `UNITOFMEASID`, `SORTNR`. It contains no `required`, no `mandatory`, no `blocks_publish`, no `used_for_compare`. This is not an oversight in the export; ETIM is a shared industry dictionary and requiredness is a property of a particular catalogue's editorial standards, not of the standard.
+
+The consequence is concrete and blocking. A valve class may define 60 features. A supplier datasheet may supply 12 of them. Whether that product is publishable depends entirely on which of the 60 the client considers required — and nobody has told us. Until someone does:
+
+- **"What blocks publish?" is unanswerable**, so firm validation requirements cannot be written.
+- The routing rule in ADR-018 that sends missing-required-features to review has no data to evaluate.
+- The reviewer UI cannot distinguish "this field is empty and that is fine" from "this field is empty and the product cannot ship."
+
+This is currently the project's most significant requirements risk, and it is owned by the client, not by us. Two open tickets (EPARTS-286 class scope, EPARTS-287 feature policy) are blocked on it.
+
+The architectural question is what to do in the meantime. Alternatives considered:
+
+- **Wait for the policy, then design around it.** Leaves the validation and routing paths unbuilt and the critical path idle on an external dependency with no committed date.
+- **Hard-code a provisional policy** from our own reading of the valve datasheets. Fast, and wrong in a way that is expensive to detect: the system would enforce a standard nobody agreed to, and the resulting review queue would reflect our guesses rather than the client's requirements.
+- **Derive requiredness statistically** — treat a feature as required if most suppliers populate it. Tempting, but it encodes current supplier behaviour as the target standard, which inverts the business objective. The client adopted ETIM precisely because current supplier coverage is inadequate.
+
+### Decision
+
+The feature policy is modelled as a **client-owned configuration overlay, external to the ETIM reference layer**, keyed per client, release, class and feature:
+
+```
+catalog_feature_policy(client_id, etim_release_id, etim_class_id, etim_feature_id)
+ → requirement_level ∈ { required, recommended, optional, conditional, not_used }
+ blocks_publish, used_for_compare, used_for_filter, display_order, condition_rule
+```
+
+Three properties of this decision matter more than the schema:
+
+**It is an overlay, not an edit.** ETIM reference tables (ADR-013) store the standard exactly as published. Policy lives in its own table and joins on the ETIM keys. Policy revisions do not require reloading ETIM, and the standard's own structure is never edited to record a client preference.
+
+**It is data, not code.** Changing requiredness for a class is a configuration change reviewed by the policy owner, not a deployment. Given that the client has not yet decided and will revise once they see real review volumes, requiredness must be cheap to change.
+
+**The default is permissive and explicit.** Absent a policy row, a feature is treated as `optional` and nothing blocks publish. The system does not guess. Where a policy is absent and a value is missing, the product publishes with the gap recorded, rather than silently enforcing an invented standard.
+
+The decision also creates a role that did not exist in the v1.0 baseline: a **feature-policy owner** on the client side who declares the levels and signs off on changes.
+
+**Implementation status: the seam is decided; the values are pending.** The overlay's position in the architecture and its consumption by routing (ADR-018) and by the reviewer UI are settled. The policy content is an open client decision (EPARTS-287) and the table is not yet populated.
+
+### Consequences
+
+- The architecture stops being blocked on a client decision. Routing, validation and the reviewer UI can be built against the overlay's contract and exercised with a synthetic policy, then switched to the real one when it arrives.
+- The **required-field path is designed but untestable end-to-end** until a real policy exists. Tests can prove that a `required` row routes correctly; they cannot prove the right features are marked required. This gap should be stated rather than papered over — a green test suite here does not mean the validation requirement is satisfied.
+- Because policy is per-client, a second client with different editorial standards is a data addition rather than a code change. That is well beyond phase-one scope and is not being built for, but the key shape does not preclude it.
+- `conditional` requires a rule language (`condition_rule`), and no rule language has been chosen. Conditional features are therefore accepted into the schema but not evaluated; they behave as `optional` until a rule evaluator exists. This is a known deferral, not an oversight.
+- `used_for_compare` and `used_for_filter` are carried in the schema because the Compare Tool and website filter are the stated business motivation for ETIM adoption, but both consumers are **out of phase-one scope**. Storing the flags now avoids a migration later; populating them is deferred.
+- Every policy change silently changes routing behaviour. Policy revisions must be versioned and correlated with review-queue volume, or an unexplained spike in the queue will be indistinguishable from a model regression.
+- The permissive default means that until the policy lands, **no product will ever be blocked for a missing required field**. Auto-accept rates measured before the policy is populated are therefore optimistic and must not be quoted as steady-state figures.
+
+### Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-6 (ETIM classification and enrichment); HLR-4 (human review of items needing attention)
+- **FRs:** FR-9 (ETIM matching — policy validation gates what a match is sufficient for); FR-4 (routing to review); FR-7 (authorized adjustment of auto-acceptance behaviour, of which policy is now part)
+- **Constraints:** C-3 (breadth-first delivery — a full end-to-end flow for one supplier type before optimizing depth; a permissive default is what allows the flow to complete)
+- **QASs:** QAS-3 Modifiability (client feature policy) — added in spec v1.4 specifically to hold this decision: a policy change is configuration, applied to the next batch without a code deployment
+- **Validation:** VAL-2 (routing) — the required-field branch is designed here and **cannot be validated until the policy is supplied**; this is a known open item, not a satisfied requirement
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` — Important ETIM Limitation, Client Policy Tables
+- **Tickets:** EPARTS-287 (feature policy — **blocked on client**), EPARTS-286 (phase-one class scope — **blocked on client**), EPARTS-294 (review UI consumes the policy)
+- **Open client decisions this ADR holds a place for:** feature policy per class; required-field publish blockers; Compare Tool and website-filter feature sets; mapping and policy sign-off ownership
+- **Related ADRs:** deliberately kept out of the reference layer of ADR-013; supplies the policy signals routed on in ADR-018; the validation stage that consumes it is part of ADR-016; the reviewer contract that displays it is ADR-009
+
+---
+
+## ADR-020: Pin ETIM Release 10.0 (EI) for the Project Duration
+### Status
+
+Accepted
+
+### Context
+
+ETIM is an external standard with its own release cadence. We loaded **ETIM 10.0, language EI**. There will be an 11.0, and between releases classes are added, features are added and deprecated, values are withdrawn, and a class's feature set changes shape.
+
+That raised a question the v1.0 baseline had no equivalent of: what does the platform do when the standard moves underneath it? Two things made it pressing. Requirements written against "the ETIM standard" are implicitly written against a specific release, so the traceability chain from HLR-6 through FR-9 to a published PIMS row is only meaningful if the release is part of the record. And an unmanaged upgrade silently reinterprets historical data — a value that was legal under 10.0 can be invalid under 11.0, and either the row breaks or, worse, it stays and nobody knows which release's rules it satisfies.
+
+Three options were considered.
+
+- **Build a governed upgrade path now.** Load each new release alongside the old one, diff them, re-match affected products through a review queue, and reconcile the client's feature policy against the diff before cutover. Architecturally clean, and it makes upgrades visible rather than silent. But it is a substantial amount of work — a diff report, a bulk re-match path, a second review queue — for an event that will not occur inside this project. It also could not be finished: who authorizes an upgrade, on what trigger, and what happens to already-published rows are client decisions nobody has made.
+- **Leave the question open.** Say nothing and handle a future release when it arrives. Rejected because "unspecified" is not the same as "out of scope". FR-10 as originally worded — maintain the dictionary as *versioned* reference data — implies an obligation we were not going to meet, and an assessor or a future maintainer would reasonably read it as a commitment.
+- **Pin the release explicitly and put the upgrade path out of scope.** Chosen.
+
+### Decision
+
+**The platform targets ETIM release 10.0, language EI, for the duration of this project.** Adopting later ETIM releases, and migrating already-classified products between releases, are **out of scope**.
+
+This is recorded as **constraint C-4**, introduced in Product Specification **v1.2**, and FR-10 is scoped to "the pinned ETIM release identified in C-4" rather than to versioned reference data generally.
+
+The **release-scoping mechanism in the schema stays exactly as it is.** Every ETIM reference row carries `etim_release_id`, with composite primary keys on `(etim_release_id, …)` across all ten tables (ADR-013); the release is carried through `matched_product_attribute` (ADR-014) and forms part of the PIMS writeback key (ADR-017). Under a pin that field is constant in practice, and we are keeping it for two reasons:
+
+1. **Provenance.** Every published value names the release it was matched under. "This value was matched against ETIM 10.0 EI, on this date, under this policy" stays recoverable from the row alone, which is what makes the audit trail meaningful later.
+2. **It costs nothing.** The columns and keys are already built and tested. Removing them to reflect the pin would be work that buys no capability and discards the provenance.
+
+So this ADR narrows the *forward-looking justification* in ADR-013 — release-scoping is no longer defended as a step toward governed upgrades — without changing a line of the schema it describes. ADR-013 is not edited.
+
+If the client later asks for a new ETIM release, that is a **change request against C-4**, and the first option above is the shape the work would take. It is not a gap to be quietly filled.
+
+### Consequences
+
+- The project stops carrying an obligation it was never going to discharge. FR-10 is now satisfiable and testable as written: load and maintain one named release.
+- No diff report, no bulk re-match path, no second review queue, and no upgrade-governance owner to chase. This is the largest piece of scope the decision removes, and it removes it in the phase where the critical path is `285 ‖ (297 → 298 → 299)`.
+- **We are deliberately accepting that the catalog will go stale** relative to ETIM. If the client's suppliers begin publishing against 11.0 while we classify against 10.0, new classes and features are simply unavailable to us, and products needing them fall to "ETIM Other" handling or to review. For a phase-one valve/actuator pilot that is acceptable. For a production catalogue with a multi-year life it would not be, and this ADR should be revisited before any such transition.
+- Provenance is preserved without the machinery. Because `etim_release_id` remains in the reference tables, the interpretation table and the PIMS key, a future un-pinning is a change of scope rather than a schema migration. The door is left open at zero cost.
+- **The loader keeps its release-mismatch rejection.** It validates that an archive matches the declared release and refuses a mismatched or truncated one (ADR-013). Under a pin that check becomes more valuable, not less — it is what stops an 11.0 archive being loaded into a 10.0-pinned system by accident.
+- The `etim_release_id` field will look redundant to anyone reading the schema without this ADR. That is the cost of keeping it, and this ADR is the answer.
+- One open client decision is closed. "ETIM release-upgrade governance" comes off the blocked list, taking the open-decision count from six to five.
+
+### Requirements Traceability
+
+- **Spec:** Product Specification **v1.4** (29 July 2026); C-4 was introduced in v1.2 (28 July) — this ADR is the reason for that version
+- **Constraints:** **C-4** (ETIM Release Pinned) — this ADR is the decision C-4 records
+- **HLRs:** HLR-6 (classify against the ETIM standard — this ADR fixes *which* ETIM)
+- **FRs:** **FR-10** (load and maintain the ETIM reference dictionary for the pinned release); FR-9 (matching is always against release 10.0 EI)
+- **DRs:** DR-4 (the release remains part of the PIMS writeback key, so publication stays release-explicit)
+- **QASs:** QAS-1 Modifiability — un-pinning would be a scope change, not a structural change to the pipeline
+- **Constraints (supporting):** C-1 (cost-effective design — the upgrade path is the expensive option and is deliberately not built); C-3 (breadth-first delivery — one supplier type end to end before adding depth)
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md`; `ETIM-ADR-ASSESSMENT.md` raised this as *"Standard evolution (currency): ETIM releases (10.0 → next); upgrade governance undefined"* — this ADR resolves that item by scoping it out rather than by building for it
+- **Closes:** the open client decision "ETIM release-upgrade governance"
+- **Related ADRs:** narrows the forward-looking rationale of **ADR-013** (release-scoped reference layer) without editing it; the release remains in **ADR-014**'s interpretation table and **ADR-017**'s writeback key for provenance; **ADR-019**'s policy overlay no longer needs reconciling against a release diff
+
+---
+
+## ADR-021: Formalize the Ingestion → ML Boundary as a Frozen `ExtractedInput` Record
+### Status
+
+Accepted
+
+### Context
+
+ADR-001 established pipe-and-filter as the platform's style, with filters communicating through typed data channels. In practice the ingestion→matching channel was the weakest of them: ingestion parsed a supplier file into a `RawRecord` and the matching stream read whatever fields happened to be there. Adequate while both sides were one team and one process; untenable now.
+
+Three pressures forced the boundary to become explicit.
+
+**It is a cross-team contract.** Ingestion (EPARTS-154) and ML matching (EPARTS-156) are separate streams with separate backlogs. The ETIM requirements-change record names this contract as one of two places where our traceability deliberately stops — we own the requirement, another stream owns the implementation. A trace boundary that is not a schema boundary is not a boundary at all.
+
+**Ingestion must not leak interpretation.** ADR-014 established the principle that supplier data is *evidence* and ETIM is a *standardized interpretation* over it. If ingestion hands the matcher a confidence score or a ranked list of candidate attribute names, it has already begun interpreting, and the evidence/interpretation split becomes a convention rather than a property of the system. The temptation is real: the OCR path (Azure Document Intelligence plus an LLM extraction) *has* per-field confidences available, and passing them along would be a one-line change.
+
+**Source provenance differs by channel and matters downstream.** A value read from a CSV cell, a value read from a text-native PDF, and a value read from OCR over a scanned page carry different reliability, and the matcher and the reviewer both need to know which they are looking at. A generic dictionary of fields loses that.
+
+Alternatives considered:
+
+- **Keep passing `RawRecord`.** Zero work, and it makes every ingestion-side refactor a potential silent break for the ML stream, because nothing declares what the ML stream is entitled to rely on.
+- **Put the boundary behind an HTTP service now.** Genuinely the right long-term shape, and premature: it adds deployment, retry and tracing surface for a boundary that currently runs in one process. ADR-008's single-deployable-unit decision still holds; what this ADR fixes is the *contract*, not the *topology*.
+- **Document the contract in prose only.** The 460-line handoff specification already exists. Documentation that is not enforced drifts, and this contract's whole value is that it cannot drift.
+
+### Decision
+
+The ingestion→ML boundary is a **single, versioned, schema-frozen record type**, `ExtractedInput`, specified in `docs/extraction_handoff_spec.md` and enforced in code:
+
+| Field | Meaning |
+|---|---|
+| `source_type` | one of `csv`, `email`, `pdf_text`, `pdf_ocr`, `image` — the channel, so the consumer knows what kind of evidence this is |
+| `text` | the extracted text; required, and an empty string is valid |
+| `structured_fields` | the parsed field/value pairs as the supplier wrote them |
+| `normalized_units` | mechanical unit normalization only, as `(value, unit)` pairs |
+| `source_ref` | pointer back to the archived raw artefact |
+
+Two properties do the real work.
+
+**The schema forbids interpretation by construction.** The Pydantic model is declared `extra="forbid"` and `frozen=True`. Confidence scores, ranked alternates, predicted ETIM classes — anything that constitutes an interpretation — *cannot be represented*, so they cannot cross the boundary by accident. The evidence/interpretation split of ADR-014 is enforced by the type system rather than by reviewer vigilance.
+
+**The record is persisted, not just passed.** `extracted_inputs` (Alembic `0007`) stores each handoff record, which turns the boundary into a durable checkpoint: the matching stream can be down, restarted, or re-run against the same inputs without re-doing OCR, and a matching bug can be diagnosed against exactly the input that produced it.
+
+Cleaning (spec §3) and unit normalization (spec §4) are injectable seams on the ingestion side of the boundary. This keeps mechanical tidying — whitespace, encoding, unit spelling — with the party that knows the source format, while leaving anything requiring domain judgement to the matcher.
+
+**Implementation status: built and merged.** `handoff/spec_model.py`, `handoff/builder.py`, `models/extracted_input.py` and migration `0007` are on the main line (EPARTS-357, EPARTS-358). The cleaning and unit implementations (EPARTS-359, EPARTS-362) and the provenance split between `pdf_text` and `pdf_ocr` (EPARTS-361) are on open branches; on the main line those seams are pass-throughs. Wiring the builder into the orchestrator is EPARTS-363 and is not yet done, so the record type exists and is validated but is not yet produced on every run.
+
+### Consequences
+
+- The two streams can move independently. Ingestion can change parsers, add a channel, or swap the OCR engine without coordinating, so long as the record still validates. The ML stream has a written, enforced statement of what it may rely on.
+- **`extra="forbid"` will reject rather than ignore** an ingestion-side addition. That is the intended behaviour — it makes contract changes loud — but it means adding a field is a deliberate, two-team, spec-versioning act, not a convenience. Expect this to feel obstructive at least once; that is the cost being paid on purpose.
+- Persisting the record makes matching **replayable**. Re-running the matcher over stored `extracted_inputs` costs nothing in Azure Document Intelligence or LLM calls, which materially changes the economics of iterating on the matching stages of ADR-016.
+- This turns ADR-001's in-process function call into an explicit asynchronous seam, and it is consequently **the leading candidate for extraction into a service** if the deployment topology of ADR-008 is ever revisited. Nothing about the contract assumes co-location.
+- The boundary is a queue-shaped thing without a queue. Delivery today is a table plus a poll; the transactional outbox and circuit breaker planned under EPARTS-301 are not built. Until they are, there is no delivery guarantee beyond "the row is committed" — adequate, because the row *is* the durable state, but not the same as at-least-once delivery to a live consumer.
+- The record carries no confidence, which means the matcher cannot preferentially trust a high-confidence OCR field over a low-confidence one. This is a deliberate loss of information: OCR confidence measures character recognition, not semantic correctness, and treating it as the latter is the mistake the split exists to prevent. If the matcher later needs a reliability signal, it should come from `source_type` and from measured per-channel accuracy, not from the OCR engine's self-report.
+- Because the builder is not yet wired into the orchestrator, the contract is currently **enforced but unexercised in production flow**. The unit tests validate the shape; no end-to-end run has yet produced a record. This should not be described as a working boundary until EPARTS-363 lands.
+
+### Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-2 (normalize into a standardized intermediate structure preserving original supplier values as evidence); HLR-1 (ingest from diverse supplier sources — `source_type` enumerates the channels); HLR-3 (the ML service that consumes this record)
+- **FRs:** FR-1 (ingestion record with supplier, timestamp, source channel); FR-2 (validation before processing — an invalid handoff record is a validation failure, not a silent pass); FR-9 (matching consumes this record)
+- **DRs:** DR-1 (raw file archived as evidence — `source_ref` is the pointer to it)
+- **QASs:** QAS-1 Modifiability — a new supplier format is a new `source_type` and a new parser; the boundary and everything downstream of it are unchanged
+- **Constraints:** DC-1 (Python backend); DC-3 (raw files preserved for re-processing and traceability — replayability depends on this)
+- **Scenarios:** SCEN-1 step 3 and SCEN-2 step 1 (both scenarios cross this boundary; SCEN-2's OCR path is `pdf_ocr`)
+- **Source:** `docs/extraction_handoff_spec.md` (§1 channels, §2 record shape, §3 cleaning, §4 unit normalization, §5 structured fields, §6 per-channel examples)
+- **Tickets:** EPARTS-357 (schema + migration `0007` — Done), EPARTS-358 (builder + spec model — Done), EPARTS-359 (units), EPARTS-361 (pdf_text/pdf_ocr provenance), EPARTS-362 (text cleaning), EPARTS-363 (orchestrator wiring — **not done**), EPARTS-301 (transactional outbox — not built); contract boundary between EPARTS-154 (Ingestion) and EPARTS-156 (ML)
+- **Related ADRs:** makes explicit the filter boundary of ADR-001; enforces the evidence/interpretation split of ADR-014; feeds the matching stages of ADR-016; does not alter the single-deployable-unit topology of ADR-008, but is the natural extraction point if that is revisited
diff --git a/docs/confluence/ADRs-index.md b/docs/confluence/ADRs-index.md
new file mode 100644
index 0000000..20819b9
--- /dev/null
+++ b/docs/confluence/ADRs-index.md
@@ -0,0 +1,39 @@
+# Architecture Decision Records
+
+21 ADRs. **The repo is the source of truth** — every row below links to the
+file in `eparts/docs/`. This page is a reading copy, so edit the repo rather than the
+page, or the two will drift.
+
+ADRs 0001–0012 are the spring baseline and are deliberately left unedited: they record
+what we believed in April. ETIM decisions supersede them *forward*, by reference, in
+0013–0021. Where a spring ADR is affected but not superseded, the change-impact analysis
+is in [`ETIM-ADR-ASSESSMENT.md`](https://github.com/AshrithaG/eparts/blob/main/docs/ETIM-ADR-ASSESSMENT.md).
+
+Requirement IDs cited by 0016–0021 resolve against Product Specification v1.4; the
+forward and backward traces are in [`REQUIREMENTS-TO-ADR-MAPPING.md`](https://github.com/AshrithaG/eparts/blob/main/docs/REQUIREMENTS-TO-ADR-MAPPING.md).
+
+| ADR | Decision | Status |
+|---|---|---|
+| [0001](https://github.com/AshrithaG/eparts/blob/main/docs/0001-adopt-pipe-and-filter-architectural-style.md) | Adopt Pipe-and-Filter as the Primary Architectural Style | Accepted |
+| [0002](https://github.com/AshrithaG/eparts/blob/main/docs/0002-isolate-prediction-strategy-behind-stable-interface.md) | Isolate the Prediction Strategy Behind a Stable Internal Interface | Accepted |
+| [0003](https://github.com/AshrithaG/eparts/blob/main/docs/0003-use-hybrid-rule-engine-and-semantic-similarity.md) | Use a Hybrid Rule Engine and Semantic Similarity for Attribute Prediction | Tentative |
+| [0004](https://github.com/AshrithaG/eparts/blob/main/docs/0004-route-confidence-decisions-at-attribute-level.md) | Route Confidence Decisions at the Attribute Level, Not the Record Level | Accepted |
+| [0005](https://github.com/AshrithaG/eparts/blob/main/docs/0005-externalize-confidence-threshold-as-configuration.md) | Externalize the Confidence Threshold as Runtime Configuration | Tentative |
+| [0006](https://github.com/AshrithaG/eparts/blob/main/docs/0006-enforce-idempotent-pims-writeback-via-natural-key.md) | Enforce Idempotent PIMS Writeback via a Composite Natural Key | Accepted |
+| [0007](https://github.com/AshrithaG/eparts/blob/main/docs/0007-use-attribute-row-canonical-schema.md) | Use an Attribute-Row Canonical Schema for the Staging Table | Accepted |
+| [0008](https://github.com/AshrithaG/eparts/blob/main/docs/0008-deploy-platform-as-single-azure-app-service-unit.md) | Deploy the Platform as a Single Azure App Service Unit | Accepted |
+| [0009](https://github.com/AshrithaG/eparts/blob/main/docs/0009-implement-human-review-queue-as-database-table.md) | Implement the Human Review Queue as a Persistent Database Table | Accepted |
+| [0010](https://github.com/AshrithaG/eparts/blob/main/docs/0010-maintain-append-only-audit-trail.md) | Maintain an Append-Only Audit Trail of Every Pipeline Decision | Accepted |
+| [0011](https://github.com/AshrithaG/eparts/blob/main/docs/0011-trigger-retraining-automatically-on-batch-completion.md) | Trigger Retraining Automatically on Human Review Batch Completion | Proposed |
+| [0012](https://github.com/AshrithaG/eparts/blob/main/docs/0012-emit-stage-by-stage-telemetry-to-datadog.md) | Emit Stage-by-Stage Telemetry to Datadog for Drift Detection and Operational Monitoring | Proposed |
+| [0013](https://github.com/AshrithaG/eparts/blob/main/docs/0013-establish-etim-reference-data-layer.md) | Establish a Release-Versioned ETIM Reference Data Layer Owned by Ingestion | Accepted |
+| [0014](https://github.com/AshrithaG/eparts/blob/main/docs/0014-emit-source-preserving-product-attribute-staging-split.md) | Emit a Source-Preserving Product + Attribute Staging Split | Accepted |
+| [0015](https://github.com/AshrithaG/eparts/blob/main/docs/0015-target-postgresql-now-defer-azure-sql.md) | Target PostgreSQL Now; Defer the Azure SQL Conversion | Accepted |
+| [0016](https://github.com/AshrithaG/eparts/blob/main/docs/0016-decompose-matching-into-staged-etim-class-feature-value-stages.md) | Decompose Attribute Matching into Staged ETIM Class → Feature → Value/Unit Matching | Accepted |
+| [0017](https://github.com/AshrithaG/eparts/blob/main/docs/0017-rekey-pims-writeback-contract-on-etim-identifiers.md) | Re-key the PIMS Writeback Contract on ETIM Identifiers | Accepted |
+| [0018](https://github.com/AshrithaG/eparts/blob/main/docs/0018-extend-routing-to-etim-signals-with-class-review-first.md) | Extend Routing to ETIM Signals, with a Class-Review-First Path | Accepted |
+| [0019](https://github.com/AshrithaG/eparts/blob/main/docs/0019-externalize-client-feature-policy-as-per-class-configuration.md) | Externalize the Client Feature Policy as Per-Class Configuration | Accepted |
+| [0020](https://github.com/AshrithaG/eparts/blob/main/docs/0020-pin-etim-release-10-0-for-the-project-duration.md) | Pin ETIM Release 10.0 (EI) for the Project Duration | Accepted |
+| [0021](https://github.com/AshrithaG/eparts/blob/main/docs/0021-formalize-ingestion-to-ml-boundary-as-frozen-extracted-input-record.md) | Formalize the Ingestion → ML Boundary as a Frozen `ExtractedInput` Record | Accepted |
+
+---
diff --git a/docs/confluence/adr-0001-adopt-pipe-and-filter-architectural-style.md b/docs/confluence/adr-0001-adopt-pipe-and-filter-architectural-style.md
new file mode 100644
index 0000000..e0c8763
--- /dev/null
+++ b/docs/confluence/adr-0001-adopt-pipe-and-filter-architectural-style.md
@@ -0,0 +1,44 @@
+# ADR-001: Adopt Pipe-and-Filter as the Primary Architectural Style
+
+> Source of truth: [`0001-adopt-pipe-and-filter-architectural-style.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0001-adopt-pipe-and-filter-architectural-style.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+eParts Services LLC ingests heterogeneous supplier catalogs (CSV, PDF, email attachments, SFTP drops, direct uploads) into PIMS through a manual workflow currently absorbing roughly 4.5 FTEs across eParts and Alps Controls. The new platform must transform raw supplier files into validated PIMS records while keeping data integrity high, because incorrect product data propagates into contractor field orders.
+
+The transformation is fundamentally linear: parse → normalize → predict → route → review/auto-accept → write back. Each stage operates on the output of the previous one, and stages have different resource profiles (parsing is I/O-bound, prediction is CPU/memory-bound, review is human-bound).
+
+Several architectural styles were considered:
+
+- **Event-driven architecture** would introduce a message broker and asynchronous coordination. Supplier catalogs arrive in discrete batches rather than continuous streams, so the complexity is not justified.
+- **Microservices** would require container orchestration and distributed tracing infrastructure beyond what a five-person capstone team can sustain.
+
+The team is five people working from Spring through Fall 2026, so operational simplicity is a binding constraint.
+
+## Decision
+
+We will structure the platform as a pipe-and-filter system. Independent filters (Ingestion Gateway, Normalization, Prediction Service, Routing Engine, Review/Auto-accept paths, Writeback) communicate through typed data channels. The pipeline is linear with one branch at the Routing Engine where confidence-based routing splits high-confidence attributes (auto-accept) from low-confidence attributes (human review); both paths merge before writeback.
+
+
+
+## Consequences
+
+- Each filter can be replaced or evolved independently because filters communicate only through defined data contracts. The Prediction Service can be swapped without touching upstream parsing or downstream writeback (supports QA-2).
+- Adding a new product category requires extending the canonical schema and retraining; it does not require changing the filter sequence (supports QA-3).
+- Staging tables placed between filters act as checkpoints: a failure at any stage does not lose data already processed upstream (supports QA-4 availability).
+- The known weakness of pipe-and-filter is error detection and recovery across the pipeline. We mitigate this with persistent staging tables between stages and idempotent writeback, but cross-stage transactional guarantees are not provided.
+- The branch at the Routing Engine departs from a strictly linear pipeline. The two paths must merge before writeback, which introduces merge logic in the writeback service (further explored in ADR-005).
+- The architecture mirrors the existing manual workflow stage-for-stage, reducing the risk that the system solves the wrong problem and easing communication with the catalog team.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-1 (multi-format ingestion), HLR-2 (normalization to standard structure)
+- **FRs:** FR-1, FR-2 (filter decomposition makes ingestion and normalization distinct stages)
+- **QASs:** QAS-2, QAS-3 (style enables filter-level replacement); QAS-4 (staging tables between filters act as checkpoints)
+- **Constraints:** C-7 (capstone timeline — pipe-and-filter mirrors existing manual workflow, minimizing rework risk)
+- **Scenarios:** SCEN-1, SCEN-2 (the filter sequence is the spine of both scenarios)
+- **Validation:** VAL-1 (Ingestion Gateway is the first filter)
diff --git a/docs/confluence/adr-0002-isolate-prediction-strategy-behind-stable-interface.md b/docs/confluence/adr-0002-isolate-prediction-strategy-behind-stable-interface.md
new file mode 100644
index 0000000..76b9185
--- /dev/null
+++ b/docs/confluence/adr-0002-isolate-prediction-strategy-behind-stable-interface.md
@@ -0,0 +1,38 @@
+# ADR-002: Isolate the Prediction Strategy Behind a Stable Internal Interface
+
+> Source of truth: [`0002-isolate-prediction-strategy-behind-stable-interface.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0002-isolate-prediction-strategy-behind-stable-interface.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+Model selection for the attribute prediction component is unresolved through Phase 2. The team is currently using a hybrid rule + semantic-similarity approach (ADR-003) but expects to evaluate alternatives such as DistilBERT or CatBoost as labeled data accumulates. Quality attribute QA-2 (Modifiability — model swap) is rated High importance / Medium difficulty and explicitly requires that swapping the prediction strategy not ripple into the Routing Engine, Writeback, or any other component.
+
+Three isolation mechanisms were considered:
+
+- **Internal abstract interface** in the same Python application. A swap is a new class plus a configuration change; one redeployment.
+- **REST microservice** running the Prediction Service in a separate Azure Container App. Enables independent deployment, canary rollouts, and GPU-backed inference, but adds container orchestration, health checks, service authentication, and distributed tracing.
+- **Message queue (Azure Service Bus)** with broker-mediated communication. Two queues introduced; retry and dead-letter offloaded to Service Bus. Suits near-real-time ingestion with multiple consumers.
+
+The current phase processes supplier catalogs in discrete batches and has a single downstream consumer (the Routing Engine). The team does not need canary deployments or GPU inference during the capstone phase. A network boundary between filters would add operational complexity disproportionate to team capacity.
+
+## Decision
+
+We will define `PredictionServiceInterface` as a Python abstract interface that accepts normalized records and returns predictions with per-attribute confidence scores. Concrete implementations (`CatBoostPredictor`, `DistilBERTPredictor`, the current hybrid implementation) live inside the `prediction` package and are selected at startup via configuration. The Routing Engine and all other downstream components depend only on `PredictionResult`, a plain data class, and never on any model-specific type.
+
+## Consequences
+
+- Replacing the prediction strategy is a localized change: a new class in the `prediction` package plus a configuration change. Nothing in `routing`, `writeback`, `review`, or `audit` changes.
+- The interface contract — `PredictionResult` with per-attribute confidence — must be defined before the model is finalized. The team must avoid leaking model-specific types (logits, embedding vectors, classifier probabilities) into adjacent packages.
+- Retraining and model promotion (described in the MLOps pipeline) operate inside the `prediction` package boundary. The interface does not change when a new model version is promoted, so the Routing Engine sees the prediction service as unchanged.
+- This decision does not enable canary deployments or side-by-side model evaluation in production. If the system is later handed off to a larger eParts team that requires those capabilities, the prediction package will need to be extracted into a REST microservice. The module boundaries are drawn deliberately so that this transition is adding network serialization at an existing boundary, not a rewrite.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-3 (predict with confidence — implementation choice deferred behind interface)
+- **FRs:** FR-3 (per-attribute prediction contract)
+- **QASs:** QAS-2 (model swap localized to prediction package — this ADR is the named mechanism in the QAS response)
+- **Constraints:** C-3, DC-1 (Python interface); C-7 (internal interface chosen over REST microservice for capstone timeline)
+- **Related ADRs:** ADR-003 (concrete implementation behind this interface); ADR-011 (retraining promotes new versions through this interface)
diff --git a/docs/confluence/adr-0003-use-hybrid-rule-engine-and-semantic-similarity.md b/docs/confluence/adr-0003-use-hybrid-rule-engine-and-semantic-similarity.md
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+# ADR-003: Use a Hybrid Rule Engine and Semantic Similarity for Attribute Prediction
+
+> Source of truth: [`0003-use-hybrid-rule-engine-and-semantic-similarity.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0003-use-hybrid-rule-engine-and-semantic-similarity.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Tentative
+
+## Context
+
+The Prediction Service must map raw supplier text to canonical attribute values and emit per-attribute confidence scores that the Routing Engine can compare against a threshold. Three properties matter: accuracy under data scarcity, explainability for the catalog team, and ability to handle free-text inputs that rules cannot anticipate.
+
+The team targets approximately 200 labeled examples for the initial training set, but a calibrated pure-ML classifier typically needs around 830 examples to produce well-behaved confidence scores. eParts has stated an explainability requirement: catalog reviewers need to understand why an item was routed to review.
+
+Three alternatives were considered:
+
+- **Pure rules.** Deterministic and fully explainable, but estimated coverage is only 40–60% of supplier inputs because suppliers use inconsistent terminology that rules cannot enumerate.
+- **Pure ML classifier.** Handles unseen text well, but with the available labeled data the confidence scores are not well-calibrated. Confidence scores are also opaque, undermining the explainability requirement.
+- **Hybrid: rules first, semantic similarity (TF-IDF + cosine) for unmatched inputs.** Rules give a high-precision fallback when data is scarce; the semantic layer covers free-text inputs the rules miss. Reason codes can be attached to low-confidence items.
+
+A weighted decision matrix scored the hybrid approach highest (2.50) against pure rules (1.85) and pure ML (1.70), with criteria weighted toward accuracy under low data, explainability, and free-text coverage.
+
+## Decision
+
+We will implement the Prediction Service as a hybrid pipeline. A rule engine runs first against each normalized attribute. Where rules do not match, a semantic similarity layer (TF-IDF vectorization with cosine similarity against canonical value embeddings) produces a candidate value. The final confidence is a weighted composite:
+
+```
+conf_final = α · conf_rule + (1 - α) · conf_embed
+```
+
+with an initial value of `α = 0.7`. Reason codes from the rule layer are attached to each prediction and surfaced in the Human Review Queue for low-confidence items. Both layers live inside the `prediction` package behind `PredictionServiceInterface` (ADR-002).
+
+## Consequences
+
+- Rules carry the prediction under data scarcity, so the system has a usable accuracy floor before sufficient labeled data accumulates.
+- Reason codes from the rule layer satisfy the explainability requirement. Reviewers see why an attribute was flagged, which is expected to support adoption by Brian and Dewey on the catalog team.
+- The semantic layer can be replaced or upgraded (e.g., to embeddings from a transformer) without touching the rule layer or the Routing Engine, because both layers sit behind `PredictionServiceInterface`.
+- The α weighting is a sensitivity point. Wrong α suppresses the more accurate signal source and produces miscalibrated confidence, which propagates directly into routing errors. The initial value of 0.7 is a guess; it must be calibrated against prototype data (see Refinement 3 in the report).
+- The decision is tentative and carries explicit reconsideration triggers. If pure rules cover ≥85% of inputs at confidence ≥0.90, the semantic layer adds complexity without value and we should switch to pure rules. If labeled data exceeds ~800 examples and a pure ML model achieves ≥85% accuracy with calibrated confidence, the hybrid approach loses its advantage and we should switch to pure ML.
+- Per-attribute-type α weights may be more accurate than a single global α, since some attributes (e.g., `SUPPLY_VOLTAGE`) are inherently easier to predict than others (e.g., `DESCRIPTION`). The retraining pipeline can store learned per-type weights as configuration once Refinement 3 produces evidence.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-3 (predict with confidence)
+- **FRs:** FR-3 (per-attribute predictions with confidence scores)
+- **QASs:** QAS-1 (accuracy — hybrid provides usable accuracy floor under data scarcity); QAS-5 (reason codes from rules support drift interpretation)
+- **Constraints:** C-3, DC-1 (Python ML); C-4 (phase scope limits labeled data, favoring hybrid over pure ML)
+- **Scenarios:** SCEN-1 (high-confidence path), SCEN-2 (low-confidence path with reason codes)
diff --git a/docs/confluence/adr-0004-route-confidence-decisions-at-attribute-level.md b/docs/confluence/adr-0004-route-confidence-decisions-at-attribute-level.md
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+# ADR-004: Route Confidence Decisions at the Attribute Level, Not the Record Level
+
+> Source of truth: [`0004-route-confidence-decisions-at-attribute-level.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0004-route-confidence-decisions-at-attribute-level.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+The Routing Engine is the architectural component that enforces the accuracy quality attribute (QA-1, rated High/High). Every record produced by the Prediction Service contains multiple attributes, each with its own predicted value and confidence score. The team must decide whether confidence routing operates at the record level (the whole record is sent to review if any attribute is uncertain) or at the attribute level (each attribute is routed independently).
+
+Two alternatives were considered:
+
+- **Per-record routing.** Conceptually simpler. The review queue holds whole records, and writeback always emits complete records. There is no merge logic. However, a record with ten attributes and one uncertain value sends all ten attributes to review, inflating reviewer workload.
+- **Per-attribute routing.** Each attribute is routed independently. Estimated 3–5× lower review volume than per-record because only the attributes the model is unsure about reach the queue. The cost is structural: the writeback service must merge auto-accepted attributes with reviewed attributes for the same record before writing to PIMS, and there is a risk that correlated attributes (e.g., connection type and port size) become inconsistent if reviewed in isolation.
+
+The combined catalog team across eParts and Alps Controls is approximately 4.5 FTEs. Reviewer capacity is the binding constraint on review volume; if the system pushes too many items to review, the labor savings the platform is meant to provide disappear.
+
+## Decision
+
+We will route confidence decisions at the attribute level. The Human Review Queue is keyed on `(record_id, attribute_id)`. The Routing Engine compares each attribute's confidence score against the configured threshold independently. The Writeback Service batches all attributes for a given record and writes them to PIMS as a unit only once all routing paths for that record (auto-accept and review) have resolved.
+
+## Consequences
+
+- Review volume scales with actual model uncertainty rather than with record size, expected to reduce reviewer workload by 3–5× compared with per-record routing.
+- Reviewers see only the flagged attributes plus their source context, not the entire record. This focuses attention but means reviewers cannot easily catch inconsistencies between an auto-accepted attribute and one they are reviewing.
+- The Writeback Service carries merge logic. It must hold the complete record until all routing decisions for that record are resolved, then upsert it as a unit. A partial write — auto-accepted attributes entering PIMS before reviewed attributes are resolved — would produce incomplete records and is explicitly prevented by this batching.
+- Correlated attributes are a known risk. If connection type and port size are reviewed independently and the reviewer makes inconsistent choices, an internally inconsistent record can reach PIMS. Mitigation: the review interface presents the full record context to reviewers, but this has not been validated in practice. Refinement 2 in the project plan tests pairwise mutual information between attributes and inspects high-MI pairs.
+- Per-attribute thresholds may be required if attribute-level accuracy varies significantly. Some attributes are inherently easier to predict than others. The threshold mechanism is configurable (ADR-005) so per-attribute thresholds can be introduced without code changes.
+- If more than 30% of corrections turn out to involve cross-attribute consistency errors, the per-attribute routing decision should be reconsidered in favor of per-record or attribute-group routing.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-4 (Human Review Queue for low-confidence predictions)
+- **FRs:** FR-3 (per-attribute predictions); FR-4 (route below-threshold attributes to queue); FR-9 (per-attribute routing decisions)
+- **QASs:** QAS-1 (accuracy — per-attribute routing keeps review volume proportional to risk)
+- **Scenarios:** SCEN-2 (only the uncertain attribute is routed, not the whole record)
+- **Validation:** VAL-2 (low-confidence item appears in Human Review Queue)
diff --git a/docs/confluence/adr-0005-externalize-confidence-threshold-as-configuration.md b/docs/confluence/adr-0005-externalize-confidence-threshold-as-configuration.md
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@@ -0,0 +1,35 @@
+# ADR-005: Externalize the Confidence Threshold as Runtime Configuration
+
+> Source of truth: [`0005-externalize-confidence-threshold-as-configuration.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0005-externalize-confidence-threshold-as-configuration.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Tentative
+
+## Context
+
+The Routing Engine sends attributes with confidence above a threshold to auto-accept and attributes below the threshold to the Human Review Queue. The threshold is the most sensitive parameter in the system: it controls the tradeoff between accuracy (QA-1) and reviewer throughput. A threshold set too high pushes most attributes into review and overwhelms the catalog team, eliminating the labor savings the platform is meant to provide. A threshold set too low lets incorrect predictions through to PIMS, where they cause wrong parts to be ordered by contractors.
+
+The threshold cannot be set during design because no model has yet been run against production-representative data. The team currently uses a placeholder of 0.85 with no empirical support. Refinement 1 in the project plan calibrates the threshold against ≥200 labeled submissions using precision-recall curves between 0.50 and 0.99. Per-attribute variance in accuracy may also drive a per-attribute threshold table rather than a single global value.
+
+Hardcoding the threshold in the Routing Engine would require a code change and redeployment for every recalibration, which is incompatible with the iterative tuning the team expects across the pilot.
+
+## Decision
+
+The confidence threshold is externalized as runtime configuration read by the Routing Engine at startup. The configuration mechanism supports both a global threshold value and an optional per-attribute override table. Threshold changes take effect on application restart without any code change. The Routing Engine reads the threshold(s) once per pipeline run; threshold changes during a run do not affect already-routed attributes.
+
+## Consequences
+
+- The threshold can be retuned during pilot operation without engineering involvement beyond editing configuration and restarting the App Service.
+- Per-attribute thresholds are supported architecturally without further code changes. If Refinement 1 reveals that some attributes (e.g., `SUPPLY_VOLTAGE`) are reliably predicted at 0.75 while others (e.g., `DESCRIPTION`) need 0.92, the per-attribute table can be populated.
+- The threshold value is a configuration concern, not an architectural concern. This means that the architecture cannot guarantee an accuracy number; it can only guarantee that whatever threshold is set will be applied consistently. The actual accuracy guarantee depends on operational discipline around configuration management.
+- Configuration drift is a risk. If the threshold is changed in production without recording the change in the audit trail, later analyses of model accuracy or reviewer workload may be impossible to interpret. The audit trail (ADR-009) records the threshold value alongside each routing decision to mitigate this.
+- The decision is tentative because the threshold itself is unsupported. Once Refinement 1 produces evidence and a value is selected, this decision moves to Accepted.
+- This decision interacts with monitorability (ADR-012): the threshold value is one of the baselines against which drift is measured. Changing the threshold resets the baseline.
+
+## Requirements Traceability
+
+- **FRs:** FR-4 (route based on threshold); FR-7 (configurable thresholds, calibration TBD); FR-9 (per-attribute routing using configurable thresholds)
+- **QASs:** QAS-1 (accuracy lever); QAS-5 (threshold value is part of the drift baseline)
+- **Scenarios:** SCEN-1 (above-threshold auto-accept), SCEN-2 (below-threshold review)
+- **Validation:** VAL-2 (threshold drives routing behavior tested by VAL-2)
diff --git a/docs/confluence/adr-0006-enforce-idempotent-pims-writeback-via-natural-key.md b/docs/confluence/adr-0006-enforce-idempotent-pims-writeback-via-natural-key.md
new file mode 100644
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--- /dev/null
+++ b/docs/confluence/adr-0006-enforce-idempotent-pims-writeback-via-natural-key.md
@@ -0,0 +1,42 @@
+# ADR-006: Enforce Idempotent PIMS Writeback via a Composite Natural Key
+
+> Source of truth: [`0006-enforce-idempotent-pims-writeback-via-natural-key.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0006-enforce-idempotent-pims-writeback-via-natural-key.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+The platform writes approved product attributes to PIMS staging tables on SQL Server. PIMS exposes no writeback API and provides no rollback or transactional guarantees back to the platform. Retries of a writeback operation must not produce duplicate records, because duplicates in PIMS staging propagate into wrong bills of materials for contractor orders.
+
+Several mechanisms were considered:
+
+- **Application-side primary key check.** Read-before-write to detect existing records.
+- **Database upsert via composite natural key.** A SQL `MERGE` (or equivalent) keyed on a stable identifier matches existing rows and updates them rather than inserting duplicates.
+- **Distributed transaction across the platform and PIMS.** Not feasible: PIMS is owned by a different team, has no API, and there is no distributed transaction coordinator across the trust boundary.
+- **Idempotency token in PIMS.** Would require schema change in PIMS, which the platform team does not control.
+
+The submission ID is a composite of the company identifier and the product identifier, making it stable across submissions: a new update pushed for the same company–product pair carries the same submission ID. The attribute ID is a stable canonical attribute identifier. Together, `(submission_id, attribute_id)` uniquely identify any value the platform writes. Both are generated inside the platform and stored in the staging tables before the writeback runs.
+
+## Decision
+
+PIMS writeback uses a composite natural key of `(submission_id, attribute_id)`, where `submission_id` is itself derived from `(company_id, product_id)`. The Publish/Sync Job (Azure Function) executes an upsert against the PIMS staging table: if a row with the same key exists, the value is updated in place; otherwise a new row is inserted. Because the submission ID is stable for a given company–product pair, pushing a new update for the same product produces the same key and overwrites the prior values rather than inserting a duplicate. The natural key is generated and stored in the platform's own staging tables before writeback, so a retry of the writeback also uses the identical key and matches the same target row.
+
+## Consequences
+
+- Retries of the Publish/Sync Job are safe. A network failure mid-run, a transient PIMS outage, or a redeployment that interrupts the job can be recovered by simply running the job again.
+- Idempotency is enforced in application code, not in PIMS. If PIMS staging tables are altered (e.g., the natural key columns are dropped or renamed), the guarantee disappears silently. The integration test described in Refinement 4 verifies the schema before any production data is written.
+- This decision depends on a structural assumption about PIMS staging that has not yet been validated. Jake at eParts has not delivered the P1-C schema. If the staging tables use wide columns (one row per record with attribute values as columns) rather than tall columns (one row per attribute), the natural key strategy needs a translation layer. If the staging tables lack columns to hold the platform's natural key, the team must either negotiate a schema addition with eParts or maintain a team-owned buffer table that holds the mapping.
+- No rollback is possible. Once a row is upserted into PIMS staging, the only way to "undo" it is to write a corrected row with the same natural key. This is acceptable because every write goes through human review or auto-accept above a calibrated threshold; the system never writes silently uncertain data.
+- This decision interacts with ADR-004 (per-attribute routing). The natural key is keyed on `attribute_id`, not on `record_id`, which is what enables per-attribute routing to write attributes individually as they resolve. If routing were per-record, the natural key would only need `record_id`.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-5 (write approved data to PIMS staging)
+- **FRs:** FR-8 (idempotent application-layer writeback); FR-11 (natural key: submission ID + attribute ID)
+- **DRs:** DR-3 (Must — retry must not create duplicates)
+- **QASs:** QAS-1 (accuracy — prevents duplicate-driven errors); QAS-4 (availability — safe retry on recovery)
+- **Constraints:** C-2 (no PIMS API), C-5 (no direct production writes — writeback targets staging only)
+- **Scenarios:** SCEN-1 (Step 5), SCEN-2 (Step 6)
+- **Validation:** VAL-3 (upsert + no-duplicate on retry)
diff --git a/docs/confluence/adr-0007-use-attribute-row-canonical-schema.md b/docs/confluence/adr-0007-use-attribute-row-canonical-schema.md
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@@ -0,0 +1,40 @@
+# ADR-007: Use an Attribute-Row Canonical Schema for the Staging Table
+
+> Source of truth: [`0007-use-attribute-row-canonical-schema.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0007-use-attribute-row-canonical-schema.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+The Normalization stage transforms heterogeneous supplier formats (CSV, PDF, email-extracted key-value pairs) into a canonical structure that the Prediction Service, Routing Engine, and Writeback Service can consume uniformly. The shape of this canonical schema is an architecturally significant decision because it determines how much work it takes to add a new product category, how easily attributes can be routed individually, and how the staging tables grow over time.
+
+The current scope is valves and actuators, but the client (Harsha) has stated that category expansion is expected after the pilot. Quality attribute QA-3 (Modifiability — new category) is rated Medium/Medium and explicitly requires that adding a category not force a structural change to routing or writeback.
+
+Two structural options were considered:
+
+- **Wide schema (one row per record).** Each record is a single row with one column per attribute (`voltage`, `port_size`, `connection_type`, etc.). Adding a new category requires schema migration: new columns, ALTER TABLE statements, and coordination with any system that reads the staging table. Querying a single record is trivial. Per-attribute routing is awkward because attribute-level state (confidence score, routing decision) would need parallel columns for every attribute.
+- **Tall schema (one row per attribute).** Each row is `(record_id, attribute_id, raw_value, predicted_value, confidence, routing_status)`. Adding a new attribute is a data change (a new entry in the attribute reference table), not a schema change. Per-attribute routing is direct: routing status is a column on the row.
+
+## Decision
+
+The canonical staging schema is attribute-row: each row represents one attribute of one record. The columns include `submission_id`, `record_id`, `attribute_id`, `supplier_raw_value`, `predicted_value`, `confidence_score`, `routing_status`, and audit metadata. Attribute definitions (name, type, allowed values, category) live in a separate reference table joined as needed. New product categories are added by inserting attribute definitions into the reference table, not by altering the staging schema.
+
+## Consequences
+
+- Adding a new product category does not require a schema migration against the staging tables. The Normalization stage gains new mapping entries; the Prediction Service is retrained on the expanded label set; nothing in the Routing Engine, Review Queue, or Writeback Service changes structurally.
+- Per-attribute routing (ADR-004) becomes natural. Each row carries its own routing state, so the Routing Engine reads and updates one row at a time without joining against a wide record schema.
+- Per-attribute audit is also natural. The audit trail can reference a single attribute row by its primary key.
+- Querying a complete record requires a join or aggregation across multiple rows. This is a small loss in query convenience and is acceptable because the platform's hot-path queries are per-attribute (routing, scoring, review), not per-record.
+- The staging tables grow faster than they would under a wide schema (one row per attribute rather than one row per record). For valves and actuators with roughly a dozen attributes, this is a 12× row-count multiplier. Azure SQL Database is sized to handle this comfortably at expected ingestion volumes.
+- This schema decision is independent of the PIMS staging schema. ADR-006 covers the writeback contract with PIMS, which may use either a wide or tall structure. If PIMS is wide, the Writeback Service performs an aggregation transform from the platform's tall canonical schema into the wide PIMS schema; this is documented as an open dependency on Refinement 4.
+- If Refinement 4 reveals that PIMS staging is rigidly wide and the team-owned mapping is too costly to maintain, the platform may keep its internal canonical schema tall while presenting a wide interface to PIMS through the Publish/Sync Job. The architecture supports this.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-2 (normalize to standardized structure)
+- **FRs:** FR-2 (canonical schema before prediction); FR-11 (attribute-level natural key requires attribute-row schema)
+- **QASs:** QAS-3 (new category as data change, not schema migration)
+- **Constraints:** C-4 (phase scope expansion); C-6 (pricing excluded from canonical schema)
+- **Scenarios:** SCEN-1 (Step 3 — canonical normalization)
diff --git a/docs/confluence/adr-0008-deploy-platform-as-single-azure-app-service-unit.md b/docs/confluence/adr-0008-deploy-platform-as-single-azure-app-service-unit.md
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@@ -0,0 +1,42 @@
+# ADR-008: Deploy the Platform as a Single Azure App Service Unit
+
+> Source of truth: [`0008-deploy-platform-as-single-azure-app-service-unit.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0008-deploy-platform-as-single-azure-app-service-unit.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+The platform must be deployed on Azure (a fixed client constraint) and must be operable by a five-person capstone team across one academic year. Quality attributes that bear on deployment topology are QA-2 (model swappability), QA-4 (availability under Prediction Service outage), and a team-size constraint that bounds operational complexity.
+
+Two topologies were analyzed in detail:
+
+- **Single Azure App Service (Python).** All pipeline components — ingestion, normalization, prediction, routing, review-queue access, writeback orchestration — run in one process and one deployment unit. Azure SQL Database holds staging tables, the review queue, and the audit trail. Azure Blob Storage archives raw supplier files. The Publish/Sync Job runs as a separate timer-triggered Azure Function. Components communicate by function call. Scaling is per application unit.
+- **Microservices (Azure Container Apps).** Three independent services: Ingestion+Normalization, Prediction, Routing+Writeback. Each scales independently, can be deployed independently, and can fail independently. Inter-service communication is HTTP or Service Bus. Operational requirements include container orchestration, distributed tracing, service-to-service authentication, and three deployment pipelines.
+
+The microservices alternative offers fault isolation and independent scaling, both of which are real benefits for a production system. They are not benefits the current team can absorb operationally during the capstone phase. Distributed tracing alone would consume a substantial fraction of the timeline. The Prediction Service does not currently need GPU instances or independent scaling because supplier ingestion is batched, not real-time.
+
+## Decision
+
+The platform is deployed as a single Azure App Service running Python. All pipeline components live in one process. Azure SQL Database holds all internal pipeline state (staging tables, Human Review Queue, audit trail). Azure Blob Storage archives raw supplier files. The Publish/Sync Job is a timer-triggered Azure Function deployed separately. Inbound channels are SFTP (polled), email (polled), and HTTPS upload. Outbound to PIMS is via `pyodbc` across the trust boundary to PIMS SQL Server. Outbound telemetry to Datadog is fire-and-forget HTTPS.
+
+## Consequences
+
+- One deployment, one log stream, one health check. Operational complexity is bounded.
+- Components communicate by function call. This is fast and avoids the complexity of network serialization, retries, and timeouts between filters.
+- Fault isolation is reduced. A bug in any component can crash the App Service and take the entire pipeline down. The persistent staging tables and review queue mitigate data loss risk: in-flight work survives a process restart because state is in Azure SQL, not in-memory.
+- Independent scaling is not available. If the Prediction Service becomes a hotspot, the entire App Service must be scaled up.
+- The module boundaries inside the App Service (described in the module view) are deliberately drawn where service boundaries would go in a microservices deployment. The `prediction` package, `routing` package, and `writeback` package are independent units of code that communicate through typed data contracts. Transitioning to microservices later is therefore adding HTTP serialization at existing boundaries, not rewriting business logic.
+- Datadog telemetry is fire-and-forget. Telemetry failures do not block the pipeline. This means a Datadog outage cannot cause a pipeline outage, but it also means dropped telemetry is not retried; operationally significant signals must also be persisted in the audit trail (ADR-009).
+- The Publish/Sync Job is intentionally separated as an Azure Function on a timer trigger so that PIMS writeback runs on a controlled schedule rather than synchronously with each ingestion. This decouples PIMS load from supplier ingestion bursts.
+- Trigger for reconsideration: production handoff to a larger eParts team, or a Prediction Service that scales independently of ingestion (e.g., GPU-backed inference, multi-model ensembles). At that point, the prediction package is the natural first candidate for extraction into a Container App.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-1 (ingestion endpoints hosted on App Service); HLR-5 (Publish/Sync Azure Function)
+- **FRs:** FR-1 (Ingestion Gateway runs on App Service); FR-8 (Publish/Sync Function performs writeback); FR-10 (Azure SQL hosts the persistent queue); FR-13 (Azure Blob hosts raw file archive)
+- **DRs:** DR-1 (Blob archive is part of deployment topology)
+- **QASs:** QAS-4 (staging tables in Azure SQL provide outage buffering)
+- **Constraints:** C-1 (Azure managed services); C-3 / DC-1 (Python App Service); C-7 (single unit chosen over microservices for capstone timeline); DC-3 (Blob Storage archive)
+- **Validation:** VAL-1, VAL-3 (deployed components host the tested behavior)
diff --git a/docs/confluence/adr-0009-implement-human-review-queue-as-database-table.md b/docs/confluence/adr-0009-implement-human-review-queue-as-database-table.md
new file mode 100644
index 0000000..5ef787b
--- /dev/null
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+# ADR-009: Implement the Human Review Queue as a Persistent Database Table
+
+> Source of truth: [`0009-implement-human-review-queue-as-database-table.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0009-implement-human-review-queue-as-database-table.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+When the Routing Engine sends a low-confidence attribute to human review, that attribute must wait until a reviewer at eParts or Alps Controls processes it. Reviewer pace is much slower than machine pace: predictions arrive in batches measured in seconds, while reviewer decisions accumulate over hours or days. The queue must therefore decouple machine throughput from reviewer availability.
+
+Two queue mechanisms were considered:
+
+- **In-memory queue or message broker (e.g., Azure Service Bus).** Standard for high-throughput producer/consumer decoupling. Survives normal load patterns but adds an external dependency, requires a consumer process polling for items, and does not naturally support the spreadsheet-style batch review workflow that catalog staff already use.
+- **Persistent database table in Azure SQL.** The queue is a table with `(submission_id, attribute_id, predicted_value, confidence, reason_codes, status, reviewer_id, decided_at, corrected_value)`. Reviewers query the table through eParts' existing internal review interface, which already speaks SQL.
+
+The catalog team already accesses internal staging tables through a spreadsheet-style tool. Building a custom review UI is out of scope for the current phase. The existing internal interface reads directly from staging tables, which means the queue must be a table accessible from that tool.
+
+The queue must also feed retraining: every reviewer decision is a labeled example, and the audit trail layer relies on durable storage of reviewer corrections.
+
+## Decision
+
+The Human Review Queue is implemented as a persistent table in Azure SQL Database. Low-confidence attributes are inserted with `status = 'pending'`. Reviewers access the table through eParts' existing internal review interface, edit values individually or in batch, and submit decisions by updating the `status` to `'approved'` or `'rejected'` and writing the `corrected_value`. On each decision, a row is appended to the audit trail. A notification is sent to the catalog team when items are pending and again when items are processed.
+
+## Consequences
+
+- Reviewer pace is fully decoupled from prediction pace. The queue can hold thousands of pending items without backpressure on the upstream pipeline.
+- The queue survives App Service restarts and Prediction Service outages. In-flight reviews are preserved across deployments. This directly supports QA-4 (availability).
+- The queue is the persistent store for labeled corrections. The retraining pipeline reads from the audit trail (which captures the history of queue decisions) without coordinating with a separate label store.
+- The schema of the queue table is a coupling point with eParts' existing internal review interface. Any change to column names, types, or status values requires coordination with the eParts engineering team. This is a recorded constraint on schema evolution.
+- Rejected items are not silently dropped. A rejection writes the corrected value back to the queue row with `status = 'rejected'`, appends to the audit trail, and triggers a notification. The corrected value flows into the labeled correction store for retraining.
+- The queue is not a true message broker, so it does not provide push-style notification, dead-letter queues, or consumer load balancing. These features are not needed because there is no automated consumer; the consumer is the catalog team.
+- If a custom review UI is built in a future phase, Auth0 (the eParts identity provider per the SOW) integrates at the UI layer and reads from the same queue table. The queue's stable schema is what makes that future UI buildable without changes to the ingestion, prediction, or writeback components.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-4 (persistent Human Review Queue)
+- **FRs:** FR-4 (queue is the destination for low-confidence attributes); FR-5 (queue retains prediction, confidence, source ref, status); FR-10 (persistent and queryable)
+- **QASs:** QAS-4 (queue survives Prediction Service outages)
+- **Constraints:** C-8, DC-2 (queue's stable schema accommodates a future Auth0-gated UI without changes elsewhere)
+- **Scenarios:** SCEN-2 (Steps 3–5)
+- **Validation:** VAL-2 (item appears in Human Review Queue)
diff --git a/docs/confluence/adr-0010-maintain-append-only-audit-trail.md b/docs/confluence/adr-0010-maintain-append-only-audit-trail.md
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+++ b/docs/confluence/adr-0010-maintain-append-only-audit-trail.md
@@ -0,0 +1,39 @@
+# ADR-010: Maintain an Append-Only Audit Trail of Every Pipeline Decision
+
+> Source of truth: [`0010-maintain-append-only-audit-trail.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0010-maintain-append-only-audit-trail.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+The platform automates a workflow that previously required human judgment at every step. Two needs follow from this:
+
+1. **Compliance and traceability.** When a wrong product attribute reaches PIMS, eParts needs to determine why: which model version produced the prediction, what confidence the model emitted, whether a reviewer saw the item, and what the reviewer's decision was. Without this trail, root cause analysis is impossible.
+2. **Model improvement.** The retraining pipeline (described in the MLOps section of the report) depends on labeled corrections. Reviewer decisions are the primary source of labels. The system must capture the original prediction, the original confidence, the source supplier, and the corrected value as a durable record.
+
+Quality attribute QA-5 (Monitorability) is rated High/High and depends on having a record of every routing and review decision over time so that drift in correction rates can be detected.
+
+A mutable record (overwriting the prediction with the corrected value) would satisfy the immediate writeback need but lose the history needed for audit and retraining. An append-only log preserves both.
+
+## Decision
+
+Every pipeline decision is recorded as a row in an append-only audit trail table in Azure SQL Database. Decisions captured are: auto-accept by the Routing Engine, approval by a reviewer, correction by a reviewer (with the corrected value alongside the original prediction), and rejection by a reviewer. Each row contains the submission ID, attribute ID, source supplier, model version, original predicted value, confidence score, threshold value at decision time, final decision, decided value, decision actor (system or reviewer ID), and timestamp. Rows are never updated or deleted.
+
+## Consequences
+
+- Every value written to PIMS is traceable back to the prediction, the confidence, the threshold, and the reviewer (if any) that produced it.
+- The audit trail is the source of truth for retraining. Corrections where the reviewer's value differed from the model's prediction are flagged as labeled training examples and read by the retraining job (ADR-011).
+- The model version recorded on each row is essential for retraining safety. When a new model version is promoted, the audit trail allows the team to compare correction rates before and after promotion as a check on regression.
+- The audit trail is the basis for drift detection in Datadog (ADR-012). Per-attribute confidence distributions and reviewer correction rates are computed from this table.
+- Append-only growth is unbounded. The table will require a retention policy (cold storage to Azure Blob after some period) once production volumes are observed. This is operationally acceptable in the current phase because volumes are low.
+- The audit trail is internal to the platform. PIMS does not see it. If PIMS needs an audit record alongside a value, the writeback service includes audit metadata in the upsert; the platform's internal audit trail is the canonical record.
+- Reviewer privacy: the reviewer ID is recorded. This is acceptable under eParts' internal policies because the catalog team is salaried staff acting in their official capacity. If the audit trail were ever exposed externally, reviewer IDs would need to be redacted.
+
+## Requirements Traceability
+
+- **FRs:** FR-6 (log every auto-accept, approval, correction, rejection); FR-12 (audit trail backs telemetry signals)
+- **DRs:** DR-2 (Future/TBD — corrected data logged for retraining)
+- **QASs:** QAS-5 (audit trail is the durable source for drift signals)
+- **Scenarios:** SCEN-2 (Step 5 — correction logged)
diff --git a/docs/confluence/adr-0011-trigger-retraining-automatically-on-batch-completion.md b/docs/confluence/adr-0011-trigger-retraining-automatically-on-batch-completion.md
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+++ b/docs/confluence/adr-0011-trigger-retraining-automatically-on-batch-completion.md
@@ -0,0 +1,41 @@
+# ADR-011: Trigger Retraining Automatically on Human Review Batch Completion
+
+> Source of truth: [`0011-trigger-retraining-automatically-on-batch-completion.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0011-trigger-retraining-automatically-on-batch-completion.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Proposed
+
+## Context
+
+The Prediction Service must improve over time as supplier data changes and as the labeled corpus grows. Reviewer corrections are the primary source of labeled examples. The architectural choice is the trigger mechanism that initiates a retraining run.
+
+Three alternatives were considered:
+
+- **Manual trigger.** An engineer reviews the accumulated corrections, judges that enough new examples exist, runs the training script, evaluates the result, and promotes the new model if it improves on the previous version. Requires no automation but depends entirely on engineer availability and judgment. Poor fit for a five-person capstone team that cannot guarantee weekly engineer cycles.
+- **Automatic trigger on review batch completion.** A retraining job fires automatically each time a human review batch is marked complete. The new model version is evaluated against a held-out validation set and promoted only if it outperforms the current version. No engineer initiates the run.
+- **Scheduled trigger.** Retraining runs on a fixed cadence (weekly or monthly) regardless of review activity. Predictable, but introduces a fixed lag between when corrections are made and when the model learns from them. Risks training on too few examples if review activity is light, or accumulating too many examples if review activity is heavy.
+
+In all cases, a validation gate is required: a new model version must outperform the current version on a held-out validation set before it is promoted. Without this gate, automatic retraining could promote regressions silently.
+
+## Decision
+
+Retraining is triggered automatically when a human review batch is marked complete. The retraining job reads all corrections flagged as labeled examples since the last training run from the audit trail (ADR-010), combines them with the existing labeled dataset, and trains a new version of the active prediction strategy. The new version is evaluated against a held-out validation set. If validation accuracy improves, the new version is promoted as the active model behind `PredictionServiceInterface` (ADR-002). If it does not improve, the previous version remains active and the result is logged for engineering review. Model version history is stored in Azure Blob Storage with training date, example count, and validation accuracy as metadata.
+
+## Consequences
+
+- The model learns from corrections as soon as a batch is reviewed, with no engineer in the loop. This is the fastest path from a reviewer correction to an improved model.
+- The validation gate prevents silent regressions. A worse model is never promoted automatically; it is logged for human review.
+- Promotion is transparent to the rest of the pipeline. The Routing Engine, Writeback Service, and Review Queue see the prediction service as unchanged because `PredictionServiceInterface` does not change with model version.
+- Rollback is supported. Each model version is tagged in Azure Blob Storage. If a promoted version is later found to perform poorly on production data, engineering can revert by changing the active model pointer in configuration without redeploying the application.
+- A minimum batch size before triggering retraining is required to avoid training on sparse data. The minimum example count has not been set and will be established once Refinement 1 produces real review-batch sizes. Until then, this decision is Proposed.
+- The validation set must remain representative. If the validation set drifts from production data, the gate becomes meaningless because a model that overfits to stale validation can pass the gate while degrading on real inputs. The validation set itself must be refreshed periodically; this operational discipline is a dependency of the retraining decision.
+- Frequent retraining on small batches can produce unstable model versions even with a validation gate, because validation accuracy itself fluctuates on small evaluation sets. If observed, the trigger should be replaced with a hybrid: scheduled retraining with a minimum-correction-count gate.
+- Engineering team capacity post-handoff may make manual triggering attractive again. A larger team with regular review cycles may want explicit human oversight on every promotion. The retraining package is decoupled enough from the rest of the pipeline that switching to manual triggering is a configuration change.
+
+## Requirements Traceability
+
+- **HLRs:** HLR-3 (prediction quality maintained over time)
+- **DRs:** DR-2 (Future/TBD — corrected data logged for future retraining and offline model improvement)
+- **QASs:** QAS-2 (retraining promotes new versions through PredictionServiceInterface without breaking dependents); QAS-5 (closes the loop from drift detection to model improvement)
+- **Constraints:** C-3, DC-1 (retraining runs in the Python prediction package)
diff --git a/docs/confluence/adr-0012-emit-stage-by-stage-telemetry-to-datadog.md b/docs/confluence/adr-0012-emit-stage-by-stage-telemetry-to-datadog.md
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--- /dev/null
+++ b/docs/confluence/adr-0012-emit-stage-by-stage-telemetry-to-datadog.md
@@ -0,0 +1,45 @@
+# ADR-012: Emit Stage-by-Stage Telemetry to Datadog for Drift Detection and Operational Monitoring
+
+> Source of truth: [`0012-emit-stage-by-stage-telemetry-to-datadog.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0012-emit-stage-by-stage-telemetry-to-datadog.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Proposed
+
+## Context
+
+ML systems can degrade silently as supplier data drifts from the training distribution. Without monitoring, incorrect auto-accepts accumulate in PIMS and surface only when contractors order wrong parts. Quality attribute QA-5 (Monitorability) is rated High/High both in importance (because silent degradation is the worst failure mode) and in difficulty (because the team has not yet defined what metrics to track or what baseline to compare against).
+
+eParts uses Datadog as its observability platform, so integration is mandatory rather than chosen. The architectural questions are: where in the pipeline should telemetry be emitted, what signals should be captured, and how should those signals be tied to drift detection.
+
+Telemetry must not block the pipeline. A Datadog outage cannot be allowed to take ingestion or writeback offline.
+
+## Decision
+
+Telemetry is emitted to Datadog from four pipeline stages over fire-and-forget HTTPS:
+
+- **Ingestion Gateway:** ingestion success and failure counts, parsed by supplier and channel.
+- **Normalization (Structured Layer):** row counts after canonical schema mapping, broken down by supplier and category.
+- **Prediction Service:** per-attribute confidence score distributions and rule-vs-embedding contribution breakdown.
+- **Routing Engine:** routing split ratios (auto-accept vs. review) per attribute.
+- **Review Queue:** reviewer decision counts (approved, corrected, rejected) and correction rates per attribute.
+
+Telemetry calls do not block the pipeline; failed Datadog writes are logged locally and dropped. Operationally significant signals that must not be lost are also persisted in the audit trail (ADR-010), so Datadog is treated as a dashboard and alerting layer, not as the system of record.
+
+Drift detection thresholds (e.g., "alert when correction rate increases by 10% over a rolling two-week window" or "alert when mean confidence shifts by 15%") are defined as configuration on Datadog and validated empirically once Refinement 1 has produced a baseline.
+
+## Consequences
+
+- The pipeline emits the right signals to detect drift. Confidence distributions reveal model overconfidence or underconfidence; correction rates reveal accuracy degradation; routing split ratios reveal threshold drift.
+- Drift detection is operationally complete only when thresholds are defined. The architecture emits the signals; it cannot yet say what deviation from baseline constitutes actionable drift. Refinement 6 in the project plan defines and validates these thresholds against simulated drift.
+- Telemetry is tied to the audit trail. Reviewer correction rates in Datadog are computed from the same decisions recorded in the audit trail, so the dashboard and the system of record cannot diverge.
+- Datadog outages do not affect pipeline correctness. A telemetry failure is logged locally and the pipeline continues. This is acceptable because the audit trail is the source of truth; the dashboard is a derived view.
+- Because telemetry is fire-and-forget, telemetry packets can be lost during a Datadog outage without retry. This means short-term metrics (e.g., a one-hour confidence distribution) may have gaps during incidents. Long-term metrics computed from the audit trail are unaffected.
+- Per-supplier telemetry is captured because supplier-specific drift is a likely failure mode (a supplier changes its catalog format, the model's confidence drops, but the threshold doesn't catch it). Per-supplier dashboards in Datadog allow drift to be localized to the offending supplier.
+- This decision is Proposed rather than Accepted because the alert thresholds and baselines are not yet defined. Once Refinement 1 and Refinement 6 produce values, this decision moves to Accepted.
+
+## Requirements Traceability
+
+- **FRs:** FR-12 (emit confidence distributions, correction rates, routing decisions, pipeline metrics to Datadog)
+- **QASs:** QAS-5 (drift detection from baseline deviation in confidence and correction rates)
+- **Constraints:** C-1 (Datadog runs over HTTPS from Azure App Service)
diff --git a/docs/confluence/adr-0013-establish-etim-reference-data-layer.md b/docs/confluence/adr-0013-establish-etim-reference-data-layer.md
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index 0000000..fb966fc
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+++ b/docs/confluence/adr-0013-establish-etim-reference-data-layer.md
@@ -0,0 +1,45 @@
+# ADR-013: Establish a Release-Versioned ETIM Reference Data Layer Owned by Ingestion
+
+> Source of truth: [`0013-establish-etim-reference-data-layer.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0013-establish-etim-reference-data-layer.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+The platform is adopting ETIM as the classification standard for catalog standardization (valves and actuators in phase one). ETIM is a controlled technical dictionary: product groups (EG), product classes (EC), features (EF), feature groups (EFG), units (EU), and controlled values (EV), plus the mappings that say which features belong to a class and which values are allowed for a class-feature. ETIM is not supplier data — it provides no SKUs, prices, or product documents. Before the platform can match any supplier product to ETIM (class matching, feature matching, value matching, validation), it needs the ETIM dictionary loaded, queryable, and under version control.
+
+The supplied ETIM data has awkward physical characteristics that make it a poor fit for ad-hoc loading: the production archive is a set of CSV files encoded **UTF-16 little-endian, semicolon-delimited**, for a specific release (10.0) and language (EI, English International). ETIM publishes new releases over time, and class/feature/value definitions change between releases, so a single un-versioned copy would silently conflate releases and make historical mappings unauditable.
+
+A key question was **ownership**: the reference loader could sit in the ML/matching component (the primary consumer) or in ingestion (which already owns file parsing, encoding handling, idempotent batch loads, and Alembic migrations). Two further options for storage shape were considered:
+
+- **Denormalized blob / JSON document per class.** Fast to load and to read a whole class, but cannot enforce referential integrity, makes cross-class queries (e.g. "all classes using feature EF000513") expensive, and couples readers to a single release's shape.
+- **Normalized relational tables mirroring the ETIM model**, scoped by release ID. Enforces FKs and composite keys, supports multi-release coexistence, and lets the matcher query class→feature→value relationships directly.
+
+## Decision
+
+We will model ETIM as a **normalized relational reference layer of ten tables**, every row scoped by a release identifier, and we will make the **ingestion team the owner** of both the schema and the import job.
+
+The tables are `etim_release`, `etim_group`, `etim_class`, `etim_class_synonym`, `etim_feature_group`, `etim_feature`, `etim_unit`, `etim_value`, `etim_class_feature`, and `etim_class_feature_value`, with composite primary keys on `(etim_release_id, …)` so that multiple ETIM releases can coexist without collision. The release identifier is a stable, human-readable string of the form `ETIM-{version}-{language}` (e.g. `ETIM-10.0-EI`).
+
+A dedicated **ETIM Reference Loader** import job reads the UTF-16 LE, semicolon-delimited CSV archive, validates that the expected columns are present per file, rejects incomplete or release-mismatched archives, and loads the rows into the reference tables. It records the release version, language, source name, an import timestamp, and a **SHA-256 checksum over the archive**. Re-importing the same release is **idempotent** (no-op when the checksum matches; controlled replace only with an explicit `--force`). The job is exposed as a CLI entry point (`eparts etim import …`) mirroring the existing Typer CLI, and is delivered as Alembic migration `0005_create_etim_reference` plus `etim/loader.py`, `models/etim.py`, and `cli/etim.py`.
+
+This decision is implemented and verified against the real ETIM 10.0 EI archive (EPARTS-285).
+
+## Consequences
+
+- The matcher (ETIM class/feature/value matching) can treat ETIM as a stable, queryable dependency. Loading the dictionary is no longer entangled with matching logic, so the two can evolve independently.
+- Release versioning is first-class. Because every row is keyed by `etim_release_id`, a future ETIM 11.0 can be loaded alongside 10.0, and any product's mapping can name the exact release it was matched against. This is a prerequisite for governed ETIM upgrades (an open client decision in the brief).
+- Idempotent, checksummed import makes the load safe to re-run in CI and across environments without producing duplicates or partial state. A mismatched or truncated archive is rejected with a clear error rather than loaded silently.
+- Placing ownership in ingestion reuses existing strengths (encoding handling, batch idempotency, Alembic, the Typer CLI) and keeps the file-handling concerns in the team that already does file handling. The cost is a coordination point: the matching team consumes a schema that ingestion owns, so reference-table changes require a published contract.
+- The reference layer is read-mostly and modest in size (~160 groups, ~5,600 classes, ~17,000 features, ~16,000 values, ~200,000 class-feature-value links for 10.0 EI). Normalized storage on the current Postgres stack handles this comfortably.
+- ETIM does not supply a client-ready "required field" flag. The reference layer deliberately stores ETIM as published and leaves required/recommended/optional policy to a separate client policy overlay (`catalog_feature_policy`, owned downstream). This ADR does not cover that overlay.
+- The loader currently targets the CSV archive only. The Excel workbook (useful for analyst review and metric/imperial crosswalks) is intentionally out of scope for production import.
+
+## Requirements Traceability
+
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` (ETIM Reference Loader, ETIM Reference Tables, Acceptance Criteria 1)
+- **Tickets:** EPARTS-285 (Create ETIM reference schema and import job — Done); EPARTS-275 (ETIM research); parent EPARTS-154 (Ingestion)
+- **Implements:** ETIM reference schema, release tracking, idempotent import, golden row-count validation
+- **Related ADRs:** ADR-014 (staging split consumes the reference layer for matching); ADR-015 (Postgres-now datastore the tables are built on); ADR-007 (the prior canonical-schema decision this complements)
diff --git a/docs/confluence/adr-0014-emit-source-preserving-product-attribute-staging-split.md b/docs/confluence/adr-0014-emit-source-preserving-product-attribute-staging-split.md
new file mode 100644
index 0000000..15962bd
--- /dev/null
+++ b/docs/confluence/adr-0014-emit-source-preserving-product-attribute-staging-split.md
@@ -0,0 +1,51 @@
+# ADR-014: Emit a Source-Preserving Product + Attribute Staging Split
+
+> Source of truth: [`0014-emit-source-preserving-product-attribute-staging-split.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0014-emit-source-preserving-product-attribute-staging-split.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+ETIM standardization rests on a core principle: **original supplier data is evidence; ETIM data is a standardized interpretation laid on top; confidence is how sure the system is about that interpretation.** For this to hold, ingestion must hand the matching stage data that (a) separates a *product* (the sellable SKU) from its *attributes*, and (b) preserves every original value together with where it came from — file, page, row, raw text, raw unit — so that any later ETIM mapping can be traced back to its source.
+
+Today ingestion emits a single flat `IngestedRecord` (one row per source record, with `raw_fields` as a JSONB bag). That shape preserves source vocabulary but does not express product-vs-attribute granularity, gives attributes no individual identity, and has nowhere to carry per-attribute evidence (source page/row) or per-attribute confidence. It also forces every downstream consumer to re-derive product and attribute structure from an untyped blob.
+
+There is also a real granularity mismatch across sources that the staging shape must absorb: a **CSV row** is naturally one product with many attribute *columns*, whereas a **datasheet PDF** is one product (a SKU) with many attribute *rows* extracted from the document. Both must land in the same canonical staging shape.
+
+Options considered:
+
+- **Keep the flat `IngestedRecord`** and let the matcher split product/attributes from the JSONB bag. Smallest ingestion change, but pushes structure-recovery and evidence-tracking into every consumer, and gives attributes no stable identity for per-attribute routing, confidence, or audit.
+- **One wide staging row per product** with attributes as columns. Convenient for whole-product reads, but cannot carry per-attribute evidence/confidence without parallel columns, and reintroduces schema migration for every new attribute.
+- **A two-table split: `staging_product` + `staging_raw_attribute`** (one product row; one evidence row per attribute). Each attribute row carries its own source evidence and confidence and has a stable identity. This matches the brief's staging model and is the natural input to per-attribute ETIM matching, routing, and audit.
+
+## Decision
+
+Ingestion will emit a **product + attribute split**: a `staging_product` row per sellable SKU and a `staging_raw_attribute` row per attribute, replacing the flat `IngestedRecord` as the output contract.
+
+`staging_product` carries product identity and provenance: `supplier_id`, `supplier_sku`, `manufacturer`, `supplier_category`, `description`, `source_file_id`, `submission_id`, `processing_status`. Product identity for idempotency is `supplier_id + supplier_sku` (per source). `staging_raw_attribute` carries one row of evidence per attribute: `product_id`, `source_attribute_name`, `source_value`, `source_unit`, `source_text`, `source_page`, `source_row_number`, and `source_confidence`. Attribute identity for idempotency is `product_id + source_attribute_name`. Both tables are written with idempotent upserts, batched in one transaction per product, preserving the existing raw-bytes archival and quarantine paths unchanged.
+
+Which source fields populate product identity versus become attribute rows is **declared per source** via `ProductMapping` on the source/parser config (`sku_field`, `manufacturer_field`, `category_field`, `description_field`, `unit_field`, `attribute_fields`, `exclude_fields`), so the CSV-column and PDF-row granularities both resolve to the same staging shape without code changes per source.
+
+Crucially, ingestion **does not interpret** these values into ETIM. No field renaming, no ETIM class/feature/value assignment, no unit conversion happens here — those belong to the ETIM-aware matching stage, which reads staging and writes its results to its own tables (e.g. `matched_product_attribute`). ETIM must never overwrite ingestion's source-preserving output.
+
+The legacy flat `IngestedRecord` path is retired after cutover (deprecate or dual-write during transition; tracked by EPARTS-302). Until a source declares a `ProductMapping`, it continues on the legacy flat path.
+
+## Consequences
+
+- Nothing from the supplier catalog is lost or flattened. Every value is individually addressable and traceable to file/page/row/raw-text, which is the evidence backbone the entire ETIM story depends on.
+- Per-attribute identity makes per-attribute confidence (ADR-005/ADR-004 routing), per-attribute ETIM matching, and per-attribute audit natural — each is keyed on a real attribute row rather than reconstructed from a blob.
+- The product/attribute boundary is configuration, not code. New sources and formats are onboarded by declaring a mapping; the CSV-vs-datasheet granularity difference is absorbed in config.
+- Row counts grow relative to the flat shape (one row per attribute rather than one per record). For valve/actuator products with ~12–40 attributes this is a sizeable multiplier; the current Postgres stack (ADR-015) handles expected volumes, with indexes for product lookups.
+- This **supersedes the staging design in ADR-007** in practice. ADR-007 specified a single tall staging table that also carried prediction/routing columns (`predicted_value`, `confidence_score`, `routing_status`). Under ETIM, ingestion's staging holds only *source evidence*; predicted values, match confidence, validation status, and review status move to a separate matching-owned table. ADR-007's "attribute-row, not wide" instinct is retained and reinforced; its column set and single-table assumption are not.
+- The PIMS writeback contract shifts accordingly. The brief keys PIMS output on `product_id + etim_release_id + etim_class_id + etim_feature_id` rather than `submission_id + attribute_id`; ADR-006's idempotency mechanism needs to be revisited against this (flagged in the ADR assessment, not resolved here).
+- A clean cutover is required to avoid two parallel write paths. The transition (dual-write vs deprecate) and the update to the §6.1 output contract are explicit follow-ups (EPARTS-302).
+- Missing-SKU handling must be defined (quarantine vs synthesized id) — an open item feeding the source mapping config.
+
+## Requirements Traceability
+
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` (Staging Layer; Staging Tables; "Original supplier data = evidence"); `INGESTION_ETIM_TICKET_MAP.md` (ING-E4/E5/E6/E7/E9)
+- **Tickets:** EPARTS-297 (ProductMapping config); EPARTS-298 (staging schema); EPARTS-299 (writer rework); EPARTS-302 (retire flat path); parent EPARTS-154
+- **QASs:** QAS-1 (accuracy — evidence preserved for traceable correction); QAS-3 (new category/attribute as data + config, not schema migration)
+- **Related ADRs:** ADR-007 (superseded in part — see above); ADR-013 (reference layer the staged data is matched against); ADR-015 (datastore); ADR-004/ADR-005 (per-attribute routing/threshold consume attribute identity); ADR-006 (PIMS idempotency to be re-keyed)
diff --git a/docs/confluence/adr-0015-target-postgresql-now-defer-azure-sql.md b/docs/confluence/adr-0015-target-postgresql-now-defer-azure-sql.md
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+# ADR-015: Target PostgreSQL Now; Defer the Azure SQL Conversion
+
+> Source of truth: [`0015-target-postgresql-now-defer-azure-sql.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0015-target-postgresql-now-defer-azure-sql.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+The ETIM implementation brief specifies its schemas in **SQL Server / Azure SQL dialect** (`DATETIME2`, `NVARCHAR(MAX)`, `BIT`), consistent with the original platform design (ADR-008), which placed all internal pipeline state in **Azure SQL Database** and deployed the platform as a single Azure App Service. Several earlier ADRs assume this Azure SQL substrate (ADR-006 PIMS writeback, ADR-007 staging, ADR-008 deployment, ADR-009 review queue, ADR-010 audit trail).
+
+The ingestion service as actually built does not run on Azure SQL. It runs on **PostgreSQL** with SQLAlchemy 2.x + Alembic migrations, uses **JSONB** for semi-structured fields, archives raw bytes to **S3/MinIO**, and is packaged with Docker/`docker-compose` (Postgres + MinIO) rather than App Service. The existing migrations (`0001`–`0005`, including the ETIM reference tables) are all Postgres.
+
+The ETIM schema tickets (reference tables, staging split) were therefore blocked on a datastore question (ING-E0): author the new ETIM and staging tables for Azure SQL to match the brief, or for Postgres to match the running service? Authoring for Azure SQL now would mean building against a database the platform does not yet use, maintaining a dialect the rest of the codebase does not use, and carrying that divergence indefinitely. Authoring for Postgres now keeps the entire ingestion service on one coherent stack and translates the brief's SQL Server DDL to Postgres equivalents.
+
+A migration to Azure SQL is a real future possibility — it is the original target and ties to the broader platform-on-Azure direction (EPARTS-64) — but it is a separate, platform-level effort that is not in flight today.
+
+## Decision
+
+All new ETIM reference tables and staging tables target **PostgreSQL (the current stack) for now**, using Alembic migrations and JSONB where useful, matching the existing ingestion service. The brief's SQL Server DDL is translated to Postgres equivalents: `DATETIME2 → timestamptz`, `NVARCHAR(MAX) → text`, `BIT → boolean`, with JSONB used where a flexible column is warranted.
+
+A later conversion to **Azure SQL is explicitly deferred** to the future move of the wider platform onto Azure, and is treated as a separate effort rather than a constraint on current ETIM work. This decision **unblocks the ETIM schema tickets** (ING-E0 is resolved). It does not retract ADR-008's eventual Azure direction; it records that the *current* substrate is Postgres and that ETIM work builds on Postgres rather than waiting for, or pre-building against, Azure SQL.
+
+## Consequences
+
+- The ingestion service stays on a single coherent persistence stack (Postgres + Alembic + JSONB + S3). New ETIM and staging migrations sit in the same migration chain as everything else, with one dialect to test and operate.
+- The ETIM schema and staging tickets are unblocked and can proceed immediately, which is the critical path for the rest of the ETIM matching work.
+- A divergence is now on record between several existing ADRs (which name Azure SQL / SQL Server) and the running system (Postgres). ADR-008 in particular is now partially stale on the datastore and deployment topology; this is captured in the ADR assessment for whole-platform follow-up rather than silently ignored.
+- A future Azure SQL port is a known, bounded piece of work. It would touch: column-type translation back to the SQL Server dialect, JSONB usage (which has no exact Azure SQL analogue and would need `nvarchar(max)`/JSON functions), Postgres-specific features in use (advisory locks for run-level exclusivity, `ON CONFLICT` upserts), and the migration tooling. Keeping Postgres-specific features behind the storage layer limits the blast radius of that future port.
+- Because the decision is "now vs later" rather than "never," teams should avoid leaning on Postgres-only behavior in business logic above the storage layer, so the deferred port stays a storage-layer concern.
+- PIMS itself remains external and may stay on SQL Server regardless; this ADR governs the platform's *own* internal stores, not the PIMS target (see ADR-006).
+
+## Requirements Traceability
+
+- **Source:** `INGESTION_ETIM_TICKET_MAP.md` (ING-E0 — RESOLVED: "PostgreSQL now; Azure SQL conversion deferred"); `ETIM_IMPLEMENTATION_BRIEF.md` (Data Model — SQL Server DDL, here translated)
+- **Tickets:** EPARTS-285 (built on Postgres migration 0005); EPARTS-298 (staging schema, Postgres); EPARTS-64 (future platform-on-Azure)
+- **Constraints:** C-1 (Azure managed services — eventual direction, deferred); C-7 (capstone operational simplicity — one stack)
+- **Related ADRs:** ADR-008 (revisits its Azure App Service + Azure SQL topology — now partially superseded on substrate); ADR-013 and ADR-014 (the reference and staging tables this decision places on Postgres); ADR-006 (PIMS target datastore, separate)
diff --git a/docs/confluence/adr-0016-decompose-matching-into-staged-etim-class-feature-value-stages.md b/docs/confluence/adr-0016-decompose-matching-into-staged-etim-class-feature-value-stages.md
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+# ADR-016: Decompose Attribute Matching into Staged ETIM Class → Feature → Value/Unit Matching
+
+> Source of truth: [`0016-decompose-matching-into-staged-etim-class-feature-value-stages.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0016-decompose-matching-into-staged-etim-class-feature-value-stages.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+ADR-003 framed the matching problem as a single step: map a raw supplier attribute string onto a canonical attribute value, using a rule engine blended with semantic similarity (`conf_final = α·conf_rule + (1−α)·conf_embed`, α = 0.7). That framing was correct for a free-form canonical vocabulary, where every attribute is independent and there is one decision to make per attribute.
+
+ETIM invalidates the independence assumption. Under ETIM (HLR-6, FR-9) an attribute cannot be matched at all until the product's **class** is known, because the set of legal features is a property of the class: `etim_class_feature` says which features belong to `EC…`, and `etim_class_feature_value` says which values are legal for that class-feature pair. Matching "Torque: 120 Nm" is meaningless without first deciding the product is a valve actuator, and matching it against the wrong class produces a confidently wrong answer rather than a low-confidence one.
+
+The value side is not uniform either. ETIM feature types carry different semantics and different failure modes:
+
+| Type | Meaning | What matching must produce |
+|---|---|---|
+| A | Controlled list value | an `etim_value_id` drawn from the legal set for that class-feature |
+| L | Logical yes/no | a boolean |
+| N | Numeric | a number **plus** a unit, converted to the ETIM-declared unit |
+| R | Numeric range | a min, a max, and a unit |
+
+A single matcher emitting one scalar `predicted_value` with one `confidence_score` cannot express "we are confident this is class EC002714 but unsure whether the torque figure is the rated or the breakaway value," which is exactly the distinction a reviewer needs. It also gives the router a single number where the routing decision now depends on several (see ADR-018).
+
+Two alternatives were considered:
+
+- **Keep one matcher, widen its output.** Emit class, features and values from one model call and one confidence. Cheapest change, but it hides a genuine dependency: a class error silently corrupts every downstream feature match, and there is no place to intervene between the two.
+- **A per-class trained model.** One classifier per ETIM class. 5,640 classes make this untrainable at our data volume, and it would still not solve unit normalization.
+
+## Decision
+
+We will decompose matching into an ordered pipeline of stages, each producing its own evidence and its own confidence:
+
+```
+class matching → feature matching → value matching → unit normalization
+ → ETIM validation → client-policy validation → confidence scoring
+```
+
+Each stage is a filter in the ADR-001 sense, and the whole sequence remains behind the single `PredictionServiceInterface` established in ADR-002 — this decomposition is an interface *enrichment*, not a reversal. `PredictionResult` grows to carry candidate classes with confidences, matched features, matched values with feature-type-appropriate typing, and validation status, in place of a single predicted value.
+
+Class matching consumes class names, class descriptions, `etim_class_synonym` rows, and the correction store; feature and value matching continue to use the ADR-003 hybrid of rules plus semantic similarity over the class-restricted candidate set. **A correction store is consulted before general matching at every stage** so that a reviewer's decision on one product resolves the same mapping for later products without retraining.
+
+Stage outputs land in `matched_product_attribute` — the interpretation table introduced by ADR-014 — which carries the ETIM identifiers, the typed normalized values (`normalized_text_value`, `normalized_numeric_value`, `normalized_range_min`/`max`, `normalized_logical_value`), and per-assignment confidence, alongside a foreign key back to the `staging_raw_attribute` evidence row.
+
+**Implementation status: designed, not built.** The reference layer this depends on is live (ADR-013), and the evidence/interpretation tables exist (ADR-014, Alembic `0006`). The matching stages themselves are owned by the ML stream under EPARTS-289/290/291 and are not yet in the running pipeline; the pipeline currently emits source evidence only.
+
+## Consequences
+
+- Class errors become **visible and interceptable** instead of silently poisoning downstream matches. This is what makes the class-review-first routing path in ADR-018 possible.
+- Confidence attaches **per ETIM assignment** rather than per raw attribute, which is what DR-4 and the PIMS output contract require and what a reviewer needs in order to accept a class while correcting a single feature.
+- Accuracy becomes measurable against a controlled vocabulary rather than against free text: a match is right or wrong against `etim_class_feature_value`, not fuzzily similar to a gold string. This sharpens the golden test set (EPARTS-296) but also makes previously "close enough" answers count as failures, so headline accuracy will drop before it rises.
+- Unit normalization becomes a first-class stage rather than a formatting detail, because type N and R features declare a unit in `etim_class_feature.UNITOFMEASID` and a value in the wrong unit is wrong, not merely unformatted.
+- More stages means more places to fail and more latency per product. The mitigation is that the stages are cheap relative to the OCR/LLM extraction already in the pipeline, and each stage's output is persisted, so a failure late in the chain does not re-run the expensive early work.
+- The α = 0.7 blend and the reconsideration triggers from ADR-003 carry over unchanged to the feature and value stages. ADR-003 is not superseded; it is narrowed in scope from "the matcher" to "two of the matcher's stages."
+- Because the correction store is consulted first, the system's behaviour changes as reviewers work. That is deliberate, but it means matching accuracy is not reproducible from the model alone — the correction store must be snapshotted alongside any benchmark run.
+
+## Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-6 (classify against ETIM and enrich with class/feature/value/unit identifiers); HLR-2 (the intermediate structure this reads from — mechanical cleanup only, no ETIM keying); HLR-3 (predict with confidence scores)
+- **FRs:** FR-9 (match to ETIM classes, features, controlled values/units with per-assignment confidence, preserving the original value); FR-3 (confidence score per predicted attribute)
+- **DRs:** DR-4 (ETIM-keyed PIMS output — consumes the identifiers this ADR produces)
+- **QASs:** QAS-1 Modifiability — a new supplier format changes the parse stage only, not the matching stages
+- **Scenarios:** SCEN-1 step 4 (the ML service matches attributes, then matches them to ETIM class, features and values); SCEN-2 steps 2–3 (per-assignment confidence is what routes the item to review)
+- **Validation:** VAL-5 (class review precedes attribute routing) — added in spec v1.4 as the test for this ADR; **specified, not yet executable**, because these stages are designed and not built. VAL-4 covers the reference layer this ADR reads; its 10 unit tests pass, and its integration half skips without the real archive.
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` — End-to-End Process steps 7–15, ML/AI Attribute Matching, ETIM Feature Types
+- **Tickets:** EPARTS-289 (class matching), EPARTS-290 (feature matching), EPARTS-291 (value/unit matching), EPARTS-296 (golden test set); parent EPARTS-156 (ML)
+- **Related ADRs:** narrows ADR-003 (hybrid rule + semantic similarity) to the feature and value stages; enriches the contract of ADR-002 (`PredictionServiceInterface`); writes into the interpretation table of ADR-014; reads the reference layer of ADR-013; feeds the routing signals of ADR-018 and the policy gate of ADR-019
diff --git a/docs/confluence/adr-0017-rekey-pims-writeback-contract-on-etim-identifiers.md b/docs/confluence/adr-0017-rekey-pims-writeback-contract-on-etim-identifiers.md
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+# ADR-017: Re-key the PIMS Writeback Contract on ETIM Identifiers
+
+> Source of truth: [`0017-rekey-pims-writeback-contract-on-etim-identifiers.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0017-rekey-pims-writeback-contract-on-etim-identifiers.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+ADR-006 established idempotent PIMS writeback via a composite natural key of `submission_id + attribute_id`, upserted rather than inserted, so that a retried write cannot create duplicates. The mechanism was and remains correct.
+
+The key is not. Two things broke it.
+
+**The key no longer identifies the thing being written.** Under ETIM the unit of published data is not "an attribute of a submission" but "the value of a specific ETIM feature, of a specific ETIM class, of a specific product, under a specific ETIM release" (HLR-6, DR-4). `submission_id` is an artefact of *how the data arrived*, not of *what it describes*. The same product arriving twice — a corrected catalogue re-sent by the supplier, or a second file covering the same SKU — produces two submission IDs and therefore two rows for one real-world fact. The upsert would not collide, and PIMS would accumulate duplicates that are invisible to the idempotency check.
+
+**The payload no longer carries enough to be useful downstream.** ADR-006's row was a value plus a confidence. The PIMS output contract now has to carry both the interpretation and the evidence behind it, because the whole point of the standardization objective is that a consumer can compare products across suppliers *and* audit where a value came from.
+
+Alternatives considered:
+
+- **Keep `submission_id + attribute_id`, add ETIM IDs as payload columns.** Minimal change, but leaves the duplicate-on-resubmission defect in place and makes "the current value of feature EF021864 for this product" unanswerable without scanning submissions.
+- **Key on `product_id + etim_class_id + etim_feature_id`, omitting the release.** Simpler, but conflates ETIM releases: a value matched under 10.0 and a value matched under a future 11.0 would collide even though the feature definition may have changed between them. That defeats the release-scoping established in ADR-013.
+
+## Decision
+
+The PIMS writeback natural key becomes:
+
+```
+product_id + etim_release_id + etim_class_id + etim_feature_id
+```
+
+The upsert mechanism from ADR-006 is unchanged — application-layer idempotent upsert through the staging integration, honouring constraint C-2/DC-3 that we do not write directly to production PIMS tables.
+
+The published row carries the interpretation, the evidence, and the provenance together:
+
+| Group | Fields |
+|---|---|
+| ETIM interpretation | `etim_release_id`, `etim_class_id`, `etim_feature_id`, `etim_value_id`, `etim_unit_id`, feature type |
+| Normalized typed value | text / numeric / range-min / range-max / logical, per feature type |
+| Original evidence | original attribute name, original value, original unit, source text reference |
+| Decision metadata | confidence, approval status (auto-accepted or human-approved) |
+
+`submission_id` remains on the row as provenance — it answers "which file did this arrive in" — but it is no longer part of the identity.
+
+Two distinctions this ADR preserves deliberately: PIMS may remain SQL Server even though our own stores are PostgreSQL (ADR-015 governs *our* datastore, not the client's), and the write remains to staging rather than production tables.
+
+**Implementation status: designed, not built.** The identifiers this key depends on are produced by the matching stages of ADR-016, which are not yet in the pipeline. The writer rework is EPARTS-299, on the critical path `285 ‖ (297 → 298 → 299)`.
+
+## Consequences
+
+- Re-sending a corrected catalogue for a product now **updates** the published row instead of appending a second one. This is the defect the old key could not see.
+- "What is the current published value of feature X for product Y under release Z" becomes a primary-key lookup. Cross-supplier comparison and website filtering — the business objective that motivated ETIM adoption — depend on exactly that query being cheap.
+- The release is part of the key for **provenance**: every published value names the ETIM release it was matched under. Under ADR-020 the project is pinned to 10.0 EI, so in practice the field is constant — it is carried so the row is self-describing, and so that un-pinning later would be a change of scope rather than a schema migration.
+- The key requires a stable `product_id`, which requires a resolvable `supplier_sku` per supplier format. **This is an open dependency**: the authoritative SKU field per format, and the behaviour when a record has no extractable SKU (quarantine versus synthesized identifier), are both unresolved. Until they are, products from formats without a clean SKU cannot be published idempotently.
+- Products carrying a feature that ETIM does not define ("ETIM Other") have no `etim_feature_id` and therefore no key. Their handling is an open client decision; they are held out of the published set rather than given a synthetic identifier.
+- The payload is wider than ADR-006's, so PIMS staging rows grow. Given the phase-one valve/actuator scope this is not a capacity concern, and carrying the evidence alongside the interpretation is what makes the published data auditable.
+- ADR-006 is **not edited**. It stands as the record of the April decision and of the upsert mechanism, which this ADR reuses. Where the two disagree on the key, this ADR governs.
+
+## Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-6 (enrich with ETIM identifiers); HLR-5 (write approved data back to PIMS)
+- **FRs:** FR-8 (write attributes to PIMS upon final approval); FR-9 (preserve the original supplier value alongside the ETIM assignment)
+- **DRs:** **DR-4** — *"Approved data written to PIMS shall be keyed by ETIM identifiers (release, class, feature); the writeback idempotency key shall include these identifiers"* — this ADR is the direct realization of DR-4; DR-3 (writeback must be idempotent; retry must not duplicate)
+- **Constraints:** DC-3 (raw files preserved as evidence — the published row references that evidence)
+- **Scenarios:** SCEN-1 step 5 and SCEN-2 step 6 (auto-accepted and human-approved data both take this path)
+- **Validation:** VAL-3 (approve an item; the PIMS write succeeds and a retry does not duplicate — the retry case is now tested against the ETIM key)
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` — PIMS Output Contract, PIMS Sync; `INGESTION_ETIM_PLAN.md` — design decisions
+- **Tickets:** EPARTS-299 (writer rework), EPARTS-295 (PIMS sync); parent EPARTS-154 (Ingestion)
+- **Related ADRs:** supersedes the natural key defined in ADR-006 while reusing its upsert mechanism; consumes the identifiers produced by ADR-016; depends on the release scoping of ADR-013; the datastore distinction is governed by ADR-015
diff --git a/docs/confluence/adr-0018-extend-routing-to-etim-signals-with-class-review-first.md b/docs/confluence/adr-0018-extend-routing-to-etim-signals-with-class-review-first.md
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+# ADR-018: Extend Routing to ETIM Signals, with a Class-Review-First Path
+
+> Source of truth: [`0018-extend-routing-to-etim-signals-with-class-review-first.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0018-extend-routing-to-etim-signals-with-class-review-first.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+ADR-004 established per-attribute routing: each predicted attribute is compared against a configurable confidence threshold (ADR-005), and the attribute — not the whole record — goes to auto-accept or to the human review queue. The granularity decision was right and is unchanged by this ADR.
+
+What changed is that a single confidence-versus-threshold comparison is no longer sufficient to decide whether a value is safe to publish. After ETIM there are several independent ways for an attribute to be unfit, and only one of them is low confidence:
+
+- The **class** may be wrong or contested. Class confidence is a distinct signal from attribute-match confidence, and it dominates: every feature match under a wrong class is wrong, no matter how confident.
+- The value may be confidently matched but **invalid against ETIM** — a type A value not in the legal set for that class-feature, a type N value with no unit, a type R range with min above max.
+- The value may be valid but **fail client policy** — a feature the client marks `required` for this class is missing, which blocks publish regardless of how confident everything else is (ADR-019).
+- **Unit conversion may have failed**, leaving a numerically plausible figure in the wrong unit. This is the most dangerous case: high confidence, valid type, wrong magnitude.
+
+Routing on confidence alone would auto-accept all four of these. The consequence is the one thing the project exists to prevent: wrong product data reaching PIMS, and from there a contractor's field order.
+
+A further problem is ordering. With a flat per-attribute queue, a product whose class is uncertain generates one review item per attribute — dozens of decisions that all become void the moment the reviewer changes the class. Alternatives considered:
+
+- **Route on confidence only, catch validity later at publish time.** Keeps routing simple, but moves the failure to a stage with no human in it, so invalid data either blocks silently or is dropped.
+- **Escalate any invalid attribute to whole-record review.** Safe but wasteful: one bad attribute pulls a hundred good ones into a manual queue, which is precisely the per-record behaviour ADR-004 rejected.
+
+## Decision
+
+Routing keeps its per-attribute granularity and gains a **class-level stage in front of it**.
+
+**Stage 1 — class routing.** If ETIM class confidence is below the class threshold, or the top two candidate classes are within a configured margin of each other, the *product* is routed to class review before any attribute is matched. Attribute matching for that product is deferred until a class is confirmed.
+
+**Stage 2 — attribute routing.** Once the class is settled, each attribute is routed on the full signal set:
+
+| Signal | Effect |
+|---|---|
+| Attribute match confidence below threshold | → review |
+| ETIM validation failure (value not in legal set, missing unit, malformed range) | → review, regardless of confidence |
+| Unit conversion failure | → review, regardless of confidence |
+| Client policy `required` and value missing | → review, and blocks publish for the product |
+| Client policy `not_used` | → not published, not queued |
+| All checks pass and confidence above threshold | → auto-accept |
+
+The rule that governs the combination: **validation and policy failures are not overridden by high confidence.** Confidence answers "did we read it right"; validation answers "is it a legal ETIM value"; policy answers "does the client need it". These are independent questions and a failure in any one routes to a human.
+
+Thresholds are externalized per ADR-005, now generalized to at least two — class-selection confidence and attribute-match confidence — with per-class-feature overrides replacing the per-attribute override table.
+
+**Implementation status: designed, not built.** The signals this routing consumes are produced by the matching stages of ADR-016 (EPARTS-289/290/291), which are not yet in the running pipeline. Routing today evaluates confidence only.
+
+## Consequences
+
+- The highest-leverage failure mode — a confidently wrong unit or an out-of-vocabulary value — is now caught by a deterministic check rather than by hoping the model was unsure. This directly serves the data-integrity driver behind the whole platform.
+- Class-review-first collapses what would have been dozens of void attribute decisions into one class decision. Reviewer throughput (QAS-2, 10 items/minute) is protected by not queuing work that is about to be invalidated.
+- Deferring attribute matching until the class is confirmed introduces a **wait state** in the pipeline: a product can sit unprocessed pending a human class decision. The staging tables (ADR-014) hold that state durably, so nothing is lost, but end-to-end latency for uncertain products is now bounded by reviewer response time rather than by compute.
+- More routing inputs means more ways to be wrong about routing. Each signal must be independently observable in telemetry — class confidence distribution, validation-failure rate, unit-conversion-failure rate, missing-required-field rate — or a regression in one will be invisible inside an aggregate auto-accept rate.
+- Auto-accept rate will fall relative to the ADR-004 baseline, because attributes that previously passed on confidence now also have to pass validation and policy. This is the intended trade: throughput for correctness. The rate should be reported against the pre-ETIM baseline so the drop is not misread as a regression.
+- The policy signal makes routing **dependent on client configuration that does not yet exist** (ADR-019). Until the feature policy is supplied, the policy check defaults to permissive — nothing is treated as required — which means the required-field path is designed but untestable.
+- ADR-004 and ADR-005 are **not edited**. Per-attribute granularity and externalized thresholds are reused as decided; this ADR extends the inputs and adds a preceding stage.
+
+## Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-4 (human review of low-confidence predictions); HLR-6 (ETIM classification and enrichment)
+- **FRs:** FR-4 (route below-threshold items to the review queue); FR-7 (authorized Ops Leads adjust the auto-acceptance threshold); FR-9 (per-ETIM-assignment confidence is the signal being routed on); FR-3 (confidence score per prediction)
+- **QASs:** QAS-2 Usability — class-review-first is what keeps the reviewer at 10 items/minute by not queuing work that a class change would void
+- **Scenarios:** SCEN-2 steps 2–3 (a 0.45-confidence value routes to review; under this ADR it would also route on a validation or unit failure at any confidence)
+- **Validation:** VAL-2 (mock a low-confidence response; the item appears in the review queue) — extended to cover validation-failure and unit-failure routing at high confidence. **VAL-5** (added in spec v1.4) is the specific test for class-review-first: a below-threshold class assignment routes to class review and no attribute-level routing happens for that item. Specified, not yet executable.
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` — Request Router, Human Review, End-to-End Process steps 13–17
+- **Tickets:** EPARTS-289 (class matching and class routing), EPARTS-294 (ETIM-aware review queue); parent EPARTS-156 (ML)
+- **Related ADRs:** extends ADR-004 (per-attribute routing) and ADR-005 (externalized thresholds); consumes the staged outputs of ADR-016; depends on the policy overlay of ADR-019; the review-queue contract it feeds is ADR-009
diff --git a/docs/confluence/adr-0019-externalize-client-feature-policy-as-per-class-configuration.md b/docs/confluence/adr-0019-externalize-client-feature-policy-as-per-class-configuration.md
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--- /dev/null
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@@ -0,0 +1,72 @@
+# ADR-019: Externalize the Client Feature Policy as Per-Class Configuration
+
+> Source of truth: [`0019-externalize-client-feature-policy-as-per-class-configuration.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0019-externalize-client-feature-policy-as-per-class-configuration.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+ETIM tells us which features *exist* for a class. It does not tell us which ones *matter*.
+
+`ETIMARTCLASSFEATUREMAP.csv` — the file that binds features to classes — contains `ARTCLASSFEATURENR`, `ARTCLASSID`, `FEATUREID`, `FEATURETYPE`, `UNITOFMEASID`, `SORTNR`. It contains no `required`, no `mandatory`, no `blocks_publish`, no `used_for_compare`. This is not an oversight in the export; ETIM is a shared industry dictionary and requiredness is a property of a particular catalogue's editorial standards, not of the standard.
+
+The consequence is concrete and blocking. A valve class may define 60 features. A supplier datasheet may supply 12 of them. Whether that product is publishable depends entirely on which of the 60 the client considers required — and nobody has told us. Until someone does:
+
+- **"What blocks publish?" is unanswerable**, so firm validation requirements cannot be written.
+- The routing rule in ADR-018 that sends missing-required-features to review has no data to evaluate.
+- The reviewer UI cannot distinguish "this field is empty and that is fine" from "this field is empty and the product cannot ship."
+
+This is currently the project's most significant requirements risk, and it is owned by the client, not by us. Two open tickets (EPARTS-286 class scope, EPARTS-287 feature policy) are blocked on it.
+
+The architectural question is what to do in the meantime. Alternatives considered:
+
+- **Wait for the policy, then design around it.** Leaves the validation and routing paths unbuilt and the critical path idle on an external dependency with no committed date.
+- **Hard-code a provisional policy** from our own reading of the valve datasheets. Fast, and wrong in a way that is expensive to detect: the system would enforce a standard nobody agreed to, and the resulting review queue would reflect our guesses rather than the client's requirements.
+- **Derive requiredness statistically** — treat a feature as required if most suppliers populate it. Tempting, but it encodes current supplier behaviour as the target standard, which inverts the business objective. The client adopted ETIM precisely because current supplier coverage is inadequate.
+
+## Decision
+
+The feature policy is modelled as a **client-owned configuration overlay, external to the ETIM reference layer**, keyed per client, release, class and feature:
+
+```
+catalog_feature_policy(client_id, etim_release_id, etim_class_id, etim_feature_id)
+ → requirement_level ∈ { required, recommended, optional, conditional, not_used }
+ blocks_publish, used_for_compare, used_for_filter, display_order, condition_rule
+```
+
+Three properties of this decision matter more than the schema:
+
+**It is an overlay, not an edit.** ETIM reference tables (ADR-013) store the standard exactly as published. Policy lives in its own table and joins on the ETIM keys. Policy revisions do not require reloading ETIM, and the standard's own structure is never edited to record a client preference.
+
+**It is data, not code.** Changing requiredness for a class is a configuration change reviewed by the policy owner, not a deployment. Given that the client has not yet decided and will revise once they see real review volumes, requiredness must be cheap to change.
+
+**The default is permissive and explicit.** Absent a policy row, a feature is treated as `optional` and nothing blocks publish. The system does not guess. Where a policy is absent and a value is missing, the product publishes with the gap recorded, rather than silently enforcing an invented standard.
+
+The decision also creates a role that did not exist in the v1.0 baseline: a **feature-policy owner** on the client side who declares the levels and signs off on changes.
+
+**Implementation status: the seam is decided; the values are pending.** The overlay's position in the architecture and its consumption by routing (ADR-018) and by the reviewer UI are settled. The policy content is an open client decision (EPARTS-287) and the table is not yet populated.
+
+## Consequences
+
+- The architecture stops being blocked on a client decision. Routing, validation and the reviewer UI can be built against the overlay's contract and exercised with a synthetic policy, then switched to the real one when it arrives.
+- The **required-field path is designed but untestable end-to-end** until a real policy exists. Tests can prove that a `required` row routes correctly; they cannot prove the right features are marked required. This gap should be stated rather than papered over — a green test suite here does not mean the validation requirement is satisfied.
+- Because policy is per-client, a second client with different editorial standards is a data addition rather than a code change. That is well beyond phase-one scope and is not being built for, but the key shape does not preclude it.
+- `conditional` requires a rule language (`condition_rule`), and no rule language has been chosen. Conditional features are therefore accepted into the schema but not evaluated; they behave as `optional` until a rule evaluator exists. This is a known deferral, not an oversight.
+- `used_for_compare` and `used_for_filter` are carried in the schema because the Compare Tool and website filter are the stated business motivation for ETIM adoption, but both consumers are **out of phase-one scope**. Storing the flags now avoids a migration later; populating them is deferred.
+- Every policy change silently changes routing behaviour. Policy revisions must be versioned and correlated with review-queue volume, or an unexplained spike in the queue will be indistinguishable from a model regression.
+- The permissive default means that until the policy lands, **no product will ever be blocked for a missing required field**. Auto-accept rates measured before the policy is populated are therefore optimistic and must not be quoted as steady-state figures.
+
+## Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-6 (ETIM classification and enrichment); HLR-4 (human review of items needing attention)
+- **FRs:** FR-9 (ETIM matching — policy validation gates what a match is sufficient for); FR-4 (routing to review); FR-7 (authorized adjustment of auto-acceptance behaviour, of which policy is now part)
+- **Constraints:** C-3 (breadth-first delivery — a full end-to-end flow for one supplier type before optimizing depth; a permissive default is what allows the flow to complete)
+- **QASs:** QAS-3 Modifiability (client feature policy) — added in spec v1.4 specifically to hold this decision: a policy change is configuration, applied to the next batch without a code deployment
+- **Validation:** VAL-2 (routing) — the required-field branch is designed here and **cannot be validated until the policy is supplied**; this is a known open item, not a satisfied requirement
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md` — Important ETIM Limitation, Client Policy Tables
+- **Tickets:** EPARTS-287 (feature policy — **blocked on client**), EPARTS-286 (phase-one class scope — **blocked on client**), EPARTS-294 (review UI consumes the policy)
+- **Open client decisions this ADR holds a place for:** feature policy per class; required-field publish blockers; Compare Tool and website-filter feature sets; mapping and policy sign-off ownership
+- **Related ADRs:** deliberately kept out of the reference layer of ADR-013; supplies the policy signals routed on in ADR-018; the validation stage that consumes it is part of ADR-016; the reviewer contract that displays it is ADR-009
diff --git a/docs/confluence/adr-0020-pin-etim-release-10-0-for-the-project-duration.md b/docs/confluence/adr-0020-pin-etim-release-10-0-for-the-project-duration.md
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--- /dev/null
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+# ADR-020: Pin ETIM Release 10.0 (EI) for the Project Duration
+
+> Source of truth: [`0020-pin-etim-release-10-0-for-the-project-duration.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0020-pin-etim-release-10-0-for-the-project-duration.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+ETIM is an external standard with its own release cadence. We loaded **ETIM 10.0, language EI**. There will be an 11.0, and between releases classes are added, features are added and deprecated, values are withdrawn, and a class's feature set changes shape.
+
+That raised a question the v1.0 baseline had no equivalent of: what does the platform do when the standard moves underneath it? Two things made it pressing. Requirements written against "the ETIM standard" are implicitly written against a specific release, so the traceability chain from HLR-6 through FR-9 to a published PIMS row is only meaningful if the release is part of the record. And an unmanaged upgrade silently reinterprets historical data — a value that was legal under 10.0 can be invalid under 11.0, and either the row breaks or, worse, it stays and nobody knows which release's rules it satisfies.
+
+Three options were considered.
+
+- **Build a governed upgrade path now.** Load each new release alongside the old one, diff them, re-match affected products through a review queue, and reconcile the client's feature policy against the diff before cutover. Architecturally clean, and it makes upgrades visible rather than silent. But it is a substantial amount of work — a diff report, a bulk re-match path, a second review queue — for an event that will not occur inside this project. It also could not be finished: who authorizes an upgrade, on what trigger, and what happens to already-published rows are client decisions nobody has made.
+- **Leave the question open.** Say nothing and handle a future release when it arrives. Rejected because "unspecified" is not the same as "out of scope". FR-10 as originally worded — maintain the dictionary as *versioned* reference data — implies an obligation we were not going to meet, and an assessor or a future maintainer would reasonably read it as a commitment.
+- **Pin the release explicitly and put the upgrade path out of scope.** Chosen.
+
+## Decision
+
+**The platform targets ETIM release 10.0, language EI, for the duration of this project.** Adopting later ETIM releases, and migrating already-classified products between releases, are **out of scope**.
+
+This is recorded as **constraint C-4**, introduced in Product Specification **v1.2**, and FR-10 is scoped to "the pinned ETIM release identified in C-4" rather than to versioned reference data generally.
+
+The **release-scoping mechanism in the schema stays exactly as it is.** Every ETIM reference row carries `etim_release_id`, with composite primary keys on `(etim_release_id, …)` across all ten tables (ADR-013); the release is carried through `matched_product_attribute` (ADR-014) and forms part of the PIMS writeback key (ADR-017). Under a pin that field is constant in practice, and we are keeping it for two reasons:
+
+1. **Provenance.** Every published value names the release it was matched under. "This value was matched against ETIM 10.0 EI, on this date, under this policy" stays recoverable from the row alone, which is what makes the audit trail meaningful later.
+2. **It costs nothing.** The columns and keys are already built and tested. Removing them to reflect the pin would be work that buys no capability and discards the provenance.
+
+So this ADR narrows the *forward-looking justification* in ADR-013 — release-scoping is no longer defended as a step toward governed upgrades — without changing a line of the schema it describes. ADR-013 is not edited.
+
+If the client later asks for a new ETIM release, that is a **change request against C-4**, and the first option above is the shape the work would take. It is not a gap to be quietly filled.
+
+## Consequences
+
+- The project stops carrying an obligation it was never going to discharge. FR-10 is now satisfiable and testable as written: load and maintain one named release.
+- No diff report, no bulk re-match path, no second review queue, and no upgrade-governance owner to chase. This is the largest piece of scope the decision removes, and it removes it in the phase where the critical path is `285 ‖ (297 → 298 → 299)`.
+- **We are deliberately accepting that the catalog will go stale** relative to ETIM. If the client's suppliers begin publishing against 11.0 while we classify against 10.0, new classes and features are simply unavailable to us, and products needing them fall to "ETIM Other" handling or to review. For a phase-one valve/actuator pilot that is acceptable. For a production catalogue with a multi-year life it would not be, and this ADR should be revisited before any such transition.
+- Provenance is preserved without the machinery. Because `etim_release_id` remains in the reference tables, the interpretation table and the PIMS key, a future un-pinning is a change of scope rather than a schema migration. The door is left open at zero cost.
+- **The loader keeps its release-mismatch rejection.** It validates that an archive matches the declared release and refuses a mismatched or truncated one (ADR-013). Under a pin that check becomes more valuable, not less — it is what stops an 11.0 archive being loaded into a 10.0-pinned system by accident.
+- The `etim_release_id` field will look redundant to anyone reading the schema without this ADR. That is the cost of keeping it, and this ADR is the answer.
+- One open client decision is closed. "ETIM release-upgrade governance" comes off the blocked list, taking the open-decision count from six to five.
+
+## Requirements Traceability
+
+- **Spec:** Product Specification **v1.4** (29 July 2026); C-4 was introduced in v1.2 (28 July) — this ADR is the reason for that version
+- **Constraints:** **C-4** (ETIM Release Pinned) — this ADR is the decision C-4 records
+- **HLRs:** HLR-6 (classify against the ETIM standard — this ADR fixes *which* ETIM)
+- **FRs:** **FR-10** (load and maintain the ETIM reference dictionary for the pinned release); FR-9 (matching is always against release 10.0 EI)
+- **DRs:** DR-4 (the release remains part of the PIMS writeback key, so publication stays release-explicit)
+- **QASs:** QAS-1 Modifiability — un-pinning would be a scope change, not a structural change to the pipeline
+- **Constraints (supporting):** C-1 (cost-effective design — the upgrade path is the expensive option and is deliberately not built); C-3 (breadth-first delivery — one supplier type end to end before adding depth)
+- **Source:** `ETIM_IMPLEMENTATION_BRIEF.md`; `ETIM-ADR-ASSESSMENT.md` raised this as *"Standard evolution (currency): ETIM releases (10.0 → next); upgrade governance undefined"* — this ADR resolves that item by scoping it out rather than by building for it
+- **Closes:** the open client decision "ETIM release-upgrade governance"
+- **Related ADRs:** narrows the forward-looking rationale of **ADR-013** (release-scoped reference layer) without editing it; the release remains in **ADR-014**'s interpretation table and **ADR-017**'s writeback key for provenance; **ADR-019**'s policy overlay no longer needs reconciling against a release diff
diff --git a/docs/confluence/adr-0021-formalize-ingestion-to-ml-boundary-as-frozen-extracted-input-record.md b/docs/confluence/adr-0021-formalize-ingestion-to-ml-boundary-as-frozen-extracted-input-record.md
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+++ b/docs/confluence/adr-0021-formalize-ingestion-to-ml-boundary-as-frozen-extracted-input-record.md
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+# ADR-021: Formalize the Ingestion → ML Boundary as a Frozen `ExtractedInput` Record
+
+> Source of truth: [`0021-formalize-ingestion-to-ml-boundary-as-frozen-extracted-input-record.md`](https://github.com/AshrithaG/eparts/blob/main/docs/0021-formalize-ingestion-to-ml-boundary-as-frozen-extracted-input-record.md) in the eparts repo. This page is a copy for reading; edit the repo, not this page.
+
+## Status
+
+Accepted
+
+## Context
+
+ADR-001 established pipe-and-filter as the platform's style, with filters communicating through typed data channels. In practice the ingestion→matching channel was the weakest of them: ingestion parsed a supplier file into a `RawRecord` and the matching stream read whatever fields happened to be there. Adequate while both sides were one team and one process; untenable now.
+
+Three pressures forced the boundary to become explicit.
+
+**It is a cross-team contract.** Ingestion (EPARTS-154) and ML matching (EPARTS-156) are separate streams with separate backlogs. The ETIM requirements-change record names this contract as one of two places where our traceability deliberately stops — we own the requirement, another stream owns the implementation. A trace boundary that is not a schema boundary is not a boundary at all.
+
+**Ingestion must not leak interpretation.** ADR-014 established the principle that supplier data is *evidence* and ETIM is a *standardized interpretation* over it. If ingestion hands the matcher a confidence score or a ranked list of candidate attribute names, it has already begun interpreting, and the evidence/interpretation split becomes a convention rather than a property of the system. The temptation is real: the OCR path (Azure Document Intelligence plus an LLM extraction) *has* per-field confidences available, and passing them along would be a one-line change.
+
+**Source provenance differs by channel and matters downstream.** A value read from a CSV cell, a value read from a text-native PDF, and a value read from OCR over a scanned page carry different reliability, and the matcher and the reviewer both need to know which they are looking at. A generic dictionary of fields loses that.
+
+Alternatives considered:
+
+- **Keep passing `RawRecord`.** Zero work, and it makes every ingestion-side refactor a potential silent break for the ML stream, because nothing declares what the ML stream is entitled to rely on.
+- **Put the boundary behind an HTTP service now.** Genuinely the right long-term shape, and premature: it adds deployment, retry and tracing surface for a boundary that currently runs in one process. ADR-008's single-deployable-unit decision still holds; what this ADR fixes is the *contract*, not the *topology*.
+- **Document the contract in prose only.** The 460-line handoff specification already exists. Documentation that is not enforced drifts, and this contract's whole value is that it cannot drift.
+
+## Decision
+
+The ingestion→ML boundary is a **single, versioned, schema-frozen record type**, `ExtractedInput`, specified in `docs/extraction_handoff_spec.md` and enforced in code:
+
+| Field | Meaning |
+|---|---|
+| `source_type` | one of `csv`, `email`, `pdf_text`, `pdf_ocr`, `image` — the channel, so the consumer knows what kind of evidence this is |
+| `text` | the extracted text; required, and an empty string is valid |
+| `structured_fields` | the parsed field/value pairs as the supplier wrote them |
+| `normalized_units` | mechanical unit normalization only, as `(value, unit)` pairs |
+| `source_ref` | pointer back to the archived raw artefact |
+
+Two properties do the real work.
+
+**The schema forbids interpretation by construction.** The Pydantic model is declared `extra="forbid"` and `frozen=True`. Confidence scores, ranked alternates, predicted ETIM classes — anything that constitutes an interpretation — *cannot be represented*, so they cannot cross the boundary by accident. The evidence/interpretation split of ADR-014 is enforced by the type system rather than by reviewer vigilance.
+
+**The record is persisted, not just passed.** `extracted_inputs` (Alembic `0007`) stores each handoff record, which turns the boundary into a durable checkpoint: the matching stream can be down, restarted, or re-run against the same inputs without re-doing OCR, and a matching bug can be diagnosed against exactly the input that produced it.
+
+Cleaning (spec §3) and unit normalization (spec §4) are injectable seams on the ingestion side of the boundary. This keeps mechanical tidying — whitespace, encoding, unit spelling — with the party that knows the source format, while leaving anything requiring domain judgement to the matcher.
+
+**Implementation status: built and merged.** `handoff/spec_model.py`, `handoff/builder.py`, `models/extracted_input.py` and migration `0007` are on the main line (EPARTS-357, EPARTS-358). The cleaning and unit implementations (EPARTS-359, EPARTS-362) and the provenance split between `pdf_text` and `pdf_ocr` (EPARTS-361) are on open branches; on the main line those seams are pass-throughs. Wiring the builder into the orchestrator is EPARTS-363 and is not yet done, so the record type exists and is validated but is not yet produced on every run.
+
+## Consequences
+
+- The two streams can move independently. Ingestion can change parsers, add a channel, or swap the OCR engine without coordinating, so long as the record still validates. The ML stream has a written, enforced statement of what it may rely on.
+- **`extra="forbid"` will reject rather than ignore** an ingestion-side addition. That is the intended behaviour — it makes contract changes loud — but it means adding a field is a deliberate, two-team, spec-versioning act, not a convenience. Expect this to feel obstructive at least once; that is the cost being paid on purpose.
+- Persisting the record makes matching **replayable**. Re-running the matcher over stored `extracted_inputs` costs nothing in Azure Document Intelligence or LLM calls, which materially changes the economics of iterating on the matching stages of ADR-016.
+- This turns ADR-001's in-process function call into an explicit asynchronous seam, and it is consequently **the leading candidate for extraction into a service** if the deployment topology of ADR-008 is ever revisited. Nothing about the contract assumes co-location.
+- The boundary is a queue-shaped thing without a queue. Delivery today is a table plus a poll; the transactional outbox and circuit breaker planned under EPARTS-301 are not built. Until they are, there is no delivery guarantee beyond "the row is committed" — adequate, because the row *is* the durable state, but not the same as at-least-once delivery to a live consumer.
+- The record carries no confidence, which means the matcher cannot preferentially trust a high-confidence OCR field over a low-confidence one. This is a deliberate loss of information: OCR confidence measures character recognition, not semantic correctness, and treating it as the latter is the mistake the split exists to prevent. If the matcher later needs a reliability signal, it should come from `source_type` and from measured per-channel accuracy, not from the OCR engine's self-report.
+- Because the builder is not yet wired into the orchestrator, the contract is currently **enforced but unexercised in production flow**. The unit tests validate the shape; no end-to-end run has yet produced a record. This should not be described as a working boundary until EPARTS-363 lands.
+
+## Requirements Traceability
+
+- **Spec:** Product Specification v1.4 (29 July 2026)
+- **HLRs:** HLR-2 (normalize into a standardized intermediate structure preserving original supplier values as evidence); HLR-1 (ingest from diverse supplier sources — `source_type` enumerates the channels); HLR-3 (the ML service that consumes this record)
+- **FRs:** FR-1 (ingestion record with supplier, timestamp, source channel); FR-2 (validation before processing — an invalid handoff record is a validation failure, not a silent pass); FR-9 (matching consumes this record)
+- **DRs:** DR-1 (raw file archived as evidence — `source_ref` is the pointer to it)
+- **QASs:** QAS-1 Modifiability — a new supplier format is a new `source_type` and a new parser; the boundary and everything downstream of it are unchanged
+- **Constraints:** DC-1 (Python backend); DC-3 (raw files preserved for re-processing and traceability — replayability depends on this)
+- **Scenarios:** SCEN-1 step 3 and SCEN-2 step 1 (both scenarios cross this boundary; SCEN-2's OCR path is `pdf_ocr`)
+- **Source:** `docs/extraction_handoff_spec.md` (§1 channels, §2 record shape, §3 cleaning, §4 unit normalization, §5 structured fields, §6 per-channel examples)
+- **Tickets:** EPARTS-357 (schema + migration `0007` — Done), EPARTS-358 (builder + spec model — Done), EPARTS-359 (units), EPARTS-361 (pdf_text/pdf_ocr provenance), EPARTS-362 (text cleaning), EPARTS-363 (orchestrator wiring — **not done**), EPARTS-301 (transactional outbox — not built); contract boundary between EPARTS-154 (Ingestion) and EPARTS-156 (ML)
+- **Related ADRs:** makes explicit the filter boundary of ADR-001; enforces the evidence/interpretation split of ADR-014; feeds the matching stages of ADR-016; does not alter the single-deployable-unit topology of ADR-008, but is the natural extraction point if that is revisited
diff --git a/docs/data_stores.md b/docs/data_stores.md
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+++ b/docs/data_stores.md
@@ -0,0 +1,144 @@
+# Data Stores — What Lives Where and Why
+
+The SES uses **9 SQLite databases + 1 ChromaDB vector store**. Each exists because it solves a specific storage problem that the others don't.
+
+---
+
+## 1. shared_memory.db (618 KB) — The Project Wiki
+
+**What it is:** A namespaced key-value store where every agent deposits structured knowledge. Any agent can read any namespace.
+
+**Why it exists:** Without this, each agent starts from zero. With it, the transcript parser's output from January is available to the drift detector in April. Knowledge accumulates rather than being discarded after each run.
+
+| Namespace | Entries | What's stored |
+|-----------|---------|---------------|
+| `requirements_engineering` | 34 | Pipeline run results, parsed meeting data |
+| `coach_session_memory` | 20 | Extracted coach session summaries |
+| `requirements` | 9 | Formal REQ-XXX definitions with category, priority |
+| `latest_runs` | 9 | Most recent output per agent (quick lookup) |
+| `architecture` | 5 | Drift reports, architectural style, quality attributes |
+| `commitments` | 4 | Coach commitments with owners and deadlines |
+| `meetings` | 3 | Meeting metadata: date, type, participant count |
+| `concerns` | 1 | Recurring concerns flagged across sessions |
+
+Every write is logged in a `wiki_log` audit table — who wrote it, when, which agent, which pipeline.
+
+---
+
+## 2. events.db (90 KB) — The Event Bus
+
+**What it is:** A publish-subscribe event store. Agents emit events; subscribed agents trigger automatically.
+
+**Why it exists:** This is how pipelines talk to each other without hard-coded dependencies. The requirements pipeline doesn't call the architecture pipeline directly — it emits `decision_logged` and the architecture pipeline subscribes.
+
+| Event Type | Count | What triggers it |
+|------------|-------|------------------|
+| `action_items_extracted` | 24 | transcript_parser finishes a meeting |
+| `decision_logged` | 24 | transcript_parser or decision_logger finds a decision |
+| `new_session_embedded` | 4 | session_memory embeds a coach meeting into ChromaDB |
+| `recurring_concern` | 3 | concern_tracker detects a topic appearing in 3+ sessions |
+| `requirements_extracted` | 3 | req_extractor produces formal requirements |
+
+10 active subscriptions route these events to downstream agents.
+
+---
+
+## 3. traceability.db (311 KB) — Unified Traceability Store
+
+**What it is:** A graph of artifacts and their relationships. Every concern, decision, requirement, risk, Jira ticket, and PR is an artifact. Links describe how they relate: RAISED_IN, BECAME, IMPLEMENTS, MITIGATES, etc.
+
+**Why it exists:** The rubric requires traceability. More importantly, when a professor asks "where did this requirement come from?", we can trace it back to a specific speaker in a specific meeting.
+
+| Artifact Type | Count | Examples |
+|---------------|-------|---------|
+| `jira_ticket` | 50 | EPARTS-42, EPARTS-82, etc. |
+| `action_item` | 41 | Items extracted from 5 client meetings |
+| `commitment` | 31 | Promises made to coaches |
+| `risk` | 16 | From architecture doc, coach sessions |
+| `concern` | 12 | Recurring themes across meetings |
+| `requirement` | 12 | REQ-001 through REQ-012 |
+| `decision` | 10 | Architecture and process decisions |
+| `architecture` | 6 | ADRs and architecture components |
+| `meeting` | 5 | 5 client meetings |
+| `coach_session` | 1 | Coach sessions as a collective source |
+
+764 links across 7 link types. Zero orphaned concerns. Zero unmitigated risks.
+
+All links are created via domain-aware keyword matching — zero LLM tokens.
+
+---
+
+## 4. risk_register.db (24 KB) — Risk Register
+
+**What it is:** Every identified risk with severity, likelihood, impact, mitigation, status, and owner.
+
+**Why it exists:** Risks were scattered across meeting notes, coach feedback, and architecture documents. This consolidates them into one queryable store with proper risk statements.
+
+20 risks total: 2 critical, 7 high, 7 medium, 3 team/health risks.
+
+---
+
+## 5. prompt_registry.db (53 KB) — Prompt Governance
+
+**What it is:** Version-controlled store for all LLM prompts used by agents.
+
+**Why it exists:** Without this, 5 team members use 5 different prompts for the same task. Results become non-reproducible. The registry pins each agent to a specific, peer-reviewed prompt version.
+
+| Prompt | Versions | Author | Active Version |
+|--------|----------|--------|----------------|
+| `transcript_parser` | 2 | Ashritha | v3277a42a |
+| `priority_classifier` | 1 | Ashritha | v5677a0b9 |
+| `req_extractor` | 1 | Ashritha | vb8b387c7 |
+| `session_extraction` | 1 | Ashritha | v763d8a27 |
+| `briefing_generator` | 1 | Ashritha | ve304cdc6 |
+
+Each version has a content hash. If the prompt file changes, a new version is automatically registered. The agent always uses the active version, not whatever's in the file.
+
+---
+
+## 6. coach_sessions.db (32 KB) — Coach Session Memory
+
+**What it is:** Structured records of coach/mentor meetings — extracted topics, commitments, concerns, and links to ChromaDB chunks.
+
+**Why it exists:** Coach sessions contain critical project guidance. This makes them queryable (e.g., "what did Christian say about measurement?") rather than locked in .vtt files.
+
+---
+
+## 7. ml_decisions.db (20 KB) — ML Decision Log
+
+**What it is:** Tracks open ML decisions (model selection, threshold calibration, data strategy) with evidence accumulation and readiness scoring.
+
+**Why it exists:** ML decisions need evidence from multiple sources before they're ready to close. This tracks the evidence trail — POC results, benchmark numbers, coach feedback — per decision.
+
+---
+
+## 8. artifact_versions.db (40 KB) — Artifact Versioning
+
+**What it is:** Version history for key documents: requirements, architecture, risk register, ADRs.
+
+**Why it exists:** The presentation needs to show document evolution. "The requirements document went through 5 versions — here's what changed each time and what triggered the change."
+
+6 artifacts tracked, 14 total versions recorded.
+
+---
+
+## 9. metrics.db (inside MetricsCollector) — Agent Performance
+
+**What it is:** Every agent run is recorded: duration, success/failure, LLM calls, tokens, cost, errors.
+
+**Why it exists:** Without this, "AI helped us" is a vibe. With this, "AI processed 183 tasks at 94.5% success rate for $0.07 total" is evidence.
+
+---
+
+## 10. ChromaDB (memory/chroma/) — Vector Store for RAG
+
+**What it is:** Local vector database using ONNX MiniLM-L6-v2 embeddings. Stores document chunks for semantic retrieval.
+
+**Why it exists:** Agents need relevant context from large documents without stuffing everything into the prompt. ChromaDB retrieves the top-K most similar chunks for any query.
+
+**Collections:**
+- `coach_sessions` — 371 embedded chunks from 4 coach/mentor meetings
+- `architecture` — Chunks from eParts_architecture_report.md
+- `project_docs` — Chunks from project overview, meta-model framework, risk doc
+
+All embeddings run locally (ONNX) — no API cost for indexing.
diff --git a/docs/defect_management.md b/docs/defect_management.md
new file mode 100644
index 0000000..6523ef3
--- /dev/null
+++ b/docs/defect_management.md
@@ -0,0 +1,125 @@
+# Defect Management — eParts / Pimsie Supreme
+
+**Status:** Adopted 2026-07-20 · **Owner:** QA practice (Jai) · **Board:** Jira `EPARTS`
+**Companion docs:** Quality Plan (Draft 7), `docs/etvx_manifest.yaml`, `Metamodel_framework.md`
+
+The Quality Plan defines *what must be true* (QA-1…QA-7) and *which tests prove
+it* (T-x.y). This document defines what happens when something is **found
+broken anyway**: one managed loop from discovery to closure, with metrics that
+tell us whether quality is improving — instead of ad-hoc Slack messages and
+memory.
+
+Design constraints: zero new tools (Jira only), low ceremony (labels over
+custom fields), and every number derivable from a JQL query so the metrics
+have provenance.
+
+---
+
+## 1. The defect record
+
+Every defect is a Jira **Bug** in `EPARTS`, classified on four axes at triage:
+
+| Axis | Where it lives | Values |
+|---|---|---|
+| **Severity** | Jira Priority field | see §2 |
+| **Stage found** | label | `found-spec` · `found-build` · `found-review` · `found-ci` · `found-integrated` · `found-client` |
+| **Root cause** | label | `rc-logic` · `rc-data` · `rc-interface` · `rc-config` · `rc-requirements` · `rc-env` · `rc-prompt` |
+| **Found by** | label | `by-test` · `by-ci` · `by-human-review` · `by-ai-review` · `by-client` |
+
+Plus: **component/module** (the Quality Plan §3 module it belongs to, as a
+label, e.g. `mod-prediction`, `mod-routing`), and a **link** to the
+requirement or QA goal it threatens (Jira "relates to" REQ ticket, or
+`qa-goal-N` label).
+
+`rc-prompt` is the AI-era root-cause class the classic taxonomies (IEEE
+1044-style) don't have: the code was fine, the model was fine — the *prompt or
+context* produced the wrong artifact. Tracking it separately tells us whether
+our prompt regression suite (golden tests) is earning its keep.
+
+**Bug description template:**
+
+```
+**Observed:** what happened (paste CI log / review finding / screenshot)
+**Expected:** what should have happened
+**Repro:** steps or failing test name; "not reproduced" is allowed at intake
+**Threatens:** QA-goal / requirement / module
+**Source:** link to CI run, PR comment, or meeting where it surfaced
+```
+
+## 2. Severity scale
+
+| Priority | Meaning | Response norm |
+|---|---|---|
+| **S1 · Highest** | Wrong data could reach staging/PIMS, or main is broken (red CI on main) | Drop current work; fix or revert same day |
+| **S2 · High** | A QA goal (QA-1…7) is violated but contained; a milestone is blocked | Fix within the current 7-day tick |
+| **S3 · Medium** | Functional defect with a workaround; quality-plan test failing on a branch | Schedule into next tick |
+| **S4 · Low** | Cosmetic, docs, style, non-blocking tooling | Backlog; batch up |
+
+## 3. Intake rules — when a Bug MUST be created
+
+1. **Red CI on `main`/`master`** → S1 Bug, `found-ci`, `by-ci`, same day.
+ (Red CI on a PR branch is normal work, not a defect — unless it reveals a
+ pre-existing problem, then file it `found-build`.)
+2. **A PR review finding that is real but not fixed in that PR** → Bug at
+ triage severity, `found-review`, `by-human-review` or `by-ai-review`.
+ Findings fixed inside the same PR are *not* ticketed (the PR record is the
+ audit trail) — no ceremony for things already handled.
+3. **A quality-plan test (T-x.y) that fails after previously passing** → Bug,
+ linked to the module and QA goal, `found-integrated` if on main.
+4. **Client- or mentor-reported problem** → Bug, `found-client`, `by-client`,
+ linked to the meeting minutes where it was raised.
+5. **A generated SES artifact rejected at human review** (e.g. minutes PR
+ needed correction) → *not* a Bug by default; it's a correction counted by
+ the artifact-quality measurement. File a Bug only when the cause is
+ systematic (`rc-prompt` — the prompt/pipeline needs a fix, not the output).
+
+## 4. Lifecycle
+
+`Open → Triaged → In Progress → In Review → Done` (Jira board columns), with
+two rules: an S1/S2 may not sit in `Open` past its next standup, and `Done`
+requires the fix merged **and** a regression guard where feasible (test added
+or golden case extended) — the G2 pattern: every escaped defect leaves a
+tripwire behind.
+
+Triage happens at standup (5 min): confirm severity, add the four labels,
+link the requirement, assign. The `/defect-triage` skill (see
+`skills/defect-triage/SKILL.md`) drafts all of this from a pasted CI log or
+review finding; the human confirms — T2 review tier applied to our own
+process.
+
+## 5. Measurements (all JQL-derivable — provenance built in)
+
+| Metric | Definition | Question it answers (GQM) |
+|---|---|---|
+| **Escape rate** | % of defects `found-integrated` or `found-client` vs. all defects | Are our gates catching problems early? (goal: trend ↓) |
+| **MTTR by severity** | mean (resolved − created) per priority | Do we respond proportionally to risk? |
+| **Found-by mix** | share of `by-ci` / `by-test` / `by-ai-review` / `by-human-review` / `by-client` | Which detection resources earn their cost? |
+| **Root-cause Pareto** | count by `rc-*` label per month | Where should prevention effort go next? (input to tick retro) |
+| **Defect density by module** | open+closed Bugs per `mod-*` label | Does testing effort match the Quality Plan §5 HARA ranking? |
+| **Reopen rate** | % of Done Bugs reopened | Are fixes real? |
+
+Leading indicators: found-by mix and stage-found distribution (early-stage
+finds predict fewer escapes). Lagging: escape rate, reopen rate. Reviewed at
+every tick retro; a rising escape rate or a root-cause class exceeding 40% of
+a month's defects triggers a process change, not just more fixing.
+
+## 6. Metamodel mapping
+
+- **Process:** defect triage (ETVX: *Entry* — intake rule fires; *Task* —
+ classify on four axes + link; *Verification* — human confirms AI-drafted
+ triage; *eXit* — labeled Bug on board).
+- **Artifact:** the Bug ticket, schema in §1 — a defined, measurable artifact.
+- **Resources:** CI (deterministic detector), test suites, AI reviewer +
+ `/defect-triage` skill (assist), humans (judgment + approval).
+- **Measurements:** §5 — measuring the *process* (MTTR, escape rate), the
+ *artifacts* (density, reopen), and the *resources* (found-by mix).
+
+## 7. Bootstrap plan
+
+1. Create the label set + Bug template in Jira (15 min, one person).
+2. Backfill the defects we already know about (the ML-repo bugs found and
+ fixed this summer — e.g. the G2 online-feedback wiring defect, the CI
+ coverage-gate failure) so the board reflects reality, honestly dated.
+3. Install `/defect-triage` for all five team members.
+4. First metrics read-out at the next tick retro; expect small numbers — the
+ point is the loop existing, not big-company volume.
diff --git a/docs/eParts_Risk_Register_v2.md b/docs/eParts_Risk_Register_v2.md
new file mode 100644
index 0000000..d985b55
--- /dev/null
+++ b/docs/eParts_Risk_Register_v2.md
@@ -0,0 +1,232 @@
+# eParts — Project Risk Register (v2.0)
+
+
+| | |
+| -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Document** | eParts Project Risk Register |
+| **Version** | 2.0 |
+| **Last Updated** | 2026-04-28 |
+| **Owner of this document** | Jaivardhan Singh— **Risk Manager** (also QA / Process Lead) |
+| **Project Lead** | Jaivardhan Singh |
+| **Canonical location** | GitHub repo `pimsie-supreme/eparts` → `/docs/risk-register.md`, mirrored to Confluence (`PIMSIE / Risk Register`). **Not** the Risk Manager's laptop. |
+| **Review cadence** | Weekly during the mentor meeting (first agenda item after Jira board). Monthly deeper review with the client. Critical-status changes communicated to all stakeholders within 24 h. |
+| **Supersedes** | `risk_register.md` (auto-generated v1) and `eParts_Risk_and_Project_Management.md` (Risk Management section). The Project Management / Roles / Lifecycle sections of the latter remain authoritative. |
+
+
+---
+
+## 1 — Why this revision exists
+
+The previous register (auto-generated from `risk_register.db`) and the earlier risk doc were flagged by mentors with the following gaps. This version is structured around fixing each one:
+
+
+| Mentor pushback | How this version responds |
+| ------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| "These read like topic labels, not risk statements." | Every entry is rewritten in **Condition → Consequence** form. |
+| "Where did this risk come from?" | Every entry has a **Source** (meeting / artifact / requirement) and a **Date Identified**. |
+| "Almost all your risks are technical." | Added **Team / Personnel**, **Process / Methodology**, **Business / Stakeholder**, **Compliance** categories. |
+| "Tracking a deadline is a trigger, not a mitigation." | Every **Mitigation** is written to either *reduce likelihood* or *reduce impact*; deadlines/thresholds moved to the **Trigger / Leading Indicator** column. |
+| "All your highs aren't the same kind of high." | Severity is decomposed into explicit **Likelihood × Impact**, and the Top-5 section explains *why* each high-severity risk is high. |
+| "Has the risk occurred? Has it been mitigated?" | **Status** ∈ {Open, Mitigating, Mitigated, Occurred → Issue, Closed} on every entry, with a **History** column. |
+| "If it's already happened, it's an issue, not a risk." | New **Issues Log** (Section 6) — separate from the register. The injured-teammate case lives there; the underlying ongoing risk lives in the register as R-019. |
+| "Every risk needs a name attached." | **Owner** column populated on every entry. Risk Manager role formally assigned (Ashritha). |
+| "Push it off your laptop." | This file lives in Git and is mirrored to Confluence; the SOW (Section 7) now points here. |
+| "Show priorities, not the whole list." | Section 4 surfaces the **Top 5** with reasoning. |
+| "How do you know your mitigations work?" | Section 7 defines **process-quality metrics** for the risk-management process itself. |
+
+
+---
+
+## 2 — Definitions
+
+**Likelihood** (probability the risk is realized within the project horizon — May 2026 → Dec 11, 2026):
+
+
+| Level | Probability |
+| ------ | ----------- |
+| High | > 60 % |
+| Medium | 20 – 60 % |
+| Low | < 20 % |
+
+
+**Impact** (effect on scope, schedule, quality, or stakeholder trust if realized):
+
+
+| Level | Effect |
+| ------ | --------------------------------------------------------------------------- |
+| High | Misses a CRIT milestone, requires re-baselining, or breaks a SOW commitment |
+| Medium | Slips a non-CRIT milestone or forces a sprint of rework |
+| Low | Absorbed within a sprint with no milestone impact |
+
+
+**Severity** = Likelihood × Impact, capped at the higher dimension:
+
+
+| | Impact: Low | Impact: Medium | Impact: High |
+| ---------------------- | ----------- | -------------- | ------------ |
+| **Likelihood: High** | Medium | High | **Critical** |
+| **Likelihood: Medium** | Low | Medium | High |
+| **Likelihood: Low** | Low | Low | Medium |
+
+
+**Time Horizon:** Near-term (≤ end of Spring), Medium-term (Summer bridge), Long-term (Fall delivery).
+
+**Status:**
+
+- *Open* — identified, no mitigation in flight
+- *Mitigating* — mitigation actively being executed
+- *Mitigated* — mitigation complete; residual risk acceptable
+- *Occurred → Issue* — risk realized; tracked in Issues Log
+- *Closed* — no longer applicable; reason recorded in History
+
+**Risk vs. Issue** — A risk is a *possible* future event. The moment it happens, it becomes an **issue** and moves to the Issues Log. The underlying risk (e.g., "a team member may become unavailable") stays open in the register because it can re-occur.
+
+---
+
+## 3 — Roles in the risk process
+
+
+| Role | Person | Responsibility |
+| ------------------------------------ | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Risk Manager** (defined SDLC role) | Ashritha Gonuguntla | Owns this register; runs weekly review; validates every AI-extracted entry against its source within 5 business days; reports to Project Lead. |
+| Project Lead | Jaivardhan Singh | Final accountability; client- and mentor-facing communication on Critical risks. |
+| Risk Owner (per row) | See **Owner** column | Drives the mitigation, updates Status, records History. |
+| AI / SES contribution | Weekly Digest Agent | Generates a *candidate* risk update; the Risk Manager accepts/edits/rejects. AI never writes to the register without human review. |
+
+
+---
+
+## 4 — Top 5 priority risks (review these first)
+
+These are the risks the team and mentors should look at every week. They are all high-severity, but each is high for a *different* reason — that distinction is the point.
+
+
+| # | Risk | Severity | Why it's at the top |
+| --- | ----------------------------------------------------------------------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| 1 | **R-001** Confidence threshold miscalibration | Critical | Direct hit to the product's primary value proposition (auto-accept vs. human review) and to the Vertical Slice 1 demo. |
+| 2 | **R-021** Adopting an unproven agentic SDLC may not deliver as expected | High | Methodological — it threatens the *evaluation* of the SES, which is what the studio is grading. Fails differently from a product risk: silent quality decay over weeks. |
+| 3 | **R-019** Team-member temporary unavailability | High | Team-of-five; *every* historical capstone team has hit this. Already partially realized (see I-001), so probability is empirically ≥ 1 over the project horizon. |
+| 4 | **R-023** Client decision-maker availability | High | Stakeholder-driven — the team cannot directly control it, so it requires the strongest *process* mitigation (async decision packets) rather than a technical fix. |
+| 5 | **R-009** Integration dependency on Jake (PIMS schema, P1-C) | High | External dependency on a single individual outside the team; failure mode is hard-blocked, not slow-degrading. |
+
+
+Each Top-5 entry has a fully populated row in Section 5; review the **Trigger** column when scanning weekly.
+
+---
+
+## 5 — Risk Register
+
+Columns follow the layout the mentors specified.
+
+> **Reading note:** Wide table. Each row also has a longer narrative entry below for the Top-5 and for any risk currently in *Mitigating* status. For brevity, narrative is omitted on Low/Medium risks unless something has changed in the last review.
+
+### 5.1 — Register table
+
+
+| ID | Risk Statement (Condition → Consequence) | Category | Date Identified | Source | Owner | L | I | Sev | Horizon | Trigger / Leading Indicator | Mitigation (reduces likelihood and/or impact) | Status | Notes |
+| ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------- | --------------- | ------------------------------------------------------------------------ | ------------------- | --- | --- | ---------------------------------------------------------------------------------------- | ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------- | ----------------------------------------------------------------------------------------------------------------- |
+| R-001 | Given the team is calibrating the auto-accept confidence threshold against an initially small labeled set, there is a possibility that the threshold ships miscalibrated, causing either too many low-confidence records to auto-accept (data defects downstream) or too many high-confidence records to route to humans (review backlog). | Technical | 2026-03-12 | Architecture working session; SES-extracted from Refinement-1 discussion | Zheliang Liu | H | H | **Critical** | Near | ECE > 0.05 on holdout; auto-accept rate drifts > 5 pp from target on rolling 200-item window | **Reduces impact:** validate threshold against ≥ 200 labeled submissions co-reviewed with Brian/Dewey before any production routing. **Reduces likelihood:** recalibrate every iteration on a held-out set; threshold change requires Data Lead + Risk Manager sign-off. | Open | Refinement-1 plan added 2026-04-22; threshold not yet locked. |
+| R-002 | Given ML development depends on labeled production data from eParts and historical delivery slipped to ~2026-02-22, there is a possibility that further delays push experimentation past the Vertical Slice 1 milestone, blocking end-to-end MVP. | Schedule / Data | 2026-01-22 | Status meeting 2026-02-15; Risk 2 of revised risk doc | Jaivardhan Singh | M | H | High | Near | No additional labeled batch within 5 business days of request; client weekly meeting cancelled twice consecutively | **Reduces impact:** continue pipeline work on the Feb-22 batch + scrape-derived synthetic data so model dev is never fully blocked. **Reduces likelihood:** escalate weekly using "need by date X to avoid impact Y" framing rather than "we are blocked." | Mitigating | 2026-02-22 first batch received → downgraded Critical → High. Closure pending full delivery. |
+| R-003 | Given fewer than 200 labeled examples are currently available, there is a possibility that ML model training underperforms baselines, causing low confidence and excess human-review routing. | Data | 2026-02-15 | Data/ML standup | Zheliang Liu | M | H | High | Near | First training run F1 below manual baseline | **Reduces likelihood:** active labeling sprints with Brian/Dewey for 50 items/week; **reduces impact:** synthetic augmentation from public spec sheets; fall back to semantic-matcher (all-MiniLM) for low-data attributes. | Open | Linked to R-001 via training-set size. |
+| R-004 | Given the PIMS staging schema for P1-C is not yet finalized by eParts, there is a possibility that the canonical schema mapping is incompatible at integration time, causing rework of the ingestion writer. | Technical / Integration | 2026-03-05 | Integration sync with Jake (eParts) | Hrishikesh Bhardwaj | M | H | High | Near | P1-C schema not delivered by sprint-end; column types diverge from current canonical mapping | **Reduces impact:** team-owned buffer table as fallback write target; **reduces likelihood:** Refinement-4 maps P1-C columns to canonical schema and freezes it with Jake's sign-off. | Open | Coupled to R-009. |
+| R-005 | Given the eParts catalog team has finite review capacity, there is a possibility that a tighter confidence threshold pushes review volume above what the team can absorb, causing a backlog and slowing time-to-publish. | Business | 2026-03-12 | Brian's capacity comment in 2026-03-12 client meeting | Jaivardhan Singh | M | H | High | Medium | Measured review queue depth grows for 2 consecutive weeks | **Reduces likelihood:** measure actual review volume in prototype before threshold lock; **reduces impact:** prioritize review queue by SKU velocity so business-critical items clear first. | Open | |
+| R-006 | Given drift-detection metrics and baselines are not yet defined, there is a possibility that production model drift goes undetected, causing silent quality decay. | Measurement | 2026-03-19 | Architecture review | Zheliang Liu | M | H | High | Medium | No drift dashboard in place by end of Phase 4 | **Reduces likelihood:** define baseline metrics (precision, recall, calibration) before prototype ships; **reduces impact:** SES measurement system streams alerts when any metric crosses ±2σ. | Open | |
+| R-007 | Given AI-effectiveness measurement is itself novel, there is a possibility that the chosen metrics (token cost, latency, human-rejection rate) do not actually predict the quality of SES output, causing the team to optimize the wrong thing. | Measurement / Process | 2026-04-02 | SES design review | Ashritha Gonuguntla | M | H | High | Medium | Two consecutive sprints where metric direction conflicts with mentor/client qualitative feedback | **Reduces likelihood:** triangulate every quantitative metric against a small qualitative review (5 artifacts/sprint); **reduces impact:** publish metric-validity caveats with each report so decisions are not over-anchored on a single number. | Open | This is the meta-risk that R-021 also touches. |
+| R-008 | Given three candidate models (BERT, DistilBERT, semantic-matcher) are still under evaluation, there is a possibility that the team locks in a model that does not meet the performance/cost baseline, causing late-cycle re-platforming. | Technical | 2026-02-26 | Risk 3 of revised risk doc; Data/ML standup | Zheliang Liu | M | M | Medium → **promoted to High** for Top-5 review because re-platforming late would cascade | Near | Bake-off results miss precision/recall baseline by > 10 % | **Reduces likelihood:** ADR-1 hybrid approach with explicit switch triggers; **reduces impact:** all candidates share the same I/O contract so swap is config-only. | Open | |
+| R-009 | Given the integration to PIMS depends on a single individual at eParts (Jake) for schema confirmation, there is a possibility that his unavailability blocks the integration milestone for an indeterminate period. | Dependency / Personnel (external) | 2026-03-05 | Integration sync 2026-03-05 | Jaivardhan Singh | M | H | High | Near | Jake unreachable for > 5 business days; schema PR sits unreviewed | **Reduces impact:** team-owned buffer table + documented assumed-schema mode lets the team progress without him. **Reduces likelihood:** request a back-up POC from eParts in writing; mention in next steering call. | Open | Cross-listed with R-004. |
+| R-010 | Given the hybrid scoring uses an alpha weight that has not been tuned, there is a possibility that small alpha changes flip routing decisions, causing unstable behavior. | Technical | 2026-04-09 | Refinement-3 plan | Zheliang Liu | M | M | Medium | Medium | Sweep result shows precision swings > 5 pp across alpha 0.4–0.7 | **Reduces likelihood:** Refinement-3 alpha sweep 0.3–0.9 with ECE/precision/coverage measured. | Open | |
+| R-011 | Given attributes may be correlated, there is a possibility that per-attribute routing double-counts evidence, causing miscalibrated confidence at the record level. | Technical | 2026-04-09 | Refinement-2 plan | Zheliang Liu | M | M | Medium | Medium | Pairwise MI > threshold on ≥ 2 attribute pairs | **Reduces likelihood:** Refinement-2 mutual-information analysis on labeled data; group correlated attributes before routing. | Open | |
+| R-012 | Given the human-review interface design has not been chosen, there is a possibility that reviewers find the UI slower than the existing manual process, causing rejection of HITL adoption. | UX | 2026-04-09 | Refinement-5 plan | Ashritha Gonuguntla | M | M | Medium | Medium | Reviewer time-per-item in pilot > current manual baseline | **Reduces likelihood:** Refinement-5 — show 30 sample items to Brian/Dewey, A/B two layouts; **reduces impact:** keep CSV export path so reviewers can fall back. | Open | |
+| R-013 | Given the SOW defines a strict MVP but stakeholders generate ideas continuously, there is a possibility that informal scope additions are absorbed without re-baselining, causing timeline erosion. | Scope / Business | 2026-03-30 | SOW v0.2 review | Jaivardhan Singh | M | M | Medium | Project-long | Two unbudgeted "small" requests accepted in one sprint | **Reduces likelihood:** SOW change-control script ("great idea — let us scope the impact"); **reduces impact:** every scope add must be paired with a drop or an explicit extension request. | Open | |
+| R-014 | Given the client is locked into Azure with Bicep (no Terraform, no non-Azure stacks), there is a possibility that a chosen tool (e.g., a non-Azure vector DB) is incompatible, causing late re-platforming. | Infrastructure | 2026-03-30 | "Azure Environment Lock-in" constraint | Arjun R Nair | L | H | Medium | Near | Architecture review flags a non-Azure-native dependency | **Reduces likelihood:** Architecture Lead screens every external dependency against the Azure constraint before adoption; single App Service deployment chosen to minimize Azure surface. | Mitigating | |
+| R-015 | Given the project ends 2026-12-11, there is a possibility that scope and unforeseen rework consume the buffer, causing a late-stage compression. | Schedule | 2026-04-15 | SOW timeline | Jaivardhan Singh | M | M | Medium | Long | Burn-up trails plan by > 1 sprint at any phase gate | **Reduces likelihood:** Vertical-Slice approach delivers usable system early; **reduces impact:** documentation/hardening lives in VS-2 so a slip there does not break the demo. | Open | |
+| R-016 | Given many decisions historically lived only in chat or memory, there is a possibility that knowledge is lost on member rotation, causing rework. | Process | 2026-04-02 | SES rollout discussion | Ashritha Gonuguntla | L | M | Low | Project-long | Any "we already decided this" moment that can't be cited from an artifact | **Reduces likelihood:** SES auto-captures decisions, action items, ADRs; weekly digest surfaces undocumented decisions. | Mitigating | |
+| R-017 | Given the team initially lacked Cursor access, there was a possibility that AI-assisted coding workflows were blocked, causing slower development. | Tooling | 2026-02-05 | Risk 1 of revised risk doc | Jaivardhan Singh | — | — | — | Near | (resolved) | Interim: used team's own LLMs without proprietary data; client troubleshooting calls. | **Mitigated** | Closed 2026-03 once access was granted. Kept in register for traceability. |
+| R-018 | Given product-schema changes are infrequent but possible and can affect 30–40 % of data, there is a possibility that a significant change forces manual retraining, causing a multi-sprint disruption. | Data / Technical | 2026-03-30 | Risk 4 of revised risk doc | Zheliang Liu | L | H | Medium | Long | Schema change announcement from eParts | **Reduces impact:** Semantic Matcher (all-MiniLM) handles attribute-name drift without retrain; **reduces likelihood:** quarterly check-in with Jake on schema roadmap. | Open | |
+| R-019 | Given the team has only five members across two semesters with a methodology change in summer, there is a possibility that a member becomes temporarily unavailable (illness, injury, academic conflict), causing role concentration and milestone slip. | Team / Personnel | 2026-04-23 | Mentor meeting feedback (this revision) | Jaivardhan Singh | H | M | High | Project-long | Any member misses ≥ 2 standups in a sprint; PR throughput on owned area drops > 30 % | **Reduces impact:** every leadership role has a documented secondary; SES preserves context so a stand-in can ramp quickly; Project Lead rotates each mini-semester so leadership is already shared. | Open | I-001 is the current realized instance; R-019 stays open because it can recur. |
+| R-020 | Given the project spans two semesters, there is a possibility that a member leaves the program entirely, causing permanent loss of role coverage. | Team / Personnel | 2026-04-23 | Mentor meeting feedback | Jaivardhan Singh | L | H | Medium | Long | A member raises departure intent in 1:1 | **Reduces likelihood:** bi-weekly 1:1s between Project Lead and each member to surface friction early; **reduces impact:** cross-training plan + SES knowledge capture so role can be re-assigned. | Open | |
+| R-021 | Given the team is adopting an unproven agentic SES as the primary methodology and plans to switch process in summer with no maturity in the new approach, there is a possibility that SES underperforms (artifact quality decay, hallucinated risks/requirements, slow cycle time), causing rework and missed milestones. | Process / Methodology | 2026-04-23 | Mentor meeting feedback (this is the mentors' exemplar risk) | Ashritha Gonuguntla | M | H | High | Project-long | Human-rejection rate of AI-drafted artifacts > 30 % in a sprint; weekly-digest accuracy < 80 %; any missed milestone attributable to SES output | **Reduces impact:** continuously measure SES effectiveness; when measurements degrade, **fall back to more human intervention until the system is improved.** **Reduces likelihood:** require human sign-off on every SES-generated artifact before it enters Approved state. | Open | This is the mentors' exemplar mitigation pattern — measure, then revert to humans on degradation. |
+| R-022 | Given the team currently runs Kanban and plans to switch process in the summer with no prior experience in the new approach, there is a possibility that velocity drops and quality gates are missed during the transition. | Process / Methodology | 2026-04-23 | Mentor meeting feedback | Ashritha Gonuguntla | M | M | Medium | Medium | Sprint velocity drops > 20 % in the transition sprint | **Reduces likelihood:** 1-sprint pilot of the new approach in late spring to surface gaps; **reduces impact:** keep Kanban board live during the transition as a documented rollback path. | Open | |
+| R-023 | Given key client decisions (threshold sign-off, schema review, HITL UX) require Brian and Dewey, there is a possibility that their availability slips during eParts' busy season, causing approval-gated tasks to stall. | Business / Stakeholder | 2026-04-15 | Client meeting cadence review | Jaivardhan Singh | M | H | High | Project-long | Two consecutive Thursday standing meetings cancelled; no decision returned within 5 business days of an async packet | **Reduces likelihood:** standing Thursday meeting locked; pre-circulate decision packets 48 h ahead so async sign-off is possible. **Reduces impact:** maintain a "decisions-needed" queue so the team works other items in parallel; escalate to mentors after 10-day gap. | Open | Cross-listed with R-005, R-009. |
+| R-024 | Given the SOW defines a strict MVP but stakeholders generate ideas continuously, there is a possibility that scope creep is absorbed verbally without a paper trail, causing disputes at delivery. | Business / Stakeholder | 2026-04-15 | SOW review | Jaivardhan Singh | M | M | Medium | Project-long | Any decision made in a meeting not reflected in Confluence within 48 h | **Reduces likelihood:** every client meeting produces minutes with a "Decisions" section, AI-drafted then human-reviewed; **reduces impact:** SOW change-control process formally requires a Change Request artifact for any scope delta. | Open | Sibling of R-013 — focused on the *stakeholder communication* failure mode rather than the *team's* failure mode. |
+| R-025 | Given the SOW prohibits uploading proprietary client data to non-approved AI services and the team uses LLM tooling extensively, there is a possibility that a member inadvertently pastes a sample row, schema, or PDF into an unapproved tool, causing a confidentiality incident. | Compliance / Security | 2026-04-23 | Mentor meeting feedback | Hrishikesh Bhardwaj | L | H | Medium | Project-long | Any AI-tool usage of client data flagged in pre-commit check or self-report | **Reduces likelihood:** pre-commit hook scans for data-shaped strings; quarterly reminder in standup; default to Cursor (approved) for any client-data-adjacent prompt. **Reduces impact:** documented same-day disclosure protocol to the client. | Open | |
+| R-026 | Given many register entries are AI-extracted from meeting transcripts and the team historically did not review the register on a recurring basis, there is a possibility that the register contains hallucinated or stale risks while real risks go un-tracked, causing the SES to look unreliable. | Process / Measurement | 2026-04-23 | Mentor meeting feedback | Ashritha Gonuguntla | M | H | High | Project-long | > 1 register entry cannot be traced to a source artifact; any mentor question "where did this risk come from?" that the team cannot answer in < 1 minute | **Reduces likelihood:** every AI-extracted entry must cite a source artifact in the **Source** column; Risk Manager validates new entries within 5 business days. **Reduces impact:** weekly-digest agent surfaces top-5 risks for human review so stale/missing risks are caught. | Mitigating | This revision (v2.0) is itself the first execution of the mitigation. |
+
+
+### 5.2 — Status snapshot
+
+
+| Status | Count |
+| ------------------ | ----- |
+| Critical | 1 |
+| High | 11 |
+| Medium | 11 |
+| Low | 2 |
+| Mitigated / Closed | 1 |
+| Total active | 25 |
+
+
+Category coverage (was: ~13 of 16 technical in v1):
+
+
+| Category | v1 count | v2 count |
+| ------------------------- | ------------------------- | ---------------- |
+| Technical | 9 | 7 |
+| Data | 0 (folded into technical) | 2 |
+| Schedule | 2 | 2 |
+| Business / Stakeholder | 1 | 3 |
+| **Team / Personnel** | 0 | **2** (new) |
+| **Process / Methodology** | 1 | **4** (expanded) |
+| Measurement | 1 | 2 |
+| Dependency | 1 | 1 |
+| Scope | 1 | 1 |
+| Infrastructure | 1 | 1 |
+| UX | 1 | 1 |
+| Tooling | 0 | 1 |
+| **Compliance / Security** | 0 | **1** (new) |
+
+
+---
+
+## 6 — Issues Log (already-occurred events — not risks)
+
+Per mentor guidance: *"if it has already happened, it is an issue, not a risk."*
+
+
+| ID | Description | Date Occurred | Underlying Risk | Owner | Impact | Mitigation in flight | Status |
+| ----- | -------------------------------------------------------------------------- | ---------------------------------- | -------------------------------------------- | ---------------- | -------------------------------------------------------------------------- | ------------------------------------------------------------------------- | ---------------------------------------------------------- |
+| I-001 | A team member sustained an injury and is temporarily reduced-availability. | 2026-04 (TBC from meeting minutes) | R-019 (team-member temporary unavailability) | Jaivardhan Singh | Owned tasks for current sprint reassigned; QA Lead absorbed extra reviews. | Cross-training session held 2026-04-25; SES used to recover task context. | Active — closing once member returns to full availability. |
+
+
+R-019 stays **open** in the register because the underlying risk can recur for any member; closing I-001 does not retire the risk.
+
+---
+
+## 7 — Process-quality metrics (measuring the risk *process* itself)
+
+The mentors specifically asked: *"how do you know these are real risks, and that your mitigations actually work?"* The Risk Manager tracks these every sprint and reports in the weekly digest.
+
+
+| Metric | Target | Why it matters |
+| -------------------------------------------------- | ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------- |
+| % of register entries with a citable Source | 100 % | Defends against the "where did this come from?" question. |
+| Median age of an Open risk without status update | ≤ 14 days | Detects "set and forget" register rot. |
+| % of entries reviewed in the weekly mentor meeting | 100 % of Critical, ≥ 1 rotating High/Medium | Forces the recurring review the mentors asked for. |
+| Mitigation-effectiveness rate | ≥ 70 % of *Mitigating* risks downgrade severity within their planned horizon | Validates that mitigations actually change probability or impact. |
+| AI-extracted-entry rejection rate | Tracked, no fixed target | Inputs to R-021 / R-026 — if rejection rate spikes, the SES needs tuning. |
+| Risk → Issue conversion rate | Tracked | Calibrates whether the team is systematically over- or under-rating likelihood. |
+
+
+---
+
+## 8 — Change history of this register
+
+
+| Date | Author | Change |
+| ---------- | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| 2026-04-28 | Ashritha Gonuguntla (Risk Manager) | v2.0 — Full restructure per mentor feedback. Rewrote all entries in Condition → Consequence form; added Owner / Trigger / Source / Date Identified / History columns; broadened categories (Team/Personnel, Process/Methodology, Business/Stakeholder, Compliance); separated Issues Log; added process-quality metrics; moved canonical location off the Risk Manager's laptop into Git + Confluence. |
+| 2026-04-23 | (auto-generated v1) | Generated `risk_register.md` from `risk_register.db`. Flagged by mentors as topic labels rather than risk statements; superseded by v2.0. |
+| 2026-04-15 | Jaivardhan Singh | Risk 1 (Cursor access) marked mitigated. |
+| 2026-04-02 | Jaivardhan Singh | Initial four risks captured in `eParts_Risk_and_Project_Management.md`. |
+
+
diff --git a/docs/elevator_pitch.md b/docs/elevator_pitch.md
new file mode 100644
index 0000000..cc76260
--- /dev/null
+++ b/docs/elevator_pitch.md
@@ -0,0 +1,100 @@
+# eParts SES - The Pitch
+
+> Deliver this as a narrative flow, not a feature list. The audience is intelligent. Don't explain what requirements engineering is. Show them what's different about how you do it.
+
+---
+
+## The Setup (30 seconds)
+
+Our team is building an ML-powered catalog system for eParts. That's the product.
+
+But the product is not what we're presenting today. We're presenting *how we build it* - our Software Engineering System. Specifically, what happens when you take 25 AI agents, connect them through a shared event-driven infrastructure, and point them at the actual engineering overhead of a five-person capstone team.
+
+---
+
+## The Hook (60 seconds)
+
+Here is a recording of our last client meeting. It's a 45-minute .vtt transcript.
+
+I upload it. Within 30 seconds, seven agents execute in sequence:
+
+1. The transcript is parsed into structured data - speakers, decisions, action items, concerns.
+2. Every item is priority-classified: P0 (blocks delivery, held for human review), P1 (this sprint), P2 (backlog).
+3. Formal requirement documents are synthesized - categorized, with acceptance criteria - and committed to GitHub.
+4. Jira tickets are created with auto-populated fields.
+5. Meeting minutes are published to Confluence.
+6. Decisions are logged to a running decision record.
+7. Those decisions are compared against our architecture document using retrieval-augmented generation. If something contradicts what we documented three meetings ago, a drift event fires - and that triggers an entirely separate pipeline.
+
+That last part is the key. These are not seven independent scripts. They are a connected system. The output of one pipeline becomes the input of another through a publish-subscribe event bus. When the requirements pipeline detects drift, the architecture pipeline picks it up and drafts an ADR for the team to review.
+
+---
+
+## What Makes This Different (90 seconds)
+
+Three things separate this from "we used ChatGPT to help with our project."
+
+**First: the agents talk to each other.** Every agent deposits what it learns into a shared memory store - a persistent, namespaced knowledge base we call the project wiki. When the knowledge pipeline runs before our next meeting, it pulls from that wiki, from Jira, from the event bus, and produces a briefing document. Everyone walks into the meeting with the same context. That is not a chatbot. That is institutional memory.
+
+**Second: we measure whether AI actually helps.** Our coach Christian pushed us on this, and he was right. It is not enough to say "AI saved us time." We apply a counterfactual framework: for each task, what would happen if we did not use AI at all? A human takes 2-3 hours to parse a 45-minute meeting and produces inconsistent output. The pipeline takes 30 seconds and produces the same structured format every time. But we also track the review overhead - the 15 minutes a human spends verifying P0 items - because AI is not free. It introduces review cost, rework cost, and token cost. We track all of that.
+
+**Third: we built traceability without spending a single API token.** Our traceability store links concerns to decisions to requirements to risks to Jira tickets to pull requests. That entire matrix is built using structured SQLite queries and domain-aware keyword matching. No LLM calls. The store currently holds 189 artifacts connected by 764 links across 10 relationship types. Zero orphaned artifacts. When someone asks "where did this requirement come from?" we can trace it back to the exact meeting, the exact speaker, and the exact minute of the transcript.
+
+---
+
+## The Design Choices That Matter (60 seconds)
+
+We made three deliberate design choices worth calling out.
+
+**Offline-first.** Every agent has a fallback that works without an LLM. If the API key expires mid-demo, the system degrades gracefully to pattern matching and keyword heuristics. We chose this because a capstone team cannot depend on API availability for their engineering process.
+
+**Human-in-the-loop, not human-out-of-the-loop.** P0 items are held for human approval. ADRs are submitted as pull requests, not auto-merged. The system augments the team's judgment; it does not replace it.
+
+**Prompt governance.** We version-control every prompt in a centralized registry with hash-based versioning, a peer review workflow, and regression testing against golden datasets. When someone on the team changes a prompt, we can measure whether the output got better or worse. This is how you operate AI systematically across a five-person team where everyone uses the same model but could get different results.
+
+---
+
+## The Honest Part (30 seconds)
+
+Not everything is live. Three of our seven pipelines are fully operational - requirements, coach session memory, and knowledge. Two are partially working - architecture and project management. Two are designed but not yet developed - coding and ML decision. We are transparent about this because the value of the system is not that everything is finished. The value is that the infrastructure is in place: the event bus, the shared memory, the traceability store, the prompt registry. When we start the coding phase next sprint, the coding pipeline plugs into the same architecture. Nothing breaks. Nothing gets rebuilt.
+
+---
+
+## The Close (15 seconds)
+
+We did not build a tool. We built an engineering system - one where a single meeting transcript triggers a chain of agents that produce requirements, tickets, minutes, decisions, drift reports, and traceability links, all connected through shared memory and events.
+
+The question we kept asking ourselves was not "can AI do this?" It was "should AI do this, and is the result worth the cost of verifying it?" For the tasks we chose, the answer is yes - and we have the numbers to show it.
+
+---
+
+## Timing Guide
+
+| Section | Duration | Purpose |
+|---|---|---|
+| The Setup | 30 sec | Frame the distinction: product vs. engineering system |
+| The Hook | 60 sec | Live walkthrough of one transcript triggering 7 agents |
+| What Makes This Different | 90 sec | Three selling points: connected agents, counterfactual measurement, zero-token traceability |
+| Design Choices | 60 sec | Offline-first, HITL gates, prompt governance |
+| The Honest Part | 30 sec | Transparency about what's live vs. planned |
+| The Close | 15 sec | Restate the core thesis |
+| **Total** | **~5 min** | |
+
+---
+
+## Anticipated Questions and One-Line Answers
+
+**"Why not just use ChatGPT directly?"**
+ChatGPT is a tool. This is a system. The difference is shared memory, cross-pipeline events, traceability, and prompt governance. A tool gives you an answer. A system gives you accountability.
+
+**"How do you know the AI output is correct?"**
+We don't assume it is. P0 items require human approval. We track ticket retention rate (created vs. deleted by humans), drift detection precision, and P0 override rate.
+
+**"Is this over-engineered for a capstone?"**
+The infrastructure serves the actual project. Every meeting we have with the client runs through this pipeline. The Jira board is populated by it. The decision log is maintained by it. This is not a demo - it is the team's operating process.
+
+**"What would you do differently?"**
+Start the measurement framework earlier. We built the counterfactual analysis after Christian's feedback. If we had it from sprint one, we would have better longitudinal data on AI effectiveness.
+
+**"How does this scale beyond your team?"**
+Every component is modular. A new pipeline is a list of agents in a config file. A new MCP server is a class with three methods. The event bus and shared memory are pipeline-agnostic. Adding a "deployment pipeline" or a "security review pipeline" follows the same pattern.
diff --git a/docs/etim-requirements-change.md b/docs/etim-requirements-change.md
new file mode 100644
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+++ b/docs/etim-requirements-change.md
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+# ETIM — requirements change record
+
+**Framing:** ETIM is not a new project. It is a **mid-project requirements change against the v1.0 baseline**, managed as a change and recorded as versions 1.1, 1.2, 1.3 and 1.4 of the Product Specification (23, 28 and 29 July 2026).
+
+Companion artifacts: [`product-spec-v1.4.pdf`](product-spec-v1.4.pdf) · [`product-spec-changelog.md`](product-spec-changelog.md) · [`ETIM-ADR-ASSESSMENT.md`](ETIM-ADR-ASSESSMENT.md) · ADRs 0013–0021 in [`adr-index.md`](adr-index.md).
+
+## What ETIM is
+
+A standardized technical product-classification model — **reference data, not supplier data**:
+
+```
+Product group (EG) → Class (EC) → Feature (EF) → Value (EV) / Unit (EU)
+```
+
+Loaded and verified: **ETIM 10.0 (EI)** — 159 groups, 5,640 classes, 17,377 features, 201,284 class-feature-values. **The project is pinned to this release** for its duration; adopting later ETIM releases is out of scope (C-4, ADR-020). Feature types A / L / N / R (controlled value / boolean / numeric / range). Phase-one scope: **valves and actuators only**.
+
+The principle that now drives every downstream decision:
+
+> **Original supplier data = evidence · ETIM data = standardized interpretation · confidence = how sure we are of the interpretation.**
+
+## 1. How the requirements shifted
+
+**The root change:** the system's *WHAT* moved from *"predict arbitrary product attributes"* to *"classify each product into the ETIM standard and map its attributes to a controlled vocabulary."* Constrained classification, not free prediction. Everything else cascades from that.
+
+Framed on the four dimensions of requirements:
+
+| Dimension | Before ETIM | After ETIM |
+|---|---|---|
+| **WHY** (objectives) | Automate manual entry into PIMS | **+ New business objective: standardize the catalog to an industry standard** (cross-supplier comparability, website filtering, Compare Tool). Data integrity sharpened to "evidence vs. interpretation." |
+| **WHAT** (function) | Predict attributes (Voltage, Material…) with confidence | Attribute matching is unchanged; **ETIM matching is added behind it** — class / feature / value / unit against a controlled vocabulary, still owned by ML. **+ New requirement type: reference-data management** (load and maintain the ETIM dictionary for one pinned release). |
+| **WHO** (responsibility) | Ops Reviewer, Supplier | **+ Feature-policy owner** (declares required/optional features per class), **+ Compare-Tool and website-filter consumers** |
+| **HOW WELL** (quality) | ≥95% attribute accuracy | Accuracy measured **against a controlled vocabulary**; **+ new modifiability axis: "add a new ETIM class"**; **+ metric-canonical storage constraint** |
+
+The per-ID detail — what was added (HLR-6, FR-9, FR-10, DR-4), what was amended (HLR-1, HLR-2, §2.1, §3.1, glossary, SCEN-1, SCEN-2), and what no longer holds (FR-3, the flat `IngestedRecord`) — is in [`product-spec-changelog.md`](product-spec-changelog.md).
+
+## 2. How the change is being managed
+
+Structured on the four classes of requirements management.
+
+### (a) Change control
+ETIM is treated as a formal change against the v1.0 baseline, not a rewrite. The spec is bumped to **v1.1 with a version-history entry** — that entry *is* the change record. Impact was analysed on Wiegers' dimensions (benefit, penalty, cost, risk, effort, quality impact) through ticket gap-analysis.
+
+The same discipline was applied three more times. Pinning the ETIM release removed an obligation FR-10 implied, so it went in as **v1.2 with its own history entry and a new constraint (C-4)**. Then we found that the v1.1 edit had put ETIM keying in the wrong component — it read as though normalization produced ETIM-keyed rows, when ETIM matching is an ML decision that runs after attribute matching — and that correction went in as **v1.3**. Then we noticed that correction had left the quality scenarios and the validation tests still describing a pre-ETIM system, and closing that went in as **v1.4** with QAS-3, VAL-4 and VAL-5. None of the three was edited quietly into v1.1.
+
+### (b) Version control
+Doc versioned 1.0 → 1.1 → 1.2 → 1.3 → 1.4. New IDs were **added** (HLR-6, FR-9/FR-10, DR-4, then C-4, then QAS-3/VAL-4/VAL-5) rather than renumbering the existing set — deliberately, to **preserve existing trace links**. Only items that changed, are reused, or are depended upon were versioned.
+
+### (c) Status tracking
+
+| Status | Items |
+|---|---|
+| **Implemented** | EPARTS-285 (ETIM reference schema + import), EPARTS-297 (field mapping) |
+| **Approved / in progress** | EPARTS-274 (canonical schema design), 298 (staging migrations), 299 (writer rework) |
+| **Proposed / blocked on client** | EPARTS-286 (class scope), 287 (feature policy) |
+
+### (d) Tracing
+The chain for the new requirements:
+
+```
+business objective (standardization) → HLR-6 → FR-9 / FR-10 → tickets → golden test set (EPARTS-296) → QAS
+```
+
+**Forward trace = completeness** (does every new ETIM requirement have downstream tickets and tests?). **Backward trace = currency** (does every new ticket trace to a requirement — catching gold plating?).
+
+**Traceability boundary:** two cross-team contracts are interface requirements — the **ML input contract (EPARTS-156)** and the **OCR output contract (EPARTS-159)**. Our tracing stops at those contracts; we own the requirement, another stream owns the implementation.
+
+### Re-scoped baseline
+
+- **In:** valve/actuator classes, ETIM 10.0 EI (pinned), class/feature/value matching, evidence preservation, PIMS ETIM-keyed sync.
+- **Out / deferred:** all-classes coverage, pricing (still out), Compare-Tool feature sets, ETIM "Other" handling, valve+actuator assemblies, **any ETIM release after 10.0**.
+- **Structure:** a 12-ticket MVP (EPARTS-285 → 296) plus 7 ingestion engineering tickets (297 → 303).
+- **Critical path:** `285 ‖ (297 → 298 → 299)` — everything hangs off 299.
+
+Prioritization follows the critical path and dependencies rather than ad-hoc judgement: 274 → 298 → 299 are Must and time-sensitive; evidence preservation is a regulatory Must; 286/287 are Must but **blocked on the client**, which is a dependency risk rather than a scheduling one.
+
+## 3. Risks
+
+| Risk | Handling tactic |
+|---|---|
+| **Validation blocker** — ETIM files carry **no required-field flag**, so "what blocks publish?" is unanswerable and firm validation requirements cannot be written | Client must define a **feature policy** per class (required / recommended / optional / conditional). The seam is recorded in **ADR-019** so the architecture does not stall while the values are pending. |
+| **Feasibility** — matching accuracy against a controlled vocabulary is unproven; threshold uncalibrated | Prototyping + **golden test set (EPARTS-296)**. Owned by ML (EPARTS-289/290/291). |
+| **Structural / dependency** — two hard cross-team contracts (ML-156, OCR-159) gate Phase-3 tickets (300/301/303) | Freeze both contracts early. **ADR-021** makes the ingestion→ML side of this an explicit, schema-frozen record. |
+| **Data-model migration** — flat `IngestedRecord` → product + attribute split | Zero-data-loss cutover (EPARTS-301/302). Schema landed in ADR-014 / migration `0006`. |
+| **Standard evolution (currency)** — ETIM will publish releases after 10.0 | **Scoped out.** The project is pinned to ETIM 10.0 EI (C-4, **ADR-020**). We deliberately accept that the catalog goes stale relative to ETIM rather than half-build an upgrade path. `etim_release_id` stays in the schema so published rows name the release they were matched under. Un-pinning would be a change request against C-4. |
+| **Scope creep** — Compare-Tool / website-filter feature sets could pull in more classes and features | Held out of the phase-one baseline explicitly (see re-scope above). |
+
+## 4. Open client decisions
+
+These are requirements we must still elicit. They are blockers, not nice-to-haves.
+
+- Phase-one valve/actuator **class list** (gates EPARTS-286)
+- **Feature policy per class** — required / recommended / optional / conditional (gates EPARTS-287)
+- Required-field **publish blockers**
+- **Compare Tool / website filter** feature sets
+- ETIM **"Other"** handling (attributes with no ETIM home)
+- **Metric-canonical** storage and UI display units
+- **PIMS ETIM-ID** storage format
+- **One primary class per SKU** (confirm the rule)
+- **Valve + actuator assemblies** handling
+- **Mapping/policy approval ownership** — who signs off
+
+*Closed since v1.1: **ETIM release-upgrade governance** — C-4 pins the project to release 10.0 EI and puts upgrades out of scope (ADR-020).*
diff --git a/docs/etvx_manifest.yaml b/docs/etvx_manifest.yaml
new file mode 100644
index 0000000..d3c6dc9
--- /dev/null
+++ b/docs/etvx_manifest.yaml
@@ -0,0 +1,840 @@
+# eParts SES — ETVX Process Manifest
+# Maps every agent activity to the CMU meta-model: Entry, Task, Verification, Exit
+# Resource types: auton (agent-driven), assist (AI-assisted), human (human-driven)
+#
+# References: Metamodel_framework.md (Steps 3/4), instruction_and_rubric_for_the_presentation.txt
+
+meta:
+ project: "eParts Agentic SE System"
+ team: "Pimsie Supreme — CMU MSE Studio"
+ framework: "AASE/LASE Meta-Model"
+ sdlc_pattern: "Custom agentic pipeline — not Scrum, not RUP"
+ version: "1.0.0"
+
+# ─────────────────────────────────────────────────────────────────────────
+# REQUIREMENTS DOMAIN
+# ─────────────────────────────────────────────────────────────────────────
+
+processes:
+ - id: REQ-PARSE
+ name: "Transcript Parsing"
+ domain: requirements
+ agent: transcript_parser
+ resource_type: auton
+ description: "Ingest raw VTT meeting transcript, extract structured minutes."
+ entry:
+ - "Raw .vtt file available (Google Drive or manual upload)"
+ - "Prompt template transcript_parser.txt exists and is version-tracked"
+ task:
+ - "Clean VTT formatting (timestamps, speaker labels)"
+ - "Send cleaned text to Claude with extraction prompt"
+ - "Parse JSON response into structured minutes"
+ - "Format as markdown and commit to Bitbucket"
+ verification:
+ - "JSON output passes schema validation (attendees, decisions, action_items, questions)"
+ - "Markdown minutes committed successfully"
+ - "Token usage and latency recorded in metrics DB"
+ exit:
+ - "Structured minutes artifact available in repository"
+ - "Downstream agents (priority_classifier, drift_detector) can consume output"
+ artifacts_produced:
+ - "minutes/YYYY-MM-DD-meeting.md"
+ - "pipeline/logs/agent_runs.jsonl entry"
+ artifacts_consumed:
+ - "Raw .vtt transcript"
+ - "prompts/transcript_parser.txt"
+ measurements:
+ - "Tokens used (input/output)"
+ - "Latency (ms)"
+ - "Extraction completeness (fields populated vs. empty)"
+ - "Human correction count post-extraction"
+
+ - id: REQ-CLASSIFY
+ name: "Priority Classification"
+ domain: requirements
+ agent: priority_classifier
+ resource_type: assist
+ description: "Classify extracted items into P0/P1/P2. P0 requires human review."
+ entry:
+ - "Structured items available from transcript parser output"
+ - "Priority classifier prompt template exists"
+ task:
+ - "Send items + context to Claude for priority classification"
+ - "Parse priority assignments"
+ - "Flag P0 items for human-in-the-loop review"
+ verification:
+ - "Each item assigned exactly one priority (P0, P1, P2)"
+ - "P0 items flagged with requires_human_review=true"
+ exit:
+ - "Classified items ready for ticket creation or requirement extraction"
+ artifacts_produced:
+ - "Priority-annotated item list (in-memory, passed to downstream agents)"
+ artifacts_consumed:
+ - "Parsed transcript items"
+ - "prompts/priority_classifier.txt"
+ measurements:
+ - "P0/P1/P2 distribution per meeting"
+ - "Human override rate on priority assignments"
+ - "Tokens used per classification batch"
+
+ - id: REQ-EXTRACT
+ name: "Requirement Extraction"
+ domain: requirements
+ agent: req_extractor
+ resource_type: auton
+ description: "Format new requirements as REQ-XXX.md files and commit to repo."
+ entry:
+ - "Parsed and classified items available"
+ - "Current REQ numbering state known (scan existing files)"
+ task:
+ - "Generate REQ-XXX.md from structured requirement data"
+ - "Commit files to requirements/ directory in Bitbucket"
+ verification:
+ - "Each file follows REQ-XXX.md naming convention"
+ - "Commit succeeds without conflicts"
+ exit:
+ - "New requirements tracked in version control"
+ - "Jira agent can link tickets to REQ IDs"
+ artifacts_produced:
+ - "requirements/REQ-XXX.md files"
+ artifacts_consumed:
+ - "Classified items from priority classifier"
+ measurements:
+ - "Requirements generated per meeting"
+ - "Requirements-to-ticket conversion rate"
+
+ - id: REQ-STALE
+ name: "Stale Requirement Detection"
+ domain: requirements
+ agent: stale_detector
+ resource_type: auton
+ description: "Identify requirements lacking Jira tickets or recent activity."
+ entry:
+ - "Cron trigger (Monday 8am)"
+ - "Jira and Bitbucket APIs accessible"
+ task:
+ - "Scan all REQ-XXX.md files"
+ - "Cross-reference with Jira tickets"
+ - "Identify stale items (no activity in N days)"
+ - "Alert team via Slack"
+ verification:
+ - "Alert includes actionable list of stale REQs"
+ exit:
+ - "Team notified, stale items visible in dashboard"
+ artifacts_produced:
+ - "Slack alert message"
+ artifacts_consumed:
+ - "requirements/REQ-*.md files"
+ - "Jira sprint state"
+ measurements:
+ - "Stale requirement count over time"
+ - "Time-to-resolution after alert"
+
+# ─────────────────────────────────────────────────────────────────────────
+# ARCHITECTURE DOMAIN
+# ─────────────────────────────────────────────────────────────────────────
+
+ - id: ARCH-DRIFT
+ name: "Architectural Drift Detection"
+ domain: architecture
+ agent: drift_detector
+ resource_type: auton
+ description: "Compare meeting discussions against canonical architecture diagram."
+ entry:
+ - "New meeting transcript parsed"
+ - "Canonical Mermaid diagram exists in docs/architecture.mmd"
+ task:
+ - "Extract architecture-relevant statements from transcript"
+ - "Compare against current diagram components/connections"
+ - "Score drift severity"
+ verification:
+ - "Drift report includes specific component references"
+ - "No false positives on trivial mentions"
+ exit:
+ - "Drift report available; triggers diagram_updater if significant"
+ artifacts_produced:
+ - "Drift analysis report"
+ artifacts_consumed:
+ - "Parsed meeting minutes"
+ - "docs/architecture.mmd"
+ measurements:
+ - "Drift events detected per sprint"
+ - "False positive rate (human-reviewed)"
+
+ - id: ARCH-ADR
+ name: "ADR Generation"
+ domain: architecture
+ agent: adr_generator
+ resource_type: assist
+ description: "Auto-draft Architecture Decision Records when significant decisions detected."
+ entry:
+ - "Significant technical decision detected in meeting or PR"
+ task:
+ - "Draft ADR using standard template (Context, Decision, Consequences)"
+ - "Create PR for human review — never auto-merge"
+ verification:
+ - "ADR follows template structure"
+ - "PR created successfully with reviewers assigned"
+ exit:
+ - "ADR PR open for team review"
+ artifacts_produced:
+ - "docs/adr/ADR-XXX.md (as PR)"
+ artifacts_consumed:
+ - "Decision context from transcript/discussion"
+ measurements:
+ - "ADRs generated per sprint"
+ - "ADR approval rate vs. rejection/revision rate"
+ - "Time from decision detection to ADR draft"
+
+ - id: ARCH-DIAGRAM
+ name: "Diagram Update"
+ domain: architecture
+ agent: diagram_updater
+ resource_type: assist
+ description: "Propose Mermaid diagram updates via PR when drift is detected."
+ entry:
+ - "Drift detector reports significant architectural drift"
+ task:
+ - "Generate updated Mermaid diagram reflecting new components/connections"
+ - "Create PR with before/after comparison"
+ verification:
+ - "Updated Mermaid parses without errors"
+ - "Changes are minimal and targeted (not full rewrites)"
+ exit:
+ - "Diagram update PR open for human review"
+ artifacts_produced:
+ - "docs/architecture.mmd update (as PR)"
+ artifacts_consumed:
+ - "Drift analysis report"
+ - "Current docs/architecture.mmd"
+ measurements:
+ - "Diagram update frequency"
+ - "Diagram accuracy (human assessment)"
+
+ - id: ARCH-TRACE
+ name: "Traceability Matrix"
+ domain: architecture
+ agent: traceability_builder
+ resource_type: auton
+ description: "Maintain living traceability matrix: REQ → Jira → PR → Test."
+ entry:
+ - "New requirement, ticket, PR, or test result available"
+ task:
+ - "Cross-reference REQ IDs, Jira keys, PR numbers, test status"
+ - "Update traceability matrix document"
+ verification:
+ - "Matrix has no orphaned entries"
+ - "Each REQ links to at least one downstream artifact"
+ exit:
+ - "Updated matrix committed to repository"
+ artifacts_produced:
+ - "docs/traceability_matrix.md"
+ artifacts_consumed:
+ - "requirements/REQ-*.md, Jira tickets, Bitbucket PRs"
+ measurements:
+ - "Traceability coverage percentage"
+ - "Orphaned requirements count"
+
+# ─────────────────────────────────────────────────────────────────────────
+# CODING DOMAIN
+# ─────────────────────────────────────────────────────────────────────────
+
+ - id: CODE-SCAFFOLD
+ name: "Boilerplate Generation"
+ domain: coding
+ agent: boilerplate_generator
+ resource_type: auton
+ description: "Scaffold new service/module directories with interface stubs and test files."
+ entry:
+ - "ADR approved or new module requirement identified"
+ task:
+ - "Generate directory structure based on ADR context"
+ - "Create interface stubs, __init__.py, basic test file"
+ - "Submit as PR for review"
+ verification:
+ - "Generated code passes linting"
+ - "Test file imports and runs (even if tests are empty)"
+ exit:
+ - "Scaffolding PR open for developer review"
+ artifacts_produced:
+ - "New module directory with stubs (as PR)"
+ artifacts_consumed:
+ - "ADR, requirement context"
+ measurements:
+ - "Scaffold-to-implementation time"
+ - "Developer modification rate on scaffolded code"
+
+ - id: CODE-REVIEW
+ name: "Automated PR Review"
+ domain: coding
+ agent: pr_reviewer
+ resource_type: assist
+ description: "Auto-comment on PRs checking style, coverage, traceability, and docs."
+ entry:
+ - "New PR opened or updated in Bitbucket"
+ task:
+ - "Analyze diff for style violations, missing tests, undocumented APIs"
+ - "Check REQ/Jira references in PR description"
+ - "Post review comments"
+ verification:
+ - "Comments are specific and actionable (not generic)"
+ - "False positive rate tracked"
+ exit:
+ - "Review comments posted; developer can proceed"
+ artifacts_produced:
+ - "PR review comments"
+ artifacts_consumed:
+ - "PR diff, REQ documents, coding standards"
+ measurements:
+ - "Issues caught per PR review"
+ - "False positive rate (developer dismissals)"
+ - "Time from PR open to first review comment"
+
+ - id: CODE-TEST
+ name: "Test Stub Generation"
+ domain: coding
+ agent: test_generator
+ resource_type: auton
+ description: "Generate unit test stubs from function signatures in new modules."
+ entry:
+ - "New module scaffolded or significant functions added"
+ task:
+ - "Parse function signatures"
+ - "Generate pytest test stubs with descriptive names"
+ verification:
+ - "Test file imports without errors"
+ - "All public functions have at least one test stub"
+ exit:
+ - "Test stubs committed or included in scaffold PR"
+ artifacts_produced:
+ - "tests/test_*.py files"
+ artifacts_consumed:
+ - "Source module files"
+ measurements:
+ - "Test stub coverage (% of public functions)"
+ - "Stub-to-implementation completion rate"
+
+ - id: CODE-DOC
+ name: "API Documentation Update"
+ domain: coding
+ agent: doc_generator
+ resource_type: auton
+ description: "Auto-update API documentation when endpoints are added or changed."
+ entry:
+ - "PR contains changes to API endpoint files"
+ task:
+ - "Detect new/changed endpoints in diff"
+ - "Generate or update API documentation"
+ - "Add documentation changes to PR"
+ verification:
+ - "Documentation matches actual endpoint signatures"
+ exit:
+ - "Updated docs included in PR"
+ artifacts_produced:
+ - "docs/api/ documentation updates"
+ artifacts_consumed:
+ - "PR diff containing endpoint changes"
+ measurements:
+ - "Documentation coverage (% of endpoints documented)"
+ - "Documentation staleness (age of last update)"
+
+# ─────────────────────────────────────────────────────────────────────────
+# PROJECT MANAGEMENT DOMAIN
+# ─────────────────────────────────────────────────────────────────────────
+
+ - id: PM-TICKET
+ name: "Ticket Creation"
+ domain: project_mgmt
+ agent: ticket_creator
+ resource_type: assist
+ description: "Create Jira tickets from classified items. P0 queued for human approval."
+ entry:
+ - "Priority-classified items available"
+ task:
+ - "Create Jira tickets for P1/P2 items automatically"
+ - "Queue P0 items with human approval prompt"
+ - "Suggest assignees based on keyword analysis"
+ verification:
+ - "Tickets created with correct priority, labels, sprint"
+ - "P0 items visible in human review queue"
+ exit:
+ - "All items tracked in Jira"
+ artifacts_produced:
+ - "Jira tickets"
+ artifacts_consumed:
+ - "Classified items from priority classifier"
+ measurements:
+ - "Tickets created per sprint"
+ - "P0 human approval time"
+ - "Assignee accuracy (% accepted by suggested assignee)"
+
+ - id: PM-WBS
+ name: "WBS Maintenance"
+ domain: project_mgmt
+ agent: wbs_updater
+ resource_type: auton
+ description: "Maintain work breakdown structure synced with Jira sprint states."
+ entry:
+ - "Sprint state changed or periodic sync trigger"
+ task:
+ - "Query Jira for current sprint contents"
+ - "Update sprint/wbs.md with current state"
+ verification:
+ - "WBS matches Jira sprint exactly"
+ exit:
+ - "Updated WBS available for team reference"
+ artifacts_produced:
+ - "sprint/wbs.md"
+ artifacts_consumed:
+ - "Jira sprint API response"
+ measurements:
+ - "WBS sync frequency"
+ - "Drift between WBS and actual sprint state"
+
+ - id: PM-DIGEST
+ name: "Weekly Digest"
+ domain: project_mgmt
+ agent: weekly_digest
+ resource_type: auton
+ description: "Generate comprehensive weekly digest for Slack and Confluence."
+ entry:
+ - "Cron trigger (Friday 6pm)"
+ task:
+ - "Gather commits, decisions, sprint health, metrics from the week"
+ - "Format as digest report"
+ - "Post to Slack and Confluence"
+ verification:
+ - "Digest covers all activity categories"
+ - "Successfully posted to both channels"
+ exit:
+ - "Team has visibility into weekly progress"
+ artifacts_produced:
+ - "Slack message, Confluence page"
+ artifacts_consumed:
+ - "Bitbucket commits, Jira sprint, decisions log, metrics DB"
+ measurements:
+ - "Digest completeness score"
+ - "Team engagement (Slack reactions/views)"
+
+ - id: PM-ALERT
+ name: "Project Health Alerting"
+ domain: project_mgmt
+ agent: alert_agent
+ resource_type: auton
+ description: "Monitor sprint health and fire Slack alerts for risks."
+ entry:
+ - "Periodic check (every 6 hours)"
+ task:
+ - "Check sprint velocity, unassigned P0s, blocked items"
+ - "Fire alerts for threshold violations"
+ verification:
+ - "Alerts include specific risk and suggested action"
+ exit:
+ - "Team aware of project risks"
+ artifacts_produced:
+ - "Slack alert messages"
+ artifacts_consumed:
+ - "Jira sprint state, metrics data"
+ measurements:
+ - "Alerts fired per sprint"
+ - "Alert-to-resolution time"
+ - "False alert rate"
+
+# ─────────────────────────────────────────────────────────────────────────
+# KNOWLEDGE DOMAIN
+# ─────────────────────────────────────────────────────────────────────────
+
+ - id: KN-PUBLISH
+ name: "Minutes Publishing"
+ domain: knowledge
+ agent: minutes_publisher
+ resource_type: auton
+ description: "Publish meeting minutes to Confluence as human-readable pages."
+ entry:
+ - "Meeting minutes markdown committed to Bitbucket"
+ task:
+ - "Convert markdown to Confluence storage format"
+ - "Create or update Confluence page"
+ verification:
+ - "Page renders correctly in Confluence"
+ exit:
+ - "Minutes accessible to all stakeholders"
+ artifacts_produced:
+ - "Confluence page"
+ artifacts_consumed:
+ - "minutes/*.md from Bitbucket"
+ measurements:
+ - "Publishing latency (commit to Confluence)"
+ - "Page view counts"
+
+ - id: KN-DECISION
+ name: "Decision Logging"
+ domain: knowledge
+ agent: decision_logger
+ resource_type: auton
+ description: "Extract and maintain a running decision log from all sources."
+ entry:
+ - "New transcript or discussion with decisions"
+ task:
+ - "Extract decisions from parsed content"
+ - "Append to minutes/decisions.log.md"
+ - "Commit to Bitbucket"
+ verification:
+ - "No duplicate decisions logged"
+ - "Each entry has date, context, and rationale"
+ exit:
+ - "Decision history updated and committed"
+ artifacts_produced:
+ - "minutes/decisions.log.md"
+ artifacts_consumed:
+ - "Parsed meeting minutes, Slack discussions"
+ measurements:
+ - "Decisions logged per sprint"
+ - "Decision reference frequency in later meetings"
+
+ - id: KN-PROMPT-REG
+ name: "Prompt Regression Testing"
+ domain: knowledge
+ agent: prompt_regression
+ resource_type: auton
+ description: "Run regression tests on modified prompts against golden dataset."
+ entry:
+ - "PR modifies a file in prompts/ directory"
+ task:
+ - "Run modified prompt against golden test cases"
+ - "Compare outputs to baseline (semantic similarity)"
+ - "Block PR if quality drops > threshold"
+ verification:
+ - "All golden cases produce acceptable outputs"
+ - "Quality score meets or exceeds baseline"
+ exit:
+ - "PR approved for prompt change or blocked with report"
+ artifacts_produced:
+ - "Prompt regression report"
+ artifacts_consumed:
+ - "prompts/*.txt, data/golden/*.json"
+ measurements:
+ - "Prompt version count per template"
+ - "Quality score per version"
+ - "Regression rate (% of changes that degrade quality)"
+
+ - id: KN-CONTEXT
+ name: "Context Packaging"
+ domain: knowledge
+ agent: context_packager
+ resource_type: auton
+ description: "Package relevant project context for mentor meetings."
+ entry:
+ - "Cron trigger (Monday 8am or pre-meeting)"
+ task:
+ - "Gather recent commits, open REQs, active ADRs"
+ - "Format as concise briefing document"
+ verification:
+ - "Briefing is under 2 pages"
+ - "Includes only relevant, recent items"
+ exit:
+ - "Context package available for meeting preparation"
+ artifacts_produced:
+ - "Context briefing document"
+ artifacts_consumed:
+ - "Bitbucket commits, requirements, ADRs, Jira sprint"
+ measurements:
+ - "Briefing preparation time"
+ - "Content relevance score (meeting feedback)"
+
+# ─────────────────────────────────────────────────────────────────────────
+# COACH SESSION MEMORY (eParts-specific)
+# ─────────────────────────────────────────────────────────────────────────
+
+ - id: COACH-INGEST
+ name: "Session Memory Ingestion"
+ domain: coach_memory
+ agent: session_memory
+ resource_type: auton
+ description: "Chunk and embed coach session transcripts into ChromaDB for RAG."
+ entry:
+ - "Coach/mentor session transcript available"
+ - "ChromaDB vector store accessible"
+ task:
+ - "Chunk transcript into semantic segments"
+ - "Generate embeddings via sentence-transformers"
+ - "Store in ChromaDB with session metadata"
+ - "Record session in SQLite index"
+ verification:
+ - "All chunks successfully embedded (count matches)"
+ - "Session retrievable via semantic query"
+ exit:
+ - "Session queryable in vector store for future RAG"
+ artifacts_produced:
+ - "ChromaDB embeddings, SQLite session record"
+ artifacts_consumed:
+ - "Coach session transcript"
+ measurements:
+ - "Chunks per session"
+ - "Embedding latency"
+ - "Retrieval quality (relevance score on test queries)"
+
+ - id: COACH-COMMIT
+ name: "Commitment Tracking"
+ domain: coach_memory
+ agent: commitment_tracker
+ resource_type: auton
+ description: "Identify and track commitments made during coach sessions."
+ entry:
+ - "Session ingested into memory"
+ task:
+ - "Use Claude to extract commitments from session text"
+ - "Store commitments with owner, deadline, status"
+ - "Check for overdue commitments"
+ verification:
+ - "Commitments have owner and deadline"
+ - "Overdue items flagged for follow-up"
+ exit:
+ - "Commitment database updated"
+ artifacts_produced:
+ - "SQLite commitment records"
+ artifacts_consumed:
+ - "Session transcripts, prompts/session_extraction.txt"
+ measurements:
+ - "Commitments tracked per session"
+ - "On-time delivery rate"
+ - "Overdue commitment count over time"
+
+ - id: COACH-CONCERN
+ name: "Concern Detection"
+ domain: coach_memory
+ agent: concern_tracker
+ resource_type: auton
+ description: "Detect recurring themes/concerns from coach sessions."
+ entry:
+ - "Multiple sessions ingested"
+ task:
+ - "Analyze session history for recurring themes"
+ - "Track concern frequency and evolution"
+ - "Flag persistent unresolved concerns"
+ verification:
+ - "Concerns linked to specific session references"
+ - "Frequency counts accurate"
+ exit:
+ - "Concern report available for briefing generation"
+ artifacts_produced:
+ - "SQLite concern records with frequency data"
+ artifacts_consumed:
+ - "Session memory (ChromaDB + SQLite)"
+ measurements:
+ - "Unique concerns per session"
+ - "Concern resolution rate"
+ - "Concern persistence duration"
+
+ - id: COACH-BRIEF
+ name: "Pre-Meeting Briefing"
+ domain: coach_memory
+ agent: briefing_generator
+ resource_type: auton
+ description: "Generate pre-meeting briefing from session memory + commitments + concerns."
+ entry:
+ - "Cron trigger or manual trigger before meeting"
+ - "Session memory, commitments, and concerns data available"
+ task:
+ - "Query session memory for recent context"
+ - "Gather open commitments and active concerns"
+ - "Use Claude to synthesize briefing"
+ - "Post to Slack"
+ verification:
+ - "Briefing covers open items, recent decisions, concerns"
+ - "Posted successfully to Slack"
+ exit:
+ - "Team prepared for upcoming meeting"
+ artifacts_produced:
+ - "Slack briefing message"
+ artifacts_consumed:
+ - "Session memory, commitment DB, concern DB, prompts/briefing_generator.txt"
+ measurements:
+ - "Briefing generation time"
+ - "Content coverage (% of open items mentioned)"
+ - "Team feedback score"
+
+# ─────────────────────────────────────────────────────────────────────────
+# ML DECISION MEMORY (eParts-specific)
+# ─────────────────────────────────────────────────────────────────────────
+
+ - id: ML-LOG
+ name: "ML Decision Log"
+ domain: ml_decision
+ agent: decision_log
+ resource_type: auton
+ description: "Maintain SQLite-backed log of open ML architectural decisions."
+ entry:
+ - "New ML decision identified or system initialization"
+ task:
+ - "Record decision with context, options, criteria"
+ - "Maintain decision status (open/evidence_gathering/ready_to_close/closed)"
+ verification:
+ - "All decisions have required fields"
+ - "No duplicate decision IDs"
+ exit:
+ - "Decision log current and queryable"
+ artifacts_produced:
+ - "SQLite ml_decisions records"
+ artifacts_consumed:
+ - "ADRs, meeting transcripts, manual input"
+ measurements:
+ - "Open decisions count"
+ - "Average time to decision closure"
+ - "Decision-to-implementation gap"
+
+ - id: ML-EVIDENCE
+ name: "Evidence Accumulation"
+ domain: ml_decision
+ agent: evidence_accumulator
+ resource_type: auton
+ description: "Parse POC results and update decision log with empirical evidence."
+ entry:
+ - "New POC result file (e.g., poc_results.json) available"
+ task:
+ - "Parse POC metrics"
+ - "Match results to relevant open decisions"
+ - "Record evidence with metrics, source, timestamp"
+ verification:
+ - "Evidence linked to correct decision(s)"
+ - "Metrics parsed accurately"
+ exit:
+ - "Decision log enriched with evidence"
+ artifacts_produced:
+ - "SQLite evidence records"
+ artifacts_consumed:
+ - "data/seed/poc_results.json, experiment outputs"
+ measurements:
+ - "Evidence items per decision"
+ - "Time from experiment completion to evidence logging"
+
+ - id: ML-READINESS
+ name: "Decision Readiness Detection"
+ domain: ml_decision
+ agent: readiness_detector
+ resource_type: auton
+ description: "Check evidence against thresholds and alert when a decision is ready to close."
+ entry:
+ - "New evidence accumulated or periodic check"
+ task:
+ - "Evaluate evidence count/quality against decision thresholds"
+ - "Fire Slack alert when ready"
+ verification:
+ - "Threshold logic is deterministic and auditable"
+ - "Alert includes summary of evidence"
+ exit:
+ - "Team notified of decisions ready for closure"
+ artifacts_produced:
+ - "Slack readiness alert"
+ artifacts_consumed:
+ - "Decision log, evidence records, threshold config"
+ measurements:
+ - "Readiness alerts per sprint"
+ - "Alert-to-closure time"
+ - "Threshold accuracy (were decisions actually ready?)"
+
+ - id: ML-LINK
+ name: "Coach-ML Linking"
+ domain: ml_decision
+ agent: coach_linker
+ resource_type: auton
+ description: "Search coach session memory for ML-related mentions and link to open decisions."
+ entry:
+ - "Open ML decisions exist and session memory is populated"
+ task:
+ - "For each open decision, query session memory for related keywords"
+ - "Link high-relevance session chunks to decisions"
+ verification:
+ - "Links are semantically relevant (not just keyword matches)"
+ exit:
+ - "ML decisions enriched with coach context for briefings"
+ artifacts_produced:
+ - "Decision-to-session links"
+ artifacts_consumed:
+ - "ML decision log, ChromaDB session embeddings"
+ measurements:
+ - "Links created per decision"
+ - "Link relevance score (semantic similarity)"
+
+# ─────────────────────────────────────────────────────────────────────────
+# CROSS-CUTTING PROCESSES (non-agent)
+# ─────────────────────────────────────────────────────────────────────────
+
+ - id: SYS-METRICS
+ name: "Metrics Collection"
+ domain: system
+ agent: "N/A (built into BaseAgent)"
+ resource_type: auton
+ description: "Automatic measurement of every agent operation."
+ entry:
+ - "Any agent.execute() call"
+ task:
+ - "Record LLM call metrics (tokens, latency, cost, model, prompt)"
+ - "Record agent run metrics (duration, success, outputs)"
+ - "Track prompt version hashes"
+ verification:
+ - "Metrics DB contains entry for every agent run"
+ - "No gaps in the audit trail"
+ exit:
+ - "Metrics queryable via /metrics API and dashboard"
+ artifacts_produced:
+ - "pipeline/metrics.db records"
+ - "pipeline/logs/agent_runs.jsonl entries"
+ artifacts_consumed:
+ - "Agent execution telemetry"
+ measurements:
+ - "Metrics completeness (runs recorded / runs executed)"
+ - "DB query latency for dashboard"
+
+ - id: SYS-ORCHESTRATE
+ name: "Event Orchestration"
+ domain: system
+ agent: "Central Orchestrator (FastAPI)"
+ resource_type: auton
+ description: "Route triggers to agents, manage task queue, serve API."
+ entry:
+ - "Incoming webhook, cron event, or manual trigger"
+ task:
+ - "Resolve trigger type to agent list via routing table"
+ - "Enqueue tasks for sequential execution"
+ - "Return task IDs for status tracking"
+ verification:
+ - "All triggers routed to correct agents"
+ - "Queue processes tasks without drops"
+ exit:
+ - "Agent results available via /task/{id} endpoint"
+ artifacts_produced:
+ - "Task queue entries, API responses"
+ artifacts_consumed:
+ - "Webhooks, cron signals, manual API calls"
+ measurements:
+ - "Events processed per day"
+ - "Queue depth over time"
+ - "End-to-end latency (trigger to result)"
+
+ - id: HUMAN-REVIEW
+ name: "Human-in-the-Loop Review"
+ domain: system
+ agent: "N/A (human process)"
+ resource_type: human
+ description: "Human review of P0 items, ADR drafts, diagram changes, and flagged outputs."
+ entry:
+ - "Agent flags output with requires_human_review=true"
+ task:
+ - "Review flagged item in context"
+ - "Approve, modify, or reject"
+ - "Record correction if output was modified"
+ verification:
+ - "Correction recorded in metrics DB"
+ - "Downstream agents receive corrected version"
+ exit:
+ - "Item either approved or corrected and re-processed"
+ artifacts_produced:
+ - "Human corrections in metrics DB"
+ artifacts_consumed:
+ - "Agent outputs flagged for review"
+ measurements:
+ - "Review queue depth"
+ - "Review turnaround time"
+ - "Correction rate by agent (quality indicator)"
+ - "Re-prompt rate (corrections leading to re-execution)"
diff --git a/docs/evals.md b/docs/evals.md
new file mode 100644
index 0000000..689f31f
--- /dev/null
+++ b/docs/evals.md
@@ -0,0 +1,121 @@
+# Agent Evals
+
+**Status:** Adopted 2026-07-27 · **Owner:** Ashritha (Engineering System) · **Harness:** [`evals/`](../evals)
+**Companion docs:** [`Metamodel_framework.md`](../Metamodel_framework.md), [`defect_management.md`](defect_management.md), [`ses_product_repo_integration.md`](ses_product_repo_integration.md)
+
+---
+
+## 1. Why
+
+Deterministic tooling — types, linters, coverage, security scanning — covers
+deterministic failure. Agents are not deterministic: the same prompt can produce
+different output tomorrow, and an agent can quietly *lose* an ability it used to
+have when a prompt, a model, or a routing table changes.
+
+Evals close that gap. The practice and its priority come from the AI-tools
+coaching session with **Cory Gwin** (Senior Software Engineer, GitHub Copilot),
+2026-07-24, where it was his single strongest recommendation. His framing:
+
+> For a given skill, define scenarios and validate that the agent calls the
+> correct tools and skills under each. Agents are non-deterministic, so evals
+> establish whether behaviour holds under known conditions. **The key benefit is
+> regression detection — knowing whether an agent has lost an ability it
+> previously had.**
+
+This also closes a gap we had already identified in our own measurement system:
+every metric we tracked was a *process* metric (time saved, handoff time, rework
+rate). None of them measured whether the output was actually any good.
+
+## 2. What existed before, honestly
+
+`agents/knowledge/prompt_regression.py` already had the *mechanism*: golden
+cases, a scoring function, baselines, and a rule to block a PR when quality drops
+more than 10%. But it never called a model — it scored the golden *input* against
+the expected structure as a proxy (see its `_run_regression_tests`, "use input as
+a proxy to validate the framework works"), and no baseline file had ever been
+written. So it validated that golden data was self-consistent; it did not
+evaluate agent behaviour.
+
+We had the machinery and not the practice. The harness in `evals/` is the
+practice.
+
+## 3. Two tiers, so the cheap one can block
+
+| Tier | Evaluates | Needs a model? | Runs |
+|---|---|---|---|
+| **`routing`** | Which agents the orchestrator dispatches for a trigger — membership, ordering, and isolation of manual overrides | No | Every PR, blocking |
+| **`skill_selection`** | Whether a skill picks the correct tools and emits labels from its controlled vocabulary | Yes, for the full check | On demand (`--live`) |
+
+The routing tier executes the real `orchestrator.router.resolve_agents` against
+declared expectations, using nothing but the standard library — so it is cheap
+enough to gate every push. The skill tier validates its own contract offline
+(every expected label must exist in the vocabulary; every named tool must exist
+in the tool surface) and additionally runs the model under `--live`.
+
+**Scenarios marked `critical` encode required capabilities.** A critical failure
+fails the run regardless of the aggregate score, and the report names the missing
+ability rather than just reporting a lower number.
+
+## 4. Running it
+
+```bash
+python3 -m evals.runner # offline tiers (blocking gate)
+python3 -m evals.runner --live # also run the model tier (needs ANTHROPIC_API_KEY)
+python3 -m evals.runner --suite routing # one suite
+python3 -m evals.runner --update-baseline # record current scores
+python3 -m evals.runner --json report.json # machine-readable report
+```
+
+Exit codes: `0` all passed · `1` failure or regression · `2` the harness itself
+could not run (malformed scenarios). A run that loads zero suites is an error,
+not a pass — silence must not read as success.
+
+CI: [`.github/workflows/quality-gates.yml`](../.github/workflows/quality-gates.yml),
+job `evals`. The offline tier gates every PR; the live tier is opt-in via
+`workflow_dispatch` because it costs tokens.
+
+## 5. Verified behaviour
+
+Recorded 2026-07-27, on 20 scenarios across 2 suites:
+
+- Clean run: **20/20 pass, exit 0**, in under a second, with no API key.
+- Regression detection, deliberately induced: removing `prompt_regression` from
+ the `pr_event` route caused
+ `FAIL [CRITICAL] pr_event.review_and_regression_capability — lost capability: missing ['prompt_regression']`
+ and exit 1. Restoring the route returned the run to 20/20.
+
+**Not yet demonstrated:** the `--live` model tier has never executed — there was
+no API key in the environment where it was written. It is wired to CI, where the
+secret exists. Its first CI run is its validation, and it should not be presented
+as a demonstrated result before then.
+
+## 6. Measurements
+
+| Measurement | Source | Question it answers |
+|---|---|---|
+| Scenarios passing / total | runner report | Does behaviour hold under known conditions? |
+| Regressions per run | baseline comparison | Have we lost an ability we had? |
+| Critical-scenario failures | runner report | Is a required capability missing right now? |
+| Scenario coverage per skill | suite files | Which skills have no eval at all? |
+| Live-tier score vs. offline contract | `--live` report | Does the model actually choose what we expect? |
+
+## 7. Metamodel mapping
+
+- **Process:** eval gate (ETVX — *Entry:* a PR or dispatch; *Task:* run scenarios
+ and score; *Verification:* a human reads the named failure and decides whether
+ it is a real regression or an intended change; *eXit:* green gate, or a
+ baseline deliberately updated).
+- **Artifacts:** scenario suites (`evals/scenarios/*.json`), the baseline file,
+ and the run report — each defined and inspectable.
+- **Resources:** the harness, the routing table under test, CI, and (for the live
+ tier) the model plus an API key.
+- **Measurements:** §6.
+- **Context management:** scenarios are the encoded, versioned statement of what
+ each agent is *supposed* to do — the reference an eval judges against.
+
+## 8. Extending it
+
+Add a `*.json` suite under `evals/scenarios/`. Give every scenario a stable `id`
+(it is the baseline key, so renaming one reads as "old capability gone, new one
+added" — rename deliberately). Mark a scenario `critical` only when its failure
+genuinely means a lost capability; overusing it makes the signal useless.
diff --git a/docs/framework.mmd b/docs/framework.mmd
new file mode 100644
index 0000000..c34f5af
--- /dev/null
+++ b/docs/framework.mmd
@@ -0,0 +1,188 @@
+%%{init: {'theme': 'dark', 'themeVariables': {'primaryColor': '#6c63ff', 'primaryTextColor': '#e1e4ed', 'primaryBorderColor': '#2a2d3a', 'lineColor': '#6c63ff', 'secondaryColor': '#1a1d27', 'tertiaryColor': '#0f1117'}}}%%
+
+graph TB
+ %% ════════════════════════════════════════════════════════════
+ %% TRIGGERS (left column)
+ %% ════════════════════════════════════════════════════════════
+ subgraph TRIGGERS["🔌 Triggers"]
+ T1[/"Zoom VTT\nTranscript"/]
+ T2[/"Coach Session\nTranscript"/]
+ T3[/"Jira\nWebhook"/]
+ T4[/"GitHub/BB\nPR Event"/]
+ T5[/"POC Result\nJSON"/]
+ T6[/"Cron\nSchedule"/]
+ end
+
+ %% ════════════════════════════════════════════════════════════
+ %% CENTRAL ORCHESTRATOR
+ %% ════════════════════════════════════════════════════════════
+ subgraph ORCH["⚙️ Central Orchestrator"]
+ O1["FastAPI Router"]
+ O2["Pipeline Executor"]
+ O3["Task Queue"]
+ O4["Metrics Collector"]
+ O1 --> O2 --> O3
+ O3 --> O4
+ end
+
+ %% ════════════════════════════════════════════════════════════
+ %% REQUIREMENTS PIPELINE (end-to-end showcase)
+ %% ════════════════════════════════════════════════════════════
+ subgraph REQ["📋 Requirements Engineering Pipeline"]
+ direction LR
+ R1["Transcript\nParser"] --> R2["Priority\nClassifier"]
+ R2 --> R3["Req\nExtractor"]
+ R2 --> R4["Ticket\nCreator"]
+ R1 --> R5["Decision\nLogger"]
+ R1 --> R6["Drift\nDetector"]
+ R1 --> R7["Minutes\nPublisher"]
+ end
+
+ %% ════════════════════════════════════════════════════════════
+ %% COACH SESSION PIPELINE
+ %% ════════════════════════════════════════════════════════════
+ subgraph COACH["🧠 Coach Session Memory Pipeline"]
+ direction LR
+ C1["Session\nMemory"] --> C2["Commitment\nTracker"]
+ C1 --> C3["Concern\nTracker"]
+ C1 --> C4["Coach\nLinker"]
+ C2 --> C5["Briefing\nGenerator"]
+ C3 --> C5
+ end
+
+ %% ════════════════════════════════════════════════════════════
+ %% ARCHITECTURE PIPELINE
+ %% ════════════════════════════════════════════════════════════
+ subgraph ARCH["🏗️ Architecture Pipeline"]
+ direction LR
+ A1["Drift\nDetector"] --> A2["ADR\nGenerator"]
+ A1 --> A3["Diagram\nUpdater"]
+ A2 --> A4["Traceability\nBuilder"]
+ end
+
+ %% ════════════════════════════════════════════════════════════
+ %% CODING PIPELINE
+ %% ════════════════════════════════════════════════════════════
+ subgraph CODE["💻 Coding Pipeline"]
+ direction LR
+ CD1["PR\nReviewer"] --> CD2["Test\nGenerator"]
+ CD1 --> CD3["Doc\nGenerator"]
+ CD1 --> CD4["Prompt\nRegression"]
+ end
+
+ %% ════════════════════════════════════════════════════════════
+ %% ML DECISION PIPELINE
+ %% ════════════════════════════════════════════════════════════
+ subgraph ML["🤖 ML Decision Memory Pipeline"]
+ direction LR
+ M1["Evidence\nAccumulator"] --> M2["Readiness\nDetector"]
+ M1 --> M3["Coach\nLinker"]
+ end
+
+ %% ════════════════════════════════════════════════════════════
+ %% PROJECT MANAGEMENT
+ %% ════════════════════════════════════════════════════════════
+ subgraph PM["📊 Project Management Pipeline"]
+ direction LR
+ P1["WBS\nUpdater"] --> P2["Weekly\nDigest"]
+ P1 --> P3["Alert\nAgent"]
+ end
+
+ %% ════════════════════════════════════════════════════════════
+ %% MCP SERVERS (external integrations)
+ %% ════════════════════════════════════════════════════════════
+ subgraph MCP["🔗 MCP Servers"]
+ MCP1[("Jira")]
+ MCP2[("Slack")]
+ MCP3[("Bitbucket")]
+ MCP4[("Confluence")]
+ MCP5[("ChromaDB")]
+ MCP6[("Google Drive")]
+ end
+
+ %% ════════════════════════════════════════════════════════════
+ %% OUTPUTS
+ %% ════════════════════════════════════════════════════════════
+ subgraph OUT["📤 Outputs"]
+ OUT1["Meeting\nMinutes"]
+ OUT2["REQ Docs\n& ADRs"]
+ OUT3["Jira\nTickets"]
+ OUT4["Slack\nBriefings"]
+ OUT5["Metrics\nDashboard"]
+ OUT6["Human Review\nQueue"]
+ end
+
+ %% ════════════════════════════════════════════════════════════
+ %% MEASUREMENT (overlays everything)
+ %% ════════════════════════════════════════════════════════════
+ subgraph MEASURE["📏 Measurement System"]
+ MS1["Token Usage\n& Cost"]
+ MS2["Latency &\nThroughput"]
+ MS3["Human Correction\nRate"]
+ MS4["Prompt Version\nTracking"]
+ end
+
+ %% ═══ CONNECTIONS ═══
+
+ %% Triggers → Orchestrator
+ T1 --> O1
+ T2 --> O1
+ T3 --> O1
+ T4 --> O1
+ T5 --> O1
+ T6 --> O1
+
+ %% Orchestrator → Pipelines
+ O2 --> REQ
+ O2 --> COACH
+ O2 --> ARCH
+ O2 --> CODE
+ O2 --> ML
+ O2 --> PM
+
+ %% Pipelines → MCP
+ R4 --> MCP1
+ R7 --> MCP4
+ R3 --> MCP3
+ C1 --> MCP5
+ C5 --> MCP2
+ A2 --> MCP3
+ CD1 --> MCP3
+ M2 --> MCP2
+ P2 --> MCP2
+ P2 --> MCP4
+
+ %% MCP → Outputs
+ MCP1 --> OUT3
+ MCP4 --> OUT1
+ MCP3 --> OUT2
+ MCP2 --> OUT4
+
+ %% Measurement
+ O4 --> MEASURE
+ MEASURE --> OUT5
+
+ %% Human review
+ R2 -.->|"P0 items"| OUT6
+ A2 -.->|"ADR drafts"| OUT6
+ CD1 -.->|"Review flags"| OUT6
+
+ %% Cross-pipeline links
+ C4 -.-> M1
+ R6 -.-> A1
+
+ %% Styling
+ classDef trigger fill:#6c63ff,stroke:#6c63ff,color:#fff
+ classDef orch fill:#0f1117,stroke:#6c63ff,color:#e1e4ed
+ classDef agent fill:#1a1d27,stroke:#2a2d3a,color:#e1e4ed
+ classDef mcp fill:#0d3b66,stroke:#3b82f6,color:#e1e4ed
+ classDef output fill:#0a3622,stroke:#22c55e,color:#e1e4ed
+ classDef measure fill:#3d1f00,stroke:#f59e0b,color:#e1e4ed
+ classDef review fill:#3d0a0a,stroke:#ef4444,color:#e1e4ed
+
+ class T1,T2,T3,T4,T5,T6 trigger
+ class O1,O2,O3,O4 orch
+ class MCP1,MCP2,MCP3,MCP4,MCP5,MCP6 mcp
+ class OUT1,OUT2,OUT3,OUT4,OUT5 output
+ class OUT6 review
+ class MS1,MS2,MS3,MS4 measure
diff --git a/docs/infra_notes.md b/docs/infra_notes.md
new file mode 100644
index 0000000..7cf13a5
--- /dev/null
+++ b/docs/infra_notes.md
@@ -0,0 +1,174 @@
+# Infrastructure Notes — Answers to Open Questions
+
+---
+
+## 1. Coding Pipeline — Tests and Quality Management
+
+**Current state:** The coding pipeline has 4 steps: `pr_reviewer → test_generator → doc_generator → prompt_regression`. It triggers on PR events.
+
+**"Create tests (done by human)" — what does the agent do then?**
+
+Right now the `test_generator` agent generates *test stubs* — it reads the source code, identifies public functions, and produces a pytest skeleton with descriptive test names, fixture setup, and TODO comments where assertions need real values. The human writes the actual assertions and edge cases. The agent handles the boilerplate that nobody wants to write (imports, class setup, parameterization scaffolding), and the human handles the judgment: what *should* this function return? What's the boundary condition that matters?
+
+This is a deliberate design choice. Fully AI-generated test assertions are dangerous because the test would just mirror whatever the code does — it's circular. If the code has a bug, the AI-generated assertion would test *for* the bug. Human-written assertions encode *intent*, which is independent of the implementation.
+
+**Quality Management — how are we ensuring it?**
+
+Quality assurance runs at multiple levels:
+
+| Level | Mechanism | What it catches |
+|-------|-----------|-----------------|
+| **Per-agent** | Every agent has ETVX (Entry/Task/Verification/Exit) criteria | Agent-level correctness — did it produce valid output? |
+| **Per-pipeline** | Pipeline executor tracks step success/failure, skips, duration | Pipeline-level health — did the chain complete? |
+| **Per-prompt** | Prompt regression testing against golden datasets | Prompt-level quality — did a prompt change degrade output? |
+| **Per-LLM-call** | Structured JSON output with regex fallback parsing | LLM output format reliability |
+| **Cross-pipeline** | EventBus triggers (e.g., drift_detected → architecture review) | System-level consistency — are pipelines contradicting each other? |
+| **Human gates** | `requires_human_review` flag, P0 items held | High-stakes decisions need human judgment |
+| **Metrics** | MetricsCollector tracks success rate (93.75%), failure rate (6.2%), human correction rate (<1%) | Aggregate quality tracking over time |
+
+The quality *system* is: agents do the work → pipeline executor validates each step → metrics record everything → anomalies trigger alerts → humans review high-stakes items. Quality isn't a separate phase — it's embedded in every pipeline step via the ETVX verification criteria.
+
+---
+
+## 2. Project Management — SDLC, Sprints, Velocity
+
+**Which SDLC?**
+
+We use a bespoke "Agent-Augmented Iterative Lifecycle" (documented in `docs/sdlc_choice.md`). We deliberately don't use Scrum, RUP, or XP because the meta-model framework says existing SDLCs assume authoring code is the bottleneck. With AI agents, the bottleneck shifts to validation, measurement, and integration. So we designed a lifecycle around that.
+
+**Sprints? Velocity tracking?**
+
+Not traditional sprints. We have 2 iterations (not sprints):
+- **Iteration 1 (Prototype):** Prove accuracy is achievable — core ML pipeline, offline evaluation
+- **Iteration 2 (Pilot):** Prove operational viability — production deployment, real data, review workflow
+
+Instead of sprint velocity, we track:
+- **Agent metrics:** 160 runs, 93.75% success rate, $0.03 total cost
+- **Pipeline throughput:** end-to-end duration per pipeline (e.g., requirements pipeline: ~33s)
+- **Human review rate:** <1% — meaning 99% of agent output is good enough without correction
+- **Risk evolution:** risk register tracks status changes over time (16 risks: 2 mitigating, 14 open)
+
+The `wbs_updater` agent syncs with Jira board state to maintain a work breakdown structure, and the `weekly_digest` agent generates progress summaries. The `alert_agent` monitors for anomalies (stale tickets, blocked items, overdue commitments).
+
+**How are decisions made?**
+
+Decisions are tracked automatically:
+- The `decision_logger` agent extracts decisions from every meeting and commits them to `minutes/decisions.log.md` on GitHub
+- Architectural decisions become ADRs (tracked in `artifact_versions.db` with version history)
+- The risk register auto-populates from architecture docs, coach sessions, and meetings
+- Everything is linked in the traceability store (189 artifacts, 764 links)
+
+---
+
+## 3. Principled Use of AI — Shared Context in Byte-Sized Chunks
+
+**"How are we making sure of this?"**
+
+Three mechanisms:
+
+**a) Prompt Registry — same prompt for everyone**
+When any team member triggers the transcript parser, they all use the same version-pinned prompt (e.g., `transcript_parser v3277a42a`). Nobody can accidentally use a different prompt. Every change requires a review. This eliminates the "I got a different answer" problem that comes from probabilistic models + inconsistent prompts.
+
+**b) SharedMemory wiki — accumulated context, not one-shot**
+Every agent deposits structured knowledge. After 160 runs, the wiki has 62 entries across 8 namespaces. When a new agent runs, it doesn't start from zero — it queries the wiki for relevant prior context. This means the 50th meeting processed benefits from the knowledge accumulated from the first 49.
+
+**c) Chunked, retrievable context via RAG — not "dump everything"**
+Instead of stuffing 40,000 words of meeting transcripts into a prompt (which would exceed context windows and dilute signal), we embed everything into ChromaDB in semantic chunks. An agent retrieves only the 3-5 most relevant chunks for its specific task. This is principled because:
+- It respects token budgets (cost-efficient)
+- It improves relevance (only related context, not noise)
+- It's auditable (we can see exactly which chunks were retrieved for any given run)
+
+---
+
+## 4. LLM-as-Judge — Can We Apply It?
+
+**"LLMs judging each other — can that concept be applied here?"**
+
+Yes, and there are at least three places it fits naturally:
+
+**a) PR Review (already exists, can be strengthened)**
+The `pr_reviewer` agent already reviews code and posts comments. A natural extension: after the `test_generator` produces test stubs and a human fills in assertions, a *second* LLM call could evaluate whether the test actually covers the requirement it claims to cover. Agent 1 generates, Agent 2 evaluates. This is the LLM-as-judge pattern — one model produces, another critiques.
+
+**b) Requirement Quality Check**
+After `req_extractor` produces REQ-XXX documents, a validation agent could check: "Is this requirement testable? Is the acceptance criteria measurable? Does it conflict with any existing requirement?" This is essentially chain-of-verification — the first agent extracts, the second verifies.
+
+**c) Prompt Regression as Judge**
+The `prompt_regression` agent already tests prompt changes against golden datasets. This could be extended to use an LLM to *judge* output quality rather than just checking for structural correctness. Feed it the old output, the new output, and ask "which is better and why?" — that's LLM-as-judge for prompt evaluation.
+
+**For the coding pipeline specifically:**
+
+The ideal chain would be:
+```
+PR submitted
+ → Agent 1: test_generator (generates test stubs from code)
+ → Human: fills in assertions (encodes intent)
+ → Agent 2: pr_reviewer (reviews code + tests for coverage, style, traceability)
+ → Agent 3: quality_judge (LLM evaluates: do these tests actually verify the requirement?)
+ → Human: final merge decision
+```
+
+Three agents, two human touchpoints. Each agent has a different role. The human handles judgment (what should this do? should we merge?), the agents handle analysis (is this well-formed? is this consistent?).
+
+---
+
+## 5. Engineering Harness — What Is It? Who Orchestrates?
+
+**"What is the engineering harness?"**
+
+The engineering harness is the entire shared infrastructure layer that all agents plug into. It's the "factory floor" that individual agents stand on:
+
+```
+┌──────────────────────────────────────────────────────────────────┐
+│ ENGINEERING HARNESS │
+│ │
+│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │
+│ │ BaseAgent │ │ Pipeline │ │ Central Orchestrator │ │
+│ │ (abstract │ │ Executor │ │ (FastAPI server) │ │
+│ │ base class) │ │ (chains │ │ │ │
+│ │ │ │ agents) │ │ Routes triggers to │ │
+│ │ Provides: │ │ │ │ correct pipeline. │ │
+│ │ - LLM calls │ │ Provides: │ │ Manages task queue. │ │
+│ │ - Wiki access│ │ - Context │ │ Exposes health API. │ │
+│ │ - Event emit │ │ threading │ │ │ │
+│ │ - Metrics │ │ - Step skip │ │ Decides WHAT runs │ │
+│ │ - Logging │ │ - Failure │ │ and WHEN. │ │
+│ │ - Retry │ │ handling │ │ │ │
+│ └──────────────┘ └──────────────┘ └──────────────────────┘ │
+│ │
+│ ┌──────────────────────────────────────────────────────────┐ │
+│ │ SHARED INFRASTRUCTURE │ │
+│ │ │ │
+│ │ SharedMemory ── EventBus ── MetricsCollector │ │
+│ │ PromptRegistry ── RiskRegister ── TraceabilityStore │ │
+│ │ ArtifactVersioning ── ChromaDB (RAG) │ │
+│ │ │ │
+│ │ 9 SQLite databases + 1 vector store │ │
+│ └──────────────────────────────────────────────────────────┘ │
+│ │
+│ ┌──────────────────────────────────────────────────────────┐ │
+│ │ MCP SERVERS (Tool Layer) │ │
+│ │ │ │
+│ │ Jira ── GitHub ── Confluence ── Slack ── Drive │ │
+│ │ ChromaDB ── Bitbucket ── VectorStore │ │
+│ └──────────────────────────────────────────────────────────┘ │
+└──────────────────────────────────────────────────────────────────┘
+```
+
+**"Who is orchestrating this?"**
+
+The **Central Orchestrator** (`orchestrator/main.py`) — a FastAPI server. It:
+1. **Receives triggers** (POST `/webhook` for external events, POST `/trigger` for manual, POST `/pipeline/{name}` for direct pipeline execution)
+2. **Routes to the correct pipeline** using `orchestrator/router.py` which maps trigger types → agent names
+3. **Manages a task queue** (`orchestrator/queue.py`) that executes agents sequentially within a pipeline
+4. **Registers all 28 agents** at startup via `orchestrator/registry.py`, wiring each agent to its MCP dependencies
+
+The orchestrator is pure routing — it doesn't make LLM calls or contain business logic. It decides *what* runs and *when*. The agents decide *how*.
+
+The Pipeline Executor is the next level down — it chains agents within a single pipeline, threading context from step to step. Step 1's output becomes Step 2's input. If a required step fails, the pipeline stops. If an optional step fails, it skips and continues.
+
+So the hierarchy is:
+- **Orchestrator** decides which pipeline to run (based on trigger type)
+- **Pipeline Executor** runs the steps in order within that pipeline
+- **BaseAgent** provides the common capabilities each step uses
+- **Shared Infrastructure** provides persistence, memory, events, metrics
+- **MCP Servers** provide external tool access
diff --git a/docs/plan_template.md b/docs/plan_template.md
new file mode 100644
index 0000000..19ef699
--- /dev/null
+++ b/docs/plan_template.md
@@ -0,0 +1,110 @@
+# Implementation Plan Template
+
+**Status:** Adopted 2026-07-24 · **Process:** `docs/spec_to_plan_process.md`
+**Produced by:** `agents/planning/plan_generator.py` (prompt: `prompts/plan_generator.txt`)
+
+Every spec gets a plan before any code is written. The plan answers *how this
+is built in this codebase* — not *what the feature is*. It is reviewed and
+accepted or rejected by a human while changing the design is still cheap.
+
+Keep it to one page-ish. If a section genuinely does not apply, write
+"none — "; do not delete the heading.
+
+---
+
+## 0. Header
+
+| Field | Value |
+|---|---|
+| **Plan ID** | `PLAN-YYYY-MM-DD-` |
+| **Spec / work item** | link or path (Jira key, `requirements/…`, spec file) |
+| **Planning model** | model that produced this plan (frontier tier) |
+| **Implementation tier** | `cheap` \| `frontier` — which tier may implement it |
+| **Status** | `draft` → `awaiting review` → `approved` \| `rejected` |
+
+## 1. Work summary
+
+2–4 sentences: what the work is, and what "done" looks like. Restated in the
+planner's words — a summary that just echoes the spec is a signal the spec was
+not understood.
+
+## 2. Clarifying questions (the "Grill Me" step)
+
+Every question the planner needed answered *before* planning, each marked
+**blocking** (a wrong guess changes the design) or **non-blocking**.
+
+| # | Question | Blocking? | Why it matters | Answer / assumption |
+|---|---|---|---|---|
+
+A plan may not be written while a blocking question is unanswered. Assumptions
+recorded here are the ones a reviewer must check hardest.
+
+## 3. Files to change — and how
+
+| File | Change | What changes | Why |
+|---|---|---|---|
+| `path/to/file.py` | new / modify / delete | the specific edit, not "update logic" | which part of §1 it serves |
+
+Real paths only. A path the planner invented is a rejection reason.
+
+## 4. Code structures supporting the feature
+
+What has to exist for the feature to be expressible: data schemas, dataclasses,
+config keys, prompt files, DB tables/migrations, interfaces, events, CLI entry
+points. For each: name, kind, where it lives, and its purpose.
+
+## 5. Class / module breakdown
+
+For each class or module introduced or materially changed:
+
+- **Name + module** — where it lives
+- **Responsibility** — one sentence; if it needs "and", consider splitting
+- **Key methods** — name, signature, what it does
+- **Collaborators** — what it calls / what calls it (existing code included)
+
+## 6. Required tests — and what each one proves
+
+| Test | File | Level | What it tests | Fails when |
+|---|---|---|---|---|
+| `test_…` | `tests/…` | unit / integration / regression | the behaviour it pins down | the concrete break it catches |
+
+Rules: every item in §3 traces to at least one test here; every test states the
+failure it catches ("tests the happy path" is not an entry); tests named here
+are the definition of done for the implementation step.
+
+## 7. Implementation sequence
+
+Ordered steps a cheaper model can follow without re-deriving the design.
+Each step should be independently verifiable (compiles / test passes).
+
+## 8. Out of scope
+
+What this plan deliberately does not do, so the implementer does not drift into
+it and the reviewer does not expect it.
+
+## 9. Risks
+
+| Risk | Mitigation |
+|---|---|
+
+## 10. Stop conditions
+
+When the implementing agent must stop and hand off to a human instead of
+trying again. Be specific — these are the conditions the team kept getting
+wrong when work was under-specified.
+
+| Condition | Action |
+|---|---|
+| e.g. a planned test cannot be made to pass without changing a file not in §3 | stop; return to plan review |
+| e.g. two consecutive failed attempts on the same step | stop; escalate with the failing output |
+
+## 11. Review gate
+
+- [ ] **Accepted** — organization, class breakdown, and test set are right; implementation may start (at the tier in §0)
+- [ ] **Rejected** — reason: `organization` \| `missing-tests` \| `wrong-files` \| `scope` \| `unanswered-ambiguity`
+
+Reviewer: ______ · Date: ______ · Time spent: ____ min
+
+Rejection is cheap and expected — it is the point of the gate. Record the
+reason; the reasons are a measured input to prompt and process improvement
+(see `docs/spec_to_plan_process.md` §7).
diff --git a/docs/practice_area_requirements.md b/docs/practice_area_requirements.md
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+++ b/docs/practice_area_requirements.md
@@ -0,0 +1,183 @@
+# Practice Area: Requirements Engineering — End-to-End
+
+> "For at least one Practice Area, there should be an end-to-end connection
+> between the Activities in that area." — Presentation Rubric
+
+This document shows the complete end-to-end flow for Requirements Engineering,
+with each Activity documented per the meta-model: Artifacts, Processes, Resources, Measurements.
+
+## Overview Flow
+
+```
+Meeting Recording (.vtt)
+ │
+ ▼
+┌──────────────────────┐
+│ A1: Transcript Parse │ Resource: auton (agent) + assist (Claude optional)
+│ ETVX: REQ-PARSE │ Artifacts IN: .vtt file
+│ │ Artifacts OUT: structured JSON (attendees, actions, decisions)
+└──────────┬───────────┘
+ │ parsed_minutes (data bridge)
+ ▼
+┌──────────────────────┐
+│ A2: Priority Classify │ Resource: auton (agent) + assist (Claude optional)
+│ ETVX: REQ-CLASS │ Artifacts IN: parsed action items
+│ │ Artifacts OUT: classified items (P0/P1/P2)
+└──────────┬───────────┘
+ │ classified_items (data bridge)
+ ▼
+┌──────────────────────┐
+│ A3: Requirement │ Resource: auton (agent)
+│ Extraction │ Artifacts IN: classified P0/P1 items
+│ ETVX: REQ-EXTRACT │ Artifacts OUT: REQ-XXX.md files committed to repo
+└──────────┬───────────┘
+ │ new_requirements (data bridge)
+ ▼
+┌──────────────────────┐
+│ A4: Ticket Creation │ Resource: auton (agent) → Jira MCP
+│ ETVX: PM-TICKET │ Artifacts IN: classified items + requirements
+│ │ Artifacts OUT: Jira tickets with labels
+└──────────┬───────────┘
+ │ action_items_extracted (event)
+ ▼
+┌──────────────────────┐
+│ A5: Minutes Publish │ Resource: auton (agent) → Confluence MCP
+│ ETVX: KN-PUBLISH │ Artifacts IN: parsed minutes + classified items
+│ │ Artifacts OUT: Confluence page
+└──────────┬───────────┘
+ │ decision_logged (event)
+ ▼
+┌──────────────────────┐
+│ A6: Decision Log │ Resource: auton (agent)
+│ ETVX: KN-DECISION │ Artifacts IN: decisions from parsed minutes
+│ │ Artifacts OUT: decision register entry
+└──────────┬───────────┘
+ │ wiki deposit → SharedMemory
+ ▼
+┌──────────────────────┐
+│ A7: Architecture │ Resource: auton (agent) + assist (Claude optional)
+│ Drift Detection │ Artifacts IN: meeting content + canonical architecture
+│ ETVX: ARCH-DRIFT │ Artifacts OUT: drift report, drift_detected event
+└──────────────────────┘
+ │
+ ▼ drift_detected event → Architecture Pipeline (cross-pipeline trigger)
+```
+
+## Activity Details (Meta-Model Format)
+
+### A1: Transcript Parsing
+
+| Element | Detail |
+|----------------|-----------------------------------------------------------|
+| **Process** | Parse raw .vtt transcript into structured JSON |
+| **Entry** | .vtt file exists, meeting type known |
+| **Task** | Clean VTT formatting, extract speaker turns, identify action items/decisions/attendees |
+| **Verification**| Output has attendees, action items, decisions fields |
+| **Exit** | Structured JSON with parsed_minutes available for next step |
+| **Resource** | `auton` (agent: `transcript_parser`) — offline structural extraction via regex; `assist` (Claude) for deeper extraction when API key available |
+| **Artifacts IN** | `.vtt` transcript file |
+| **Artifacts OUT**| Structured JSON: `{attendees, action_items, decisions, new_requirements}` |
+| **Measurements**| Tokens used (if Claude), action items extracted count, decisions extracted count, duration_ms |
+| **Wiki Deposit**| `meetings/{date}-{type}` — meeting summary with counts |
+| **Events Emitted**| `action_items_extracted`, `decision_logged` |
+
+### A2: Priority Classification
+
+| Element | Detail |
+|----------------|-----------------------------------------------------------|
+| **Process** | Classify extracted items as P0 (critical), P1 (important), P2 (nice-to-have) |
+| **Entry** | `parsed_minutes` available from A1 |
+| **Task** | Apply priority heuristics (keyword-based offline, or Claude-powered) |
+| **Verification**| Every item has a priority label; P0 items flagged for human review |
+| **Exit** | `classified_items` with `p0_items`, `p1_items`, `p2_items` |
+| **Resource** | `auton` (agent: `priority_classifier`) — offline heuristic; `assist` (Claude) for nuanced classification |
+| **Artifacts IN** | Action items from A1 |
+| **Artifacts OUT**| Classified item list with P0/P1/P2 labels |
+| **Measurements**| Distribution of P0/P1/P2, human review rate for P0, reclassification rate |
+
+### A3: Requirement Extraction
+
+| Element | Detail |
+|----------------|-----------------------------------------------------------|
+| **Process** | Generate formal REQ-XXX.md requirement documents from classified items |
+| **Entry** | `classified_items` available, at least one P0 or P1 item |
+| **Task** | Template-fill requirement document with rationale, acceptance criteria, traceability |
+| **Verification**| Each REQ has title, rationale, acceptance criteria, priority |
+| **Exit** | REQ-XXX.md files committed to repo |
+| **Resource** | `auton` (agent: `req_extractor`) → Bitbucket MCP for commit |
+| **Artifacts IN** | P0/P1 classified items |
+| **Artifacts OUT**| `requirements/REQ-XXX.md` files in repository |
+| **Measurements**| Requirements generated count, commit success rate |
+
+### A4: Ticket Creation
+
+| Element | Detail |
+|----------------|-----------------------------------------------------------|
+| **Process** | Create Jira tickets from extracted action items |
+| **Entry** | `classified_items` available |
+| **Task** | Map action items to Jira tickets with priority, labels, assignee |
+| **Verification**| Ticket created in correct project with `ai-generated` label |
+| **Exit** | Jira tickets created, task IDs recorded |
+| **Resource** | `auton` (agent: `ticket_creator`) → Jira MCP |
+| **Artifacts IN** | Classified action items |
+| **Artifacts OUT**| Jira tickets |
+| **Measurements**| Tickets created count, ticket accuracy (human review rate) |
+
+### A5: Minutes Publication
+
+| Element | Detail |
+|----------------|-----------------------------------------------------------|
+| **Process** | Publish formatted meeting minutes to Confluence |
+| **Entry** | `parsed_minutes` and `classified_items` available |
+| **Task** | Format markdown minutes with action items, decisions, attendees |
+| **Verification**| Published page has all sections populated |
+| **Exit** | Confluence page published and linked |
+| **Resource** | `auton` (agent: `minutes_publisher`) → Confluence MCP |
+| **Artifacts IN** | Parsed minutes + classified items |
+| **Artifacts OUT**| Confluence page |
+| **Measurements**| Publication success rate, page completeness |
+
+### A6: Decision Logging
+
+| Element | Detail |
+|----------------|-----------------------------------------------------------|
+| **Process** | Extract and log decisions from meeting to persistent register |
+| **Entry** | `decisions` available from A1 |
+| **Task** | Record each decision with context, rationale, participants |
+| **Verification**| Decision has context, at least one participant |
+| **Exit** | Decision register updated in wiki |
+| **Resource** | `auton` (agent: `decision_logger`) |
+| **Artifacts IN** | Decisions from parsed minutes |
+| **Artifacts OUT**| Wiki entry in `decisions/` namespace |
+| **Measurements**| Decisions logged count, decisions with full context rate |
+| **Events Emitted**| `decision_logged` → Knowledge pipeline |
+
+### A7: Architecture Drift Detection
+
+| Element | Detail |
+|----------------|-----------------------------------------------------------|
+| **Process** | Compare meeting discussion against canonical architecture for contradictions |
+| **Entry** | Meeting content available AND architecture doc in ChromaDB |
+| **Task** | Semantic search for architecture-related discussion; compare against ADRs and constraints |
+| **Verification**| Drift items have specific reference to contradicted architecture element |
+| **Exit** | Drift report generated; `drift_detected` event emitted if found |
+| **Resource** | `auton` (agent: `drift_detector`) + ChromaDB (architecture collection) |
+| **Artifacts IN** | Meeting content + canonical architecture (31 chunks in ChromaDB) |
+| **Artifacts OUT**| Drift report; triggers Architecture pipeline if drift found |
+| **Measurements**| Drift items detected, false positive rate (human verified) |
+| **Events Emitted**| `drift_detected` → Architecture pipeline (cross-pipeline) |
+
+## End-to-End Connection
+
+The seven activities form a complete pipeline:
+1. **Input**: Raw `.vtt` recording from a client meeting
+2. **Processing**: Each activity consumes the previous activity's output via the `PipelineContext` data bridge
+3. **Output**: Requirements docs, Jira tickets, Confluence pages, decision register, drift reports
+4. **Cross-Pipeline**: Events emitted (`action_items_extracted`, `decision_logged`, `drift_detected`) trigger other practice areas
+5. **Persistent Knowledge**: Every activity deposits structured data into SharedMemory wiki
+6. **Measurement**: Every step is metered (tokens, duration, success rate) via MetricsCollector
+
+This is the "end-to-end connection between Activities" the rubric requires:
+a single meeting recording flows through all seven activities, producing
+artifacts at each step, with data bridging between steps, events triggering
+other pipelines, and measurements captured throughout.
diff --git a/docs/presentation_guide.md b/docs/presentation_guide.md
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+++ b/docs/presentation_guide.md
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+# SES Presentation Guide — Jai, Hrishi & Ashritha
+
+**Section 5: Software Engineering System [~7 min]**
+
+Per rubric: "SE System overview, SDLC choice, Processes/artifacts/measurements/resources per meta-model, Evidence that process will be effective particularly with AI, Decisions and reasoning about tradeoffs"
+
+---
+
+## Slide Flow
+
+### Slide 1: SES Overview (1 min)
+**Title:** "Our Engineering System Is Engineered"
+
+**Key message:** We don't just use AI ad-hoc — we built a multi-agent framework where every repeatable activity is documented, measured, and connected.
+
+**Visual:** Framework diagram (docs/framework.mmd) showing:
+- 7 pipelines, 29 steps, 25 agents
+- Triggers → Orchestrator → Domain Agents → MCP Servers → Outputs
+- Measurement system wrapping everything
+
+**Speaker notes:**
+> "We took the meta-model seriously. Instead of bolting AI onto a traditional SDLC, we engineered a system where Artifacts, Processes, Resources, and Measurements are first-class concepts. Every agent is a Resource that implements a Process, generates Artifacts, and is measured. This is 56 Python files, 7,800+ lines of production code."
+
+---
+
+### Slide 2: SDLC Choice & Meta-Model Mapping (1 min)
+**Title:** "Bespoke SDLC — Not Scrum, Not RUP"
+
+**Visual:** Four-quadrant diagram:
+
+| | Artifacts | Measurements |
+|---|---|---|
+| **Processes** | 31 ETVX-documented | Tokens, latency, success rate |
+| **Resources** | 25 agents + 6 MCP servers | Human review rate, corrections |
+
+**Speaker notes:**
+> "Following Christian's guidance, we didn't pick an off-the-shelf SDLC. We created a bespoke lifecycle with 7 practice areas. Each has its own pipeline — an ordered chain of agents where data flows from one step to the next. Every process is documented in ETVX format — Entry criteria, Tasks, Verification, Exit. We have 31 documented processes with 100% agent coverage."
+
+---
+
+### Slide 3: End-to-End Requirements Pipeline (2 min) — LIVE DEMO
+**Title:** "Requirements Engineering — End-to-End Connected"
+
+**This is the money slide.** Run the demo live.
+
+**Before demo, say:**
+> "The rubric asks for at least one practice area with end-to-end connection. Let me show you Requirements Engineering running on a real client meeting from April 2nd."
+
+**Run:** `python demo.py --section requirements --no-pause`
+
+**What audience sees:**
+1. Real VTT transcript → transcript_parser extracts 10 action items
+2. priority_classifier → 2 P1, 8 P2
+3. req_extractor, ticket_creator, minutes_publisher, decision_logger, drift_detector
+4. 7/7 SUCCESS in ~120ms
+
+**After demo, say:**
+> "That's a real Zoom recording from our April sprint review, going through 7 agents in sequence. The transcript parser does structural extraction without any API calls — it runs entirely locally. Each agent's output feeds the next. The same pipeline processes all 5 of our client meetings."
+
+---
+
+### Slide 4: Coach Session Memory — RAG Pipeline (1.5 min) — LIVE DEMO
+**Title:** "Coach Memory — Never Lose Context Between Sessions"
+
+**Before demo:**
+> "This is eParts-specific. We have coaching sessions with Cory, Ben, and Dennis. The system embeds every session into ChromaDB so we never walk into a meeting blind."
+
+**Run:** `python demo.py --section coach --no-pause` then `python demo.py --section search --no-pause`
+
+**What audience sees:**
+1. Cory's session → 6 pipeline steps → 95 chunks embedded
+2. Semantic search: "agent visibility" → Cory's exact advice surfaces
+3. "ETVX process documentation" → finds Dennis's risk doc + project overview
+
+**After demo:**
+> "317 chunks from 3 coach sessions plus 79 chunks from project documents — all searchable by meaning, not keywords. Before every meeting, the briefing generator assembles last session's recap, open commitments, recurring concerns, and relevant past context into a structured briefing."
+
+---
+
+### Slide 5: Measurement System (1 min)
+**Title:** "Every Agent Call Is Measured"
+
+**Visual:** Dashboard screenshot (dashboard/metrics.html) or live browser
+
+**Key metrics to highlight:**
+- 8 meetings processed, 53,745 words analyzed, 123 action items extracted
+- 396 ChromaDB chunks (317 sessions + 79 knowledge docs)
+- 30 commitments tracked, concern patterns detected across 3 sessions
+- Per-agent: tokens, latency, success rate, human review rate, corrections
+
+**Speaker notes:**
+> "The meta-model requires a measurement system. Ours is automatic — every LLM call records tokens, latency, and cost. Every agent run records success, duration, and whether it needed human review. This feeds a real-time dashboard. We use GQIM: our Goal is to get the best out of AI; our Question is how effective are our prompts; our Indicator is re-prompt frequency; our Metric is interactions per task type."
+
+---
+
+### Slide 6: Tradeoffs & Decisions (30 sec)
+**Title:** "Decisions and Reasoning"
+
+**Visual:** Decision table
+
+| Decision | Rationale |
+|----------|-----------|
+| Local embeddings (ONNX MiniLM) over API | No API dependency, runs offline, same model as client POC |
+| ChromaDB over Pinecone | Local-first, zero config, swappable to Azure AI Search |
+| Prompts as versioned files | Enables regression testing, A/B testing, git history |
+| Offline-first agent design | Demo without API keys, Claude enhances but isn't required |
+| SQLite → Azure SQL migration path | Start simple, proven upgrade path |
+
+**Speaker notes:**
+> "Two key tradeoffs: First, we designed every agent to work offline with pattern matching, then enhance with Claude when available. This means the framework runs anywhere — no API keys needed for the demo you just saw. Second, we version-control prompts as files and run regression tests against golden datasets before deploying changes."
+
+---
+
+## Demo Commands Quick Reference
+
+```bash
+# Full demo with pauses between sections (for presentation)
+python demo.py
+
+# Individual sections (for targeted demos)
+python demo.py --section stats --no-pause
+python demo.py --section requirements --no-pause
+python demo.py --section coach --no-pause
+python demo.py --section search --no-pause
+python demo.py --section briefing --no-pause
+
+# Open dashboard in browser
+open dashboard/metrics.html
+
+# Start FastAPI server (if needed for live API demo)
+cd /Users/ashritha/eparts && source .venv/bin/activate
+uvicorn orchestrator.main:app --port 8000
+```
+
+---
+
+## Connecting to Other Sections
+
+**For Project Context (Arjun/Liu):**
+- "We've processed all 5 client meeting transcripts — Jan 22 through Apr 16"
+- "The system extracted 123 action items and 14 decisions automatically"
+- Timeline data is in the dashboard Overview tab
+
+**For Management (Arjun/Liu):**
+- "30 commitments tracked from coach sessions, with overdue alerting"
+- "Measurement plan maps directly to GQIM framework"
+- Risk doc from Dennis session is in the knowledge base
+
+**For Requirements (Liu/Arjun):**
+- "Requirements pipeline runs end-to-end: transcript → classify → extract → Jira → Confluence → drift check"
+- "P0 items automatically flagged for human review"
+
+**For Architecture (Liu/Arjun):**
+- "Architecture pipeline includes drift detection — when a meeting discussion contradicts the canonical architecture, it flags it"
+- "ADR generation agent drafts Architecture Decision Records from detected decisions"
+
+---
+
+## Rubric Checklist
+
+- [x] SE System overview — framework diagram + dashboard
+- [x] SDLC choice — bespoke, not off-the-shelf, justified
+- [x] Processes per meta-model — 31 ETVX-documented processes
+- [x] Artifacts per meta-model — prompts versioned, ADRs, REQ docs, minutes, briefings
+- [x] Measurements per meta-model — tokens, latency, success rate, human review, corrections
+- [x] Resources per meta-model — 25 agents (auton/assist) + human reviewers
+- [x] Evidence of effectiveness — live demo on real data, 8 meetings processed
+- [x] AI use justified — offline-first design, enhancement with Claude, prompts as artifacts
+- [x] End-to-end connection — Requirements pipeline (7 steps) + Coach Memory pipeline (6 steps)
+- [x] Decisions and reasoning — tradeoff table with rationale
+
+---
+
+## Key Numbers to Remember
+
+| Metric | Value |
+|--------|-------|
+| Total code | 57 files, 7,800+ lines |
+| Pipelines | 7 pipelines, 29 steps |
+| Agents | 25 unique |
+| MCP Servers | 6 |
+| Meetings processed | 8 (5 client + 3 coach) |
+| Total words analyzed | 53,745 |
+| Action items extracted | 123 |
+| ChromaDB chunks | 396 |
+| Coach commitments tracked | 30 |
+| ETVX processes documented | 31 |
+| Pipeline success on real data | 100% (all steps pass) |
diff --git a/docs/presentation_qa.md b/docs/presentation_qa.md
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+++ b/docs/presentation_qa.md
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+# eParts Capstone Presentation — Comprehensive Q&A
+
+**SES / SDLC demo (shorter speaker Q&A):** see [`ses_presentation_qa.md`](ses_presentation_qa.md) — harnesses, Shared Memory, trace dashboards, REQ-001 / IDs, Liu handoff.
+
+---
+
+## Section 1: Generic Questions
+
+### 1.1 "What do you mean by GitHub and wiki? Isn't the same?"
+
+No — they serve different roles. **GitHub** is the remote repository where committed artifacts live: REQ markdown files (`requirements/parsed/REQ-XXX.md`), ADRs (`docs/adr/ADR-*.md`), meeting minutes (`minutes/{date}-{type}.md`), decision logs (`minutes/decisions.log.md`), and drift reports (`docs/drift/YYYY-MM-DD.md`). These are version-controlled, diffable, and reviewable via PRs.
+
+**Wiki** refers to `SharedMemory` (`pipeline/shared_memory.py`), backed by `memory/shared_memory.db` — a local SQLite key-value store where agents deposit and query structured knowledge at runtime. It has 9 namespaces: `requirements/`, `architecture/`, `decisions/`, `risks/`, `commitments/`, `concerns/`, `ml_decisions/`, `meetings/`, and `metrics/` (lines 9–17 of `shared_memory.py`). Each entry carries `namespace`, `key`, `value` (JSON), `source_agent`, `source_pipeline`, `timestamp`, and optional tags. Agents write to it via `self.wiki.put()` and read via `self.wiki.get()`.
+
+**Confluence** is the external tool for stakeholder-facing meeting summaries. The `weekly_digest` agent publishes there if the Confluence MCP is configured; otherwise digests are committed to GitHub. These three are distinct systems: GitHub = artifact version control, wiki = agent knowledge store, Confluence = stakeholder communication.
+
+### 1.2 "If meetings trigger this, are requirements/ADRs always evolving? Good or bad?"
+
+Yes, they evolve after every processed meeting — and this is by design. Iterative refinement is a core SE principle: requirements are living documents, not frozen specs. Each pipeline run can add new requirements, reclassify priorities, or flag architectural drift. The `req_extractor` agent checks existing requirements via `_get_existing_reqs()` (line 286 of `req_extractor.py`) before extracting, specifically to avoid duplicates and track evolution rather than duplication.
+
+The `ArtifactVersioning` component in the engineering harness tracks every change to every artifact. The risk is AI-introduced drift — an LLM hallucinating a requirement that nobody discussed. Mitigation: P0 items require human review (`requires_human_review=True` on line 90 of `priority_classifier.py`), ADR drafts go through PR approval (`requires_human_review=True` on line 78 of `adr_generator.py`), and the PromptRegistry pins prompt versions so outputs are reproducible. Evolution is good; uncontrolled mutation is the risk we mitigate.
+
+### 1.3 "Why ChromaDB? What is ChromaDB?"
+
+ChromaDB is a local vector database used for semantic search (RAG — Retrieval-Augmented Generation). It stores text as 384-dimensional vector embeddings generated by ONNX MiniLM-L6-v2, a small (~23 MB) model that runs locally with zero API cost. When the `drift_detector` needs architecture context, it calls `_get_architecture_context()` (line 101 of `drift_detector.py`), which queries the `"architecture"` collection in ChromaDB with `n_results=5` — retrieving the 5 most semantically relevant chunks from `eParts_architecture_report.md`.
+
+Why not just stuff the whole architecture document into every prompt? Because the document is too long. ChromaDB lets agents ask targeted questions and get only the relevant paragraphs, keeping prompts focused and token costs low. The `session_memory` agent uses the same approach for coach sessions: it chunks transcripts into 800-character segments with 100-character overlap, embeds them, and stores them in ChromaDB for later retrieval by `coach_linker`. ChromaDB data lives at `memory/chroma/`.
+
+### 1.4 "What is our engineering harness?"
+
+The engineering harness is the infrastructure that agents run on — the non-AI rails. It consists of: **FastAPI orchestrator** (the API layer that exposes pipeline execution), **PipelineExecutor** (runs pipeline steps sequentially with context threading, skip conditions, and ETVX gates — `pipeline/pipelines.py`), **SharedMemory** (the wiki — `pipeline/shared_memory.py`), **EventBus** (cross-pipeline communication — `pipeline/event_bus.py`), **MetricsCollector** (tracks run duration, token count, success rate per agent), **PromptRegistry** (version-controls prompts with SHA-256 hashing — `pipeline/prompt_registry.py`), **TraceabilityStore** (189 artifacts, 764 links — `pipeline/traceability.py`), and **ArtifactVersioning** (tracks changes to committed files — `pipeline/artifact_versioning.py`).
+
+These components are deterministic — no LLM calls, no probabilistic behavior. They provide the guarantees: every run is logged, every event is persisted, every artifact is versioned.
+
+### 1.5 "What is our agentic harness?"
+
+The agentic harness is the AI-powered layer built on top of the engineering harness. It starts with the `BaseAgent` class (`agents/base.py`), which provides every agent with: LLM integration via `call_claude()` with automatic offline fallback, wiki access via `self.wiki`, event emission via `self.emit()`, prompt loading via `self.load_prompt()` from the PromptRegistry, and MCP client access via `self.mcp`.
+
+The agentic patterns: **Pipeline chaining** — agents run in sequence where each agent's output becomes the next agent's input via `PipelineContext`. **Prompt governance** — prompts are externalized to `/prompts/*.txt`, version-controlled, and peer-reviewed before activation. **Metrics per run** — every agent execution records duration, token usage, success/failure, and human review status. **Offline-first** — every agent has an `_offline()` fallback (e.g., `_classify_offline()` in `priority_classifier.py` line 100, `_parse_offline()` in `transcript_parser.py` line 145) so the system functions without API keys, just at lower quality.
+
+### 1.6 "Ralph Wiggum loop tool? Emerging techniques?"
+
+The feedback loops manifest through two mechanisms. **EventBus cycles**: the drift_detector in the requirements pipeline fires a `drift_detected` event (line 78 of `drift_detector.py`); the architecture pipeline subscribes to this event and triggers a full drift analysis → ADR generation → diagram update → traceability rebuild. The `recurring_concern` event from coach sessions feeds into PM alerts. These cycles create continuous improvement loops — meetings feed requirements, requirements feed architecture checks, drift feeds ADR generation, ADRs feed traceability.
+
+**SharedMemory accumulation**: every agent deposits knowledge into the wiki, and downstream agents query it. The `briefing_generator` reads recent decisions, concerns, and requirements from the wiki to produce pre-meeting briefings. Over time, the wiki becomes richer and briefings become more informed — an emergent intelligence loop. Emerging techniques used: **RAG** (ChromaDB for semantic retrieval), **tool use** (MCP servers for Jira, GitHub, Confluence, Slack), **chain-of-agents** (pipeline chaining where output feeds input), and **offline-first with LLM upgrade** (keyword fallbacks that work without API keys).
+
+### 1.7 "Where are events stored?"
+
+Events are stored in `memory/events.db`, a SQLite database managed by the `EventBus` class (`pipeline/event_bus.py`). The schema includes: `event_id` (UUID), `event_type`, `source_agent`, `source_pipeline`, `data` (JSON blob), `timestamp`, and `consumed_by` (tracks which agents have consumed each event). As documented in lines 1–17 of `event_bus.py`, the EventBus is the "cross-pipeline communication backbone" — when one pipeline produces a significant output, it publishes an event, and other pipelines auto-trigger.
+
+The event types fired so far include: `action_items_extracted` (from `transcript_parser`, line 108), `decision_logged` (from `transcript_parser`, line 116), `requirements_extracted` (from `req_extractor`, line 248), `drift_detected` (from `drift_detector`, line 78), and `recurring_concern` (from `concern_tracker`). Events are persistent, providing a full audit trail of system-level communication across all pipeline runs.
+
+### 1.8 "What is the one strong WHY?"
+
+A 5-person team cannot manually process 5+ client meetings (225 min each to parse), 4 coach sessions (chunking, embedding, theme extraction), extract and categorize requirements from each meeting, create 50 Jira tickets with correct priorities and assignees, maintain 760 traceability links across 189 artifacts, track 23 risks from 3 sources, version and review 12 ADRs, generate pre-meeting briefings, run drift checks against the canonical architecture — and still write the actual ML pipeline code.
+
+SES automates the software engineering overhead so the team focuses on the ML pipeline. The numbers from `docs/ai_measurements.md`: 202 min saved per meeting cycle for parsing alone (90% reduction), 182 min saved for priority classification (91%), 128 min saved for requirements extraction (71%). Total cost: $0.069 for 183 agent runs. The traceability store — 764 links across 189 artifacts — simply would not exist without automation; as the measurements document states: "Humans link 15-20 items then give up."
+
+### 1.9 "For which do we need LLM calls?"
+
+Five agents require LLM calls: **transcript_parser** (`_parse_with_claude()`, line 198 of `transcript_parser.py`), **priority_classifier** (`_classify_items()`, line 117 of `priority_classifier.py`), **req_extractor** (`_extract_with_llm()`, line 301 of `req_extractor.py`), **session_memory** (session extraction from coach transcripts), and **briefing_generator** (generates pre-meeting briefings). These are the 5 agents listed in `prompts/` — each has a corresponding `.txt` prompt file governed by the PromptRegistry.
+
+Everything else runs without LLM: drift_detector uses keyword matching against 9 contradiction patterns and 7 new-technology patterns (lines 142–186 of `drift_detector.py`), ticket_creator uses domain keyword → assignee mapping (lines 21–40 of `ticket_creator.py`), decision_logger formats markdown tables, traceability uses keyword overlap for linking, and all storage operations are SQLite. Total LLM cost from `ai_measurements.md`: **$0.069 for 183 runs** (7 actual API calls consuming 18,817 tokens).
+
+### 1.10 "Storage? Deployed?"
+
+All data is stored locally in SQLite databases within the `memory/` directory: `shared_memory.db` (wiki — 85 entries, 8 namespaces), `events.db` (EventBus — 58 events), `coach_sessions.db` (session data, concerns, commitments), `traceability.db` (189 artifacts, 764 links), `prompt_registry.db` (prompt versions, reviews, metrics), and `ml_decisions.db` (ML decision tracking). ChromaDB vector data lives in `memory/chroma/`. Committed artifacts (REQ files, ADRs, minutes, decision logs) live in the GitHub repository.
+
+The system is **not cloud-deployed** — it runs locally on a developer machine. There is no Azure App Service deployment for SES itself (Azure is the target for the eParts ML product, not the SES framework). To run: start the FastAPI server locally, execute pipelines via API or directly via `PipelineExecutor`.
+
+### 1.11 "Common knowledge bank? Chatbot?"
+
+The common knowledge bank is `SharedMemory` (`pipeline/shared_memory.py`) — the project wiki. It contains 85 entries across 8 namespaces (`requirements/`, `architecture/`, `decisions/`, `risks/`, `commitments/`, `concerns/`, `ml_decisions/`, `meetings/`). Every agent deposits structured data: the `decision_logger` writes to `decisions/{date}:{N}` (line 69 of `decision_logger.py`), `req_extractor` writes to `requirements/{REQ-ID}` (line 207 of `req_extractor.py`), `drift_detector` writes to `architecture/drift-{date}` (line 85 of `drift_detector.py`).
+
+It is **not a chatbot** — it is a structured key-value store with JSON values, queryable by namespace and key. Agents read from it programmatically (e.g., `self.wiki.get("architecture", "decisions")`). It could power a chatbot by adding a natural language query interface on top, but currently it serves as the persistent memory substrate for agent-to-agent communication.
+
+### 1.12 "What common AI workspace? Same model?"
+
+All agents share the same AI workspace through three mechanisms. **PromptRegistry** (`pipeline/prompt_registry.py`) pins prompt versions via SHA-256 content hashing — everyone uses the same pinned active version, and changes require peer review before activation (see `docs/prompt_management.md`). **BaseAgent** (`agents/base.py`) sets `temperature=0` for deterministic outputs across all agents. The system supports both Claude and Gemini as providers, selectable via configuration.
+
+For model updates, the PromptRegistry enables **prompt regression testing**: run the new model against golden test cases using the existing prompts. If output quality drops (measured by the `prompt_metrics` table — run count, average quality score, correction rate), the old version stays active. The A/B testing capability (`ab_tests` table) allows comparing two prompt versions on the same input to determine the winner before activating.
+
+### 1.13 "Principled AI use on model updates?"
+
+The principle is: **no model or prompt change takes effect without evidence that it doesn't degrade quality.** The mechanism is the PromptRegistry's version control and metrics tracking (`docs/prompt_management.md`). Every prompt version has tracked metrics: run count, average tokens consumed, average quality score, and correction rate (how often a human overrode the output). When a prompt is edited, the new version enters `pending_review` status — the old pinned version continues running.
+
+Activation requires: (1) peer review of the prompt diff, (2) regression testing against golden test cases (the `prompt_regression` step in the coding pipeline), and (3) comparison of metrics between old and new versions. If the new version's quality score is lower or correction rate is higher, the old version stays. This applies equally to model updates — if switching from Claude 3.5 to Claude 4 degrades transcript parsing accuracy, the system reverts. Convention #6 from the team conventions table: "Golden test cases for every prompt."
+
+---
+
+## Section 2: Requirements Pipeline Questions
+
+### 2.1 "Why P0 human reviewed? How? Interface?"
+
+P0 means the item blocks project delivery or a client commitment with a hard deadline — a wrong P0 decision cascades into wasted sprints. The `priority_classifier` sets `requires_human_review=True` when P0 items exist (line 90 of `priority_classifier.py`) and populates `review_items` with entries of type `"p0_ticket_approval"` (lines 76–84). The `ticket_creator` holds P0 tickets in a review queue rather than auto-creating them (line 69–76 of `ticket_creator.py`) — P1 and P2 are auto-created, but P0 items are added to `review_items` with the suggested assignee.
+
+The review interface is **Jira** — tickets flagged with the `"human-review-required"` label appear in the Jira board's review queue. There is no custom review UI. Cost-benefit rationale: building a custom review dashboard for a 5-person team costs more engineering hours than simply reviewing P0 items in Jira, which the team already uses daily.
+
+### 2.2 "Time saved step 1 (parsing)?"
+
+From `docs/ai_measurements.md`, Task 1 — Meeting Transcript Parsing: **Without AI:** 5 meetings × 45 min each = **225 min** of human transcription time. **With AI:** 5 meetings × 30 sec processing = 2.5 min + 5 min spot-checking + 15 min fixing 1 bad parse = **22.5 min total** at a cost of **$0.02** (3 LLM calls, ~5K tokens). **Net savings: 202 min (90%)**. The `transcript_parser` agent (`agents/requirements/transcript_parser.py`) handles VTT cleaning, Claude-powered structured extraction, and offline fallback via `parse_vtt` + `generate_offline_summary` from `pipeline/vtt_processor.py`.
+
+### 2.3 "P0/P1/P2 definition?"
+
+Defined in the `priority_classifier.py` docstring (lines 4–6): **P0** = blocks delivery or a client commitment with a hard deadline. **P1** = important for the current sprint; ticket created immediately. **P2** = future sprint; deferred. Classification is based on blocking potential, stakeholder urgency, and dependency count. The offline classifier (`_classify_offline()`, line 100) uses keyword matching: P0 keywords include `{"deadline", "demo", "block", "urgent", "critical", "p0", "client"}`, P1 keywords include `{"should", "need", "sprint", "important", "this week"}`, and anything else defaults to P2.
+
+### 2.4 "How does classification work?"
+
+Two modes. **LLM mode** (`_classify_items()`, line 117 of `priority_classifier.py`): formats items as a text list with owners, loads the prompt from `prompts/priority_classifier.txt` via the PromptRegistry, sends it to Claude with the current sprint focus context, and parses the JSON response into a list of items with P0/P1/P2 assignments and rationale. If the LLM call fails, it falls back to offline (line 133).
+
+**Offline mode** (`_classify_offline()`, line 100): pure keyword matching against two sets — `p0_keywords = {"deadline", "demo", "block", "urgent", "critical", "p0", "client"}` and `p1_keywords = {"should", "need", "sprint", "important", "this week"}`. Each item's text is lowercased and scanned; first P0 match wins, then P1, then default P2. Accuracy: LLM ~87% (matches human on 87/100 items), offline ~40% (per `ai_measurements.md`).
+
+### 2.5 "REQ md file fields? Who constructed?"
+
+The `_format_req_file()` method (line 413 of `req_extractor.py`) generates markdown files with these fields in a table: **ID** (e.g., REQ-001), **Category** (FUNCTIONAL, NON_FUNCTIONAL, USER_GOAL, SOFT_GOAL, or CONSTRAINT — mapped via `CATEGORY_LABELS` on line 143), **Priority** (P0/P1/P2), **Date Identified**, **Source Meeting** (client/coach), **Source** (speaker name), and **Status** (draft). Below the table: **Requirement Statement** (formal "shall" statement), **Rationale**, **Acceptance Criteria**, **Related Concerns / Open Questions**, and a **Traceability** section with pending links to Jira ticket, architecture decision, and test coverage.
+
+The prompt was authored by the team in `prompts/req_extractor.txt`. The offline domain patterns (`EPARTS_REQUIREMENT_PATTERNS`, lines 32–141 of `req_extractor.py`) were also team-authored — 12 patterns covering the known eParts requirements with pre-written statements, rationale, and acceptance criteria.
+
+### 2.6 "How effective? Metrics? Quality definition?"
+
+Quality means: a clear "shall" statement, testable acceptance criteria, correct category assignment, and traceable source. From `ai_measurements.md` Task 6: LLM extraction achieves **~85% accuracy** with broader coverage but occasional imprecision. Offline extraction (keyword matching against `EPARTS_REQUIREMENT_PATTERNS`) covers known patterns but misses novel requirements. Human review caught and corrected 2 out of 9 requirements — the rework time was 20 min for fixing those 2 bad requirements.
+
+The `req_extractor` emits a `requirements_extracted` event (line 248) with count and category breakdown. Category accuracy is ~87% per `ai_measurements.md`, with the main confusion being between CONSTRAINT and NON_FUNCTIONAL (a boundary that is fuzzy even for humans). The PromptRegistry tracks per-version metrics: run count, average quality score, and correction rate — answering whether prompt changes improved or degraded extraction quality.
+
+### 2.7 "What else from REQ files? Categorization?"
+
+Requirements feed into three downstream consumers: **Jira tickets** (the `ticket_creator` agent creates tickets from classified items with summary, description, priority, and labels), **TraceabilityStore** (requirements become `requirement` type artifacts linked to source meetings via `RAISED_IN`, to architecture decisions via `DECIDED_BY`, and to Jira tickets via `IMPLEMENTS`), and the **goal model** (USER_GOAL and SOFT_GOAL categories trace to high-level project goals).
+
+The five categories from `CATEGORY_LABELS` (line 143 of `req_extractor.py`): **FUNCTIONAL** ("The system shall…"), **NON_FUNCTIONAL** (quality attributes like confidence scoring), **USER_GOAL** (user-facing objectives like human-in-the-loop review), **SOFT_GOAL** (aspirational targets like 60% effort reduction), and **CONSTRAINT** (non-negotiable limits like Azure deployment or pricing exclusion).
+
+### 2.8 "Time saved step 2+3?"
+
+From `ai_measurements.md`: **Step 2 — Priority Classification:** Without AI: ~100 items × 2 min team discussion = 200 min. With AI: 3 min processing + 10 min reviewing P0 items + 5 min adjusting 3 mis-classified items = **18 min + $0.01**. Net savings: **182 min (91%)**. **Step 3 — Requirements Extraction:** Without AI: reading meeting notes and writing formal REQs manually = 180 min. With AI: 2 min LLM synthesis + 30 min review + 20 min fixing 2 bad requirements = **52 min + $0.03**. Net savings: **128 min (71%)**.
+
+### 2.9 "Kanban → SDLC choice?"
+
+The Jira board (Kanban-style with To Do, In Progress, In Review, Done columns) is a **tool**, not the methodology. The SDLC is the team's bespoke **"Agent-Augmented Iterative Lifecycle"** — a custom methodology that incorporates AI agents into every phase: requirements are extracted by agents, architecture is checked by agents, project management is augmented by agents, and measurements are continuous. The Jira board is one component of the PM practice area; the SDLC encompasses the full lifecycle from meeting processing through architecture governance to coding.
+
+### 2.10 "Who moves tickets? Non-coding tracking?"
+
+**Humans move tickets.** There is no auto-completion detection — tickets are created by the `ticket_creator` agent with correct priorities and suggested assignees (via `DOMAIN_ASSIGNEES` mapping on lines 21–40 of `ticket_creator.py`: ml/pipeline/threshold → Arjun, frontend/ui → Zheliang, pims/database → Hrishikesh, architecture/monitoring → Jaivardhan, ingestion/agents → Ashritha), but status transitions (To Do → In Progress → In Review → Done) are manual. Non-coding work is tracked via Jira labels (`"auto-created"`, priority labels, `"human-review-required"`). This is an acknowledged gap — no automatic detection of when non-coding tasks are complete.
+
+### 2.11 "Non-coding PR mapping?"
+
+The `TraceabilityStore` (`pipeline/traceability.py`) maintains directed links between artifacts: `action_item → requirement → jira_ticket`. Each Jira ticket created by `ticket_creator` carries source metadata in its description (lines 114–127 of `ticket_creator.py`): the original item text, priority, owner, rationale, deadline, and source REQ reference. The link types `BECAME` (action item became a requirement) and `IMPLEMENTS` (ticket implements a requirement) establish the chain. For non-coding work specifically, tickets have labels that distinguish them — but there is no automated link from a completed non-coding task back to its source requirement.
+
+### 2.12 "P0 tickets?"
+
+Yes, P0 tickets ARE created — but they go through a mandatory review gate first. In `ticket_creator.py` (lines 69–76), when `priority == "P0"`, the item is added to `review_items` instead of being auto-created. The review item includes the suggested assignee (from `_suggest_assignee()`, line 106) and a message like `"P0 ticket needs approval: {item text}"`. The agent returns `requires_human_review=True` (line 102). After human approval, the ticket is created with the `"human-review-required"` label. P1 and P2 tickets are auto-created immediately (lines 78–96) — only P0 requires the approval gate.
+
+### 2.13 "Jira field correctness?"
+
+The `ticket_creator` populates: **summary** (item text), **description** (built by `_build_description()`, line 114 — includes item text, priority, owner, rationale, deadline, source REQ), **issue_type** ("Task"), **priority** ("High" for P1, "Medium" for P2, line 80), and **labels** (priority tag + "auto-created"). Fields NOT populated: **assignee** (only suggested, not set — `_suggest_assignee()` returns a suggestion but `create_issue()` does not pass it), **story points**, **sprint**, **epic link**. From `ai_measurements.md` Task 3: 46 out of 50 tickets were accurate (92%), with 4 needing description fixes (10 min rework).
+
+### 2.14 "Minutes publisher quality? Confluence?"
+
+The `transcript_parser` produces structured markdown minutes via `_format_minutes()` (line 225 of `transcript_parser.py`): header with date, type, and attendees; sections for Key Discussion Points, Decisions (bold text + context), Action Items (checkbox format with owner and deadline), Open Questions (with assignee), and New Requirements. The format is clean and professional.
+
+Confluence integration: the `weekly_digest` agent publishes to Confluence if the MCP client is configured. Currently **offline** — no Confluence API token is set. Minutes are committed to GitHub instead (`minutes/{date}-{type}.md`, line 80 of `transcript_parser.py`). The gap is acknowledged: stakeholder-facing summaries should ideally go to Confluence for non-technical readers.
+
+### 2.15 "Decision logger format? Categorized?"
+
+The `decision_logger` agent (`agents/knowledge/decision_logger.py`) produces a markdown table in `minutes/decisions.log.md` via `_build_log_update()` (line 92): columns are **Date**, **Decision**, **Source**, and **People Present**. Each decision is also deposited to the SharedMemory wiki under the `"decisions"` namespace with key `"{date}:{index}"` (line 69) — e.g., `"2026-02-05:3"` for the 3rd decision from Feb 5th. The wiki entry stores: `text` (truncated to 300 chars), `source`, `date`, and `participants`.
+
+Currently decisions are **not categorized by type**. All items — firm architectural decisions, coach guidance, and open questions — are logged in the same table. A valid enhancement would add a "Type" column (DECISION / GUIDANCE / QUESTION) to distinguish firm commitments from advisory input.
+
+### 2.16 "Versions of ADRs vs appending to log?"
+
+These are two different artifacts serving different purposes. The **decision log** (`minutes/decisions.log.md`) is a raw chronological stream — every decision from every meeting, appended in table format. It is the audit trail. The **ADRs** (`docs/adr/ADR-001` through `ADR-004`, plus AI-generated drafts in `docs/adrs/`) are refined, structured documents with Context, Decision, Options Considered, Consequences, and Reconsideration Triggers. Both coexist. The real ADRs — `ADR-001-threshold-calibration.md`, `ADR-002-staging-tables.md`, `ADR-003-human-in-loop.md`, `ADR-004-per-attribute-routing.md` — are **team-written**, not AI-generated. AI-generated ADRs are drafts created by `adr_generator` that go through PR review.
+
+### 2.17 "wiki: decisions/{date:N}?"
+
+The `decision_logger` deposits each decision to SharedMemory using the key pattern `"{date}:{index}"` (line 69 of `decision_logger.py`): `self.wiki.put("decisions", f"{date}:{i}", {...})`. So `"decisions/2026-02-05:3"` means the 4th decision (0-indexed) logged from February 5th. The value is a JSON object with `text`, `source`, `date`, and `participants`. Other agents can query this namespace — for example, `briefing_generator` reads recent decisions from the wiki to include in pre-meeting briefings, and `weekly_digest` reads the last 10 decisions for the digest.
+
+### 2.18 "Drift detector uses ADRs/decision log?"
+
+No. The drift detector does **not** read ADRs or the decision log. It uses the canonical architecture document `eParts_architecture_report.md`, which is indexed into ChromaDB as the `"architecture"` collection. The `_get_architecture_context()` method (line 101 of `drift_detector.py`) queries ChromaDB with `n_results=5` to get the 5 most relevant architecture chunks. It also reads structured architecture data from the SharedMemory wiki via `_get_wiki_architecture()` (line 115): `style`, `components`, `quality_attributes`, `constraints`, and `decisions` from the `"architecture"` namespace. The drift detection compares meeting content against this canonical baseline — not against ADRs or the decision log.
+
+### 2.19 "What does 'similar' mean in ChromaDB?"
+
+Similarity is computed as **cosine similarity** on 384-dimensional vectors produced by the ONNX MiniLM-L6-v2 embedding model. When `_get_architecture_context()` calls `col.query(query_texts=[query[:1000]], n_results=5)` (line 109 of `drift_detector.py`), ChromaDB: (1) embeds the query text into a 384-dim vector, (2) computes cosine similarity between this vector and every stored chunk vector, (3) returns the top-K results (k=5). There is no hard similarity cutoff — the top 5 are returned regardless of score. The `coach_linker` agent uses a stricter threshold: only results with distance < 0.5 are considered mentions.
+
+### 2.20 "Keyword match for contradictions? Better way?"
+
+The drift detector's offline mode (`_detect_drift_offline()`, line 128 of `drift_detector.py`) checks 9 contradiction patterns (lines 142–152) and 7 new-technology patterns (lines 168–176) using regex. For example, `"microservice"` triggers a contradiction flag against AD-6 ("Single App Service chosen over microservices"), and `"per.record.*rout"` flags a contradiction against the per-attribute routing decision.
+
+An LLM would be better — it could understand semantic contradictions that keyword matching misses (e.g., "let's use a separate service for each attribute type" doesn't contain the word "microservice" but implies a microservices pattern). The trade-off: keyword matching is **free** and instant; LLM-based detection would cost ~$0.01 per check. Given that 5 meetings produced 0 drift events (architecture was well-aligned), the current approach is sufficient. LLM enhancement is a valid future improvement when real drift occurs.
+
+### 2.21 "Where is stale_detector?"
+
+Located at `agents/requirements/stale_detector.py`. It is a standalone agent — **not included in any pipeline definition**. It checks for requirements that haven't been updated or linked to downstream artifacts within a configurable timeframe. It exists as a utility that can be run ad-hoc but has not been integrated into the requirements pipeline steps. If added, it would run after `req_extractor` and before `drift_detector` to flag stale requirements for human attention.
+
+### 2.22 "Human review volume for drift?"
+
+Across 5 client meetings processed through the requirements pipeline, **0 drift events were fired** — every meeting's decisions aligned with the canonical architecture. This means the human review volume for drift is currently zero. The system is correctly producing no false positives (0% false positive rate for these 5 meetings). Human review volume will become meaningful when actual architectural drift occurs — at that point, each drift report (committed to `docs/drift/YYYY-MM-DD.md`) will need review to determine whether the architecture document should be updated or the meeting discussion was a deviation that should be corrected.
+
+### 2.23 "Where do drift reports go?"
+
+Three destinations. (1) **SharedMemory wiki**: deposited under namespace `"architecture"` with key `"drift-{date}"` (line 85 of `drift_detector.py`), containing the date, drift list, and confidence score. (2) **GitHub/Bitbucket**: committed as `docs/drift/{date}.md` (line 64) formatted by `_format_drift_report()` with severity, description, evidence, and suggested action for each drift item. (3) **EventBus**: a `drift_detected` event is emitted (line 78) with drift count and details, which the architecture pipeline subscribes to — triggering ADR generation, diagram updates, and traceability rebuilds.
+
+### 2.24-2.25 "Meta model processes and resources for requirements?"
+
+**Processes** (the activities in the pipeline): (1) **Parsing** — `transcript_parser` extracts structured JSON from `.vtt` transcripts (ETVX: REQ-PARSE). (2) **Classification** — `priority_classifier` assigns P0/P1/P2 to items (ETVX: REQ-CLASSIFY). (3) **Extraction** — `req_extractor` synthesizes formal requirements with categories (ETVX: REQ-EXTRACT). (4) **Ticket Creation** — `ticket_creator` creates Jira tickets from P1/P2 items, queues P0 for approval (ETVX: REQ-TICKET). (5) **Publishing** — `minutes_publisher` commits minutes and publishes to Confluence. (6) **Decision Logging** — `decision_logger` maintains the running decision log. (7) **Drift Checking** — `drift_detector` compares meeting content against canonical architecture.
+
+**Resources** (agents and their responsibilities): `TranscriptParserAgent` — VTT cleaning, LLM/offline parsing, minute formatting. `PriorityClassifierAgent` — LLM/keyword priority assignment, P0 flagging. `ReqExtractorAgent` — LLM/domain-pattern requirement synthesis, GitHub commit. `TicketCreatorAgent` — Jira ticket creation, domain-based assignee suggestion. `MinutesPublisherAgent` — Confluence/GitHub publishing. `DecisionLoggerAgent` — decision log maintenance, wiki deposit. `DriftDetectorAgent` — ChromaDB RAG queries, contradiction pattern matching, event emission.
+
+### 2.26 "More measurements?"
+
+Reference `docs/ai_measurements.md` for the full measurement framework. The **GQIM framework** (Goal-Question-Indicator-Metric) maps: Goal "Reduce manual effort" → Question "How much time saved per cycle?" → Indicator "Time comparison" → Metric **202 min saved (90%)**. Goal "Ensure quality" → Rework rate → **5.5% failure, 0.55% needs review**. Goal "Control cost" → $/min saved → **$0.0003/min saved**. Goal "Maintain traceability" → Coverage → **0 orphaned artifacts out of 184**. Goal "Improve decisions" → Risk coverage → **16 risks from 3 sources vs ~5 manual**.
+
+The **counterfactual analysis** compares total cost WITH AI (including review, correction, rework) against WITHOUT AI for each task. The honest assessment section documents where AI does NOT help: offline transcript parsing drops to ~40% quality, keyword traceability has ~15% false positives, short meetings produce thin outputs, and prompt sensitivity means same input can give different outputs.
+
+### 2.27 "Who reviews? Who maintains Jira?"
+
+**Ashritha** reviews requirements — she is the SES framework owner and the agent pipeline author. The team reviews tickets collectively during sprint planning. **Jaivardhan** owns the Jira board (he is the architecture lead and project lead per the risk register). The `DOMAIN_ASSIGNEES` mapping in `ticket_creator.py` (lines 21–40) reflects the team's domain split: Arjun (ML/pipeline/threshold), Zheliang (frontend/UI/review queue), Hrishikesh (PIMS/database/schema), Jaivardhan (architecture/monitoring), Ashritha (ingestion/agents/orchestrator).
+
+### 2.28 "How often human reviews? How notified?"
+
+Human review is triggered **after every pipeline run that produces P0 items or sets `requires_human_review=True`**. The `priority_classifier` sets this flag when P0 items exist (line 90). The `adr_generator` sets it for every ADR draft (line 78 of `adr_generator.py`). The `traceability_builder` emits a `human_review_needed` event when traceability gaps are found.
+
+Currently, notification is **manual** — a human checks the pipeline output or the Jira board for items with `"human-review-required"` labels. There are no push notifications (no email, no SMS, no Slack alert for review items specifically). If Slack MCP is configured, the `alert_agent` sends alerts there, but the review trigger is not yet wired to Slack. This is an acknowledged gap.
+
+### 2.29 "What action does human take?"
+
+Three action contexts. **Jira tickets:** review the auto-created ticket for accuracy, edit summary/description if needed, assign to the right team member, move to the appropriate status, and set story points. For P0 tickets: approve or reject the queued item (which then creates or discards the ticket). **Requirements:** review the auto-generated REQ markdown file on GitHub, approve or edit via PR review, correct category or priority if misclassified, and add missing acceptance criteria. **Drift reports:** read the drift report at `docs/drift/YYYY-MM-DD.md`, decide whether the detected drift represents a real architectural change or a false positive, and if real, initiate an architecture document update or ADR revision.
+
+---
+
+## Section 3: Architecture Pipeline
+
+### 3.1 "How is this pipeline triggered?"
+
+The architecture pipeline (`ARCHITECTURE_PIPELINE` in `pipeline/pipelines.py`, line 558) accepts two trigger types: `"transcript"` and `"pr_event"`. In practice this means three activation paths:
+
+1. **EventBus event.** When the requirements pipeline's drift_detector (step 7) fires a `drift_detected` event, the EventBus routes it to the architecture pipeline's drift_detector (step 1) for full analysis.
+2. **GitHub webhook.** A `pr_event` trigger fires when a PR is opened or merged, allowing the architecture pipeline to check whether code changes introduce architectural drift.
+3. **Manual invocation.** A human calls `PipelineExecutor.execute(ARCHITECTURE_PIPELINE, {...})` directly — used during demos and ad-hoc architecture reviews.
+
+The pipeline has four steps: drift_detector (ETVX: ARCH-DRIFT) → adr_generator (ARCH-ADR, skip_if_empty on drift_report) → diagram_updater (ARCH-DIAGRAM, skip_if_empty on drift_report) → traceability_builder (ARCH-TRACE).
+
+### 3.2 "What is the difference between drift_detector in requirements vs architecture pipeline?"
+
+The same `DriftDetectorAgent` class (`agents/architecture/drift_detector.py`) is invoked in both pipelines, but the context and purpose differ:
+
+**Requirements pipeline (step 7, ETVX: REQ-DRIFT-CHECK):** A lightweight, tail-end sanity check that runs after every client meeting. It takes the parsed minutes and decisions as input, compares them against the canonical architecture stored in ChromaDB and the SharedMemory wiki, and fires a `drift_detected` event only if contradictions are found. Its purpose is early warning — catch contradictions while the meeting is still fresh.
+
+**Architecture pipeline (step 1, ETVX: ARCH-DRIFT):** The full drift analysis that kicks off the entire architecture practice. It is the entry point for ADR generation, diagram updates, and traceability linking. When it detects drift, the subsequent pipeline steps activate: adr_generator drafts an ADR, diagram_updater proposes a Mermaid diagram change, and traceability_builder updates the matrix.
+
+Both invocations use the same offline detection logic (`_detect_drift_offline`), which checks contradiction patterns (e.g., mentions of "microservice" against AD-6 "Single App Service"), new technology mentions (e.g., Redis, Kafka), and cross-references architecture chunks from ChromaDB.
+
+### 3.3 "What is eParts_architecture_report.md?"
+
+The canonical architecture document authored by the team. It describes the pipe-and-filter architecture, component breakdown, deployment view, quality attributes, and architectural decisions (AD-1 through AD-12). This document is:
+
+- **Indexed into ChromaDB** as the `"architecture"` collection, enabling RAG queries. The drift_detector's `_get_architecture_context()` method (line 101 of `drift_detector.py`) queries this collection with the meeting content to retrieve the 5 most relevant architecture chunks.
+- **Structured in the SharedMemory wiki** under the `"architecture"` namespace with keys for style, components, quality_attributes, constraints, and decisions. The drift_detector's `_get_wiki_architecture()` method (line 115) pulls this structured data.
+- **The baseline against which drift is measured.** Every meeting's decisions are compared against this document. If a meeting discusses "per-record routing" but the architecture specifies "per-attribute routing" (pattern: `per.record.*rout`), that is flagged as a contradiction against the `"routing"` decision.
+
+### 3.4 "What is the definition of drift?"
+
+Drift is a meeting decision or discussion that contradicts, diverges from, or extends the canonical architecture without a corresponding update to the architecture document. The drift_detector operationalizes this through two categories:
+
+1. **Contradictions.** Discussions that conflict with existing architectural decisions. The detector checks 9 specific patterns: microservices (contradicts AD-6), Kubernetes (contradicts AD-6), message queues (contradicts ADR-1), REST API for prediction (contradicts AD-2), GCP/AWS (contradicts Azure constraint), per-record routing (contradicts per-attribute decision), direct production writes (contradicts staging-first policy), and pricing ML (contradicts scope exclusion).
+
+2. **New components.** Technologies mentioned that are absent from the current architecture: Redis, Kafka, MongoDB, GraphQL, gRPC, Terraform, Docker Compose. These are flagged at severity "low" with a suggested action to evaluate whether they should be added.
+
+Example: if the architecture document says "per-attribute routing" (ADR-004) but a meeting discusses "single model for everything" or "per-record routing," the drift_detector flags it as a contradiction with severity "medium" and suggests reviewing the routing decision.
+
+### 3.5 "What is the quality of drift_detected events?"
+
+Across 5 client meetings processed through the requirements pipeline, 0 drift events were fired. This is the correct result — every meeting's decisions were aligned with the canonical architecture. The team was not contradicting its own architecture during this period.
+
+Quality measurement of drift detection requires two conditions: (a) actual drift must occur, and (b) the detector must correctly identify it. Since no real drift occurred in our meetings, we cannot report precision/recall numbers. We can confirm the false-positive rate is 0% for these 5 meetings. To validate true-positive detection, we would need to inject synthetic drift (e.g., add "let's use microservices" to a transcript) and verify the detector catches it — this is a valid future test.
+
+### 3.6 "If significant decision detected, draft ADR, commit as PR — what does that mean?"
+
+The `adr_generator` agent (`agents/architecture/adr_generator.py`) implements this as follows:
+
+1. It receives decisions from the pipeline context (passed from drift_detector's output via `PipelineContext`).
+2. For each decision, it calls `_generate_adr()` which uses a Claude prompt to generate a full ADR in the standard template: Status, Context, Decision, Options Considered, Consequences, Reconsideration Triggers.
+3. "Commit as PR" is literal. The agent uses the GitHub/Bitbucket MCP client to:
+ - Create a feature branch: `adr/{adr_id}` (line 53)
+ - Commit the ADR markdown file to `docs/adrs/{adr_id}.md` on that branch (line 55)
+ - Open a pull request with title `[ADR] {decision text}` and description including the decision context (line 62)
+4. The `AgentResult` is returned with `requires_human_review=True` (line 78), ensuring the PR is flagged for human approval before merge.
+
+The agent never commits directly to main. Every ADR goes through a PR review cycle.
+
+### 3.7 "In adr_generator, drift_report input — where does that come from?"
+
+The data flows through the pipeline's context-threading mechanism:
+
+1. drift_detector (step 0 of the architecture pipeline) produces its result with `data={"drift_report": drift_report}` (line 98 of `drift_detector.py`).
+2. The `PipelineExecutor` (line 233 of `pipelines.py`) takes this result and, because the step's `output_key` is `"drift_report"`, stores it in `PipelineContext.data["drift_report"]`.
+3. Additionally, all keys from `result.data` are merged directly into the context (line 240-241): `for key, val in result.data.items(): ctx.set(key, val)`.
+4. When adr_generator (step 1) runs, the executor builds an `AgentTrigger` with `metadata={"pipeline_context": ctx.data}` (line 210), making the drift_report available as `trigger.metadata["pipeline_context"]["drift_report"]`.
+5. The adr_generator's `skip_if_empty` is set to `"drift_report"`, so if drift_detector found no drift, the adr_generator step is skipped entirely.
+
+### 3.8 "ADR requires PR approval? What criteria? Who reviews?"
+
+Yes. The adr_generator commits ADR files to feature branches and opens PRs (never direct commits). The review criteria:
+
+- **Format compliance:** Proper ADR template with Context, Decision, Options Considered, Consequences, and Reconsideration Triggers sections.
+- **Technical accuracy:** Does the ADR correctly capture the decision and its implications for eParts?
+- **Completeness:** Are alternatives documented? Are consequences (both positive and negative) enumerated?
+
+The real ADRs (docs/adr/ADR-001 through ADR-004, visible in the git status) were written by the team — not generated by AI. The AI-generated ADRs are drafts that require human editing before they become canonical. The reviewer is the architecture lead or Project Lead (Jaivardhan Singh, per the risk register document header). The `requires_human_review=True` flag in the agent result ensures these drafts are surfaced in the human review queue.
+
+### 3.9 "Which architecture diagrams? Was there a base diagram? How created?"
+
+The `diagram_updater` agent (`agents/architecture/diagram_updater.py`) proposes updates to Mermaid architecture diagrams. Its mechanism:
+
+1. It receives the list of detected drifts and the current `architecture.mmd` diagram content from the pipeline context (line 30).
+2. It calls `_propose_update()` which sends both the current diagram and the drift descriptions to Claude, requesting an updated Mermaid diagram.
+3. The updated diagram is committed to a branch `arch/diagram-update-{date}` and a PR is opened with the drift descriptions as the PR body.
+
+The base diagram is the initial architecture Mermaid file (`docs/architecture.mmd`). The team also created `dashboard/architecture.html` and `dashboard/interactive_architecture.html` as visual renderings.
+
+Limitation: the "PR with diagram diff" is currently a text-level diff of Mermaid syntax. GitHub renders Mermaid natively, so reviewers can see the visual change in the PR preview, but there is no side-by-side visual diff tool integrated. The PR description quotes the meeting excerpt that triggered each proposed change.
+
+### 3.10 "What does traceability store contain? Where does it live? What does 'seed from all sources' mean?"
+
+The traceability store lives at `memory/traceability.db` (defined in `pipeline/traceability.py`, line 42). It is a SQLite database with two tables:
+
+**`artifacts` table:** Every traceable item with fields: id, artifact_type, title, description, status, source_meeting, source_speaker, source_timestamp, source_quote, owner, jira_key, pr_number, priority, created_at, updated_at, metadata. Indexed on type, status, jira_key, and meeting.
+
+**`links` table:** Directed edges between artifacts with fields: source_id, target_id, link_type, description, created_at. Link types include BECAME, DECIDED_BY, IMPLEMENTS, MITIGATES, ADDRESSES, RAISED_IN, ASSIGNED_TO, TRIGGERED, VERIFIED_BY, SUPERSEDES, DEPENDS_ON, RELATES_TO.
+
+The store contains **189 artifacts** and **764 links** across 10 artifact types (meeting, coach_session, concern, decision, action_item, commitment, requirement, architecture, risk, jira_ticket).
+
+**"Seed from all sources"** means the `seed_traceability.seed()` function (invoked by traceability_builder at line 33) populates the store from every data source in the project:
+- Client meeting JSON files (parsed transcripts)
+- Coach session database (`memory/coach_sessions.db`)
+- Jira tickets (via Jira MCP)
+- Risk register entries (`docs/eParts_Risk_Register_v2.md`)
+- Architecture decisions (ADRs)
+- EventBus events (cross-pipeline triggers)
+
+### 3.11 "Report gaps (unaddressed concerns, unmitigated risks) — what does this mean?"
+
+The traceability store can identify orphaned artifacts — items that should be connected to downstream work but are not.
+
+**Unaddressed concern:** A concern artifact that has no outgoing BECAME link (it never became a requirement or decision) and no incoming ADDRESSES link (no architecture decision addresses it). The SQL query in `get_coverage()` (line 271-275 of `traceability.py`) counts concerns with no outgoing links. The `traceability_builder` agent emits a `human_review_needed` event when gap_count > 0 (line 57-62 of `traceability_builder.py`).
+
+**Unmitigated risk:** A risk artifact that has no incoming MITIGATES link — no requirement, architecture component, or Jira ticket is recorded as mitigating it. The SQL query (line 280-284) counts risks without MITIGATES links.
+
+These gaps are reported in the auto-generated `docs/traceability.md` file under the "Traceability Gaps" section, and available via the `get_unlinked()` method which finds artifacts with no outgoing links at all.
+
+### 3.12 "Where does the human see these reports? Are they actionable?"
+
+Reports are accessible through two channels:
+
+1. **FastAPI API endpoints.** The traceability store exposes its data through the API layer. Queries like `GET /traceability/gaps/concerns` and `GET /traceability/gaps/risks` return specific gap lists.
+
+2. **intelligence.html dashboard.** The `dashboard/intelligence.html` file (2039 lines) includes a traceability tab that visualizes the artifact graph, coverage percentages, and gap reports.
+
+Each gap is actionable because it points to a specific artifact that needs attention. Example: "Concern C-005 'data quality from vendors' has no requirement addressing it" tells the team to either create a requirement that addresses data quality, or link an existing requirement to this concern. The traceability_builder emits `human_review_needed` events with structured data (`unaddressed_concerns` count and `unmitigated_risks` count) so that the alert_agent can surface them.
+
+### 3.13 "Where is the meta model framework for architecture pipeline?"
+
+This needs to be written. The meta-model framework for the architecture pipeline should map:
+
+- **Artifacts:** Drift reports (`docs/drift/YYYY-MM-DD.md`), ADR drafts (`docs/adrs/ADR-*.md`), architecture diagrams (`docs/architecture.mmd`), traceability matrix (`docs/traceability.md`)
+- **Processes:** Drift detection (ARCH-DRIFT), ADR generation (ARCH-ADR), diagram update (ARCH-DIAGRAM), traceability linking (ARCH-TRACE)
+- **Resources:** drift_detector (autonomous agent), adr_generator (Claude-assisted), diagram_updater (Claude-assisted), traceability_builder (autonomous agent), human reviewers (PR approval)
+- **Measurements:** Drift detection accuracy (true positive rate, false positive rate), ADR coverage (decisions with ADRs vs. total decisions), traceability coverage (189 artifacts, 764 links, % linked), diagram currency (days since last update)
+
+### 3.14 "Counterfactuals for architecture pipeline"
+
+**Without AI:** After every client meeting, a human architect would need to: re-read the canonical architecture document, manually compare meeting notes against each architectural decision, identify contradictions, draft an ADR from scratch if a new decision was made, update Mermaid diagrams by hand, and manually update the traceability matrix. Estimated time: 3-4 hours per meeting cycle.
+
+**With AI:** drift_detector runs the full contradiction check in ~30 seconds (offline keyword matching against 9 contradiction patterns and 7 new-technology patterns, plus ChromaDB RAG query). adr_generator drafts a complete ADR in ~2 minutes (one Claude API call). diagram_updater proposes Mermaid changes in ~1 minute. But every output requires human review: ~20 minutes to validate a drift report, ~15 minutes to review and edit an ADR draft, ~10 minutes to review a diagram PR.
+
+**Net value:** Saves approximately 3 hours per meeting cycle. The AI catches contradictions that humans might miss (systematic pattern matching vs. cognitive load). But AI-generated ADRs are drafts, not final — the real ADRs (ADR-001 through ADR-004 in `docs/adr/`) were team-written. The AI contribution is speed-to-first-draft, not quality-of-final-artifact.
+
+---
+
+## Section 4: Project Management Pipeline
+
+### 4.1 "Sync WBS with Jira — why commit to GitHub?"
+
+The WBS markdown file (`sprint/wbs.md`) serves as a point-in-time snapshot of the project's work breakdown structure. Committing it to GitHub provides:
+
+- **Git history as a project timeline.** Each commit represents the WBS state at a specific date. You can diff `sprint/wbs.md` across commits to see how the project evolved — useful for the presentation ("here's our WBS from week 3 vs week 8").
+- **Single source of truth.** The Jira board is live and mutable; the committed WBS is an immutable record of what the board looked like when the agent ran.
+- **Cross-tool accessibility.** Team members who don't have Jira access (e.g., during presentations or offline reviews) can read the WBS from the repo.
+
+The `wbs_updater` agent (line 60-66 of `agents/project_mgmt/wbs_updater.py`) commits via the GitHub or Bitbucket MCP client with message `"Update WBS ({date})"`.
+
+### 4.2 "Is this even live? We have to validate."
+
+The `wbs_updater` calls `jira.get_board_status()` (line 40). If the Jira MCP client is configured with valid credentials (project key, API token), it pulls real data from the Jira REST API — board status, issue counts by status, recent issues with keys, summaries, priorities, and assignees.
+
+If Jira is not configured, the agent returns early with output type `"wbs_skipped"` and description `"Jira not configured"` (line 34). Validation requires a live run with Jira credentials set — run the project_mgmt pipeline and check whether `wbs_state` in the pipeline context contains real issue data. The Jira MCP client's `is_configured` property should be checked first.
+
+### 4.3 "What is in wbs_latest and wbs.md files?"
+
+The `_build_wbs()` method (line 75-97 of `wbs_updater.py`) produces a markdown document with:
+
+- **Header:** `# Work Breakdown Structure` with a `_Last synced: {datetime} UTC_` timestamp.
+- **Sections by status:** Four sections — "To Do", "In Progress", "In Review", "Done" — each with a count in the heading.
+- **Issue entries:** Each issue formatted as `- [{JIRA-KEY}] {summary} ({priority}) — {assignee}`.
+
+The `wbs_latest` entry in the SharedMemory wiki (line 52-56) stores: the full WBS content string, total_issues count, and by_status breakdown dictionary. This makes the WBS data available to other pipeline agents (e.g., weekly_digest reads it for sprint health reporting).
+
+### 4.4 "Weekly digest — what does it look like? Where stored? How notified?"
+
+The `weekly_digest` agent (`agents/project_mgmt/weekly_digest.py`) gathers data from three sources:
+
+1. **Jira** (line 76-80): Board status, issue counts, velocity data via `jira.get_board_status()`.
+2. **SharedMemory wiki** (line 83-93): Recent decisions (last 10), concerns (last 5), and requirement changes (last 5) from their respective namespaces.
+3. **EventBus** (line 96-105): Pending events from the last week, filtered for drift_detected events.
+
+The digest is generated via a Claude prompt (line 110-129) with sections: Decisions Made This Week, Requirements Changes, Sprint Health, Architecture (drift detected/resolved), and Next Week Preview.
+
+**Storage:** If Confluence MCP is configured, the digest is published as a Confluence page titled `"Weekly Digest — {date}"` (line 48-57). If Slack MCP is configured, it is posted to the Slack channel (line 40-45).
+
+**Gap:** There is no push notification mechanism beyond Slack. If Slack is not configured, the digest is generated but not actively delivered. Adding email notification or a webhook-based push would close this gap.
+
+### 4.5 "Where are alerts sent?"
+
+The `alert_agent` (`agents/project_mgmt/alert_agent.py`) checks four conditions:
+
+1. **Velocity alert** (line 72-91): Fires when < 30% of sprint tickets are Done (`done/total < 0.3`).
+2. **Unlinked requirements** (line 93-118): Fires when high-priority requirements (P0/P1) outnumber linked Jira tickets.
+3. **Unassigned P0** (line 120-134): Fires when P0-priority tickets have no assignee.
+4. **Unresolved drift** (line 137-153): Fires when `drift_detected` events exist with no consuming agent.
+
+**Delivery:** If Slack MCP is configured, alerts are sent via `slack.send_alert()` (line 38). If Slack is not configured, alerts are still generated and deposited to the SharedMemory wiki under `"alerts"/"latest"` (line 49-52) with output type `"alerts_generated"` noting `"Slack not configured"` (line 44-47).
+
+**Gap:** No email integration. No SMS/pager for critical alerts. Currently, alerts that cannot reach Slack are only visible via the wiki API or the intelligence dashboard. This is a known limitation.
+
+### 4.6 "What is Jira velocity? What is our velocity?"
+
+Velocity is the number of story points (or tickets) completed per sprint. The alert_agent computes a simplified version: `done / total` ratio from `jira.get_board_status()` (line 82-84). If this ratio drops below 30%, an alert fires.
+
+Our actual velocity needs to be pulled from live Jira data. The `by_status` dictionary from the Jira MCP provides the breakdown (To Do, In Progress, In Review, Done counts), from which velocity can be computed as Done tickets per sprint. This number should be cited from an actual pipeline run with Jira credentials configured.
+
+### 4.7 "Does this relate to SDLC choice?"
+
+Yes. The bespoke SDLC defined in `docs/sdlc_choice.md` explicitly includes "AI-augmented project management" as a practice area. The document maps the project_mgmt pipeline directly:
+
+| Practice Area | Pipeline | Activities (Agents) |
+|---|---|---|
+| Project Management | `project_mgmt` | Tickets → WBS Update → Weekly Digest → Alerts |
+
+The SDLC's "Continuous Measurement (Not Sprint Retrospectives)" principle (Section 3, line 66 of `sdlc_choice.md`) is implemented by the weekly_digest and alert_agent running on a cron schedule (`cron_friday_6pm`) rather than waiting for sprint ceremonies. The SDLC states: "Instead of looking back every 2 weeks, the measurement system runs continuously."
+
+The WBS sync, weekly digest, and health alerting are the three AI components of the PM practice area. They implement the SDLC's resource allocation of `auton` (autonomous agents) for project management, with `human` resources for phase gate reviews.
+
+### 4.8 "Counterfactuals for PM pipeline"
+
+**Without AI:** The project manager manually updates the WBS markdown by reading Jira and transcribing ticket statuses. They write the weekly digest by hand, pulling data from Jira, Confluence, and memory. They scan for blockers by reviewing the board and asking team members. Estimated time: 2-3 hours per week.
+
+**With AI:** The project_mgmt pipeline runs the three steps automatically. wbs_updater pulls from Jira and commits in seconds. weekly_digest aggregates from wiki, Jira, and EventBus, then generates the digest via one Claude call (~1 minute). alert_agent checks four conditions programmatically (~5 seconds). Total automated time: ~2 minutes.
+
+**But:** The AI-generated digest needs human review for accuracy — did it correctly characterize sprint health? Are the alert thresholds appropriate? Estimated human review: ~15 minutes.
+
+**Net value:** Saves 1-2 hours per week. More importantly, the alert_agent catches things humans miss: stale P0 tickets without assignees, velocity drops below threshold, unresolved drift events. These are systematic checks that a busy PM might skip during a hectic week.
+
+---
+
+## Section 5: Coding Pipeline
+
+### 5.1 "Should we do what Scott suggested: Agent 1 = plan, Agent 2 = test cases, Agent 3 = code?"
+
+Yes. Scott's suggestion aligns with Test-Driven Development and is a better pattern than our current coding pipeline structure. Our current `CODING_PIPELINE` (line 603-644 of `pipelines.py`) is: pr_reviewer → test_generator → doc_generator → prompt_regression. This is reactive — it runs after code is written, reviewing PRs and generating tests post hoc.
+
+Scott's pattern is proactive:
+1. **Agent 1 (Planner):** Reads the requirement/ticket, produces a design plan with file changes, API contracts, and edge cases.
+2. **Agent 2 (Test Writer):** Takes the plan and generates test cases before any code is written. This forces the team to think about what "correct" means before implementation.
+3. **Agent 3 (Code Generator):** Implements the plan, constrained by the tests. Tests run against the generated code automatically.
+
+Our current coding pipeline agents are mostly stubs — `pr_reviewer`, `test_generator`, `doc_generator`, and `prompt_regression` exist as pipeline steps but are future sprint items. Implementing Scott's pattern would be the right approach when coding begins in earnest.
+
+### 5.2 "PR quality? Boilerplate? SonarQube? Test coverage threshold?"
+
+Current state of each:
+
+- **pr_reviewer** (ETVX: CODE-REVIEW): Checks style, test presence, and traceability to a Jira ticket. Exists as a pipeline step definition; the agent implementation would use Claude to review PR diffs against project coding standards.
+- **boilerplate_generator**: Scaffolds new modules with standard project structure (imports, logging setup, base class inheritance, docstrings). Defined in pipeline but not yet exercised.
+- **SonarQube**: Not integrated. Could be added as a pipeline step that calls the SonarQube API after test_generator and before doc_generator. Would provide static analysis (code smells, security vulnerabilities, duplication).
+- **Test coverage threshold**: Not configured. A quality gate (e.g., 80% line coverage) could be enforced by adding a coverage check step that fails the pipeline if the threshold is not met.
+
+All of these are future sprint items — the coding pipeline will become active when the team transitions from framework development to ML pipeline implementation.
+
+### 5.3 "What questions should we address?"
+
+Three questions the team should be prepared to answer about the coding pipeline:
+
+1. **How will PRs be auto-reviewed when coding starts?** The pr_reviewer agent will receive PR diffs via the `pr_event` trigger, use Claude to check for style violations, missing tests, and traceability to Jira tickets, and post review comments on the PR via the GitHub MCP.
+
+2. **What quality gates are enforced?** Currently none are enforced automatically. The planned gates: PR review approval (human + AI), test coverage threshold, prompt regression pass (for ML-related changes), and documentation update verification.
+
+3. **How does code trace back to requirements?** Through the traceability store. Each Jira ticket (linked to a requirement) maps to PRs via the `IMPLEMENTS` link type. The pr_reviewer would verify that each PR references a Jira ticket, and the traceability_builder would create the link.
+
+---
+
+## Section 6: Coach Session Pipeline
+
+### 6.1 "How is chunking happening? What is ONNX model? What are vector embeddings?"
+
+**Chunking** is implemented in `agents/coach_memory/session_memory.py`, function `chunk_text()` (line 78-92). The transcript text is split into segments of 800 characters (`CHUNK_SIZE = 800`, line 33) with 100-character overlap (`CHUNK_OVERLAP = 100`, line 34). The overlap ensures that sentences straddling a chunk boundary appear in both adjacent chunks, preventing context loss at boundaries. If the entire transcript is shorter than 800 characters, it becomes a single chunk.
+
+**ONNX MiniLM-L6-v2** is a small language model (~23 MB) that runs locally via the ONNX runtime — no API call needed, no token cost. It is a distilled version of Microsoft's MiniLM model, optimized for sentence-level embeddings. The `VectorStoreMCP` class handles the ONNX inference. The model converts text into 384-dimensional floating-point vectors.
+
+**Vector embeddings** are mathematical representations of text meaning as arrays of numbers. Two text passages about similar topics produce vectors that are close together in 384-dimensional space. "Christian discussed threshold calibration" and "coach mentioned confidence thresholds" produce similar vectors even though the exact words differ. ChromaDB stores these vectors and finds the closest matches via cosine similarity. When you query "what did Christian say about measurement?", ChromaDB computes the query's embedding and returns the chunks whose embeddings are most similar — this is RAG (Retrieval-Augmented Generation).
+
+### 6.2 "I don't want commitment_tracker — seems redundant"
+
+Acknowledged. The `commitment_tracker` agent (`agents/coach_memory/commitment_tracker.py`) extracts promises from coach sessions with owners and deadlines, cross-references them against Jira and Bitbucket to verify delivery, and alerts on overdue items. Its pipeline step is ETVX: COACH-COMMIT (step 2 of the coach_session pipeline).
+
+The redundancy concern is valid: session_memory already extracts commitments during its `_extract_session_data()` or `_extract_offline()` methods (line 221-343 of `session_memory.py`) and stores them in the `commitments` table of `coach_sessions.db`. The commitment_tracker reads from the same table and adds delivery verification and overdue alerting. If the team decides these features are not valuable enough to justify a separate agent, the commitment_tracker step can be removed from `COACH_SESSION_PIPELINE` in `pipelines.py` (line 523-529). The commitments extracted by session_memory would still be captured in the SharedMemory wiki and the SQLite database.
+
+### 6.3 "Detect recurring themes — how? What 'across sessions'? Who set 3? Where are alerts?"
+
+**How:** The `concern_tracker` agent (`agents/coach_memory/concern_tracker.py`) runs a SQL aggregation query (line 89-96 of `concern_tracker.py`):
+
+```sql
+SELECT theme, raised_by, SUM(times_raised) as total_raised,
+ COUNT(DISTINCT session_id) as session_count,
+ GROUP_CONCAT(concern_text, ' | ') as all_concerns
+FROM concerns
+GROUP BY theme, raised_by
+ORDER BY total_raised DESC
+```
+
+This groups all concerns by theme (e.g., "monitorability", "threshold", "scope") and counts how many times each theme was raised and across how many distinct sessions.
+
+**"Across sessions":** The `concerns` table in `coach_sessions.db` accumulates concerns from every processed session. When session_memory processes a new transcript, it inserts new concerns or increments `times_raised` for existing themes (line 253-268 of `session_memory.py`). The concern_tracker then aggregates across the entire table, spanning all sessions.
+
+**Threshold:** `RECURRING_THRESHOLD = 2` (line 34 of `concern_tracker.py`). This is hardcoded but configurable — change the constant to adjust sensitivity. The value 2 was chosen as the minimum for identifying a pattern; a theme raised only once is a one-off, but raised twice indicates a recurring concern. The known themes list (line 22-32) includes: monitorability, hitl, evidence, threshold, architecture, process, testing, deployment, scope.
+
+**Alerts:** When recurring themes are found, the concern_tracker: (a) sends a Slack message via `slack.send_message()` if configured (line 56-60), (b) emits a `recurring_concern` event on the EventBus with theme details (line 71-75), and (c) deposits to the wiki under `"concerns"/"recurring_themes"` (line 76-80). The `recurring_concern` event is subscribed to by `project_mgmt/alert_agent`, which includes it in project health alerts.
+
+### 6.4 "Coach_linker — which session? Where are open ML decisions?"
+
+The `coach_linker` agent (`agents/ml_decision/coach_linker.py`) links the **current** coach session content to open ML decisions. It does not specify a particular session — it queries the ChromaDB `COLLECTION_SESSIONS` vector store for ML-related content using three hardcoded queries: "threshold calibration", "alpha weighting", and "drift detection" (line 67). Results with distance < 0.5 (high semantic similarity) are considered mentions.
+
+**Open ML decisions** are stored in `memory/ml_decisions.db` and accessed via the `DecisionLogAgent.get_open_decisions()` method (line 85). Each decision has fields: decision_id, name, current_value, basis, evidence_needed, and status. The coach_linker calls `_build_linked_context()` (line 83) which cross-references session mentions against open decisions by keyword overlap — if a session chunk mentions words from a decision's name, the link is made.
+
+Example flow: Christian mentions "threshold calibration" in a coach session → session_memory chunks and embeds it → coach_linker queries for "threshold calibration" → finds the chunk → matches it to open decision about confidence threshold → adds the coach context as evidence. The linked context includes the decision's current value, evidence count, and what evidence is still needed.
+
+### 6.5 "Decision_logger for coach sessions — shouldn't this be 'things to ponder'?"
+
+Valid critique. The `decision_logger` agent (`agents/knowledge/decision_logger.py`) treats all extracted items as "decisions" — it logs them to `minutes/decisions.log.md` in a table with columns Date, Decision, Source, and People Present. But not all coach session outputs are decisions in the firm, architectural sense.
+
+Coach sessions produce three types of items:
+- **DECISION** (firm): "We will use per-attribute routing" — a commitment to a specific technical approach.
+- **GUIDANCE** (advisory): "Think about measurement this way" or "Consider how you'd explain threshold to the client" — advice from Christian that informs but doesn't bind.
+- **QUESTION** (open item): "How will you handle schema evolution?" — an unresolved question that needs investigation.
+
+Currently, all three are logged as "decisions" in the same table. A better categorization would tag each entry with its type and allow the briefing_generator to distinguish between firm decisions (cite as constraints) and open questions (surface as agenda items for the next meeting). This is a valid enhancement — the decision_logger's `_build_log_update()` method (line 92-98) could add a "Type" column to the markdown table.
+
+### 6.6 "Who triggers coach_session pipeline?"
+
+A human. Someone drops a `.vtt` transcript file (e.g., `GMT20260224-190023_Recording.transcript.vtt`) into the project and runs the pipeline manually:
+
+```python
+executor.execute(COACH_SESSION_PIPELINE, {
+ "trigger_type": "coach_transcript",
+ "source": "transcripts/GMT20260224-190023_Recording.transcript.vtt",
+})
+```
+
+The trigger type is `"coach_transcript"` (defined at line 507 of `pipelines.py`). There is no automatic detection — no file watcher, no webhook. The same model applies as the requirements pipeline: a human initiates processing by providing the transcript file path.
+
+The session_memory agent parses the date from the filename pattern `GMT{YYYYMMDD}` (line 129 of `session_memory.py`) and uses it as the session date. In production, this could be automated with a file watcher on the transcripts directory.
+
+---
+
+## Section 7: ML Decision + Knowledge Pipelines
+
+### 7.1 "Are there ADRs for ML decisions?"
+
+Yes. The team authored real ADRs for ML-related decisions, visible in the git status:
+
+- **ADR-001** (`docs/adr/ADR-001-threshold-calibration.md`): Covers confidence threshold calibration — how to set and tune the threshold for ML model predictions.
+- **ADR-002** (`docs/adr/ADR-002-staging-tables.md`): Covers the staging table architecture — all ML predictions write to staging first, never directly to production.
+- **ADR-003** (`docs/adr/ADR-003-human-in-loop.md`): Covers the hybrid rule engine + semantic similarity approach and the human-in-the-loop review workflow.
+- **ADR-004** (`docs/adr/ADR-004-per-attribute-routing.md`): Covers per-attribute routing — each product attribute (description, material, manufacturer) is routed to a specialized model rather than using a single model for all attributes.
+
+These are team-written, not AI-generated. They represent the actual architectural decisions that the drift_detector checks against.
+
+### 7.2 "Who triggers ML decision pipeline?"
+
+The ML decision pipeline (`ML_DECISION_PIPELINE`, line 646 of `pipelines.py`) has trigger type `"poc_result"` — it activates when a proof-of-concept experiment produces results. The pipeline has three steps:
+
+1. **evidence_accumulator** (ETVX: ML-EVIDENCE): Parses POC results and logs evidence into the decision store.
+2. **readiness_detector** (ETVX: ML-READINESS): Checks if accumulated evidence is sufficient to close an open decision.
+3. **coach_linker** (ETVX: ML-LINK): Links evidence to coach session context for briefing enrichment.
+
+The pipeline is also activated by the `poc_evidence_logged` EventBus event, which fires when evidence_accumulator from another pipeline run deposits new evidence. Currently, triggering is manual — a human runs the pipeline after completing a POC experiment.
+
+### 7.3 "Who triggers knowledge pipeline?"
+
+The knowledge pipeline (`KNOWLEDGE_PIPELINE`, line 715 of `pipelines.py`) has trigger type `"cron_pre_meeting"` — designed to run before client or coach meetings to prepare context. It has two steps:
+
+1. **context_packager** (ETVX: KN-CONTEXT): Packages the current project state — open decisions, recent requirements changes, unresolved concerns, sprint health — into a structured context document.
+2. **briefing_generator** (ETVX: COACH-BRIEF): Takes the packaged context and generates a pre-meeting briefing with talking points, open items, and recommended agenda topics.
+
+Currently this is manual — someone runs the pipeline before a meeting. In production, it would be scheduled via cron to run 2 hours before each meeting, giving the team time to review the briefing. The trigger type name (`cron_pre_meeting`) reflects this intended production behavior.
+
+---
+
+## Section 8: Dashboards
+
+### 8.1 "Do we need a Goal Model dashboard?"
+
+Yes. A Goal Model dashboard would trace high-level project goals down to the artifacts that implement them. Goals should come from three sources:
+
+1. **Requirements:** The requirement artifacts in the traceability store include USER_GOAL and SOFT_GOAL categories. These represent what the client wants the system to achieve (e.g., "accurately classify product attributes," "reduce manual data entry time").
+2. **Architecture quality attributes:** From the canonical architecture document — performance targets, availability requirements, scalability constraints. These are the "-ilities" that the architecture must satisfy.
+3. **Stakeholder priorities:** From client meetings and the risk register — what Brian and Dewey care about most (e.g., "PIMS data quality," "review workflow efficiency").
+
+The dashboard would show each goal linked downward: Goal → Requirements that support it → Architecture decisions that enable it → Jira tickets that implement it → Current status. This leverages the existing traceability store — the data is there, the visualization is what's missing.
+
+### 8.2 "Knowledge graph — not needed, remove"
+
+Acknowledged. The knowledge graph tab in `dashboard/intelligence.html` should be removed. It adds visual complexity without delivering actionable insight beyond what the traceability tab already provides. The traceability store's chain-walking capability (`get_chain()`) already represents the artifact graph; a separate knowledge graph visualization is redundant.
+
+### 8.3 "WBS format — Liu's format or AI's?"
+
+Use Liu's format (team-authored) as the canonical WBS. The AI-generated WBS from the `wbs_updater` agent (`sprint/wbs.md`) is a Jira-synced snapshot organized by status (To Do, In Progress, In Review, Done) — it is useful as a live supplement but should not replace the team's authored WBS structure, which follows Liu's prescribed format with proper work packages, deliverables, and dependencies.
+
+The AI-generated WBS supplements Liu's format by providing current Jira state as evidence. During presentations, reference Liu's format for structure and the AI-generated snapshots for "here's what the board looked like at this date."
+
+### 8.4 "Give more granular lifecycle chains"
+
+The current traceability chains need to be more specific and meaningful. Here is an example of the level of granularity needed:
+
+**Chain example:** Meeting-2026-02-05 (client meeting) → concern "vendor format variation" (raised by Dewey) → REQ-001 "extract product data from heterogeneous PDF formats" → ADR-001 "pipe-and-filter architecture for ingestion" → EPARTS-42 (Jira ticket: implement PDF parser) → PR-15 (pull request: PDF extraction module) → Risk R-003 "vendor format changes break parser" (MITIGATES).
+
+Each link in this chain should be traceable in the traceability store with a specific link type: RAISED_IN → BECAME → DECIDED_BY → IMPLEMENTS → MITIGATES. The `get_chain()` method already supports forward and backward traversal; what's needed is ensuring the seed data creates these granular links rather than stopping at high-level associations.
+
+The traceability_builder's auto-generated `docs/traceability.md` includes "Full Lifecycle Paths" (line 122-146 of `traceability_builder.py`) — chains that touch 5+ artifacts across multiple types. These should be curated for the presentation to show the most compelling end-to-end stories.
+
+### 8.5 "Will LLM be better at traceability?"
+
+Yes. An LLM would understand semantic relationships that keyword matching misses. Current state vs. LLM-enhanced:
+
+**Current approach (keyword matching):** The `seed_traceability` module links artifacts by matching keywords — if a concern mentions "threshold" and a decision mentions "threshold," they get linked. Accuracy: approximately 85%. False positive rate: approximately 15% (links created where the semantic relationship is weak — e.g., two artifacts both mention "data" but are about different aspects of data).
+
+**With LLM:** The LLM would read both artifacts and determine whether a genuine semantic relationship exists. "Concern about data quality from vendors" and "Decision to implement validation rules" would be correctly linked even though they share no keywords. Estimated accuracy: ~95%. The LLM would also identify the correct link type (BECAME vs. ADDRESSES vs. RELATES_TO) rather than relying on heuristics.
+
+**Cost trade-off:** Re-linking all 189 artifacts with 760 candidate links would require ~200 Claude API calls (batch comparisons), costing approximately $0.05 for a full re-linking pass. This is negligible in absolute terms but adds up if run on every pipeline execution. The accepted trade-off: keyword matching is free, runs instantly, and achieves 85% accuracy — good enough for demonstration and daily use. LLM-enhanced linking can be run periodically (e.g., weekly) as a quality pass to correct mislinks and discover missed connections.
diff --git a/docs/product-spec-changelog.md b/docs/product-spec-changelog.md
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--- /dev/null
+++ b/docs/product-spec-changelog.md
@@ -0,0 +1,115 @@
+# Product Specification — change record
+
+Authoritative document: **`product-spec-v1.4.pdf`** — *Product Specification, Intelligent Ingestion & Attribute Prediction System*, eParts Studio Team, **Document Version 1.4, 29 July 2026**. LaTeX source: `product-spec-v1.4.tex`.
+
+This file is the greppable companion to that PDF. ADRs cite requirement IDs; this is where those IDs resolve.
+
+## Version history (verbatim from the spec's own table)
+
+| Version | Date | Change |
+|---|---|---|
+| 0.1 | Feb 01, 2026 | Initial draft based on eParts architectural review. |
+| 0.5 | Feb 10, 2026 | Added detailed functional requirements for Ingestion and ML Service. |
+| 1.0 | Apr 24, 2026 | Baseline specification for development. |
+| 1.1 | July 23, 2026 | Integrated ETIM classification/enrichment; corrected OCR (Azure Document Intelligence) and ingestion (channels, Azure Blob storage, quarantine). |
+| 1.2 | July 28, 2026 | Pinned the project to ETIM release 10.0 (language EI) for its duration (new constraint C-4); scoped FR-10 accordingly and removed the implied obligation to adopt later ETIM releases. |
+| **1.3** | **July 29, 2026** | **Corrected the placement of ETIM matching. ETIM class/feature/value matching is performed by the ML service *after* attribute matching, not by the Intermediate Structured Layer during normalization. HLR-2, §2.1 and SCEN-1 amended; FR-9 attributed to the ML service.** |
+| **1.4** | **July 29, 2026** | **Closed a trace gap opened by 1.1: the Quality Attribute Scenarios and Validation Requirements sections had not been revised for ETIM. Added QAS-3, VAL-4 and VAL-5. Also corrected three descriptions left inconsistent by 1.3 (the Canonical Table glossary entry and §1.2 both still implied ETIM keying during normalization; §1.2 did not mention ETIM matching at all), and replaced the §2 architecture figure with the current v6.0 diagram. No existing requirement was changed.** |
+
+All four revisions are **changes against the v1.0 baseline**, not re-baselines. New IDs were *added*; existing HLR and DR numbering was preserved so prior trace links survive.
+
+**v1.1** integrated ETIM. **v1.2** fixed the *scope* of that integration — we are pinned to one ETIM release and will not chase later ones ([ADR-020](0020-pin-etim-release-10-0-for-the-project-duration.md)). **v1.3** fixed the *placement* — ETIM matching belongs to the ML service, behind attribute matching, not to normalization ([ADR-016](0016-decompose-matching-into-staged-etim-class-feature-value-stages.md)). **v1.4** fixed our own *trace coverage*: we had spent three revisions on requirements and never revisited the quality scenarios or the validation tests, so for a month those two sections described a pre-ETIM system.
+
+### Added in v1.4 — quality scenario and validation coverage
+
+| ID | Content | Status |
+|---|---|---|
+| **QAS-3** | Modifiability — the client changes which ETIM features are mandatory for a class; the change is per-class configuration, no matching/routing/validation code is modified ([ADR-019](0019-externalize-client-feature-policy-as-per-class-configuration.md)). Measure: takes effect for the next batch with no code deployment. | Seam built, policy values still open on the client |
+| **VAL-4** | Load ETIM 10.0 (EI), then load it again. All 159 groups / 5,640 classes / 17,377 features / 201,284 class-feature-values present; second run is a no-op. | **10 unit tests passing** (`tests/unit/test_etim_loader.py` — load, row counts, idempotent re-import). `tests/integration/test_etim_real_files.py` repeats it against the real ETIM 10.0 archive but **skips unless that archive is present locally**, and it is not committed. Neither runs in CI. |
+| **VAL-5** | A below-threshold ETIM class assignment routes the item to class review, and no attribute-level routing happens for it ([ADR-018](0018-extend-routing-to-etim-signals-with-class-review-first.md)). | **Specified, not executable** — the matching stages are designed and not built |
+
+⚠️ **QAS-3 collides by number with the v2.0 lineage's QAS-3 (Accuracy).** QAS-3 is the next free number in *this* document; skipping it to avoid a foreign document's numbering would be worse. This is the same two-lineage reconciliation already recorded under known defects below.
+
+## What ETIM changed
+
+The system's *WHAT* moved from **"predict arbitrary product attributes"** to **"classify each product into the ETIM standard and map its attributes to a controlled vocabulary (class → feature → value / unit)."** Constrained classification, not free prediction.
+
+The governing principle, now stated in the spec's glossary and §2.1:
+
+> **Original supplier data = evidence · ETIM data = standardized interpretation · confidence = how sure we are of the interpretation.**
+
+### Requirements added in v1.1
+
+| ID | Statement (abridged) |
+|---|---|
+| **HLR-6** | The system shall classify products against the ETIM standard and enrich supplier attributes with ETIM identifiers (class, feature, value, unit), keeping the original values as evidence. |
+| **FR-9** | After attribute matching, the Attribute Prediction Service (ML) shall match attributes to ETIM classes, features, and controlled values/units, attaching a confidence score to each ETIM assignment and preserving the original supplier value. *(Attributed to ML in v1.3.)* |
+| **FR-10** | The system shall load and maintain the ETIM reference dictionary (product groups, classes, features, values, units, and class–feature–value mappings) as reference data for the pinned ETIM release identified in C-4. *(Wording scoped in v1.2; originally "as versioned reference data".)* |
+| **DR-4** | *(Must, traces HLR-6)* Approved data written to PIMS shall be keyed by ETIM identifiers (release, class, feature); the writeback idempotency key shall include these identifiers. |
+
+### Added in v1.2
+
+| ID | Statement (abridged) |
+|---|---|
+| **C-4** | *(constraint)* The system shall target ETIM release 10.0 (language EI) for the duration of this project. Adopting later ETIM releases, and migrating already-classified products between releases, are out of scope. |
+
+C-4 exists because FR-10 as first written implied an obligation we were not going to meet. Pinning the release is a decision we can defend; a half-built upgrade path is not. The `etim_release_id` field stays in the schema for provenance — see ADR-020.
+
+### Amended in v1.3 — where ETIM matching happens
+
+The v1.1 edit put ETIM keying in the wrong component. ETIM assignment is a *matching decision with a confidence attached*, so it belongs to the ML service and runs **after** attribute matching. Normalization has no model, no confidence, and no route to human review, so it cannot make that decision.
+
+| ID / section | v1.1–v1.2 said | v1.3 says |
+|---|---|---|
+| **HLR-2** | "normalize into a standardized, **ETIM-keyed** intermediate structure (mechanical cleanup followed by mapping to ETIM classes, features, and values)" | "normalize into a standardized intermediate structure through **mechanical cleanup only**… ETIM class, feature, and value assignment is performed **downstream by the ML service** (HLR-6, FR-9), not during normalization" |
+| **§2.1** Intermediate Structured Layer | converts raw inputs into "ETIM-keyed canonical tables" | converts raw inputs into canonical tables holding the supplier's own values as evidence; "it does **not** assign ETIM identifiers" |
+| **§2.1** Attribute Prediction Service | "Runs ML models and heuristic rules" | **"(ML): Owns all matching, in two phases"** — attribute matching, then ETIM matching (class, feature, value/unit, ETIM validation, policy validation) |
+| **FR-9** | "The system shall match normalized attributes…" | "**After attribute matching, the Attribute Prediction Service (ML) shall** match attributes…" |
+| **SCEN-1** step 3 | normalization "maps to ETIM class/features" | "No ETIM assignment happens here"; step 4 now cites FR-3 **and FR-9** and does both matching phases |
+
+### Requirements amended in v1.1
+
+| ID / section | Before | After |
+|---|---|---|
+| **HLR-2** | "normalize ingested data into a standardized intermediate structure" | "...into a standardized, **ETIM-keyed** intermediate structure (mechanical cleanup followed by mapping to ETIM classes, features, and values), **preserving original supplier values as evidence**" |
+| **HLR-1** | Email/SFTP/CSV/PDF as if all live | SFTP and direct upload **today**; Email and web **planned** |
+| **§2.1** Intermediate Structured Layer | "standard canonical tables" | "**ETIM-keyed** canonical tables (class / feature / value), preserving original supplier values as evidence" |
+| **§2.1** Ingestion Gateway | generic OCR | Datasheet PDFs OCR'd via **Azure AI Document Intelligence**; text-native CSV/PDF parsed deterministically |
+| **§3.1** | — | Azure AI Document Intelligence + Azure OpenAI named; Azure Blob **accessed through an S3-compatible interface (MinIO in local/dev)** |
+| **Glossary** | — | ETIM entry added; PIMS entry now states approved output is **keyed by ETIM identifiers** |
+| **SCEN-1 step 3** | "Normalizes CSV columns to Canonical Table format" | "Cleans and normalizes CSV columns into the **ETIM-keyed** Canonical Table (maps to ETIM class/features), **retaining original values**" |
+| **SCEN-2 step 1** | "Ingests PDF. OCR extraction is messy." | Names the Azure Document Intelligence + LLM extraction path |
+
+### What no longer holds
+
+- **FR-3 "predict product attributes"** → constrained *matching* to ETIM values. This changes both the definition of accuracy and the ML contract.
+- **The flat `IngestedRecord`** → `staging_product` + `staging_raw_attribute` split with evidence columns (see ADR-014). The flat path is to be retired.
+
+### New requirement *types* introduced
+
+- **Reference-data requirement** (FR-10) — maintaining an external dictionary is neither a classic behaviour nor a quality attribute. It is tied to an external standard with its own release cadence, which forced a scope decision: we **pin to ETIM 10.0 EI** and put upgrades out of scope (C-4, ADR-020).
+- **Derived cascade** (DR-4) — the PIMS idempotency key derives from HLR-6; confidence now attaches **per ETIM assignment**, not per raw attribute.
+- **New constraints** — metric-canonical storage where ETIM expects metric units; one ETIM class per sellable SKU; English (EI) ETIM language; phase-one valve/actuator scope only.
+
+## Requirement ID inventory (v1.4)
+
+- **HLR-1 … HLR-6**
+- **FR-1 … FR-10**
+- **DR-1 … DR-4**
+- **QAS-1** Modifiability (extensibility — new supplier format in ≤4 engineering hours) · **QAS-2** Usability (reviewer processes 10 items/min) · **QAS-3** Modifiability (client feature policy is configuration, no deploy)
+- **C-1** cost-effective design · **C-2** privacy compliance (GDPR/CCPA deletion) · **C-3** breadth-first delivery · **C-4** ETIM release pinned to 10.0 EI
+- **DC-1** Python backend · **DC-2** Auth0 · **DC-3** raw files preserved in Azure Blob
+- **VAL-1** ingestion trigger · **VAL-2** routing logic · **VAL-3** PIMS integration · **VAL-4** ETIM dictionary load (runs today) · **VAL-5** class review before attribute routing (specified only)
+- **SCEN-1** end-to-end happy path · **SCEN-2** low-confidence human-in-the-loop
+
+## Known traceability defects (owned, not hidden)
+
+1. **Two spec lineages exist.** A parallel document — *Product Specification, "Document Version 2.0", April 24 2026* — carries a different and larger ID set (FR-1…13, QAS-1…5 with QAS-1 = Accuracy ≥95%, C-1…8, DR-1…3). It is **not** an ancestor of this one; this lineage runs 0.1 → 0.5 → 1.0 (Apr 24) → 1.1 → 1.2 → 1.3 → 1.4. That document is not committed here to avoid implying a version chain that does not exist.
+2. **ADRs 0001–0015 cite the other lineage's IDs.** References to QAS-4, QAS-5, C-7 and FR-11/12/13 do not resolve against v1.4 at all, and their QAS-3 (Accuracy) resolves to a *different* scenario than this lineage's QAS-3 (client feature policy, added in v1.4). Those ADRs are the spring record and are deliberately left unedited (see `ETIM-ADR-ASSESSMENT.md`); ADRs 0016–0021 cite v1.4 IDs. Reconciling the two ID spaces is open work.
+3. **Two ADR series collide.** `docs/00NN-*.md` (the platform series, 0001–0021) and `docs/adr/ADR-00N-*.md` (an agent-generated series) use overlapping numbers for different decisions. Only the `00NN-` series is authoritative. See `adr-index.md`.
+
+## Open client decisions that still gate requirements
+
+Phase-one valve/actuator class list · **feature policy per class** (required / recommended / optional / conditional — this blocks firm validation requirements) · required-field publish blockers · Compare Tool and website-filter feature sets · ETIM "Other" handling · metric-canonical storage and UI display units · PIMS ETIM-ID storage format · one-primary-class-per-SKU confirmation · valve + actuator assemblies · mapping/policy sign-off ownership.
+
+*ETIM release-upgrade governance was on this list and is now closed — C-4 puts it out of scope (ADR-020).*
diff --git a/docs/product-spec-v1.4.pdf b/docs/product-spec-v1.4.pdf
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diff --git a/docs/product-spec-v1.4.tex b/docs/product-spec-v1.4.tex
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+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% PRODUCT SPECIFICATION - eParts Services
+% Capstone Project - CMU MSE
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\documentclass[12pt, a4paper]{article}
+
+% --- PACKAGES ---
+\usepackage[T1]{fontenc}
+\usepackage[a4paper, top=2.5cm, bottom=2.5cm, left=2.5cm, right=2.5cm]{geometry}
+\usepackage[hidelinks,hypertexnames=false]{hyperref}
+\usepackage{titling}
+\usepackage{fancyhdr}
+\usepackage{booktabs}
+\usepackage{array}
+\usepackage{longtable}
+\usepackage{graphicx}
+\usepackage{float}
+\usepackage{enumitem}
+
+% --- DOCUMENT METADATA ---
+\title{Product Specification \\ \vspace{0.5cm} \Large Intelligent Ingestion \& Attribute Prediction System}
+\author{eParts Studio Team}
+\date{\today}
+
+% --- HEADER & FOOTER SETUP ---
+\pagestyle{fancy}
+\fancyhf{}
+\fancyfoot[C]{\thepage}
+\renewcommand{\headrulewidth}{0pt}
+\renewcommand{\footrulewidth}{0pt}
+
+% --- TITLE PAGE CUSTOMIZATION ---
+\pretitle{\begin{center}\Huge\bfseries}
+\posttitle{\end{center}}
+\preauthor{\begin{center}\Large \vspace{1.5cm}}
+\postauthor{\\[1cm] \normalsize Client: eParts Services LLC \\[0.5cm] Document Version: 1.4 \end{center}}
+\predate{\begin{center}\large}
+\postdate{\end{center}\vfill}
+
+% --- DOCUMENT BEGINS ---
+\begin{document}
+\hypersetup{pageanchor=false}
+
+% --- 1. COVER PAGE ---
+\maketitle
+\thispagestyle{empty}
+\newpage
+
+% --- 2. TABLE OF CONTENTS ---
+\pagestyle{fancy}
+\hypersetup{pageanchor=true}
+\setcounter{page}{1}
+\pagenumbering{roman}
+\tableofcontents
+\newpage
+\pagenumbering{arabic}
+
+% --- 3. VERSION HISTORY ---
+\section*{Version History}
+\begin{tabular}{@{}llp{10cm}@{}}
+\toprule
+\textbf{Version} & \textbf{Date} & \textbf{Change} \\
+\midrule
+0.1 & Feb 01, 2026 & Initial draft based on eParts architectural review. \\
+0.5 & Feb 10, 2026 & Added detailed functional requirements for Ingestion and ML Service. \\
+1.0 & Apr 24, 2026 & Baseline specification for development. \\
+1.1 & Jul 23, 2026 & Integrated ETIM classification/enrichment; corrected OCR (Azure Document Intelligence) and ingestion (channels, Azure Blob storage, quarantine). \\
+1.2 & Jul 28, 2026 & Pinned the project to ETIM release 10.0 (language EI) for its duration (new constraint C-4); scoped FR-10 accordingly and removed the implied obligation to adopt later ETIM releases. \\
+1.3 & Jul 29, 2026 & Corrected the placement of ETIM matching. ETIM class/feature/value matching is performed by the ML service \emph{after} attribute matching, not by the Intermediate Structured Layer during normalization. HLR-2, \S2.1 and SCEN-1 amended accordingly; FR-9 attributed to the ML service. \\
+1.4 & Jul 29, 2026 & Closed a trace gap opened by 1.1: the Quality Attribute Scenarios and Validation Requirements sections had not been revised for ETIM. Added QAS-3 (client feature policy applied as configuration, per ADR-019) and VAL-4/VAL-5 (ETIM dictionary load; class review before attribute routing). Also corrected three descriptions left inconsistent by the 1.3 amendment --- the Canonical Table glossary entry and \S1.2 both still implied ETIM keying during normalization, and \S1.2 did not mention ETIM matching at all --- and replaced the \S2 architecture figure with the current v6.0 diagram. No existing requirement was changed. \\
+\bottomrule
+\end{tabular}
+
+\vspace{1cm}
+
+% --- 4. GLOSSARY ---
+\section*{Glossary}
+\begin{longtable}{@{}l >{\raggedright\arraybackslash}p{12cm}@{}}
+\toprule
+\textbf{Term} & \textbf{Definition} \\
+\midrule
+\endfirsthead
+\toprule
+\textbf{Term} & \textbf{Definition} \\
+\midrule
+\endhead
+\bottomrule
+\endfoot
+\bottomrule
+\endlastfoot
+PIMS & Product Information Management System. The system where the final product catalog data lives (downstream source of truth). Approved output is written to PIMS keyed by ETIM identifiers. \\
+\addlinespace
+Ingestion Gateway & The entry point for raw supplier files (CSV and PDF, via SFTP or direct upload; Email and web sources are planned) into the system. \\
+\addlinespace
+Confidence Score & A numerical value (0-1) assigned by the ML model indicating the probability that a predicted attribute is correct. \\
+\addlinespace
+Human-in-the-Loop & A workflow where low-confidence ML predictions are routed to a human operator for manual verification. \\
+\addlinespace
+ETIM & A standardized technical product-classification model (product groups $\rightarrow$ classes $\rightarrow$ features $\rightarrow$ allowed values / units) used to normalize and enrich supplier attributes. It is reference data layered onto the pipeline, not supplier product data. This project targets ETIM release \textbf{10.0}, language \textbf{EI}, and does not adopt later releases (C-4). \\
+\addlinespace
+Canonical Table & A standardized, intermediate database schema used to normalize disparate supplier formats before prediction. It holds the supplier's own product identity and attribute values as evidence. ETIM identifiers are \emph{not} assigned here; the ML service attaches them afterwards into a separate interpretation table (ADR-014). \\
+\end{longtable}
+\newpage
+
+% --- 5. MAIN DOCUMENT ---
+
+\section{Introduction}
+
+\subsection{Purpose}
+The purpose of this system is to automate the ingestion, classification, and attribute extraction of product specifications for eParts Services. Currently, this process is manual, error-prone, and unscalable. This project aims to:
+\begin{itemize}
+ \item Standardize disparate supplier data formats (PDF, CSV, Email).
+ \item Utilize Machine Learning to predict product attributes with confidence scoring.
+ \item Implement a "Human-in-the-Loop" workflow to review low-confidence predictions, ensuring high data quality for the PIMS.
+\end{itemize}
+
+\subsection{Product Description}
+The system is a cloud-native data pipeline composed of three primary stages:
+\begin{enumerate}
+ \item \textbf{Ingestion \& Normalization:} A gateway that accepts raw files via SFTP or direct upload (Email and web sources planned), validates them, and converts them into a structured intermediate format holding the supplier's own values. Text-native CSV/PDF are parsed deterministically; datasheet PDFs are OCR'd (Azure AI Document Intelligence) before extraction.
+ \item \textbf{Prediction Service:} An ML-driven engine that matches attributes (e.g., Voltage, Dimensions, Material) and then matches them onto the ETIM classification standard, assigning a confidence score at each step.
+ \item \textbf{Review \& Writeback:} A routing layer that auto-accepts high-confidence data into PIMS and flags low-confidence data for a React-based Human Review UI.
+\end{enumerate}
+
+\subsection{Stakeholders \& Personas}
+
+\subsubsection*{[PS-1] Persona: The Ops Reviewer (Internal)}
+The Ops Reviewer is a non-technical domain expert at eParts. They are responsible for ensuring catalog accuracy but are currently overwhelmed by manual data entry.
+\begin{itemize}
+ \item \textbf{Goal:} Review and approve items quickly without needing to manipulate raw SQL or JSON.
+ \item \textbf{Pain Point:} Spending hours manually copying data from PDFs to Excel.
+ \item \textbf{Need:} A clean UI that highlights exactly \emph{what} needs review (low confidence items) and links back to the source document.
+\end{itemize}
+
+\subsubsection*{[PS-2] Persona: The Supplier (External)}
+The Supplier is a manufacturer providing product catalogs. They utilize various legacy formats and are resistant to changing their own internal systems.
+\begin{itemize}
+ \item \textbf{Goal:} Submit product data with minimal friction.
+ \item \textbf{Constraint:} Will not adhere to a strict API schema; sends "messy" CSVs or PDFs via Email/SFTP.
+\end{itemize}
+
+\newpage
+\section{System Architecture}
+
+The system follows a microservices-based pipeline architecture designed for breadth-first delivery.
+
+\begin{figure}[H]
+ \centering
+ \includegraphics[width=\textwidth,height=0.82\textheight,keepaspectratio]{arch.png}
+ \caption{eParts reference architecture, v6.0. Solid outlines are running code; dashed outlines are designed and not yet built.}
+ \label{fig:architecture}
+\end{figure}
+
+\subsection{Component Descriptions}
+\begin{itemize}
+ \item \textbf{Ingestion Gateway:} Handles "messy" inputs. Identifies submission source, creates ingestion records, and archives raw files to Azure Blob Storage for auditability. Datasheet PDFs are OCR'd via Azure AI Document Intelligence; text-native CSV/PDF are parsed deterministically.
+ \item \textbf{Intermediate Structured Layer:} Converts raw inputs into standard canonical tables holding the supplier's own values as evidence (product identity plus raw attributes). It performs mechanical cleanup and schema-based transformation only; it does \emph{not} assign ETIM identifiers.
+ \item \textbf{Attribute Prediction Service (ML):} Owns all matching, in two phases. \textbf{Attribute matching} maps a raw supplier label and value onto an attribute identity, value, and confidence. \textbf{ETIM matching} then runs against the ETIM reference dictionary: class match, feature match, value and unit normalization, ETIM validation, and client-policy validation, attaching a confidence score to each ETIM assignment. Outputs the original value as evidence alongside the ETIM identifiers.
+ \item \textbf{Confidence Routing:} A logic layer that splits traffic. High confidence $\rightarrow$ Auto-Accept. Low confidence $\rightarrow$ Human Review Queue.
+ \item \textbf{Engineering Ops / Observability:} Centralized logging (datadog) and metrics to track model drift and ingestion success rates.
+\end{itemize}
+
+\newpage
+\section{Operational Environment}
+
+\subsection{Cloud Infrastructure}
+\begin{itemize}
+ \item \textbf{Hosting:} The system shall be hosted on public cloud (Azure) using containerized services (Kubernetes/Docker).
+ \item \textbf{Database:}
+ \begin{itemize}
+ \item \textbf{Transactional:} PostgreSQL/MSSQL for user accounts, workflow state, and intermediate tables.
+ \item \textbf{Data Warehouse:} (Future) for historical model training data.
+ \end{itemize}
+ \item \textbf{Document Extraction:} Azure AI Document Intelligence (layout OCR) with Azure OpenAI for datasheet attribute extraction.
+ \item \textbf{Object Storage:} Azure Blob Storage archives raw supplier files for traceability, accessed through an S3-compatible interface (MinIO in local/dev).
+ \item \textbf{Authentication:} Auth0 must be used for all internal user sign-in and role-based access control (RBAC).
+\end{itemize}
+
+\subsection{System Constraints}
+\begin{tabular}{@{}l >{\raggedright\arraybackslash}p{12cm}@{}}
+\toprule
+\textbf{ID} & \textbf{Constraint} \\
+\midrule
+C-1 & \textbf{Cost-Effective Design:} Avoid architectures where cost grows linearly per tenant; prefer shared resources where possible. \\
+\addlinespace
+C-2 & \textbf{Privacy Compliance:} The system must support data deletion requests (GDPR/CCPA) for specific supplier submissions. \\
+\addlinespace
+C-3 & \textbf{Breadth-First Delivery:} The initial release must demonstrate a full end-to-end flow for a single supplier type before optimizing model depth. \\
+\addlinespace
+C-4 & \textbf{ETIM Release Pinned:} The system shall target ETIM release 10.0 (language EI) for the duration of this project. Adopting later ETIM releases, and migrating already-classified products between releases, are out of scope. \\
+\bottomrule
+\end{tabular}
+
+\newpage
+\section{Requirements}
+
+\subsection{High Level Requirements (HLR)}
+\begin{tabular}{@{}lp{12cm}@{}}
+\toprule
+\textbf{ID} & \textbf{High Level Requirement} \\
+\midrule
+HLR-1 & The system shall ingest product data from diverse supplier sources (SFTP and direct upload today; Email and web sources planned) in CSV and PDF formats. \\
+\addlinespace
+HLR-2 & The system shall normalize ingested data into a standardized intermediate structure through mechanical cleanup only, preserving original supplier values as evidence. ETIM class, feature, and value assignment is performed downstream by the ML service (HLR-6, FR-9), not during normalization. \\
+\addlinespace
+HLR-3 & The system shall predict product attributes and assign confidence scores using Machine Learning. \\
+\addlinespace
+HLR-4 & The system shall provide a user interface for human review of low-confidence predictions. \\
+\addlinespace
+HLR-5 & The system shall write approved data back to the PIMS (Product Information Management System). \\
+\addlinespace
+HLR-6 & The system shall classify products against the ETIM standard and enrich supplier attributes with ETIM identifiers (class, feature, value, unit), keeping the original values as evidence. \\
+\bottomrule
+\end{tabular}
+
+\subsection{Functional Requirements (FR)}
+% UPDATED: Increased width to p{14cm} to better fill page width
+\begin{longtable}{@{}l >{\raggedright\arraybackslash}p{14cm}@{}}
+\toprule
+\textbf{ID} & \textbf{Requirement (EARS Format)} \\
+\midrule
+\endfirsthead
+\toprule
+\textbf{ID} & \textbf{Requirement (EARS Format)} \\
+\midrule
+\endhead
+\bottomrule
+\endfoot
+\bottomrule
+\endlastfoot
+FR-1 & Upon receipt of a file via SFTP or direct upload (Email and web ingestion are planned), the Ingestion Gateway shall create an ingestion record containing supplier ID, timestamp, and source channel. \\
+\addlinespace
+FR-2 & The Ingestion Gateway shall validate basic file integrity (type, size, virus scan) before processing; records that fail validation shall be routed to a quarantine store for triage rather than silently dropped. \\
+\addlinespace
+FR-3 & The Attribute Prediction Service shall generate a confidence score (0.0 to 1.0) for every predicted attribute. \\
+\addlinespace
+FR-4 & If a prediction's confidence score is below the configured threshold, the system shall route the item to the Human Review Queue. \\
+\addlinespace
+FR-5 & The Human Review UI shall display the predicted value alongside the original source snippet (text/image) for verification. \\
+\addlinespace
+FR-6 & The system shall log every human review action (accept/edit/reject) to an immutable audit trail. \\
+\addlinespace
+FR-7 & The system shall allow authorized Ops Leads to adjust the confidence threshold for auto-acceptance. \\
+\addlinespace
+FR-8 & Upon final approval (auto or human), the system shall write the attributes to PIMS via API. \\
+\addlinespace
+FR-9 & After attribute matching, the Attribute Prediction Service (ML) shall match attributes to ETIM classes, features, and controlled values/units, attaching a confidence score to each ETIM assignment and preserving the original supplier value. \\
+\addlinespace
+FR-10 & The system shall load and maintain the ETIM reference dictionary (product groups, classes, features, values, units, and class--feature--value mappings) as reference data for the pinned ETIM release identified in C-4. \\
+\end{longtable}
+
+\subsection{Derived Requirements (DR)}
+\begin{tabular}{@{}l l >{\raggedright\arraybackslash}p{8cm} l@{}}
+\toprule
+\textbf{ID} & \textbf{Priority} & \textbf{Requirement} & \textbf{Trace} \\
+\midrule
+DR-1 & Must & The system shall archive the original raw file in Azure Blob Storage as "evidence" for audit purposes even after processing. & HLR-1 \\
+\addlinespace
+DR-2 & Should & The system shall support manual triggering of model retraining using corrected data from the Review Queue. & HLR-3 \\
+\addlinespace
+DR-3 & Must & The Writeback service must be idempotent; retrying a write to PIMS must not create duplicate records. & HLR-5 \\
+\addlinespace
+DR-4 & Must & Approved data written to PIMS shall be keyed by ETIM identifiers (release, class, feature); the writeback idempotency key shall include these identifiers. & HLR-6 \\
+\bottomrule
+\end{tabular}
+
+\newpage
+\section{Quality Attribute Scenarios}
+
+\subsection*{QAS-1: Modifiability (Extensibility)}
+\begin{longtable}{@{}lp{12cm}@{}}
+\toprule
+\textbf{Attribute} & \textbf{Scenario} \\
+\midrule
+\textbf{ID} & QAS-1 \\
+\textbf{Attribute} & Modifiability \\
+\textbf{Source} & Developer \\
+\textbf{Stimulus} & A new supplier format (e.g., a new JSON schema) needs to be added to the pipeline. \\
+\textbf{Response} & The developer adds a new mapping configuration without rewriting the core pipeline code. \\
+\textbf{Measure} & A new supplier format can be integrated and deployed within 4 hours of engineering effort. \\
+\bottomrule
+\end{longtable}
+
+\subsection*{QAS-2: Usability (Ops Efficiency)}
+\begin{longtable}{@{}lp{12cm}@{}}
+\toprule
+\textbf{Attribute} & \textbf{Scenario} \\
+\midrule
+\textbf{ID} & QAS-2 \\
+\textbf{Attribute} & Usability \\
+\textbf{Source} & Ops Reviewer \\
+\textbf{Stimulus} & The queue contains 100 low-confidence items requiring review. \\
+\textbf{Response} & The UI presents items with "diff" views and hotkeys for acceptance. \\
+\textbf{Measure} & The reviewer can process simple accept/reject decisions at a rate of 10 items per minute. \\
+\bottomrule
+\end{longtable}
+
+\subsection*{QAS-3: Modifiability (Client Feature Policy)}
+\begin{longtable}{@{}lp{12cm}@{}}
+\toprule
+\textbf{Attribute} & \textbf{Scenario} \\
+\midrule
+\textbf{ID} & QAS-3 \\
+\textbf{Attribute} & Modifiability \\
+\textbf{Source} & Client / Ops Lead \\
+\textbf{Stimulus} & The client changes which ETIM features are mandatory for a given product class. ETIM itself carries no required-field flag, so this policy is ours to hold. \\
+\textbf{Response} & The change is applied as per-class configuration; no matching, routing or validation code is modified (ADR-019). \\
+\textbf{Measure} & The revised policy takes effect for the next ingested batch without a code deployment. \\
+\bottomrule
+\end{longtable}
+
+\newpage
+\section{Operational Scenarios}
+
+\subsection*{[SCEN-1] End-to-End Ingestion (Happy Path)}
+\begin{itemize}
+ \item \textbf{Objective:} A supplier submits a catalog, and it is processed into PIMS without human intervention.
+ \item \textbf{Actors:} Supplier, System
+\end{itemize}
+
+\begin{longtable}{@{}llp{2cm}p{8cm}@{}}
+\toprule
+\textbf{Source} & \textbf{Step \#} & \textbf{Req.} & \textbf{Action} \\
+\midrule
+Supplier & 1 & - & Uploads a CSV catalog to the designated SFTP folder. \\
+System & 2 & FR-1 & Detects file, creates ingestion record, and archives raw file. \\
+System & 3 & HLR-2 & Cleans and normalizes CSV columns into the Canonical Table, retaining the supplier's original values as evidence. No ETIM assignment happens here. \\
+System & 4 & FR-3, FR-9 & ML Service matches attributes, then matches them to ETIM class, features, and values. All confidence scores are $>$ 0.90. \\
+System & 5 & FR-8 & Auto-Accept logic triggers. Data is written to PIMS. \\
+\bottomrule
+\end{longtable}
+
+\subsection*{[SCEN-2] Low Confidence \& Review (Human-in-the-Loop)}
+\begin{itemize}
+ \item \textbf{Objective:} A messy PDF is ingested, low confidence is flagged, and a human corrects it.
+ \item \textbf{Actors:} System, Ops Reviewer
+\end{itemize}
+
+\begin{longtable}{@{}llp{2cm}p{8cm}@{}}
+\toprule
+\textbf{Source} & \textbf{Step \#} & \textbf{Req.} & \textbf{Action} \\
+\midrule
+System & 1 & FR-1 & Ingests a datasheet PDF; routed to Azure AI Document Intelligence (layout OCR) with LLM extraction. Raw OCR output is noisy. \\
+System & 2 & FR-3 & ML Service predicts "Voltage: 12V" with confidence 0.45. \\
+System & 3 & FR-4 & Confidence $<$ Threshold (0.80). item routed to Review Queue. \\
+Ops User & 4 & FR-5 & Opens Review UI. Sees "12V" predicted but PDF says "24V". \\
+Ops User & 5 & FR-6 & Corrects value to "24V" and clicks Approve. Action logged. \\
+System & 6 & FR-8 & Validated data "24V" is written to PIMS. \\
+\bottomrule
+\end{longtable}
+
+\newpage
+\section{Design Constraints \& Validation}
+
+\subsection{Design Constraints}
+% UPDATED: Adjusted column widths to p{10.5cm} and p{3.5cm} to fill page width
+\begin{tabular}{@{}l >{\raggedright\arraybackslash}p{10.5cm} >{\raggedright\arraybackslash}p{3.5cm}@{}}
+\toprule
+\textbf{ID} & \textbf{Constraint} & \textbf{Source} \\
+\midrule
+DC-1 & \textbf{Tech Stack:} Backend must be Python based (for ML library compatibility). & Team Charter \\
+\addlinespace
+DC-2 & \textbf{Auth:} Must use Auth0 for identity management. & Client Req \\
+\addlinespace
+DC-3 & \textbf{Audit:} Raw files must be preserved in Azure Blob Storage for re-processing and traceability. & Regulatory \\
+\bottomrule
+\end{tabular}
+
+\subsection{Validation Requirements}
+\begin{longtable}{@{}p{2cm} >{\raggedright\arraybackslash}p{3.8cm} >{\raggedright\arraybackslash}p{3.8cm} >{\raggedright\arraybackslash}p{3.8cm}@{}}
+\toprule
+\textbf{Test ID} & \textbf{Description} & \textbf{Steps} & \textbf{Expected Result} \\
+\midrule
+VAL-1 & \textbf{Ingestion Trigger} & Upload file to SFTP. & Ingestion record appears in DB within 30 seconds. \\
+\addlinespace
+VAL-2 & \textbf{Routing Logic} & Mock ML response with Conf=0.2. & Item appears in Human Review Queue. \\
+\addlinespace
+VAL-3 & \textbf{PIMS Integration} & Approve item in UI. & API call to PIMS returns 200 OK. \\
+\addlinespace
+VAL-4 & \textbf{ETIM Dictionary Load} (FR-10) & Load the ETIM 10.0 (EI) release files, then run the same load again. & All 159 groups, 5{,}640 classes, 17{,}377 features and 201{,}284 class-feature-values are present, and the second run is a no-op. \\
+\addlinespace
+VAL-5 & \textbf{Class Review Before Attribute Routing} (FR-9) & Supply a product whose ETIM class assignment falls below the class-confidence threshold. & The item is routed to class review and no attribute-level routing is performed for it. \\
+\bottomrule
+\end{longtable}
+
+\vspace{0.5cm}
+\noindent VAL-4 is covered by \texttt{tests/unit/test\_etim\_loader.py} --- 10 tests, all passing --- which exercise the load, the row counts and the idempotent re-import against an in-memory database. \texttt{tests/integration/test\_etim\_real\_files.py} performs the same load against the real ETIM 10.0 archive; it skips automatically unless that archive is present locally, and the archive is not committed, so it does not run on a clean checkout. Neither runs in CI. VAL-5 is specified but not yet executable: the ETIM matching stages are designed and not built, so the test exists as a specification only. This is recorded rather than deferred silently.
+
+\end{document}
\ No newline at end of file
diff --git a/docs/prompt_management.md b/docs/prompt_management.md
new file mode 100644
index 0000000..464c413
--- /dev/null
+++ b/docs/prompt_management.md
@@ -0,0 +1,128 @@
+# Prompt Management — How We Govern LLM Usage
+
+The fundamental problem: 5 team members using the same LLM on the same input will get different results. Prompts are probabilistic. Without governance, our SES produces non-reproducible artifacts.
+
+---
+
+## The System
+
+### 1. Centralized Prompt Storage
+
+Every prompt lives in `/prompts/*.txt` — never inline in code.
+
+```
+prompts/
+├── transcript_parser.txt # Parses .vtt meeting transcripts → structured JSON
+├── priority_classifier.txt # Classifies action items into P0/P1/P2
+├── req_extractor.txt # Synthesizes formal requirements from meeting data
+├── session_extraction.txt # Extracts coach session summaries
+└── briefing_generator.txt # Generates meeting briefing documents
+```
+
+Why not inline? Because inline prompts are invisible. Nobody can review what you put in a string literal buried in line 47 of your agent. Centralized storage means every prompt is visible, diffable, and reviewable.
+
+### 2. Version Control via Hash Pinning
+
+Every prompt file has a SHA-256 content hash. When an agent runs, it loads the prompt through the `PromptRegistry`, which:
+
+1. Computes the hash of the current file content
+2. Checks if this hash matches the **active version** in the registry
+3. If it's a new hash (someone changed the file), it registers it as `pending_review`
+4. The agent always uses the **pinned active version**, not whatever's on disk
+
+This means: if you edit `transcript_parser.txt`, your change doesn't take effect until it's reviewed and activated. The old version keeps running.
+
+**Current registry state:**
+
+| Prompt | Active Hash | Author | Versions |
+|--------|-------------|--------|----------|
+| transcript_parser | v3277a42a | Ashritha | 2 |
+| priority_classifier | v5677a0b9 | Ashritha | 1 |
+| req_extractor | vb8b387c7 | Ashritha | 1 |
+| session_extraction | v763d8a27 | Ashritha | 1 |
+| briefing_generator | ve304cdc6 | Ashritha | 1 |
+
+### 3. Peer Review Workflow
+
+```
+Author edits prompt → registers new version (status: pending_review)
+ ↓
+ Reviewer examines diff
+ ↓
+ approve / reject / request_changes
+ ↓
+ If approved → can be activated
+ ↓
+ Activation writes to prompts/ dir and pins as active
+```
+
+The review is stored in `prompt_reviews` table: who reviewed, what action, comment, timestamp.
+
+### 4. Performance Tracking Per Version
+
+Every time an agent uses a prompt, the registry records:
+- Run count
+- Average tokens consumed
+- Average quality score
+- Correction rate (how often a human overrode the output)
+
+This answers: "Did the new version of transcript_parser actually improve things, or just cost more tokens?"
+
+### 5. A/B Testing
+
+When two prompt versions both seem good, run them both on the same input and compare:
+
+```
+Same meeting transcript → Version A → Output A (score: 0.82)
+Same meeting transcript → Version B → Output B (score: 0.91)
+Winner: B
+```
+
+A/B results accumulate in `ab_tests` table with input hashes, scores, and winner.
+
+---
+
+## Team Conventions (Enforced)
+
+These are stored in the `team_conventions` table and represent agreed-upon practices:
+
+| # | Convention | Enforced By |
+|---|-----------|-------------|
+| 1 | All prompts in /prompts/ as .txt, never inline | Auto-scan on startup |
+| 2 | Every prompt change requires peer review | Review workflow |
+| 3 | temperature=0 for deterministic tasks | BaseAgent default |
+| 4 | Every artifact carries provenance metadata | MetricsCollector |
+| 5 | Human-in-the-loop for P0 items and ADRs | Pipeline config |
+| 6 | Golden test cases for every prompt | Regression agent |
+| 7 | Offline-first, LLM as upgrade | BaseAgent fallback |
+| 8 | Deposit outputs to SharedMemory wiki | Pipeline executor |
+| 9 | Cross-pipeline events, not function calls | EventBus |
+| 10 | Weekly measurement dashboard review | Manual practice |
+
+---
+
+## Why This Matters
+
+Without prompt governance:
+- Team member A uses a prompt they found on Reddit. Team member B writes their own. Neither knows what the other is doing.
+- A "small tweak" to a prompt breaks 40% of outputs. Nobody notices for a week.
+- Measurement is meaningless because you can't compare runs across different prompt versions.
+
+With prompt governance:
+- Everyone uses the same pinned version. Outputs are reproducible.
+- Changes are reviewed before activation. Regressions are caught.
+- Performance is tracked per version. "Better" has a number.
+
+---
+
+## Where It Lives
+
+- **Prompt files:** `/prompts/*.txt`
+- **Registry DB:** `memory/prompt_registry.db`
+ - `prompt_versions` — all versions with hashes, authors, status
+ - `prompt_reviews` — review history
+ - `prompt_metrics` — usage stats per version
+ - `ab_tests` — A/B comparison results
+ - `team_conventions` — enforced practices
+- **Code:** `pipeline/prompt_registry.py`
+- **Integration:** `agents/base.py` → `load_prompt()` reads from registry
diff --git a/docs/quality_plan_implementation.md b/docs/quality_plan_implementation.md
new file mode 100644
index 0000000..84e49df
--- /dev/null
+++ b/docs/quality_plan_implementation.md
@@ -0,0 +1,70 @@
+# Quality Plan → Implementation Matrix
+
+**Verified:** 2026-07-20, against `epartsservices/intelligent-attribute-prediction`
+`master` (post CI-adoption merge) and the two **pending** test PRs
+(`zhelianl/ml-ct-and-docs`, `zhelianl/ml-it-and-feedback-fix`, open since 2026-06-24).
+**Scope:** Quality Plan (Draft 7) modules **3.3 Prediction** and **3.4 Routing** —
+the ML modules whose tests exist today. Ingestion/normalization/writeback
+modules are pre-implementation and their T-x.y rows remain *Planned* as the
+plan states.
+
+**Method:** every claimed status was checked against actual pytest functions
+(file::name cited below), not against docs. This is the provenance the
+impromptu review asked for.
+
+---
+
+## Headline facts
+
+- **`master` today: 219 tests**, all green in CI on every PR (ruff + black +
+ mypy + pytest with **branch coverage ≥ 85% hard gate** on `src/`).
+- **+27 tests (246 total), the `ml_ct`/`ml_it` markers, the exact-boundary
+ tests, and the G2 online-feedback fix + regression guard are in the two
+ PENDING PRs** — approved-count zero since Jun 24. ⚠️ **Action: review and
+ merge PR #2 and PR #3 before the crit.** Several PARTIALs below flip to
+ VERIFIED on merge.
+- The audit-trail, online-feedback, calibration, and drift-monitoring areas
+ (plan modules 3.8–3.10 analogues) already have real coverage on master —
+ better than the plan's "Planned" labels admit (details §3).
+
+## 1. Module 3.3 — Prediction Service
+
+| Test | Plan claim | On `master` today | After pending PRs merge | Implementing tests (master) |
+|---|---|---|---|---|
+| T-3.1 exact PN terminal @1.0; near-miss not exact | Passing | ✅ **VERIFIED** | ✅ | `test_layer4_fusion.py::test_conf_final_equals_one_iff_tier1_terminal`, `::test_tier1_terminal_emits_single_auto_processed_prediction`, `test_engine.py::test_tier1_*_terminates`, `test_part_numbers.py::test_is_exact_only_for_exact_match` |
+| T-3.2 manufacturer fuzzy flips at cutoff | Passing | ⚠️ **PARTIAL** — above/below covered; no just-below/at/above triplet at one fixed cutoff | ⚠️ PARTIAL (not in pending PRs) | `test_manufacturers.py::test_below_threshold_returns_none`, `::test_partial_match_below_min_score_drops`, `test_engine.py::test_tier2_below_threshold_no_hit` |
+| T-3.3 index identical across reload; recall ≥ 0.95 | Passing | ⚠️ **PARTIAL** — reload identity fully verified; recall≥0.95 is a per-vector spot check, no aggregate assertion | ⚠️ PARTIAL | `test_layer3_index.py::test_persistence_roundtrip`, `::test_search_returns_top_k_with_self_as_first_neighbor` |
+| T-3.4 ambiguous PT → below auto-accept → review | Passing | ✅ **VERIFIED** (band + 0.75 cap each asserted; end-to-end chain implicit) | ✅ (+ exclusive-boundary test at 0.60) | `test_layer3_consensus.py::test_ambiguous_band_below_0_60`, `test_layer4_fusion.py::test_pt_ambiguity_cap_when_pt_conf_below_band_low` |
+| T-3.5 thin clusters treated cautiously | Passing | ✅ **VERIFIED** | ✅ | `test_layer3_clusters.py::test_mahalanobis_uses_identity_for_low_sample`, `::test_ill_conditioned_cluster_is_demoted_to_low_sample`, `test_layer4_fusion.py::test_low_sample_cap_when_top_candidate_is_low_sample` |
+| T-3.6 conf ∈ [0,1]; bands tested at each cutoff | Passing | ⚠️ **PARTIAL** — [0,1] fully verified (+ defensive clamp); bands sampled mid-range (0.985/0.835/0.15), never exactly at 0.85/0.50 | ✅ **VERIFIED** — pending PR adds routing tests at exact 0.85 and 0.50 | `test_layer4_fusion.py::test_conf_final_always_in_unit_interval`, `::test_routing_*` |
+
+## 2. Module 3.4 — Routing Engine
+
+| Test | Plan claim | On `master` today | After pending PRs merge | Notes |
+|---|---|---|---|---|
+| T-4.1 bands route correctly; flip only at cutoffs | Passing | ⚠️ **PARTIAL** — same mid-band sampling gap as T-3.6; `>=` inclusivity at exactly 0.85/0.50 unpinned | ✅ **VERIFIED** | boundary tests are in the pending ml_ct PR |
+| T-4.2 100% branch coverage on routing module | Being written | ❌ **NOT IMPLEMENTED as specified** — global `fail_under=85` branch gate exists and is enforced in CI, but no per-module 100% gate | ❌ unchanged | decide: add a routing-module coverage check, or amend the plan to the global gate with rationale |
+| T-4.3 capped attribute can never auto-accept | Passing | ⚠️ **PARTIAL** — caps (0.75/0.70) tested and numerically below 0.85, but no test asserts `routing != AUTO_PROCESS` on a capped prediction; invariant only holds via config numbers | ⚠️ PARTIAL | one small invariant test closes it |
+
+## 3. Coverage the plan under-claims (already real on master)
+
+| Plan area | Status in plan | Reality on master |
+|---|---|---|
+| Audit trail (3.8 analogue: feedback audit log) | Planned | `test_layer4_feedback.py` — append-exactly-one-line, JSON round-trip, snapshot archival, replay-without-drift, concurrent-writes-don't-lose-updates |
+| Learning loop (3.9 analogue: online updates) | Planned | spec-formula tests for confirm/pushback + service-level `/feedback` tests |
+| Monitoring/drift (3.10 analogue) | Being written | drift **KL gauge tested** (`test_service.py::test_drift_kl_set_after_predictions_with_baseline`), Prometheus endpoint tested |
+| Calibration honesty (QA-2) | — | 16 tests in `test_layer4_calibration.py` (Brier, ECE, per-PT σ recovery) |
+
+## 4. Follow-up actions (each is a ticket)
+
+1. **Review + merge pending PRs #2/#3** (owner: reviewers listed on the PRs) — unblocks 246 tests, markers, boundary tests, G2 regression guard. ⚠️ also a process lesson: 3+ weeks of review latency on our highest-value test work.
+2. Add the **capped-never-auto invariant test** (T-4.3, ~30 min).
+3. Add **cutoff-triplet test for manufacturer fuzzy** (T-3.2, ~30 min).
+4. Add **aggregate self-retrieval recall ≥ 0.95 test** (T-3.3, ~1 h).
+5. **Decide T-4.2**: per-module 100% branch gate for routing vs. amending the plan to the enforced global 85% gate. Recommend amending with rationale — a documented decision beats an unenforced claim.
+6. Update Quality Plan statuses per §3 (several "Planned" areas are further along than claimed — under-claiming is also a provenance defect).
+
+---
+
+*Every VERIFIED/PARTIAL verdict above cites the implementing test functions;
+re-derive any row with `pytest ::` against the stated branch.*
diff --git a/docs/risk_register.md b/docs/risk_register.md
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+++ b/docs/risk_register.md
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+# eParts Risk Register
+
+**Total Risks:** 20
+**Critical:** 4 | **High:** 7 | **Medium:** 9
+
+**Mitigating:** 3 **Open:** 17
+
+| # | Severity | Category | Title | Risk Statement | Mitigation | Status | Owner |
+|---|----------|----------|-------|----------------|------------|--------|-------|
+| 1 | **critical** | technical | Confidence threshold miscalibration | IF the 0.85 confidence threshold is not calibrated with empirical data THEN the review queue either overwhelms the catalog team (threshold too high) or lets incorrect data into PIMS (threshold too lo… | Refinement 1: Run prototype on >=200 labeled submissions, compute precision-recall curves | open | team |
+| 2 | **critical** | schedule | Data access delay blocking ML development | IF client data is not available for model training THEN ML development stalls and the team cannot validate the hybrid approach RESULTING IN schedule delays and inability to meet prototype milestones. | Data received ~Feb 22; team started basic model tests. Continue pressing for complete dataset. | mitigating | team |
+| 3 | **critical** | health | Team burnout from capstone + coursework overlap | IF team members are overloaded with concurrent capstone and coursework demands THEN productivity and code quality decline as fatigue accumulates RESULTING IN missed deadlines, increased defect rates,… | Establish sustainable sprint cadence; enforce work-hour limits; rotate intensive tasks across members. | open | team |
+| 4 | **critical** | team | Single point of failure — key person unavailable | IF a key team member becomes unavailable (illness, emergency, dropout) THEN critical knowledge and in-progress work are inaccessible RESULTING IN blocked deliverables and schedule delays until knowle… | Cross-train on all subsystems; maintain pair-programming rotation; document decisions in SharedMemory. | open | team |
+| 5 | **high** | technical | Insufficient training data (<200 labeled examples) | IF fewer than 200 labeled examples are available for training THEN the embedding layer will be undertrained and the hybrid approach falls back to pure rules with limited coverage (~40-60%) RESULTING… | Secure labeled data from eParts; augment with synthetic examples if needed | open | team |
+| 6 | **high** | technical | PIMS staging schema incompatibility (P1-C pending) | IF Jake does not deliver the P1-C schema or staging tables use incompatible columns THEN the writeback mechanism requires redesign RESULTING IN schedule delays and potential data integration failures. | Refinement 4: Map P1-C columns to canonical schema; integration-test 10 sample records | open | team |
+| 7 | **high** | business | Catalog team capacity vs review volume | IF per-attribute routing still produces too many review items THEN the 1.5 + 3 FTE catalog team cannot handle the volume RESULTING IN no labor savings and failure of the core value proposition. | Measure actual review volume in prototype; adjust threshold iteratively | open | team |
+| 8 | **high** | technical | Drift detection metrics and baselines undefined | IF baseline metrics, alert thresholds, and feedback loops are not defined before deployment THEN model drift will go undetected RESULTING IN silent accuracy degradation and no trigger for retraining. | Define baseline metrics before prototype; SES measurement system can track these | open | team |
+| 9 | **high** | measurement | Measurement validity for AI effectiveness | IF AI effectiveness is not measured with rigorous before/after comparisons THEN the team cannot demonstrate genuine AI-driven improvement RESULTING IN weak capstone evaluation and inability to justif… | SES measurement system tracks tokens, cost, latency, human review rate, correction rate per agent. Prompt regression testing validates quality over time. | open | team |
+| 10 | **high** | technical | Model selection uncertainty | IF the ML model selection remains unresolved and the hybrid approach (ADR-1) is not validated THEN the prediction pipeline lacks a stable foundation RESULTING IN rework risk and delayed confidence in… | ADR-1 hybrid approach with clear trigger conditions for switching to pure ML or pure rules | open | team |
+| 11 | **high** | dependency | Integration dependency on Jake (PIMS schema) | IF Jake does not deliver the P1-C staging table schema on time THEN the writeback mechanism cannot be implemented against the real target RESULTING IN critical-path schedule slip and potential redesi… | Refinement 4 scheduled; team-owned buffer table as fallback | open | Hrishik |
+| 12 | **medium** | technical | Alpha weighting sensitivity in hybrid scoring | IF small tuning errors occur in alpha weighting (currently 0.7) THEN routing behavior changes disproportionately, suppressing the more accurate signal source RESULTING IN misrouted items and unreliab… | Refinement 3: Sweep alpha 0.3-0.9; measure ECE, precision, coverage | open | team |
+| 13 | **medium** | technical | Attribute correlation invalidates per-attribute routing | IF correlated attributes are reviewed independently THEN inconsistent records are produced when cross-attribute errors exceed 30% RESULTING IN need to switch from per-attribute to per-record routing,… | Refinement 2: Pairwise mutual information analysis on labeled data | open | team |
+| 14 | **medium** | ux | Human review interface design not decided | IF the reviewer walkthrough reveals that tabular export is insufficient for review tasks THEN a custom review UI must be added to scope RESULTING IN scope expansion, additional development effort, an… | Refinement 5: Present 30 sample items to Brian/Dewey; measure time and accuracy | open | team |
+| 15 | **medium** | dependency | ETIM release pin leaves the catalog progressively stale | IF the client's suppliers begin publishing against ETIM 11.0 while the platform remains pinned to release 10.0 EI (constraint C-4) THEN new classes, features and values are unavailable to the matcher… | Release pinned explicitly as constraint C-4 rather than left unspecified. Every ETIM reference row, the interpretation table and the PIMS writeback key all carry etim_release_id (ADR-013/014/017), so… | mitigating | team |
+| 16 | **medium** | scope | Scope creep risk | IF the team expands beyond valves/actuators scope before the core pipeline is validated THEN development effort is diluted across unvalidated categories RESULTING IN an incomplete core pipeline and m… | Strict phase scoping; architecture designed for category extension without structural change | open | team |
+| 17 | **medium** | technical | Azure tool constraints | IF Azure platform limitations (GPU availability, service quotas) conflict with architecture needs THEN design decisions must be reworked for the constrained environment RESULTING IN reduced model per… | Single App Service deployment chosen to minimize Azure operational complexity | open | team |
+| 18 | **medium** | team | Communication gaps between distributed team members | IF distributed team members have infrequent or asynchronous-only communication THEN misalignments on requirements, design, and priorities go undetected RESULTING IN integration conflicts, rework, and… | Weekly sync meetings; shared Slack channel for async updates; meeting summaries auto-generated by SES. | open | team |
+| 19 | **medium** | schedule | Capstone timeline constraint | IF the 5-person team cannot prototype within the Spring-Fall 2026 semester THEN operational complexity exceeds team capacity RESULTING IN incomplete deliverables and a failed capstone milestone. | Architecture favors simplicity (single App Service, internal interfaces). SES agents automate repetitive tasks to free team capacity. | open | team |
+| 20 | **medium** | process | Knowledge loss from manual processes | IF meeting decisions, action items, and rationale are captured manually THEN information is lost or inconsistently documented across artifacts RESULTING IN duplicated effort, contradictory decisions,… | Agentic SE system auto-captures decisions, action items, and commitments from meetings. SharedMemory wiki maintains persistent project knowledge. | mitigating | team |
+
+## Traceability
+
+15 of 20 risks are linked to the requirements or architecture artifacts they threaten.
+
+| Risk | Title | Threatens (requirements) | Threatens (architecture) |
+|------|-------|--------------------------|--------------------------|
+| `RISK-ARCH-01` | Confidence threshold miscalibration | QA-1, FR-4 | AD-4, routing |
+| `RISK-COACH-01` | Data access delay blocking ML development | REQ-DATA | — |
+| `RISK-ARCH-02` | Insufficient training data (<200 labeled examples) | FR-3 | ADR-1, prediction |
+| `RISK-ARCH-03` | PIMS staging schema incompatibility (P1-C pending) | FR-6 | AD-3, AD-5, writeback |
+| `RISK-ARCH-06` | Catalog team capacity vs review volume | QA-1 | routing, review |
+| `RISK-ARCH-07` | Drift detection metrics and baselines undefined | QA-5 | observability |
+| `RISK-COACH-04` | Measurement validity for AI effectiveness | REQ-SES | — |
+| `RISK-COACH-05` | Model selection uncertainty | — | ADR-1 |
+| `RISK-PM-02` | Integration dependency on Jake (PIMS schema) | — | AD-3, AD-5 |
+| `RISK-ARCH-04` | Alpha weighting sensitivity in hybrid scoring | QA-1 | ADR-1 |
+| `RISK-ARCH-05` | Attribute correlation invalidates per-attribute routing | QA-1, FR-4 | routing |
+| `RISK-ARCH-08` | Human review interface design not decided | FR-5 | review |
+| `RISK-ARCH-09` | ETIM release pin leaves the catalog progressively stale | FR-10, HLR-6, FR-9 | ADR-020, ADR-013, ADR-014, ADR-017, C-4 |
+| `RISK-COACH-02` | Scope creep risk | QA-3 | — |
+| `RISK-COACH-03` | Azure tool constraints | — | AD-6 |
+
+## Exit-condition check
+
+All risks have both an owner and a mitigation, so every entry has cleared the exit gate.
+
+---
+_Generated from `memory/risk_register.db` by `pipeline/render_risk_register.py` on 2026-07-29. Do not edit by hand: change the source in `pipeline/risk_register.py` and re-run._
diff --git a/docs/sdlc_choice.md b/docs/sdlc_choice.md
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+++ b/docs/sdlc_choice.md
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+# SDLC Choice: Agent-Augmented Iterative Lifecycle
+
+## Why Not Scrum/RUP/XP
+
+Per the meta-model framework: "Existing SDLCs and methodologies are founded, in part,
+on the idea of authoring software being the most labor-intensive part of development."
+With AI agents handling artifact generation, the bottleneck shifts from *authoring*
+to *validation, measurement, and integration*.
+
+We deliberately avoid naming an existing SDLC. Instead, we compose a bespoke lifecycle
+from the meta-model's four elements: **Artifacts, Processes, Resources, Measurements**.
+
+## Our Lifecycle Pattern
+
+```
+ ┌─────────────────────────────────────────────────┐
+ │ ENGINEERING OPERATIONS │
+ │ (measurement collection + process tuning) │
+ └─────────┬───────────────────────────┬───────────┘
+ │ │
+ ┌─────────────────────────▼───────────────────────────▼────────────────────┐
+ │ │
+ │ Requirements → Architecture → Construction → Quality │
+ │ Engineering Design (ML + App) Assurance │
+ │ │
+ │ │ │ │ │ │
+ │ ▼ ▼ ▼ ▼ │
+ │ ┌─────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
+ │ │ Agent │ │ Agent │ │ Agent │ │ Agent │ │
+ │ │ Pipeline│ │ Pipeline │ │ Pipeline │ │ Pipeline │ │
+ │ │ (7 steps│ │ (4 steps)│ │ (4 steps)│ │ (cron) │ │
+ │ └────┬────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ │
+ │ │ │ │ │ │
+ │ └──────────────────┴─────────────────────┴───────────────┘ │
+ │ │ │
+ │ ┌──────▼──────┐ │
+ │ │ Shared │ │
+ │ │ Memory + │ ← every agent deposits knowledge │
+ │ │ Event Bus │ ← cross-pipeline triggers fire │
+ │ └─────────────┘ │
+ │ │
+ │ ◆ Phase Gate (human review of agent outputs before proceeding) │
+ │ │
+ └──────────────────────────────────────────────────────────────────────────┘
+ ▲ │
+ │ Iterate (prototype → pilot) │
+ └───────────────────────────────────────────┘
+```
+
+## Key Characteristics
+
+### 1. Agent-First, Human-Verified
+
+Every repeatable activity has an agent implementation. But agents don't ship — humans
+verify. The HITL (human-in-the-loop) is explicit at every phase gate:
+
+| Phase | Agent Role | Human Role |
+|--------------------|--------------------------------------|-----------------------------------|
+| Requirements | Parse transcripts, classify, extract | Review extracted reqs, approve P0 |
+| Architecture | Detect drift, generate ADR drafts | Approve ADRs, validate tradeoffs |
+| Construction | Generate boilerplate, review PRs | Approve merges, design decisions |
+| Quality | Run regression tests, track metrics | Interpret metrics, tune thresholds|
+
+### 2. Continuous Measurement (Not Sprint Retrospectives)
+
+Instead of looking back every 2 weeks, the measurement system runs continuously:
+- Every LLM call: tokens, latency, cost, prompt version
+- Every agent run: success rate, human review rate, correction rate
+- Every pipeline: end-to-end duration, step failures, data quality
+- Cross-pipeline: event propagation, wiki enrichment, risk evolution
+
+### 3. Two Iterations, Not Sprints
+
+Following the meta-model's guidance:
+- **Iteration 1 (Prototype):** Core pipeline (ingestion → prediction → routing → writeback)
+ with hybrid model, offline evaluation. Focus: prove accuracy is achievable.
+- **Iteration 2 (Pilot):** Production deployment, real data, review workflow, monitoring.
+ Focus: prove operational viability.
+
+Phase gate between iterations requires: threshold calibrated, PIMS schema validated,
+review workflow tested with Brian/Dewey.
+
+### 4. Practice Areas as Pipelines
+
+Each practice area maps to an agent pipeline with defined data flow:
+
+| Practice Area | Pipeline | Activities (Agents) |
+|-------------------------|-----------------------|-------------------------------------------------------------|
+| Requirements Engineering| `requirements` | Parse → Classify → Extract → Create Tickets → Drift Check |
+| Architecture | `architecture` | Drift Detect → ADR Generate → Diagram Update → Traceability|
+| Construction | `coding` | Boilerplate → PR Review → Test Generate → Doc Generate |
+| Coach/Mentor Memory | `coach_session` | Parse → Embed → Commitments → Concerns → Link → Log |
+| ML Decision Memory | `ml_decision` | Log → Evidence → Readiness → Coach Link |
+| Project Management | `project_mgmt` | Tickets → WBS Update → Weekly Digest → Alerts |
+
+### 5. Cross-Practice Communication via Event Bus
+
+The lifecycle isn't just vertical (within a practice area) — it's horizontal:
+- Requirements drift → triggers Architecture review
+- Coach concern recurrence → triggers PM alert
+- ML evidence accumulation → triggers Coach briefing refresh
+- Action items from any meeting → trigger Ticket creation
+
+This is what the meta-model means by "practice areas working together as a system."
+
+## Resource Allocation
+
+| Resource Type | What | Where Used |
+|---------------|-----------------------------------------|-------------------------------------|
+| `auton` | Agent pipelines, event bus, wiki writes | 28 agent processes |
+| `assist` | Claude API for extraction/generation | LLM-backed agents when API key set |
+| `human` | Phase gate reviews, threshold tuning | All P0 items, ADR approval, metrics |
+| `tool` | ChromaDB, SQLite, FastAPI, Datadog | Infrastructure layer |
+
+## Justification: Why This Works for eParts
+
+1. **The client problem is a data pipeline** — pipe-and-filter architecture maps directly
+ to a linear lifecycle with clear phase boundaries.
+2. **Team size (5) precludes heavyweight processes** — no sprint ceremonies, no Scrum Master
+ role. Agents handle the repetitive work; humans make decisions.
+3. **AI must be measured to be justified** — the meta-model requires evidence of AI
+ effectiveness. Continuous measurement gives us data, not anecdotes.
+4. **Coach sessions revealed specific risks** — the lifecycle explicitly incorporates risk
+ tracking (auto-populated risk register) and commitment tracking (from coach memory).
diff --git a/docs/ses_architecture.md b/docs/ses_architecture.md
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+++ b/docs/ses_architecture.md
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+# eParts SES — Software Engineering System Architecture
+
+This note is for presenters and integrators who need a **plug-and-play mental model**: what fires the system, what runs inside, how pieces connect across practice areas, and the **meta model** for artifacts and knowledge.
+
+SES is intentionally a **framework** (agents + pipelines + shared state + events), not a set of unrelated scripts.
+
+---
+
+## 1. Big picture — three layers
+
+| Layer | Role |
+|-------|------|
+| **Triggers** | External inputs: transcripts, Git PRs, Jira hooks, POC results, crons. Each has a **`trigger_type`** string. |
+| **Orchestration** | **FastAPI** (`orchestrator/main.py`): `/webhook` and `/trigger` enqueue work on a **TaskQueue**; agents run asynchronously. **`demo.py`** uses **`PipelineExecutor`** directly to run a **whole pipeline** synchronously—best for scripted demos. |
+| **Agents + pipelines** | **Pipelines** are ordered **`PipelineStep`** chains (**`pipeline/pipelines.py`**). Each step invokes one **registered agent**. Data flows via **`PipelineContext`**: upstream outputs merge into keyed context fields for downstream steps. |
+| **Side-effect buses** | **Shared Memory** (“wiki”: namespaced KV), **Event Bus** (pub/sub audit + subscriptions), **Traceability Store** (artifact graph). Agents also call **MCP clients** (Jira, GitHub, Confluence, etc.) via `agents/base.BaseAgent`. |
+
+```mermaid
+flowchart LR
+ subgraph ingress["Ingress"]
+ T[Triggers: transcript · PR · cron · poc_result · …]
+ end
+ subgraph orch["Orchestration"]
+ API[FastAPI + TaskQueue]
+ DEMO[demo.py + PipelineExecutor]
+ end
+ subgraph core["SES core"]
+ P[Named pipelines Practice areas]
+ A[Agents]
+ end
+ subgraph stores["Stores & integrations"]
+ W[SharedMemory wiki]
+ E[EventBus SQLite]
+ X[TraceabilityStore graph]
+ M[MCP servers]
+ end
+ T --> API
+ T --> DEMO
+ API --> A
+ DEMO --> P
+ P --> A
+ A --> W
+ A --> E
+ A --> X
+ A --> M
+```
+
+**Plug-and-play idea:** Swap or add an **agent** in **`orchestrator/registry.py`**, register it by name; wire it into a **pipeline step** or a **trigger route**. Connect cross-practice workflows by **`emit(...)`** on **`BaseAgent`** and **EventBus** subscriptions (see §4).
+
+---
+
+## 2. Pipelines vs triggers (“which pipeline fires when?”)
+
+Pipelines are defined in **`pipeline/pipelines.py`** as **`Pipeline`** objects. Each declares **`trigger_types`**—the contract for which **incoming trigger categories** could start that logical flow. The router **`orchestrator/router.py`** maps **`trigger_type`** to **agent lists** for the API path; **`TRIGGER_PIPELINES`** is built programmatically from those same **`Pipeline`** definitions.
+
+**Canonical catalogue (7 pipelines)**
+
+| Pipeline `name` | Practice area | `trigger_types` | Purpose (compact) |
+|-----------------|---------------|-----------------|-------------------|
+| `requirements` | Requirements Engineering | `transcript` | VTT/text → parse → classify → REQ files → Jira → minutes → decisions → drift check |
+| `coach_session` | Coach Session Memory | `coach_transcript` | Parse → embed session → concerns → linker → decisions |
+| `architecture` | Architecture | `transcript`, `pr_event` | Full drift vs canon → ADR → diagram PR → traceability matrix |
+| `coding` | Coding | `pr_event` | PR review → tests → docs → prompt regression |
+| `ml_decision` | ML Decision Memory | `poc_result` | Evidence → readiness → coach links |
+| `project_mgmt` | Project Management | `cron_friday_6pm` | WBS sync → digest → alerts |
+| `knowledge` | Knowledge Management | `cron_pre_meeting` | Context pack → briefing |
+
+**Representative sequence (requirements)** — each row is one **`PipelineStep`**; context keys **`input_keys`/`output_key`** thread data (`parsed_minutes` → `classified_items` → `requirements`, etc.):
+
+1. `transcript_parser`
+2. `priority_classifier`
+3. `req_extractor`
+4. `ticket_creator`
+5. `minutes_publisher`
+6. `decision_logger`
+7. `drift_detector`
+
+Conditional steps use **`skip_if_empty`**: if a context key is empty, the step is skipped without failing the run.
+
+Inside a run:
+
+1. Build **`PipelineContext`** from **`trigger_payload`** (`trigger_type`, `source`, merged data).
+2. For each step: resolve **`AgentTrigger`**, run **`agent.execute`**, **`_deposit_to_wiki`** (when applicable), merge **`AgentResult.data`** and outputs into **`ctx.data`**.
+3. Return **`PipelineResult`** (metrics, artifacts, human-review flags).
+
+Reference: **`PipelineExecutor`** docstring and **`PipelineContext`**, **`Pipeline`** in **`pipeline/pipelines.py`**.
+
+---
+
+## 3. APIs and two ways to execute work
+
+### 3.1 FastAPI orchestrator (async, queue-backed)
+
+- **`POST /webhook`** — body includes **`trigger_type`**, **`source`**, **`metadata`**; resolves agents via **`resolve_agents()`** (**`orchestrator/router.py`**: **`TRIGGER_ROUTES`**).
+- **`POST /trigger`** — run a named agent with a payload (**manual override**).
+- **`GET /agents`** — registered agents plus **`TRIGGER_ROUTES`** for observability.
+
+This path is geared toward **routing and fan-out**, not necessarily one full **`Pipeline`** object per HTTP call—it depends how tasks are wired in **`TaskQueue`** and registry.
+
+### 3.2 Direct pipeline executor (demo / batch)
+
+Scripts such as **`demo.py`** instantiate **`PipelineExecutor(agents)`** and call **`execute(REQUIREMENTS_PIPELINE, {trigger_type: "transcript", source: path})`**.
+
+That guarantees **exactly one pipeline DAG** runs end-to-end with **ordering and skip rules** as defined in code.
+
+---
+
+## 4. Event bus — what events exist and what they unlock
+
+Agents call **`emit(event_type, data, pipeline=...)`** on **`BaseAgent`**. **`EventBus.publish`** persists to SQLite (**`memory/events.db`**) and returns **matching subscriptions**.
+
+### 4.1 Well-known **`EVENT_TYPES`** (contract)
+
+Declared in **`pipeline/event_bus.py`** (readable labels). Examples:
+
+- **`drift_detected`** — requirement discussion contradicts canon architecture
+- **`new_requirements`** — requirements extracted
+- **`action_items_extracted`** — action items surfaced
+- **`decision_logged`** — captured decision
+- **`recurring_concern`**, **`commitment_overdue`**, **`new_session_embedded`** — coach/mentor lineage
+- **`decision_ready`**, **`poc_evidence_logged`** — ML decision flow
+- **`human_review_needed`**, **`artifact_produced`** — governance / outputs
+
+These names are the **interop contract** between teams and pipelines—treat them as API surface area.
+
+### 4.2 Default subscriptions (routing table snapshot)
+
+Inserted in **`EventBus._setup_default_subscriptions()`** unless already present—a **subscriber** row means “when **`event_type`** fires, logically notify **`target_pipeline`** (+ optional **`target_agent`**).” Illustrative mappings:
+
+| Event | Typical downstream (from defaults) |
+|-------|--------------------------------------|
+| `drift_detected` | **`architecture`** — deeper architecture review loop |
+| `new_requirements` | **`architecture`**, agent **`drift_detector`** subscription row |
+| `action_items_extracted` | **`project_mgmt`**, **`ticket_creator`** — align ticketing |
+| `recurring_concern` / `commitment_overdue` | **`project_mgmt`**, **`alert_agent`** |
+| `new_session_embedded` | **`knowledge`**, **`briefing_generator`** |
+| `decision_logged` | **`knowledge`**, **`decision_logger`** |
+| `human_review_needed` | **`project_mgmt`**, **`alert_agent`** |
+| … | *(see DB table `subscriptions` for full wiring)* |
+
+**Runtime note:** In-process **`subscribe_handler`** callbacks run synchronously for demos; production could attach a queue consumer.
+
+---
+
+## 5. Meta model — artifacts, wiki, traceability
+
+SES encodes CMU-studio traceability explicitly in two complementary stores plus events.
+
+### 5.1 **Shared Memory** (**“wiki pattern”**) — **`pipeline/shared_memory.py`**
+
+- **SQLite** KV with **namespaces** (e.g. `requirements`, `architecture`, `decisions`, `risks`, `meetings`, `ml_decisions`, …).
+- Each write records **agent + pipeline**, enabling “who enriched the wiki?”
+- Enables **agents to read cumulative project context** rather than isolated outputs.
+
+### 5.2 **Traceability Store** — **`pipeline/traceability.py`**
+
+- **Artifacts**: typed nodes (`concern`, `requirement`, `decision`, `risk`, **jira_ticket**, **pull_request**, `test`, **`adr`**, **`meeting`**, **`coach_session`**, …).
+- **Links**: directed typed edges (**`BECAME`**, **`IMPLEMENTS`**, **`MITIGATES`**, **`RAISED_IN`**, **`DECIDED_BY`**, **`TRIGGERED`**, **`VERIFIED_BY`**, **`SUPERSEDES`**, **`DEPENDS_ON`**, …).
+
+This is the graph answer to:
+
+> Trace from *client sentence* → REQ → architecture → Jira → PR → test → risk closed.
+
+The **meta model** for architecture talks is:
+
+**Artifact (typed) —(typed link)→ Artifact**, plus **status** and **provenance**; parallel **wiki** entries for narrative and fast agent lookup.
+
+### 5.3 **ETVX / process IDs**
+
+Each **`PipelineStep`** can carry **`etvx_id`** (e.g. **REQ-PARSE**, **ARCH-DRIFT**) to align pipeline steps with process documentation and dashboards.
+
+### 5.4 **Meta-model diagram (conceptual)**
+
+```mermaid
+flowchart TB
+ subgraph meta["Meta model"]
+ M[Meeting / trigger]
+ C[Concern]
+ R[Requirement]
+ D[Decision]
+ A[Architecture / ADR]
+ J[Jira ticket]
+ P[PR / test]
+ K[Risk]
+ end
+ M -->|RAISED_IN / extracted| C
+ C -->|BECAME| R
+ R -->|DECIDED_BY| D
+ D -->|TRIGGERED / informs| A
+ R -->|IMPLEMENTS| J
+ J -->|VERIFIED_BY| P
+ D -->|MITIGATES| K
+```
+
+---
+
+## 6. MCP (Model Context Protocol) — external systems
+
+Agents receive **`mcp_clients`** (Jira, GitHub, Confluence, etc.). **When credentials are missing**, agents **degrade gracefully** (log + offline behaviour), consistent with demos.
+
+---
+
+## 7. How to extend (checklist)
+
+1. **New agent**: Implement **`BaseAgent`**, **`run(trigger) -> AgentResult`**, register in **`orchestrator/registry.py`**.
+2. **New pipeline or step**: Add **`Pipeline`** / **`PipelineStep`** in **`pipeline/pipelines.py`**, extend **`trigger_types`** and **`TRIGGER_PIPELINES`**.
+3. **New trigger from outside**: Extend **`WebhookPayload`** descriptions and **`TRIGGER_ROUTES`** in **`router.py`**; ensure **`execute`** payloads match **`input_keys`**.
+4. **New cross-cutting reaction**: **`emit`** a new **`event_type`** (add to **`EVENT_TYPES`**) or reuse an existing one; insert a **subscription** row strategy (code or DB bootstrap).
+5. **Traceability**: On new outward artifacts, **`TraceabilityStore`** updates from agents like **`traceability_builder`**; keep **`ARTIFACT_TYPES` / LINK_TYPES`** in mind.
+
+---
+
+## 8. References in repo
+
+| File | Contents |
+|------|----------|
+| `pipeline/pipelines.py` | **`ALL_PIPELINES`**, **`PipelineExecutor`**, **`TRIGGER_PIPELINES`**, **`get_framework_summary()`** |
+| `pipeline/event_bus.py` | **`EVENT_TYPES`**, **`EventBus`**, default **subscriptions** |
+| `pipeline/shared_memory.py` | Wiki namespaces |
+| `pipeline/traceability.py` | Artifact/link meta model |
+| `orchestrator/main.py`, `orchestrator/router.py` | HTTP API, **`TRIGGER_ROUTES`** |
+| `agents/base.py` | **`AgentTrigger`**, **`emit`**, MCP hooks |
+
+Programmatic introspection for slides:
+
+```python
+from pipeline.pipelines import get_framework_summary
+import json
+print(json.dumps(get_framework_summary(), indent=2))
+```
+
+---
+
+*Synthetic scenario content (e.g. demo transcripts) is for education—see disclaimers beside bundled examples.*
diff --git a/docs/ses_architecture_speakable.md b/docs/ses_architecture_speakable.md
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+# SES architecture — speakable script (natural language)
+
+Use this when you present the **Software Engineering System** to teammates. Read it almost as-is; breathe at the paragraph breaks. Pause after “So in plain terms” lines.
+
+---
+
+## Opening (about 45 seconds)
+
+Hey — I want to give you a mental model of SES, our **Software Engineering System**, in one go. The main idea is: this is **not** a pile of one-off scripts that someone runs by hand. It’s a **framework**. You’ve got specialized **agents**, wired into **pipelines** that belong to real **practice areas**, like requirements or architecture. Things that happen in the outside world — a meeting transcript lands, a PR opens, a cron fires — we call those **triggers**. Inside, data flows through steps in order, and agents can read and write **shared memory**, raise **events**, and update a **traceability graph**. If you remember only one sentence: **things come in, pipelines run in order, artifacts and links get recorded, and events can fan out to other parts of the system.**
+
+---
+
+## Layer 1 — Something has to start the work (triggers)
+
+So first: **what kicks this off?** In practice it’s things like: we drop a **Zoom-style transcript** — that’s usually a `transcript` trigger. A **pull request** — that’s often a `pr_event`. A **proof-of-concept result** might be `poc_result`. There are also **scheduled** triggers, like Friday evening for project-management digests, or a pre-meeting window for prep. Each of these has a small string label, a **trigger type**, and that label decides which **family of workflows** is even in play.
+
+Takeaway for the room: **nothing magical** — it’s “something external happened, we label it, that label routes the work.”
+
+---
+
+## Layer 2 — Two ways to actually run the workflows (orchestration)
+
+There are **two** execution styles worth naming, because we use both.
+
+**First**, the **live service** path: FastAPI exposes **webhooks**. When the outside world POSTs an event, we don’t block the HTTP call on a half-hour pipeline; we **enqueue** work on a **task queue** and agents pick it up. Good for production-style, async operation.
+
+**Second**, the **demo / batch** path: something like **`demo.py`** runs **one full pipeline** synchronously with a **pipeline executor** — same ordered steps every time, deterministic for a customer demo. That’s why the live demo feels like a script: it’s **explicitly** walking the whole DAG in order.
+
+Say this clearly: **Same building blocks; different shell — async API versus one-shot runner.**
+
+---
+
+## Layer 3 — Pipelines are the spine (what happens inside)
+
+Think of a **pipeline** as a **recipe**: step one, step two, step three. Each step calls **one agent by name**. Output from step one doesn’t vanish; it lands in a **context object** — keyed fields like “parsed minutes,” “classified items,” “requirements.” Step two reads those keys. If a step depends on something being empty — we can **skip** it without failing the whole run. So the system is **honest** about partial data.
+
+Walk them through **one** chain they care about — usually **requirements** on a transcript:
+
+1. **Parse** the transcript into structured minutes — action items, decisions, that kind of thing.
+2. **Classify priority** — what’s urgent versus what can wait.
+3. **Pull out formal requirements** — the REQ-style artifacts.
+4. **Create tickets** where it makes sense — often with human review for the highest risk items.
+5. **Publish minutes** where integrations exist.
+6. **Log decisions** so we have an audit trail.
+7. **Check drift** against the architecture we’ve already agreed on — lightweight sanity check after the meeting.
+
+You can say: **“That’s seven stations on an assembly line — each station is an agent, one after the other.”**
+
+There are **other** pipelines in the framework — coach sessions, heavier architecture work on a PR, coding support on a PR, ML decision evidence, Friday PM rollups, knowledge prep for meetings. Don’t list all seven unless someone asks; the point is **multiple practice areas**, each with its own recipe.
+
+---
+
+## Layer 4 — How pieces talk without being glued together (events)
+
+Agents don’t only hand data to the **next** step. They can **emit events** — publish-and-subscribe style. Example: we noticed **drift** versus the canonical architecture. Something else might care — the **architecture** side of the house, or alerting. Another example: **action items** hit the wire — project management or ticketing might react. Those event names are basically a **contract** between subsystems: **if you emit this, downstream is allowed to subscribe and react.**
+
+In the real implementation, events get **stored** so we can audit what happened and who should have reacted. For demos, handlers often run **in process**; in a bigger deployment you’d picture a **queue** behind that.
+
+Sound bite: **“Pipelines are sequential; events are how the rest of the organism hears about important outcomes.”**
+
+---
+
+## Meta model — how we think about “truth” in the product (about 60 seconds)
+
+Two stores, one story.
+
+**Shared memory** — we sometimes call it the **wiki pattern**. Think namespaced buckets: requirements-ish stuff, decisions, risks, meetings, whatever your agents need to **read before they write the next thing**. Every write can say **which agent** did it, so you’re not blindly trusting a black box.
+
+**Traceability** — think **graph**, not document. There are **artifacts**: a concern, a requirement, a decision, a ticket, a PR, a risk, an ADR — typed nodes. Between them we have **links** with meaning: this concern **became** a requirement; this ticket **implements** a requirement; this decision **mitigates** a risk. That’s how you answer an auditor or a PM who asks: **“Where did this requirement come from, and where did it land in engineering?”** — you follow the chain.
+
+Optional line: we also tag some steps with **process IDs** — ETVX-style — so **process documentation** and **automation** stay aligned.
+
+Closer for this section: **“The wiki is fast context for agents; the traceability store is the evidence graph for humans and compliance.”**
+
+---
+
+## Touching the outside world (MCP)
+
+Agents don’t hallucinate Jira tickets into the void — they go through **integrations**: Jira, GitHub, Confluence, whatever we’ve wired as **MCP clients**. If credentials aren’t there, the code is written to **fail soft**: log it, fall back to heuristics or skip, which is why demos still run on a laptop without every secret.
+
+---
+
+## If someone asks “how do we extend this?”
+
+Short answer: **Register a new agent**, **drop it into a pipeline step** or **trigger route**, and if it should notify other areas, **emit an event** and/or **write traceability**. Four moving parts, same pattern every time.
+
+---
+
+## One-liner endings (pick one)
+
+- **“Triggers in, ordered pipelines, shared wiki, event bus, traceability graph — that’s SES.”**
+- **“It’s connected agents, not lonely scripts.”**
+- **“We can show the live path in the demo, and the architecture doc has the full catalogue when you need to wire something new.”**
+
+---
+
+*Paired with `ses_architecture.md` for diagrams and file references. Synthetic demo content in the repo stays clearly labeled for education.*
diff --git a/docs/ses_assessment.md b/docs/ses_assessment.md
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+++ b/docs/ses_assessment.md
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+# eParts Software Engineering System — Assessment & Measurement Plan
+
+## Part 1: Rubric Self-Assessment
+
+### Criterion 1: Viability of Project and Risk Management (3 pts)
+
+| Requirement | What We Have | Status |
+|---|---|---|
+| Project plan to end of year | Two-iteration lifecycle (Prototype → Pilot), WBS auto-generated from meetings | ✅ |
+| Key risks and mitigation | Risk Register: 16 risks auto-populated from arch report + 4 coach sessions + meetings. 2 critical, 7 high, 7 medium. Each has mitigation + contingency. | ✅ |
+| Team roles and responsibilities | SES: Jai, Hrishik, Ashritha. Software System: Arjun, Liu. Roles mapped to pipeline ownership. | ✅ |
+| Measurement plan | See Part 2 below — GQIM-based, 4 goals, 12 metrics, all auto-collected | ✅ |
+| Resources to implement | See Part 3 below — every activity classified auton/assist/human with justification | ✅ |
+| Tracking | MetricsCollector (SQLite): per-LLM-call and per-agent-run metrics. Risk register updated by agents. Commitment tracker from coach sessions. | ✅ |
+
+### Criterion 2: Soundness of Software System Definition (5 pts)
+*Arjun/Liu section — architecture report covers this*
+
+| Requirement | Status |
+|---|---|
+| Goals, requirements, priorities | Architecture report §2: FRs, QAs, priority matrix |
+| Quality attributes | 5 QAs with scenarios + utility tree (H/H, H/M, M/M, M/L, H/H) |
+| Constraints | 7 constraints with sources and architectural impact |
+| Context diagram | System boundary defined §1.3; C&C view §3.3.1 |
+| Architectural drivers, tradeoffs | 3 tradeoffs analyzed §5.3 (accuracy vs throughput, simplicity vs modifiability, explainability vs sophistication) |
+| Decisions | ADR-1 + 5 ADs with status, rationale, and reconsideration triggers |
+
+### Criterion 3: Strength of Engineering System — DECISION QUALITY (5 pts)
+
+| Requirement | How We Meet It |
+|---|---|
+| 1. Clear decisions | Every activity has explicit resource allocation (auton/assist/human). SDLC choice documented with rationale. Each agent has a defined ETVX process. |
+| 2. Strong justification with evidence | See Part 4: Human-vs-AI Comparison Matrix. Each AI use is justified with measurable cost, time, and quality data. |
+| 3. Tradeoffs explained | See Part 5: AI Use Tradeoff Register. Every decision to use/not-use AI has explicit gains, risks, and conditions for reversal. |
+| 4. Consistent reasoning | Single framework (meta-model) applied uniformly: Artifacts → Processes → Resources → Measurements for all 28 agent activities. |
+
+### Criterion 4: Strength of Engineering System — ELEMENTS (5 pts)
+
+| Element | What We Have | Count |
+|---|---|---|
+| **Processes** | 7 pipelines, 28 agent activities, each with ETVX documentation | 28 |
+| **Key artifacts** | VTT transcripts, parsed minutes, REQ docs, ADRs, risk register, meeting wiki, decision log, drift reports, prompt versions, metrics DB | 12 types |
+| **Measurements** | 12 metrics across 4 GQIM goals (see Part 2). Auto-collected by MetricsCollector on every LLM call and agent run. | 12 |
+| **Resources** | 28 agents (auton), Claude API (assist), 5 humans, 8 MCP server integrations, ChromaDB, SQLite | mapped per activity |
+
+### Criterion 5: Crit Performance (2 pts)
+
+| Requirement | Strategy |
+|---|---|
+| Effective communication | Visual-first: knowledge graph, goal model, WBS, agent flow diagram, traceability matrix |
+| Deep, thoughtful reflection | Part 5 (tradeoff register) + Part 6 (lessons learned) |
+| Actively engaged with feedback | Coach session memory tracks all commitments and concerns; briefing generator prepares for each session |
+| Balanced participation | SES owned by Jai/Hrishik/Ashritha; Software System by Arjun/Liu |
+
+---
+
+## Part 2: Measurement Plan (GQIM)
+
+The meta-model says: "Use GQIM. LLM-related measurements will be very helpful as an indicator of system performance."
+
+### Goal 1: Validate that AI improves engineering productivity
+
+| Question | Indicator | Metric | Collection Method |
+|---|---|---|---|
+| How much manual effort does AI save per meeting? | Time comparison: manual minutes vs agent-generated | **M1: Minutes per meeting transcript processed** — manual baseline ~45 min, agent target <1 min | Timer in pipeline executor |
+| How accurate is AI extraction vs human? | Agreement rate between agent output and human review | **M2: Human correction rate** — % of agent outputs that require human editing | `record_human_correction()` API |
+| What does AI cost vs human cost? | Dollar comparison per activity | **M3: Cost per activity** — LLM tokens × price vs estimated human hourly cost | MetricsCollector tracks tokens + cost per call |
+
+### Goal 2: Ensure AI quality doesn't degrade over time
+
+| Question | Indicator | Metric | Collection Method |
+|---|---|---|---|
+| Are prompts getting better or worse? | Regression test pass rate across prompt versions | **M4: Prompt regression score** — golden test score per prompt version | `prompt_regression.py` runs on every prompt change |
+| How often do we reprompt? | Reprompting frequency by task type | **M5: Reprompt rate** — LLM calls per agent run (>1 means retries/reprompts) | `_run_llm_calls` counter in BaseAgent |
+| Is the knowledge base growing? | Wiki entries over time | **M6: Knowledge accumulation rate** — new wiki entries per week | SharedMemory change log timestamps |
+
+### Goal 3: Measure engineering system effectiveness
+
+| Question | Indicator | Metric | Collection Method |
+|---|---|---|---|
+| Are pipelines completing successfully? | End-to-end success rate | **M7: Pipeline success rate** — % of pipeline runs where all required steps succeed | PipelineResult in metrics DB |
+| Are agents triggering the right cross-pipeline actions? | Event emission and consumption | **M8: Event utilization** — % of events that trigger at least one downstream action | EventBus consumed_by tracking |
+| Are risks being mitigated? | Risk status changes over time | **M9: Risk mitigation velocity** — risks moving from open → mitigating → closed per week | Risk register status history |
+
+### Goal 4: Justify AI use with cost-benefit evidence
+
+| Question | Indicator | Metric | Collection Method |
+|---|---|---|---|
+| What is the total AI spend? | Cumulative token cost | **M10: Total LLM cost (USD)** — sum of all API calls | MetricsCollector `llm_calls` table |
+| What is the human time saved? | Estimated hours saved | **M11: Hours saved** — (manual baseline per activity) × (activities automated) | Manual baseline × pipeline run count |
+| What is the ROI? | Cost saved vs cost spent | **M12: AI ROI** — (M11 × hourly rate) / (M10 + infrastructure cost) | Computed weekly |
+
+### Baseline Collection Schedule
+
+| Interval | What | Why |
+|---|---|---|
+| Per LLM call | Tokens, latency, cost, model, prompt version | Granular cost tracking |
+| Per agent run | Duration, success, outputs, human review flag | Activity-level effectiveness |
+| Per pipeline run | End-to-end duration, steps completed, events emitted | Process-level health |
+| Weekly | Aggregate dashboard refresh, risk review, metric trends | Management-level visibility |
+
+### Current Measured State (as of April 2026)
+
+| Metric | Current Value | Interpretation |
+|---|---|---|
+| M1: Processing time per transcript | <1 sec (offline) | 2700× faster than manual (~45 min) |
+| M2: Human correction rate | Not yet measured | Need human review sessions to establish |
+| M3: Cost per activity | $0 (offline mode) | Will measure when Claude API enabled |
+| M5: Reprompt rate | 1.0 (no retries) | Offline agents don't need retries |
+| M6: Knowledge accumulation | 50 wiki entries from 12 meetings + 4 coach sessions | ~3 entries per meeting processed |
+| M7: Pipeline success rate | 100% (all runs succeeded) | 7/7 steps, 6/6 steps consistently |
+| M8: Event utilization | 47 events published | 10 active cross-pipeline subscriptions |
+| M9: Risk mitigation | 2/16 in "mitigating" status | 14 still "open" — need action |
+
+---
+
+## Part 3: Resource Allocation — AI Use/Non-Use Justification
+
+The meta-model says: "Use and non-use of AI should be justified with evidence."
+
+The professors want to see REASONING, not just "we used AI because it's cool."
+Here is our principled classification for every activity:
+
+### Why Not Just Have Humans Do Everything?
+
+A human *can* do everything an agent does. The question isn't capability — it's **cost, consistency, and scalability**:
+
+| Factor | Human | Agent | Winner |
+|---|---|---|---|
+| Parse a 1-hour VTT transcript into structured minutes | ~45 min, varies by person | <1 sec, deterministic format | Agent (2700× faster) |
+| Classify 10 action items by priority | ~10 min, subjective disagreements | <1 sec, consistent heuristics | Agent for draft, human for P0 review |
+| Check meeting against architecture for contradictions | ~30 min, requires reading entire arch doc | <1 sec, queries 31 ChromaDB chunks | Agent (can check every meeting, human can't) |
+| Track commitments across 4 coach sessions | Manual spreadsheet, things get lost | Automatic, persistent, queryable | Agent (zero commitment tracking overhead) |
+| Detect recurring concerns across sessions | Requires re-reading all session notes | Pattern matching across SQLite, 371 ChromaDB chunks | Agent (impossible to do reliably by hand) |
+| Generate pre-meeting briefing with context | ~20 min gathering notes from past meetings | <1 sec, pulls from wiki + ChromaDB | Agent (ensures no context is forgotten) |
+
+### Why Not Have AI Do Everything?
+
+Because AI **lacks judgment** on things that matter most:
+
+| Activity | Why Human, Not AI | Evidence |
+|---|---|---|
+| P0 requirement approval | Business impact of wrong priority is high. Agent doesn't know client politics. | Architecture report §5.3: "incorrect data causes wrong parts ordered" |
+| ADR approval | Architectural decisions have long-term consequences. AI can draft, human must judge tradeoffs in context. | ADR-1 is "Tentative" precisely because empirical validation is needed |
+| Threshold calibration | Most sensitive parameter in the system. Small tuning errors have outsized effects. | RISK-ARCH-01 (critical): "0.85 is unsupported by empirical data" |
+| Coach session interpretation | Nuance in coach feedback requires understanding the relationship and history | Christian's session: measurement validity concerns are contextual to CMU expectations |
+| Risk mitigation decisions | Accepting risk vs mitigating it is a project management judgment call | RISK-ARCH-06: Catalog team capacity is a business decision |
+
+### Per-Activity Resource Classification
+
+| Activity | Resource | Why This Classification |
+|---|---|---|
+| **Transcript parsing** | `auton` | Structural extraction is deterministic. VTT format is fixed. No judgment needed for extraction. |
+| **Priority classification** | `auton` → `human` (P0 only) | Agent draft is fast + consistent. But P0 items (business-critical) must be human-verified because wrong priority = wrong resource allocation. |
+| **Requirement extraction** | `auton` | Template-filling from classified items. Format is standardized. |
+| **Ticket creation** | `auton` | Mechanical: item → Jira ticket. Agent adds `ai-generated` label so humans can filter. |
+| **Architecture drift detection** | `auton` | Checking 31 architecture chunks against every meeting is infeasible by hand. Agent queries ChromaDB in <1 sec. Human reviews only flagged drifts. |
+| **Coach session embedding** | `auton` | Chunking + embedding is mechanical. 371 chunks across 4 sessions — no human would do this manually. |
+| **Commitment tracking** | `auton` | Pattern extraction from text. Persistent storage in SQLite. Human reviews commitment status weekly. |
+| **Concern pattern detection** | `auton` | Cross-session analysis requires querying all past sessions. Agent detects recurring themes; human decides action. |
+| **Pre-meeting briefing** | `auton` → `human` review | Agent gathers context from wiki + ChromaDB. Human reviews briefing before meeting to add judgment. |
+| **ADR generation** | `assist` → `human` approval | AI drafts ADR from meeting discussions. Human architect approves because ADRs are binding decisions. |
+| **Prompt regression testing** | `auton` | Automated: run golden tests against new prompt versions. No human judgment needed for pass/fail. |
+| **Weekly digest** | `auton` → `human` review | Agent aggregates metrics, decisions, risks. Human reviews before sending to team. |
+| **Threshold tuning** | `human` (AI provides data) | The system provides precision-recall curves and confidence distributions. Human makes the final call because threshold = accuracy vs throughput tradeoff. |
+| **Risk register updates** | `auton` seeding → `human` status changes | Agent populates from sources. Human updates status because risk acceptance is a judgment call. |
+
+---
+
+## Part 4: Human vs AI Comparison — Evidence Table
+
+This is the table to show the professors. For each activity, what happens manually vs with the agent:
+
+| Activity | Manual (Human) | Automated (Agent) | Gain | Evidence |
+|---|---|---|---|---|
+| Parse 1 meeting transcript | 45 min, inconsistent format, misses items | <1 sec, consistent JSON, extracts all speaker turns | **2700× faster**, zero format variance | Pipeline logs: 7 steps in <300ms |
+| Classify 10 items by priority | 10 min, subjective, team disagrees | <1 sec, consistent heuristic | **600× faster**, eliminates subjectivity (but needs human P0 review) | Classifier output: 0 P0, 1 P1, 9 P2 consistently |
+| Check meeting vs architecture | 30 min per meeting, requires reading entire 406-line arch doc | <1 sec, queries 31 ChromaDB chunks, checks against 6 ADRs + 7 constraints | **Checks every meeting automatically** — human would skip most | drift_detector runs on every pipeline execution |
+| Track commitments across 4 sessions | Manual spreadsheet, updated sporadically | Automatic: 31 commitments tracked in SQLite, queryable | **Zero tracking overhead**, nothing forgotten | coach_sessions.db: 31 commitments across 4 sessions |
+| Detect recurring concerns | Re-read all session notes, remember patterns | Pattern matching: "general" concern raised 4× across sessions | **Cross-session memory** — impossible to do reliably by hand at scale | concern_tracker found 1 recurring theme ≥2 sessions |
+| Generate pre-meeting briefing | 20 min gathering notes, reviewing past sessions | <1 sec, pulls from 371 ChromaDB chunks + wiki + SQLite | **Complete context** — human would inevitably forget something | briefing_generator queries all data sources |
+| Maintain decision register | Manual doc, gets stale, items missed | Auto-logged from every meeting, queryable | **Every decision captured**, none lost | wiki: decision entries from all meetings |
+| Monitor for scope creep | Relies on team awareness | Agent detects when discussion contradicts constraints | **Continuous monitoring** vs periodic human review | drift_detector checks against 7 architecture constraints |
+
+### What We **Cannot** Automate (and Why)
+
+| Activity | Why Human Required | What AI Provides Instead |
+|---|---|---|
+| Setting the confidence threshold | Business-critical parameter with cascading effects on accuracy, review volume, and labor savings | Precision-recall curves, confidence distributions, sensitivity analysis — the DATA for the human to decide |
+| Deciding whether a risk is acceptable | Risk tolerance is a business judgment, not a technical one | Risk register with severity scores, mitigation options, and links to related architecture decisions |
+| Approving an ADR | Architectural decisions bind the team for months. Context matters. | ADR draft with alternatives analysis, traceability to drivers, and evidence from meetings |
+| Interpreting coach feedback | Coaches communicate with nuance, context, and relationship history | Structured extraction of commitments, concerns, and decisions — the raw material for human interpretation |
+| Choosing between ML approaches | Model selection depends on data volume, accuracy targets, and explainability requirements that evolve | Evidence accumulation dashboard showing POC results, confidence metrics, and readiness scores |
+
+---
+
+## Part 5: AI Use Tradeoff Register
+
+For each significant decision about AI use, the tradeoff and reversal condition:
+
+### Decision 1: Offline-first agents (pattern matching) before Claude API
+
+**Choice:** Agents work offline with regex/heuristics first. Claude is an upgrade, not a dependency.
+
+**Reasoning:** The meta-model says "take some risks, monitor performance, make changes." Starting offline lets us:
+- Validate the pipeline architecture independent of LLM quality
+- Establish baselines for what structural extraction can achieve
+- Measure the DELTA when Claude is added (M2: correction rate improvement)
+
+**Tradeoff:** Offline extraction is less accurate than Claude-powered extraction.
+**Gain:** System works without API key, costs $0, and we can measure improvement when Claude is added.
+**Reversal condition:** If offline accuracy is sufficient (>80% agreement with human review), Claude may not be worth the cost.
+
+### Decision 2: ChromaDB with local ONNX embeddings, not OpenAI embeddings
+
+**Choice:** Use `ONNXMiniLM_L6_V2` for local embeddings instead of OpenAI/Anthropic embedding APIs.
+
+**Reasoning:** Embedding is a commodity operation. The marginal quality difference between local MiniLM and cloud embeddings doesn't justify the API dependency, cost, and latency for our use case (matching meeting chunks, not precision ranking).
+
+**Tradeoff:** Slightly lower embedding quality (MiniLM-L6 vs text-embedding-3-large).
+**Gain:** Zero API cost for embeddings, zero latency, works offline, no auth needed.
+**Evidence:** 371 coach session chunks + 31 architecture chunks + 68 knowledge base chunks all indexed locally in <5 seconds.
+**Reversal condition:** If semantic search recall drops below acceptable threshold when evaluated against human-curated ground truth.
+
+### Decision 3: Event bus for cross-pipeline communication, not direct function calls
+
+**Choice:** Agents publish events to an EventBus rather than directly calling other pipelines.
+
+**Reasoning:** Direct coupling between pipelines creates a maintenance nightmare. If the requirements pipeline directly calls the architecture pipeline, both must change when either changes. The event bus decouples them: the requirements pipeline publishes "drift_detected" and doesn't care who subscribes.
+
+**Tradeoff:** Indirection adds a layer of complexity. Events can be missed if no subscriber is registered.
+**Gain:** Any new pipeline can subscribe to any event without modifying existing code. 10 subscriptions wired with zero cross-pipeline imports.
+**Reversal condition:** If the team finds event-based communication too hard to debug, switch to explicit pipeline chaining.
+
+### Decision 4: SharedMemory wiki instead of passing files between agents
+
+**Choice:** Agents deposit structured data into a namespaced SQLite store (the "wiki") instead of writing files that other agents read.
+
+**Reasoning:** This is the Karpathy wiki pattern — the system accumulates intelligence over time. Files are static; a wiki is queryable. An agent can ask "what are all the commitments related to data access?" and get an answer from across all meetings and sessions.
+
+**Tradeoff:** Another database to maintain. Data format must be kept consistent.
+**Gain:** 50 wiki entries across 7 namespaces, fully queryable, with 112 change log entries tracking how knowledge evolved. Any agent can search across all project knowledge in <1ms.
+**Reversal condition:** If wiki maintenance becomes a burden or data quality degrades, simplify to file-based artifacts.
+
+### Decision 5: Bespoke SDLC, not Scrum
+
+**Choice:** Agent-Augmented Iterative Lifecycle with two iterations, continuous measurement, and pipeline-based practice areas.
+
+**Reasoning:** The meta-model explicitly says "Avoid using a preexisting SDLC pattern (i.e., Scrum, RUP) which are fabricated on the idea of authoring software being the most labor-intensive part." With AI agents, authoring is cheap. The bottleneck is validation, integration, and measurement. Our lifecycle is designed around that reality.
+
+**Tradeoff:** No established ceremony cadence (no sprint planning, no retrospectives).
+**Gain:** Continuous measurement replaces periodic retrospectives. Agent pipelines enforce process consistency. Phase gates replace sprint reviews.
+**Reversal condition:** If the team struggles without ceremony structure, adopt lightweight standup practices — but keep continuous measurement.
+
+---
+
+## Part 6: What's Missing / Gaps to Close
+
+Being honest about what we haven't done yet:
+
+| Gap | Impact | Plan |
+|---|---|---|
+| **M2 (Human correction rate) not measured** | Can't prove AI quality without human baseline | Run 3 meetings through pipeline, have team member review outputs, record corrections |
+| **Claude API not yet enabled** | All agents running offline — can't show LLM-powered quality difference | Add API key, measure quality delta between offline and Claude-powered extraction |
+| **Jira/Bitbucket not connected** | Can't demonstrate real ticket creation or file commits | Provide creds, wire MCP clients, demonstrate end-to-end |
+| **Prompt versions not A/B tested** | Meta-model says "A/B test prompts" — we have the framework but no data | Create 2 prompt variants for transcript_parser, run both on same meeting, compare M4 scores |
+| **Drift detector hasn't found real drift yet** | Demonstrates capability but not impact | Process a meeting where team discusses something contradicting the architecture (or simulate one) |
+| **Risk mitigation tracking is manual** | Agents seed risks but don't auto-update status | Wire agents to update risk status when evidence appears (e.g., data received → RISK-COACH-01 status changes) |
+
+---
+
+## Part 7: Creative Elements — Beyond the Basics
+
+### 7.1 Prompt as First-Class Artifact
+
+The meta-model says: "Reusable prompts should be treated as version-controlled artifacts."
+
+Our system implements this:
+- **Prompt files** stored in `/prompts/` with version hashes
+- **Prompt regression testing** (`prompt_regression.py`) runs golden tests on every prompt change
+- **MetricsCollector** tracks which prompt version was used for every LLM call
+- **Prompt performance dashboard** shows accuracy per prompt version over time
+
+This means we can answer: "Did that prompt change make extraction better or worse?" — with data.
+
+### 7.2 Knowledge Accumulation as a Metric
+
+Most teams treat meetings as isolated events. Our system treats them as **incremental knowledge deposits**:
+
+| After Processing | Wiki Entries | ChromaDB Chunks | Events | Risks Tracked |
+|---|---|---|---|---|
+| 0 meetings | 0 | 0 | 0 | 0 |
+| 5 client meetings | 10 | 0 | 20 | 3 |
+| + 4 coach sessions | 50 | 371 | 47 | 16 |
+| + architecture doc | 55 | 470 | 47 | 16 |
+
+The knowledge base **grows monotonically**. Every meeting makes every future agent smarter. The briefing generator for meeting #10 has context from meetings 1-9 that a human would never fully review.
+
+### 7.3 Decision Provenance Tracking
+
+Every decision in the system has a provenance chain:
+```
+Raw utterance in meeting → extracted by transcript_parser → classified by priority_classifier
+→ logged by decision_logger → deposited in wiki → queryable by drift_detector
+→ linked to architecture decisions via traceability
+```
+
+A professor can ask: "Where did this requirement come from?" and we can trace it back to the exact meeting, speaker, and timestamp.
+
+### 7.4 Cost Transparency
+
+When Claude API is enabled, every single LLM call records:
+- Input tokens, output tokens, total cost
+- Which agent, which prompt version, which pipeline
+- Aggregated per-agent, per-pipeline, per-day
+
+This lets us answer: "How much does it cost to process one meeting?" and compare it to the human alternative ($50/hr × 45 min = $37.50 per meeting). If the agent costs $0.15 in tokens, that's a **250× cost reduction** — and we have the data to prove it.
+
+### 7.5 The Agentic Patterns We Use (and Don't Use)
+
+The meta-model says: "Be mindful of agentic patterns and know when to use them."
+
+| Pattern | We Use It? | Where / Why Not |
+|---|---|---|
+| **Sequential Pipeline** | Yes | All 7 pipelines chain agents in sequence with data bridging |
+| **Fan-out / Fan-in** | No | Our pipelines are linear; fan-out would add complexity without clear benefit for our data flow |
+| **Publish-Subscribe** | Yes | EventBus: 10 cross-pipeline subscriptions. Agents publish, pipelines subscribe. |
+| **RAG (Retrieval-Augmented Generation)** | Yes | ChromaDB stores 470 chunks; briefing generator and drift detector query for context |
+| **Persistent Memory (Wiki)** | Yes | SharedMemory: 50 entries across 7 namespaces, queryable by all agents |
+| **Human-in-the-Loop** | Yes | Phase gates at P0 approval, ADR approval, threshold tuning, risk acceptance |
+| **Prompt Versioning + Regression** | Yes | Prompts as files, golden test suite, version tracking in MetricsCollector |
+| **Self-improving (fine-tuning on corrections)** | No | Insufficient correction data volume yet. Would require Claude fine-tuning API access. |
+| **Multi-model routing** | No | Single model (Claude Sonnet) sufficient. Would add complexity without clear quality gain. |
+
+---
+
+## Part 8: Systematic Team Operation — Solving the Consistency Problem
+
+### The Problem
+
+Five team members, all using LLMs. Even with the same model and the same prompt,
+outputs differ because LLMs are probabilistic. Without governance:
+- Hrishik's meeting minutes format ≠ Ashritha's format
+- A "small prompt tweak" by one person silently degrades quality for everyone
+- No one knows which prompt version produced which artifact
+- No way to prove quality improved or regressed
+
+### The Solution: Three Layers
+
+#### Layer 1: Prompt Registry (Centralized, Version-Controlled)
+
+Every prompt is a **versioned artifact** in `/prompts/`, tracked in a SQLite registry:
+
+```
+/prompts/
+├── transcript_parser.txt ← v=3277a42a (active, reviewed)
+├── priority_classifier.txt ← v=5677a0b9 (active, reviewed)
+├── session_extraction.txt ← v=763d8a27 (active, reviewed)
+└── briefing_generator.txt ← v=e304cdc6 (active, reviewed)
+```
+
+- **4 prompts** registered, each with a content hash
+- Agents always load from the registry, never from inline strings
+- If someone changes a prompt, the old version is preserved and the new one requires review
+
+#### Layer 2: Prompt Peer Review (Like Code Review)
+
+Prompt changes follow a review workflow:
+
+```
+Author submits new version → status: pending_review
+ → Peer reviews (approve/reject/request_changes)
+ → If approved: status: approved → can be activated
+ → If rejected: author revises and resubmits
+```
+
+**Example from our system:**
+1. Ashritha submits new `transcript_parser` v2 (adds priority field to action items)
+2. Hrishik reviews: "Good — priority field aligns with classifier downstream" → **approved**
+3. Only after approval can the version be activated as the team's canonical prompt
+
+This means no single person can silently change a prompt that affects everyone's outputs.
+
+#### Layer 3: Regression Testing (Golden Test Suites)
+
+Every prompt has golden test cases in `/tests/golden/`:
+- Input: a known transcript
+- Expected output: the correct extraction result
+
+When a prompt changes, `prompt_regression` agent runs all golden tests and **blocks
+the change if quality drops >10%** from baseline. This is the unit test of prompt engineering.
+
+### 10 Team Conventions (Enforced Rules)
+
+| # | Convention | How Enforced |
+|---|---|---|
+| 1 | All prompts in `/prompts/` as .txt files, never inline | Registry auto-scan detects |
+| 2 | Every prompt change requires peer review before activation | Registry review workflow |
+| 3 | Temperature=0 for deterministic tasks | BaseAgent default |
+| 4 | Every LLM artifact has provenance (agent, prompt version, timestamp, model) | MetricsCollector |
+| 5 | HITL required for P0 items and ADRs | Pipeline step config |
+| 6 | Golden test cases required for every prompt | Regression agent on PR |
+| 7 | Offline-first, Claude as upgrade | BaseAgent fallback pattern |
+| 8 | Outputs deposited to wiki, not just files | BaseAgent.wiki |
+| 9 | Cross-pipeline events, not direct calls | EventBus |
+| 10 | Weekly measurement dashboard review | Team practice |
+
+### Why This Matters
+
+Without these layers, "we used AI" is an anecdote. With them:
+- We can prove which prompt version produced which result (traceability)
+- We can prove quality didn't degrade when we changed a prompt (regression)
+- We can prove all team members used the same prompt (registry)
+- We can prove peer review happened (review log)
+- We can A/B test prompts with evidence (ab_tests table)
+
+This is the difference between **ad-hoc AI use** and **principled AI use** that the
+rubric explicitly asks for.
+
+---
+
+## Part 9: Answers to Likely Professor Questions
+
+**Q: "Everyone's using the same LLM — how do you ensure consistency across team members?"**
+A: Three layers. (1) Prompt Registry: every prompt is version-controlled with a content hash. All agents load from the registry, so everyone uses the exact same prompt. (2) Prompt Peer Review: no one can change a prompt unilaterally — it requires approval, just like code review. (3) Temperature=0 for deterministic tasks: we eliminate stochasticity where consistency matters. For the remaining variance, golden test suites catch regressions before they ship.
+
+**Q: "What if someone changes a prompt and it breaks things?"**
+A: The prompt regression agent catches it. Every prompt has golden test cases (known input → expected output). When a prompt changes, regression tests run automatically. If quality drops >10% from baseline, the change is blocked. This is literally the same principle as code regression testing, applied to prompts.
+
+**Q: "If a human can do all of this, why build agents?"**
+A: A human can write meeting minutes. They can't write them in <1 second, cross-reference against 31 architecture chunks, check for commitment violations across 4 past sessions, and deposit the results into a queryable knowledge base — all simultaneously, for every meeting, consistently. The agent doesn't replace the human. It gives the human superpowers: complete context, zero forgetting, and continuous monitoring.
+
+**Q: "How do you know the AI is actually helping?"**
+A: We measure it. M1 (processing time: 2700× faster), M5 (reprompt rate), M7 (pipeline success rate: 100%). When we enable Claude, we'll measure M2 (correction rate) and M3 (cost per activity) to quantify the quality-cost tradeoff. We don't assert "AI helps" — we show the numbers.
+
+**Q: "What happens if the AI is wrong?"**
+A: Three safeguards. (1) P0 items always go to human review. (2) Every agent output is logged with provenance — we can audit any decision. (3) Prompt regression testing catches quality degradation before it reaches production. The system is designed to be wrong sometimes and catch it.
+
+**Q: "Why not just use ChatGPT/Copilot?"**
+A: ChatGPT is a conversation. Our system is an engineering pipeline. ChatGPT doesn't remember last week's meeting. It doesn't track commitments. It doesn't emit events that trigger other processes. It doesn't maintain a queryable wiki. The value isn't in the LLM — it's in the orchestration, memory, and measurement infrastructure around it.
+
+**Q: "What's your SDLC?"**
+A: We designed a bespoke Agent-Augmented Iterative Lifecycle per the meta-model. Not Scrum. Two iterations (prototype → pilot), continuous measurement, pipeline-based practice areas, phase gates for human decisions. Every repeatable activity is automated; every non-repeatable decision is human. The measurement system runs continuously, not retrospectively.
diff --git a/docs/ses_explained.md b/docs/ses_explained.md
new file mode 100644
index 0000000..bbb9438
--- /dev/null
+++ b/docs/ses_explained.md
@@ -0,0 +1,823 @@
+# The eParts Software Engineering System — Explained
+
+> A complete explanation of how our team builds software, why we built it this way,
+> and how AI agents work together as a connected system — not isolated tools.
+
+---
+
+## Table of Contents
+
+1. [What Is This?](#1-what-is-this)
+2. [The Big Picture](#2-the-big-picture)
+3. [How a Meeting Becomes a Jira Ticket (End-to-End Walkthrough)](#3-how-a-meeting-becomes-a-jira-ticket)
+4. [The 7 Pipelines and 28 Agents](#4-the-7-pipelines-and-28-agents)
+5. [How Agents Talk to Each Other](#5-how-agents-talk-to-each-other)
+6. [The Shared Infrastructure](#6-the-shared-infrastructure)
+7. [The Unified Traceability Store](#7-the-unified-traceability-store)
+8. [Mapping to the Meta-Model Framework](#8-mapping-to-the-meta-model-framework)
+9. [Measuring AI Effectiveness — Counterfactuals](#9-measuring-ai-effectiveness)
+10. [Our SDLC: Agent-Augmented Iterative Lifecycle](#10-our-sdlc)
+11. [Key Design Decisions](#11-key-design-decisions)
+
+---
+
+## 1. What Is This?
+
+Our team (Pimsie Supreme) is building a product for eParts — a catalog management system that uses ML to extract product attributes from vendor spec sheets. That's the **Software System** (the product for the client).
+
+But there's a second system: the **Software Engineering System (SES)** — the system we use *to build* the product. Think of it like a factory that produces cars. The car is the product. The factory — with its assembly lines, quality checks, inventory tracking, and worker coordination — is the engineering system.
+
+Our SES is powered by **28 AI agents** organized into **7 automated pipelines**. These agents don't write the product code. They handle the engineering overhead:
+
+- Parsing meeting transcripts into structured requirements
+- Creating Jira tickets from action items
+- Detecting when architecture decisions drift from what was discussed
+- Tracking commitments made to coaches and verifying delivery
+- Maintaining a living traceability matrix connecting every artifact to its origin
+- Generating pre-meeting briefings so the team walks in prepared
+
+The key insight: **authoring software is now cheap, but engineering coordination is where teams fail.** Our SES automates the repeatable 80% of coordination while humans own the judgment-heavy 20%.
+
+---
+
+## 2. The Big Picture
+
+```
+┌─────────────────────────────────────────────────────────────────────┐
+│ TRIGGERS │
+│ │
+│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
+│ │ .vtt file │ │ GitHub PR │ │ Cron │ │ Manual API │ │
+│ │ upload │ │ (future) │ │ schedule │ │ call │ │
+│ │ [ACTIVE] │ │ [READY] │ │ [ACTIVE] │ │ [ACTIVE] │ │
+│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │
+└─────────┼───────────────┼───────────────┼───────────────┼──────────┘
+ └───────────────┴───────┬───────┴───────────────┘
+ ▼
+┌─────────────────────────────────────────────────────────────────────┐
+│ CENTRAL ORCHESTRATOR │
+│ FastAPI server · 29 REST endpoints │
+│ Routes triggers → pipelines · Task queue · Agent registry │
+└──────────────────────────────┬──────────────────────────────────────┘
+ │ selects pipeline based on trigger type
+ ▼
+┌─────────────────────────────────────────────────────────────────────┐
+│ 7 AGENT PIPELINES │
+│ │
+│ Requirements (7) │ Architecture (4) │ Coding (4) │ PM (3) │
+│ Coach Session (6) │ ML Decision (3) │ Knowledge (2) │
+│ │
+│ Total: 28 unique agents, 29 pipeline steps │
+│ (some agents appear in multiple pipelines) │
+└──────────┬──────────────────────────┬───────────────────────────────┘
+ │ │
+ ▼ ▼
+┌─────────────────────┐ ┌─────────────────────────────────────────────┐
+│ SHARED INFRA │ │ MCP SERVERS (External API Wrappers) │
+│ (all SQLite-based) │ │ │
+│ │ │ Jira [LIVE] GitHub [LIVE] │
+│ SharedMemory │ │ ChromaDB [LIVE] Bitbucket [READY] │
+│ EventBus │ │ Confluence [READY] Slack [READY] │
+│ TraceabilityStore │ │ Google Drive [READY] Anthropic [READY] │
+│ PromptRegistry │ │ │
+│ RiskRegister │ │ LIVE = wired + tested │
+│ MetricsCollector │ │ READY = code complete, needs credentials │
+└─────────┬───────────┘ └──────────────────────┬──────────────────────┘
+ │ │
+ ▼ ▼
+┌─────────────────────────────────────────────────────────────────────┐
+│ STORAGE │
+│ │
+│ All stored locally in the memory/ folder: │
+│ │
+│ memory/shared_memory.db — wiki entries (meetings, decisions...) │
+│ memory/events.db — cross-pipeline event history │
+│ memory/traceability.db — artifact links (concerns → Jira) │
+│ memory/coach_sessions.db — coach session structured data │
+│ memory/ml_decisions.db — ML experiment logs │
+│ memory/risk_register.db — identified risks + mitigations │
+│ memory/prompt_registry.db — prompt versions + review status │
+│ memory/metrics.db — agent run performance data │
+│ memory/chroma/ — ChromaDB vector store (for RAG) │
+│ │
+│ Why 8 DBs instead of 1? Each can be independently inspected, │
+│ backed up, or reset. Zero infrastructure — no Postgres needed. │
+└─────────────────────────────────────────────────────────────────────┘
+ │
+ ▼
+┌─────────────────────────────────────────────────────────────────────┐
+│ OUTPUTS │
+│ REQ docs │ ADRs │ Jira tickets │ Meeting minutes │ Traceability │
+│ Risk register │ Weekly digest │ Dashboards │ Pre-meeting briefings │
+└─────────────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## 3. How a Meeting Becomes a Jira Ticket
+
+Let's trace what happens when someone uploads a client meeting recording. This is the **end-to-end flow** for the Requirements Engineering practice area.
+
+### Step-by-Step
+
+```
+ YOU SYSTEM EXTERNAL
+ │ │ │
+ │ Upload meeting.vtt │ │
+ │──────────────────────▶│ │
+ │ │ │
+ │ ┌───────┴───────┐ │
+ │ │ ORCHESTRATOR │ │
+ │ │ Detects │ │
+ │ │ trigger_type= │ │
+ │ │ "transcript" │ │
+ │ │ Routes to │ │
+ │ │ requirements │ │
+ │ │ pipeline │ │
+ │ └───────┬───────┘ │
+ │ │ │
+ │ Step 1: transcript_parser │
+ │ ┌───────┴───────┐ │
+ │ │ Parse VTT: │ │
+ │ │ • Clean text │ │
+ │ │ • Identify │ │
+ │ │ speakers │ │
+ │ │ • Extract: │ │
+ │ │ 3 decisions │ │
+ │ │ 7 actions │ │
+ │ │ 2 concerns │ │
+ │ └───────┬───────┘ │
+ │ │ │
+ │ │ ① Deposits to SharedMemory: │
+ │ │ wiki["meetings"]["2026-01-22"]│
+ │ │ = {summary, actions, concerns}│
+ │ │ │
+ │ │ ② Emits events: │
+ │ │ action_items_extracted │
+ │ │ decision_logged │
+ │ │ │
+ │ Step 2: priority_classifier │
+ │ ┌───────┴───────┐ │
+ │ │ For each item:│ │
+ │ │ Does it │ │
+ │ │ contain │ │
+ │ │ "deadline", │ │
+ │ │ "demo", │ │
+ │ │ "block"? →P0 │ │
+ │ │ "need", │ │
+ │ │ "sprint"? →P1 │ │
+ │ │ Otherwise →P2 │ │
+ │ ◄────────────│ │ │
+ │ "Review 2 P0│ With Claude │ │
+ │ items" │ API: context- │ │
+ │ │ aware classify│ │
+ │ └───────┬───────┘ │
+ │ │ │
+ │ Step 3: req_extractor │
+ │ ┌───────┴───────┐ │
+ │ │ Format as │ │
+ │ │ REQ-XXX.md: │ │
+ │ │ • Title │──── commit ────────────▶│ GitHub
+ │ │ • Rationale │ │ requirements/
+ │ │ • Priority │ │ parsed/
+ │ │ • Acceptance │ │ REQ-013.md
+ │ │ criteria │ │
+ │ └───────┬───────┘ │
+ │ │ │
+ │ │ Deposits to wiki: │
+ │ │ wiki["requirements"]["REQ-013"] │
+ │ │ │
+ │ Step 4: ticket_creator │
+ │ ┌───────┴───────┐ │
+ │ │ For each P1/ │ │
+ │ │ P2 item: │──── create ticket ─────▶│ Jira
+ │ │ • summary │ │ EPARTS-XX
+ │ │ • description │ │
+ │ │ • labels: │ │
+ │ │ [P1, │ │
+ │ │ auto- │ │
+ │ │ created] │ │
+ │ │ • priority │ │
+ │ └───────┬───────┘ │
+ │ │ │
+ │ Step 5: minutes_publisher │
+ │ ┌───────┴───────┐ │
+ │ │ Format as │── (publish) ───────────▶│ Confluence
+ │ │ Confluence │ │ (when
+ │ │ page │ │ configured)
+ │ └───────┬───────┘ │
+ │ │ │
+ │ Step 6: decision_logger │
+ │ ┌───────┴───────┐ │
+ │ │ For each │ │
+ │ │ decision: │ │
+ │ │ • Log to wiki │ │
+ │ │ ["decisions"│ │
+ │ │ /"date:0"] │──── commit ────────────▶│ GitHub
+ │ │ • Append to │ │ minutes/
+ │ │ decisions. │ │ decisions.
+ │ │ log.md │ │ log.md
+ │ └───────┬───────┘ │
+ │ │ │
+ │ Step 7: drift_detector │
+ │ ┌───────┴───────┐ │
+ │ │ Take meeting │ │
+ │ │ decisions │ │
+ │ │ │──── RAG query ─────────▶│ ChromaDB
+ │ │ Query arch │◀─── similar chunks ────│ (vectors of
+ │ │ report chunks │ │ architecture
+ │ │ │ │ report)
+ │ │ Compare via │ │
+ │ │ keyword match:│ │
+ │ │ does meeting │ │
+ │ │ contradict │ │
+ │ │ architecture? │ │
+ │ └───────┬───────┘ │
+ │ │ │
+ │ │ If drift found: │
+ │ │ emits "drift_detected" event │
+ │ │ │
+ │ PIPELINE COMPLETE │
+ │ │ │
+ │ The emitted events trigger MORE: │
+ │ │ │
+ │ action_items_extracted ──▶ project_mgmt pipeline │
+ │ decision_logged ──────▶ architecture pipeline │
+ │ drift_detected ───────▶ architecture pipeline │
+ │ │
+```
+
+**What just happened in human terms:**
+
+1. You uploaded a 45-minute meeting recording
+2. The system parsed it into 3 decisions, 7 action items, and 2 concerns
+3. Each requirement was formatted as a document and committed to GitHub
+4. Items were classified by priority — P0 items were flagged for your review
+5. Decisions were logged to a running decision log (wiki + GitHub)
+6. The system checked if any meeting decisions contradicted the architecture
+7. Jira tickets were automatically created with proper labels and priority
+8. Three other pipelines were triggered to handle the downstream effects
+
+**Time for a human to do all of this manually: ~3 hours.**
+**Time for the system: ~30 seconds.**
+
+---
+
+## 4. The 7 Pipelines and 28 Agents
+
+### Definitions
+
+- **Agent**: A single Python class that does one specific job. It takes a trigger (input), runs logic, and produces a result (output). Each agent has access to the SharedMemory wiki and EventBus.
+- **Pipeline**: An ordered chain of agents where each agent's output feeds the next agent's input. Like an assembly line — each station does one job, then passes the work forward.
+
+### Pipeline → Agent Mapping (from actual code)
+
+```
+PIPELINE: requirements
+PRACTICE AREA: Requirements Engineering
+TRIGGER: .vtt transcript upload
+STEPS:
+ 1. transcript_parser — Parse VTT into structured JSON
+ 2. priority_classifier — Classify items as P0/P1/P2
+ 3. req_extractor — Format as REQ-XXX.md, commit to GitHub
+ 4. ticket_creator — Create Jira tickets (P0 needs human approval)
+ 5. minutes_publisher — Publish minutes to Confluence
+ 6. decision_logger — Log decisions to wiki + GitHub
+ 7. drift_detector — Check for architecture contradictions via RAG
+
+PIPELINE: coach_session
+PRACTICE AREA: Coach Session Memory
+TRIGGER: coach/mentor .vtt upload
+STEPS:
+ 1. transcript_parser — Parse coach transcript
+ 2. session_memory — Chunk + embed into ChromaDB for future RAG
+ 3. commitment_tracker — Extract commitments with owners/deadlines
+ 4. concern_tracker — Detect recurring themes across sessions
+ 5. coach_linker — Link session content to open ML decisions
+ 6. decision_logger — Log coach session decisions
+
+PIPELINE: architecture
+PRACTICE AREA: Architecture
+TRIGGER: transcript processed, PR event, drift_detected event
+STEPS:
+ 1. drift_detector — Compare discussion vs canonical architecture
+ 2. adr_generator — Draft Architecture Decision Record if needed
+ 3. diagram_updater — Propose diagram updates
+ 4. traceability_builder — Update the unified traceability matrix
+
+PIPELINE: coding
+PRACTICE AREA: Coding
+TRIGGER: PR event (future — for when actual coding begins)
+STEPS:
+ 1. pr_reviewer — Automated PR review (style, tests, traceability)
+ 2. test_generator — Generate test stubs for new functions
+ 3. doc_generator — Update API documentation
+ 4. prompt_regression — Test prompt changes against golden dataset
+
+PIPELINE: ml_decision
+PRACTICE AREA: ML Decision Memory
+TRIGGER: POC result submitted
+STEPS:
+ 1. evidence_accumulator — Parse POC results and log evidence
+ 2. readiness_detector — Check if enough evidence to close decision
+ 3. coach_linker — Link evidence to coach session context
+
+PIPELINE: project_mgmt
+PRACTICE AREA: Project Management
+TRIGGER: Cron (weekly, Friday 6pm)
+STEPS:
+ 1. wbs_updater — Sync WBS with Jira board state
+ 2. weekly_digest — Generate weekly progress digest
+ 3. alert_agent — Check project health, fire alerts
+
+PIPELINE: knowledge
+PRACTICE AREA: Knowledge Management
+TRIGGER: Cron (pre-meeting) or new_session_embedded event
+STEPS:
+ 1. context_packager — Aggregate project context from wiki + Jira + events
+ 2. briefing_generator — Generate pre-meeting briefing document
+```
+
+### Agent Count
+
+- **28 unique agents** registered in the system
+- **25 appear in pipelines** (some in multiple: `transcript_parser` in 2, `decision_logger` in 2, `drift_detector` in 2, `coach_linker` in 2)
+- **3 standalone agents** (triggered on-demand, not part of a pipeline chain):
+ - `stale_detector` — finds requirements with no Jira ticket
+ - `boilerplate_generator` — scaffolds code from templates
+ - `decision_log` — standalone ML decision logger
+
+---
+
+## 5. How Agents Talk to Each Other
+
+Agents don't talk directly. They communicate through two mechanisms:
+
+### Mechanism 1: SharedMemory (The Project Wiki)
+
+Think of it as a shared whiteboard organized into folders (namespaces). Every agent can read and write to it.
+
+**Physically:** A SQLite database at `memory/shared_memory.db` with a `wiki` table.
+
+**How agents use it:**
+
+```python
+# transcript_parser deposits a meeting summary
+self.wiki.put("meetings", "2026-01-22", {
+ "summary": "Discussed ML extraction approach with eParts team...",
+ "action_items": ["Set up confidence threshold testing", ...],
+ "decisions": ["Primary approach: LLM extraction, not OCR"],
+ "concerns": ["Data quality from vendor spec sheets unclear"]
+})
+
+# Later, context_packager reads it to build a briefing
+meetings = self.wiki.list_namespace("meetings")
+# Returns all stored meeting summaries
+```
+
+Every write is audited — the `wiki_log` table records who changed what, when, and the old vs new value.
+
+### Namespaces (the "folders")
+
+| Namespace | What's Stored | Example Entry | Written By | Read By |
+|-----------|--------------|---------------|-----------|---------|
+| `meetings` | Parsed meeting summaries | `{summary: "...", action_items: [...], decisions: [...]}` | transcript_parser | context_packager, weekly_digest |
+| `decisions` | All decisions from all sources | `{text: "Use LLM extraction", speaker: "Harsha", context: "..."}` | decision_logger | adr_generator, traceability_builder |
+| `concerns` | Recurring themes from coaches | `{theme: "data quality", sessions_raised: 3, severity: "high"}` | concern_tracker | alert_agent, briefing_generator |
+| `requirements` | Extracted requirements | `{id: "REQ-003", title: "ML confidence scoring", priority: "P0"}` | req_extractor | stale_detector, traceability_builder |
+| `commitments` | Coach session commitments | `{text: "deliver prototype by March 15", owner: "team", status: "pending"}` | session_memory | commitment_tracker, briefing_generator |
+| `project_mgmt` | WBS state, sprint data | `{total_tickets: 50, done: 12, in_progress: 8}` | wbs_updater | weekly_digest, alert_agent |
+
+### Mechanism 2: EventBus (Cross-Pipeline Triggers)
+
+When something important happens in one pipeline, it fires an event that can trigger another pipeline.
+
+**Physically:** A SQLite database at `memory/events.db` with an `events` table (history) and a `subscriptions` table (wiring).
+
+**How it works:**
+
+```
+1. transcript_parser finishes parsing a meeting
+2. It calls: self.emit("action_items_extracted", data={"items": [...]})
+3. EventBus stores the event in events.db
+4. EventBus looks up subscriptions table:
+ "action_items_extracted" → target: project_mgmt pipeline
+5. project_mgmt pipeline is queued for execution
+```
+
+### The 10 Cross-Pipeline Event Subscriptions
+
+| Event | Fired By | Triggers | What Happens |
+|-------|----------|----------|-------------|
+| `action_items_extracted` | transcript_parser | project_mgmt | Ticket creation for action items |
+| `decision_logged` | decision_logger | knowledge | Decision gets indexed |
+| `drift_detected` | drift_detector | architecture | ADR drafting + diagram update |
+| `new_session_embedded` | session_memory | knowledge | Briefing refresh |
+| `recurring_concern` | concern_tracker | project_mgmt | PM alert for team |
+| `commitment_overdue` | commitment_tracker | project_mgmt | Overdue alert |
+| `decision_ready` | readiness_detector | coach_session | Link to coach context |
+| `poc_evidence_logged` | evidence_accumulator | ml_decision | Readiness check |
+| `human_review_needed` | traceability_builder | project_mgmt | Alert for human review |
+| `new_requirements` | req_extractor | architecture | Drift check on new reqs |
+
+**The key insight:** No pipeline is an island. The Requirements pipeline's output triggers the Architecture pipeline. The Coach Session pipeline's recurring concerns feed back into Project Management. This is what makes it a **framework** instead of a collection of scripts.
+
+---
+
+## 6. The Shared Infrastructure
+
+### What Each Component Does
+
+**SharedMemory** (`memory/shared_memory.db`)
+- A key-value store organized into namespaces
+- Agents deposit knowledge → other agents read it later
+- Inspired by Karpathy's "wiki" pattern: accumulated intelligence over time
+- Includes full audit trail (every write logged with before/after values)
+
+**EventBus** (`memory/events.db`)
+- Pub-sub notification system
+- Agents fire events → subscribed pipelines get triggered
+- Events are persistent (stored in SQLite), not fire-and-forget
+- 10 active subscriptions wiring 7 pipelines together
+
+**TraceabilityStore** (`memory/traceability.db`)
+- Connects artifacts to each other: concern → requirement → Jira ticket → risk
+- Two tables: `artifacts` (the nodes) and `links` (the edges)
+- Currently: 189 artifacts, 764 links, 10 artifact types, 7 link types
+- All links created via keyword matching — zero LLM calls
+
+**PromptRegistry** (`memory/prompt_registry.db`)
+- Every prompt used by agents is version-controlled here
+- Each version has: author, content, review_status, active flag
+- Ensures consistent AI use across team members (same prompt = same behavior)
+
+**RiskRegister** (`memory/risk_register.db`)
+- Identified project risks with severity, likelihood, mitigation status
+- Auto-seeded from architecture report + coach sessions
+- 16 risks tracked
+
+**MetricsCollector** (`memory/metrics.db`)
+- Every agent run logs: duration_ms, success/fail, llm_calls, tokens_used
+- Human corrections logged via POST /metrics/correction
+- Enables measurement of AI effectiveness (override rates, cost per artifact)
+
+**ChromaDB** (`memory/chroma/`)
+- Vector database for semantic similarity search (RAG)
+- Stores text as numerical vectors using local ONNX model (no API needed)
+- Used by: drift_detector (compare meeting vs architecture), session_memory (recall past coach sessions), briefing_generator (find relevant context)
+- Why not SQL? SQL can only do exact text matching (`LIKE '%keyword%'`). ChromaDB finds semantically similar text ("confidence threshold" matches "accuracy calibration")
+
+### Why SQLite? Why Not One Database?
+
+| Consideration | SQLite (our choice) | Postgres |
+|--------------|-------------------|----------|
+| Setup | Zero — it's a file | Install server, manage connections |
+| Cost | Free, runs locally | Free but needs infrastructure |
+| Portability | Zip the `memory/` folder, share it | Database dump/restore |
+| Concurrent writes | Limited (locks whole file) | Excellent |
+| Our use case | Sequential pipeline execution, 1 user | Multi-user production system |
+
+For a capstone demo and team of 5, SQLite is the right choice. For production, you'd migrate to Postgres — but the SQL schema is identical, so migration is straightforward.
+
+---
+
+## 7. The Unified Traceability Store
+
+This answers: **"Where did this come from, and what does it connect to?"**
+
+### What It Is
+
+A SQLite database (`memory/traceability.db`) with two tables:
+- `artifacts` — every traceable item (concern, decision, requirement, risk, Jira ticket, etc.)
+- `links` — directed edges between artifacts (e.g., concern BECAME requirement)
+
+### Artifact Types and Counts
+
+| Type | Count | Source |
+|------|-------|--------|
+| meeting | 5 | Parsed from .vtt client meeting files |
+| coach_session | 1 | From coach session memory DB |
+| concern | 12 | Extracted from meeting transcripts and coach sessions |
+| decision | 10 | Logged by decision_logger from meetings |
+| action_item | 41 | Extracted by transcript_parser |
+| commitment | 31 | Extracted by commitment_tracker from coach sessions |
+| requirement | 12 | REQ-001 through REQ-012, defined from project knowledge |
+| architecture | 6 | Key decisions from the architecture report |
+| risk | 16 | From the risk register |
+| jira_ticket | 50 | From live Jira API |
+
+### Link Types
+
+| Link Type | Count | Meaning | Example |
+|-----------|-------|---------|---------|
+| RAISED_IN | 122 | Originated from a meeting/session | "Data quality concern" RAISED_IN "Meeting Jan 22" |
+| BECAME | 60 | Evolved into a different artifact | Concern BECAME Requirement |
+| DECIDED_BY | 32 | Shaped by a decision | Requirement DECIDED_BY Architecture decision |
+| ADDRESSES | 13 | Responds to a concern | Decision ADDRESSES Concern |
+| TRIGGERED | 9 | Spawned another artifact | Decision TRIGGERED Architecture change |
+| IMPLEMENTS | 204 | Jira ticket tracks/fulfills | Jira ticket IMPLEMENTS Requirement |
+| MITIGATES | 320 | Reduces a risk | Requirement MITIGATES Risk |
+
+### Example Chains (Simple and Meaningful)
+
+**Chain 1: From a client concern to a Jira ticket**
+```
+[MEETING] Client Meeting Jan 22
+ │
+ └── RAISED_IN ──▶ [CONCERN] "How do we handle low-confidence predictions?"
+ │
+ └── BECAME ──▶ [REQUIREMENT] REQ-003: ML confidence scoring
+ │
+ ├── DECIDED_BY ──▶ [ARCHITECTURE] ARCH-003:
+ │ Threshold calibration design
+ │
+ ├── IMPLEMENTS ──▶ [JIRA] EPARTS-72:
+ │ Explore Azure AI services
+ │
+ └── MITIGATES ──▶ [RISK] Confidence threshold
+ miscalibration
+```
+
+**Why this matters:** If someone asks "why does EPARTS-72 exist?", trace backward:
+Jira ticket → REQ-003 → client concern about low-confidence predictions → Meeting Jan 22.
+
+**Chain 2: From a coach commitment to evidence**
+```
+[COACH SESSION] Christian Feb 20
+ │
+ └── RAISED_IN ──▶ [COMMITMENT] "We'll deliver a working prototype by March 15"
+ │
+ └── IMPLEMENTS ──▶ [JIRA] EPARTS-74:
+ Finalize requirements by April 15
+```
+
+**Chain 3: From a risk to what mitigates it**
+```
+[RISK] "Vendor spec sheet formats vary widely"
+ │
+ └── mitigated by ── [REQUIREMENT] REQ-008: Support multiple document formats
+ (if we handle multiple formats, the format variation risk is reduced)
+
+[RISK] "Confidence threshold miscalibration"
+ │
+ ├── mitigated by ── [REQUIREMENT] REQ-003: ML confidence scoring on every prediction
+ │
+ └── mitigated by ── [ARCHITECTURE] ARCH-003: Per-attribute threshold calibration
+```
+
+### Where Did All This Data Come From?
+
+The script `pipeline/seed_traceability.py` populates the store from existing data:
+1. Client meeting JSONs (from transcript parsing) → meeting + action_item + concern + decision artifacts
+2. Coach session DB → commitment artifacts
+3. Jira API (live) → jira_ticket artifacts
+4. Risk register DB → risk artifacts
+5. Architecture report → architecture artifacts (manually curated)
+6. Project knowledge → 12 formal requirement artifacts (REQ-001 to REQ-012)
+
+**Links** are created using domain-aware keyword matching and Jira label-based linking — **zero LLM calls**. The data is structured enough (meetings have dates, tickets have labels, requirements have IDs) that pattern matching works.
+
+---
+
+## 8. Mapping to the Meta-Model Framework
+
+The CMU meta-model framework says every engineering system has four elements:
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│ META-MODEL FRAMEWORK │
+│ │
+│ Resources ──implements──▶ Processes │
+│ Processes ──generates──▶ Artifacts │
+│ Artifacts ──consumed by──▶ Processes │
+│ Measurement ──measures──▶ Resources, Processes, Artifacts │
+└─────────────────────────────────────────────────────────────┘
+```
+
+### ARTIFACTS (what gets produced)
+
+| Artifact | Format | Generated By | Validation Gate |
+|----------|--------|-------------|-----------------|
+| Meeting minutes | JSON + Markdown | transcript_parser | Human reviews summary |
+| Requirements (REQ-001..012) | Markdown | req_extractor | P0 items require human approval |
+| Architecture Decision Records | Markdown | adr_generator | All ADRs require PR approval |
+| Jira tickets | Jira Cloud | ticket_creator | P0 tickets need 1 approval |
+| Traceability matrix | SQLite + Markdown | traceability_builder | Gaps flagged automatically |
+| Risk register | SQLite | seed_risk_register | Human reviews mitigations |
+| Decision log | Markdown | decision_logger | Committed to version control |
+| Weekly digest | Markdown | weekly_digest | Published to team |
+| Pre-meeting briefings | Text | briefing_generator | Sent before meetings |
+| Versioned prompts | SQLite | PromptRegistry | Peer review before activation |
+
+### PROCESSES (how work gets done)
+
+Each process follows **ETVX** (Entry, Task, Verification, Exit):
+
+```
+PROCESS: Requirements Engineering (end-to-end)
+
+ ENTRY: .vtt transcript uploaded to system
+
+ TASK: 7-step pipeline executes:
+ parse → classify → extract → create_tickets →
+ publish_minutes → log_decisions → detect_drift
+
+ VERIFY: • P0 items flagged for human review
+ • Drift detector compares vs architecture (RAG)
+ • All outputs deposited to SharedMemory with audit trail
+ • Traceability links established
+
+ EXIT: Requirements committed to GitHub
+ Jira tickets created with labels
+ Events emitted for downstream pipelines
+```
+
+All 7 pipelines are documented processes with ETVX definitions.
+
+### RESOURCES (who/what does the work)
+
+| Resource Type | Count | Role |
+|--------------|-------|------|
+| AI Agents | 28 | Automated execution of repeatable tasks |
+| MCP Servers | 8 | Interface to external services (Jira, GitHub, ChromaDB...) |
+| Shared Infra | 6 components | Cross-agent coordination and knowledge |
+| Human Team | 5 | Judgment, review, approval gates |
+| External Tools | Python 3.12, FastAPI, SQLite, ChromaDB | Runtime platform |
+
+### MEASUREMENTS (how we know it's working)
+
+See Section 9 for the full measurement framework.
+
+---
+
+## 9. Measuring AI Effectiveness — Counterfactuals
+
+> "When evaluating whether it is worth using AI, we should ask what would happen
+> if we did not use AI at all." — Christian (Coach)
+
+### The Counterfactual Framework
+
+For every AI-assisted task, we ask: **What is the total cost WITH AI vs WITHOUT AI?**
+
+```
+ WITHOUT AI WITH AI
+ ────────── ───────
+TASK COST Human time to do the task AI execution time (seconds)
+ + Human review time
+ + Token/API cost
+
+ERROR COST Human mistakes AI mistakes
+ (forgotten items, (misclassification,
+ inconsistent formats) hallucinated items)
+ + Time to detect & fix
+
+DOWNSTREAM COST If error propagates: If error propagates:
+ rework in later phases same, but errors are
+ consistent and detectable
+
+MEASUREMENT COST Time to evaluate quality Automated metrics collection
+ (manual spot-checks) (agent logs everything)
+```
+
+### Applying This to Our Tasks
+
+| Task | Without AI | With AI | Net Assessment |
+|------|-----------|---------|----------------|
+| **Parse 45-min transcript** | 2-3 hours/meeting × 5 meetings = 10-15 hrs total. Human misses items, inconsistent format. | 30 sec/meeting. May miss nuanced items. Human reviews output (15 min). Repeated weekly → savings accumulate. | **AI worth it.** Repeated task, big time savings, review catches errors. |
+| **Classify priority (P0/P1/P2)** | Team discusses each item in a meeting (30-60 min). Subjective, inconsistent across members. | Keyword heuristic: instant but less accurate. With Claude: context-aware but costs tokens. P0 requires human approval regardless. | **AI worth it.** P0 human gate limits downside risk. Consistency across meetings is the real value. |
+| **Create Jira tickets** | Team member does it manually, maybe next day. Forgets context. Inconsistent labels. Some items never get ticketed. | Immediate creation with labels. May create duplicate or low-quality tickets. | **AI worth it for P1/P2.** The cost of a missed ticket (forgotten work) exceeds the cost of deleting a bad ticket. |
+| **Architecture drift detection** | Someone remembers "didn't we say something different last time?" — unreliable. | RAG query + keyword match: detects contradictions automatically. False positive rate unknown. | **AI worth it.** The cost of undetected drift (building the wrong thing) is very high. Even imperfect detection is better than none. |
+| **Traceability matrix** | Manual maintenance: always out of date. Teams skip it because it's tedious. | 764 links via keyword matching, zero tokens. May have false links. | **AI worth it.** Zero marginal cost. The alternative (no traceability) is worse than imperfect traceability. |
+| **Pre-meeting briefing** | Check Jira, wiki, notes, coach feedback... 30-60 min per person. | Aggregates from 7 sources in seconds. May miss context a human would catch. | **AI worth it.** Repeated weekly. Even a 70% useful briefing saves team 4+ hours/week. |
+
+### What We Actually Measure (MetricsCollector)
+
+| Metric | How Collected | What It Tells Us |
+|--------|--------------|-----------------|
+| Agent run duration | Automatic per run | Time cost of AI |
+| Success/failure rate | Automatic per run | Reliability |
+| LLM token usage | Automatic per call | Dollar cost |
+| Human override rate | POST /metrics/correction | AI accuracy proxy |
+| Traceability coverage % | TraceabilityStore.get_coverage() | Completeness |
+| Tickets created vs deleted | Jira API diff | Precision of ticket creation |
+| Commitment delivery rate | commitment_tracker | Team accountability |
+
+### Key Principle: Measurement Should Be Cheaper Than the Decision
+
+> "If a more precise measurement costs more than the value of the better decision,
+> it is not worth doing." — Christian
+
+- For **repeated tasks** (meeting parsing, ticket creation): measure carefully. The benefit compounds weekly.
+- For **one-time tasks** (architecture doc creation): a rough estimate is enough. Don't over-measure.
+- For **low-value tasks**: improve or remove the task rather than measuring AI performance on it.
+
+---
+
+## 10. Our SDLC: Agent-Augmented Iterative Lifecycle
+
+### Why Not Use an Existing SDLC?
+
+The meta-model framework explicitly warns: *"Avoid using a preexisting SDLC pattern which are fabricated on the idea that authoring software is the most labor-intensive part of development."*
+
+With AI agents, authoring (parsing, classifying, drafting) is cheap. The bottleneck shifts to **validation, measurement, and judgment**.
+
+| SDLC | Why We Rejected It |
+|------|--------------------|
+| **Scrum** | Sprint ceremonies assume the bottleneck is coordination between humans. With agents handling routine coordination, we don't need daily standups for ticket updates — the `weekly_digest` agent does that. |
+| **RUP** | Too heavyweight for a 5-person team. RUP's elaborate phase/discipline matrix assumes large teams with dedicated roles. |
+| **XP** | Pair programming and continuous integration are valuable, but XP doesn't account for AI agents as first-class team members. |
+| **Waterfall** | Our work is iterative (prototype → pilot), not sequential. |
+
+### What Our SDLC Actually Looks Like
+
+```
+┌──────────────────────────────────────────────────────────────────┐
+│ AGENT-AUGMENTED ITERATIVE LIFECYCLE │
+│ │
+│ Two iterations with human phase gates between them: │
+│ │
+│ ┌─────────────────────────┐ ┌─────────────────────────┐ │
+│ │ ITERATION 1: │ │ ITERATION 2: │ │
+│ │ PROTOTYPE │ │ PILOT │ │
+│ │ │ │ │ │
+│ │ Goal: Prove ML │ │ Goal: Prove │ │
+│ │ accuracy is achievable │ │ operational viability │ │
+│ │ │ │ │ │
+│ │ Core pipeline: │ │ Production deployment: │ │
+│ │ ingest → predict → │ │ real data, review │ │
+│ │ route → writeback │ │ workflow, monitoring │ │
+│ └────────────┬────────────┘ └────────────┬────────────┘ │
+│ │ │ │
+│ ▼ ▼ │
+│ [PHASE GATE] [PHASE GATE] │
+│ Human verifies: Human verifies: │
+│ • Threshold calibrated • Review workflow tested │
+│ • PIMS schema validated • Monitoring in place │
+│ • Coach concerns addressed • Client sign-off │
+│ │
+│ ──────────────────────────────────────────────────────────────── │
+│ │
+│ CONTINUOUS ACROSS BOTH ITERATIONS: │
+│ │
+│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
+│ │Requirem- │ │Architect-│ │ Project │ │Knowledge │ │
+│ │ents │ │ure │ │ Mgmt │ │ │ │
+│ │Pipeline │ │Pipeline │ │Pipeline │ │Pipeline │ │
+│ └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ │
+│ └─────────────┴────────────┴─────────────┘ │
+│ │ │
+│ SharedMemory + EventBus │
+│ (continuous measurement) │
+│ │
+│ MEASUREMENT: Not sprint retrospectives. │
+│ Every agent run logs: duration, tokens, success, corrections. │
+│ Measurement is continuous, automated, and queryable. │
+└──────────────────────────────────────────────────────────────────┘
+```
+
+### How Practice Areas Map to Pipelines
+
+| Practice Area | Pipeline | Key Activities | Human Gate |
+|--------------|----------|---------------|------------|
+| Requirements Engineering | `requirements` | Parse → Classify → Extract → Ticket → Log → Drift Check | P0 approval |
+| Architecture | `architecture` | Drift → ADR → Diagram → Traceability | ADR PR approval |
+| Construction | `coding` | PR Review → Test → Doc → Prompt Regression | Merge approval |
+| Coach Memory | `coach_session` | Parse → Embed → Commitments → Concerns → Link | None (advisory) |
+| ML Decisions | `ml_decision` | Evidence → Readiness → Coach Link | Decision closure |
+| Project Management | `project_mgmt` | WBS → Digest → Alerts | Alert triage |
+| Knowledge | `knowledge` | Context Package → Briefing | None (informational) |
+
+### Why This Works for eParts
+
+1. **The client problem is a data pipeline** — linear architecture maps to linear iterations with clear phase boundaries.
+2. **Team size (5) precludes heavyweight processes** — no sprint ceremonies, no Scrum Master. Agents handle repetitive coordination.
+3. **AI must be measured to be justified** — continuous measurement gives us data, not anecdotes.
+4. **Coach sessions revealed specific risks** — the lifecycle explicitly incorporates risk tracking and commitment tracking.
+5. **The bottleneck is judgment, not authoring** — agents draft, humans approve. The SDLC reflects this by putting human phase gates between iterations, not between sprints.
+
+---
+
+## 11. Key Design Decisions
+
+### 1. Offline-First Architecture
+Every agent has a pattern-matching fallback when no LLM API key is available. ChromaDB uses local ONNX embeddings. The system is fully functional without any external AI service. You can use any LLM (Anthropic Claude, OpenAI, Gemini, local Llama) by changing one method in `agents/base.py`.
+
+### 2. SQLite Everywhere
+7 small database files in `memory/`, each serving one purpose. No infrastructure to maintain. Portable — the entire system state is a folder. Cost: zero.
+
+### 3. Zero LLM Calls for Traceability
+764 links created via domain-aware keyword matching. Structured data (dates, labels, IDs) enables deterministic pattern matching. Cost: zero tokens.
+
+### 4. Prompt Governance
+PromptRegistry version-controls every prompt, requires peer review before activation. 10 team conventions ensure consistent AI use across all team members.
+
+### 5. Event-Driven Cross-Pipeline Communication
+Adding a new pipeline only requires subscribing to events — no existing code changes. This is how the system scales to the coding phase.
+
+### 6. Human-in-the-Loop at Every Critical Point
+P0 requirements need human approval. ADRs need PR approval. ML decisions need human closure. The system proposes; humans decide.
+
+---
+
+*Auto-generated from the eParts SES codebase. Last updated: April 2026.*
+*CMU MSE Studio 2026 · Pimsie Supreme · Python 3.12 · FastAPI · SQLite · ChromaDB*
diff --git a/docs/ses_presentation_qa.md b/docs/ses_presentation_qa.md
new file mode 100644
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+++ b/docs/ses_presentation_qa.md
@@ -0,0 +1,104 @@
+# SES demo — Question & Answer (speaker notes)
+
+Companion to **`talking_script_ses_infra_trace_liuhandoff.*`** / **`talking_script_ses_six_minute.*`**. Answers here stay **short and speakable** for audience Q&A during or after the SES + trace segment. For file-level detail, line references, and deeper pipeline Q&A, see **`presentation_qa.md`** (Sections 1–2, 8).
+
+---
+
+## SES in one sentence
+
+**Q: What is SES?**
+**A:** SES is our **Software Engineering System**—the way we run **ordered automation** (pipelines of steps), **persist what matters** in SQLite and Git, and **link** meetings, requirements, architecture, risks, and backlog in one trace graph so the story does not live only in chat history.
+
+---
+
+## Harnesses, hierarchy, agents (no jargon version)
+
+**Q: Engineering harness vs agentic harness—why two names?**
+**A:** **Engineering harness** is the **agreement on outcomes**: what each practice must leave behind (files in Git, REQ markdown, DB rows, gates like P0 review). **Agentic harness** is the **runtime mechanic**: register agents once, order them in pipelines, pass results step to step, optionally skip if there is nothing to process. Same system—**policy** plus **execution**.
+
+**Q: How do pipelines and agents relate—who’s on top?**
+**A:** **Pipelines** are named flows; **steps** inside a pipeline are ordered slots; **agents** are the reusable implementations plugged into those slots (~28 registered once, combined differently per pipeline). Hierarchy: **pipeline → ordered steps → agent executes step**.
+
+**Q: Why ordered steps instead of “let the model figure it out”?**
+**A:** Order gives **repeatable behavior**, auditable hand-offs, and predictable failure modes. You can explain “step six did not run because step three produced no ticket payload”—harder when everything is one prompt.
+
+**Q: Is SES the same as using ChatGPT as a team assistant?**
+**A:** No—the difference is **persisted state**, **versioned artifacts**, **typed trace edges**, and **pipelines you can re-run** after a trigger. Chat threads do not replace Git history, `traceability.db`, or an executor that enforces step order.
+
+---
+
+## Shared memory, events, three databases
+
+**Q: What is Shared Memory vs “context in the model”?**
+**A:** **Shared Memory** here means the **wiki-style SQLite store** namespaces agents write to across runs—concerns, decisions, architecture snippets, etc.—so the next trigger does not start from zero. “Context in the model” is ephemeral per call; ** namespaces in `shared_memory.db`** accumulate **project truth** the team agreed should persist (see also **`presentation_qa.md` §1.11**).
+
+**Q: What are `shared_memory.db`, `events.db`, and `traceability.db` for—in plain English?**
+**A:** **Shared memory:** durable key-value style knowledge for agents between runs. **Events:** pub/sub log so one pipeline can signal another (and you keep an audit trail). **Traceability:** the graph of **artifacts and typed links** (meetings, REQ, ARCH, risks, Jira, etc.) powering dashboards and REQ docs.
+
+**Q: Why SQLite locally instead of “a real database” in the cloud?**
+**A:** Capstone choice: **zero ops**, single-machine demos, easy backup (copy files), and alignment with “engineering system we can show on a laptop.” Production could move stores to managed DBs without changing the conceptual model.
+
+---
+
+## Execution, triggers, deployment
+
+**Q: What wakes a pipeline up?**
+**A:** Anything normalized to a **trigger type** plus payload—client transcript ingest, cron-style hooks, Git/PR style triggers, etc.—then **routing picks which pipeline** runs and the **executor walks the steps** (details in **`presentation_qa.md`** infra answers).
+
+**Q: Is SES deployed on Azure?**
+**A:** **SES framework as shown** runs **locally** for this capstone; **eParts product** target infra can be Azure—that is a **separate** deployment story. Don’t conflate “where the ML app runs” with “where SES notebooks/pipelines run today” (see **`presentation_qa.md` §1.10**).
+
+**Q: If one step fails, does the whole pipeline die?**
+**A:** Depends on the step: some paths use **skip** when inputs are missing; hard failures should surface in logs/metrics. **Honest answer for demo:** we design for graceful skips where possible; not every branch is production-hardened—call out **demo vs production** if pressed.
+
+---
+
+## Traceability: diagrams, IDs, REQ-001
+
+**Q: What is `ses_traceability.html` vs `traceability_diagram.html`?**
+**A:** **`ses_traceability.html`**—**SES Traceability** overview: **generic** artifact types and relationship idea. **`traceability_diagram.html`**—same **concept** plus a **compact REQ-001** slice for slides. **`traceability_story.html`**—**same graph as a readable tree** for REQ-001. **`intelligence.html`**—full tabular/trace explorer on the bundled dataset.
+
+**Q: What do IDs like REQ-001, ARCH-002, CON-… mean?**
+**A:** Stable handles in the **trace store**: **REQ-*** requirement records (also align with REQ markdown under `requirements/parsed/`), **ARCH-*** architecture records (ADR-aligned intent), **CON-*** concerns from conversation, **DEC-*** decisions, **RIS-*** risks, **MTG-*** meetings. They let human and machine **cite the same node** across Git, dashboards, and DB.
+
+**Q: Are these traces “real” or just a diagram?**
+**A:** Same nodes and edges **exported** into what dashboards load (**e.g.** `dashboard/traceability_data.json` seeded from ingest); REQ markdown is **committed** separately. Trace is **documentation + data**, not a one-off illustration.
+
+**Q: Why bother with typed links (`MITIGATES`, `BECAME`, etc.)?**
+**A:** **Searchability and audit**: you can query “everything that mitigates this risk” or “what requirement this concern became”—and justify sponsor questions without re-watching recordings.
+
+---
+
+## People, governance, Liu handoff
+
+**Q: Who owns requirements vs architecture vs trace?**
+**A:** **Team convention:** SES framework / pipeline authoring (ownership per your roster—**Ashritha** is cited as SES pipeline owner in **`presentation_qa.md`** around ticket routing); **Liu** and software-system leads own **WBS / SDLC narrative** depth—**explicitly hand off** “why we chose this SDLC” to Liu rather than debating it under SES infra slides.
+
+**Q: Human in the loop where?**
+**A:** **P0** items are flagged for human review before auto-behaviors that could derail sprint (see **`presentation_qa.md` §2.1**); ADR and REQ changes belong in normal **Git/PR review** culture.
+
+---
+
+## Limits & future (honest pivots)
+
+**Q: Accuracy of automated trace links?**
+**A:** Heuristic/fuzzy linking is **good but not oracle** (~85%-class behavior discussed in **`presentation_qa.md` §8.5**). LLM-assisted relinking is a plausible quality pass—not run on every row in demo scope.
+
+**Q: What’s missing you’d build next quarter?**
+**A:** Example pivots: **goal-model** layering on traceability (**§8.1** discussion), tighter **cron** operationalization for briefings, **cloud-hosted** SQLite replacements for team-wide runtime, richer **human review UX** beyond Jira queues.
+
+---
+
+## Quick index to the big doc
+
+| Topic | Primary section in `presentation_qa.md` |
+|-------|----------------------------------------|
+| Git vs wiki vs Confluence | §1.1 |
+| Engineering / agentic harness (deep) | §1.4–1.5 |
+| Events store | §1.7 |
+| Why / measurement | §1.8 |
+| LLM vs offline agents | §1.9 |
+| Local storage & deploy | §1.10 |
+| SharedMemory / “common bank” | §1.11 |
+| Requirements / P0 / REQ files | Section 2 |
+| Dashboards / trace / WBS | Section 8 |
diff --git a/docs/ses_product_repo_integration.md b/docs/ses_product_repo_integration.md
new file mode 100644
index 0000000..4d455c8
--- /dev/null
+++ b/docs/ses_product_repo_integration.md
@@ -0,0 +1,137 @@
+# SES ↔ Product Repo Integration
+
+**Status:** Proposed (team decision pending) · **Owner:** Ashritha (Engineering System) · **Date:** 2026-07-27
+**Companion docs:** [`Metamodel_framework.md`](../Metamodel_framework.md), [`agentic-augmented-scrum.md`](../agentic-augmented-scrum.md), [`defect_management.md`](defect_management.md), [`evals.md`](evals.md)
+
+---
+
+## 1. The principle: the harness is not the product
+
+The engineering system is a **harness that operates on the product repo from the
+outside**. It is versioned, tested, and deployed as its own system; the product
+repo stays clean and shippable.
+
+| | Repo | Hosts | CI |
+|---|---|---|---|
+| **Harness (this repo)** | `AshrithaG/eparts` | agents, prompts, orchestrator, evals, linters, QA interfaces, dashboards | GitHub Actions |
+| **Product** | `epartsservices/intelligent-attribute-prediction` | the ML attribute-prediction system delivered to eParts | Bitbucket Pipelines |
+
+This separation is the reason the harness can be changed aggressively — new
+agents, new prompts, new gates — without any of that churn touching client
+deliverables.
+
+## 2. Decision: integrate by configuration, not migration
+
+**Decision.** Keep the SES in its own repo and have it act on the product repo
+through the Bitbucket API client (`mcp/bitbucket.py`).
+
+**Alternatives considered and rejected:**
+
+| Option | Rejected because |
+|---|---|
+| **Migrate the SES into the product repo** | Puts team-process tooling — coach-session memory, meeting-transcript pipelines, program-health dashboards — inside the client's deliverable, degrading handover. The client bought an attribute-prediction system, not our project management. |
+| **Duplicate the SES into both repos** | Two copies of every agent and prompt diverge immediately; the prompt registry's whole purpose is one reviewed version per prompt. |
+| **Rebuild the SES natively on Bitbucket Pipelines** | Throws away working GitHub Actions workflows and couples the harness to one CI vendor. The harness should be portable across product repos, because the *next* project will have a different one. |
+| **Keep them fully disconnected (status quo)** | The engineering system then governs only its own artifacts, and cannot see or act on the code it is supposed to govern. This is the gap being closed. |
+
+**Consequence being accepted:** two CI platforms to understand. Mitigated by
+keeping the boundary narrow — the harness talks to the product repo over one
+documented API client, not through shared build config.
+
+## 3. What crosses the boundary
+
+`mcp/bitbucket.py` is the single crossing point. It exposes `get_pr_status`,
+`add_pr_comment`, `open_pr`, `create_branch`, and `commit_file`, configured by
+three environment variables (see `.env.example`):
+
+```
+BITBUCKET_WORKSPACE=epartsservices
+BITBUCKET_REPO=intelligent-attribute-prediction
+BITBUCKET_TOKEN=
+```
+
+Agents that act on the product repo once configured:
+
+| Agent | Acts on the product repo by | Human gate |
+|---|---|---|
+| `pr_reviewer` | Commenting on every product PR (style, test coverage, REQ traceability, API surface) | Comment-only; never merges |
+| `drift_detector` | Flagging product changes that diverge from the documented architecture | Raises for review |
+| `traceability_builder` | Linking product PRs to requirements and decisions in the traceability graph | Read-only |
+| `test_review_agent` | Reviewing whether product tests cover sad paths and whether coverage is meaningful | Comment-only |
+
+Everything else in the harness (transcripts, coach memory, requirements
+extraction, program health) operates on project artifacts and Jira, and never
+touches product code.
+
+## 4. Staged rollout, gated on safety
+
+Integration is deliberately staged, because the product repo is shared team
+property and agent write access to it is not reversible by a single person.
+
+| Stage | Grants | Precondition |
+|---|---|---|
+| **1. Observe** | PR read | none — safe today |
+| **2. Advise** | PR read + comment | team agreement that agent comments are welcome on their PRs |
+| **3. Propose** | branch create + commit to a **feature branch**, PR opened for human review | **SES005 fixed** (below) |
+| **4. — (not planned)** | direct commit to `main` | never; violates the human-approval rule |
+
+**SES005 is a hard precondition for stage 3.** `commit_file()` defaults to
+`branch="main"` (`mcp/bitbucket.py:52`, `mcp/github.py:74`), and seven agents
+call it without naming a branch. A write-scoped token today would let an agent
+commit directly to the team's `main` with no PR and no human approval — which
+contradicts this system's core rule. The custom linter
+(`python3 tools/lint_ses.py --strict`) reports every affected call site, and this
+finding is exactly the kind of implementation-drift-from-stated-policy that
+deterministic tooling is supposed to catch.
+
+## 5. Process definition (ETVX)
+
+**Process:** *Product-change governance* — the harness observing and advising on
+a product-repo change.
+
+- **Entry:** a PR is opened in `epartsservices/intelligent-attribute-prediction`.
+- **Task:** `pr_reviewer` posts a structured review; `traceability_builder` links
+ the PR to the requirement or decision it implements; `drift_detector` compares
+ the change against the documented architecture; `test_review_agent` assesses
+ test meaningfulness.
+- **Verification:** a human reviewer reads the agent output alongside the diff.
+ Agent output is advice, never authority — the tier policy in
+ `agentic-augmented-scrum.md` (T1/T2/T3) sets how much human review the change
+ requires, indexed to its risk.
+- **eXit:** the PR is merged by a human, with the traceability link recorded and
+ any agent-raised concern either addressed or explicitly dismissed.
+
+## 6. Measurements
+
+All derivable without new instrumentation:
+
+| Measurement | Source | Question it answers |
+|---|---|---|
+| Product PRs receiving agent review | Bitbucket PR comments by the agent account | Is the harness actually reaching the product? |
+| Agent comments that led to a code change | PR comment → subsequent commit in the same PR | Is the review useful, or noise? |
+| Product PRs linked to a requirement | traceability graph vs. total merged PRs | Can we still trace code to intent? |
+| Drift findings per tick | `drift_detector` output | Is the implementation diverging from the architecture? |
+| Stage-3 blockers outstanding | `tools/lint_ses.py --strict` violation count | Are we safe to grant write access yet? |
+
+## 7. Metamodel mapping
+
+- **Process:** product-change governance (§5), as ETVX, with the human
+ verification step explicit.
+- **Artifacts:** the product PR, the agent review comment, the traceability link,
+ the drift finding. Each is a defined, inspectable artifact.
+- **Resources:** the Bitbucket MCP client, the four agents above, CI on both
+ sides, and human reviewers holding the tier-appropriate authority.
+- **Measurements:** §6 — measuring reach (is it connected), usefulness (does
+ advice change outcomes), and safety (are write preconditions met).
+- **Context management:** the harness supplies agents with the requirement,
+ decision, and architecture context a product PR relates to — which is what
+ makes the review specific to *this* system rather than generic.
+
+## 8. Provenance
+
+The harness/product separation and the staged-trust rollout follow the AI-tools
+coaching session with **Cory Gwin** (Senior Software Engineer, GitHub Copilot),
+2026-07-24 — specifically his framing that trust should be a function of risk,
+that loop tightness is the control, and that a building harness is a distinct
+artifact whose quality is measured by what it lets you ship correctly the first
+time.
diff --git a/docs/studio-req+arch.md b/docs/studio-req+arch.md
new file mode 100644
index 0000000..eb7ab13
--- /dev/null
+++ b/docs/studio-req+arch.md
@@ -0,0 +1,155 @@
+# Studio Crit — Requirements & Architecture
+
+**Section:** Software System (requirements + architecture) · ~5 minutes, 4 slides · script: [`talking_script.md`](talking_script.md)
+**Closing:** Reflection is a separate deck — `eParts_Reflection_Closing.pptx` · script: [`talking_script_reflection.md`](talking_script_reflection.md)
+**Crit:** Thursday 30 July 2026, MSE 265, 300 South Craig Street
+**Deck:** [`eParts_Section3_SoftwareSystem.pptx`](../../eParts_Section3_SoftwareSystem.pptx) · rebuild with [`build_section3_deck.py`](build_section3_deck.py)
+
+This page is the index and the argument. It does not restate the artifacts — it points at them and says what each one is for.
+
+---
+
+## The one-line story
+
+ETIM was a **mid-project requirements change against the v1.0 baseline**, not a new project, and it cascaded into the architecture. The system's *WHAT* moved from *"predict arbitrary product attributes"* to *"classify each product into an ETIM class and match its attributes to a controlled vocabulary."* Constrained classification, not free prediction.
+
+The principle everything hangs off:
+
+> **Original supplier data = evidence · ETIM data = standardized interpretation · confidence = how sure we are of the interpretation.**
+
+The rubric asks for exactly this — *"if the architecture and requirements have changed significantly since Spring, these should be mentioned"* — and for requirements and system drivers to be **traceable through architecture, implementation, and validation**. The chain below closes.
+
+---
+
+## Artifact index
+
+### Requirements
+
+| Artifact | What it is |
+|---|---|
+| [`product-spec-v1.4.pdf`](product-spec-v1.4.pdf) · [`.tex`](product-spec-v1.4.tex) | The authoritative spec. **Version 1.4, 29 July 2026.** The version-history table *is* the change record: 1.1 integrated ETIM, 1.2 pinned the release, 1.3 corrected where ETIM matching happens, 1.4 closed our own trace gap (QAS-3, VAL-4, VAL-5). |
+| [`product-spec-changelog.md`](product-spec-changelog.md) | Greppable companion: every ID added and amended, what no longer holds, the requirement inventory, and three owned defects. |
+| [`etim-requirements-change.md`](etim-requirements-change.md) | How the change was **managed** — the four classes of requirements management, the re-scoped baseline, risks with handling tactics, open client decisions. |
+
+**Added in v1.1:** HLR-6 (classify against ETIM, enrich with class/feature/value/unit IDs) · FR-9 (match to ETIM classes/features/controlled values+units, confidence per assignment, preserve the original) · FR-10 (load and maintain the ETIM dictionary) · DR-4 (PIMS writes keyed by ETIM identifiers).
+
+**Added in v1.2:** C-4 — the project targets **ETIM release 10.0 (EI) for its duration**; later releases and cross-release migration are out of scope. FR-10 is scoped to that pinned release.
+
+**Corrected in v1.3:** ETIM matching is performed by the **ML service, after attribute matching** — not by the Intermediate Structured Layer during normalization. HLR-2, §2.1 and SCEN-1 amended; FR-9 attributed to ML. The v1.1 edit had put ETIM keying in the wrong component.
+
+**Amended:** HLR-1, HLR-2, §2.1, §3.1, glossary, SCEN-1 step 3, SCEN-2 step 1.
+**No longer holds:** FR-3 "predict attributes" · the flat `IngestedRecord`.
+
+New IDs were **added, not renumbered** — deliberately, so every existing trace link survives.
+
+### Architecture
+
+| Artifact | What it is |
+|---|---|
+| [`../diagrams/pipe-filter-architecture-v6.png`](../diagrams/pipe-filter-architecture-v6.png) | **v6.0, July 2026, post-ETIM.** Source: [`.svg`](../diagrams/pipe-filter-architecture-v6.svg). Solid outline = running code, dashed = designed but not built. |
+| [`../diagrams/pipe-filter-architecturev5.png`](../diagrams/pipe-filter-architecturev5.png) | v5.0, May 2026, pre-ETIM. Kept for the side-by-side. |
+| [`adr-index.md`](adr-index.md) | All 21 ADRs, with a "Built?" column and the ETIM verdict on each spring ADR. |
+| [`ETIM-ADR-ASSESSMENT.md`](ETIM-ADR-ASSESSMENT.md) | The change-impact analysis over ADRs 0001–0012, bucketed A/B/C. 29 June. |
+| [`REQUIREMENTS-TO-ADR-MAPPING.md`](REQUIREMENTS-TO-ADR-MAPPING.md) | §1–9 map v2.0 → ADRs 0001–0012. **§10** maps v1.2 → ADRs 0013–0021, with forward/backward coverage and known gaps. |
+
+### The five architectural deltas, v5.0 → v6.0
+
+| # | Delta | ADR | Built? |
+|---|---|---|---|
+| 1 | **ETIM reference layer** — 10 release-scoped tables, ETIM 10.0 EI (pinned, C-4) | 0013 | Yes — alembic `0005` |
+| 2 | **ETIM matching added behind ML** — attribute matching unchanged; then class → feature → value/unit → ETIM validation → policy validation | 0016 | Phase 1 exists; phase 2 no — EPARTS-289/290/291 |
+| 3 | **Staging split** — evidence (`staging_product` + `staging_raw_attribute`) vs. interpretation (`matched_product_attribute`) | 0014 | Evidence yes (alembic `0006`); interpretation no |
+| 4 | **Explicit ingestion → ML seam** — frozen `ExtractedInput`, `extra="forbid"` so no interpretation can cross | 0021 | Partly — alembic `0007` merged; orchestrator wiring EPARTS-363 outstanding |
+| 5 | **PIMS contract re-keyed** — `product_id + etim_release_id + etim_class_id + etim_feature_id` | 0017 | No — writer rework EPARTS-299 |
+
+**Unchanged, and that is the point:** the pipe-and-filter spine (ADR-001), per-attribute routing granularity (ADR-004), the `PredictionServiceInterface` boundary (ADR-002), human-in-the-loop, and the append-only audit trail (ADR-010) all survived a major requirements change.
+
+### New ETIM ADRs (0016–0021)
+
+Written this cycle, against spec v1.4. ADRs 0001–0012 were **deliberately left unedited** — they record what the team decided in April. New ADRs supersede *forward* by reference.
+
+| ADR | Decision | Status |
+|---|---|---|
+| [0016](0016-decompose-matching-into-staged-etim-class-feature-value-stages.md) | Add ETIM matching as a second ML phase after attribute matching | Accepted |
+| [0017](0017-rekey-pims-writeback-contract-on-etim-identifiers.md) | Re-key the PIMS writeback contract on ETIM identifiers | Accepted |
+| [0018](0018-extend-routing-to-etim-signals-with-class-review-first.md) | Extend routing to ETIM signals, class-review-first | Accepted |
+| [0019](0019-externalize-client-feature-policy-as-per-class-configuration.md) | Externalize the client feature policy as per-class configuration | Accepted (values blocked on client) |
+| [0020](0020-pin-etim-release-10-0-for-the-project-duration.md) | Pin ETIM release 10.0 (EI) for the project duration | Accepted |
+| [0021](0021-formalize-ingestion-to-ml-boundary-as-frozen-extracted-input-record.md) | Formalize the ingestion → ML boundary as a frozen record | Accepted |
+
+---
+
+## Traceability
+
+```
+business objective (industry-standard catalog)
+ → HLR-6
+ → FR-9 / FR-10 / DR-4
+ → ADRs 0013–0021
+ → tickets EPARTS-285…303
+ → golden test set (EPARTS-296) / VAL-1–3
+```
+
+**Forward trace = completeness.** Every v1.4 requirement maps to at least one ADR, except **DC-2 (Auth0)** — an open gap, recorded rather than hidden.
+
+**Backward trace = currency.** All nine ETIM ADRs anchor to a v1.4 requirement; none is orphaned, and none was written for work with no requirement behind it.
+
+**Boundary.** Tracing stops at two cross-team interface contracts — ML input (EPARTS-156) and OCR output (EPARTS-159). We own those requirements; another stream implements them. ADR-021 is the ingestion-side half made explicit and schema-enforced.
+
+---
+
+## Evidence, verified
+
+| Claim | Verified against |
+|---|---|
+| ETIM 10.0 EI: 159 groups, 5,640 classes, 17,377 features, 201,284 class-feature-values | `e2e-ocr-ing/docs/INGESTION_ETIM_PLAN.md`; loader tested against the real archive |
+| 10 release-scoped reference tables | `src/eparts_ingestion/models/etim.py` — 10 model classes |
+| ETIM 10.0 EI pinned; release-mismatch rejected on import | `src/eparts_ingestion/etim/loader.py` (checksum + release validation), C-4 in spec v1.2 |
+| Ingestion does not assign ETIM identifiers | `e2e-ocr-ing/docs/architecture.md` decision log Q1.2 — "preserve source vocabulary only"; ETIM matching is ML stream EPARTS-156 |
+| Idempotent checksummed import | `src/eparts_ingestion/etim/loader.py`, `cli/etim.py` |
+| Evidence/interpretation split | `src/eparts_ingestion/models/staging.py`, `alembic/versions/0006_create_staging_tables.py` |
+| Handoff record forbids interpretation | `src/eparts_ingestion/handoff/spec_model.py` — `extra="forbid"`, `frozen=True` |
+| Handoff not yet wired into the pipeline | `src/eparts_ingestion/orchestrator.py` — zero references to `handoff` |
+| Migrations | `alembic/versions/0005`, `0006`, `0007` |
+
+---
+
+## Owned defects
+
+Naming these is worth more than hiding them.
+
+1. **Two spec lineages exist.** This one runs 0.1 → 0.5 → 1.0 (24 Apr) → 1.1 → 1.2 → 1.3 → 1.4, with FR-1…10, QAS-1…3, C-1…4, VAL-1…5. A parallel *"Document Version 2.0"* (24 Apr) carries FR-1…13, QAS-1…5 (QAS-1 = Accuracy ≥95%), C-1…8. It is **not** an ancestor of this one, so it is not committed here — pairing them would imply a version chain that does not exist.
+2. **ADRs 0001–0015 cite the other lineage's IDs.** QAS-4/5, C-7 and FR-11/12/13 do not resolve against v1.4, and their QAS-3 (Accuracy) is a different scenario from this lineage's QAS-3 (client feature policy, added in v1.4). Reconciling the two ID spaces is open work; the spring ADRs stay unedited.
+3. **Two ADR series collide.** `docs/00NN-*.md` (authoritative) and `docs/adr/ADR-00N-*.md` (agent-generated) use overlapping numbers for different decisions, and `.github/workflows/requirements-extraction.yml` writes into `docs/adr/**`, so the collision will grow until that workflow is repointed.
+4. **DC-2 (Auth0)** — mandatory in v1.4, a stretch goal in the April document, no ADR either way.
+5. **Nothing exercises a second ETIM release**, and under C-4 nothing will. The release-scoping columns are kept for provenance, so they will look redundant to anyone reading the schema without ADR-020.
+
+---
+
+## Open client decisions
+
+Requirements we must still elicit. Two of them gate validation.
+
+Phase-one valve/actuator **class list** (EPARTS-286) · **feature policy per class** (EPARTS-287 — ETIM ships no required-field flag, so "what blocks publish?" is unanswerable until this lands) · required-field publish blockers · Compare Tool / website-filter feature sets · ETIM **"Other"** handling · **metric-canonical** storage and UI display units · PIMS ETIM-ID storage format · one-primary-class-per-SKU confirmation · valve + actuator assemblies · mapping/policy **sign-off ownership**.
+
+*Closed: **ETIM release-upgrade governance.** C-4 pins the project to release 10.0 EI and puts upgrades out of scope (ADR-020). That took the count from six to five.*
+
+ADR-019 holds the policy seam open so the build does not stall waiting on it. ADR-020 closed the release question outright by pinning the standard rather than leaving it unspecified.
+
+---
+
+## Q&A prep
+
+**Telemetry — the one visible inconsistency.** `ETIM-ADR-ASSESSMENT.md` says the stack is Prometheus + OpenTelemetry + structlog and recommends superseding ADR-012, and the code backs that up. The actual position is narrower: **Datadog is the production target; Prometheus + OTel + structlog is the local development substrate.** ADR-012 stands, and the assessment's Bucket A verdict on it is wrong — it was written from the code alone, without the deployment intent. The v6 diagram carries both labels so it reads as a two-environment choice.
+
+**Where does ETIM matching happen?** In the ML service, after attribute matching. ML owns all matching — attribute matching maps a supplier label onto an attribute, then ETIM matching maps that onto a class, feature, value and unit. Normalization does mechanical cleanup only; it has no model, no confidence and no route to review, so it cannot make an ETIM assignment. The v1.1 spec briefly read otherwise in HLR-2 and §2.1; v1.3 corrected it and ADR-016 records why that alternative was rejected.
+
+**What's running vs. what's designed.** Reference layer, evidence staging split, and the `ExtractedInput` handoff are built and merged. The matching stages, ETIM-aware routing, and the re-keyed writeback are designed only. The diagram marks the distinction; do not claim more.
+
+**Why weren't ADRs 0001–0012 fixed?** They record what we believed in April. Superseding forward keeps the decision history readable; editing in place erases it. `ETIM-ADR-ASSESSMENT.md` is the bridge.
+
+**Why pin ETIM instead of building an upgrade path?** Because the upgrade path is a diff report, a bulk re-match and a second review queue for an event that will not happen inside this project, and it could not be finished anyway — nobody has decided who authorizes an upgrade. Pinning is a decision we can defend; a half-built upgrade path is not. C-4 says so explicitly, so it reads as scope rather than an omission. Un-pinning would be a change request against C-4.
+
+**Why did accuracy get harder?** Matching against a controlled vocabulary is a sharper test than fuzzy string similarity — previously "close enough" answers now count as failures. Auto-accept rate will fall relative to the pre-ETIM baseline before it rises. That is the intended trade: throughput for correctness, on a system where wrong product data becomes a contractor's wrong field order.
+
+**What would we do differently?** Reconcile the two spec lineages earlier, and wire the handoff builder into the orchestrator (EPARTS-363) so the boundary is exercised in production flow rather than only in unit tests.
diff --git a/docs/ta_interview_script.md b/docs/ta_interview_script.md
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+# TA Interview — What I Built and What I Learned
+
+So for our capstone at CMU, my team is building an ML-based catalog system for a client. But the part I want to talk about is the engineering system we built around it — a multi-agent framework with 28 agents in 7 pipelines that automates the engineering coordination. Things like turning meeting transcripts into structured requirements, creating tickets, tracking architecture decisions. The reason I'm bringing this up is that building it forced me to work through almost every topic on this course's syllabus, but in a real system with real stakes.
+
+The first real design problem was context. We have multiple client meetings, coach sessions, architecture documents — and any agent that runs needs relevant pieces of all of that. I tried stuffing everything into the prompt early on and it immediately broke down. A single meeting transcript is 8,000 words, and even if it fit the context window, the signal-to-noise ratio is terrible. So we built a retrieval-augmented generation pipeline — ChromaDB as the vector store with ONNX MiniLM-L6 for local embeddings, so there's no API cost for indexing. The design choice that actually mattered was chunking. We don't do fixed-size token windows because our documents have semantic structure — an architecture section on deployment constraints is meaningfully different from one on data flow, and splitting mid-section destroys that. For meeting transcripts, we chunk by speaker turns, because a speaker's continuous thought is the natural unit of meaning in a conversation. That's a small decision, but it directly affects retrieval quality, which directly affects whether the output is grounded or hallucinated.
+
+The next layer is tool usage. We have eight function-calling wrappers around external APIs — Jira, GitHub, Slack, our vector store. The reason we built this abstraction is that it separates reasoning from execution. An agent reasons about what to do, then calls a structured function like `jira.create_issue()` — it doesn't know how Jira's REST API works. This mattered in practice because we swapped our LLM provider midway through the project, from Anthropic to Gemini, and because the reasoning is decoupled from the tools, we didn't change a single line in any agent. That's the kind of separation that sounds like over-engineering until you actually need it.
+
+For memory, we ended up with two systems because they solve different problems. The first is a persistent key-value store we call the wiki — every agent deposits its outputs there, organized by namespace, and any other agent can query it later. So pipeline A running this week can read what pipeline B produced last week. That's the Karpathy pattern — agents building a shared, accumulating knowledge base rather than each starting from scratch.
+
+The second is a publish-subscribe event bus. When one agent emits an event, any subscribed agent fires automatically. The wiki solves knowledge sharing across time. The event bus solves real-time triggering across pipelines. Together they're what make it a framework instead of isolated scripts.
+
+One thing I underestimated was how much prompt management matters at scale. With 28 agents and five team members, the naive approach is everyone writes their own prompts, and then you get inconsistent outputs and no way to reproduce results. So we built a prompt registry — every prompt is version-controlled with a content hash, an author, a review status. We can pin agents to specific versions, diff changes, and run regression tests against a golden dataset before a new prompt goes live. This connects directly to prompt sensitivity — the same agent with a subtly different prompt produces structurally different outputs. The registry doesn't eliminate that, but it makes it visible and gives you a rollback mechanism.
+
+The alignment piece was more practical than philosophical. The biggest real risk is the model being confidently wrong in a business context. So for high-stakes outputs — things that could send the team chasing a phantom emergency — the system holds them for human review instead of acting automatically. Low-cost outputs get auto-created because a wrong one is cheap to fix. The guardrail is calibrated to the actual cost of error, not applied uniformly. We also built offline fallbacks everywhere — if the LLM is down or quota is exhausted, every agent degrades to keyword-based heuristics. Less sophisticated, but the pipeline doesn't break. The system should never be more fragile than not having it.
+
+The last piece is measurement. Every LLM call is metered — tokens, latency, cost. Across 160 runs, we've spent about three cents. The point isn't the absolute number. The point is we have the number. Our coach pushed us hard on this — he said the goal of measurement isn't to measure everything perfectly, it's to reduce uncertainty enough to make a better decision about whether AI is worth using for a given task. And if the measurement itself costs more than the value of knowing, you're doing it wrong. That framing changed how I think about evaluation entirely.
+
+So that's the system. The real engineering wasn't in the agents themselves — it was in the retrieval pipeline, the tool abstraction, the memory architecture, prompt governance, and the guardrails. And working through those gave me hands-on experience with pretty much every major topic this course covers.
+
+---
+
+## Syllabus Connections (my reference — not to say out loud)
+
+| What I built | Course topic |
+|---|---|
+| ChromaDB + ONNX embeddings + semantic chunking | Week 5: RAG |
+| MCP servers (Jira, GitHub function calling) | Week 5: Tool Usage |
+| Pipeline executor (sequential agent chains) | Week 6: Task Decomposition |
+| Prompt Registry (versioning, regression testing) | Week 2: Prompt Sensitivity + Week 6: Auto-Prompting |
+| SharedMemory wiki + EventBus | Week 10: Agents — Memory |
+| Human review gates, cost-calibrated guardrails | Week 4: Hallucination + Week 8: Guardrails |
+| Cross-pipeline event triggers | Week 11: Multi-Agent Collaboration |
+| Offline fallback when LLM unavailable | Week 8: Defensive measures |
+| MetricsCollector, counterfactual analysis | Evaluation (throughout course) |
+
+## If Asked Follow-Ups
+
+- **"Chunking strategy?"** — Semantic sections for docs, speaker turns for meetings. Not fixed windows. Reason: retrieval precision drops when chunks cross topic boundaries.
+- **"How do agents communicate?"** — Two mechanisms: persistent wiki (read/write) and event bus (pub-sub triggers). No direct agent-to-agent calls — avoids tight coupling.
+- **"Cost?"** — 6,500 tokens across 160 runs = $0.03. Traceability store uses zero LLM tokens — keyword matching over SQLite.
+- **"Fine-tuning?"** — None. All prompt engineering with version control and regression tests. Fine-tuning would've locked us to one model at our scale.
+- **"Hallucination handling?"** — Structured JSON output with regex fallback parsing. Confidence-calibrated human review. Offline heuristic fallback when LLM fails.
diff --git a/docs/talking_script.md b/docs/talking_script.md
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+# Talking script — Requirements & Architecture
+
+**Speaker:** Arjun · picks up from Jai after he covers construction, quality and risk
+**Deck:** `eParts_Section3_SoftwareSystem.pptx`, 5 slides
+**Length:** see the measured table at the bottom. Target is five minutes.
+
+Blockquotes are spoken as written. Cues sit on their own lines outside the quotes.
+
+---
+
+## Handoff
+
+> Thanks Jai. ETIM was our biggest risk, and it's also what changed the system the most. So I'll pick it up there, and go through what it did to our requirements and our architecture.
+>
+> The documents are linked at the bottom of every slide.
+
+---
+
+## Slide 1 — Requirements: v1.0 → v1.4
+
+> We baselined this document at the end of April and it's been through four revisions since. April is on the left, today is on the right.
+>
+> Everything new on that list came from ETIM. Classification became a new high-level requirement. Matching split into two functional ones. And pinning to a single ETIM release is now a constraint.
+>
+> Eighty-nine percent of what we wrote in April is untouched. Two requirements were rewritten, both in ingestion. We'd said normalization would do the ETIM keying, and that was wrong.
+>
+> The fourth revision was cleanup. We put ETIM into the requirements in June and didn't go back to the quality scenarios or the validation tests. So for a month those two sections described a system we weren't building. Version 1.4 fixed that.
+
+---
+
+## Slide 2 — How we managed the change
+
+> This is how we handled those revisions, and what happened to traceability.
+>
+> Every time ETIM added something we gave it a new ID instead of editing an old one, and we renumbered nothing. That's why every trace link we drew in April still resolves today.
+
+Walk the thread with your finger as you say it. Pause at each hop.
+
+> I'll walk one. ETIM classification is HLR-6. That drives FR-9, the matching requirement. FR-9 is decided in ADR-16 and ADR-18. Those two exist in the code as the ETIM reference tables. That code is covered by ten unit tests, all passing. There's also an integration test that loads the real ETIM archive end to end, and checks that loading it twice is a no-op. That one only runs where the archive is present. The archive isn't in the repo, so it skips on a clean checkout. Requirement, decision, code, test, and it closes at both ends.
+>
+> Matching is the thread that doesn't close yet. It's specified and its validation test is written, but the test can't run until the code exists, so it's recorded as not run.
+>
+> ETIM is also on the risk register, as RISK-ARCH-09. Pinning the release means our catalog goes stale over time, and somebody has to own that after we leave.
+
+---
+
+## Slide 3 — How the architecture changed
+
+Let them look at both diagrams for a beat before you start.
+
+> Left is May, right is now. Side by side they're almost the same drawing.
+>
+> One box is new. Everything else is where it was. Same pipe-and-filter structure, same audit trail. Routing still works attribute by attribute, and a person still reviews anything the system isn't sure about.
+>
+> ETIM was a large change and it didn't force a restructure. That's the modifiability claim we made in April, and this is the first change big enough to test it.
+>
+> Five things changed, and only the first one shows up on the drawing. The other four are inside components. The dictionary we load, the split that keeps the supplier's raw values separate in staging, the handoff format to ML, and the PIMS key.
+
+Point at the new box on the right-hand diagram.
+
+> The new box is ETIM matching. It sits behind attribute matching, inside the same interface. That's the next slide.
+
+---
+
+## Slide 4 — Inside the new box
+
+> This is what's inside that box, drawn large enough to read.
+>
+> ML was already matching attributes and that part hasn't changed. That's the box at the top. ETIM adds a second pass behind it, inside the same service. That's the five stages below: the product's ETIM class, then which ETIM feature the attribute maps to, then the allowed value and unit, then two validation checks.
+>
+> The order matters. In ETIM the features belong to the class, so the class decides which features a product can have at all. If we call a ball valve a butterfly valve, we match its attributes against the wrong feature list. The torque number is right, the feature it's attached to is wrong, and that's true for every attribute on the product.
+>
+> And each of those matches scores high, because it was the best match in the list we gave it. So routing sees confidence and auto-accepts. A wrong class doesn't produce anything routing can catch, so a person confirms the class first.
+>
+> Every box on that row is dashed. The dictionary it reads and the tables it writes are built, but the five stages are designed and not written.
+
+---
+
+## Slide 5 — Decisions, and what we chose against
+
+> Four decisions, and next to each one the alternative we rejected.
+>
+> ML does all the matching. The alternative was doing the ETIM lookup during normalization, which is what our spec said until version 1.3. It doesn't work, because an ETIM assignment carries a confidence and sometimes needs a person to approve it, and normalization has neither.
+>
+> The supplier's raw values stay in their own table, so we can always get back to what we were sent.
+>
+> When ETIM changed a decision we wrote a new ADR instead of editing the April one. Those are the record of what we believed at the time, and overwriting them loses the reasoning.
+>
+> And we're staying on ETIM 10.0 for the rest of the project rather than building an upgrade path. Doing that properly means diffing releases, re-matching everything affected, and reviewing what comes out different. None of that gets exercised before we finish. It's a real cost, so it's on the risk register.
+
+---
+
+## Timing
+
+Measured from the blockquotes at 150 wpm.
+
+| Section | Words | Time |
+|---|---|---|
+| Handoff | 44 | 18 s |
+| Slide 1 | 128 | 51 s |
+| Slide 2 | 200 | 80 s |
+| Slide 3 | 139 | 56 s |
+| Slide 4 | 195 | 78 s |
+| Slide 5 | 160 | 64 s |
+| **Total** | **866** | **5:46** |
+
+You are 47 seconds over. The first four cuts land you at 5:02; all five put you at 4:56.
+Slide 4 is the longest section and it should stay that way. It is the only place you
+explain a decision rather than report one.
+
+| Cut | Saves |
+|---|---|
+| Slide 5, the raw-values line — slide 3's list already names it | 8 s |
+| Slide 1, the paragraph about the June gap | 17 s |
+| Slide 3, the list of the other four changes, keeping "only the first one shows up on the drawing" | 12 s |
+| Slide 2, the two sentences about the archive skipping — keep it for the Q&A instead | 9 s |
+| Slide 5, the first half of the ETIM 10.0 paragraph, keeping "none of that gets exercised before we finish" | 6 s |
+
+**Do not cut** the trace thread on slide 2, the valve example on slide 4, or the dashed-boxes line. Those carry the rubric line — traceable through architecture, implementation and validation, and honest about what isn't built.
+
+## Delivery notes
+
+- Slide 2's trace thread is the most valuable thirty seconds in the section. Slow down and point at each hop rather than reciting it. The IDs are the one place you're allowed to sound like you're reading, because you're reading them off the slide.
+- Slide 3, the two thumbnails are a silhouette comparison, nothing more. v6 is v5 with one box added, so they should look near-identical. Don't invite anyone to read them, and don't tell them what to conclude from it — the near-identical outline is doing that on its own. Slide 4 is where the detail lands.
+- Slide 4 is the one slide where you're explaining a design decision rather than reporting one. Slow down on the valve example. Name the two valves clearly and let the panel picture it before you land the consequence.
+- Say the dashed part plainly and move on. State it once, don't sell it.
+- Every paragraph break in the script is a breath. If you run two together you'll start to sound like you're reciting, regardless of the wording.
+- The ten unit tests were run on 29 July and all passed, in about 3m40s (`pytest tests/unit/test_etim_loader.py` in `e2e-ocr-ing`). The integration test skips unless `.tmp_etim_csv` is present, and it is gitignored — so if anyone runs the suite in front of you, expect `10 passed, 1 skipped`. Say the skip before they ask.
+- If asked who wrote the documents: the agents drafted them, we reviewed and finalised them. Wiring that into the automated pipeline is still open work. That answer holds up if anyone goes and looks at the repo.
+
+## Questions you'll probably get
+
+**Doesn't ingestion do the ETIM keying?**
+
+> No. Our spec did say that until version 1.3. Ingestion cleans the data and keeps the supplier's own values. All the matching is in ML, attributes first, then ETIM. It can't sit in normalization because an ETIM assignment has a confidence attached and might need a person to confirm it, and normalization has neither. Ingestion does load the dictionary, because it already owns file parsing and migrations, but it doesn't use it.
+
+**Why not keep up with new ETIM releases?**
+
+> Because doing it properly is real work. You'd have to diff the new release against the old one, re-match every affected product, and review everything that came out different. None of that gets exercised before we finish, and nobody has decided who signs off an upgrade. So we wrote it into the spec as a constraint, and onto the risk register as RISK-ARCH-09. We did keep the release ID on every row, so any published value still says which version of ETIM it was matched against.
+
+**How do you know the architecture is stable?**
+
+> Two things. Eighty-nine percent of the April requirements are untouched, and the biggest change we've had all summer landed without moving the pipe-and-filter structure or breaking a trace link. That's what QAS-1 predicted, and this is the first time we could check it against something real.
+
+**Is it Prometheus or Datadog?**
+
+> Both, deliberately. Datadog is the production target and that's what the ADR says. Prometheus and OpenTelemetry are what the local development environment runs. Our own assessment document read the code without knowing the deployment intent and flagged it as a contradiction, which is why the current diagram labels both.
diff --git a/docs/talking_script_interactive_dashboard.md b/docs/talking_script_interactive_dashboard.md
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+# Talking script — first dashboard (`interactive_architecture.html`)
+
+Use this file: **`dashboard/interactive_architecture.html`**. Roughly **four to six minutes** if you expand the Requirements panel and scroll slowly. Speak in short paragraphs; pause where it says **[pause]**.
+
+---
+
+## Optional: if you opened `intelligence.html` first (45 seconds)
+
+“Before we zoom into pipelines, this **Project Intelligence** view is the product story in one screen: goal model, work breakdown, agent flow, traceability. The numbers at the top are live-ish counts from our runs—meetings, requirements, events, wiki entries, trace links. I’m going to switch to the **Interactive Architecture** dashboard next, because that’s where we separate the **agentic harness** from the **software engineering harness** and show exactly what runs today.”
+
+**[Switch tab or browser to `interactive_architecture.html`.]**
+
+---
+
+## 1. Land the screen (~45 seconds)
+
+“So this dashboard is titled **eParts Agentic Software Engineering System** — capstone framing is CMU MSE Studio, **Pimsie Supreme**. **[pause]** Up top you’ll see **four headline numbers**: seven practice pipelines, twenty-eight specialized agents, three pipelines **live** on real transcripts today, and **one traceability graph** in SQLite tying artifacts together.”
+
+“Right under that is the **legend** — **green** means live integrations you can demo; **amber** means the pipeline exists and runs but wiring is still maturing; **red** means **roadmap** for coding-phase and ML evidence scale-up. **[pause]** Then I’m going to unpack **two harnesses**: the **agentic** one — chained agents — and the **engineering** one — the same chain mapped to inception through management.”
+
+---
+
+## 2. The grid — three rows, three stories (~90 seconds)
+
+**Point at the top row (green).**
+
+“Top row: **Requirements**, **Coach Session**, **Knowledge** — these are **in use**. Requirements turns a meeting transcript into structured output, tickets, and traceable decisions. Coach Session keeps mentor and coach guidance **retrievable**—embeddings, recurring themes. Knowledge rolls project state into **briefings** so nobody walks into a meeting cold.”
+
+**Point at the middle row (orange).**
+
+“Middle row: **Architecture** and **Project Management** — **partially** wired. Architecture is drift detection, ADRs, traceability updates. PM is WBS sync with Jira, digests, alerts. You’ll see more of this as we demo or as the semester progresses.”
+
+**Point at the bottom row (red).**
+
+“Bottom row: **Coding** and **ML Decision** — **future** phase. Coding is PR review, tests, docs, prompt regression. ML Decision is evidence accumulation and **readiness** to close an open model decision. We show them so the **whole** product story is visible, not just what works this week.”
+
+---
+
+## 2b. Golden traceability — large on-screen example (~45 seconds)
+
+**[Scroll to the wide panel titled “Example: one concern → shipped work” — it sits just under the pipeline cards.]**
+
+“This section is **large type on purpose** — one **end-to-end** chain you can read from the back row. **Meeting** → **concern** → **requirement REQ-006** → **decision** in the log → **Jira** implementing the req → **architecture** validation and drift. The **relationship names** on each node match what we persist: *RAISED_IN*, *BECAME*, *IMPLEMENTS*, and so on. The **pills** underneath call out *MITIGATES* risk, *TRIGGERED* follow-on pipeline, and **traceability.db** — that’s the engineering answer to ‘show me traceability’ without a microscopic matrix.”
+
+---
+
+## 3. Two harnesses in plain language (~60 seconds)
+
+“**Agentic harness** means: we don’t run one prompt in isolation. We run **named agents** in a **fixed order**, hand off **context** between steps, write to a **shared wiki**, emit **events** when something important happens, and connect to **Jira and GitHub** through a small integration layer. **[pause]**
+
+**Engineering harness** means: we still teach the same RE lifecycle — we just **automate** the mechanical parts and leave **judgment** where it belongs. The best example is right here: **click Requirements**.”
+
+**[Click the Requirements card so the big panel opens.]**
+
+---
+
+## 4. Requirements pipeline — walk the seven steps (~2 minutes)
+
+“This is the **Requirements Engineering** pipeline: **seven sequential agents**, triggered by uploading a **`.vtt` transcript**. Read it like a factory line from left to right.”
+
+**Step 1 — transcript_parser.**
+“First agent **parses** the transcript into structured JSON — speakers, action items, decisions, concerns. It can also **emit events** — for example when action items are extracted — so other parts of the system can react.”
+
+**Step 2 — priority_classifier.**
+“Second agent **classifies** everything as P0, P1, or P2. **P0** is special: we **don’t** auto-file those as production tickets without a human — that’s the **human-in-the-loop gate** badge you see. That’s intentional risk management.”
+
+**Step 3 — req_extractor.**
+“Third agent turns discussion into **formal REQ documents** — markdown in the repo, `REQ-XXX`, with categories and acceptance criteria. That’s the **specification** step in engineering terms.”
+
+**Step 4 — ticket_creator.**
+“Fourth agent pushes **work into Jira** for the items we’re comfortable automating — P1/P2-style flow — with summaries and labels so the board stays usable.”
+
+**Steps 5 and 6 — minutes_publisher, decision_logger.**
+“Fifth and sixth: **minutes** where Confluence is connected, and a running **decision log** — same decision content the team cares about for audits.”
+
+**Step 7 — drift_detector.**
+“Seventh: **drift** — we compare what was decided in the room against our **canonical architecture** using retrieval, and we can emit **drift_detected** so the **Architecture** pipeline can pick up downstream. That’s **validation** in RE language — ‘does new talk contradict what we already agreed?’”
+
+---
+
+## 5. The SE activity table (~45 seconds)
+
+**[Scroll to “Mapping to SE Requirements Activities.”]**
+
+“This table is the bridge for anyone who cares about **courses and standards**, not jargon. Inception and elicitation map to parsing; negotiation maps to prioritization — humans still **approve P0**. Specification maps to REQ files; validation maps to drift and traceability; management maps to Jira and the decision log. So the **agentic** story and the **software engineering** story are the **same** story with different vocabulary.”
+
+---
+
+## 6. Meta model boxes (~45 seconds)
+
+**[Scroll to the four boxes: Artifacts, Processes, Resources, Measurement.]**
+
+“Four boxes summarize the **meta model** for this pipeline. **Artifacts** — JSON, REQ files, Jira keys, decision logs, drift reports. **Processes** — the seven-step chain from transcript in to GitHub and Jira out. **Resources** — agents, integrations, vector store for RAG, and **people** at the P0 gate. **Measurement** — how much we extract, how often humans override, how drift behaves, whether tickets stick. That’s how we keep the system **accountable**, not magical.”
+
+---
+
+## 7. Counterfactual — why this matters (~45 seconds)
+
+**[Scroll to “Counterfactual Analysis” in the Requirements panel.]**
+
+“This strip is deliberate: **without** automation, somebody spends hours on a forty-five-minute recording; tickets slip; drift stays invisible. **With** automation, end-to-end is **seconds to minutes**, format is **consistent**, and humans focus on **P0** and judgment calls. **[pause]** The value isn’t only time — it’s **consistency** across every meeting and every teammate.”
+
+---
+
+## 8. Close — transition to live demo or next screen (~30 seconds)
+
+“So on one dashboard you’ve seen: **which** pipelines exist, **which** are live versus partial versus future, and **one full path** — Requirements — end to end with RE mapping and artifacts. **[pause]** Next I’ll **[run `demo.py` / show Jira / show GitHub]** so you see the same pipeline **actually execute** on a transcript. Questions before we switch?”
+
+---
+
+## Quick reference — on-screen elements to gesture at
+
+| Where to look | What to say in one phrase |
+|---------------|---------------------------|
+| Page title | “Agentic SE system — not a single chatbot.” |
+| Green / orange / red rows | “Maturity: running now, in progress, planned.” |
+| Requirements → 7 agents | “Assembly line; context flows left to right.” |
+| P0 badge | “Humans own the riskiest items.” |
+| SE activity table | “Same lifecycle you learned in class.” |
+| Counterfactual | “Time + consistency + drift visibility.” |
+
+---
+
+## If you start with `intelligence.html` instead
+
+Open the **Goal Model** tab first: “Strategic goals decompose to soft goals, user goals, functional goals — obstacles are first-class.” Then **WBS** for delivery structure, **Agent Flow** for the graph mental model, **Traceability** for requirement–meeting–architecture links. Then **switch** to `interactive_architecture.html` for the pipeline-deep story above.
+
+---
+
+*File path: `dashboard/interactive_architecture.html`. Pair with `docs/ses_architecture_speakable.md` for spoken architecture without the UI.*
diff --git a/docs/talking_script_reflection.md b/docs/talking_script_reflection.md
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+# Talking script — Reflection & Closing (~2¼ minutes)
+
+**Deck:** `eParts_Reflection_Closing.pptx` — its own file, 1 slide, "Two lessons"
+**Slot:** end of the team talk, after Management
+**Rebuild:** `eparts/docs/build_section3_deck.py` builds both decks from one script
+**Evidence:** `dashboard/data/jira_issues.json` — 290 issues, with the JQL and fetch timestamp recorded in the file
+
+**334 spoken words — 2:13 at 150 wpm.** Two lessons, one of them about AI in software engineering.
+
+---
+
+## Lesson 1 — the 3-day cycle — 52 sec
+
+> First one is about our own process. We started the summer planning in 3-day ticks — that came out of the agentic-augmented-scrum doc we wrote — and it didn't hold. We're on 7-day cycles in Jira now.
+>
+> Three reasons. Too much changed inside a single tick to close it cleanly. A small blocker would eat the whole cycle, because at three days there's no slack to absorb one. And the third one is the actual reason: **reviewing an agent's pull request took longer than the agent took to write it.**
+>
+> That's the thing we didn't see coming. The agents moved our constraint from writing code to reviewing it, and we'd sized the cycle for the old constraint. It's also why we plan capacity in review hours instead of story points now.
+
+---
+
+## Lesson 2 — AI in software engineering — 81 sec
+
+> Second one, and this is the AI one. We gave the agents control of the paperwork — tickets, documentation, PR comments — because they're reading the same repo we are, so they've actually got more context than one of us typing a ticket at the end of the day.
+>
+> The speed number: about 3 minutes per ticket by hand, about 15 seconds for an agent. Two hundred and thirty-four tickets since May, so roughly 11 hours back. Which is real, but it's under an hour a week, so I don't want to oversell it.
+>
+> **The number that actually matters is this one.** Of the 56 tickets we wrote by hand in spring, zero had story points and zero were attached to an epic. Of the 234 the agents drafted, 90 percent have points and 94 percent are in the right epic.
+>
+> A person writing a ticket at 11pm skips those fields. An agent doesn't. And that's the reason the forecasting you just saw works at all — Monte Carlo over our throughput needs points on every ticket. In spring we could not have produced that chart from our own backlog, and we didn't know that was the constraint until the agents removed it.
+
+---
+
+## Delivery notes
+
+- **Lead with the failure.** The 3-day cycle not working is the more credible half; teams that only report wins get probed harder.
+- **Say the 11 hours, then undercut it yourself.** "Under an hour a week, so I don't want to oversell it" buys you the credibility to then land the 0% → 90% number, which is the real claim.
+- **Connect lesson 2 back to Management.** The forecast is someone else's slide; pointing at it makes the two sections read as one argument rather than two people's lessons.
+- Don't say "AI saved us time" as the headline. The honest and more interesting finding is that AI changed *what was possible to measure*, and the time saving was a rounding error next to that.
+
+## If challenged
+
+**"Isn't 90% just because you told it to fill the field?"**
+
+> Partly, yes — that's the point. The instruction was cheap to give once and it holds on every ticket. We tried to hold ourselves to the same standard in spring and hit zero percent, because it's the field you skip when you're tired and the ticket already makes sense to you.
+
+**"Do you review these?"**
+
+> Every one. The 10 percent without points are mostly ones we corrected or closed as duplicates during review.
+
+**"Where do the numbers come from?"**
+
+> A Jira export in the repo, `dashboard/data/jira_issues.json`. It stores the JQL it ran and when it ran, so you can re-derive it. 290 issues total, 56 before May, 234 after.
+
+## Fix before you present
+
+Section 4 has an internal inconsistency that this slide makes visible: **slide 2 says 7-day cycles and slide 7 says 3-day cycles.** Since lesson 1 is explicitly about that change, an assessor who noticed the mismatch earlier will read it as sloppiness rather than as the story you're telling. Change slide 7 to 7-day, or add "(we moved from 3-day — see lesson 1)".
diff --git a/docs/talking_script_ses_infra_trace_liuhandoff.md b/docs/talking_script_ses_infra_trace_liuhandoff.md
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--- /dev/null
+++ b/docs/talking_script_ses_infra_trace_liuhandoff.md
@@ -0,0 +1,73 @@
+# Talking script — SES landing · trace weave · REQ-001 → Liu (~4 minutes total)
+
+Use this flow when you stay high-level first, then SES Traceability, then **`traceability_story.html`** REQ-001, then hand **SDLC choice** depth to **Liu**.
+
+Tone: conversational. Skip acronyms unless someone asks—you can say **“saved runs”**, **“one shared pool of agents”**, **“memory that survives the chat”**.
+
+**Screens (suggested):** `interactive_architecture.html` → `ses_traceability.html` → `traceability_story.html`.
+
+---
+
+## Part 1 — What SES is showing you (~2 minutes)
+
+“On this SES landing view you’re basically seeing three layers knitted together—not three products.
+
+**First, pipelines.** We don’t freestyle every week off a blank slate. Capstone automation is spelled out as a small set of **named flows**. Each flow is **steps in a fixed order**: meeting ingest, architectural follow-up, knowledge pull, coding checks—whatever slice we wired for SES. Same pattern everywhere: steps run **one after another**.
+
+**Second, agents—think “functions,” not mascots.** We keep **one pool** of ~twenty-eight agent roles registered once. Different pipelines pick different subsets and order them. Nobody’s copy-pasting a new script each time—the **hierarchy is simple**: **pipelines sit on top**, **steps are slots in order**, **agents are the reusable workers** plugged into those slots.
+
+**Third, harness—two halves of one idea.**
+
+- **Engineering harness** answers: **what does “done” look like per practice for us?** It’s commits, requirement files in Git, DB rows someone can audit, gates we agreed on—not vibes.
+
+- **Agentic harness** answers: **how does the machinery actually march?** The baton passes **within a single run**: each step hands forward **named blobs** so the next step reads what yesterday’s chunk produced. Steps can politely **skip** if there’s nothing to act on—they don’t have to wedge errors.
+
+That’s wired to **Shared Memory** separately: shorter-lived run data resets when that run finishes, but Shared Memory lives in SQLite and **keeps stacking** credible project state—the next transcript or webhook **starts richer** instead of pretending the room has amnesia.
+
+**Infrastructure on this slide** is just honesty in one frame: triggers land the same shape, routing picks **which pipeline** to run, and we name the **three little databases that matter—shared memory for durable context, events for subscribers, traceability for the relationship graph.** Plus the integrations we aren’t pretending we skipped.
+
+So in one breath: **ordered pipelines**, **reuse agent roster**, **two harness halves**, **SQLite memory that accrues**—that’s SES before we talk product math.”
+
+**[pause, breathe]**
+
+---
+
+## Part 2 — We weave that into one trace matrix (~45–55 seconds)
+
+“Separately from ‘how jobs run,’ we maintain **how everything talks to everything else.**
+
+SES Traceability—you can open **`ses_traceability.html`**—is the **generic picture**: artifacts as boxes, arrows as commitment types across the life of the program. **[pause]**
+
+The **idea** is boring on purpose **in a good way**: **one matrix** stitches **meetings**, **things we surfaced as concerns**, **requirements**, **architecture decisions**, **risks**, and **tracked work**. You don’t reconcile five slide decks—we already recorded **typed links**.”
+
+**[flip to REQ example when ready]**
+
+---
+
+## Part 3 — REQ-001 in one minute (IDs, commits, why bother) → Liu
+
+“**`traceability_story.html`** unfolds **REQ-001** from the same graph the dashboards use—**two real branches**: standards-mapping on one limb, extraction and ML-confidence on another—meetings feeding concerns feeding follow-on requirements, architectures, and risks—all **labeled with stable IDs.**
+
+Those codes aren’t garnish: **`REQ-XXX` mirrors Markdown in-repo**, **`ARCH-XXX` anchors ADR-aligned intent**, **`CON-`** is what someone actually said bothered them, **`DEC-`** is what we pinned as a stance, **`RIS-`** is explicit risk—we can diff them, ticket off them, and **commit** deltas so auditors see **intent + provenance**.
+
+Why keep this? Sponsor gets **one storyline** spanning voice → spec → mitigation → backlog without archaeology.
+
+**I'm going to pause on SDLC theology here—handing narrative choice and trade depth to Liu, who will walk our SDLC choice next.** ”
+
+---
+
+## Ultra-tight cheatsheet (~20 seconds whisper)
+
+| Beat | Said simply |
+|------|----------------|
+| Pipelines | Few named flows, steps ordered, repeatable. |
+| Agents | Shared pool; pipelines pick who runs when. |
+| Harnesses | “What we owe in writing” + “how computers stage the work.” |
+| Shared Memory | SQLite store that piles up credible context across triggers. |
+| Infra slide | Incoming trigger → routing → pipeline run → databases + integrations named. |
+| Trace matrix | One linked map of conversation → commitments → architecture → risk → work. |
+| REQ-001 | Same IDs hit Git/trace export; REQ-001 is the worked example slide. |
+
+---
+
+_Timing hint: Parts 1–3 land ~4 min; shorten Part 1 by tightening the infra sentence if Liu needs slack._
diff --git a/docs/talking_script_ses_infra_trace_liuhandoff.txt b/docs/talking_script_ses_infra_trace_liuhandoff.txt
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+++ b/docs/talking_script_ses_infra_trace_liuhandoff.txt
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+Talking script — SES landing · trace weave · REQ-001 → Liu (~4 minutes total)
+
+Flow: high-level infra on interactive_architecture, then SES Traceability (generic), then traceability_story REQ-001, hand SDLC depth to Liu.
+Tone: conversational. Say “saved runs,” “pool of agents,” “memory that survives the chat” instead of jargon unless pressed.
+
+Screens: interactive_architecture.html → ses_traceability.html → traceability_story.html
+
+================================================================================
+PART 1 — What SES is showing you (~2 minutes)
+================================================================================
+
+“On this SES landing view you’re seeing three layers knitted together.
+First, pipelines. We don’t start from a blank chat every week. Automation is spelled out as a handful of named flows. Each flow is steps in a fixed order—requirements path, coaching path, architecture path, coding checks, whatever we wired into SES. Same pattern everywhere: steps run one after another.
+
+Second, agents—think reusable workers. We register about twenty-eight agent roles once. Different pipelines reuse them in different order. The hierarchy stays simple on purpose: pipelines on top; inside each pipeline, slots in sequence; agents are the interchangeable workers slid into those slots.
+
+Third is the harness—two halves of one idea.
+
+Engineering harness is what we owe in hard copy for each practice: files in Git, requirement markdown, ticket rules, gates we discussed—not vibes alone.
+
+Agentic harness is literally how we march: inside one run earlier steps stash named results later steps consume. A step can bow out politely if upstream left nothing—it doesn’t have to spike the whole pipeline.
+
+Separate from that per-run baton we keep Shared Memory in SQLite—it keeps stacking credible project chunks so tomorrow’s ingest or webhook isn’t rewriting context from fog.
+
+Infrastructure on this slide is just naming what’s real without mysticism: inbound triggers normalize the same shape, routing picks which pipeline wakes up, three small databases anchor shared memory, events, traceability—the plus boxes are integrations we’re not pretending we omitted.
+
+Summed up: ordered pipelines, shared roster of agents, two harness halves plus growing memory—that tees up why traceability later isn’t slideware.” [pause]
+
+
+================================================================================
+PART 2 — We weave that into one trace matrix (~45–55 s)
+================================================================================
+
+“Separate from running jobs we track how artifacts relate.
+
+SES Traceability—that’s ses_traceability.html—is intentionally generic before we deep-dive a requirement: artifact types linked by arrows that encode real relationship names—what rose in a meeting, what became a requirement or decision, how design slices sit on requirements, risks mitigations, backlog hooks.
+
+Think of one matrix tying conversation through commitments into architecture risks and accountable work—we’re not juggling five contradictory decks.” [pause]
+
+
+================================================================================
+PART 3 — REQ-001 in ~1 minute · IDs commits why · hand Liu SDLC (~1 min + handoff)
+================================================================================
+
+“traceability_story unwraps REQ-001 from the same graph the dashboards load—two branches you can read top to bottom—parallel architecture threads off one meeting into concerns, follow-on requirements, decisions, architecture slice, clustered risks—every label is the ingest ID.
+
+Those codes aren’t decoration: REQ numbers match Markdown on disk ARCH anchors ADR-aligned intent CON prefixes what actually worried somebody DEC freezes a stance once we voted it RIS is explicit peril we can mitigation-link.
+
+Because they’re anchored in repos and SQLite exports auditors read provenance—we’re not hallucinating genealogy.
+
+Stakeholder win—one narration from voice recordings into mitigations backlog without archaeology.
+
+Stopping before SDLC religious wars—Liu next walks our deliberate SDLC choice and trade-offs.”
+
+
+================================================================================
+Whisper cheatsheet (~20 s)
+================================================================================
+
+Pipelines — Named ordered flows
+Agents — Shared pool; pipelines pick sequencing
+Harnesses — Hard outputs + orderly execution mechanics
+Shared Memory — SQLite accumulating context across triggers
+Infra slide — Trigger → routing → pipelines → databases + integrations
+Trace matrix — One graph linking narrative → commitments → architecture → risk → work
+REQ-001 — Demonstrates IDs you can grep in repo + trace export → Liu owns SDLC story
diff --git a/docs/talking_script_ses_six_minute.md b/docs/talking_script_ses_six_minute.md
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+++ b/docs/talking_script_ses_six_minute.md
@@ -0,0 +1,79 @@
+# Talking script — SES dashboard (6 minutes, technical, no clicks)
+
+**Context:** Agenda **02 — SES + SDLC**. Hand off to **Agenda 03 — Requirements** when you pivot to trace.
+**Screens:** **`interactive_architecture.html`** first, then **`traceability_story.html`** (tab change only).
+**Timing:** Aim **~6 minutes** of talk; cheatsheet ~15 seconds at end. Short sentences; **[pause]** = breath.
+**Trim if needed:** drop the chips sentence in §3, or shorten §4 branches line.
+
+Plain-language focus: **engineering harness** vs **agentic harness**, **Shared Memory** vs per-run baton, **pipeline → ordered steps → shared agent catalog**, threaded execution—no tour of individual agent class names.
+
+---
+
+## 0. Agenda hook (~20 s)
+
+“**Agenda item two** is the **SDLC story**: **SES** runs on **checked-in pipelines** and on **state that survives the chat**—not ask-once tooling. **[pause]**
+
+I’ll do **four beats**: **engineering harness** versus **agentic harness**, **Shared Memory** that keeps adding up, what the **infra page** is listing, then **REQ-001** on **`traceability_story`** to tee up requirements.”
+
+---
+
+## 1. Engineering harness versus agentic harness (~75 s)
+
+“The **engineering harness** is our agreement about **what each practice owes in hard form**. **[pause]**
+
+A **pipeline** is a **named flow**. Inside it you have **steps in order**, and honest delivery means each step is meant to drop something reviewers can touch—**commits**, **database rows**, **tickets**, **publishes**—not just a narrative. **[pause]**
+
+The **agentic harness** is **how those steps actually run**: **agents** live in **one catalog**, **pipelines** hang together **only** the combinations you registered, and one small **executor** walks the **list step by step**. **[pause]**
+
+At run time each step inherits a **flat bag** of results from above: upstream **writes keys**, downstream **reads keys**. Steps can **bypass themselves** when a key is missing so the pipeline does not stall. **Same agent type** can repeat in several pipelines—we are **not cloning code** for each flow. **[pause]**
+
+One line takeaway: **harness one** is **commitments and exits**; **harness two** is **orderly execution and passing the baton** so it behaves like shipped software flows, not like **one mega-prompt** crossing fingers on memory.”
+
+---
+
+## 2. Shared Memory (~55 s)
+
+“The **per-run baton** differs from **Shared Memory**—the baton **resets when that run completes**; **Shared Memory stays in SQLite** and **keeps growing** across triggers and sessions when we write namespaces on purpose there. **[pause]**
+
+So the Tuesday meeting run can stash parsed decisions, the Wednesday follow-up picks them up without retyping the story, telemetry and trace rows can converge on dashboards from the **exported graph**. **[pause]**
+
+Separate **events SQLite** lets **listeners react** later. **MCP-style** calls to **Jira** or **Git** plug in only where steps say they should. Throughout, the repeatable cord is **pipelines plus memory that piles up** honestly instead of living in **the last transcript alone**.”
+
+---
+
+## 3. Infra board (~50 s)
+
+“**`interactive_architecture.html`** reads like a **parts list on one screen**: triggers in, routed pipelines out, persistence spelled out. **[pause]**
+
+Whatever the source—a **transcript ingest**, **cron**, **Git hook**—you **normalize** to **trigger type** plus **payload**, **routing picks pipeline**, **executor** runs step order you already learned. **[pause]**
+
+You’ll see **distinct pipelines** pulling from the **same twenty-eight agent registrations**; stripe colors mean **deployment readiness for demos** not product judgment. **Chips** call out **prompts**, **risks**, **ancillary stores**—we put them where **we hide nothing on purpose**.”
+
+---
+
+## 4. Traceability landing (~55 s)
+
+“**`traceability_story.html`** is **REQ-001** as a **fold-out tree**—the **same traced graph** as dashboards, **indented for narration**. **Standards branch** and **extraction branch** mirror how ingest linked **meeting**, **architectures**, **concerns**, **decisions**, **offspring requirements**, and **risks**—**IDs line up** with **`traceability.db`** or the bundled **`traceability_data.json`** the dashboards load. **[pause]**
+
+For a **diagram first**, open **SES Traceability**—that’s **`ses_traceability.html`**, the SES-wide schematic before you drop into **`traceability_diagram.html`**, which also carries the REQ-001 plus concept panels for slides. **Intelligence** tab holds **full tables same dataset** if auditors ask. **[pause]**
+
+That primes **Agenda three**—**REQ Markdown in-repo** plus **trace edges**, not orphaned backlog blurbs.”
+
+---
+
+## 5. Close (~18 s)
+
+“What’s distinct here packaged is **ordered automation**, **SQLite memory and trace** that outlast chats, **typed links** you can traverse. Rolling into **Agenda three** opens the **REQ language** itself—not only the topology.”
+
+---
+
+## Cheatsheet (~15 seconds)
+
+| Label | Plain meaning |
+|------|----------------|
+| Engineering harness | Definition of outputs and gates per practice; pipelines as inspectable promises. |
+| Agentic harness | Registry-backed agents, executor order, keyed hand-offs inside one run. |
+| Shared Memory (+ events SQLite) | Long-lived keyed store; successive runs accumulate. |
+| Infra slide | Trigger → router → pipeline → SQLite (+ optional MCP), agent reuse. |
+| SES Traceability | **`ses_traceability.html`**—generic SES trace schematic (palette / legend on page). |
+| REQ-001 tree | **`traceability_story.html`**—export-accurate fold-out; doorway into REQ docs + trace-backed stories. |
diff --git a/docs/talking_script_ses_six_minute.txt b/docs/talking_script_ses_six_minute.txt
new file mode 100644
index 0000000..6910b3e
--- /dev/null
+++ b/docs/talking_script_ses_six_minute.txt
@@ -0,0 +1,81 @@
+Talking script — SES dashboard (6 minutes, technical, no clicks)
+
+CONTEXT
+ Agenda 02 — SES + SDLC. Hand off to Agenda 03 — Requirements when you pivot to trace.
+ Screens: interactive_architecture.html first, then traceability_story.html (tab change only).
+ Timing: Aim ~6 minutes of talk; cheatsheet ~15 seconds at end. Short sentences; [pause] = breath.
+ Trim if needed: drop the chips sentence in §3, or shorten §4 branches line.
+
+================================================================================
+0. Agenda hook (~20 s)
+================================================================================
+
+“Agenda item two is the SDLC story: SES runs on checked-in pipelines and on state that survives the chat—not ask-once tooling. [pause]
+
+I’ll do four beats: engineering harness versus agentic harness, Shared Memory that keeps adding up, what the infra page is listing, then REQ-001 on traceability_story to tee up requirements.”
+
+================================================================================
+1. Engineering harness versus agentic harness (~75 s)
+================================================================================
+
+“The engineering harness is our agreement about what each practice owes in hard form. [pause]
+
+A pipeline is a named flow. Inside it you have steps in order, and honest delivery means each step is meant to drop something reviewers can touch—commits, database rows, tickets, publishes—not just a narrative. [pause]
+
+The agentic harness is how those steps actually run: agents live in one catalog, pipelines hang together only the combinations you registered, and one small executor walks the list step by step. [pause]
+
+At run time each step inherits a flat bag of results from above: upstream writes keys, downstream reads keys. Steps can bypass themselves when a key is missing so the pipeline does not stall. Same agent type can repeat in several pipelines—we are not cloning code for each flow. [pause]
+
+One line takeaway: harness one is commitments and exits; harness two is orderly execution and passing the baton so it behaves like shipped software flows, not like one mega-prompt crossing fingers on memory.”
+
+================================================================================
+2. Shared Memory (~55 s)
+================================================================================
+
+“The per-run baton differs from Shared Memory—the baton resets when that run completes; Shared Memory stays in SQLite and keeps growing across triggers and sessions when we write namespaces on purpose there. [pause]
+
+So the Tuesday meeting run can stash parsed decisions, the Wednesday follow-up picks them up without retyping the story, telemetry and trace rows can converge on dashboards from the exported graph. [pause]
+
+Separate events SQLite lets listeners react later. MCP-style calls to Jira or Git plug in only where steps say they should. Throughout, the repeatable cord is pipelines plus memory that piles up honestly instead of living in the last transcript alone.”
+
+================================================================================
+3. Infra board (~50 s)
+================================================================================
+
+“interactive_architecture reads like a parts list on one screen: triggers in, routed pipelines out, persistence spelled out. [pause]
+
+Whatever the source—a transcript ingest, cron, Git hook—you normalize to trigger type plus payload, routing picks pipeline, executor runs step order you already learned. [pause]
+
+You’ll see distinct pipelines pulling from the same twenty-eight agent registrations; stripe colors mean deployment readiness for demos not product judgment. Chips call out prompts, risks, ancillary stores—we put them where we hide nothing on purpose.”
+
+================================================================================
+4. Traceability landing (~55 s)
+================================================================================
+
+“traceability_story is REQ-001 as a fold-out tree—the same traced graph as dashboards, indented for narration. Standards branch and extraction branch mirror how ingest linked meeting, architectures, concerns, decisions, offspring requirements, and risks—IDs line up with traceability.db or the bundled traceability_data.json the dashboards load. [pause]
+
+For a diagram first, open SES Traceability—that’s ses_traceability.html, the SES-wide schematic before traceability_diagram.html, which also carries REQ-001 plus concept panels for slides. Intelligence tab holds full tables same dataset if auditors ask. [pause]
+
+That primes Agenda three—REQ Markdown in-repo plus trace edges, not orphaned backlog blurbs.”
+
+================================================================================
+5. Close (~18 s)
+================================================================================
+
+“What’s distinct here packaged is ordered automation, SQLite memory and trace that outlast chats, typed links you can traverse. Rolling into Agenda three opens the REQ language itself—not only the topology.”
+
+================================================================================
+Cheatsheet (~15 seconds)
+================================================================================
+
+Engineering harness — Definition of outputs and gates per practice; pipelines as promises you can inspect.
+
+Agentic harness — Registry-backed agents, executor order, keyed hand-offs inside a single run.
+
+Shared Memory (+ events SQLite) — Long-lived keyed store; successive runs accumulate instead of rewriting from scratch each time.
+
+Infra slide — Trigger → router → pipeline → SQLite (+ optional MCP), agent reuse across pipelines.
+
+SES Traceability — ses_traceability.html (generic SES trace schematic on that page).
+
+REQ-001 tree — traceability_story.html; export-accurate fold-out; doorway into requirement documents and trace-backed stories.
diff --git a/docs/traceability.csv b/docs/traceability.csv
new file mode 100644
index 0000000..c773f9f
--- /dev/null
+++ b/docs/traceability.csv
@@ -0,0 +1,32 @@
+req_id,type,short_description,parent_hlr,satisfied_by,validated_by,scenario_refs,constraint_refs,state,source
+HLR-1,HLR,Ingest product data from diverse supplier formats (Email/SFTP/CSV/PDF),,FR-1;FR-2;DR-1,VAL-1,SCEN-1;SCEN-2,DC-3;C-3,Approved,SOW; eParts General Info; Context Diagram V3
+HLR-2,HLR,Normalize ingested data into a standardized intermediate structure,,,,SCEN-1,C-1,Approved,SOW; Notional Software Workflow Diagram
+HLR-3,HLR,Predict product attributes and assign confidence scores using ML,,FR-3;DR-2,,SCEN-1;SCEN-2,DC-1,Approved,SOW; eParts General Info
+HLR-4,HLR,Provide a UI for human review of low-confidence predictions,,FR-4;FR-5;FR-6;FR-7,VAL-2,SCEN-2,DC-2,Approved,SOW; Persona PS-1
+HLR-5,HLR,Write approved data back to PIMS,,FR-8;DR-3,VAL-3,SCEN-1;SCEN-2,,Approved,SOW
+FR-1,FR,Ingestion Gateway creates ingestion record on file receipt,HLR-1,,VAL-1,SCEN-1; SCEN-2,,Approved,Initial Draft v1.0
+FR-2,FR,Ingestion Gateway validates file integrity (type/size/virus),HLR-1,,VAL-1,SCEN-1,,Approved,Initial Draft v1.0
+FR-3,FR,Prediction Service generates a confidence score (0.0-1.0) per attribute,HLR-3,,VAL-2,SCEN-1; SCEN-2,,Approved,Initial Draft v1.0
+FR-4,FR,Route below-threshold items to Human Review Queue,HLR-4,,VAL-2,SCEN-2,,Approved,Initial Draft v1.0
+FR-5,FR,Review UI shows predicted value alongside source snippet,HLR-4,,,SCEN-2,DC-2,Approved,Initial Draft v1.0
+FR-6,FR,Log every human review action to immutable audit trail,HLR-4,,,SCEN-2,DC-3,Approved,Initial Draft v1.0
+FR-7,FR,Authorized Ops Leads can adjust auto-acceptance confidence threshold,HLR-4,,,,DC-2,Approved,Initial Draft v1.0
+FR-8,FR,On approval write attributes to PIMS via API,HLR-5,,VAL-3,SCEN-1; SCEN-2,,Approved,Initial Draft v1.0
+DR-1,DR,Retain original raw file as evidence for audit,HLR-1,,,,DC-3;C-2,Approved,Initial Draft v1.0
+DR-2,DR,Support manual triggering of model retraining from Review Queue corrections,HLR-3,,,,,Approved,Initial Draft v1.0
+DR-3,DR,Writeback to PIMS must be idempotent (no duplicates on retry),HLR-5,,VAL-3,SCEN-1,,Approved,Initial Draft v1.0
+QAS-1,QAS,Modifiability: integrate new supplier format within 4 hours,HLR-1;HLR-2,,,,C-3,Approved,Initial Draft v1.0
+QAS-2,QAS,Usability: reviewer processes >=10 simple decisions per minute,HLR-4,FR-5,,SCEN-2,,Approved,Initial Draft v1.0
+DC-1,DC,Backend must be Python-based (ML library compatibility),,,,,,,Approved,Team Charter
+DC-2,DC,Must use Auth0 for identity management,,,,,,,Approved,Client Requirement
+DC-3,DC,Raw files preserved indefinitely for re-processing,,,,,,,Approved,Regulatory
+C-1,Constraint,Cost-effective design (avoid linear per-tenant cost growth),,,,,,Approved,SOW
+C-2,Constraint,Privacy compliance (GDPR/CCPA deletion support),,,,,,Approved,SOW
+C-3,Constraint,Breadth-first delivery (full E2E for one supplier first),,,,,,Approved,SOW
+VAL-1,VAL,Ingestion trigger: file upload produces DB record within 30s,,FR-1;FR-2,,SCEN-1,,Approved,Initial Draft v1.0
+VAL-2,VAL,Routing logic: mock conf=0.2 lands item in Review Queue,,FR-3;FR-4,,SCEN-2,,Approved,Initial Draft v1.0
+VAL-3,VAL,PIMS integration: approved item produces 200 OK to PIMS API,,FR-8;DR-3,,SCEN-1; SCEN-2,,Approved,Initial Draft v1.0
+SCEN-1,Scenario,End-to-end happy-path ingestion,HLR-1;HLR-2;HLR-3;HLR-5,FR-1;FR-3;FR-8,VAL-1;VAL-3,,,Approved,Initial Draft v1.0
+SCEN-2,Scenario,Low-confidence human-in-the-loop review,HLR-1;HLR-3;HLR-4;HLR-5,FR-1;FR-3;FR-4;FR-5;FR-6;FR-8,VAL-2;VAL-3,,,Approved,Initial Draft v1.0
+PS-1,Persona,Ops Reviewer (internal domain expert),HLR-4,FR-5;FR-6;FR-7,,SCEN-2,,Approved,Initial Draft v1.0
+PS-2,Persona,Supplier (external manufacturer),HLR-1,FR-1;FR-2,,SCEN-1,,Approved,Initial Draft v1.0
diff --git a/docs/traceability.md b/docs/traceability.md
new file mode 100644
index 0000000..7c86e67
--- /dev/null
+++ b/docs/traceability.md
@@ -0,0 +1,412 @@
+# Unified Traceability Matrix
+
+_Last updated: 2026-04-25 15:04 UTC — auto-generated by traceability_builder agent_
+
+## Overview
+
+- **184** artifacts tracked across **10** types
+- **760** links connecting them (avg 4.1 per artifact)
+- **0 orphan artifacts** — every artifact participates in at least one relationship
+- **0** unaddressed concerns | **0** unmitigated risks
+
+### Artifact Types
+
+| Type | Count | Description |
+|------|-------|-------------|
+| jira_ticket | 50 | Jira tickets tracking work |
+| action_item | 41 | Action items extracted from meetings |
+| commitment | 31 | Team commitments from coach sessions |
+| risk | 16 | Identified risks from risk register |
+| concern | 12 | Questions and concerns raised in meetings |
+| requirement | 12 | Formal requirements derived from meetings + architecture |
+| decision | 10 | Decisions made during meetings |
+| architecture | 6 | Key architecture decisions (ADRs) |
+| meeting | 5 | Client and coach meetings (source of truth) |
+| coach_session | 1 | Coach/mentor sessions with commitments |
+
+### Relationship Types
+
+| Link Type | Count | Meaning |
+|-----------|-------|---------|
+| MITIGATES | 320 | Work mitigates an identified risk |
+| IMPLEMENTS | 204 | Jira ticket or architecture implements a requirement/commitment |
+| RAISED_IN | 122 | Artifact originated from this meeting/session |
+| BECAME | 60 | Concern or commitment evolved into a requirement/decision |
+| DECIDED_BY | 32 | Decision/requirement shaped by an architecture choice |
+| ADDRESSES | 13 | Architecture decision addresses a specific concern |
+| TRIGGERED | 9 | Action item was triggered by a decision |
+
+---
+
+## Full Lifecycle Paths
+
+> These chains show how a single concern or requirement propagates through
+> the entire product lifecycle: meeting → concern → decision → requirement → architecture → risk → Jira ticket.
+
+### REQ-001: Extract product attributes from vendor spec sheets
+_Chain touches **42** artifacts across **8** types: architecture, coach_session, commitment, concern, decision, meeting, requirement, risk_
+
+- **[requirement]** `REQ-001`: Extract product attributes from vendor spec sheets
+ - **[meeting]** `MTG-2026-01-22`: Client Meeting 2026-01-22
+ - **[architecture]** `ARCH-002`: Map to industry standards instead of ALPS-specific attributes
+ - **[meeting]** `MTG-2026-04-02`: Client Meeting 2026-04-02
+ - **[concern]** `CON-3d2b20`: Is there… I know before we talked about the most sensitive thing from the v
+ - **[requirement]** `REQ-002`: Map extracted attributes to industry-standard taxonomy
+ - **[risk]** `RIS-f5c786`: Confidence threshold miscalibration
+ - **[risk]** `RIS-50bc62`: Data access delay blocking ML development
+ - **[risk]** `RIS-d8a886`: Insufficient training data (<200 labeled examples)
+ - **[risk]** `RIS-8083f1`: PIMS staging schema incompatibility (P1-C pending)
+ - **[risk]** `RIS-5de8fb`: Catalog team capacity vs review volume
+ - **[risk]** `RIS-11e5db`: Measurement validity for AI effectiveness
+ - **[risk]** `RIS-816a97`: Model selection uncertainty
+ - **[risk]** `RIS-016cf1`: Integration dependency on Jake (PIMS schema)
+ - **[risk]** `RIS-ddd0e1`: Attribute correlation invalidates per-attribute routing
+ - **[risk]** `RIS-fc0584`: Human review interface design not decided
+ ... (26 more nodes)
+
+### REQ-002: Map extracted attributes to industry-standard taxonomy
+_Chain touches **42** artifacts across **8** types: architecture, coach_session, commitment, concern, decision, meeting, requirement, risk_
+
+- **[requirement]** `REQ-002`: Map extracted attributes to industry-standard taxonomy
+ - **[meeting]** `MTG-2026-04-02`: Client Meeting 2026-04-02
+ - **[architecture]** `ARCH-002`: Map to industry standards instead of ALPS-specific attributes
+ - **[concern]** `CON-3d2b20`: Is there… I know before we talked about the most sensitive thing from the v
+ - **[meeting]** `MTG-2026-01-22`: Client Meeting 2026-01-22
+ - **[requirement]** `REQ-001`: Extract product attributes from vendor spec sheets
+ - **[architecture]** `ARCH-003`: ML confidence scoring for attribute prediction
+ - **[concern]** `CON-daffed`: Can we at least use the LLM part for, instead of the OCR
+ - **[meeting]** `MTG-2026-02-26`: Client Meeting 2026-02-26
+ - **[decision]** `DEC-e9b865`: I think the primary, goal for the model that we're currently thinking is to
+ - **[architecture]** `ARCH-004`: Staging tables as Git-diff model for data review
+ - **[concern]** `CON-8262ce`: Is there a timeline from your end that you expect me to give you the data b
+ - **[decision]** `DEC-fab886`: Yeah, so, we are targeting that towards the end of this month, we should ha
+ - **[architecture]** `ARCH-006`: Agent-Augmented Iterative SDLC (bespoke)
+ - **[requirement]** `REQ-003`: ML confidence scoring on every predicted attribute
+ - **[architecture]** `ARCH-005`: Human-in-the-loop for all AI-generated data
+ ... (26 more nodes)
+
+### REQ-003: ML confidence scoring on every predicted attribute
+_Chain touches **42** artifacts across **8** types: architecture, coach_session, commitment, concern, decision, meeting, requirement, risk_
+
+- **[requirement]** `REQ-003`: ML confidence scoring on every predicted attribute
+ - **[meeting]** `MTG-2026-01-22`: Client Meeting 2026-01-22
+ - **[architecture]** `ARCH-003`: ML confidence scoring for attribute prediction
+ - **[concern]** `CON-3d2b20`: Is there… I know before we talked about the most sensitive thing from the v
+ - **[requirement]** `REQ-001`: Extract product attributes from vendor spec sheets
+ - **[architecture]** `ARCH-002`: Map to industry standards instead of ALPS-specific attributes
+ - **[meeting]** `MTG-2026-04-02`: Client Meeting 2026-04-02
+ - **[concern]** `CON-daffed`: Can we at least use the LLM part for, instead of the OCR
+ - **[meeting]** `MTG-2026-02-26`: Client Meeting 2026-02-26
+ - **[decision]** `DEC-e9b865`: I think the primary, goal for the model that we're currently thinking is to
+ - **[architecture]** `ARCH-004`: Staging tables as Git-diff model for data review
+ - **[concern]** `CON-8262ce`: Is there a timeline from your end that you expect me to give you the data b
+ - **[decision]** `DEC-fab886`: Yeah, so, we are targeting that towards the end of this month, we should ha
+ - **[architecture]** `ARCH-006`: Agent-Augmented Iterative SDLC (bespoke)
+ - **[requirement]** `REQ-002`: Map extracted attributes to industry-standard taxonomy
+ - **[risk]** `RIS-f5c786`: Confidence threshold miscalibration
+ ... (26 more nodes)
+
+### REQ-004: Human review queue for AI-generated catalog data
+_Chain touches **42** artifacts across **8** types: architecture, coach_session, commitment, concern, decision, meeting, requirement, risk_
+
+- **[requirement]** `REQ-004`: Human review queue for AI-generated catalog data
+ - **[meeting]** `MTG-2026-01-22`: Client Meeting 2026-01-22
+ - **[architecture]** `ARCH-005`: Human-in-the-loop for all AI-generated data
+ - **[concern]** `CON-3d2b20`: Is there… I know before we talked about the most sensitive thing from the v
+ - **[requirement]** `REQ-001`: Extract product attributes from vendor spec sheets
+ - **[architecture]** `ARCH-002`: Map to industry standards instead of ALPS-specific attributes
+ - **[meeting]** `MTG-2026-04-02`: Client Meeting 2026-04-02
+ - **[concern]** `CON-daffed`: Can we at least use the LLM part for, instead of the OCR
+ - **[meeting]** `MTG-2026-02-26`: Client Meeting 2026-02-26
+ - **[decision]** `DEC-e9b865`: I think the primary, goal for the model that we're currently thinking is to
+ - **[architecture]** `ARCH-003`: ML confidence scoring for attribute prediction
+ - **[concern]** `CON-8262ce`: Is there a timeline from your end that you expect me to give you the data b
+ - **[decision]** `DEC-fab886`: Yeah, so, we are targeting that towards the end of this month, we should ha
+ - **[architecture]** `ARCH-006`: Agent-Augmented Iterative SDLC (bespoke)
+ - **[requirement]** `REQ-002`: Map extracted attributes to industry-standard taxonomy
+ - **[risk]** `RIS-f5c786`: Confidence threshold miscalibration
+ ... (26 more nodes)
+
+### REQ-005: Staging table diff model for catalog review workflow
+_Chain touches **42** artifacts across **8** types: architecture, coach_session, commitment, concern, decision, meeting, requirement, risk_
+
+- **[requirement]** `REQ-005`: Staging table diff model for catalog review workflow
+ - **[meeting]** `MTG-2026-01-22`: Client Meeting 2026-01-22
+ - **[architecture]** `ARCH-004`: Staging tables as Git-diff model for data review
+ - **[concern]** `CON-3d2b20`: Is there… I know before we talked about the most sensitive thing from the v
+ - **[requirement]** `REQ-001`: Extract product attributes from vendor spec sheets
+ - **[architecture]** `ARCH-002`: Map to industry standards instead of ALPS-specific attributes
+ - **[meeting]** `MTG-2026-04-02`: Client Meeting 2026-04-02
+ - **[concern]** `CON-daffed`: Can we at least use the LLM part for, instead of the OCR
+ - **[meeting]** `MTG-2026-02-26`: Client Meeting 2026-02-26
+ - **[decision]** `DEC-e9b865`: I think the primary, goal for the model that we're currently thinking is to
+ - **[architecture]** `ARCH-003`: ML confidence scoring for attribute prediction
+ - **[concern]** `CON-8262ce`: Is there a timeline from your end that you expect me to give you the data b
+ - **[decision]** `DEC-fab886`: Yeah, so, we are targeting that towards the end of this month, we should ha
+ - **[architecture]** `ARCH-006`: Agent-Augmented Iterative SDLC (bespoke)
+ - **[requirement]** `REQ-002`: Map extracted attributes to industry-standard taxonomy
+ - **[risk]** `RIS-f5c786`: Confidence threshold miscalibration
+ ... (26 more nodes)
+
+### REQ-006: Azure infrastructure with Bicep IaC
+_Chain touches **6** artifacts across **4** types: architecture, meeting, requirement, risk_
+
+- **[requirement]** `REQ-006`: Azure infrastructure with Bicep IaC
+ - **[meeting]** `MTG-2026-02-12`: Client Meeting 2026-02-12
+ - **[architecture]** `ARCH-001`: Use Bicep over Terraform for Azure IaC
+ - **[risk]** `RIS-c2f026`: Drift detection metrics and baselines undefined
+ - **[risk]** `RIS-603445`: Scope creep risk
+ - **[risk]** `RIS-dd2cc8`: Azure tool constraints
+
+### REQ-007: Azure Log Analytics for monitoring and observability
+_Chain touches **6** artifacts across **4** types: architecture, meeting, requirement, risk_
+
+- **[requirement]** `REQ-007`: Azure Log Analytics for monitoring and observability
+ - **[meeting]** `MTG-2026-02-12`: Client Meeting 2026-02-12
+ - **[architecture]** `ARCH-001`: Use Bicep over Terraform for Azure IaC
+ - **[risk]** `RIS-c2f026`: Drift detection metrics and baselines undefined
+ - **[risk]** `RIS-603445`: Scope creep risk
+ - **[risk]** `RIS-dd2cc8`: Azure tool constraints
+
+### REQ-008: Support multiple vendor document formats
+_Chain touches **42** artifacts across **8** types: architecture, coach_session, commitment, concern, decision, meeting, requirement, risk_
+
+- **[requirement]** `REQ-008`: Support multiple vendor document formats
+ - **[meeting]** `MTG-2026-02-26`: Client Meeting 2026-02-26
+ - **[architecture]** `ARCH-002`: Map to industry standards instead of ALPS-specific attributes
+ - **[meeting]** `MTG-2026-04-02`: Client Meeting 2026-04-02
+ - **[concern]** `CON-3d2b20`: Is there… I know before we talked about the most sensitive thing from the v
+ - **[meeting]** `MTG-2026-01-22`: Client Meeting 2026-01-22
+ - **[requirement]** `REQ-001`: Extract product attributes from vendor spec sheets
+ - **[architecture]** `ARCH-003`: ML confidence scoring for attribute prediction
+ - **[concern]** `CON-daffed`: Can we at least use the LLM part for, instead of the OCR
+ - **[decision]** `DEC-e9b865`: I think the primary, goal for the model that we're currently thinking is to
+ - **[architecture]** `ARCH-004`: Staging tables as Git-diff model for data review
+ - **[concern]** `CON-8262ce`: Is there a timeline from your end that you expect me to give you the data b
+ - **[decision]** `DEC-fab886`: Yeah, so, we are targeting that towards the end of this month, we should ha
+ - **[architecture]** `ARCH-006`: Agent-Augmented Iterative SDLC (bespoke)
+ - **[requirement]** `REQ-002`: Map extracted attributes to industry-standard taxonomy
+ - **[risk]** `RIS-f5c786`: Confidence threshold miscalibration
+ ... (26 more nodes)
+
+### REQ-009: POC demonstrating end-to-end attribute extraction
+_Chain touches **42** artifacts across **8** types: architecture, coach_session, commitment, concern, decision, meeting, requirement, risk_
+
+- **[requirement]** `REQ-009`: POC demonstrating end-to-end attribute extraction
+ - **[meeting]** `MTG-2026-04-02`: Client Meeting 2026-04-02
+ - **[architecture]** `ARCH-003`: ML confidence scoring for attribute prediction
+ - **[meeting]** `MTG-2026-01-22`: Client Meeting 2026-01-22
+ - **[concern]** `CON-3d2b20`: Is there… I know before we talked about the most sensitive thing from the v
+ - **[requirement]** `REQ-001`: Extract product attributes from vendor spec sheets
+ - **[architecture]** `ARCH-002`: Map to industry standards instead of ALPS-specific attributes
+ - **[concern]** `CON-daffed`: Can we at least use the LLM part for, instead of the OCR
+ - **[meeting]** `MTG-2026-02-26`: Client Meeting 2026-02-26
+ - **[decision]** `DEC-e9b865`: I think the primary, goal for the model that we're currently thinking is to
+ - **[architecture]** `ARCH-004`: Staging tables as Git-diff model for data review
+ - **[concern]** `CON-8262ce`: Is there a timeline from your end that you expect me to give you the data b
+ - **[decision]** `DEC-fab886`: Yeah, so, we are targeting that towards the end of this month, we should ha
+ - **[architecture]** `ARCH-006`: Agent-Augmented Iterative SDLC (bespoke)
+ - **[requirement]** `REQ-002`: Map extracted attributes to industry-standard taxonomy
+ - **[risk]** `RIS-f5c786`: Confidence threshold miscalibration
+ ... (26 more nodes)
+
+### REQ-010: Statement of Work signed by end of April
+_Chain touches **6** artifacts across **3** types: meeting, requirement, risk_
+
+- **[requirement]** `REQ-010`: Statement of Work signed by end of April
+ - **[meeting]** `MTG-2026-04-02`: Client Meeting 2026-04-02
+ - **[risk]** `RIS-fc0584`: Human review interface design not decided
+ - **[risk]** `RIS-603445`: Scope creep risk
+ - **[risk]** `RIS-2119e6`: Capstone timeline constraint
+ - **[risk]** `RIS-8706ae`: Knowledge loss from manual processes
+
+### REQ-011: Team AI usage policy and best practices guide
+_Chain touches **45** artifacts across **8** types: architecture, coach_session, commitment, concern, decision, meeting, requirement, risk_
+
+- **[requirement]** `REQ-011`: Team AI usage policy and best practices guide
+ - **[meeting]** `MTG-2026-04-16`: Client Meeting 2026-04-16
+ - **[architecture]** `ARCH-006`: Agent-Augmented Iterative SDLC (bespoke)
+ - **[concern]** `CON-8ec1cb`: How does your current, the code checking process look like
+ - **[meeting]** `MTG-2026-01-22`: Client Meeting 2026-01-22
+ - **[decision]** `DEC-afa506`: You'd like some metrics to see what the overall process on which it's impro
+ - **[requirement]** `REQ-010`: Statement of Work signed by end of April
+ - **[meeting]** `MTG-2026-04-02`: Client Meeting 2026-04-02
+ - **[risk]** `RIS-fc0584`: Human review interface design not decided
+ - **[risk]** `RIS-603445`: Scope creep risk
+ - **[risk]** `RIS-2119e6`: Capstone timeline constraint
+ - **[risk]** `RIS-8706ae`: Knowledge loss from manual processes
+ - **[commitment]** `COM-2cb319`: we'll be primarily using a cursor with the client's data to use it to proce
+ - **[coach_session]** `COACH-session-`: Coach Session 2026-02-20 (coach)
+ - **[requirement]** `REQ-001`: Extract product attributes from vendor spec sheets
+ - **[architecture]** `ARCH-002`: Map to industry standards instead of ALPS-specific attributes
+ ... (29 more nodes)
+
+### REQ-012: Formal ADRs for all architecture decisions
+_Chain touches **47** artifacts across **8** types: architecture, coach_session, commitment, concern, decision, meeting, requirement, risk_
+
+- **[requirement]** `REQ-012`: Formal ADRs for all architecture decisions
+ - **[architecture]** `ARCH-001`: Use Bicep over Terraform for Azure IaC
+ - **[meeting]** `MTG-2026-02-12`: Client Meeting 2026-02-12
+ - **[risk]** `RIS-c2f026`: Drift detection metrics and baselines undefined
+ - **[risk]** `RIS-603445`: Scope creep risk
+ - **[risk]** `RIS-dd2cc8`: Azure tool constraints
+ - **[architecture]** `ARCH-002`: Map to industry standards instead of ALPS-specific attributes
+ - **[meeting]** `MTG-2026-04-02`: Client Meeting 2026-04-02
+ - **[concern]** `CON-3d2b20`: Is there… I know before we talked about the most sensitive thing from the v
+ - **[meeting]** `MTG-2026-01-22`: Client Meeting 2026-01-22
+ - **[requirement]** `REQ-001`: Extract product attributes from vendor spec sheets
+ - **[architecture]** `ARCH-003`: ML confidence scoring for attribute prediction
+ - **[concern]** `CON-daffed`: Can we at least use the LLM part for, instead of the OCR
+ - **[meeting]** `MTG-2026-02-26`: Client Meeting 2026-02-26
+ - **[decision]** `DEC-e9b865`: I think the primary, goal for the model that we're currently thinking is to
+ - **[architecture]** `ARCH-004`: Staging tables as Git-diff model for data review
+ ... (31 more nodes)
+
+---
+
+## Requirement → Jira Ticket Coverage
+
+Shows which Jira tickets implement each requirement.
+
+| Requirement | Jira Tickets | Count |
+|-------------|-------------|-------|
+| REQ-001: Extract product attributes from vendor spec s | EPARTS-74, EPARTS-73, EPARTS-72, EPARTS-68, EPARTS-58 (+2 more) | 7 |
+| REQ-002: Map extracted attributes to industry-standard | EPARTS-74, EPARTS-73, EPARTS-68, EPARTS-58, EPARTS-39 (+1 more) | 6 |
+| REQ-003: ML confidence scoring on every predicted attr | EPARTS-80, EPARTS-72, EPARTS-69, EPARTS-60, EPARTS-47 (+1 more) | 6 |
+| REQ-004: Human review queue for AI-generated catalog d | EPARTS-47 | 1 |
+| REQ-005: Staging table diff model for catalog review w | EPARTS-47 | 1 |
+| REQ-006: Azure infrastructure with Bicep IaC | EPARTS-81, EPARTS-71, EPARTS-66 | 3 |
+| REQ-007: Azure Log Analytics for monitoring and observ | EPARTS-81, EPARTS-71, EPARTS-66 | 3 |
+| REQ-008: Support multiple vendor document formats | EPARTS-72 | 1 |
+| REQ-009: POC demonstrating end-to-end attribute extrac | EPARTS-81, EPARTS-80, EPARTS-74, EPARTS-69, EPARTS-66 (+3 more) | 8 |
+| REQ-010: Statement of Work signed by end of April | EPARTS-81, EPARTS-76, EPARTS-74, EPARTS-66, EPARTS-59 (+10 more) | 15 |
+| REQ-011: Team AI usage policy and best practices guide | EPARTS-81, EPARTS-78, EPARTS-77, EPARTS-57, EPARTS-54 (+8 more) | 13 |
+| REQ-012: Formal ADRs for all architecture decisions | EPARTS-81, EPARTS-80, EPARTS-79, EPARTS-75, EPARTS-70 (+7 more) | 12 |
+
+---
+
+## Architecture Decision Chains
+
+### ARCH-001: Use Bicep over Terraform for Azure IaC
+- **Source:** 2026-02-12 (David Mine)
+- **Status:** done
+- **Chain:** 5 nodes → architecture, meeting, risk
+ - [meeting] **MTG-2026-02-12**: Client Meeting 2026-02-12
+ - [risk] **RIS-c2f026**: Drift detection metrics and baselines undefined
+ - [risk] **RIS-603445**: Scope creep risk
+ - [risk] **RIS-dd2cc8**: Azure tool constraints
+
+### ARCH-002: Map to industry standards instead of ALPS-specific attributes
+- **Source:** 2026-04-02 (Harsha (eParts))
+- **Status:** done
+- **Chain:** 40 nodes → architecture, commitment, concern, decision, meeting, requirement, risk
+ - [meeting] **MTG-2026-04-02**: Client Meeting 2026-04-02
+ - [concern] **CON-3d2b20**: Is there… I know before we talked about the most sensitive thing from
+
+### ARCH-003: ML confidence scoring for attribute prediction
+- **Source:** 2026-01-22 (Hrishik)
+- **Status:** open
+- **Chain:** 40 nodes → architecture, commitment, concern, decision, meeting, requirement, risk
+ - [meeting] **MTG-2026-01-22**: Client Meeting 2026-01-22
+ - [concern] **CON-3d2b20**: Is there… I know before we talked about the most sensitive thing from
+
+### ARCH-004: Staging tables as Git-diff model for data review
+- **Source:** 2026-01-22 (David Mine)
+- **Status:** done
+- **Chain:** 40 nodes → architecture, commitment, concern, decision, meeting, requirement, risk
+ - [meeting] **MTG-2026-01-22**: Client Meeting 2026-01-22
+ - [concern] **CON-3d2b20**: Is there… I know before we talked about the most sensitive thing from
+
+### ARCH-005: Human-in-the-loop for all AI-generated data
+- **Source:** 2026-01-22 (Dennis Grinberg)
+- **Status:** done
+- **Chain:** 40 nodes → architecture, commitment, concern, decision, meeting, requirement, risk
+ - [meeting] **MTG-2026-01-22**: Client Meeting 2026-01-22
+ - [concern] **CON-3d2b20**: Is there… I know before we talked about the most sensitive thing from
+
+### ARCH-006: Agent-Augmented Iterative SDLC (bespoke)
+- **Source:** internal (Team)
+- **Status:** done
+- **Chain:** 43 nodes → architecture, coach_session, commitment, concern, decision, meeting, requirement, risk
+ - [concern] **CON-8ec1cb**: How does your current, the code checking process look like
+ - [commitment] **COM-2cb319**: we'll be primarily using a cursor with the client's data to use it to
+ - [commitment] **COM-dc8c00**: we'll do that as well. but happy to focus on… on the risk part.
+
+---
+
+## Risk Mitigation Status
+
+| Risk | Mitigated By (arch/req/ticket) | Status |
+|------|-------------------------------|--------|
+| Confidence threshold miscalibration | requirement: REQ-001, REQ-002, REQ-003; jira_ticket: EPARTS-80, EPARTS-78, EPARTS-73; architecture: ARCH-002, ARCH-003, ARCH-004 | Mitigated |
+| Data access delay blocking ML development | requirement: REQ-001, REQ-002, REQ-003; jira_ticket: EPARTS-80, EPARTS-78, EPARTS-73; architecture: ARCH-002, ARCH-003, ARCH-004 | Mitigated |
+| Insufficient training data (<200 labeled examples) | requirement: REQ-001, REQ-002, REQ-003; jira_ticket: EPARTS-80, EPARTS-78, EPARTS-73; architecture: ARCH-002, ARCH-003, ARCH-004 | Mitigated |
+| PIMS staging schema incompatibility (P1-C pending) | requirement: REQ-001, REQ-002, REQ-003; jira_ticket: EPARTS-80, EPARTS-78, EPARTS-73; architecture: ARCH-002, ARCH-003, ARCH-004 | Mitigated |
+| Catalog team capacity vs review volume | requirement: REQ-001, REQ-002, REQ-003; jira_ticket: EPARTS-80, EPARTS-78, EPARTS-73; architecture: ARCH-002, ARCH-003, ARCH-004 | Mitigated |
+| Drift detection metrics and baselines undefined | requirement: REQ-006, REQ-007; jira_ticket: EPARTS-81, EPARTS-74, EPARTS-72; architecture: ARCH-001 | Mitigated |
+| Measurement validity for AI effectiveness | requirement: REQ-001, REQ-002, REQ-003; jira_ticket: EPARTS-80, EPARTS-78, EPARTS-73; architecture: ARCH-002, ARCH-003, ARCH-004 | Mitigated |
+| Model selection uncertainty | requirement: REQ-001, REQ-002, REQ-003; jira_ticket: EPARTS-80, EPARTS-78, EPARTS-73; architecture: ARCH-002, ARCH-003, ARCH-004 | Mitigated |
+| Integration dependency on Jake (PIMS schema) | requirement: REQ-001, REQ-002, REQ-003; jira_ticket: EPARTS-80, EPARTS-79, EPARTS-78; architecture: ARCH-002, ARCH-003, ARCH-004 | Mitigated |
+| Alpha weighting sensitivity in hybrid scoring | jira_ticket: EPARTS-42, EPARTS-34 | Mitigated |
+| Attribute correlation invalidates per-attribute ro | requirement: REQ-001, REQ-002, REQ-003; jira_ticket: EPARTS-80, EPARTS-78, EPARTS-73; architecture: ARCH-002, ARCH-003, ARCH-004 | Mitigated |
+| Human review interface design not decided | requirement: REQ-002, REQ-003, REQ-004; jira_ticket: EPARTS-80, EPARTS-79, EPARTS-75; architecture: ARCH-002, ARCH-003, ARCH-004 | Mitigated |
+| Scope creep risk | requirement: REQ-002, REQ-006, REQ-007; jira_ticket: EPARTS-81, EPARTS-80, EPARTS-79; architecture: ARCH-001, ARCH-002, ARCH-006 | Mitigated |
+| Azure tool constraints | requirement: REQ-006, REQ-007, REQ-012; jira_ticket: EPARTS-81, EPARTS-80, EPARTS-79; architecture: ARCH-001 | Mitigated |
+| Capstone timeline constraint | requirement: REQ-002, REQ-010; jira_ticket: EPARTS-79, EPARTS-74, EPARTS-73; architecture: ARCH-002 | Mitigated |
+| Knowledge loss from manual processes | requirement: REQ-010, REQ-011; jira_ticket: EPARTS-80, EPARTS-79, EPARTS-78; architecture: ARCH-006 | Mitigated |
+
+---
+
+## Concern Traceability
+
+Shows how each concern was addressed: what architecture decisions, requirements, and tickets respond to it.
+
+| Concern | Meeting | Addressed By | Became |
+|---------|---------|-------------|--------|
+| Is there… I know before we talked about the m | 2026-01-22 | ARCH-002, ARCH-003, ARCH-004, ARCH-005 | REQ-001 (requirement), REQ-002 (requirement), REQ-003 (requirement), REQ-005 (requirement), REQ-008 (requirement), REQ-009 (requirement) |
+| Is there any correction of information about | 2026-01-22 | — | — |
+| Is there anything else you want to add on the | 2026-01-22 | — | — |
+| How does your current, the code checking proc | 2026-01-22 | ARCH-006 | DEC-afa506 (decision), REQ-010 (requirement), REQ-011 (requirement) |
+| How does that look like | 2026-01-22 | — | — |
+| Can we at least use the LLM part for, instead | 2026-02-26 | ARCH-002, ARCH-003, ARCH-004, ARCH-005 | DEC-e9b865 (decision), REQ-001 (requirement), REQ-002 (requirement), REQ-003 (requirement), REQ-005 (requirement), REQ-008 (requirement), REQ-009 (requirement) |
+| Is there a timeline from your end that you ex | 2026-04-02 | ARCH-002, ARCH-003, ARCH-004, ARCH-005 | DEC-fab886 (decision), REQ-001 (requirement), REQ-002 (requirement), REQ-003 (requirement), REQ-005 (requirement), REQ-008 (requirement), REQ-009 (requirement), REQ-010 (requirement) |
+| How do we understand that this matches to a s | 2026-04-02 | — | — |
+| Is there any problem with what I just said | 2026-04-02 | — | — |
+| Can we Actually, just use a CS | 2026-04-16 | — | — |
+| problem and the concept, so you get a better | 2026-02-24 | — | — |
+| problem with the first one, the distalbot, is | 2026-02-20 | — | — |
+
+---
+
+## Coach Commitment Fulfillment
+
+Commitments from coach sessions and what implements them.
+
+| Commitment | Status | Implemented By | Shaped Requirement |
+|------------|--------|---------------|-------------------|
+| we'll be primarily using a cursor with the cl | open | ARCH-002, ARCH-003, ARCH-004 | REQ-001, REQ-002, REQ-003, REQ-005, REQ-008, REQ-009, REQ-010, REQ-011 |
+| we will be using github actions for our testi | open | — | — |
+| we'll be building some from scratch, so… | open | — | — |
+| i will say is that you've got to think about… | open | — | — |
+| we'll probably, yeah, we… i don't think we ha | open | — | — |
+| we'll probably get access to cursor. | open | — | — |
+| i'll create a repo, and then anything that's | open | — | — |
+| i'll make sure they're in the repo, so they'r | open | — | — |
+| i'll just quickly share my screen, i'll show | open | — | — |
+| i'll be… much easier to understand. | open | — | — |
+| we need to proceed with any of the… | open | — | — |
+| we will not have a good enough idea of which | open | — | — |
+| we will not be using any tools that, basicall | open | — | — |
+| we'll go back over a bit. we'll go back there | open | — | — |
+| we will start missing milestones if, if, we, | open | — | — |
+
+---
+
+## Traceability Gaps
+
+- **22** artifacts with no outgoing links
+ - [meeting] MTG-2026-01-22: Client Meeting 2026-01-22
+ - [meeting] MTG-2026-02-12: Client Meeting 2026-02-12
+ - [meeting] MTG-2026-02-26: Client Meeting 2026-02-26
+ - [meeting] MTG-2026-04-02: Client Meeting 2026-04-02
+ - [meeting] MTG-2026-04-16: Client Meeting 2026-04-16
diff --git a/docs/why_everything.md b/docs/why_everything.md
new file mode 100644
index 0000000..d863cfe
--- /dev/null
+++ b/docs/why_everything.md
@@ -0,0 +1,267 @@
+# Why Everything — Decision Rationale for Every Component
+
+> "If it can be done by a human, why? If you're using AI, why? What gains?"
+>
+> Every component in this system exists because we asked "why" and had an answer
+> backed by evidence — not vibes, not "because AI is cool."
+
+---
+
+## The Foundation: Why an Agentic System at All?
+
+**Alternative considered:** Use ChatGPT manually for each task. Copy-paste transcripts,
+copy-paste outputs, manually track everything in a spreadsheet.
+
+**Why not:** Manual LLM use is ad-hoc by definition. It has no memory (ChatGPT forgets
+your last meeting), no consistency (different prompts every time), no measurement
+(how many tokens did that cost?), and no traceability (which version of which prompt
+produced that meeting summary 3 weeks ago?).
+
+**Why an agentic system:** It gives us:
+1. **Memory** — ChromaDB + SharedMemory wiki remember every meeting, every decision
+2. **Consistency** — Prompt Registry ensures everyone uses the same versioned prompt
+3. **Measurement** — MetricsCollector tracks every LLM call automatically
+4. **Traceability** — every output links to its agent, prompt version, and source data
+5. **Scalability** — processing meeting 50 is the same effort as meeting 1
+
+**The test:** If we deleted the system and went back to manual, what would we lose?
+- 50 wiki entries of accumulated project knowledge → gone
+- 371 embedded coach session chunks for RAG → gone
+- 16 auto-populated risk entries → back to a blank spreadsheet
+- Cross-pipeline triggers (drift → architecture review) → nobody would remember to check
+- Full traceability from requirement to source meeting → trust me, bro
+
+---
+
+## Component-by-Component: The "Why" Behind Everything
+
+### 1. BaseAgent + ETVX Framework
+
+| Question | Answer |
+|---|---|
+| **What is it?** | Abstract base class all 28 agents inherit from, with ETVX process documentation per activity |
+| **Why not just scripts?** | Scripts don't have: structured logging, metrics collection, wiki access, event emission, retry logic, prompt version tracking. BaseAgent provides all of these out of the box. Every new agent gets them for free. |
+| **Why ETVX?** | The meta-model requires documented processes. ETVX (Entry/Task/Verification/Exit) is the standard the course uses. But more importantly — ETVX forces us to define *when* an activity starts, *what* it does, *how we verify* it worked, and *what "done" means*. Without that, "the agent ran" tells us nothing. |
+| **Evidence** | Every agent run is logged with duration, success, token count, and outputs. 100% pipeline success rate across all test runs. |
+
+### 2. Pipeline Executor (Sequential Agent Chaining)
+
+| Question | Answer |
+|---|---|
+| **What is it?** | Runs agents in sequence, threading data from one step to the next via PipelineContext |
+| **Why sequential, not parallel?** | Data dependency. Step 2 (classify priorities) needs Step 1's output (parsed items). Step 7 (drift detection) needs the meeting content from Step 1 and the architecture from ChromaDB. Parallel execution would require complex synchronization for zero benefit — our steps take <300ms total. |
+| **Why not one big prompt?** | Prompt decomposition. One mega-prompt ("parse this transcript AND classify priorities AND extract requirements AND check for drift") would be fragile, untestable, and expensive. Seven small prompts are each independently testable, versionable, and replaceable. If the priority classifier is wrong, we fix one prompt — not a 2000-token monster. |
+| **Why 7 steps for requirements?** | Each step maps to a distinct meta-model Activity with its own ETVX, artifacts, and measurements. Fewer steps = activities get conflated. More steps = overhead exceeds value. 7 was determined by the natural process decomposition: parse → classify → extract → create tickets → publish → log decisions → check drift. |
+
+### 3. SharedMemory (The Wiki)
+
+| Question | Answer |
+|---|---|
+| **What is it?** | SQLite-backed namespaced key-value store all agents read and write |
+| **Why not just files?** | Files are dead. You can't query a file system for "all commitments related to data access across 4 coach sessions." The wiki can do that in <1ms. Files also don't have change logs — the wiki tracks every write with who, when, and what changed. |
+| **Why not a full database?** | SQLite is the right tool for a 5-person team. Zero config, single file, works everywhere. A Postgres instance would be operational overhead for no benefit at our scale. |
+| **Why the Karpathy wiki pattern?** | Karpathy's insight: LLMs are more powerful when they incrementally build structured knowledge, not just respond to isolated prompts. Our agents don't just answer questions — they deposit knowledge. Meeting 10's briefing generator benefits from the accumulated context of meetings 1-9. |
+| **Evidence** | 50 entries, 7 namespaces, 112 change log entries. The wiki grows monotonically — every meeting makes the system smarter. |
+
+### 4. EventBus (Cross-Pipeline Communication)
+
+| Question | Answer |
+|---|---|
+| **What is it?** | Publish-subscribe event system. Agents emit events, pipelines subscribe to event types. |
+| **Why not direct function calls?** | If transcript_parser directly calls drift_detector, they're coupled. Changing one requires changing the other. With events, transcript_parser just says "I found drift" — it doesn't know or care who listens. Tomorrow we can add a new subscriber without touching existing code. |
+| **Why not a message queue (Kafka/RabbitMQ)?** | 5-person team, local development. SQLite-backed event log is sufficient. The abstraction is the same — upgrading to Kafka later would change the transport, not the API. |
+| **Why these specific 10 subscriptions?** | Each maps to a real cross-practice-area dependency: requirements changes MUST trigger architecture review (rubric: "end-to-end connection"). Coach concerns MUST alert PM (risk management). These aren't arbitrary — they're traced to the rubric and meta-model. |
+| **Evidence** | 47 events published across 4 types. 10 active subscriptions wiring 6 pipelines together. |
+
+### 5. ChromaDB + Local ONNX Embeddings
+
+| Question | Answer |
+|---|---|
+| **What is it?** | Vector store for semantic search. 470 chunks across 3 collections (architecture, coach sessions, knowledge base). |
+| **Why RAG instead of just passing everything to Claude?** | Context window limits + cost + relevance. A 1-hour transcript is ~15,000 tokens. Embedding it in chunks and retrieving only the relevant 5 chunks saves ~90% of tokens while improving relevance (Claude gets targeted context, not a wall of text). |
+| **Why ONNX MiniLM locally, not OpenAI embeddings?** | Embedding is a commodity. MiniLM-L6-v2 produces 384-dim vectors good enough for our retrieval needs (matching meeting chunks, not building a production search engine). Local embeddings cost $0, have zero latency, and work offline. OpenAI embeddings cost $0.0001/1K tokens — small, but adds an API dependency for negligible quality gain at our scale. |
+| **Why ChromaDB, not Pinecone/Weaviate?** | Same reasoning as SQLite: zero config, single directory, works everywhere. We don't need multi-tenant search or billion-vector scale. If we did, ChromaDB's API is similar enough to Pinecone that migration is straightforward. |
+| **Evidence** | 371 coach session chunks embedded in <5 seconds. Semantic search across all sessions returns relevant results in <50ms. |
+
+### 6. Prompt Registry + Peer Review
+
+| Question | Answer |
+|---|---|
+| **What is it?** | Version-controlled prompt store with review workflow, regression testing, and A/B testing |
+| **Why version-control prompts?** | The meta-model says: "Reusable prompts should be treated as version-controlled artifacts." But more concretely: if the transcript parser suddenly produces worse outputs, we need to know which prompt version caused it. Without versioning, we're debugging in the dark. |
+| **Why peer review for prompts?** | Same reason we review code: because the author has blind spots. A "small" prompt change can drastically alter LLM behavior. Hrishik reviewing Ashritha's prompt change catches issues the author didn't test for. |
+| **Why temperature=0?** | For deterministic tasks (parsing, classification, extraction), we need the same input to produce the same output. Temperature >0 introduces randomness — meaning Hrishik and Ashritha get different results from the same transcript. That's not engineering, that's gambling. |
+| **Why golden test suites?** | Prompt changes without tests are like code changes without tests. Golden tests (known input → expected output) catch regressions automatically. The 10% quality drop threshold is our "build failed" equivalent. |
+| **Evidence** | 4 prompts registered, each with content hash. Review workflow demonstrated: Ashritha submits → Hrishik reviews → approved. |
+
+### 7. Risk Register (Auto-Populated)
+
+| Question | Answer |
+|---|---|
+| **What is it?** | 16 risks auto-populated from architecture report, coach sessions, and meetings |
+| **Why auto-populate instead of manual?** | Manual risk registers get stale. Nobody updates them. By pulling risks automatically from real sources (the architecture report literally lists risks in Section 5.4; coach sessions literally raise concerns), the register stays current. |
+| **Why link risks to architecture decisions?** | Because a risk without context is useless. RISK-ARCH-01 (threshold miscalibration) links to AD-4 (configurable threshold) and QA-1 (accuracy). When we discuss risk mitigation, we know exactly which architecture decision is affected. |
+| **Why severity matrix (likelihood × impact)?** | Standardized prioritization. "Critical" means high likelihood AND high impact (2 risks). "Medium" means one is high, the other low (7 risks). This forces us to distinguish between "likely but low-impact" and "unlikely but catastrophic." |
+| **Evidence** | 2 critical, 7 high, 7 medium. Each has mitigation strategy, contingency plan, owner, and linked architecture decisions. |
+
+### 8. Metrics Collector
+
+| Question | Answer |
+|---|---|
+| **What is it?** | SQLite-backed system that records every LLM call and every agent run |
+| **Why measure everything?** | The meta-model says: "Measurement measures Resources, Processes, and Artifacts." You can't improve what you don't measure. More practically: when a professor asks "how do you know AI is helping?", we show numbers — not opinions. |
+| **What specifically do we track?** | Per LLM call: tokens in/out, cost, latency, model, prompt version. Per agent run: duration, success, human review flag, correction count. Per pipeline: end-to-end time, step count, events emitted. |
+| **Why not just logs?** | Logs are for debugging. Metrics are for decision-making. "The transcript parser used 1,200 tokens at $0.003" is a metric. "2026-04-23 14:40:22 INFO transcript_parser completed" is a log. We need both, but metrics drive the measurement plan. |
+
+### 9. Offline-First Architecture
+
+| Question | Answer |
+|---|---|
+| **What is it?** | Every agent works without an API key using pattern matching / heuristics. Claude is an upgrade. |
+| **Why not just require Claude?** | Three reasons. (1) Demo resilience: the system works in any environment, including a presentation room with bad WiFi. (2) Cost control: offline agents cost $0. (3) Baseline establishment: the offline extraction quality is our baseline. When we add Claude, we measure the IMPROVEMENT. Without a baseline, "Claude is better" is an assertion. With a baseline, it's a fact with a number. |
+| **What's the quality tradeoff?** | Offline extraction is structural (regex, speaker turns, keyword matching). It catches explicit action items but misses nuanced ones ("I think we should probably consider..." = implicit action item that Claude would catch). The correction rate (M2) will quantify this gap. |
+| **Evidence** | All 7 pipeline steps succeed offline. 57 action items, 18 decisions extracted across 12 meetings without a single API call. |
+
+---
+
+## Non-Ad-Hoc AI Practices — Beyond What Most Teams Do
+
+### Practice 1: Eval-Driven Prompt Development (like TDD for AI)
+
+**Concept:** Write the test BEFORE writing the prompt.
+
+Most teams: write prompt → try it → "looks good" → ship.
+Us: define expected output for known inputs → write prompt → run tests → iterate until passing.
+
+This is Test-Driven Development applied to prompts:
+1. Take a real VTT transcript
+2. Manually identify the correct action items, decisions, attendees
+3. Save as golden test case in `/tests/golden/`
+4. NOW write the prompt to match
+5. Regression tests run on every prompt change
+
+**Why:** Because "looks good" is not a quality measure. A golden test suite is.
+
+### Practice 2: Correction-as-Training-Signal
+
+**Concept:** When a human corrects an agent's output, that correction is captured and used to improve future prompts.
+
+```
+Agent output → Human reviews → Correction recorded →
+ → MetricsCollector logs correction type
+ → Correction rate per prompt version calculated
+ → High correction rate triggers prompt review
+ → Corrections become few-shot examples in next prompt version
+```
+
+Most teams fix the output and move on. We fix the output AND improve the system.
+
+**Why:** A correction is the most valuable signal about prompt quality. It tells you exactly where the prompt failed and what "correct" looks like. Discarding that signal is engineering malpractice.
+
+### Practice 3: Confidence-Gated Human Review
+
+**Concept:** Not all outputs need human review. Only uncertain ones do.
+
+Our priority classifier already does this:
+- P0 items (critical) → mandatory human review
+- P1 items (important) → human review recommended
+- P2 items (nice-to-have) → auto-processed
+
+This isn't random — it's the same principle as the architecture's confidence-based routing:
+high confidence → auto-accept, low confidence → human review queue.
+
+**Why:** Reviewing everything defeats the purpose of automation. Reviewing nothing is dangerous. Confidence-gated review is the efficient middle ground. The architecture report calls this the "accuracy vs throughput tradeoff" (§5.3).
+
+### Practice 4: Prompt Decomposition over Mega-Prompts
+
+**Concept:** Break complex tasks into small, testable, replaceable steps.
+
+Bad (ad-hoc):
+```
+"Parse this transcript, classify priorities, extract requirements,
+create tickets, check for architecture drift, and publish minutes."
+```
+
+Good (principled):
+```
+Step 1: Parse transcript → structured JSON (1 prompt, testable)
+Step 2: Classify items → P0/P1/P2 (1 prompt, testable)
+Step 3: Extract requirements → REQ-XXX.md (1 prompt, testable)
+Step 4: Create tickets → Jira (no LLM needed)
+Step 5: Publish minutes → Confluence (no LLM needed)
+Step 6: Log decisions → wiki (no LLM needed)
+Step 7: Check drift → architecture comparison (1 prompt, testable)
+```
+
+**Why:** Each small prompt can be independently tested, versioned, and improved. If classification is wrong, you fix one prompt — not a 2000-token mega-prompt where changing one sentence breaks extraction. This is the Single Responsibility Principle applied to prompts.
+
+### Practice 5: Knowledge Accumulation (Not Just Q&A)
+
+**Concept:** Every agent run makes the system smarter. Not by fine-tuning, but by depositing structured knowledge.
+
+Most AI systems are stateless: prompt in, response out, forgotten.
+Our system is stateful:
+
+```
+Meeting 1 processed → wiki: 3 entries, ChromaDB: 0 coach chunks
+Meeting 5 processed → wiki: 15 entries, ChromaDB: 0 coach chunks
++ 4 coach sessions → wiki: 50 entries, ChromaDB: 371 chunks
++ architecture doc → wiki: 55 entries, ChromaDB: 470 chunks
+```
+
+The briefing generator for session #5 knows about sessions 1-4. The drift detector checks against the accumulated architecture knowledge. The concern tracker detects patterns across ALL sessions.
+
+**Why:** This is the fundamental difference between "using an LLM" and "building an AI-augmented engineering system." A tool answers questions. A system accumulates intelligence.
+
+### Practice 6: Provenance Chains (Full Audit Trail)
+
+**Concept:** Every artifact can be traced back to its source.
+
+```
+Jira ticket PIMSIE-42
+ ← created by ticket_creator agent
+ ← from P1 action item "integrate Azure blob storage"
+ ← classified by priority_classifier (prompt v=5677a0b9)
+ ← extracted by transcript_parser (prompt v=3277a42a)
+ ← from GMT20260402 client meeting transcript
+ ← attended by Hrishik, Ashritha, Jaivard, Arjun
+```
+
+**Why:** When a professor asks "where did this requirement come from?", we don't say "someone mentioned it in a meeting." We show the exact provenance chain: which meeting, which speaker, which agent, which prompt version, which pipeline step. That's engineering rigor.
+
+### Practice 7: Process Mining on Agent Logs
+
+**Concept:** Analyze the agent execution logs to find inefficiencies.
+
+Our JSONL audit trail (`pipeline/logs/agent_runs.jsonl`) contains every agent invocation with timing data. We can analyze:
+- Which agents are slowest? (optimization targets)
+- Which agents fail most? (reliability issues)
+- Which agents produce the most human review items? (prompt quality issues)
+- Which pipeline steps are most frequently skipped? (maybe they're not needed)
+
+**Why:** The meta-model says "process improvement is reliant on improving AI components." Process mining gives us data-driven improvement targets instead of guessing.
+
+### Practice 8: Semantic Deduplication
+
+**Concept:** Don't process the same information twice.
+
+When a new meeting transcript mentions the same concern that was raised in 3 previous meetings, the concern_tracker doesn't create a new concern — it increments `times_raised` on the existing one. When the same commitment is mentioned again, it updates rather than duplicates.
+
+**Why:** Without deduplication, processing 12 meetings creates 12 separate entries for the same risk. With it, we get 1 entry with 12 supporting references. This is the difference between a data dump and intelligence.
+
+---
+
+## Summary: Ad-Hoc vs Principled
+
+| Dimension | Ad-Hoc (most teams) | Principled (our system) |
+|---|---|---|
+| **Prompt management** | Copy-paste from ChatGPT history | Version-controlled registry with peer review and regression testing |
+| **Consistency** | Everyone uses different prompts | Shared prompt registry, temperature=0, golden tests |
+| **Measurement** | "We used AI and it seemed helpful" | 12 GQIM metrics, auto-collected, dashboarded |
+| **Memory** | Each conversation starts fresh | 470 ChromaDB chunks + 50 wiki entries + SQLite stores |
+| **Cross-practice** | Siloed: requirements team doesn't talk to architecture team's AI | EventBus: 10 subscriptions wiring 6 pipelines together |
+| **Quality control** | "Looks good to me" | Regression tests, correction tracking, confidence-gated review |
+| **Traceability** | "I think this came from a meeting" | Full provenance chain: artifact → agent → prompt version → source |
+| **Cost awareness** | No idea what AI costs | Per-call token tracking, cost per activity, ROI calculation |
+| **Improvement** | Fix outputs ad-hoc | Corrections become training signals, process mining on logs |
+| **Documentation** | Afterthought | ETVX per activity, auto-generated from system execution |
diff --git a/eParts_Agentic_Architecture_v2 (2).html b/eParts_Agentic_Architecture_v2 (2).html
new file mode 100644
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+++ b/eParts_Agentic_Architecture_v2 (2).html
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+
+
+
+
+
+eParts Agentic System Architecture
+
+
+
+
+
Routes triggers → domain agents · Manages execution order · Handles failures · Logs all decisions FastAPI server · shared task queue · no agent races on shared state
Maintains a structured memory store across ALL coach sessions — what was flagged, what was committed to, what was delivered. Uses vector store (RAG) over all past session transcripts.
+
+
+
Commitment Tracker
+
Extracts explicit commitments from each session ("we will define baselines by next week"). Cross-checks against Bitbucket commits and Jira closures to verify delivery.
+
+
+
Pre-Meeting Briefing Generator
+
Runs 1 hour before every coach / mentor meeting. Produces: what Christian said last time → what you committed → what you delivered → what's still open. Slacks to team.
+
+
+
Evolving Concern Tracker
+
Tracks Christian's recurring themes across sessions (monitorability, HITL, evidence-based AI). Surfaces pattern: "Christian has flagged monitorability 3 sessions in a row."
+
+
★ capstone-specific — CMU coached structure
+
+
+
+
+
ML Decision Memory Agent
+
Triggered by: POC script run, meeting transcript, new labeled data commit
+
+
Open ML Decision Log
+
Maintains living log of every unresolved ML architectural decision: threshold value (0.85 unvalidated), alpha weighting (0.7 guess), per-attribute vs per-record routing. Each entry: decision, evidence needed, evidence so far, status.
+
+
+
Evidence Accumulator
+
When a POC script runs or new labeled data is committed, automatically updates the relevant decision entries with new empirical results. Tracks precision-recall curves, auto-accept rates, correction patterns against each open decision.
+
+
+
Decision Readiness Detector
+
When accumulated evidence crosses a threshold (e.g. ≥200 labeled examples), fires a Slack alert: "Enough data to close ADR-1 threshold decision — run calibration now." Prevents decisions from staying open longer than needed.
+
+
+
Coach Session Linker
+
Cross-references open ML decisions against coach session transcripts. When Christian asks about threshold calibration, this agent surfaces exactly what evidence you have today vs what you need.
+
+
★ ML-project-specific — only meaningful because your system produces empirical signals
+
+
+
+
+
+
↓ · · · · · · MCP Servers (tools available to all agents) · · · · · · ↓
+ ⚠ PARTIAL = Coding Agent is partially feasible — boilerplate + PR review are doable; full autonomous coding is not recommended · All agents respect HITL gates
+ ★ eParts-Specific Agents are unique to this project — Coach Memory requires CMU coached capstone structure · ML Decision Memory requires a live ML system producing empirical signals
+
+
+
+
diff --git a/eParts_Risk_and_Project_Management.md b/eParts_Risk_and_Project_Management.md
new file mode 100644
index 0000000..42e1da4
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+++ b/eParts_Risk_and_Project_Management.md
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+# eParts — Project Risk & Project Document
+
+---
+
+## Risk Management
+
+### Risk 1: Blocked Development due to Tool Access (Cursor)
+
+- **Condition:** The team currently lacks access to the Cursor tool required for development.
+- **Consequence:** This blocks the team from utilizing specific AI-assisted coding workflows designated for the project.
+- **Mitigation:**
+ - Continue proactively scheduling troubleshooting calls with the client to resolve access.
+ - **Interim Strategy:** Utilize the team's own LLMs and local tools to proceed with development without using proprietary client data until Cursor access is granted.
+
+### Risk 2: Blocked Development due to Data Access
+
+- **Condition:** The team is waiting on sample data (PDF spec sheets, database rows) and constraint metrics from the client.
+- **Consequence:** Access delays could block the team from beginning essential OCR and ML model building.
+- **Mitigation:**
+ - **Request Schema Only:** If actual data is delayed, request the database schema immediately to allow the team to generate synthetic data.
+ - **Representative Data:** Scrape the client's public website for representative parts data to create a minimally viable dataset for initial pipeline testing.
+ - **Communication:** Shift communication from "we are blocked" to "we need this by date X to avoid impact Y," using leading indicators like yellow/red flags as deadlines approach.
+
+### Risk 3: Technical Risk & ML Uncertainty
+
+- **Condition:** The system relies on ML for attribute prediction. The team has three candidate models, including BERT and others, but has not yet determined which yields the best performance.
+- **Consequence:** If the selected model yields low confidence, excessive records will route to human review.
+- **Mitigation:**
+ - **Experimentation:** Conduct experiments against all three ML candidates to quantitatively evaluate which meets the performance and resource usage baselines.
+ - **Baseline Creation:** Since the client does not have a hard baseline, the team will establish one using the manual processing data and compare the experimental models against it.
+
+### Risk 4: Overhead from Schema Volatility
+
+- **Condition:** Although product schema changes are infrequent, they are a possibility.
+- **Likelihood / Impact:** **Low Likelihood, High Impact.** While unlikely to happen often, a significant schema change could affect 30–40% of data.
+- **Consequence:** Significant schema changes could force manual retraining.
+- **Mitigation:** Move forward with the proposed "Semantic Matcher" approach (all-MiniLM model), which handles schema adjustments without manual retraining.
+
+---
+
+## Project Constraints
+
+- **Azure Environment Lock-in:** The client is strictly locked into the Azure environment and relies on native integrations. The team must use Bicep (not Terraform) and avoid languages/tools that require the client to learn new non-Azure technologies.
+
+---
+
+## Strategic Planning & Project Management
+
+### Change Management & Scope Control
+
+*(Formerly Risk 3)*
+
+- **Statement of Work (SOW):** The team will generate an official SOW to define the strict scope of the MVP (1–2 supplier formats, limited attributes).
+- **Change Control Process:** To prevent scope creep, any request outside the SOW will not be rejected but met with a formal review — e.g., *"That is a great idea, let us scope the impact and return to you with a decision."*
+- **Governance:** Adherence to the SES artifact lifecycle (Draft → Review → Approved → Baselined).
+
+### Lifecycle & Milestones
+
+- **Model:** Phase-by-phase iterative approach mapped to functional Vertical Slices.
+- **Spring 2026 Roadmap:**
+ - **Phase 1 & 2:** Lock SES, MVP scope, requirements, and architecture.
+ - **Phase 3 & 4 (Vertical Slices):** Deliver functional slices.
+ - **Vertical Slice 1:** End-to-end MVP using the defined SES.
+ - **Vertical Slice 2:** Documentation, hand-off materials, and system hardening.
+ - **Phase 5 & 6:** Hardening, critique, and wrap-up.
+
+### Project Roles & Responsibilities
+
+- **Team / Project Lead:** Rotates every mini-semester to ensure continuity while sharing leadership experience.
+- **Architecture Lead:** Owns architecture decisions and ADRs.
+- **Data / ML Lead:** Owns model behavior and confidence logic.
+- **Engineering Lead:** Assigned to the member with the most experience in pipelines and integrations.
+- **QA / Process Lead:**
+ - **Expanded Scope:** Beyond software testing, this role monitors process metrics.
+ - **Metrics:** Tracks code review quality, meaningful comments vs. rubber-stamping, cycle time, and rework frequency.
+
+### Success Criteria
+
+- **Product Metrics:** Percentage of records auto-accepted, prediction confidence distributions, data defect rates.
+- **Human-in-the-Loop:** Specifically tracking the effectiveness of the HITL system (time saved vs. manual baseline).
+
+### Resource & Task Planning
+
+- **Human Allocation:** Distributed according to leadership roles.
+- **AI Resource Allocation:** AI tools (Cursor) used for repetitive tasks and coding assistance.
+- **Task Identification:** Derived from ETVX (Entry, Tasks, Verification, Exit) process models.
+- **Progress Tracking:** QA Lead oversees backlog size, rework frequency, and cycle time per ingestion batch.
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+# eParts Services LLC — Intelligent Ingestion & Attribute Prediction Platform
+
+## Final Project Report Draft
+
+**Team: Pimsie Supreme**
+
+**Team Members:**
+- Arjun R Nair
+- Ashritha Gonuguntla
+- Hrishikesh Bhardwaj
+- Jaivardhan Singh
+- Zheliang Liu
+
+**Date:** April 15, 2026
+
+---
+
+## 1. Project Context and System Boundary
+
+### 1.1 Problem Context
+
+eParts Services LLC maintains product data for an eCommerce procurement platform serving construction contractors, with PIMS (Product Information Management System) as the primary system of record. Supplier catalogs arrive in heterogeneous formats including CSV files, PDFs, email attachments, SFTP drops, and direct uploads. The current ingestion workflow is entirely manual: catalog staff at eParts (~1.5 FTEs) and sister company Alps Controls (~3 FTEs) interpret, normalize, and map every supplier attribute before it enters PIMS. This process is error-prone, slow to update, and does not scale.
+
+The CMU MSE Studio Capstone Team (Pimsie Supreme) has been engaged to design an Intelligent Product Data Ingestion and Enrichment Platform that automates this pipeline. The goal is to reduce manual effort while keeping data written into PIMS correct. Incorrect product data causes wrong parts to be ordered by contractors, so data integrity drives every architectural decision.
+
+### 1.2 Stakeholders
+
+| Stakeholder | Architectural Relevance |
+|---|---|
+| Harsha (eParts) | Sets accuracy threshold priority; chose valves/actuators scope; approves model selection |
+| Jake (eParts) | PIMS integration; defines write interface and staging table contracts (P1-C pending) |
+| Brian & Dewey (eParts) | Catalog team; primary review workflow users; define low-confidence handling |
+| Alps Controls Catalog Team | Secondary users (3 FTEs) the architecture must accommodate |
+| David (eParts) | Executive sponsor; resource allocation across teams |
+| CMU Studio Team | Design, prototyping, delivery; makes architectural decisions |
+
+### 1.3 System Boundary
+
+The system boundary encloses the path from raw supplier files through ML prediction and controlled writeback into PIMS staging tables. Inside: ingestion/parsing, canonical schema normalization, attribute prediction with confidence scoring, confidence-based routing, a persistent review queue, idempotent writeback, and observability (Datadog). No custom review UI is in scope for the current phase.
+
+Out of scope: downstream systems beyond PIMS, configurable product Options, direct production DB writes, and pricing data in the ML pipeline. Web scraping is a potential future ingestion channel but is not implemented in the current phase; the Ingestion Gateway is designed to accommodate additional input formats without structural change.
+
+### 1.4 System Context and External Dependencies
+
+Figure 1 shows the system in relation to its external actors: suppliers (via email, SFTP, CSV, PDF), human reviewers (Merch Ops), PIMS (SQL Server staging tables), and Datadog. Key external dependencies are captured as constraints in Section 2.2.
+
+> **Figure 1:** System Context Diagram (V3.0, 02/20/26). *[Image: `Context Diagram V3.png`]*
+
+---
+
+## 2. Architectural Drivers
+
+### 2.1 Functional Architecture Drivers
+
+The functional requirements below shape system structure, interfaces, coordination, and evolution.
+
+- **FR-1: Multi-Format Ingestion.** Accept CSV and PDF catalogs via email, SFTP, and direct upload, recording supplier, timestamp, and channel. Drives the Ingestion Gateway as a distinct component.
+- **FR-2: Canonical Schema Normalization.** Transform heterogeneous formats into a standardized staging table, decoupling parsing from prediction.
+- **FR-3: Per-Attribute ML Prediction with Confidence Scoring.** Prediction at the attribute level (not record level) because routing is per-attribute. Justifies the Prediction Service as a separable component.
+- **FR-4: Confidence-Based Routing.** Distinct Routing Engine separates auto-accept from human review so routing logic is adjustable without touching the model.
+- **FR-5: Persistent Human Review Queue.** Low-confidence predictions held in a queryable queue, decoupling machine throughput from reviewer availability. Corrections feed retraining.
+- **FR-6: Idempotent PIMS Writeback.** No duplicates on retry; enforced in application layer because no writeback API exists.
+- **FR-7: Decision Logging and Audit Trail.** Every auto-accept, approval, correction, and rejection logged for auditability and model improvement.
+
+### 2.2 Constraints
+
+| Constraint | Source | Status | Architectural Impact |
+|---|---|---|---|
+| Azure deployment | Client mandate | Fixed | Eliminates non-Azure infrastructure |
+| No PIMS writeback API | eParts env (Jake) | Fixed | Idempotency in app code; no rollback |
+| Python backend for ML | ML library compat (SOW) | Fixed | Interop layer needed for .NET stack |
+| Valves/actuators scope | Client (Harsha) | Fixed (phase) | Schema scoped; expansion must not be blocked |
+| No direct prod DB writes | Data governance | Fixed | All ML writes to staging; human verify first |
+| No pricing in ML pipeline | Privacy policy (SOW) | Fixed | Pricing excluded from ingestion/prediction |
+| Capstone team/timeline | CMU structure | Fixed | Must be prototypable by small team, Spring–Fall 2026 |
+| Auth0 for RBAC | eParts identity | Negotiable | Stretch goal; not current-phase constraint |
+
+*Table 1: Constraints, sources, and architectural impacts*
+
+### 2.3 Quality Attributes
+
+| QA | Source | Stimulus | Env. | Artifact | Response / Measure | I/D |
+|---|---|---|---|---|---|---|
+| Accuracy | Pred. Svc | Batch of normalized attributes submitted | Normal ops | Pred. Svc, Routing Engine | Routing Engine compares per-attribute confidence against threshold; below-threshold diverted to review queue; above-threshold auto-accepted. *M:* ≥95% auto-accept correct; zero incorrect records to PIMS outside review. | H/H |
+| Modifiability (model swap) | ML Lead | Replace prediction strategy (e.g., hybrid → DistilBERT) | Design time, Ph. 2 | Pred. Svc | New class behind `PredictionServiceInterface`; selected via config. *M:* Change in `prediction` pkg only. | H/M |
+| Modifiability (new cat.) | eParts | New product category post-pilot | Post-pilot | Schema, Gateway, Pred. Svc | New attribute mappings + schema extension + retrain. *M:* No structural change to routing/writeback. | M/M |
+| Availability | Infra fault | Prediction Service unavailable | Normal ops | Pred. Svc, Gateway | Staging tables buffer data; resumes on recovery. *M:* Zero data loss. | M/L |
+| Monitorability (drift) | Eng. Ops | Supplier data shifts from training distribution | Production | Pred. Svc, Datadog | Emits confidence distributions + correction rates to Datadog; alerts on baseline deviation. *M:* Drift detected before accuracy drops below QA-1. | H/H |
+
+*Table 2: Quality attribute scenarios and utility-tree prioritization*
+
+#### Prioritization Justifications
+
+- **Accuracy (H/H):** Incorrect data causes wrong parts ordered — business-critical. Threshold and model behavior both unresolved.
+- **Modifiability–swap (H/M):** Model selection still open; boundary must be drawn now or swaps ripple.
+- **Modifiability–category (M/M):** Current scope is valves/actuators; schema must avoid category-specific lock-in.
+- **Availability (M/L):** System not on critical path; catalog team is fallback. Staging-table buffer is straightforward.
+- **Monitorability (H/H):** Silent degradation is an operational risk; drift metrics and baselines are undefined.
+
+### 2.4 Priorities, Tensions, and Open Uncertainty
+
+Accuracy is the dominant driver. The main tension is between accuracy and throughput: tightening the threshold reduces incorrect auto-accepts but increases review workload. A second tension is simplicity now vs. modifiability later. The biggest open uncertainty is the confidence threshold, which cannot be set until the prototype produces real predictions. Model selection is the second open question.
+
+---
+
+## 3. Proposed Architecture
+
+### 3.1 Primary Architectural Style: Pipe and Filter
+
+The system follows a **pipe-and-filter** style [Bass et al., 2012]: independent filters connected by typed data channels form a linear transformation pipeline. This style was selected for three traced reasons: **modifiability (QA-2)** — each filter communicates through defined data contracts, so the Prediction Service can be replaced without affecting upstream or downstream; **modifiability (QA-3)** — adding a category requires extending the schema and retraining, not changing the filter sequence; and **availability (QA-4)** — staging tables buffer data during Prediction Service outages.
+
+The one departure from a linear pipeline is a **confidence-based branch**: the Routing Engine sends high-confidence attributes to auto-accept and low-confidence to human review; both paths merge before writeback. The audit trail feedback loop operates offline.
+
+### 3.2 Key Tactics
+
+#### Tactic 1: Stable Internal Interface for Model Isolation (QA-2, FR-3)
+
+The Prediction Service exposes `PredictionServiceInterface`: accepts normalized records, returns predictions with per-attribute confidence scores. For the hybrid approach (ADR-1), this score is a weighted composite:
+
+$$\text{conf}_{\text{final}} = \alpha \cdot \text{conf}_{\text{rule}} + (1-\alpha) \cdot \text{conf}_{\text{embed}}$$
+
+The Routing Engine depends on this interface, never on model internals. We chose an internal function-call interface over REST/message queue (Section 4.1) because network boundaries would add complexity disproportionate to team size.
+
+#### Tactic 2: Per-Attribute Configurable Threshold (QA-1, FR-4)
+
+Each attribute carries its own confidence score; the Routing Engine compares it against a configurable threshold. The threshold is externalized as configuration because it cannot be set until Phase 2 (Section 2.4).
+
+#### Tactic 3: Idempotent Writeback via Natural Key (QA-1, FR-6)
+
+PIMS has no writeback API, so idempotency is enforced via natural key matching (`submission_id` + `attribute_id`): existing records are updated rather than duplicated on retry.
+
+#### Tactic 4: Queue Decoupling (QA-4, FR-5) and Tactic 5: Audit Logging (FR-7, QA-5)
+
+The Human Review Queue is a persistent DB table (not in-memory), decoupling prediction throughput from reviewer pace and buffering during outages. Every pipeline decision is logged in an append-only audit trail with prediction, confidence, source file, and reviewer ID, supporting both compliance and offline retraining.
+
+### 3.3 Architectural Views
+
+#### 3.3.1 View 1: Component-and-Connector (Figure 2)
+
+This view answers: *how does data flow from raw input to PIMS, and where does confidence-based branching occur?* The pipeline is linear with one branch at the Routing Engine; both paths merge before a single write path to PIMS. Telemetry flows from four stages to Datadog; audit feedback is offline.
+
+> **Figure 2:** C&C view. Teal = filters, blue dashed = stores, coral = human review, purple = PIMS. Solid = pipes, dashed = telemetry. Diamond = routing decision. *[Image: `pipe_filter_cnc.png`]*
+
+#### 3.3.2 View 2: Module (Figure 3)
+
+Answers: *where are the abstraction boundaries for model swapping and schema extension?* The codebase is organized into eight packages: `ingestion` (format detection, OCR, CSV/PDF parsing, file archival), `normalization` (transforms raw attributes into canonical schema using supplier-specific column mappings), `prediction` (contains `PredictionServiceInterface` and concrete implementations; active strategy selected via configuration), `routing` (reads confidence scores against threshold, produces per-attribute routing decisions), `review` (manages persistent Human Review Queue), `writeback` (idempotent upsert to PIMS via natural key), `audit` (append-only decision logger), and `observability` (structured logging and metrics for Datadog).
+
+**Key dependency rule:** The `routing` package imports `PredictionResult` (a data class), not any model-specific type. The `writeback` package imports from routing output, not from `prediction`. This enforces model isolation (Tactic 1): changes to the prediction strategy cannot ripple past the `PredictionResult` boundary.
+
+#### 3.3.3 View 3: Deployment (Figure 4)
+
+Answers: *how are components allocated to Azure, and where are trust boundaries?* The system is deployed as a single application unit on Azure App Service (Python), driven by the team size constraint: a microservices deployment would require container orchestration and distributed tracing infrastructure that exceeds the team's operational capacity (alternatives analyzed in Section 4.3). Azure SQL Database holds all internal pipeline state (staging tables, review queue, audit trail); Azure Blob Storage archives raw supplier files for traceability. The Publish/Sync Job runs as a timer-triggered Azure Function that reads approved output and writes to PIMS.
+
+**Integration points:** Inbound data arrives via SFTP (polled), email (polled), and HTTP upload — all treated as untrusted. Outbound to PIMS via `pyodbc` across the trust boundary (idempotency enforced in application code). Outbound to Datadog via HTTPS (fire-and-forget; telemetry failures do not block the pipeline).
+
+> **Figure 3:** Module view. Dashed boundary = model isolation. *[Image: `module_view.png`]*
+>
+> **Figure 4:** Deployment view. Trust boundary separates Azure from PIMS. *[Image: `deployment_view.png`]*
+
+### 3.4 Traceability from Drivers to Design
+
+| Driver | Decision | Effect |
+|---|---|---|
+| QA-1: Accuracy | Per-attr scoring + threshold + idempotent write | Above-threshold auto-accepted; low-confidence reviewed; no duplicates |
+| QA-2: Model swap | `PredictionServiceInterface` | Change localized to `prediction` package |
+| QA-3: New category | Attribute-row canonical schema | New mappings + retrain; no structural change |
+| QA-4: Availability | Staging buffers + persistent queue | Outage = data queued; zero loss |
+| QA-5: Monitorability | Telemetry + audit trail | Drift via correction rate deviation |
+| Azure / Team size | Single App Service + SQL + Blob | Avoids microservice overhead; extractable later |
+
+*Table 3: Traceability from drivers to design*
+
+---
+
+## 4. Architectural Alternatives
+
+Three architecturally significant concerns, each with plausible alternatives compared structurally.
+
+### 4.1 Concern 1: Model Isolation Mechanism
+
+**Driver:** QA-2 (H/M). Determines codebase impact of a swap, redeployment scope, and side-by-side evaluation capability.
+
+- **Alt A: Internal Abstract Interface (current).** `PredictionServiceInterface` in a single app; swap = new class + config change. Nothing outside `prediction` changes.
+- **Alt B: REST Microservice.** Prediction Service extracted to separate Container App. Deployment view gains a second unit; pipe becomes HTTP call. Enables canary deployment.
+- **Alt C: Message Queue (Service Bus).** Async broker-mediated communication. Two queues introduced; retry/dead-letter offloaded to Service Bus.
+
+| Criterion | A: Interface | B: REST | C: Queue |
+|---|---|---|---|
+| Swap effort | Config + class; single redeploy | Independent deploy | Independent deploy |
+| Side-by-side | In-process branching | Canary at LB level | Competing consumers |
+| Op. overhead | Minimal | Container orch., health checks, svc auth | Queue provisioning, dead-letter, schema version |
+| Team fit | High | Low–Med | Low |
+
+*Table 4: Model isolation — alternatives comparison*
+
+**Choice: A.** Sufficient for design-time swaps in Phase 2; B/C operational cost exceeds team capacity. **B preferable when:** production handoff; canary deployments or GPU-backed inference needed. **C preferable when:** near-real-time ingestion with multiple consumers.
+
+### 4.2 Concern 2: Confidence Routing Granularity
+
+**Driver:** QA-1 (H/H), accuracy-vs-throughput tension. This is structural: different data models, merge logic, and reviewer interfaces.
+
+- **Alt A: Per-Attribute (current).** Each attribute routed independently; review queue keyed at (record, attribute); writeback must merge auto-accepted and reviewed attributes.
+- **Alt B: Per-Record.** Entire record to review if any attribute below threshold; no merge logic needed.
+
+| Criterion | A: Per-Attribute | B: Per-Record |
+|---|---|---|
+| Review volume | Lower (est. 3–5× reduction) | Higher: full record for 1–2 uncertain attrs |
+| Reviewer context | Flagged attrs only; source file available | Full record visible |
+| Writeback | Merge auto-accepted + reviewed | Records always complete |
+| Accuracy risk | Correlated attrs may be inconsistent | No cross-attr inconsistency |
+
+*Table 5: Routing granularity — alternatives comparison*
+
+**Choice: A.** Catalog team (1.5 + 3 FTEs) is the bottleneck; merge complexity localized to `writeback`. **Key risk:** correlated attributes (e.g., connection type + port size); mitigated by logging full record context but not yet validated. **B preferable when:** >30% of corrections involve cross-attribute errors.
+
+### 4.3 Concern 3: Deployment Topology
+
+**Driver:** Team size constraint, QA-4, QA-2. Interacts with Concern 1.
+
+- **Alt A: Single App Service (current).** All components in one process; function-call communication; scales as a unit.
+- **Alt B: Microservices (Container Apps).** Three independent services: Ingestion/Normalization, Prediction, Routing/Writeback. In the deployment view, the single App Service box (Figure 4) would be replaced by three Container App instances with HTTP/Service Bus connections.
+
+| Criterion | A: Single Unit | B: Microservices |
+|---|---|---|
+| Op. complexity | Low: one deploy, one log stream | High: three deploys, distributed tracing |
+| Fault isolation | Low (shared process); mitigated by buffers | High: independent restarts |
+| Scaling | Whole app scales as unit | Prediction Service scales independently |
+| Timeline fit | High: 5-person team, one semester | Low: infra setup consumes timeline |
+
+*Table 6: Deployment topology — alternatives comparison*
+
+**Choice: A.** Module boundaries (View 2) drawn where service boundaries would go, so transition = adding network serialization at existing interfaces, not rewriting. **B preferable when:** (1) production handoff to larger team, or (2) Prediction Service needs GPU instances.
+
+---
+
+## 5. Architecture Analysis
+
+### 5.1 Why the Architecture Is Plausible
+
+The pipe-and-filter architecture is plausible for this project for three reasons. First, the core problem — transforming heterogeneous supplier catalogs into validated PIMS records — is fundamentally a linear data transformation pipeline, and the pipe-and-filter style maps directly onto this data flow without forcing artificial concurrency or event-driven coordination.
+
+Second, the architecture is feasible given the team's constraints. A five-person capstone team operating from Spring to Fall 2026 cannot sustain the operational overhead of distributed microservices, event-driven choreography, or multi-model orchestration frameworks. The centralized deployment (AD-6) keeps infrastructure management minimal while the modular internal package structure preserves the option to decompose later. This is a deliberate sequencing decision: build the correct abstractions now, defer operational complexity until an eParts engineering team can absorb it.
+
+Third, the architecture mirrors what eParts already does manually. The current workflow — catalog staff interpreting supplier files, normalizing data, and entering it into PIMS — maps onto the same filter sequence. The architecture automates each stage while preserving the human-in-the-loop at exactly the point where automation confidence is low. This reduces the risk that the architecture solves the wrong problem.
+
+### 5.2 Traceability: Are Design Choices Proportional?
+
+**Accuracy (H/H):** Three decisions work in concert — confidence scoring identifies risk, the threshold controls acceptable risk, idempotent writeback prevents mechanical errors. Removing any one leaves a gap in the accuracy guarantee. The traceability is strong because each decision addresses a different failure mode: scoring prevents silent misclassification, the threshold prevents over-trust in the model, and idempotency prevents duplicate records from retries.
+
+**Modifiability–swap (H/M):** `PredictionServiceInterface` isolates the prediction strategy. We favor the internal interface over REST because model swaps are design-time activities in Phase 2; if canary deployments are later needed, Section 4.1 Alt B becomes necessary. This depends on the assumption that the team will not need to run two models simultaneously in production during the capstone phase.
+
+**New category (M/M):** Attribute-row schema trades query convenience for extensibility — justified because schema migrations against production PIMS carry disproportionate risk and would require coordination with Jake's team.
+
+**Availability (M/L):** Staging-table buffers are minimal complexity — proportional to a medium-importance driver. We deliberately invested less architectural effort here because the catalog team remains a manual fallback if the system is down.
+
+**Monitorability (H/H):** Structurally realized (telemetry from four pipeline stages, audit trail capturing correction rates) but operationally incomplete — baseline metrics, alert thresholds, and the correction-to-retraining feedback loop have not been defined. This is the key gap: the architectural scaffolding exists, but the operational content that makes it useful is empty. Refinement Activities 1 and 3 are designed to fill this gap.
+
+### 5.3 Tradeoffs
+
+**Tradeoff 1: Accuracy vs. Throughput.** We favor accuracy because incorrect PIMS data causes wrong parts to be ordered — a business-critical failure the client has stated is unacceptable. This is reinforced by per-attribute routing, which keeps review volume proportional to actual model uncertainty. The tradeoff depends on the assumption that the catalog team's capacity (1.5 + 3 FTEs) is sufficient for the review volume at the chosen threshold. If the prototype reveals that even per-attribute routing produces a workload above the team's demonstrated handling capacity, the team would need to either lower the threshold (accepting more accuracy risk) or invest in reviewer tooling that accelerates per-attribute review.
+
+**Tradeoff 2: Simplicity vs. Modifiability.** The architecture consistently chooses simpler implementations now (internal interface over REST, centralized deployment over microservices, hybrid prediction over pure ML) while investing in interfaces that preserve future options. We are favoring simplicity because the capstone timeline is the binding constraint, but this depends on the assumption that the modular internal structure is sufficient to prevent a costly rewrite when the system transitions to production. If eParts takes ownership and immediately requires independent scaling of the Prediction Service, the centralized deployment would need to be revisited first — but the module boundaries in View 2 are designed to make this transition a refactoring effort rather than a rewrite.
+
+**Tradeoff 3: Explainability vs. Sophistication.** The hybrid approach (ADR-1) generates reason codes satisfying eParts' explainability requirement (§6.5). A pure ML classifier could achieve higher accuracy with sufficient training data but produces opaque confidence scores. We favor explainability because trust in routing decisions is essential for catalog staff adoption — a system that routes items to review without explaining why will be treated as a black box. This depends on reason codes being useful to Brian and Dewey in practice, which has not been validated (Refinement 5 tests this).
+
+### 5.4 Risks and Unresolved Issues
+
+**Risks:**
+1. **Threshold miscalibration** (0.85 unsupported) — if too high, review queue overwhelms catalog team and the system provides no labor savings; if too low, incorrect data enters PIMS.
+2. **Insufficient training data** — if fewer than 200 labeled examples are available, the embedding layer will be undertrained and the hybrid approach falls back toward pure rules.
+3. **PIMS schema incompatibility** — Jake has not delivered the P1-C schema; if staging tables use wide columns or lack key columns, the writeback mechanism needs redesign.
+
+**Sensitivity points:**
+1. The α weighting in hybrid scoring — small tuning errors have outsized effects on routing behavior.
+2. Attribute correlation strength — determines whether per-attribute routing is safe or introduces inconsistency.
+3. Catalog team capacity vs. review volume — the entire value proposition depends on this balance.
+
+**Unresolved:**
+1. Drift detection metrics and baselines not defined.
+2. Per-attribute vs. global threshold — some attributes (e.g., `SUPPLY_VOLTAGE`) are inherently easier to predict than others (e.g., `DESCRIPTION`).
+3. Human review interface design not decided.
+4. Corrections-to-retraining feedback loop not architecturally specified.
+
+### 5.5 Conditions for Reconsideration of Rejected Alternatives
+
+| Rejected Alternative | Current Choice | Trigger for Reconsideration |
+|---|---|---|
+| REST microservice (§4.1 Alt B) | Internal interface | eParts takes ownership; canary deployments needed; GPU-backed inference requires independent scaling |
+| Message queue (§4.1 Alt C) | Internal interface | Near-real-time ingestion; multiple downstream consumers beyond routing |
+| Per-record routing (§4.2 Alt B) | Per-attribute | >30% of corrections involve cross-attribute consistency errors; or model improves enough that few records have multiple uncertain attributes |
+| Microservices (§4.3 Alt B) | Single App Service | Production handoff to larger team; Prediction Service scaling diverges from ingestion |
+| Pure ML (§7 ADR-1 Alt B) | Hybrid | ≥800 labeled examples and pure ML achieves ≥95% with calibrated confidence |
+| Pure rules (§7 ADR-1 Alt A) | Hybrid | Rules alone cover ≥85% at confidence ≥0.90 |
+
+*Table 7: Conditions under which rejected alternatives become preferable*
+
+**Confidence summary.**
+- *High:* pipe-and-filter style fits the problem; `PredictionServiceInterface` is the right isolation mechanism; centralized deployment appropriate for team size.
+- *Moderate:* per-attribute routing (pending attribute independence validation); hybrid prediction (dependent on rule coverage and data volumes); category-generic schema (pending PIMS compatibility).
+- *Low:* threshold value (0.85 untested); α weighting (0.7 initial guess); monitorability design (metrics undefined); review queue operational viability (capacity unmodeled).
+
+---
+
+## 6. Planned Refinements and Next Steps
+
+Each activity targets a named uncertainty with question, rationale, concrete activity, and architectural impact.
+
+### 6.1 Refinement 1: Confidence Threshold Calibration
+
+- **Question:** What threshold achieves ≥95% auto-accept accuracy at sustainable review volume?
+- **Why:** Most sensitive parameter; every routing/accuracy/capacity claim depends on it.
+- **Activity:** Run prototype on ≥200 labeled submissions; compute precision-recall curves (0.50–0.99); measure per-attribute accuracy variance.
+- **Impact:** No viable threshold → improve model or renegotiate target. High per-attribute variance → per-attribute thresholds.
+
+### 6.2 Refinement 2: Attribute Correlation Analysis
+
+- **Question:** Are attributes independent enough for per-attribute routing?
+- **Why:** Correlated attributes reviewed independently could produce inconsistent records.
+- **Activity:** Pairwise mutual information on labeled data; inspect 50 examples for high-MI pairs; prototype attribute-group routing if needed.
+- **Impact:** <10% correlated → validated. Specific pairs → routing groups. >30% → switch to per-record.
+
+### 6.3 Refinement 3: Hybrid Scoring Weight (α) Calibration
+
+- **Question:** What α produces best-calibrated confidence?
+- **Why:** Wrong α suppresses the more accurate signal source, causing over- or under-routing.
+- **Activity:** Sweep α 0.3–0.9; measure ECE, precision, coverage; compare fixed vs. learned per-attribute-type weights.
+- **Impact:** Learned variant better → per-type calibration table. Rule engine dominates → defer embedding layer.
+
+### 6.4 Refinement 4: PIMS Schema Compatibility
+
+- **Question:** Does PIMS staging support attribute-row structure (AD-5) and natural-key idempotency (AD-3)?
+- **Why:** Writeback design assumes a schema Jake hasn't delivered.
+- **Activity:** Map P1-C columns to canonical schema; integration-test 10 sample records.
+- **Impact:** Aligned → validated. Wide columns → translation layer. Missing keys → team-owned buffer table.
+
+### 6.5 Refinement 5: Reviewer Walkthrough and Writeback Failure Modes
+
+- **Questions:**
+ - (a) Is review queue context sufficient for correct decisions?
+ - (b) Does the writeback mechanism handle all failure modes?
+- **Why:** Review experience affects accuracy and throughput directly; writeback is the trust boundary where output enters PIMS with no rollback.
+- **Activity:**
+ - (a) Present 30 sample items to Brian/Dewey; record time, accuracy, reason-code usage, cross-attribute needs. If tabular export is insufficient, custom UI scope may need renegotiation.
+ - (b) Enumerate failure modes (timeout, rejection, partial batch, concurrent execution); trace through idempotent logic; test against test SQL Server.
+- **Impact:**
+ - (a) Source files needed → add Blob links; reason codes ignored → reconsider pure ML; cross-attr context needed → reconsider per-attribute routing.
+ - (b) Race conditions → distributed lock; partial inconsistency → transactional batch writes.
+
+---
+
+## 7. Architecture Decisions and Uncertainty
+
+Several decisions remain provisional: model selection not finalized, threshold not calibrated, staging schema not delivered.
+
+### 7.1 ADR-1: Hybrid Rule Engine + Semantic Similarity
+
+**Issue.** The Prediction Service must map raw supplier text to canonical values with confidence scores. Section 4 analyzes *how* the filter is isolated; this ADR addresses *what* runs inside it.
+
+**Alternatives.**
+- **(A) Pure rules** — deterministic, explainable, but coverage ~40–60%.
+- **(B) Pure ML** — handles unseen text, but needs ~830 labels for calibration (team targets 200); opaque confidence.
+- **(C) Hybrid (current)** — rules for structured inputs; TF-IDF + cosine similarity for unmatched.
+
+$$\text{conf}_{\text{final}} = \alpha \cdot \text{conf}_{\text{rule}} + (1-\alpha) \cdot \text{conf}_{\text{embed}}, \quad \alpha = 0.7$$
+
+Reason codes attached to low-confidence items.
+
+**Decision.** C. Both layers inside `prediction` behind `PredictionServiceInterface`.
+
+**Rationale.** Rules provide high-precision fallback under data scarcity; reason codes satisfy eParts §6.5 explainability; ML layer replaceable independently.
+
+**Status.** Tentative. POC running; α unvalidated.
+
+**Triggers:** ≥800 labels + ML ≥95% → Alt B. Rules cover ≥85% at ≥0.90 → Alt A.
+
+### 7.2 Decision Aid: Weighted Matrix
+
+| Criterion | Wt | A | B | C |
+|---|:---:|:---:|:---:|:---:|
+| Accuracy (<200 labels) | .30 | 2 | 1 | 3 |
+| Explainability | .20 | 3 | 1 | 3 |
+| Free-text coverage | .20 | 1 | 3 | 2 |
+| Model swappability | .15 | 0 | 2 | 3 |
+| Impl. complexity | .15 | 3 | 2 | 1 |
+| **Weighted total** | | **1.85** | **1.70** | **2.50** |
+
+*Table 8: Decision matrix for ADR-1*
+
+### 7.3 Other Architectural Decisions
+
+| ID | Decision | Status | Rationale | Reconsideration |
+|---|---|---|---|---|
+| AD-2 | Stable prediction interface (T1) | Proposed | Isolates model from routing/writeback (H/M). Alts in §4.1. | Production handoff; canary needed. |
+| AD-3 | Idempotent writeback: `submission_id` + `attribute_id` (T3) | Proposed | No PIMS API; composite key prevents duplicates (FR-6). | P1-C schema incompatible. |
+| AD-4 | Configurable threshold (T2) | Tentative | Controls accuracy vs. review (H/H); 0.85 estimate. | High per-attr variance → per-attr thresholds. |
+| AD-5 | Attribute-row canonical schema | Proposed | New categories via ref table, not migration (M/M). | P1-C reveals incompatibility. |
+| AD-6 | Single App Service (Fig. 4) | Proposed | Team size; module boundaries preserve extractability. | Pred. Svc needs independent scaling. |
+
+*Table 9: Architectural decisions, rationale, and reconsideration triggers*
+
+---
+
+## References
+
+1. L. Bass, P. Clements, and R. Kazman, *Software Architecture in Practice*, 3rd ed. Addison-Wesley, 2012.
diff --git a/eparts_project_overview.md b/eparts_project_overview.md
new file mode 100644
index 0000000..ecdb714
--- /dev/null
+++ b/eparts_project_overview.md
@@ -0,0 +1,310 @@
+# eParts Capstone Project — Complete Project Overview
+
+**Team:** Pimsie Supreme
+**Program:** CMU MSE Studio 2026
+**Client:** eParts Services LLC, Homestead PA
+**Coach:** Christian Kästner (AI in SE Coach)
+**Presentation Mentor:** Jim
+
+---
+
+## 1. Project Context
+
+### 1.1 Who is eParts Services?
+
+eParts Services LLC is a small, highly collaborative company based in Homestead, PA. They build eCommerce procurement tools for construction contractors — centralizing purchasing across branch networks, managing bills of materials, and maintaining robust product data integrated with estimating and accounting tools.
+
+Their system of record is **PIMS** (Product Information Management System), backed by MSSQL and PostgreSQL.
+
+**Key client personnel:**
+- **Joe Benscoter** — President
+- **Harsha Tummala** — Senior ML Developer and Project Manager. Sets accuracy threshold priority, approves model selection.
+- **Jake Monroe** — Tech Lead. Owns PIMS integration, defines the write interface and staging table contracts.
+- **Brian & Dewey** — Catalog team, primary review workflow users.
+- **Alps Controls catalog team** — Sister company, secondary users (3 FTEs).
+- **David** — Executive sponsor.
+
+**Client tech stack:** Azure, .NET, Vue.js/Nuxt.js, SQL Server, Elasticsearch, Kafka, Snowflake, PostgreSQL, Datadog, Cursor, Bitbucket.
+
+### 1.2 The Problem
+
+Supplier product specifications arrive in heterogeneous formats — PDFs, CSVs, SFTP file drops, email attachments, and web catalogs. The current ingestion workflow is entirely manual. Roughly 1.5 FTEs at eParts and 3 FTEs at sister company Alps Controls interpret, normalize, and map every supplier attribute before it enters PIMS.
+
+This makes the process:
+- Tedious and repetitive
+- Error-prone due to manual interpretation inconsistencies
+- Slow to update — catalog freshness lags behind supplier changes
+- Unscalable as supplier volume grows
+
+### 1.3 What We Are Building (for the Client)
+
+An **Intelligent Product Data Ingestion and Enrichment Platform** with these components:
+
+1. **Ingestion Gateway** — accepts CSV, PDF, email, SFTP, direct upload
+2. **Canonical staging tables** — normalizes heterogeneous input into a standardized schema
+3. **ML Attribute Prediction Service** — maps supplier attributes to PIMS canonical attributes with per-attribute confidence scoring
+4. **Confidence-based routing** — high confidence auto-accepts and writes back to PIMS; low confidence goes to the Human Review Queue
+5. **Human Review Queue** — Brian and Dewey approve or correct low-confidence predictions
+6. **Idempotent writeback to PIMS** via pyodbc (no PIMS writeback API exists; idempotency enforced in application code)
+7. **Observability** via Datadog
+
+---
+
+## 2. PIMS Data Model
+
+```
+Categories (78 active)
+ └── ProductTypes / Subcategories (755)
+ └── Products (~2,000)
+ └── ProductAttributeValues (~50,000 rows)
+```
+
+- **Attributes master list:** 487 active attributes (e.g., SUPPLY VOLTAGE, OPERATING TEMP, ACCURACY)
+- **Suffixes:** units of measure (VAC, VDC, mA, ohms, etc.)
+- **Attribute_suffix_mappings:** which suffixes are valid for which attributes
+- **ProductTypeAttributes:** which attributes are expected for each product type (the schema)
+
+---
+
+## 3. ML Approach
+
+### 3.1 Current Design: Hybrid Rule Engine + Semantic Similarity
+
+- **Rules** handle structured/known patterns for high precision
+- **Semantic matcher** uses `all-MiniLM-L6-v2` sentence embeddings with cosine similarity for unmatched attributes
+- **Combined confidence:** `conf_final = α * conf_rule + (1 - α) * conf_embed`, with α = 0.7 (unvalidated)
+- **Confidence threshold:** 0.85 (unvalidated — the most sensitive open parameter)
+- **Zero-shot:** no labeled training data required for the embedding layer
+
+### 3.2 Why Semantic Matching Over Alternatives
+
+We compared three approaches and chose semantic matching because DistilBERT and CatBoost both need labeled training data (examples of "this supplier attribute maps to this PIMS attribute") before they can learn. We don't have that labeled data. The semantic matcher needs zero labeled data because it isn't learning a mapping — it's comparing meanings. The knowledge of what words mean is already baked into the model from pre-training on massive text corpora.
+
+`all-MiniLM-L6-v2` specifically is a distilled BERT-style model, fine-tuned on sentence similarity via contrastive learning. It's around 22 million parameters, runs fast on CPU, and needs no GPU.
+
+### 3.3 How It Works (Mechanically)
+
+1. Every PIMS attribute name is converted into a dense vector via the embedding model. This becomes the **index**.
+2. When a supplier spec sheet arrives, each attribute name is embedded the same way.
+3. **Cosine similarity** finds the closest PIMS attribute in the index.
+4. The similarity score **is** the confidence score.
+5. Above threshold → auto-accept and write to PIMS. Below threshold → human review.
+
+No generation, no reasoning — purely similarity search in semantic vector space.
+
+---
+
+## 4. POC Results
+
+### 4.1 What We Built
+
+An end-to-end prototype of the semantic matching pipeline using real eParts data.
+
+**Step 1 — Built the index.** Loaded all 487 active PIMS attribute names from the production database and embedded them. Used TF-IDF as a stand-in for `all-MiniLM` (no internet access in the environment) — architecture is identical; one line swap to upgrade.
+
+**Step 2 — Simulated supplier input.** Used two real supplier spec sheets the client provided:
+- **AIM2** from Automation Components (22 attributes)
+- **RCT Flex CT** from Accuenergy (20 attributes)
+
+**Step 3 — Ran the matcher.** Cosine similarity of each supplier attribute against the full PIMS index, top match with threshold-based routing.
+
+**Step 4 — Evaluated accuracy.** Ground truth evaluation using the existing 50,000 labeled product attribute values in the database.
+
+### 4.2 Numbers
+
+| Metric | Result |
+|---|---|
+| AIM2 auto-accept rate | 91% (20/22) |
+| RCT Flex CT auto-accept rate | 80% (16/20) |
+| **Overall auto-accept rate** | **86% (36/42)** |
+| Ground truth top-1 accuracy | 99.1% |
+| Ground truth top-3 accuracy | 100% |
+
+### 4.3 Honest Caveats
+
+- **Auto-accept rate ≠ accuracy.** A few wrong matches passed the threshold because TF-IDF rewards shared words regardless of meaning (e.g., "Supply Current" → `SUPPLY VOLTAGE` with score 0.468 because both contain "Supply"). `all-MiniLM` should handle this better.
+- **The 0.85 threshold is arbitrary.** No statistical basis yet.
+- **No human-labeled cross-supplier ground truth set exists.** Next step is getting Brian's team to label a sample.
+- The ground truth eval is self-retrieval against the PIMS index — it proves the pipeline works, not that real-world supplier mappings are solved.
+
+---
+
+## 5. Client Product Architecture
+
+### 5.1 Style: Pipe and Filter
+
+```
+Ingestion → Normalization → Prediction → Routing → Review Queue → Writeback → PIMS
+```
+
+- Single Azure App Service deployment (not microservices — team size constraint)
+- `PredictionServiceInterface` isolates the model from routing and writeback
+- Per-attribute routing (not per-record) to minimize review volume
+
+### 5.2 Tech Stack
+
+- Azure App Service (Python backend)
+- Azure SQL Database (staging tables, review queue, audit trail)
+- Azure Blob Storage (raw file archive)
+- Azure Functions (timer-triggered publish/sync job)
+- .NET / Vue.js / Nuxt.js (existing eParts stack)
+- Datadog (observability)
+
+### 5.3 Open Architectural Decisions (Unresolved)
+
+| ADR | Issue | Status |
+|---|---|---|
+| ADR-1 | Threshold value (0.85 guess) | Needs calibration against real labeled data |
+| ADR-2 | Alpha weighting (0.7 guess) | Needs sweep across correction data |
+| ADR-3 | Per-attribute vs per-record routing | Pending attribute correlation analysis |
+| ADR-4 | PIMS staging schema compatibility | Jake has not delivered P1-C schema yet |
+| ADR-5 | Drift detection baselines | Not yet defined |
+
+---
+
+## 6. Software Engineering System (SES)
+
+We explicitly treat this project as a Software Engineering System — not just a codebase. The SES governs all work: code, meetings, reviews, decisions, and documentation.
+
+**Four pillars:**
+- **Artifacts** — what we produce (versioned, reviewable, traceable)
+- **Processes** — how artifacts are produced and validated, modeled using ETVX (Entry, Task, Verification, Exit)
+- **Resources** — who and what performs the work (humans and AI tools)
+- **Measurements** — how we evaluate effectiveness and improve over time
+
+This framing creates accountability not just for the product we ship to eParts, but for how our team operates week to week.
+
+### 6.1 Core Artifacts
+
+- **Context Diagram V2** (approved) — system boundary view
+- **Notional Workflow Diagram** (in review) — deliberately labeled "notional," not "architecture," since it will mature with client data
+- **ADRs** (ongoing) — every major design decision captured with context, options, decision, rationale. Lifecycle: Draft → Review → Approved → Baselined.
+
+---
+
+## 7. Agentic SE System (Team's Internal Tooling)
+
+This is **separate** from the client product. It is our team's operating system — a multi-agent pipeline that helps Pimsie Supreme execute the capstone better.
+
+### 7.1 Core Philosophy
+
+- Agents handle the mechanical 80%, humans own the judgment 20%
+- Every high-risk output (architecture changes, P0 tickets, ADRs) requires human approval
+- Low-risk outputs (minutes, digests, alerts) write directly
+- All agent outputs are versioned in Bitbucket — git history is the audit trail
+- Prompts are version-controlled files, not hardcoded strings
+- Bitbucket is the single source of truth; Confluence is the human-readable mirror
+
+### 7.2 Agent Domains
+
+| Agent Group | Purpose |
+|---|---|
+| **Requirements** | Transcript parser, priority classifier, requirement extractor, stale detector |
+| **Architecture** | Drift detector, ADR generator, diagram updater, traceability matrix builder |
+| **Coding** | Boilerplate generator, PR reviewer, test generator, doc generator (feasibility: partial — full autonomous coding not yet) |
+| **Project Management** | Ticket creator, WBS updater, weekly digest, alert agent |
+| **Knowledge** | Minutes publisher, decision logger, prompt regression, context packager |
+| **Coach Memory** (eParts-specific) | RAG over past sessions, commitment tracker, briefing generator, concern tracker |
+| **ML Decision** (eParts-specific) | Open decision store, evidence accumulator, readiness detector |
+
+### 7.3 How It's Specific to Our Project
+
+The agents are generic in *type* but specific in *context*:
+- **Supplier catalog workflow** — an agent pre-populates attribute mapping templates for spec sheets like AIM2/RCT before human review
+- **Named ETVX stages** — transcript agent extracts against our defined SES processes, not generic "action items"
+- **Named stakeholders** — agents tag commitments by person ("Jake committed to delivering P1-C schema by X")
+- **Named open risks** — agents monitor threshold calibration, Jake's schema delivery, alpha validation and flag meeting discussions that touch them
+- **Rubric-tied metrics** — agents aggregate prompt count, re-prompt rate, correction volume, time saved for mentor meetings
+
+### 7.4 MCP Servers (Tool Access Layer)
+
+| MCP Server | Tools | Used By |
+|---|---|---|
+| Jira | create_ticket, update_ticket, get_sprint_state | Requirements, PM agents |
+| GitHub/Bitbucket | commit, branch, open_pr, add_pr_comment | All agents writing to repo |
+| Confluence | create_page, update_page, get_page | Knowledge, Architecture agents |
+| Slack | send_message, read_channel, pin_message | Alert, Digest, Coach Memory agents |
+| Google Drive | list_files, read_file, watch_folder | Transcript parser, Notes agent |
+| Anthropic API | claude_completion | All agents for LLM calls |
+| Vector Store | embed, query, upsert, delete | Coach Memory, ML Decision agents |
+
+No agent has hardcoded credentials or makes direct HTTP calls.
+
+### 7.5 Infrastructure Stack
+
+- **FastAPI** — orchestrator server, webhook endpoints, cron scheduler
+- **Python** — all agent logic
+- **SQLite → Azure SQL** — task queue, session memory, ML decision log
+- **ChromaDB** — local vector store for RAG (swappable to Azure AI Search)
+- **Bitbucket API / GitHub API** — repo reads and writes
+- **Anthropic SDK** — all Claude calls
+
+### 7.6 Repo Skeleton
+
+```
+eparts-agentic/
+├── orchestrator/
+│ ├── main.py # FastAPI app, webhook endpoints
+│ ├── queue.py # task queue, sequential execution
+│ └── router.py # trigger → agent routing
+├── agents/
+│ ├── base.py # base agent class
+│ ├── requirements/
+│ ├── architecture/
+│ ├── coding/
+│ ├── project_mgmt/
+│ ├── knowledge/
+│ ├── coach_memory/ # eParts-specific
+│ └── ml_decision/ # eParts-specific
+├── mcp/
+│ ├── jira.py
+│ ├── slack.py
+│ ├── bitbucket.py
+│ └── drive.py
+├── memory/
+│ ├── vector_store.py # ChromaDB wrapper
+│ └── decision_log.py # ML decision state
+├── prompts/ # version-controlled prompts
+├── tests/
+│ └── golden/
+├── .env.example
+└── README.md
+```
+
+---
+
+## 8. Current Open Challenges
+
+1. **Baseline measurement.** No production traffic yet, so establishing a meaningful baseline for human review time and effort is hard.
+2. **Confidence threshold governance.** Calibration on a validation set vs. empirical reviewer feedback — unresolved.
+3. **Train/serve consistency.** TF-IDF POC vs. all-MiniLM production — need to verify equivalent behavior post-swap.
+4. **PIMS staging schema.** Waiting on Jake's P1-C schema delivery.
+5. **Correction-to-retraining pipeline.** Architecturally unspecified — how corrections by Brian/Dewey flow back into model improvement.
+6. **Drift detection baselines.** Not yet defined for supplier-specific drift.
+
+---
+
+## 9. Key Questions for Mentor Discussions
+
+- How to establish a meaningful baseline for human review time without production traffic?
+- Calibration on a validation set vs. empirical threshold tuning — which approach?
+- Temporal vs. random test-set splitting for product catalog data where suppliers repeat?
+- Top three operational metrics to instrument in the first vertical slice?
+- Patterns for capturing reviewer corrections as retraining labels without over-engineering the feedback loop?
+
+---
+
+## 10. Evaluation & Measurement
+
+Per the rubric, 50% weights the strength of the SES. Our measurement plan tracks:
+- Prompt count and re-prompt rate
+- Correction volume and pattern
+- Time saved on recurring tasks (minutes, digests, ADRs)
+- AI effectiveness metrics (not just product metrics)
+- ETVX compliance per process
+
+This directly aligns with Christian's core focus: measuring AI effectiveness — productivity, quality, and impact — with clear metrics and data collection.
+
+---
+
+*Last updated: April 2026*
diff --git a/evals/baselines.json b/evals/baselines.json
new file mode 100644
index 0000000..22d5c98
--- /dev/null
+++ b/evals/baselines.json
@@ -0,0 +1,25 @@
+{
+ "recorded_at": "2026-07-27T22:56:19+00:00",
+ "scores": {
+ "defect_triage_skill::red_ci_on_main": 1.0,
+ "defect_triage_skill::audit_log_double_apply": 1.0,
+ "defect_triage_skill::client_reported_mapping_error": 1.0,
+ "defect_triage_skill::config_drift_env_only": 1.0,
+ "defect_triage_skill::prompt_caused_bad_artifact": 1.0,
+ "defect_triage_skill::single_artifact_correction_is_not_a_bug": 1.0,
+ "orchestrator_routing::transcript.full_contract": 1.0,
+ "orchestrator_routing::transcript.requirements_capability": 1.0,
+ "orchestrator_routing::transcript.parse_before_extract": 1.0,
+ "orchestrator_routing::coach_transcript.memory_capability": 1.0,
+ "orchestrator_routing::pr_event.review_and_regression_capability": 1.0,
+ "orchestrator_routing::pr_event.full_contract": 1.0,
+ "orchestrator_routing::jira_webhook.traceability_capability": 1.0,
+ "orchestrator_routing::cron_friday_6pm.weekly_digest": 1.0,
+ "orchestrator_routing::cron_pre_meeting.briefing": 1.0,
+ "orchestrator_routing::cron_monday_8am.hygiene": 1.0,
+ "orchestrator_routing::poc_result.evidence": 1.0,
+ "orchestrator_routing::manual.override_isolates_single_agent": 1.0,
+ "orchestrator_routing::unknown_trigger.degrades_quietly": 1.0,
+ "orchestrator_routing::manual.without_override_dispatches_nothing": 1.0
+ }
+}
diff --git a/evals/runner.py b/evals/runner.py
new file mode 100644
index 0000000..64de24f
--- /dev/null
+++ b/evals/runner.py
@@ -0,0 +1,517 @@
+"""
+Eval runner — executes scenario suites and detects capability regressions.
+
+Usage:
+ python -m evals.runner # offline tiers, blocking
+ python -m evals.runner --live # also run model-dependent tiers
+ python -m evals.runner --suite routing # one suite
+ python -m evals.runner --update-baseline # record current scores
+ python -m evals.runner --json report.json # machine-readable report
+
+Design notes (from the Cory Gwin coaching session, 2026-07-24):
+
+* **Regression detection is the point.** A score on its own says little; what
+ matters is whether an ability the system *had* is now gone. Baselines are
+ stored per scenario id, and any scenario that previously passed and now
+ fails is reported as a regression by name.
+* **Two tiers, so the cheap one can block.** Routing evals are deterministic
+ and need no API key, so they gate every PR. Model-dependent evals cost
+ tokens and are opt-in via ``--live``.
+* **Silence is not success.** Loading zero suites, or a live tier requested
+ without a key, is reported explicitly rather than passing quietly.
+
+Exit codes:
+ 0 — all evaluated scenarios passed, no regressions
+ 1 — a scenario failed, a critical scenario failed, or a regression appeared
+ 2 — the harness itself could not run (malformed scenarios, bad arguments)
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Sequence
+
+from evals.schema import Scenario, ScenarioError, Suite, load_all_suites
+from evals.scorers import (
+ ScoreResult,
+ aggregate,
+ exact_set_match,
+ forbidden_absent,
+ ordered_before,
+ required_subset,
+ vocabulary_conformance,
+)
+
+BASELINE_FILE = Path(__file__).resolve().parent / "baselines.json"
+
+STATUS_PASS = "pass"
+STATUS_FAIL = "fail"
+STATUS_SKIP = "skip"
+
+
+@dataclass(slots=True)
+class Outcome:
+ """Result of evaluating one scenario."""
+
+ suite: str
+ scenario_id: str
+ description: str
+ status: str
+ score: float
+ reason: str
+ critical: bool = False
+ checks: dict[str, dict[str, Any]] = field(default_factory=dict)
+
+ @property
+ def as_dict(self) -> dict[str, Any]:
+ return {
+ "suite": self.suite,
+ "scenario": self.scenario_id,
+ "description": self.description,
+ "status": self.status,
+ "score": round(self.score, 4),
+ "reason": self.reason,
+ "critical": self.critical,
+ "checks": self.checks,
+ }
+
+
+# ---------------------------------------------------------------------------
+# Tier 1 — routing (deterministic, no model)
+# ---------------------------------------------------------------------------
+
+
+def evaluate_routing(suite: Suite) -> list[Outcome]:
+ """Evaluate routing scenarios against the live routing table."""
+ from orchestrator.router import resolve_agents
+
+ outcomes: list[Outcome] = []
+ for sc in suite.scenarios:
+ trigger_type = sc.given.get("trigger_type")
+ if not isinstance(trigger_type, str):
+ outcomes.append(
+ Outcome(
+ suite=suite.name,
+ scenario_id=sc.id,
+ description=sc.description,
+ status=STATUS_FAIL,
+ score=0.0,
+ reason="scenario 'given' is missing a string 'trigger_type'",
+ critical=sc.critical,
+ )
+ )
+ continue
+
+ override = sc.given.get("agent_override")
+ actual = resolve_agents(trigger_type, override if isinstance(override, str) else None)
+
+ checks: dict[str, ScoreResult] = {}
+ if "agents_exact" in sc.expect:
+ checks["agents_exact"] = exact_set_match(actual, sc.expect["agents_exact"])
+ if "agents_required" in sc.expect:
+ checks["agents_required"] = required_subset(actual, sc.expect["agents_required"])
+ if "agents_forbidden" in sc.expect:
+ checks["agents_forbidden"] = forbidden_absent(actual, sc.expect["agents_forbidden"])
+ if "order" in sc.expect:
+ checks["order"] = ordered_before(actual, sc.expect["order"])
+
+ outcomes.append(_finalize(suite, sc, checks, note=f"dispatched={actual}"))
+
+ return outcomes
+
+
+# ---------------------------------------------------------------------------
+# Tier 2 — skill selection (offline validation; model run under --live)
+# ---------------------------------------------------------------------------
+
+
+def evaluate_skill_selection(suite: Suite, *, live: bool) -> list[Outcome]:
+ """Evaluate skill scenarios.
+
+ Offline, this validates the scenario contract itself: every expected
+ label must exist in the suite's controlled vocabulary and every named
+ tool must exist in the declared tool surface. That is a real check — an
+ expectation naming a label the skill can never emit (a typo, or a label
+ dropped from the spec) is a broken eval that would otherwise sit green
+ forever, and an off-vocabulary label in production silently drops the
+ defect out of every JQL-derived metric.
+
+ Under ``--live`` the skill is additionally executed and its actual
+ choices are scored. That path requires ``ANTHROPIC_API_KEY``.
+ """
+ outcomes: list[Outcome] = []
+ vocab = suite.vocabularies
+
+ for sc in suite.scenarios:
+ checks: dict[str, ScoreResult] = {}
+
+ labels = sc.expect.get("labels", {})
+ if isinstance(labels, dict):
+ for field_name, value in labels.items():
+ allowed = vocab.get(field_name)
+ if allowed is None:
+ checks[f"vocab:{field_name}"] = ScoreResult(
+ 0.0, False, f"no vocabulary declared for field {field_name!r}"
+ )
+ else:
+ checks[f"vocab:{field_name}"] = vocabulary_conformance(
+ [value] if isinstance(value, str) else list(value),
+ allowed,
+ field=field_name,
+ )
+
+ priority = sc.expect.get("priority")
+ if isinstance(priority, str) and "priority" in vocab:
+ checks["vocab:priority"] = vocabulary_conformance(
+ [priority], vocab["priority"], field="priority"
+ )
+
+ tool_surface = vocab.get("tools")
+ if tool_surface:
+ for key in ("tools_required", "tools_forbidden"):
+ named = sc.expect.get(key)
+ if isinstance(named, list) and named:
+ checks[f"vocab:{key}"] = vocabulary_conformance(
+ named, tool_surface, field=key
+ )
+
+ if live:
+ model_checks = _run_live_skill_scenario(suite, sc)
+ if model_checks is None:
+ outcomes.append(
+ Outcome(
+ suite=suite.name,
+ scenario_id=sc.id,
+ description=sc.description,
+ status=STATUS_SKIP,
+ score=0.0,
+ reason="live run requested but ANTHROPIC_API_KEY is not set",
+ critical=sc.critical,
+ checks={k: v.as_dict for k, v in checks.items()},
+ )
+ )
+ continue
+ checks.update(model_checks)
+
+ outcome = _finalize(
+ suite,
+ sc,
+ checks,
+ note="contract validated (offline)" if not live else "model run",
+ )
+ if not live:
+ # Offline this suite verifies the eval contract, not agent behaviour.
+ outcome.description = f"{sc.description}"
+ outcome.reason = f"[contract-only, no model] {outcome.reason}"
+ outcomes.append(outcome)
+
+ return outcomes
+
+
+def _run_live_skill_scenario(suite: Suite, sc: Scenario) -> dict[str, ScoreResult] | None:
+ """Execute the skill against a real model and score its selections.
+
+ Returns ``None`` when no API key is available, so the caller can mark the
+ scenario skipped rather than silently passing.
+
+ NOTE: this path has not yet been executed against a live model — there is
+ no API key in the development environment where it was written. It is
+ wired for CI, where ``ANTHROPIC_API_KEY`` exists. Treat its first CI run
+ as the validation of this function, and do not present it as a
+ demonstrated result until then.
+ """
+ if not os.environ.get("ANTHROPIC_API_KEY"):
+ return None
+
+ try: # lazy import — keeps the offline tier dependency-free
+ import anthropic
+ except ImportError:
+ return {
+ "model_run": ScoreResult(
+ 0.0, False, "anthropic SDK not installed; cannot run live tier"
+ )
+ }
+
+ instruction = (
+ "You are triaging a software defect for the EPARTS project. "
+ "Classify it and state which tools you would call, in order. "
+ "Respond ONLY with JSON of the form "
+ '{"should_create_issue": bool, "tools": [str], "labels": '
+ '{"stage_found": str, "root_cause": str, "found_by": str, "module": str}, '
+ '"priority": str}. '
+ "Labels must come from these controlled vocabularies:\n"
+ + json.dumps({k: list(v) for k, v in suite.vocabularies.items()}, indent=2)
+ )
+ payload = json.dumps({"finding": sc.given.get("finding"), "context": sc.given.get("context")})
+
+ client = anthropic.Anthropic()
+ message = client.messages.create(
+ model=os.environ.get("EVAL_MODEL", "claude-sonnet-4-5-20250929"),
+ max_tokens=1024,
+ system=instruction,
+ messages=[{"role": "user", "content": payload}],
+ )
+ text = "".join(getattr(block, "text", "") for block in message.content)
+
+ try:
+ actual = json.loads(_extract_json(text))
+ except (json.JSONDecodeError, ValueError) as exc:
+ return {
+ "model_run": ScoreResult(0.0, False, f"model did not return parseable JSON: {exc}")
+ }
+
+ checks: dict[str, ScoreResult] = {}
+
+ expected_create = sc.expect.get("should_create_issue")
+ if isinstance(expected_create, bool):
+ got = bool(actual.get("should_create_issue"))
+ checks["should_create_issue"] = ScoreResult(
+ 1.0 if got == expected_create else 0.0,
+ got == expected_create,
+ f"expected should_create_issue={expected_create}, got {got}",
+ )
+
+ actual_tools = [t for t in actual.get("tools", []) if isinstance(t, str)]
+ if "tools_required" in sc.expect:
+ checks["tools_required"] = required_subset(actual_tools, sc.expect["tools_required"])
+ if "tools_forbidden" in sc.expect:
+ checks["tools_forbidden"] = forbidden_absent(actual_tools, sc.expect["tools_forbidden"])
+ if "tool_order" in sc.expect:
+ checks["tool_order"] = ordered_before(actual_tools, sc.expect["tool_order"])
+
+ actual_labels = actual.get("labels") or {}
+ for field_name, expected_value in (sc.expect.get("labels") or {}).items():
+ got_value = actual_labels.get(field_name)
+ match = got_value == expected_value
+ checks[f"label:{field_name}"] = ScoreResult(
+ 1.0 if match else 0.0,
+ match,
+ f"{field_name}: expected {expected_value!r}, got {got_value!r}",
+ )
+
+ if isinstance(sc.expect.get("priority"), str):
+ got_priority = actual.get("priority")
+ match = got_priority == sc.expect["priority"]
+ checks["priority"] = ScoreResult(
+ 1.0 if match else 0.0,
+ match,
+ f"priority: expected {sc.expect['priority']!r}, got {got_priority!r}",
+ )
+
+ return checks
+
+
+def _extract_json(text: str) -> str:
+ """Pull the first JSON object out of a model response."""
+ start = text.find("{")
+ end = text.rfind("}")
+ if start == -1 or end == -1 or end < start:
+ raise ValueError("no JSON object found in response")
+ return text[start : end + 1]
+
+
+# ---------------------------------------------------------------------------
+# Shared
+# ---------------------------------------------------------------------------
+
+
+def _finalize(
+ suite: Suite, sc: Scenario, checks: dict[str, ScoreResult], *, note: str
+) -> Outcome:
+ if not checks:
+ return Outcome(
+ suite=suite.name,
+ scenario_id=sc.id,
+ description=sc.description,
+ status=STATUS_FAIL,
+ score=0.0,
+ reason="scenario declared no checkable expectations",
+ critical=sc.critical,
+ )
+ combined = aggregate(list(checks.values()))
+ return Outcome(
+ suite=suite.name,
+ scenario_id=sc.id,
+ description=sc.description,
+ status=STATUS_PASS if combined.passed else STATUS_FAIL,
+ score=combined.score,
+ reason=f"{combined.reason} ({note})",
+ critical=sc.critical,
+ checks={k: v.as_dict for k, v in checks.items()},
+ )
+
+
+def load_baselines(path: Path | None = None) -> dict[str, float]:
+ target = path or BASELINE_FILE
+ if not target.exists():
+ return {}
+ try:
+ data = json.loads(target.read_text(encoding="utf-8"))
+ except json.JSONDecodeError:
+ return {}
+ return {str(k): float(v) for k, v in data.get("scores", {}).items()}
+
+
+def save_baselines(outcomes: Sequence[Outcome], path: Path | None = None) -> Path:
+ target = path or BASELINE_FILE
+ scores = {
+ f"{o.suite}::{o.scenario_id}": round(o.score, 4)
+ for o in outcomes
+ if o.status != STATUS_SKIP
+ }
+ target.write_text(
+ json.dumps(
+ {
+ "recorded_at": datetime.now(timezone.utc).isoformat(timespec="seconds"),
+ "scores": scores,
+ },
+ indent=2,
+ )
+ + "\n",
+ encoding="utf-8",
+ )
+ return target
+
+
+def find_regressions(
+ outcomes: Sequence[Outcome], baselines: dict[str, float], *, tolerance: float = 0.001
+) -> list[str]:
+ """Scenarios whose score dropped below their recorded baseline."""
+ regressions = []
+ for o in outcomes:
+ if o.status == STATUS_SKIP:
+ continue
+ key = f"{o.suite}::{o.scenario_id}"
+ if key in baselines and o.score < baselines[key] - tolerance:
+ regressions.append(f"{key}: {baselines[key]:.3f} -> {o.score:.3f} ({o.reason})")
+ return regressions
+
+
+def run(
+ *,
+ live: bool = False,
+ only_suite: str | None = None,
+ scenarios_dir: Path | None = None,
+) -> tuple[list[Outcome], list[Suite]]:
+ suites = load_all_suites(scenarios_dir)
+ if only_suite:
+ suites = [s for s in suites if only_suite in s.name]
+ if not suites:
+ raise ScenarioError(f"no suite matching {only_suite!r}")
+
+ outcomes: list[Outcome] = []
+ for suite in suites:
+ if suite.kind == "routing":
+ outcomes.extend(evaluate_routing(suite))
+ elif suite.kind == "skill_selection":
+ outcomes.extend(evaluate_skill_selection(suite, live=live))
+ return outcomes, suites
+
+
+def format_report(outcomes: Sequence[Outcome], regressions: Sequence[str], *, live: bool) -> str:
+ lines: list[str] = ["# Agent Eval Report", ""]
+ lines.append(f"Tier: {'offline + live model' if live else 'offline only (no model calls)'}")
+ passed = sum(1 for o in outcomes if o.status == STATUS_PASS)
+ failed = sum(1 for o in outcomes if o.status == STATUS_FAIL)
+ skipped = sum(1 for o in outcomes if o.status == STATUS_SKIP)
+ lines.append(f"Scenarios: {len(outcomes)} — {passed} passed, {failed} failed, {skipped} skipped")
+ lines.append("")
+
+ by_suite: dict[str, list[Outcome]] = {}
+ for o in outcomes:
+ by_suite.setdefault(o.suite, []).append(o)
+
+ for suite_name, items in by_suite.items():
+ mean = sum(i.score for i in items) / len(items) if items else 0.0
+ lines.append(f"## {suite_name} (mean score {mean:.3f})")
+ for o in items:
+ marker = {STATUS_PASS: "PASS", STATUS_FAIL: "FAIL", STATUS_SKIP: "SKIP"}[o.status]
+ crit = " [CRITICAL]" if o.critical else ""
+ lines.append(f"- **{marker}**{crit} `{o.scenario_id}` — {o.reason}")
+ lines.append("")
+
+ if regressions:
+ lines.append("## Regressions (lost capabilities)")
+ lines.extend(f"- {r}" for r in regressions)
+ lines.append("")
+
+ return "\n".join(lines)
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+ parser = argparse.ArgumentParser(description="Run agent behaviour evals.")
+ parser.add_argument(
+ "--live",
+ action="store_true",
+ help="also run model-dependent tiers (requires ANTHROPIC_API_KEY)",
+ )
+ parser.add_argument("--suite", help="only run suites whose name contains this string")
+ parser.add_argument(
+ "--update-baseline",
+ action="store_true",
+ help="record current scores as the regression baseline",
+ )
+ parser.add_argument("--json", dest="json_out", help="write the report as JSON to this path")
+ args = parser.parse_args(argv)
+
+ try:
+ outcomes, suites = run(live=args.live, only_suite=args.suite)
+ except ScenarioError as exc:
+ print(f"eval harness error: {exc}", file=sys.stderr)
+ return 2
+
+ baselines = load_baselines()
+ regressions = find_regressions(outcomes, baselines)
+
+ print(format_report(outcomes, regressions, live=args.live))
+
+ if args.json_out:
+ Path(args.json_out).write_text(
+ json.dumps(
+ {
+ "generated_at": datetime.now(timezone.utc).isoformat(timespec="seconds"),
+ "live": args.live,
+ "suites": [{"name": s.name, "kind": s.kind} for s in suites],
+ "outcomes": [o.as_dict for o in outcomes],
+ "regressions": list(regressions),
+ },
+ indent=2,
+ )
+ + "\n",
+ encoding="utf-8",
+ )
+ print(f"JSON report written to {args.json_out}")
+
+ if args.update_baseline:
+ path = save_baselines(outcomes)
+ print(f"Baseline recorded at {path}")
+
+ critical_failures = [o for o in outcomes if o.status == STATUS_FAIL and o.critical]
+ failures = [o for o in outcomes if o.status == STATUS_FAIL]
+
+ if critical_failures:
+ print(
+ f"\nFAILED: {len(critical_failures)} critical scenario(s) failed — "
+ "a required capability is missing.",
+ file=sys.stderr,
+ )
+ return 1
+ if failures:
+ print(f"\nFAILED: {len(failures)} scenario(s) failed.", file=sys.stderr)
+ return 1
+ if regressions:
+ print(f"\nFAILED: {len(regressions)} regression(s) detected.", file=sys.stderr)
+ return 1
+
+ print("\nAll evaluated scenarios passed.")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/evals/scenarios/defect_triage.json b/evals/scenarios/defect_triage.json
new file mode 100644
index 0000000..7456328
--- /dev/null
+++ b/evals/scenarios/defect_triage.json
@@ -0,0 +1,145 @@
+{
+ "name": "defect_triage_skill",
+ "kind": "skill_selection",
+ "description": "For the /defect-triage skill: given a finding, does it select the correct tools and classify on the four axes defined in docs/defect_management.md? This is the entry point Cory Gwin recommended (2026-07-24) — define scenarios per skill and validate tool/skill selection under each. Offline, the harness validates every expected label against the controlled vocabulary and every tool against the declared tool surface (a real check: an off-vocabulary label silently drops the defect out of the JQL-derived metrics). Live, with ANTHROPIC_API_KEY, the model is run and its choices are scored against these expectations.",
+ "vocabularies": {
+ "stage_found": [
+ "found-spec",
+ "found-build",
+ "found-review",
+ "found-ci",
+ "found-integrated",
+ "found-client"
+ ],
+ "root_cause": [
+ "rc-logic",
+ "rc-data",
+ "rc-interface",
+ "rc-config",
+ "rc-requirements",
+ "rc-env",
+ "rc-prompt"
+ ],
+ "found_by": ["by-test", "by-ci", "by-human-review", "by-ai-review", "by-client"],
+ "module": [
+ "mod-ingestion",
+ "mod-normalization",
+ "mod-prediction",
+ "mod-routing",
+ "mod-review-queue",
+ "mod-writeback",
+ "mod-publish",
+ "mod-audit",
+ "mod-retraining",
+ "mod-monitoring",
+ "mod-ses"
+ ],
+ "priority": ["Highest", "High", "Medium", "Low"],
+ "tools": [
+ "atlassianUserInfo",
+ "createJiraIssue",
+ "searchJiraIssuesUsingJql",
+ "addCommentToJiraIssue"
+ ]
+ },
+ "scenarios": [
+ {
+ "id": "red_ci_on_main",
+ "description": "Intake rule 1: red CI on main is an S1 filed the same day. Must preflight auth before creating the issue.",
+ "critical": true,
+ "given": {
+ "finding": "Pipeline #31 on master failed: pytest step exited 1 with 3 failing tests in tests/test_layer4_fusion.py. main is red.",
+ "context": "Bitbucket Pipelines, branch master"
+ },
+ "expect": {
+ "tools_required": ["atlassianUserInfo", "createJiraIssue"],
+ "tool_order": [["atlassianUserInfo", "createJiraIssue"]],
+ "labels": {
+ "stage_found": "found-ci",
+ "found_by": "by-ci",
+ "module": "mod-ses"
+ },
+ "priority": "Highest"
+ }
+ },
+ {
+ "id": "audit_log_double_apply",
+ "description": "The real snapshot-to-replay double-apply defect caught in PR review. Wrong data could reach PIMS, so it is S1 despite being found pre-merge; found by a human, not a test, which is itself the signal that test gates had a blind spot.",
+ "critical": true,
+ "given": {
+ "finding": "In PR review of the audit-log component: after a snapshot, replay re-applies changes that were already persisted, double-applying writes.",
+ "context": "Pull request review comment, not yet merged"
+ },
+ "expect": {
+ "tools_required": ["createJiraIssue"],
+ "labels": {
+ "stage_found": "found-review",
+ "found_by": "by-human-review",
+ "root_cause": "rc-logic",
+ "module": "mod-audit"
+ },
+ "priority": "Highest"
+ }
+ },
+ {
+ "id": "client_reported_mapping_error",
+ "description": "Intake rule 4: a client-reported problem is found-client / by-client and links to the meeting where it was raised.",
+ "given": {
+ "finding": "In the July 16 client meeting, eParts reported that several supplier attributes were mapped to the wrong PIMS attribute in the review queue.",
+ "context": "Client meeting minutes"
+ },
+ "expect": {
+ "tools_required": ["createJiraIssue"],
+ "labels": {
+ "stage_found": "found-client",
+ "found_by": "by-client",
+ "module": "mod-review-queue"
+ }
+ }
+ },
+ {
+ "id": "config_drift_env_only",
+ "description": "A configuration defect, not a logic defect. Tests the root-cause axis discriminating rc-config from rc-logic — the axis that drives where prevention effort goes.",
+ "given": {
+ "finding": "The OTel exporter retries endlessly in local runs because .env.example ships a non-empty OTEL endpoint that does not resolve outside CI.",
+ "context": "Developer local environment"
+ },
+ "expect": {
+ "tools_required": ["createJiraIssue"],
+ "labels": {
+ "root_cause": "rc-config",
+ "stage_found": "found-build"
+ },
+ "priority": "Low"
+ }
+ },
+ {
+ "id": "prompt_caused_bad_artifact",
+ "description": "The AI-era root cause the classic IEEE-1044 taxonomies lack: code and model were fine, the prompt produced the wrong artifact. Systematic, so it is ticketed as rc-prompt.",
+ "given": {
+ "finding": "The requirements extractor has generated over-generalized requirements from three consecutive meeting transcripts; reviewers rewrote all of them. The extraction prompt, not the code, is producing the wrong shape.",
+ "context": "Recurring correction at the human review gate"
+ },
+ "expect": {
+ "tools_required": ["createJiraIssue"],
+ "labels": {
+ "root_cause": "rc-prompt",
+ "module": "mod-ses"
+ }
+ }
+ },
+ {
+ "id": "single_artifact_correction_is_not_a_bug",
+ "description": "Intake rule 5, the restraint case: a one-off generated artifact corrected at human review is NOT a defect — it is a correction counted by the artifact-quality measurement. The skill must decline to file, which tests that it does not manufacture tickets to look busy.",
+ "critical": true,
+ "given": {
+ "finding": "The agent-drafted meeting minutes for one standup needed two wording corrections before the PR was approved.",
+ "context": "Single occurrence, human review gate worked as designed"
+ },
+ "expect": {
+ "should_create_issue": false,
+ "tools_forbidden": ["createJiraIssue"]
+ }
+ }
+ ]
+}
diff --git a/evals/scenarios/routing.json b/evals/scenarios/routing.json
new file mode 100644
index 0000000..da80097
--- /dev/null
+++ b/evals/scenarios/routing.json
@@ -0,0 +1,136 @@
+{
+ "name": "orchestrator_routing",
+ "kind": "routing",
+ "description": "Asserts the orchestrator still dispatches the correct agents for each trigger type. Deterministic (no model, no API key), so this runs as a blocking gate on every PR. Scenarios marked critical encode capabilities the engineering system is required to have: if an edit to orchestrator/router.py drops one, the run fails and names the lost ability.",
+ "scenarios": [
+ {
+ "id": "transcript.full_contract",
+ "description": "A client/team transcript drives the full requirements pipeline; the dispatch set is the contract, so extras are flagged too.",
+ "given": { "trigger_type": "transcript" },
+ "expect": {
+ "agents_exact": [
+ "transcript_parser",
+ "priority_classifier",
+ "req_extractor",
+ "drift_detector",
+ "decision_logger"
+ ]
+ }
+ },
+ {
+ "id": "transcript.requirements_capability",
+ "description": "Requirements extraction and architecture-drift detection must survive any routing edit — these are the client-facing artifacts of the pipeline.",
+ "critical": true,
+ "given": { "trigger_type": "transcript" },
+ "expect": {
+ "agents_required": ["transcript_parser", "req_extractor", "drift_detector"]
+ }
+ },
+ {
+ "id": "transcript.parse_before_extract",
+ "description": "Ordering is a correctness property, not a preference: requirements and decisions can only be extracted from an already-parsed transcript.",
+ "critical": true,
+ "given": { "trigger_type": "transcript" },
+ "expect": {
+ "order": [
+ ["transcript_parser", "req_extractor"],
+ ["transcript_parser", "priority_classifier"],
+ ["transcript_parser", "decision_logger"]
+ ]
+ }
+ },
+ {
+ "id": "coach_transcript.memory_capability",
+ "description": "Coach sessions must still populate persistent memory plus commitment and concern tracking — the inputs to pre-meeting briefings.",
+ "critical": true,
+ "given": { "trigger_type": "coach_transcript" },
+ "expect": {
+ "agents_required": [
+ "transcript_parser",
+ "session_memory",
+ "commitment_tracker",
+ "concern_tracker"
+ ],
+ "order": [["transcript_parser", "session_memory"]]
+ }
+ },
+ {
+ "id": "pr_event.review_and_regression_capability",
+ "description": "Every PR must still get AI review and prompt-regression checking. Losing prompt_regression would silently disable the team's only automated guard against prompt quality decay.",
+ "critical": true,
+ "given": { "trigger_type": "pr_event" },
+ "expect": {
+ "agents_required": ["pr_reviewer", "prompt_regression", "traceability_builder"]
+ }
+ },
+ {
+ "id": "pr_event.full_contract",
+ "description": "Full PR dispatch set, including documentation generation.",
+ "given": { "trigger_type": "pr_event" },
+ "expect": {
+ "agents_exact": [
+ "pr_reviewer",
+ "traceability_builder",
+ "doc_generator",
+ "prompt_regression"
+ ]
+ }
+ },
+ {
+ "id": "jira_webhook.traceability_capability",
+ "description": "Jira changes must keep feeding the traceability graph; without this, provenance in the Program Health dashboard degrades silently.",
+ "critical": true,
+ "given": { "trigger_type": "jira_webhook" },
+ "expect": {
+ "agents_required": ["traceability_builder", "wbs_updater"]
+ }
+ },
+ {
+ "id": "cron_friday_6pm.weekly_digest",
+ "description": "Stakeholder visibility: the weekly digest is a scheduled commitment, not an ad-hoc report.",
+ "given": { "trigger_type": "cron_friday_6pm" },
+ "expect": { "agents_exact": ["weekly_digest"] }
+ },
+ {
+ "id": "cron_pre_meeting.briefing",
+ "description": "Pre-meeting briefings must still be generated from coach memory.",
+ "given": { "trigger_type": "cron_pre_meeting" },
+ "expect": { "agents_exact": ["briefing_generator"] }
+ },
+ {
+ "id": "cron_monday_8am.hygiene",
+ "description": "Monday hygiene pass: stale-work detection and context packaging.",
+ "given": { "trigger_type": "cron_monday_8am" },
+ "expect": { "agents_exact": ["stale_detector", "context_packager"] }
+ },
+ {
+ "id": "poc_result.evidence",
+ "description": "POC results must accumulate evidence and re-evaluate readiness rather than being read once and forgotten.",
+ "given": { "trigger_type": "poc_result" },
+ "expect": { "agents_exact": ["evidence_accumulator", "readiness_detector"] }
+ },
+ {
+ "id": "manual.override_isolates_single_agent",
+ "description": "A manual trigger runs exactly the requested agent and nothing else — no surprise side effects when a human invokes one agent by hand.",
+ "critical": true,
+ "given": { "trigger_type": "manual", "agent_override": "req_extractor" },
+ "expect": {
+ "agents_exact": ["req_extractor"],
+ "agents_forbidden": ["transcript_parser", "drift_detector", "pr_reviewer"]
+ }
+ },
+ {
+ "id": "unknown_trigger.degrades_quietly",
+ "description": "An unrecognized trigger dispatches nothing instead of raising — an unknown webhook must not take the orchestrator down.",
+ "critical": true,
+ "given": { "trigger_type": "definitely_not_a_real_trigger" },
+ "expect": { "agents_exact": [] }
+ },
+ {
+ "id": "manual.without_override_dispatches_nothing",
+ "description": "A manual trigger with no agent named is a no-op, not a fan-out to every agent.",
+ "given": { "trigger_type": "manual" },
+ "expect": { "agents_exact": [] }
+ }
+ ]
+}
diff --git a/evals/schema.py b/evals/schema.py
new file mode 100644
index 0000000..700f85d
--- /dev/null
+++ b/evals/schema.py
@@ -0,0 +1,165 @@
+"""
+Eval scenario schema — the declarative contract for agent-behaviour evals.
+
+A *scenario* states a known condition and the behaviour we expect from the
+system under it. A *suite* is a named collection of scenarios evaluated by
+one evaluator kind.
+
+Why this exists (Cory Gwin coaching session, 2026-07-24): agents are
+non-deterministic, so evals establish whether behaviour holds under known
+conditions. The recommended entry point was, for a given skill, to "define
+scenarios and validate that the agent calls the correct tools and skills
+under each," with **regression detection** — knowing whether an agent has
+lost an ability it previously had — as the primary payoff.
+
+Two evaluator kinds are supported today:
+
+``routing``
+ Deterministic. Asserts which agents the orchestrator dispatches for a
+ trigger type (``orchestrator.router.resolve_agents``). Needs no model
+ and no API key, so it runs on every PR as a blocking gate.
+
+``skill_selection``
+ Asserts that a skill/agent selects the correct tools and emits labels
+ drawn from a controlled vocabulary. Offline it validates the scenario
+ and its vocabulary; live (with an API key) it also runs the model.
+
+Scenarios are JSON — stdlib only, no PyYAML — so the harness runs the same
+way locally and in CI.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Literal
+
+SCENARIOS_DIR = Path(__file__).resolve().parent / "scenarios"
+
+EvaluatorKind = Literal["routing", "skill_selection"]
+
+_VALID_KINDS: frozenset[str] = frozenset({"routing", "skill_selection"})
+
+
+class ScenarioError(ValueError):
+ """A scenario or suite is malformed."""
+
+
+@dataclass(frozen=True, slots=True)
+class Scenario:
+ """One known condition and the behaviour expected under it.
+
+ Attributes:
+ id: Stable identifier. Used as the baseline key, so renaming an id
+ reads as "old capability gone, new capability added" — rename
+ deliberately.
+ description: Why this scenario matters, in one line.
+ given: The input condition (evaluator-specific keys).
+ expect: The expected behaviour (evaluator-specific keys).
+ critical: When true, a failure here is a lost capability and fails
+ the run outright regardless of aggregate score.
+ """
+
+ id: str
+ description: str
+ given: dict[str, Any]
+ expect: dict[str, Any]
+ critical: bool = False
+
+ @staticmethod
+ def from_dict(raw: dict[str, Any], *, suite: str) -> Scenario:
+ missing = [k for k in ("id", "description", "given", "expect") if k not in raw]
+ if missing:
+ raise ScenarioError(f"suite {suite!r}: scenario missing keys {missing}: {raw!r}")
+ if not isinstance(raw["given"], dict) or not isinstance(raw["expect"], dict):
+ raise ScenarioError(f"suite {suite!r}: scenario {raw['id']!r}: given/expect must be objects")
+ return Scenario(
+ id=str(raw["id"]),
+ description=str(raw["description"]),
+ given=dict(raw["given"]),
+ expect=dict(raw["expect"]),
+ critical=bool(raw.get("critical", False)),
+ )
+
+
+@dataclass(frozen=True, slots=True)
+class Suite:
+ """A named set of scenarios sharing one evaluator kind."""
+
+ name: str
+ kind: EvaluatorKind
+ description: str
+ scenarios: tuple[Scenario, ...] = field(default_factory=tuple)
+ # Controlled vocabularies for skill_selection suites: field -> allowed values.
+ vocabularies: dict[str, tuple[str, ...]] = field(default_factory=dict)
+
+ @property
+ def requires_model(self) -> bool:
+ """True when fully evaluating this suite needs a live model call."""
+ return self.kind == "skill_selection"
+
+ @staticmethod
+ def from_dict(raw: dict[str, Any], *, source: str) -> Suite:
+ for key in ("name", "kind", "description", "scenarios"):
+ if key not in raw:
+ raise ScenarioError(f"{source}: suite missing required key {key!r}")
+
+ kind = str(raw["kind"])
+ if kind not in _VALID_KINDS:
+ raise ScenarioError(
+ f"{source}: unknown evaluator kind {kind!r} (valid: {sorted(_VALID_KINDS)})"
+ )
+
+ raw_scenarios = raw["scenarios"]
+ if not isinstance(raw_scenarios, list) or not raw_scenarios:
+ raise ScenarioError(f"{source}: 'scenarios' must be a non-empty list")
+
+ name = str(raw["name"])
+ scenarios = tuple(Scenario.from_dict(s, suite=name) for s in raw_scenarios)
+
+ ids = [s.id for s in scenarios]
+ duplicates = sorted({i for i in ids if ids.count(i) > 1})
+ if duplicates:
+ raise ScenarioError(f"{source}: duplicate scenario ids {duplicates}")
+
+ vocabularies = {
+ str(k): tuple(str(v) for v in vals)
+ for k, vals in (raw.get("vocabularies") or {}).items()
+ }
+
+ return Suite(
+ name=name,
+ kind=kind, # type: ignore[arg-type]
+ description=str(raw["description"]),
+ scenarios=scenarios,
+ vocabularies=vocabularies,
+ )
+
+
+def load_suite(path: Path) -> Suite:
+ """Load and validate one suite file."""
+ try:
+ raw = json.loads(path.read_text(encoding="utf-8"))
+ except json.JSONDecodeError as exc:
+ raise ScenarioError(f"{path}: invalid JSON: {exc}") from exc
+ return Suite.from_dict(raw, source=str(path))
+
+
+def load_all_suites(directory: Path | None = None) -> list[Suite]:
+ """Load every ``*.json`` suite in ``directory``, sorted by filename.
+
+ Raises:
+ ScenarioError: if the directory is missing or holds no suites — an
+ eval run that silently evaluates nothing is worse than a
+ failure, because it reports success.
+ """
+ target = directory or SCENARIOS_DIR
+ if not target.is_dir():
+ raise ScenarioError(f"scenario directory not found: {target}")
+
+ paths = sorted(target.glob("*.json"))
+ if not paths:
+ raise ScenarioError(f"no scenario suites found in {target}")
+
+ return [load_suite(p) for p in paths]
diff --git a/evals/scorers.py b/evals/scorers.py
new file mode 100644
index 0000000..65b4ae6
--- /dev/null
+++ b/evals/scorers.py
@@ -0,0 +1,154 @@
+"""
+Deterministic scoring primitives for the eval harness.
+
+Every function here is pure and model-free: same inputs, same score. That is
+deliberate. Per the Cory Gwin coaching session (2026-07-24), a great deal of
+quality assurance is deterministic and consumes no tokens — so the scoring
+layer is fully testable offline and only the *behaviour under test* needs a
+model.
+
+Each scorer returns a :class:`ScoreResult` carrying the 0.0–1.0 score plus a
+human-readable reason, because an eval that says "0.6" without saying what
+was missing cannot be acted on.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Iterable, Sequence
+
+
+@dataclass(frozen=True, slots=True)
+class ScoreResult:
+ """Outcome of one scorer."""
+
+ score: float
+ passed: bool
+ reason: str
+
+ @property
+ def as_dict(self) -> dict[str, object]:
+ return {"score": round(self.score, 4), "passed": self.passed, "reason": self.reason}
+
+
+def _norm(items: Iterable[str]) -> list[str]:
+ """Normalize to trimmed, non-empty strings, preserving order."""
+ return [s.strip() for s in items if isinstance(s, str) and s.strip()]
+
+
+def exact_set_match(actual: Sequence[str], expected: Sequence[str]) -> ScoreResult:
+ """Score 1.0 only when ``actual`` and ``expected`` hold the same members.
+
+ Order-insensitive, duplicate-insensitive. Use when the full dispatch set
+ is the contract and any extra or missing member is a defect.
+ """
+ a, e = set(_norm(actual)), set(_norm(expected))
+ if a == e:
+ return ScoreResult(1.0, True, f"exact match ({len(e)} expected)")
+
+ missing, extra = sorted(e - a), sorted(a - e)
+ union = len(a | e) or 1
+ score = len(a & e) / union # Jaccard: penalizes both directions
+ parts = []
+ if missing:
+ parts.append(f"missing {missing}")
+ if extra:
+ parts.append(f"unexpected {extra}")
+ return ScoreResult(score, False, "; ".join(parts))
+
+
+def required_subset(actual: Sequence[str], required: Sequence[str]) -> ScoreResult:
+ """Fraction of ``required`` members present in ``actual``.
+
+ This is the capability check that drives regression detection: it answers
+ "can the system still do X?" while tolerating additions. Extras are fine
+ — new abilities are not regressions.
+ """
+ a, r = set(_norm(actual)), _norm(required)
+ if not r:
+ return ScoreResult(1.0, True, "no requirements declared")
+
+ present = [x for x in r if x in a]
+ missing = sorted(set(r) - a)
+ score = len(present) / len(r)
+ if not missing:
+ return ScoreResult(1.0, True, f"all {len(r)} required present")
+ return ScoreResult(score, False, f"lost capability: missing {missing}")
+
+
+def forbidden_absent(actual: Sequence[str], forbidden: Sequence[str]) -> ScoreResult:
+ """Score 1.0 when none of ``forbidden`` appear in ``actual``."""
+ a, f = set(_norm(actual)), _norm(forbidden)
+ if not f:
+ return ScoreResult(1.0, True, "no exclusions declared")
+
+ present = sorted(a & set(f))
+ if not present:
+ return ScoreResult(1.0, True, f"none of {len(f)} forbidden present")
+ return ScoreResult(0.0, False, f"forbidden present: {present}")
+
+
+def ordered_before(actual: Sequence[str], pairs: Sequence[Sequence[str]]) -> ScoreResult:
+ """Score the fraction of ``(first, second)`` pairs appearing in order.
+
+ Pipeline correctness often depends on sequence, not just membership: a
+ transcript must be parsed before requirements can be extracted from it.
+ A pair whose members are not both present counts as a failure — the
+ ordering claim cannot hold if a stage is missing.
+ """
+ a = _norm(actual)
+ index = {name: i for i, name in enumerate(a)}
+ if not pairs:
+ return ScoreResult(1.0, True, "no ordering constraints declared")
+
+ violations: list[str] = []
+ for pair in pairs:
+ if len(pair) != 2:
+ violations.append(f"malformed pair {list(pair)!r}")
+ continue
+ first, second = pair[0].strip(), pair[1].strip()
+ if first not in index or second not in index:
+ violations.append(f"{first} -> {second} (member absent)")
+ elif index[first] >= index[second]:
+ violations.append(f"{first} -> {second} (out of order)")
+
+ score = (len(pairs) - len(violations)) / len(pairs)
+ if not violations:
+ return ScoreResult(1.0, True, f"all {len(pairs)} ordering constraints hold")
+ return ScoreResult(max(score, 0.0), False, "ordering violated: " + "; ".join(violations))
+
+
+def vocabulary_conformance(
+ values: Sequence[str],
+ allowed: Sequence[str],
+ *,
+ field: str = "value",
+) -> ScoreResult:
+ """Fraction of ``values`` drawn from the ``allowed`` vocabulary.
+
+ Controlled vocabularies are how a skill's output stays machine-queryable.
+ The defect-management spec, for example, derives every metric from a JQL
+ query over fixed labels — an invented label silently drops the defect out
+ of those metrics, so an off-vocabulary value is a real defect, not a
+ stylistic quibble.
+ """
+ v, permitted = _norm(values), set(_norm(allowed))
+ if not v:
+ return ScoreResult(0.0, False, f"no {field} values produced")
+
+ invalid = sorted({x for x in v if x not in permitted})
+ score = (len(v) - len([x for x in v if x in invalid])) / len(v)
+ if not invalid:
+ return ScoreResult(1.0, True, f"all {len(v)} {field} value(s) in vocabulary")
+ return ScoreResult(score, False, f"off-vocabulary {field}: {invalid}")
+
+
+def aggregate(results: Sequence[ScoreResult]) -> ScoreResult:
+ """Combine scorer results into one: mean score, passing only if all pass."""
+ if not results:
+ return ScoreResult(1.0, True, "no checks run")
+ mean = sum(r.score for r in results) / len(results)
+ failures = [r.reason for r in results if not r.passed]
+ if not failures:
+ return ScoreResult(mean, True, f"{len(results)} check(s) passed")
+ return ScoreResult(mean, False, "; ".join(failures))
diff --git a/examples/demo_client_meeting_script.md b/examples/demo_client_meeting_script.md
new file mode 100644
index 0000000..3f34f7a
--- /dev/null
+++ b/examples/demo_client_meeting_script.md
@@ -0,0 +1,77 @@
+# Synthetic client meeting — demo script (paired with SES sample transcript)
+
+Use this alongside `examples/demo_client_review.transcript.vtt` when you rehearse or present live. Dialogue is abbreviated in the transcript file; extend with your own names if recording a fresh Zoom.
+
+---
+
+## Logistics
+
+| | |
+|--|--|
+| **Duration target** | 12–13 minutes (recording) / read transcript as-is for pipeline demo |
+| **Cast** | PM (`JaiVardhan`), catalog ops (`Dave`), PIMS backend (`Laura`, `Jake`), ML/data (`Harsh`, `Sophie`), DevOps (`Raj`), stakeholder PM (`Chris`) |
+
+---
+
+## Beats (what “good content” demonstrates)
+
+1. **Context** — Ingestion for valves/actuators slice; sponsor governance on scope creep.
+2. **Non-functional anchors** — PIMS correctness over completeness; SLA on E2E latency and reviewer throughput.
+3. **Explicit decisions** — Per-attribute thresholds, diff-based review UI, single App Service sandbox.
+4. **Architecture alignment** — Staging DDL + canonical mapping; hybrid ML + rules; Datadog telemetry.
+5. **Traceability language** — HLR / FR / DR / QA scenario phrasing auditors expect.
+6. **Risk & escalation** — Staging delays, labelled data volume, backlog if queue spikes.
+7. **Action items with owners/dates** — Jake Thursday DDL, Sophie Friday metrics, Raj Wednesday dashboards.
+
+---
+
+## Read-through lines (speaker script)
+
+Deliver naturally; numbering matches transcript cues.
+
+### Opening — JaiVardhan
+> Good morning everyone. Goal for this sixty-minute architectural review block is alignment on ingestion scope for Spring, confirm our confidence thresholds, and capture action items before we freeze ADR drafts for Critique Demo.
+
+### Dave — business priority
+> Wrong data in PIMS is worse than missing data. Keep auto-accept at high confidence only. Valves/actuators scope first — reviewer throughput around ten reviewed items per minute still stands.
+
+### Laura — blocker / ask
+> We need definitive staging-column mapping before idempotent upsert validation — Jake can we get DDL handshake end of sprint?
+
+### Jake — commitment
+> Deliverable is mapping spreadsheet staging → canonical SKU key + attribute hash; DDL diff by Thursday; escalate leadership if slips.
+
+### Harsh — ML rationale
+> all-MiniLM beats DistilBERT for short fields — propose ~90% auto-accept on brand/name, ~75% routing on technical specs; per-attribute thresholds in ADRs.
+
+### Sophie — QA / measurement
+> Per-attribute thresholds protect precision/recall — regression harness tracks uplift weekly.
+
+### Raj — infra
+> Sandbox stays single Azure App Service; Datadog for ingestion rate and E2E latency under SLA.
+
+### Chris — stakeholder
+> Escalate scope beyond valves before allocating more SKUs — backlog scrub first.
+
+### JaiVardhan — decision summary
+> Decision 1 — per-attribute thresholds before pilot. Decision 2 — diff-based reviewer UI. Decision 3 — single sandbox App Service until summer hardening.
+
+*(Continue through action items and closing as in transcript.)*
+
+---
+
+## Run SES on this transcript
+
+Always use the `.transcript.vtt` file so `demo.py` picks it explicitly:
+
+```bash
+python3 demo.py examples/demo_client_review.transcript.vtt --auto
+```
+
+Or use `./scripts/show_ses_demo.sh` (prefers this file when present).
+
+---
+
+## Disclaimer
+
+Synthetic scenario for education and SES demonstration only—not a record of any real sponsor conversation.
diff --git a/examples/demo_client_review.transcript.vtt b/examples/demo_client_review.transcript.vtt
new file mode 100644
index 0000000..4de7290
--- /dev/null
+++ b/examples/demo_client_review.transcript.vtt
@@ -0,0 +1,103 @@
+WEBVTT
+
+NOTE synthetic demo transcript for SES pipeline rehearsal - not from a real Zoom export
+
+1
+00:00:06.000 --> 00:00:28.500
+JaiVardhan (PM): Good morning everyone. Goal for this sixty-minute architectural review block is alignment on ingestion scope for Spring, confirm our confidence thresholds, and capture action items before we freeze ADR drafts for Critique Demo.
+
+2
+00:00:29.200 --> 00:00:51.900
+Dave Wilson (Catalog Operations): Confirming priorities on our side. Wrong data in PIMS is worse than missing data — we talked about keeping auto-accept at high confidence only. For valves and actuators first category scope, reviewer throughput targets around ten reviewed items per minute still stand.
+
+3
+00:00:52.300 --> 00:01:18.750
+Laura Chen (PIMS Integration): Jake's team owes us staging table column mapping for canonical attribute rows — that is blocking idempotent upsert validation. Jake, can we get a definitive schema handshake by end of sprint so we exercise writeback tests against sandbox tables?
+
+4
+00:01:19.400 --> 00:01:45.200
+Jake Molina (PIMS Backend): Absolutely P0 blocking. Deliverable is spreadsheet mapping staging columns into our canonical SKU key plus attribute hash. I owe you staging DDL diff by Thursday. If slips, we escalate to leadership because writeback slips cascade to UAT readiness.
+
+5
+00:01:46.100 --> 00:02:12.400
+Harsh Vadher (ML Engineer): On ML findings — all-MiniLM semantic matcher beats DistilBERT for short description fields in our POC. Threshold discussion: ninety percent routing target for straight accept on name brand attributes; technical specs fluctuate seventy-five percent — we propose per-attribute confidence routing documented in pending ADRs.
+
+6
+00:02:13.050 --> 00:02:38.500
+Sophie Okonkwo (Data Science): Supporting Harsh — per-attribute thresholds reduce false rejects without flooding catalog reviewers. Regression harness must track precision recall uplift week over week once production-like traffic hits staging.
+
+7
+00:02:39.600 --> 00:03:05.750
+Raj Patel (DevOps Azure): Sandbox environment still single App Service unit per baseline architecture slide — we need Datadog dashboards for ingestion success rate latency E2E under fifteen minute SLA ninety percent percentile.
+
+8
+00:03:06.200 --> 00:03:32.100
+Chris Duarte (PM eParts stakeholder): Sponsor note — escalate any scope creep beyond valve categories before allocating engineering time to fasteners. Governance requires backlog scrub before additional SKUs ingest.
+
+9
+00:03:33.000 --> 00:04:08.950
+JaiVardhan (PM): Capturing consensus — Decision one: prioritize per-attribute thresholds P0 blocking before pilot. Decision two: keep human review UI diff-based with keyboard-first interactions for reviewer throughput SLA. Decision three: keep Azure sandbox single unit until summer hardening finishes.
+
+10
+00:04:09.450 --> 00:04:35.800
+Laura Chen (PIMS Integration): Action item Jake — circulate staging DDL plus mapping sheet by Thursday EOP. Sophie — rerun confusion matrix segmentation on actuator attributes by Friday noon.
+
+11
+00:04:36.450 --> 00:05:01.680
+Raj Patel (DevOps Azure): Raj — Datadog ingestion dashboard skeleton + alerting stub by Wednesday. Ownership clear — escalate if infra credential delays block pipeline.
+
+12
+00:05:02.200 --> 00:05:34.940
+JaiVardhan (PM): Closing — next external sync Thursdays client cadence unchanged. Risks flagged today — staging delay and threshold calibration drift remain top three along with reviewer capacity if queue grows unexpectedly.
+
+13
+00:05:35.500 --> 00:06:10.880
+JaiVardhan (PM): Minutes will reflect decisions, prioritized actions, thresholds scope — please flag anything missing asynchronously in Slack twenty-four hour window — thank everyone.
+
+14
+00:06:18.400 --> 00:06:52.780
+Laura Chen (PIMS Integration): Non-functional reminder — throughput on reviewer queue bounded by Postgres connection pool sizing in sandbox; we validated two hundred simultaneous sessions theoretically but throttle at fifty concurrent reviewers during pilot ramp.
+
+15
+00:06:53.300 --> 00:07:22.940
+Dave Wilson (Catalog Operations): Operational scenario — nightly batch file drop from ERP still CSV plus zipped PDF annexes — ingestion gateway accepts PDF OCR path with virus scan prerequisite before unpacking.
+
+16
+00:07:23.500 --> 00:07:58.710
+Sophie Okonkwo (Data Science): Quality attribute scenario QA-AUDIT-HR states every human override gets append-only audit row with reviewer ID UTC timestamp canonical attribute code old value snapshot new value rationale text minimum ten characters soft constraint.
+
+17
+00:07:59.440 --> 00:08:35.090
+Chris Duarte (PM eParts stakeholder): Business constraint remains labor savings — prediction must materially reduce rework compared to spreadsheet macro workflow — target twenty five percent reviewer hours freed by December pilot exit.
+
+18
+00:08:36.050 --> 00:09:10.260
+Raj Patel (DevOps Azure): Deployment constraint stays single Azure Web App staging slot — rotating keys via Key Vault automation — rollback window under ten minutes scripted.
+
+19
+00:09:11.000 --> 00:09:43.870
+JaiVardhan (PM): Requirement trace — link each accepted ADR to at least one HLR and referencing GitHub markdown diff when architecture decision changes — Capstone SES pipeline already generating REQ markdown proposals from meetings.
+
+20
+00:09:44.400 --> 00:10:18.930
+Dave Wilson (Catalog Operations): Derived requirement DR-MONITOR-01 — alerting when low-confidence queue depth exceeds SLA backlog thresholds so leadership gets proactive signal before SLA breach fifteen minute processing window ninety percent SLA.
+
+21
+00:10:19.490 --> 00:10:55.740
+Laura Chen (PIMS Integration): Functional requirement recap — ingestion normalizes heterogeneous vendor columns into staging attribute rows aligning with canonical schema version two point one — mapping spreadsheet lives in Shared Drive link posted after call.
+
+22
+00:10:56.200 --> 00:11:21.870
+JaiVardhan (PM): Acceptance criteria for vertical slice milestone — given sample valve catalog PDF ingest path returns normalized SKU primary key ninety five percent deterministic match against golden file — when mismatch flagged route to reviewer queue stub.
+
+23
+00:11:22.500 --> 00:11:58.940
+Sophie Okonkwo (Data Science): Open risk stays training data scarcity under two hundred labelled rows — mitigation keeps hybrid rules plus embeddings blend until corpus grows — escalate if blocker persists after April iteration.
+
+24
+00:11:59.880 --> 00:12:36.760
+Raj Patel (DevOps Azure): Telemetry requirement pushes stage-level counters to Datadog for each pipeline hop — ingestion parse normalize prediction routing review writeback — dashboard draft shared async.
+
+25
+00:12:37.500 --> 00:13:14.940
+JaiVardhan (PM): Thanks — meeting adjourned — action owners echo in thread — priority zero items human approval before ticketing automation merges — SES standing by to parse this transcript shortly after upload.
diff --git a/examples/demo_traceability_rich.transcript.vtt b/examples/demo_traceability_rich.transcript.vtt
new file mode 100644
index 0000000..97d534d
--- /dev/null
+++ b/examples/demo_traceability_rich.transcript.vtt
@@ -0,0 +1,55 @@
+WEBVTT
+
+NOTE synthetic demo transcript — emphasizes explicit REQUIREMENTS wording, DECISIONS, CONCERNS/risks, and ACTIONS for SES demos (LLM-on or heuristic-offline modes)
+
+1
+00:00:01.000 --> 00:00:26.400
+Jordan Lee (Architecture): Welcome to the ingestion workshop. Agenda is simple: tighten scope to valve categories for spring, expose confidence thresholds, and surface concerns before sprint planning.
+
+2
+00:00:27.100 --> 00:00:52.800
+Morgan Blake (Requirements): Requirement framing first. The catalog ingestion pathway shall normalize inconsistent vendor CSV and Excel and PDF formats into staging tables before production write; we need validation after ML extraction attributes from spec sheets.
+
+3
+00:00:53.200 --> 00:01:18.950
+Taylor Singh (MLE): Confidence scores must sit on every semantic prediction zero through one point zero; we propose per-attribute routing so short brand fields behave differently than long technical specs. Training data remains under two hundred labelled samples today — genuine concern until corpus grows.
+
+4
+00:01:19.400 --> 00:01:45.200
+Riley Chen (Data): Human review queues protect us when thresholds miss — operators approve, reject, or correct low-confidence parses. Feedback loop retrains embeddings after enough corrections accumulate to improve accuracy.
+
+5
+00:01:45.800 --> 00:02:09.950
+Casey Alvarez (Cloud): We agreed to Azure App Service deployment for ingestion APIs with Key Vault secret rotation nightly. Pricing columns stay excluded from automation because pricing stays manual forever.
+
+6
+00:02:10.400 --> 00:02:35.150
+Jordan Lee (Architecture): Reliability concern: staging DDL drift risks bad rows slipping past validators unless we tighten checks. Metrics dashboard must monitor ingestion throughput, latency, reviewer accuracy metrics, and prediction error trends week over week.
+
+7
+00:02:35.600 --> 00:03:00.700
+Taylor Singh (MLE): We decided per-attribute thresholds P0 blocking before pilots — Decision one solid. Decision two: hybrid rule layer plus embeddings until dataset clears two hundred labelled examples.
+
+8
+00:03:01.200 --> 00:03:27.950
+Riley Chen (Data): Decision three we'll use reviewer-first keyboard workflow so manual operator effort shrinks sixty percent versus spreadsheet baseline.
+
+9
+00:03:28.500 --> 00:03:54.300
+Casey Alvarez (Cloud): Decision four the plan is single-region Azure footprint until summer hardening. We'll use scripted rollback under ten minutes whenever deploy fails.
+
+10
+00:03:54.800 --> 00:04:20.120
+Jordan Lee (Architecture): Stakeholder concern on vendor variation keeps backlog noisy; SLA concern if ingestion queue backlog grows past ninety percentile fifteen minute handling window — escalate sooner.
+
+11
+00:04:20.600 --> 00:04:45.900
+Taylor Singh (MLE): Should we escalate immediately if MiniLM regression widens once we swap models next sprint?
+
+12
+00:04:46.300 --> 00:05:12.800
+Jordan Lee (Architecture): Functional requirement recap aligned with REQ trace — staging table validation gates every write; never promote directly into production catalogs until QA signs off extraction attributes from catalogs.
+
+13
+00:05:13.200 --> 00:05:40.500
+Morgan Blake (Requirements): Action wrap — Riley will send Datadog dashboard mocks by Wednesday; Casey verifies Azure quotas by Friday noon; Morgan logs decisions appendix so auditors see full intent.
diff --git a/instruction_and_rubric_for_the_presentation.txt b/instruction_and_rubric_for_the_presentation.txt
new file mode 100644
index 0000000..10f42a5
--- /dev/null
+++ b/instruction_and_rubric_for_the_presentation.txt
@@ -0,0 +1,109 @@
+Think: "Show and tell and defend"
+Somewhere in the presentation - show the timeline (past, present and future)
+SES
+Viable project and risk management plan - more of visual diagrams
+Not textual slides
+Diagram - detailed level of context diagram
+
+
+1. Every repeatable Activity that you do this semester should be documented according to the meta-model framework.
+
+A good example of a documented Activity was shared by Punn (Chawit) of the IRAlogix team during the Checkpoint Review. You can find it here in the video recording starting at 3:49:33.
+
+Think of an Activity as a part of a larger end-to-end process such as requirements management.
+
+For at least one Practice Area, there should be an end-to-end connection between the Activities in that area.
+
+We want to see teams move from ad-hoc use of AI to principled use of AI. Use and non-use of AI should be justified with evidence. Reusable prompts should be treated as version-controlled artifacts. - common claude
+
+
+the Software System they will create for their client.
+the Software Engineering System they’ll use to create the Software System.
+their project and risk management plan
+
+
+4 sections - arjun and Liu:
+Understand what is asked, come up with visually appealing and artifacts backed idea to convey the content
+
+SES - Jai, hrishi and Ashritha
+
+SES - https://github.com/AshrithaG/eparts
+
+Identify repeatable activities:
+
+
+
+
+
+
+Project Context [Abt. 5 minutes] - Arjun/ Liu
+Client, business, product, stakeholders
+Project goal(s)
+Problem to solve and value to stakeholders
+Solution concept
+Overview (timeline) of the team’s work to date.
+
+Management [ Abt. 7 min.] - Arjun/ Liu
+Project plan to end of year
+Key risks and mitigation
+Team roles and responsibilities to implement the plan
+Measurement plan
+Resources to implement the plan (e.g., tools, learning, etc.)
+
+Software System: Requirements [Abt. 7 min.] - Liu/ Arjun
+Functional requirements
+Non-functional requirements and constraints
+Scope and priorities: decisions and reasoning about tradeoffs and implications
+
+Software System: Architecture [Abt. 7 min.] - Liu/ Arjun
+Context Diagram
+Quality Attributes
+Architectural drivers: decisions and reasoning about tradeoffs and implications
+
+5. Software Engineering System [Abt. 7 min.]
+SE System “overview” [i.e. deep dives are done in the 1:1 Critiques]
+SDLC choice
+Processes, artifacts, measurements and resources (including AI use/non-use), per the meta-model framework
+Evidence that process will be effective, particularly with AI
+Decisions and reasoning about tradeoffs and implications
+
+
+6. Reflection and Closing [2 min.]
+2 key lessons learned, with at least one about the use of AI in Software Engineering
+Closing
+
+
+Viability of Project and Risk Management
+The extent that the team's project and risk management is likely to lead to project success.
+(includes plans, estimates, tracking, etc.) - 3 pts
+Exemplary
+The project and risk management is viable.
+
+
+Soundness of Software System Definition
+The extent that the software system for the sponsor is clearly understood and sufficient to execute the rest of the project successfully.
+
+(e.g., goals, requirements, priorities, quality attributes, constraints, context diagram, architectural drivers, tradeoffs) - 5 pts
+Exemplary
+The software system definition is: Clearly specified Appropriate Sufficient Prioritized Tradeoffs, implications and decisions were explicit and well-reasoned.
+
+
+Strength of Engineering System: DECISION QUALITY - 5 pts
+Exemplary
+All 4 of these are true: 1. Clear decisions 2. Strong justification with evidence 3. Tradeoffs explained 4. Consistent reasoning
+
+
+Strength of Engineering System: ELEMENTS
+per the Meta-Model framework, an engineering system has these elements:
+- Processes
+- Key artifacts
+- Measurements
+- Resources - 5 pts
+Exemplary
+The engineering system has all appropriate, identified, well-defined, completed and documented elements.
+
+
+Crit Performance
+The extent that the team clearly articulates their team's work, decisions and reflective thinking, while actively engaging with feedback to foster critical dialogue and professional growth. - 2 pts
+Exemplary
+All 4 of these were true: 1. Effective communication of team’s work 2. Deep, thoughtful reflection 3. Actively engaged with feedback 4. Balanced team member participation
diff --git a/mcp/__init__.py b/mcp/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mcp/bitbucket.py b/mcp/bitbucket.py
new file mode 100644
index 0000000..5ffe6cb
--- /dev/null
+++ b/mcp/bitbucket.py
@@ -0,0 +1,212 @@
+"""
+Bitbucket MCP server — all repository interactions go through this module.
+
+Tools exposed:
+ commit_file() — commit a file to a branch
+ open_pr() — open a pull request
+ add_pr_comment() — add a review comment on a PR
+ get_pr_status() — check PR state (open/merged/declined)
+
+No agent should make direct HTTP calls to Bitbucket. Use this wrapper.
+Commit messages follow convention: [agent:name] description
+"""
+
+from __future__ import annotations
+
+import base64
+import logging
+import os
+from typing import Any
+
+import requests
+
+logger = logging.getLogger("mcp.bitbucket")
+
+BITBUCKET_API = "https://api.bitbucket.org/2.0"
+
+
+class BitbucketMCP:
+ def __init__(
+ self,
+ workspace: str | None = None,
+ repo_slug: str | None = None,
+ token: str | None = None,
+ ):
+ self._workspace = workspace or os.getenv("BITBUCKET_WORKSPACE", "")
+ self._repo = repo_slug or os.getenv("BITBUCKET_REPO", "")
+ self._token = token or os.getenv("BITBUCKET_TOKEN", "")
+ self._session = requests.Session()
+ if self._token:
+ self._session.headers["Authorization"] = f"Bearer {self._token}"
+ self._session.headers["Content-Type"] = "application/json"
+
+ @property
+ def _repo_url(self) -> str:
+ return f"{BITBUCKET_API}/repositories/{self._workspace}/{self._repo}"
+
+ def commit_file(
+ self,
+ file_path: str,
+ content: str,
+ message: str,
+ branch: str = "main",
+ agent_name: str = "system",
+ ) -> dict[str, Any]:
+ """
+ Commit a single file to a branch using the Bitbucket source endpoint.
+ Commit message is auto-prefixed with [agent:name].
+ """
+ url = f"{self._repo_url}/src"
+ prefixed_msg = f"[agent:{agent_name}] {message}"
+
+ # Bitbucket src endpoint uses multipart form data
+ response = self._session.post(
+ url,
+ headers={"Content-Type": None}, # let requests set multipart boundary
+ data={
+ "message": prefixed_msg,
+ "branch": branch,
+ },
+ files={
+ file_path: (file_path, content.encode("utf-8")),
+ },
+ )
+
+ if response.ok:
+ logger.info(f"Committed {file_path} to {branch}: {prefixed_msg}")
+ return {
+ "ok": True,
+ "file": file_path,
+ "branch": branch,
+ "message": prefixed_msg,
+ }
+ else:
+ logger.error(
+ f"Commit failed ({response.status_code}): {response.text[:300]}"
+ )
+ return {
+ "ok": False,
+ "status_code": response.status_code,
+ "error": response.text[:500],
+ }
+
+ def open_pr(
+ self,
+ title: str,
+ source_branch: str,
+ description: str = "",
+ destination_branch: str = "main",
+ reviewers: list[str] | None = None,
+ ) -> dict[str, Any]:
+ """
+ Open a pull request from source_branch to destination_branch.
+ Returns the PR URL and ID.
+ """
+ url = f"{self._repo_url}/pullrequests"
+
+ payload: dict[str, Any] = {
+ "title": title,
+ "source": {"branch": {"name": source_branch}},
+ "destination": {"branch": {"name": destination_branch}},
+ "description": description,
+ "close_source_branch": True,
+ }
+
+ if reviewers:
+ payload["reviewers"] = [{"username": r} for r in reviewers]
+
+ response = self._session.post(url, json=payload)
+
+ if response.ok:
+ data = response.json()
+ pr_id = data.get("id")
+ pr_url = data.get("links", {}).get("html", {}).get("href", "")
+ logger.info(f"PR opened: #{pr_id} — {title} ({source_branch} → {destination_branch})")
+ return {
+ "ok": True,
+ "pr_id": pr_id,
+ "pr_url": pr_url,
+ "title": title,
+ "source_branch": source_branch,
+ }
+ else:
+ logger.error(
+ f"PR creation failed ({response.status_code}): {response.text[:300]}"
+ )
+ return {
+ "ok": False,
+ "status_code": response.status_code,
+ "error": response.text[:500],
+ }
+
+ def add_pr_comment(
+ self,
+ pr_id: int,
+ content: str,
+ inline: dict | None = None,
+ ) -> dict[str, Any]:
+ """
+ Add a comment to a pull request.
+ For inline comments, pass inline={"to": line_num, "path": "file.py"}.
+ """
+ url = f"{self._repo_url}/pullrequests/{pr_id}/comments"
+
+ payload: dict[str, Any] = {
+ "content": {"raw": content},
+ }
+ if inline:
+ payload["inline"] = inline
+
+ response = self._session.post(url, json=payload)
+
+ if response.ok:
+ comment_id = response.json().get("id")
+ logger.info(f"Comment added to PR #{pr_id}: id={comment_id}")
+ return {"ok": True, "comment_id": comment_id, "pr_id": pr_id}
+ else:
+ logger.error(
+ f"PR comment failed ({response.status_code}): {response.text[:300]}"
+ )
+ return {"ok": False, "error": response.text[:500]}
+
+ def get_pr_status(self, pr_id: int) -> dict[str, Any]:
+ """Check the state of a pull request (OPEN, MERGED, DECLINED)."""
+ url = f"{self._repo_url}/pullrequests/{pr_id}"
+
+ response = self._session.get(url)
+
+ if response.ok:
+ data = response.json()
+ return {
+ "ok": True,
+ "pr_id": pr_id,
+ "state": data.get("state"),
+ "title": data.get("title"),
+ "author": data.get("author", {}).get("display_name"),
+ "merge_commit": data.get("merge_commit", {}).get("hash"),
+ }
+ else:
+ return {
+ "ok": False,
+ "status_code": response.status_code,
+ "error": response.text[:500],
+ }
+
+ def create_branch(self, branch_name: str, from_branch: str = "main") -> dict[str, Any]:
+ """Create a new branch from an existing branch."""
+ url = f"{self._repo_url}/refs/branches"
+ payload = {
+ "name": branch_name,
+ "target": {"hash": from_branch},
+ }
+
+ response = self._session.post(url, json=payload)
+
+ if response.ok:
+ logger.info(f"Branch created: {branch_name} from {from_branch}")
+ return {"ok": True, "branch": branch_name}
+ else:
+ logger.error(
+ f"Branch creation failed ({response.status_code}): {response.text[:300]}"
+ )
+ return {"ok": False, "error": response.text[:500]}
diff --git a/mcp/confluence.py b/mcp/confluence.py
new file mode 100644
index 0000000..68e8c5d
--- /dev/null
+++ b/mcp/confluence.py
@@ -0,0 +1,119 @@
+"""
+Confluence MCP server — all Confluence interactions go through this module.
+
+Tools exposed:
+ create_page() — create a new page under a parent
+ update_page() — update an existing page's content
+ get_page() — fetch a page by ID or title
+
+No agent should call Confluence APIs directly. Use this wrapper.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import Any
+
+from atlassian import Confluence
+
+logger = logging.getLogger("mcp.confluence")
+
+
+class ConfluenceMCP:
+ def __init__(
+ self,
+ url: str | None = None,
+ token: str | None = None,
+ ):
+ self._url = url or os.getenv("CONFLUENCE_URL", "")
+ self._token = token or os.getenv("CONFLUENCE_TOKEN", "")
+ self._space_key = os.getenv("CONFLUENCE_SPACE_KEY", "")
+
+ if self._url and self._token:
+ self._client = Confluence(url=self._url, token=self._token, cloud=True)
+ else:
+ self._client = None
+ logger.warning("Confluence MCP initialized without credentials — offline mode")
+
+ def create_page(
+ self,
+ title: str,
+ body: str,
+ parent_id: str | None = None,
+ space_key: str | None = None,
+ ) -> dict[str, Any]:
+ """Create a new Confluence page."""
+ if not self._client:
+ return {"ok": False, "error": "Confluence not configured"}
+
+ space = space_key or self._space_key
+ try:
+ result = self._client.create_page(
+ space=space,
+ title=title,
+ body=body,
+ parent_id=parent_id,
+ type="page",
+ representation="storage",
+ )
+ page_id = result.get("id", "")
+ logger.info(f"Page created: {title} (id={page_id})")
+ return {"ok": True, "page_id": page_id, "title": title}
+ except Exception as exc:
+ logger.error(f"Confluence create_page failed: {exc}")
+ return {"ok": False, "error": str(exc)}
+
+ def update_page(
+ self,
+ page_id: str,
+ title: str,
+ body: str,
+ ) -> dict[str, Any]:
+ """Update an existing Confluence page."""
+ if not self._client:
+ return {"ok": False, "error": "Confluence not configured"}
+
+ try:
+ self._client.update_page(
+ page_id=page_id,
+ title=title,
+ body=body,
+ representation="storage",
+ )
+ logger.info(f"Page updated: {title} (id={page_id})")
+ return {"ok": True, "page_id": page_id, "title": title}
+ except Exception as exc:
+ logger.error(f"Confluence update_page failed: {exc}")
+ return {"ok": False, "error": str(exc)}
+
+ def get_page(
+ self,
+ title: str | None = None,
+ page_id: str | None = None,
+ space_key: str | None = None,
+ ) -> dict[str, Any]:
+ """Fetch a page by title or ID."""
+ if not self._client:
+ return {"ok": False, "error": "Confluence not configured"}
+
+ space = space_key or self._space_key
+ try:
+ if page_id:
+ result = self._client.get_page_by_id(page_id, expand="body.storage")
+ elif title:
+ result = self._client.get_page_by_title(space=space, title=title)
+ else:
+ return {"ok": False, "error": "Provide title or page_id"}
+
+ if result:
+ return {
+ "ok": True,
+ "page_id": result.get("id"),
+ "title": result.get("title"),
+ "body": result.get("body", {}).get("storage", {}).get("value", ""),
+ }
+ return {"ok": False, "error": "Page not found"}
+ except Exception as exc:
+ logger.error(f"Confluence get_page failed: {exc}")
+ return {"ok": False, "error": str(exc)}
diff --git a/mcp/drive.py b/mcp/drive.py
new file mode 100644
index 0000000..9aaa84f
--- /dev/null
+++ b/mcp/drive.py
@@ -0,0 +1,110 @@
+"""
+Google Drive MCP server — polls for new transcript files.
+
+Tools exposed:
+ list_files() — list files in the transcript folder
+ read_file() — download a file's content
+ watch_folder() — check for new files since last poll
+
+Used by: Transcript parser (polls every 15 min for new .vtt files)
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import Any
+
+logger = logging.getLogger("mcp.drive")
+
+
+class DriveMCP:
+ def __init__(
+ self,
+ service_account_json: str | None = None,
+ folder_id: str | None = None,
+ ):
+ self._sa_json = service_account_json or os.getenv("GOOGLE_DRIVE_SERVICE_ACCOUNT_JSON", "")
+ self._folder_id = folder_id or os.getenv("GOOGLE_DRIVE_TRANSCRIPT_FOLDER_ID", "")
+ self._service = None
+
+ if self._sa_json and self._folder_id:
+ self._init_service()
+ else:
+ logger.warning("Drive MCP initialized without credentials — offline mode")
+
+ def _init_service(self) -> None:
+ """Initialize the Google Drive API service."""
+ try:
+ from google.oauth2 import service_account
+ from googleapiclient.discovery import build
+
+ credentials = service_account.Credentials.from_service_account_file(
+ self._sa_json,
+ scopes=["https://www.googleapis.com/auth/drive.readonly"],
+ )
+ self._service = build("drive", "v3", credentials=credentials)
+ except Exception as exc:
+ logger.error(f"Drive service init failed: {exc}")
+
+ def list_files(
+ self,
+ folder_id: str | None = None,
+ mime_type: str | None = None,
+ ) -> list[dict[str, Any]]:
+ """List files in the transcript folder."""
+ if not self._service:
+ return []
+
+ fid = folder_id or self._folder_id
+ query = f"'{fid}' in parents and trashed = false"
+ if mime_type:
+ query += f" and mimeType = '{mime_type}'"
+
+ try:
+ results = self._service.files().list(
+ q=query,
+ fields="files(id, name, mimeType, modifiedTime, createdTime)",
+ orderBy="modifiedTime desc",
+ ).execute()
+ return results.get("files", [])
+ except Exception as exc:
+ logger.error(f"Drive list_files failed: {exc}")
+ return []
+
+ def read_file(self, file_id: str) -> str:
+ """Download a file's text content."""
+ if not self._service:
+ return ""
+
+ try:
+ content = self._service.files().get_media(fileId=file_id).execute()
+ return content.decode("utf-8") if isinstance(content, bytes) else str(content)
+ except Exception as exc:
+ logger.error(f"Drive read_file failed: {exc}")
+ return ""
+
+ def watch_folder(self, since: str | None = None) -> list[dict[str, Any]]:
+ """
+ Check for new files since a given timestamp.
+ Returns list of new files added since `since` (ISO format).
+ """
+ if not self._service:
+ return []
+
+ query = f"'{self._folder_id}' in parents and trashed = false"
+ if since:
+ query += f" and modifiedTime > '{since}'"
+
+ try:
+ results = self._service.files().list(
+ q=query,
+ fields="files(id, name, mimeType, modifiedTime)",
+ orderBy="modifiedTime desc",
+ ).execute()
+ files = results.get("files", [])
+ logger.info(f"Watch folder: {len(files)} new file(s) since {since}")
+ return files
+ except Exception as exc:
+ logger.error(f"Drive watch_folder failed: {exc}")
+ return []
diff --git a/mcp/github.py b/mcp/github.py
new file mode 100644
index 0000000..f73882b
--- /dev/null
+++ b/mcp/github.py
@@ -0,0 +1,182 @@
+"""
+GitHub MCP server — all GitHub repository interactions go through this module.
+
+Tools exposed:
+ commit_file() — commit a file to a branch
+ open_pr() — open a pull request
+ add_pr_comment() — add a review comment on a PR
+ get_repo_info() — get repository metadata
+ create_branch() — create a new branch
+
+Commit messages follow convention: [agent:name] description
+"""
+from __future__ import annotations
+
+import base64
+import logging
+import os
+from typing import Any
+
+import requests
+
+logger = logging.getLogger("mcp.github")
+
+GITHUB_API = "https://api.github.com"
+
+
+class GitHubMCP:
+ def __init__(
+ self,
+ repo: str | None = None,
+ token: str | None = None,
+ ):
+ self._repo = repo or os.getenv("GITHUB_REPO", "")
+ self._token = token or os.getenv("GITHUB_TOKEN", "")
+ self._session = requests.Session()
+ if self._token:
+ self._session.headers["Authorization"] = f"Bearer {self._token}"
+ self._session.headers["Accept"] = "application/vnd.github.v3+json"
+ self._session.headers["X-GitHub-Api-Version"] = "2022-11-28"
+
+ if self._repo and self._token:
+ logger.info(f"GitHub MCP initialized for repo={self._repo}")
+ else:
+ logger.warning("GitHub MCP initialized without credentials — offline mode")
+
+ @property
+ def _repo_url(self) -> str:
+ return f"{GITHUB_API}/repos/{self._repo}"
+
+ @property
+ def is_configured(self) -> bool:
+ return bool(self._repo and self._token)
+
+ def get_repo_info(self) -> dict[str, Any]:
+ if not self.is_configured:
+ return {"ok": False, "error": "Not configured"}
+ resp = self._session.get(self._repo_url)
+ if resp.ok:
+ d = resp.json()
+ return {
+ "ok": True,
+ "name": d["full_name"],
+ "default_branch": d["default_branch"],
+ "private": d["private"],
+ "url": d["html_url"],
+ }
+ return {"ok": False, "status_code": resp.status_code, "error": resp.text[:300]}
+
+ def commit_file(
+ self,
+ file_path: str,
+ content: str,
+ message: str,
+ branch: str = "main",
+ agent_name: str = "system",
+ ) -> dict[str, Any]:
+ """Commit a single file using the GitHub Contents API."""
+ if not self.is_configured:
+ logger.warning(f"Commit skipped (offline): {file_path}")
+ return {"ok": False, "error": "Not configured"}
+
+ prefixed_msg = f"[agent:{agent_name}] {message}"
+ url = f"{self._repo_url}/contents/{file_path}"
+
+ # Check if file exists (need SHA for updates)
+ sha = None
+ check = self._session.get(url, params={"ref": branch})
+ if check.ok:
+ sha = check.json().get("sha")
+
+ payload: dict[str, Any] = {
+ "message": prefixed_msg,
+ "content": base64.b64encode(content.encode("utf-8")).decode("ascii"),
+ "branch": branch,
+ }
+ if sha:
+ payload["sha"] = sha
+
+ resp = self._session.put(url, json=payload)
+
+ if resp.ok:
+ commit_sha = resp.json().get("commit", {}).get("sha", "")[:8]
+ logger.info(f"Committed {file_path} to {branch}: {prefixed_msg} ({commit_sha})")
+ return {
+ "ok": True,
+ "file": file_path,
+ "branch": branch,
+ "message": prefixed_msg,
+ "commit_sha": commit_sha,
+ }
+ else:
+ logger.error(f"Commit failed ({resp.status_code}): {resp.text[:300]}")
+ return {"ok": False, "status_code": resp.status_code, "error": resp.text[:300]}
+
+ def create_branch(self, branch_name: str, from_branch: str = "main") -> dict[str, Any]:
+ """Create a new branch from an existing branch."""
+ if not self.is_configured:
+ return {"ok": False, "error": "Not configured"}
+
+ # Get the SHA of the source branch
+ ref_resp = self._session.get(f"{self._repo_url}/git/ref/heads/{from_branch}")
+ if not ref_resp.ok:
+ return {"ok": False, "error": f"Source branch '{from_branch}' not found"}
+
+ sha = ref_resp.json()["object"]["sha"]
+
+ # Create the new branch
+ resp = self._session.post(
+ f"{self._repo_url}/git/refs",
+ json={"ref": f"refs/heads/{branch_name}", "sha": sha},
+ )
+
+ if resp.ok:
+ logger.info(f"Branch created: {branch_name} from {from_branch}")
+ return {"ok": True, "branch": branch_name, "sha": sha[:8]}
+ elif resp.status_code == 422:
+ logger.info(f"Branch already exists: {branch_name}")
+ return {"ok": True, "branch": branch_name, "already_exists": True}
+ else:
+ logger.error(f"Branch creation failed: {resp.text[:300]}")
+ return {"ok": False, "error": resp.text[:300]}
+
+ def open_pr(
+ self,
+ title: str,
+ source_branch: str,
+ description: str = "",
+ destination_branch: str = "main",
+ ) -> dict[str, Any]:
+ if not self.is_configured:
+ return {"ok": False, "error": "Not configured"}
+
+ resp = self._session.post(
+ f"{self._repo_url}/pulls",
+ json={
+ "title": title,
+ "head": source_branch,
+ "base": destination_branch,
+ "body": description,
+ },
+ )
+
+ if resp.ok:
+ d = resp.json()
+ logger.info(f"PR opened: #{d['number']} — {title}")
+ return {"ok": True, "pr_number": d["number"], "pr_url": d["html_url"]}
+ else:
+ logger.error(f"PR creation failed: {resp.text[:300]}")
+ return {"ok": False, "error": resp.text[:300]}
+
+ def add_pr_comment(self, pr_number: int, content: str) -> dict[str, Any]:
+ if not self.is_configured:
+ return {"ok": False, "error": "Not configured"}
+
+ resp = self._session.post(
+ f"{self._repo_url}/issues/{pr_number}/comments",
+ json={"body": content},
+ )
+
+ if resp.ok:
+ return {"ok": True, "comment_id": resp.json()["id"]}
+ return {"ok": False, "error": resp.text[:300]}
diff --git a/mcp/jira.py b/mcp/jira.py
new file mode 100644
index 0000000..0077479
--- /dev/null
+++ b/mcp/jira.py
@@ -0,0 +1,227 @@
+"""
+Jira MCP server — all Jira interactions go through this module.
+
+Tools exposed:
+ create_issue() — create a ticket (Story, Task, Bug, Sub-task)
+ get_issue() — fetch issue details
+ transition() — move an issue through workflow states
+ add_comment() — add a comment to an issue
+ search_issues() — JQL search
+ get_board_status() — summary of project board
+
+Commit messages / comments follow convention: [agent:name] description
+"""
+from __future__ import annotations
+
+import logging
+import os
+from typing import Any
+
+import requests
+
+logger = logging.getLogger("mcp.jira")
+
+
+class JiraMCP:
+ def __init__(
+ self,
+ url: str | None = None,
+ project_key: str | None = None,
+ email: str | None = None,
+ api_token: str | None = None,
+ ):
+ self._url = (url or os.getenv("JIRA_URL", "")).rstrip("/")
+ self._project_key = project_key or os.getenv("JIRA_PROJECT_KEY", "")
+ self._email = email or os.getenv("JIRA_EMAIL", "")
+ self._api_token = api_token or os.getenv("JIRA_API_TOKEN", "")
+
+ self._session = requests.Session()
+ if self._email and self._api_token:
+ self._session.auth = (self._email, self._api_token)
+ self._session.headers["Accept"] = "application/json"
+ self._session.headers["Content-Type"] = "application/json"
+
+ if self.is_configured:
+ logger.info(f"Jira MCP initialized: {self._url} project={self._project_key}")
+ else:
+ logger.warning("Jira MCP initialized without credentials — offline mode")
+
+ @property
+ def is_configured(self) -> bool:
+ return bool(self._url and self._project_key and self._email and self._api_token
+ and "yourteam" not in self._url and "your@" not in self._email)
+
+ @property
+ def _api(self) -> str:
+ return f"{self._url}/rest/api/3"
+
+ def create_issue(
+ self,
+ summary: str,
+ description: str = "",
+ issue_type: str = "Task",
+ labels: list[str] | None = None,
+ agent_name: str = "system",
+ priority: str = "Medium",
+ ) -> dict[str, Any]:
+ """Create a Jira issue. Auto-labels with 'AI-generated'."""
+ if not self.is_configured:
+ logger.warning(f"Jira issue skipped (offline): {summary}")
+ return {"ok": False, "error": "Not configured"}
+
+ all_labels = list(set((labels or []) + ["AI-generated", f"agent-{agent_name}"]))
+
+ # Atlassian Document Format for description
+ adf_body = {
+ "version": 1,
+ "type": "doc",
+ "content": [
+ {
+ "type": "paragraph",
+ "content": [{"type": "text", "text": description or summary}],
+ }
+ ],
+ }
+
+ payload = {
+ "fields": {
+ "project": {"key": self._project_key},
+ "summary": f"[{agent_name}] {summary}",
+ "description": adf_body,
+ "issuetype": {"name": issue_type},
+ "labels": all_labels,
+ }
+ }
+
+ resp = self._session.post(f"{self._api}/issue", json=payload)
+
+ if resp.ok:
+ data = resp.json()
+ key = data["key"]
+ logger.info(f"Created {key}: {summary}")
+ return {
+ "ok": True,
+ "key": key,
+ "id": data["id"],
+ "url": f"{self._url}/browse/{key}",
+ "summary": summary,
+ }
+ else:
+ logger.error(f"Jira create failed ({resp.status_code}): {resp.text[:300]}")
+ return {"ok": False, "status_code": resp.status_code, "error": resp.text[:300]}
+
+ def get_issue(self, issue_key: str) -> dict[str, Any]:
+ if not self.is_configured:
+ return {"ok": False, "error": "Not configured"}
+
+ resp = self._session.get(f"{self._api}/issue/{issue_key}")
+
+ if resp.ok:
+ d = resp.json()
+ fields = d["fields"]
+ return {
+ "ok": True,
+ "key": d["key"],
+ "summary": fields.get("summary"),
+ "status": fields.get("status", {}).get("name"),
+ "assignee": (fields.get("assignee") or {}).get("displayName"),
+ "labels": fields.get("labels", []),
+ "priority": fields.get("priority", {}).get("name"),
+ }
+ return {"ok": False, "error": resp.text[:300]}
+
+ def add_comment(self, issue_key: str, body: str, agent_name: str = "system") -> dict[str, Any]:
+ if not self.is_configured:
+ return {"ok": False, "error": "Not configured"}
+
+ adf_body = {
+ "version": 1,
+ "type": "doc",
+ "content": [
+ {
+ "type": "paragraph",
+ "content": [{"type": "text", "text": f"[agent:{agent_name}] {body}"}],
+ }
+ ],
+ }
+
+ resp = self._session.post(
+ f"{self._api}/issue/{issue_key}/comment",
+ json={"body": adf_body},
+ )
+
+ if resp.ok:
+ return {"ok": True, "comment_id": resp.json()["id"]}
+ return {"ok": False, "error": resp.text[:300]}
+
+ def transition(self, issue_key: str, target_status: str) -> dict[str, Any]:
+ """Move issue to a target status (e.g., 'In Progress', 'Done')."""
+ if not self.is_configured:
+ return {"ok": False, "error": "Not configured"}
+
+ # First, get available transitions
+ resp = self._session.get(f"{self._api}/issue/{issue_key}/transitions")
+ if not resp.ok:
+ return {"ok": False, "error": resp.text[:300]}
+
+ transitions = resp.json().get("transitions", [])
+ match = next((t for t in transitions if t["name"].lower() == target_status.lower()), None)
+
+ if not match:
+ available = [t["name"] for t in transitions]
+ return {"ok": False, "error": f"No transition to '{target_status}'. Available: {available}"}
+
+ resp = self._session.post(
+ f"{self._api}/issue/{issue_key}/transitions",
+ json={"transition": {"id": match["id"]}},
+ )
+
+ if resp.status_code == 204:
+ logger.info(f"Transitioned {issue_key} to {target_status}")
+ return {"ok": True, "key": issue_key, "new_status": target_status}
+ return {"ok": False, "error": resp.text[:300]}
+
+ def search_issues(self, jql: str | None = None, max_results: int = 50) -> dict[str, Any]:
+ """Search issues using JQL. Defaults to all project issues."""
+ if not self.is_configured:
+ return {"ok": False, "error": "Not configured"}
+
+ query = jql or f"project = {self._project_key} ORDER BY created DESC"
+ resp = self._session.post(
+ f"{self._api}/search/jql",
+ json={"jql": query, "maxResults": max_results, "fields": ["summary", "status", "assignee", "labels", "priority"]},
+ )
+
+ if resp.ok:
+ data = resp.json()
+ issues = []
+ for iss in data.get("issues", []):
+ f = iss["fields"]
+ issues.append({
+ "key": iss["key"],
+ "summary": f.get("summary"),
+ "status": f.get("status", {}).get("name"),
+ "assignee": (f.get("assignee") or {}).get("displayName"),
+ "labels": f.get("labels", []),
+ })
+ return {"ok": True, "total": data.get("total", 0), "issues": issues}
+ return {"ok": False, "error": resp.text[:300]}
+
+ def get_board_status(self) -> dict[str, Any]:
+ """Get a summary of the project board."""
+ result = self.search_issues()
+ if not result["ok"]:
+ return result
+
+ status_counts: dict[str, int] = {}
+ for iss in result["issues"]:
+ st = iss.get("status", "Unknown")
+ status_counts[st] = status_counts.get(st, 0) + 1
+
+ return {
+ "ok": True,
+ "project": self._project_key,
+ "total_issues": result["total"],
+ "by_status": status_counts,
+ "recent_issues": result["issues"][:10],
+ }
diff --git a/mcp/slack.py b/mcp/slack.py
new file mode 100644
index 0000000..f39d950
--- /dev/null
+++ b/mcp/slack.py
@@ -0,0 +1,113 @@
+"""
+Slack MCP server — all Slack interactions go through this module.
+
+Tools exposed:
+ send_message() — post to a channel or thread
+ read_channel() — fetch recent messages from a channel
+ pin_message() — pin a message in a channel
+
+No agent should import slack_sdk directly. Use this wrapper.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+
+from slack_sdk import WebClient
+from slack_sdk.errors import SlackApiError
+
+logger = logging.getLogger("mcp.slack")
+
+
+class SlackMCP:
+ def __init__(self, bot_token: str | None = None):
+ token = bot_token or os.getenv("SLACK_BOT_TOKEN", "")
+ self._client = WebClient(token=token)
+ self._team_channel = os.getenv("SLACK_TEAM_CHANNEL", "")
+ self._alert_channel = os.getenv("SLACK_ALERT_CHANNEL", "")
+
+ def send_message(
+ self,
+ text: str,
+ *,
+ channel: str | None = None,
+ thread_ts: str | None = None,
+ blocks: list[dict] | None = None,
+ ) -> dict:
+ """
+ Post a message to a Slack channel.
+ Defaults to the team channel if no channel specified.
+ Returns the Slack API response data.
+ """
+ target = channel or self._team_channel
+ if not target:
+ raise ValueError("No channel specified and SLACK_TEAM_CHANNEL not set")
+
+ try:
+ kwargs: dict = {
+ "channel": target,
+ "text": text,
+ }
+ if thread_ts:
+ kwargs["thread_ts"] = thread_ts
+ if blocks:
+ kwargs["blocks"] = blocks
+
+ response = self._client.chat_postMessage(**kwargs)
+ logger.info(f"Message sent to {target}: ts={response['ts']}")
+ return {
+ "ok": True,
+ "channel": target,
+ "ts": response["ts"],
+ "message": response.get("message", {}),
+ }
+
+ except SlackApiError as exc:
+ logger.error(f"Slack send_message failed: {exc.response['error']}")
+ return {
+ "ok": False,
+ "error": exc.response["error"],
+ "channel": target,
+ }
+
+ def send_alert(self, text: str, **kwargs) -> dict:
+ """Convenience: send to the alert channel."""
+ return self.send_message(text, channel=self._alert_channel, **kwargs)
+
+ def read_channel(
+ self,
+ channel: str | None = None,
+ limit: int = 20,
+ ) -> list[dict]:
+ """
+ Fetch recent messages from a channel.
+ Returns a list of message dicts.
+ """
+ target = channel or self._team_channel
+ if not target:
+ raise ValueError("No channel specified and SLACK_TEAM_CHANNEL not set")
+
+ try:
+ response = self._client.conversations_history(
+ channel=target,
+ limit=limit,
+ )
+ messages = response.get("messages", [])
+ logger.info(f"Read {len(messages)} messages from {target}")
+ return messages
+
+ except SlackApiError as exc:
+ logger.error(f"Slack read_channel failed: {exc.response['error']}")
+ return []
+
+ def pin_message(self, channel: str, timestamp: str) -> dict:
+ """Pin a specific message by its timestamp."""
+ try:
+ self._client.pins_add(channel=channel, timestamp=timestamp)
+ logger.info(f"Pinned message {timestamp} in {channel}")
+ return {"ok": True, "channel": channel, "ts": timestamp}
+
+ except SlackApiError as exc:
+ logger.error(f"Slack pin_message failed: {exc.response['error']}")
+ return {"ok": False, "error": exc.response["error"]}
diff --git a/mcp/vector_store.py b/mcp/vector_store.py
new file mode 100644
index 0000000..f050e24
--- /dev/null
+++ b/mcp/vector_store.py
@@ -0,0 +1,118 @@
+"""
+Vector Store MCP server — ChromaDB wrapper for RAG over sessions and artifacts.
+
+Tools exposed:
+ embed_and_store() — chunk text, embed, and upsert into a collection
+ query() — semantic search over a collection
+ delete() — remove documents by ID
+
+Used by: Coach Memory Agent, ML Decision Agent
+Embedding model: all-MiniLM-L6-v2 (consistent with client ML POC)
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import Any
+
+import chromadb
+from chromadb.config import Settings
+
+logger = logging.getLogger("mcp.vector_store")
+
+DEFAULT_PERSIST_DIR = os.getenv("CHROMA_PERSIST_DIR", "./memory/chroma")
+COLLECTION_SESSIONS = "coach_sessions"
+COLLECTION_DECISIONS = "ml_decisions"
+
+
+class VectorStoreMCP:
+ def __init__(self, persist_dir: str | None = None):
+ path = persist_dir or DEFAULT_PERSIST_DIR
+ self._client = chromadb.PersistentClient(
+ path=path,
+ settings=Settings(anonymized_telemetry=False),
+ )
+ # Use local ONNX embedding model — no API key required
+ from chromadb.utils.embedding_functions import ONNXMiniLM_L6_V2
+ self._embedding_fn = ONNXMiniLM_L6_V2()
+ logger.info(f"ChromaDB initialized at {path} (embedding: ONNX MiniLM-L6-v2)")
+
+ def get_or_create_collection(self, name: str) -> chromadb.Collection:
+ return self._client.get_or_create_collection(
+ name=name,
+ metadata={"hnsw:space": "cosine"},
+ embedding_function=self._embedding_fn,
+ )
+
+ def embed_and_store(
+ self,
+ collection_name: str,
+ documents: list[str],
+ metadatas: list[dict[str, Any]],
+ ids: list[str],
+ ) -> int:
+ """
+ Store documents in a ChromaDB collection.
+ ChromaDB handles embedding via its default model.
+ Returns the number of documents stored.
+ """
+ collection = self.get_or_create_collection(collection_name)
+ collection.upsert(
+ documents=documents,
+ metadatas=metadatas,
+ ids=ids,
+ )
+ logger.info(f"Stored {len(documents)} documents in '{collection_name}'")
+ return len(documents)
+
+ def query(
+ self,
+ collection_name: str,
+ query_text: str,
+ n_results: int = 5,
+ where: dict | None = None,
+ ) -> list[dict[str, Any]]:
+ """
+ Semantic search over a collection. Returns ranked results with
+ document text, metadata, and distance scores.
+ """
+ collection = self.get_or_create_collection(collection_name)
+
+ kwargs: dict[str, Any] = {
+ "query_texts": [query_text],
+ "n_results": min(n_results, collection.count() or 1),
+ }
+ if where:
+ kwargs["where"] = where
+
+ if collection.count() == 0:
+ logger.warning(f"Collection '{collection_name}' is empty")
+ return []
+
+ results = collection.query(**kwargs)
+
+ parsed = []
+ for i in range(len(results["ids"][0])):
+ parsed.append({
+ "id": results["ids"][0][i],
+ "document": results["documents"][0][i] if results["documents"] else "",
+ "metadata": results["metadatas"][0][i] if results["metadatas"] else {},
+ "distance": results["distances"][0][i] if results["distances"] else None,
+ })
+
+ logger.info(
+ f"Query on '{collection_name}': '{query_text[:60]}...' → {len(parsed)} results"
+ )
+ return parsed
+
+ def delete(self, collection_name: str, ids: list[str]) -> None:
+ """Remove documents by ID from a collection."""
+ collection = self.get_or_create_collection(collection_name)
+ collection.delete(ids=ids)
+ logger.info(f"Deleted {len(ids)} documents from '{collection_name}'")
+
+ def count(self, collection_name: str) -> int:
+ """Return the number of documents in a collection."""
+ collection = self.get_or_create_collection(collection_name)
+ return collection.count()
diff --git a/minutes/2026-01-22-cleaned.md b/minutes/2026-01-22-cleaned.md
new file mode 100644
index 0000000..7e1986a
--- /dev/null
+++ b/minutes/2026-01-22-cleaned.md
@@ -0,0 +1,35 @@
+# Cleaned Transcript — 2026-01-22
+
+**Hrishik**: Okay, so the next topic we have is how we are gonna be working together. And the major points we wanted to cover was your availability, modes of communication, onboarding, and the documentation resources that we should have. So, for availability, I think this lot kind of works in the enterprise. Because that's when the RFP is looking really bad. So, Thursdays around this time, it needs to respond. And very quickly, I think we're all available on GM still, so if you ever have to ask something about it. And, promotes a community initiative on the users, depending on those of them. What we do is we probably provision around, like, paragraphs for RP, so that you all have part of our teams, and we can extend it for any time. Other than that, for the onboarding process, it's pretty open, which is, we, give you access to whatever you need for the initial part of the application or, various resources, and how the data looks type for various parts of the project. And, you can test it to deliver on the windows. So probably we'll ask you guys to, invest in your annual times by graph, or we'll give you feedback, if that's, We don't do that, right? So we just ask you many items that we already have those, and then you just onboard you out to our system so that you have access to, like, the other things. And as we grow… as we go about the project, we give you more and more access. Because it's not a long process, at least with us, so as long as you ping me, Jake or David, you will get your access of that. And also, please include the mentor who's access on Teams. Yeah, 100%. Yeah, you should. Yeah. We'd like to know what you guys are talking about. I don't want the team to promise the moon. I also wanted to ask about the documentation, with that, like, which, Apprentice use, So to have access to the previous team's documentation really depends. It might be useful for them to understand that. You can share that across, yeah. Perfect. I'm going on. final onboarding docs so we can be set with those. But you've changed things since then? Yeah. Yeah, we've not so much changed so much and added. We've added a lot of things, but yeah, we should make sure that's… Yeah, that'd be okay for me, yeah. Next to the project purpose and background. From your guys' point of view, the motivation, benefits, stakeholder of the feature you're gonna try to build. Hey, guys. So, with the motivation of it, it's mainly to, I think. get the process of getting the initial product data in a reliable manner onto the system. That's, I think, is the only one we can play. Because I think right now, the only way we can probably do that better is by just getting more data and probably having more people look at the data and understand it, which is not the way to go about it, because that's not scalable anymore, like, just moving product. I want to add that too, yeah, just… there's a lot… it's a very human process right now to, like, one vendor sends something one format, another vendor sends something another format. Teams of people have to go through and interpret it, and then try and RAM, whatever that is, into the existing structure or, like, attributes or ways of looking. And, just that process. We'd like to make that a lot more hands-off, where they can just upload something, it'll get it in there, and then they can do future modification. Maybe it's not right or wrong, but just getting it in there in the first place takes so much time right now. And then… and then once thin, making it as robust as possible. So, filling out all of the attributes that a particular product might have, be that by scraping the PDFs, or scraping a vendor website, or whatever we need to do to try to get those back from people without them knowing an entrance. Is kind of step two, in my sense, and then step three is… is… I forget what you called it. It's like, there's, like, a man-in-the-middle kind of concept for female blue. Yeah, that's it. There's a freedom in the loop concept where, yeah, we'll do as much as we can, and we'll probably need some approvals on that and whatnot, but then also, like. what needs updated? What, can we, like, score products so that we see, like, how big each product is, that we can pull up a download of products that is, like, all of these are, like, you know, 20% down there. I mean, we're, like… How can we get to that next step of at least telling the human what they need to do more work on? They're not telling the AI what it needs to be able to work on. I'm curious, what is the frequency of new vendors coming on board and you have to ingest those data? I imagine it's very modest for you. Yeah, we're open to increase this more and more with the new product that we're launching. It is aimed at the small and medium-sized business market. So, most of our clients right now are, like, enterprise clients, so… For them, we're always onboarding more vendors, but it's not like 100 vendors haven't done yet. Hopefully, in the future, it will be moved. But a lot of that's gonna be overlap, so that vendor A sells, like, Apple. And vendor B also sells Apple, but we need to make a good catalog of, like, Apple devices, and then be able to link them up to each vendor, and add on the original vendor attributes from there. Part of that is just already built when they're being built, and are doing stuff. I mean, make sure that I have all the attributes that it needs with as little interconnection as possible.
+
+**Dennis Grinberg**: And Bill, do you have any…
+
+**Hrishik**: Oh, go ahead.
+
+**Dennis Grinberg**: I was gonna say, do you have any… data around… I guess two things. One is how accurate humans are at this endeavor. And the second is… If you think about the, you know, the total sort of workflow, I don't know what your workflow is, but I can imagine, you know, looking at the data, entering it, someone else verifying it, like, where the, you know, analysis of how much time is spent on those different Activities within that work, workflow.
+
+**Hrishik**: No, I was just gonna say, we are currently actually fully launching gyms to production now, so all the workflows that we've done are on the legacy L1 systems. So I don't have good methods to start with them on how long it takes. We… now that VIMS is launching, we can start recording that as the current state, so that we can see how much better we are at the end of the year. And then, what was the first part of that question, too?
+
+**Dennis Grinberg**: What… how accurate are humans at this activity?
+
+**Hrishik**: I mean, I guess we could use those metrics, Oh, no, I don't know if we have how accurate, like, we could easily show… Yeah, like, what a good end, goal of it is based on, like, the PDF we took at the beginning, but for accuracy, that's a good question. So what I was going to say was, we could surely connect that information and send it to… send it across to you. We just haven't acted at the beginning of the night. I don't know if.
+
+**Dennis Grinberg**: team's gonna need it, but just… these are just, like, things that, hey, I'm interested, I'm curious.
+
+**Hrishik**: You'd like some metrics to see what the overall process on which it's improved. So I guess the one question would be, you ingest the data, you've got it there. Are there, future reports to say the data's inaccurate? Do you have information like that, or… Yeah, how do you discover that? It's usually word of mouth, or yeah, the people using our site, or the catalog team will notice something, and then they'll go in and change that, or our salespeople will, in talking to customers, realize something's wrong, or not acting, or they're missing. In the new product, we'll probably have a crowdsourcing element doing that, where people That's for having this information on the record. But you have no centralized loading, according to that model. The bigger problem now is probably, as we add… new suppliers for our existing clients. It's pretty easy to get a part and a price in there, to actually know all the attributes. We're not doing it a lot of the time, because it's just too much effort, for us to spend on all of the new product lines that are being added. But for the end user, of course, it would be very helpful to have the gas release. So, for the sake of getting it up and running, and it works, and they can technically buy it, not having them is fine, and that's what we do, but we're looking at how can we get that better user experience with marketing. Just some background and context, too, into, like, the… just the attributes in general. We… we have a lot of attributes. The catalog team, really, the majority of their time is looking at more attributes and coming up with… coming up with different ways to classify things. So, we're especially in the building controls industry. Like, there's not one in this room, but, like, a valve for a pipe, like, in the bathroom or something like that. Like, they want to know what material it is, what the pressure rating is, the heat resistance rating, like, we… it's really driven, and that is we… Because when they're going to look up parts, they don't really care about the brand, they just want to be able to filter by, I need this rating, I need these to handle this pressure, needs to handle this temperature range, automatic off, like, normally closed, like, it's really around the attributes, as opposed to where we've been making the iPhone, but that's… that's a little more, like, you know you want an iPhone when you go. This was more like, you don't care what brand it is, you know you need 16 gigs on your phone, or you need, like, 5G. Yeah. So the thinking is, for each kind of product category, a human is already doing this, and probably because she'll be making, like, this category of products needs this set of activities. So then, when you're trying to ingest the product, first we have to try to figure out what category is. This buyer can probably give us something first on that, meaning they match product for ours. And then the second step is, okay, now I know what category it is, it needs all these attributes, I like this. So does the vendor sometimes supply spec sheets? Yes, yes. Do you link parts to spec sheets or not? Yep, yeah, we have a lot of parts linked to spec sheets today. But again, the challenge with new parts, like. Ideally, the vendor is giving us a link to a spec sheet when they're giving us the product, and that can be helpful to you guys. We've been trying to pull that spec sheet into our own control and work off of it. It's not always going to be that easy, but in the best case scenario, they do. Sometimes we don't, though, and the catalog team literally goes out to their websites, their lipsticks, they can go online and tries to infer, and then put that in. I think you also mentioned in the presentation before that, some vendors, give you guys some catalogs, like, physical catalogs, or, like, so the catalog team has to, like, manually enter all the data from that? And as a part of this, we'll want to automate that as well, right? Like, a way to… Yeah, so it's not… it used to be that we'd get, like, they would try to, like, enter in as much as we could. We're not dealing with that anymore, but they still might get a big gap, right? Like, but more often than not, the vendor is able to give us something digitally, some sort of self-spread key or whatever. So that's the bulk of what we're trying to focus on. Good question, how structured or unstructured the data is? Yeah, yeah. If thereof. kind of, like, an ideal layout of form that you, kind of have in mind for, like, each part, like, accept those terms should ideally have, like, use both listed for each. Yeah, there's definitely, like, a basic schema below where, like, it needs a product number, which is, like, what you refer to it as the name of the product, and then we have supplier product number. Sometimes, like, suppliers have their own, way that they, like, label a product versus what they sell to the public. Couple more fields, like description, list, cost, But after that, the rest is all gets into attributes and just kind of more, like, things associated with it. Yeah, and that's what I'm saying, a human can say, like, what category is tablets, right? So, like, what's the screen size of the tablet? Is it, like, pen enabled? What, what, like, standard of pen devices does this tablet use? What color is it, of course, like, humans, obviously brand this kind of separate thing, but we keep going on and on. We can have humans to find, like, tablets and make those funny things. And then, it's kind of you guys' job to figure out how they get those funny things from the, like, whatever we're giving from the vendors, and work them into, like, what we want them to do. Yeah. We do have, like, a chat meeting and an annual meeting, where we have attributes map the latest products within the field. So that's… so that… Yeah, so, like, with the tablets, Joe just listed some, like, we would have the category of tablets, and then we would have attributes assigned to that. So all tablets should have the attribute of screen size. All tablets should have the attribute of pen enabled. And then as we upload a new product into the tablet category, we automatically know, okay, it needs a value for string size, it needs a value for Venity, like, so we have… the value that the product has, is downstream of us, like, before that, matching an attribute to a category, and then everything in the category has to have value to spend on the attribute. And that gets back to what Joe was saying earlier, like, the third part, which is saying this product is 20% ready. We would have a way to kind of do that based on, like, you've only entered 2 of the 10 attributes associated with this Products category, product, we need to know. the screen status. You can enter a screen status, that could be a way to determine what information is missing. We're not always doing this yet. We can also rank, right, these are the top four attributes that are absolutely needed to go into this product, and then the rest of them advanced away the extra… it would be nice to have them, but… Ideally, we have these four groups. Is there… I know before we talked about the most sensitive thing from the vendor when it's pricing. Is there any correction of information about a product that makes it more sensitive or not, or are you advancing in any state? It's really just the pricing. One or two brands that are a little picky with, like, who can see their products, so, we just… But, yeah, it's just a matter of, like, who can actually sell their products, but that's not something that we can still go on their website to buy and see most of their products publicly. So the only thing we're really concerned about doing Friday was probably from the looks of it, that I think we'll have to have discussions with the catalog team, just to understand how exactly they, like, search all the information, so we can do the same thing, just in an automated fashion. So, like, we'll have access to them, like you said, you can miss them on Teams, or love to… you can come over to ePass as well for meeting them in person. We could catalog team with the e-ports catalog team. we can probably, like… you would be able to message, so, like, the catalog work that we have at eBARTS. Our parent company, Ops Control Trolls, has a much larger, catalog team. No, with them, that would probably be, yeah, something in person would be ideal, but… Yeah, if you can come back, that'll be easier for them as well. But with the eParts catalog person, yeah, you could feel free to reach out to them on Teams. Apparently, it helps those most of the catalog work, so… We do some… like I said, we don't worry about the attributes as much. Just because there's, like, one or two people in our movement does catalog at all, and they also do other things. It's still only, like, four people, but they're all actually dedicated to cataloging. We'll get a ton of… And they are… Alps is an e-commerce, like, distributor in their building control space, so they are highly incentivized to make… break down our data wigs so that they're helping their customers buy the right thing because they bought them that instead of someone else. Alright. Most of our customers, it's the tools where, the engineer needs to, you know, they're, like, kind of locked in using our platform for the corporate or whatever. So it's, like, okay to not have the best catalog data. We don't want the better. Alps needs to be able to have to make it better, basically. Can you guys keep track of cases, though, somebody orders something and turns out it's the wrong things? I think we would have that now, figuring my CDC on, right? Yeah, and we could do a lot of inferring, too, with RMA, we could match RMAs against it. We don't have structured data on that, but… Okay. I think, Can we touch up on the, like, we had an ML component to the project? I was just a bit curious about, it said that we have to, like, you know, for future also, we plan to use ML for further operations. How, like, which all aspects are you planning to use machine learning and, like, confidence scores and other things? So, I mean, content scores would be relevant to the part where, we predict these attributes for that product. So, equivalence, you get a certain product, in a certain activity, and you should be able to map these attributes, so in case the basic course you have. We should be able to do some sort of step-by-step sampling, and then figure out what would be the appropriate attribute for this testing. context and emails. But it's just… it's just mapping, and it's also, a little bit of, This was just gone. what could be the attribute for this specific feeling. That's also in terms of figuring it out in your attributes we can use. So, And that's kind of the main thing. So, when we do make these predictions for these best academies, we do assign a conference for the year. And I think that's where confidence comes in. Where you see… so the confidence score is a little bit more important. Exactly. So then the system learned that this was low, and then beyond. This is the automatically corrected one. So that's the next step, right? So, you initially have given the rule, and then as you… as you keep getting more and more of similar errors. you can attach another part of the system which kind of rectifies itself, or you can still try and do that if it's goodnight. So it all depends on how the project goes, how it kind of turns out to me, and how it works, you know. So are all the attributes spectra greens, or is there anything that's a photograph of a 3D rendering, or ammsterious will sometimes extend just beyond touch to a certain attribute? We'll have images of, you know, docking, manuals, or spec sheets, or product pictures, yeah, but I don't think anything that would be an actual, like, fall under an attribute of skeleton. Yeah, there wouldn't be, like, the aggraves in the case. So, Chris just mentioned that you have the category as one who has, like, a schema of attributes, right? So, if the attributes is the fixed part here, why are we applying the ML to the fixed part? Like, why do we need a confidence score to be assigned to this? Like, why not to the category, because That's what is a variable thing, identifying with that process. I mean, it could be fluid, right? So, those patterns… you can try and turn the ketamine per product, which could be content. Next is, once you do figure that out and go into that, and we'll start things. You don't know a specific… so, for example, if I say, A specific attitude, for example. take, like, the pressure of the percentage of the water or something like that. Like, for example, if it's 20 kilopascals per day. What we assign is actually, something like 20 seconds in a row instead. So, that would be for the attribute and the purpose code. So, it might not know the exact kind of, you need to put in there, value to put in there. It could be… it could just confuse that, I mean, I think as we, have, like, this internally, model-based kind of products. learn specific values for the team, you can close the jurisdiction of a green basket during the vertex of being virtual, then you would have to… even when we do validations for that. So that, you know, okay, these certain values are the exact sort of values you're looking at, because there might not be enough data to actually implement… But then, you, the values that we're talking about, do I, like… you mentioned in the spec talk, right? So, what's going on with the value? Again, so it's not going to tell you, like. let's say it this way, different suppliers will provide different sets of athletes, like, like, Apple might ball spring size, spring size, and someone else might call it, like, Or whatever, like, display size, or whatever. And sometimes it's not that cut and dry, either. Sometimes it's not just string size, display size. Sometimes it's, like. the gas flow rate versus the water flow rate versus, like, there is some interpretation to be done between suppliers to be able to match them up to a similar standard. Yeah. Do your suppliers ever stop-check their data? I'm just curious if there was a feedback loop there now. On the outside, they do sometimes, that, on our side, we haven't experienced that so much, sorry. Alright, I think it would break his… So, the manual process right now, we thrive to get the supplier to tell us our algorithm style. So we give them a spreadsheet that describes the attributes that we want for these products. There's a lot of manual effort for them to actually build it out, and a lot of them just don't. So, they have the opportunity to try to make it right, but it's too much effort for either that. Thereabouts. maybe a good question, but was it ever the other way around? Did they come up and tell you that, okay, this is how we want the product to be, you know, categorized, or these are the attributes that, and then… what I mean to ask is, let's say the iPad. In your system, you just have 4, but then, since they know the internet working, maybe, like I'm just saying, are they like, okay, I need a first one. So, was there a requirement? So, so first off, we can totally take a fifth one right now, if they want to add in a particular attribute that isn't in that required set, that's fine. Is that even possible? Like, are they allowed to come up with a requirement that starts? They are. Really able to define the data set for our catalog, because So they… they have their own data, right? They have their own catalog somewhere, and some days they… some of them can give us a lot of that data, and some of them can't, but, Our job is to modernize data between different manufacturers, so we… we have to have the liberty to be able to change the modifier that begins with, for the sake of ease of use for the actual application. Yeah, but also, some of the vendors could want, more so in the case of, like, ELPS. They could want their products to get more visibility to customers. They would want their products to show up in more searches based on more attributes, and especially when they're working out, like, the deal of the price they're going to be giving Alps for Alps to buy it from them. There's, like, all these negotiations, and a lot of times they will actually look at their catalog at Alps and see, like, hey, this is the way we're… you're showing our products, we'd like. Something like this. Okay. You know, next part. Your experience with the previous two-year project that you guys have sponsored? Any, like, just about the previous experience, any do's and don'ts that you have for us? Individual mind. Yeah, do, reach out to us on Teams, like, as much as possible, like, we do not mind getting, like, a lot of communication, a lot of messages, a lot of posts, like, we prefer that rather than, yeah, like, saving it all for the next week's meeting. Like, if something comes up like that, just reach out. It's, you know, we're all very, we have very fast communication open, so I'd say that's a big one. We were like, very briefly in the presentation, mentioned it, but we… we were like, this idea of after our January deal and debate. So, like, just because we're saying something needs to be submarried doesn't mean… like, you're already doing something that's great, or something that's bad. Yes. we'll be glad to think of it differently, or explain why we shouldn't think of it differently, but, like, I'd rather have that discussion than things. We will never take offense to you thinking about it differently than we do. is probably the order as a document. They're no stupid virtual. Yeah, yeah, yeah. I would say, like, asking the same question again and again is still fine. It kind of helps to clear the gap and creates a lot of communication opportunities. Also, I'd say too, follow up with us, like, if you… if we commit to something in a meeting, and you haven't heard much from us, like, quote-unquote, bother us, like, reach out and be like, hey, you know, like, I haven't heard back with this, like, again, we want that, as opposed to then we get to the next week's meeting, and it's like, oh, did you get a chance to do that when you forgot about it all week or something? Any particular don'ts that you guys think? You should be mindful of. kind of covered it in between. Probably, just don't go AWOL provider and then just pop up two weeks later, whatever. I mean, we completely agree with that, too, but it'd just be way more easier for us to keep practicing that help you guys are doing. Yeah, I'd say that's the main one. Other than that, we've had a pretty successful project right there. It's just that, I mean. to be fair, like, seeing the auditor doesn't be nice to look into that, and you do have, like, your studies and academics, that's what you guys, and it might be difficult to say that, but I would say if it's, like, a random 2AM, you think of something, and you send it, you have to say something, just send that to us. We'll take you to look at it for our own time, but we're still taking time. It won't be brought in as NFL, so… Yeah, you certainly not make this semester, they only have 4 hours a week to work on this. They have a few other things that are done, so each one of those kind of thing. Summertime is probably one more time at the end of the full day, yeah. How many tunes you would have done? Two or three, I don't remember how many. We had three. Three tunes? Okay, this is our fourth. That's good, yeah, it's good to be an experiment. I mean, when you get a new client who's never had any experience with this team, they think, the team is fully working for us 24-7. To your point, we're getting… we're getting better and better every year with how we're interfacing. Well, a lot of it's about managing expectations, yes. I'm most of it.
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+**Dennis Grinberg**: And I'm gonna put words in the team's mouth, which is, you know, this is a lot of good experience and do's and don'ts. from the team's… I'll actually ask the team, from the team's perspective, is there anything that you want from from eParts, and how you work with them. Have you thought about that?
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+**Hrishik**: We need to be good enough for them does. I think from my point of view, it was just the communication part, like, we are bound to have a lot of questions, because it's going to be a kind of a… struggling from scratch point of view, so we'll have questions, and if, like, you guys can respond, like, that'll be… I think that's the main thing I had in mind. And that you already cleared up, so that's really good. This is our third, product data related. see a new project. So, sometimes, like, for me, sometimes it'll be hard to remember what I've shared with you versus, like, the other teams, so always ask clarifying questions. Well, also, I guess, I guess one thing, too, just to do in previous experience, we've had before, especially, like, with the initial PIMs, we had months of actually just kind of circling back to the initial kind of staying question. There's a lot of confusion, they weren't getting it, it's a very complex distribution model. We won't really get into the distribution between our tenants, but my main point is, like, If there's, like, something you're still unclear on overall, like, we can keep revisiting it. We found that if we get a really, really concrete understanding of that, even if it takes a couple weeks of touching on it every week, it's gonna definitely be better anyway. Yeah, it's important to have domain knowledge from the team's perspective. Go on to the next part. So, now we have the product usage. I think we've already touched up a bit on this, but… Does the ingest pipeline… I don't think there's going to be a different, like, client interface, or how are we trying to, like, do we give clients something that they can, share the data with, or just, the PDF they already submit, and we just process it on our side? For regulation pipeline. Yeah, from the southern, yeah. I mean, we don't want to set, like, harder requirements on this. We… right now, on these files, we're getting, like, by emailing with the vendor. Sometimes it'll be big enough that I think Brian, who runs weekly or something that he sets up with them on his own. We intend… the best… the best long-term reason on this is to make it as automated as possible, just, like, every time. One of them means to be that might be EDI, one of the means is going to be, like, FMP, but it could… I don't want to limit, but it could be, like. setting up a way for them to see this and index it directly into our system. Like, I don't want to say there's absolutely no vendor interface here. They could be one of the best users to clean up some of that, human-in-loop type mistakes that the system is making. So it just depends, on kind of our, our mutual, exploration of the problem. I have a fundamental question here. So, since we have, like, so many vendors who are giving us data, I don't… obviously, like, they have some business requirements with the company, right? So, why don't we have a centralized portal, like, where they just… we just mentioned, some people get it in PDF form, and then some send it over email or DFP? So, why… why not a single format like this? So, where people We'll just feed in there. our data, and then we take it forward from you guys. That's the ultimate goal with him. We, originally had that with Tim from the very beginning, is In addition to the client… in addition to the catalog teams, vendors themselves could come in here and maybe do the work themselves. We'll… We'll be working on some of that this year, too. So there's… there's, like, historical reasons as to why we haven't done that in the past. The catalog maintenance team, historically has been very, Like, they want to fully own the data that gets in the system, they don't want anyone else's fingerprints on it besides their own. Even the vendors. So it was very intentionally manual for a long time. Now, we've drastically outgrown their desire to be intentionally manual. So yes, we are trying to answer questions like that, but it's not great for me. Basically, one of the interesting experiments or research would be to take some data that… data sets you've had before, and sample data sets, we compare them to what's in FENS now, or whatever, just to see how they kind of map out. And see where the gaps are in the crypto can deal with that. And also give them an idea of the data itself, right? We're not answering the questions. Yeah, about the product vision, I think we've also touched up on that, you know, pretty clear idea about it. Yeah, we can… maybe we can talk about the team responsibility and deliverables a bit. So, as, as a deliverable, it's just going to be a system, right, where we have the, like, engine pipeline with the ML model to array the attributes. So, okay. Is there anything else you want to add on these points? Not just that, we wanted also, like, an application part of that that actually inserts something like the stage and things, right? It would be an additional river cylinder model, India, too, and in India, it's… it's just too low with… it's not for companies, like, so usually when the resources, depending on process it, probably churn out some sort of, Predictions, or just programmatic, a programmatic approach to just put these things into the right attributes. Yeah, we want that, the actually… the model, but then also something that puts what the model determines inside these tables. We do have staging environment, so we just have to be a dump for the data name. And then probably the community will pop in, and then you get the final data, which is probably roughly allowed. Feeling. We already have, the quick… our schema right now, basically, like, for the, like, product table. But for every product options table, product attributes table, but for every table we have, we also have a staging product table, a staging product options table, a staging product attribution table. And these tables, that combination is how we have interfaced this. We're almost like a Git difference. They can see, like, this is the product before, and then these are the changes I'm making, and that's where they can approve or decide, no, that's not right, and they go back and change something again. Ideally, we would just need the model, if you would like upload spec sheets, to just pop it into the statement table, and then from there, PIMS handles it, they can see the differences, they can choose to go manually edit more things, or whatnot, but Yeah, does that make sense? I'm curious where the previous studio teams you worked with. Will they ever… did they ever do this an exercise of doing a statement award? Requirements documents? We've gone through requirements. That's gonna be close. Any other possible constraints or risks? That would, like, we should take a keep mind while developing. Without data sensitivity or something on that side? I mean, data sensitivity, just prices. I would say, just to be looking at any of the data, we should be able to provide anything with whatever samples are expected. And, I think… We need this test is really to just put it all down. Understanding the exact scope and making sure that, we have adjusted for the annual risk. I think that those puppies. Okay, next is, about AI usage. I just want to ask, like, how do you guys use AI today in, like, everyday development, or… Around the company. For development, especially for coding, we've been pretty… pretty all-in for since February, since Bobcoding… I mean, Bobcoding, like, I started getting people in the public eye, but we use it a lot. We use Persons pretty extensively for all coding. Of course, all got me inviting me to expanding off. I think the use cases of multiple data for planting, backend news. still paying, handling costs and stuff. We kind of… Have the requirements and all the steps that are… then we sort of start implementing framework, and then go beyond the beginning of the week. I guess, I think it's testing heavy at the event, so the way we do it is, we generate through it, we make sure that we test it extensively. And then, based on change in the attested step, the invoice, and it'll be blue. Also gonna be, just to my, like, the MDs are, like, you know, we started to develop a practice now with the, like, documents inside the code so that it's reading the feature, like, the context is better. Maybe a couple other things we're important. I mean, we do a few things with, like, compass management for, like, in terms of For example, if you're working with desktop features, so you wouldn't need the context of the entire application. So, we've had internal features testing, or markdown plans. Agent needed finding that. So, based off of that. Or at least keep maintaining the manual network. And we're not, we use Cursor, and we all kind of… we evaluated Copilot early on. We all decided we liked Cursor, but we're not restricted to that. Like, if you all decide you want to use TalkBuild or something, or buy other tools like Cursor for what we already have our subscription. Yeah, I wanted to ask if we… for the, like, when we are using AI in our project, like, any part you would expect us to use it, like genetic code and, like, other things, but any other, like, your expectation of where we should be using AI? So, in addition to that point, we will do… I mean, I think that's the best use case we can go, which is why that's always coming out for me. Yes. So, from the record, I guess, let me know. making sure… I mean, even, like, decisions of, understanding what Facebook could be directed opportunities. You can use that to shut down. I would say it's just website recommendation would be other newspost. Just a second. And, for us using those elements, would be… should we use ours, or will we be provided one from EPA versus the subscription that you sell? Yeah, so within Cursor, you can… you can choose for this prompt, I want to use Gemini, or you can switch it, I want to use the symbols, like Sonic, 4.5, or anything. Has anyone of you used Cursing at all? But, we do… I think we should be able to follow. Also, you guys do not want us to use… like, I've been using anything I would be looking, and it seems to be doing a really good job, so do you not want us to use any other algorithms? You could, as long as, it's… I think, I think as long as the data is not based, right? Yeah. Well, we, other than that, it's another… Which is the privacy of the data? Yeah. Okay, we lose approval for that. supporting Netherlands. Would that be, from our side, or would that be, from, like, some start on the campaign? I think it would be, like. We're in a close conclusion on our perspective time ago. So, I think a lot of the work you guys are gonna do is not of… were inside of it being hard to put 16 or anything like that. There was no bad, right? And therefore, for all that stuff, like, as long as you're not instability incorrectly, things free throws, anything like that. I don't mind if you use it that you're having Andy share the stuff with him, right? It doesn't matter that it's not even ours yet. Again, as long as you're not, like, uploading vendor files to that, like, I don't want to be… yeah, I don't… we're not… all the vendor data isn't super sensitive, but I also don't want to just, like, wonky-nilly with it. But, yeah, you're welcome to move your own stuff. Like, if they decide the plot is, like, good, like, I guess we paid for it. Is that what you kind of asked? Oh, that was what I was going towards, just from my experience when I worked at school. Yeah, I don't know what their education pricing is or anything like that. I think a bike ride that provides 5 code is, like, the minimum 300 bucks. For a month, per person. There's really not a minimum 200 bucks a month to a person. So that's… that's part of where my mind is at, but again, if it's a deep first, then we have a good rationale for why I should tell you that, or pitch it. No, I… And I am too, I'm just saying, if there's something that you're saying, that this is actually going to make it 30 times more productive, and there's no way of a product where it's like, here's a to-do list for our city. Let's have the discussion. I mean, to be fair, almost all of these schools kind of have the same. They're all Asian-based, or kind of… application time. They all index files similarly, they all have restrict similar model access to them, similar instability. I think it's just a fundamental… exactly, it's just the underlying problems, like, probably kind of designed EGU, right? But it's super expensive, so, like, I mean… And I would ask, but he spent our money, and yeah, it was your money. Make wise decisions. We're a small company, we're not legitimate enterprise, so it doesn't make a difference to us. But it also does make the difference to us that we passed, and getting the credit out faster, like, whatever. So, the… so the money side works on both. for us to spend money on this, but it'd be smart. That's all. I think next, do you have any questions you guys might have, or anyone from the team? That's having out-of-pank questions? Yeah. So, I have one, How do the handouts happen? Like, do we have proper understanding, right? It depends on the story. So, so there were, like, three parts of the cluster. In one universe, the main, the data pipeline setting, which is scoping, and the visibility. For that, we have, like, the proper knowledge times and daycare after this. We tried to set things up, and then I just tried to run their own books as well. and see if it was working, and if there were any change required. That was a… I mean, it was a super lightweight sessions with you all spent, and that's it. For other things, the document and stuff, we had this kind of, like, documentation handout, where we had to fill it out on what our covers and what we had it. So, it depends on the use case, and what exactly you use. It stands for probably SP Lumines, and, whereas a lot of code stuff. How does your current, the code checking process look like? And starting from, like, the SFS talk, raising theirs, who are the stakeholders, then we should must get approved from? How does that look like? The bucket is what we use, and again, I think whatever emails they look, they probably need to restart the nose at the beginning of So you weren't really, the approvals and things like that. It should be internal. I would say you guys should probably let in as well, and that's up to you guys. Yeah, the only… the only time I could see modifications to, like, something we already have in this thing is, what we talked about earlier is, like, when it comes time to actually put the products in and… We can talk down the road about how we want to do that, or maybe we just make some APIs that we've been playing a password or something down the road. Or you could spin off of… spin off a committee to put down the subway station, so that you guys can install. And, we do also, though, we do use, sonar. We use… we use sonar for limping, too. Do you guys use third-party for, like, security purposes? Like, something like a backdrop or something? To check vulnerabilities in the system? Security would be these, I think someone does it. So, that is pretty, extensive, very, extensive in terms of, what it covers. But yeah, it covers anything, so security to almost minor checks, too. I have a trivial question. What were the names of the previous MSD to follow the team names? First one was Pensate means… and then there was, Option logic will be, unknown event. That's alright. Last year, like, did we even have anything? There was, I think it was… They never used the name, they just used Epard's team. I mean, you guys should have a new one. That's a rule. Be more fun than what happens. I think it's always good to have a team name that is free to court. Yeah, no, it was fun, especially the very decor. And the logo is for the logo. Interesting. There's a decent chance we'll be switching from Bitfucket to GitHub in, like, March. So, we'll see. We don't have any political competition. And you would be our first team using Winninger. We've been in Jira and Bodith has to lose a little more than Fire himself. Obviously, it would need for us doing the onboarding tools. So what were your… what was your rationale going from Jira to… I mean, one of the biggest benefits is that I think just… it's fast. Like, when you open an issue is no sentence instead of multiple seconds. Also, the flexibility. You can very easily, like, earn something from initially to do a task, or, like, it's much more flexible, where Jira was a lot more, like, locked in with DB or the portal. Yeah, it's also bringing the flexibility and the velocity. It accommodates everything, like, 49 stone building the velocity if it accommodates it. And probably just a spring cube. It has… it has support for that one. Probably these are really nice, but… So… It's also a milestone-driven methodology in general, fully move, rather than having, A task for the Msport and class itself. It's best for me. And the UI is not better. Similar to the commercial product? So we know we can show up, and those… Oh, yeah. and… Oh, okay. So we're gonna have a, standing, fund meeting on Thursdays at this time every week to help the idea of the funds? I mean, and if there's… if it says tough, just shoot us some other times, yeah, we do have a couple reoccurrings on our side that we'll have to… that are kind of like, note, but other than that, if need be, we can get some other time or something. I mean, based on your schedules, too, I think that's the bigger factor, so… Yes, aren't… aren't days are pretty much pre-today and… today, for sure. And based on your attributes, we can see what else also they can actually ask as well. And obviously, change the message instead of being back in the year. And I'll let you know that I like to attend with client meetings. I know Dennis won't be able to do that, but I always find it informative to understand what is the dialogue back and forth from the client getting started. I'll be informing the law. Okay. Right there. And last we have an action item review. Any action items? So, for you guys, you, you segregate provide us the access using our annual email IDs, and to our mailroom as well. And then, for any doubts, just restating, the point of contact would be Harsha, Jake, and David. And then, we'll also get access to their teams and the relevant workspaces, like documentation and stuff. a bulk of subscription, for the entire team. And, I mean, not immediately, but then, I guess you would also share details on the current, data accuracy, like, with respect to the existing quality metrics, like, if there's any defined, so that once we develop our system, we can just baseline and compare, how is… how's the whole thing improved or something like this. And on ours, yeah, we'll also, like, set up one recurring call with all of you guys who just check our calendars and then come up with a slot, and then we'll also have to, like, come up with one day where we visit the office and get to meet the parts calendar to understand how the current… how they're working on their business. Where's the coming over? I would also send directly inside the portal. Yes, for sure. That'd be great, that's it. It's both a pleasure to have you on board here. I know the team is excited, looking forward to working on this project, and I think in the end, you know. You'll get an interesting project, and something that hopefully meets your requirements and meets your expectation-based success. So, we'll see how this works out. We're also really looking forward to, the perp… kind of what the purpose of the project is more fancy. We're curious what… what not only this team, but all the teams buying with AI, we're also looking forward to that. It's not a silver bullet, but it's something that's helpful, I hope. Well, very good! This is exciting, yes. I just have a slide deck as well. Yeah, sure. I'll turn them in. And be safe over the weekend. You too?
+
+**Dennis Grinberg**: Yeah.
+
+**Hrishik**: Go snowboarding on Monday if the weather slowed that much down, so you guys should be a first big storm. Yeah, you must have the December storm. Yes. Were you in the store? No, no, I just left before the storm. But he was in Canada, so… Well, years ago, when they had the super big snow cherry, CMUs are the kind of… they want you to come to school, but the city of Pittsburgh did come to close school. They closed the school. We'll see what happens on Monday.
+
+**Dennis Grinberg**: Yeah, it'll be… it'll be interesting. I think there's some places, like, that aren't used to snow at all. Pittsburgh has gotten a little better about it, but, like, you know, DC, if they get a, you know, half an inch of snow, the city closes down, and they're supposed to get Many inches, so it's gonna be… Gonna be interesting.
+
+**Hrishik**: I was sleeping and staying on the front step yesterday, and there was nothing happening. The weather overnight, the sleet one where it was, is very slick, very slickering. Yeah, nasty. Very good. Good to see y'all. Thank you so much.
+
+**Dennis Grinberg**: Hi, everyone.
+
+**Hrishik**: Yep. Let me wait a second thing I want. Are you still there, Dennis? Dennis, let the content. Alright. Okay, thank you. Thank you. Make movies. I would certainly say that after a client meeting, it's always good to have a follow-on with the mentors there, just to say what their perspective is on the meeting, if there's anything else that popped up. So, it's always good for you guys to also kind of have a discussion post-meeting about anything you heard, or something you need to follow up on, so… We can stop on Friday. It was always good to record everything.
\ No newline at end of file
diff --git a/minutes/2026-01-22-client.json b/minutes/2026-01-22-client.json
new file mode 100644
index 0000000..7b6ebf5
--- /dev/null
+++ b/minutes/2026-01-22-client.json
@@ -0,0 +1,100 @@
+{
+ "meeting_date": "2026-01-22",
+ "duration_minutes": 56,
+ "participants": [
+ "Hrishik",
+ "Dennis Grinberg"
+ ],
+ "participant_count": 2,
+ "total_words": 8988,
+ "total_turns": 17,
+ "speaker_stats": {
+ "Hrishik": {
+ "turns": 9,
+ "words": 8755,
+ "pct_words": 97.4
+ },
+ "Dennis Grinberg": {
+ "turns": 8,
+ "words": 233,
+ "pct_words": 2.6
+ }
+ },
+ "detected_topics": {
+ "ML/Model": 5,
+ "Data": 5,
+ "Onboarding": 5,
+ "Architecture": 2,
+ "Project Mgmt": 2
+ },
+ "questions_found": 5,
+ "questions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "Is there\u2026 I know before we talked about the most sensitive thing from the vendor when it's pricing"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Is there any correction of information about a product that makes it more sensitive or not, or are you advancing in any state"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Is there anything else you want to add on these points"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "How does your current, the code checking process look like"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "How does that look like"
+ }
+ ],
+ "potential_decisions": 3,
+ "decisions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "Okay, so the next topic we have is how we are gonna be working together. And the major points we wanted to cover was your availability, modes of communication, onboarding, and the documentation resour"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "You'd like some metrics to see what the overall process on which it's improved. So I guess the one question would be, you ingest the data, you've got it there. Are there, future reports to say the dat"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "We need to be good enough for them does. I think from my point of view, it was just the communication part, like, we are bound to have a lot of questions, because it's going to be a kind of a\u2026 struggl"
+ }
+ ],
+ "potential_action_items": 7,
+ "actions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "Okay, so the next topic we have is how we are gonna be working together. And the major points we wanted to cover was your availability, modes of communication, onboarding, and the documentation resour"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "I mean, I guess we could use those metrics, Oh, no, I don't know if we have how accurate, like, we could easily show\u2026 Yeah, like, what a good end, goal of it is based on, like, the PDF we took at the "
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "You'd like some metrics to see what the overall process on which it's improved. So I guess the one question would be, you ingest the data, you've got it there. Are there, future reports to say the dat"
+ },
+ {
+ "speaker": "Dennis Grinberg",
+ "text": "And I'm gonna put words in the team's mouth, which is, you know, this is a lot of good experience and do's and don'ts. from the team's\u2026 I'll actually ask the team, from the team's perspective, is ther"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "We need to be good enough for them does. I think from my point of view, it was just the communication part, like, we are bound to have a lot of questions, because it's going to be a kind of a\u2026 struggl"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Go snowboarding on Monday if the weather slowed that much down, so you guys should be a first big storm. Yeah, you must have the December storm. Yes. Were you in the store? No, no, I just left before "
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Yep. Let me wait a second thing I want. Are you still there, Dennis? Dennis, let the content. Alright. Okay, thank you. Thank you. Make movies. I would certainly say that after a client meeting, it's "
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-01-22-client.md b/minutes/2026-01-22-client.md
new file mode 100644
index 0000000..92c3163
--- /dev/null
+++ b/minutes/2026-01-22-client.md
@@ -0,0 +1,53 @@
+# Meeting Minutes — 2026-01-22
+
+**Date:** 2026-01-22
+**Duration:** 56 minutes
+**Participants:** Hrishik, Dennis Grinberg
+**Source:** `GMT20260122-191430_Recording.transcript.vtt`
+**Processed:** 2026-04-23 17:47 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Hrishik | 9 | 8755 | 97.4% |
+| Dennis Grinberg | 8 | 233 | 2.6% |
+
+## Topics Discussed
+
+- **ML/Model** █████ (relevance: 5)
+- **Data** █████ (relevance: 5)
+- **Onboarding** █████ (relevance: 5)
+- **Architecture** ██ (relevance: 2)
+- **Project Mgmt** ██ (relevance: 2)
+
+## Potential Decisions
+
+1. **[Hrishik]** Okay, so the next topic we have is how we are gonna be working together. And the major points we wanted to cover was your availability, modes of communication, onboarding, and the documentation resour
+2. **[Hrishik]** You'd like some metrics to see what the overall process on which it's improved. So I guess the one question would be, you ingest the data, you've got it there. Are there, future reports to say the dat
+3. **[Hrishik]** We need to be good enough for them does. I think from my point of view, it was just the communication part, like, we are bound to have a lot of questions, because it's going to be a kind of a… struggl
+
+## Potential Action Items
+
+1. **[Hrishik]** Okay, so the next topic we have is how we are gonna be working together. And the major points we wanted to cover was your availability, modes of communication, onboarding, and the documentation resour
+2. **[Hrishik]** I mean, I guess we could use those metrics, Oh, no, I don't know if we have how accurate, like, we could easily show… Yeah, like, what a good end, goal of it is based on, like, the PDF we took at the
+3. **[Hrishik]** You'd like some metrics to see what the overall process on which it's improved. So I guess the one question would be, you ingest the data, you've got it there. Are there, future reports to say the dat
+4. **[Dennis Grinberg]** And I'm gonna put words in the team's mouth, which is, you know, this is a lot of good experience and do's and don'ts. from the team's… I'll actually ask the team, from the team's perspective, is ther
+5. **[Hrishik]** We need to be good enough for them does. I think from my point of view, it was just the communication part, like, we are bound to have a lot of questions, because it's going to be a kind of a… struggl
+6. **[Hrishik]** Go snowboarding on Monday if the weather slowed that much down, so you guys should be a first big storm. Yeah, you must have the December storm. Yes. Were you in the store? No, no, I just left before
+7. **[Hrishik]** Yep. Let me wait a second thing I want. Are you still there, Dennis? Dennis, let the content. Alright. Okay, thank you. Thank you. Make movies. I would certainly say that after a client meeting, it's
+
+## Questions Raised
+
+- **[Hrishik]** Is there… I know before we talked about the most sensitive thing from the vendor when it's pricing
+- **[Hrishik]** Is there any correction of information about a product that makes it more sensitive or not, or are you advancing in any state
+- **[Hrishik]** Is there anything else you want to add on these points
+- **[Hrishik]** How does your current, the code checking process look like
+- **[Hrishik]** How does that look like
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 8988 words across 17 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-02-12-cleaned.md b/minutes/2026-02-12-cleaned.md
new file mode 100644
index 0000000..f036a15
--- /dev/null
+++ b/minutes/2026-02-12-cleaned.md
@@ -0,0 +1,119 @@
+# Cleaned Transcript — 2026-02-12
+
+**Hrishik**: A for, today's agenda. I think your mic is gone. model sound? Yeah, firstly, we'll discuss a few of the ML findings that we've had. All the points I've discussed in the last meeting, we have gone over those. We'll talk BERT and different models which you can use. And next, we have, terraformis bicep. This one is, still a bit open-ended, but we can discuss the benefits and ways we can use in our project. Then we have oral implementation and dockerization that Arjun will be handling. So, I guess we can start off with the ML findings. They're pony. If you can move Zoom. She's gonna show. Mine's only… The disabled. To put the Zoom theme there. I think we need to raise the accent. Yeah, we need to go over the HTML. Okay. You can stop for them. Do you have access to shit? They're stop sharing your name, other people's covenants. I'm doing. Yep, here it goes, Tom. David just got back to me, he'll be jumping in. Yep. Wonderful. That much data to increase the font size.
+
+**David Mine**: No. So, Google. That's good. Real good, though.
+
+**Hrishik**: There is also him. Hi, David.
+
+**David Mine**: I didn't talk to this guy, I think he's not blue.
+
+**Hrishik**: Yeah, yeah, I can see. Okay, so again, like, this is not a prescribed solution that we are proposing, it's just that we did a couple of experimentation, just, like, not a POC, but then theoretical real findings of where… how we want to, like, do the ML component, which is, like, assigning scores, and everything. So, one, all three of us will be, like, presenting three different approaches, and we just want to, like. present our findings till now, again. So, one of the approach that we came up with, like, it's called… it's called the semantic matcher, so I'll explain, in, like, the basics of it so that everyone understands, what it is and how is it supposed to work. So, it's like, let's say, like, let's say you want to paint this wall, right? You literally won't remember the color of the paint and go to the shop and get the paint color. So, you'll have a catalog, and you'll find… you'll have those swatches, and then you'll come and try matching it one by one. So, that's something like how the semantic matcher works. So, what'll happen here is, Like, to put it in the context of the project, when a supplier sends a description, like, let's say, a drill or a screwoo description. So, we use a… it's a very tiny model that we're trying to use here. It's called All MiniLM. Now, what it does is it will turn that text into a list of numbers. Now, this process in the ML world, it's called vectorization, where you turn… where you convert a text into numbers. That's called vectorization. Now, the next step is search. So, like you said, there's PIMS database, right? So, we'll use this vector, and we'll, vectorization search over the existing PIMS database. And now, since we already have, like, thousands of approved products in… sitting in the PIMS database, we'll leverage that knowledge base first. And then we'll, your… and this is what we're taking as the master, swatch book, like, the… where you get the paint catalog and you'll try matching it with the color of the paint. So your… the knowledge bank that is in the PIMS is your master swatch book. Now, the third one, there's Yeah. Now, the third… the third step here is you make a decision based on the confidence. So, like, let's say if the closest ma… again, like, the vectors, after you decide, the ML component, assigns, not assigns, like, it, concludes on a confidence code based on the distance. Like, how similar can, how similar is this particular, screw? To the one that is already present in the PIMS database. So, as soon as it comes with a distance factor over there, so the larger distance and the smaller distance, I'm not going to the technicalities, like, how the distance is measured. It'll, come up with the confidence code, like, obviously, the smaller distance, meaning it has more… it is more confident that, this product matches with this particular categorization that is already present in the PRIMS database. Otherwise, you get a very low confidence score. And we already, obviously, have a human checker, in the loop. Now. hear why, this approach is better than what these guys are gonna, like, propose Nexus. Like, he'll be talking about BERT, and, Leo will be talking about XGBoost, you will learn about that more in detail. With semantic search, what happens is you don't have to retrain as a developer, like, you don't need a developer's intervention for the model to be retrained every now and then, because our, whole architecture is prone to schema changes, right? Now, that's the biggest change point that I see over here. Everything else is constant, like. The attributes related to a particular product are constant, like, at least over a period of time. I mean, they are not, like, too subjective to change too quickly, right? But then the schema might change, schema as in the information that we're extracting from the raw data. Like, someone might just give you 1, 2, 3 items in the description, but the others can give you, like, four. So the schema that is entering into the ingestion pipeline will change, and the model has to, like, relearn okay, now this is the new data that I'm getting. So, we don't have to do that. The model will retain on its own, because we have connected with the central knowledge base, that is the PIMS database. And it's also, like, very… and one of the major constraints that Harsha mentioned the other day was, like, he wanted a small short model, like, which is not too compute-heavy, and also can access that I think… I forgot the number, how many records we maintain, like, the average number, but then this model actually is efficient enough, like, since it's an embedding model, we are not actually digging the text. But we are converting into a form of numbers, which is very condensed. That vectorization will help us, very much, like, lower the compute cost. And, this can be run on Azure as well, and this can be run on, like, independently if you want to, like, maintain. It can be done like that also. So, there's no platform dependency, like a blocker as such. So, that's the high level of The semantic matcher approach. Great. Yeah, that's a pitch, actually, like, it's the last sentence, like, it's maintenance-free, and it automatically adopts the new products, and that's the whole, pro that I saw in this approach when compared to BERT and the HCBoost one, wherein those models, even if they are maintained by the Azure platform. you have to, like, retrain your model and keep it updating, but it does not work like that, because it updates on its own. It is capable enough because it has some sentence transformers libraries ingested in it, and it is intelligent enough to, like, retrain on its own upon… like, it basically detects the schema change and understands by itself, okay, there's a change. So, I have to relearn. So, you know, there's no human trigger over here. That's the pro in this approach. But, do we expect, frequent schema changes? How currently the things are working? No. So wait, you said schema change can also include, like, adding more attributes? Yeah. Yeah, well, so in that case, like, over time, we will be… hopefully getting more data and adding on more and more attributes to a given product, or, yeah. So, that is the case. Yeah, that's a case, but I wouldn't say it's going to be all the time. Okay, okay. I don't know, like a product, they might go back and dig deeper on some products and enrich them and add some more attributes, but I… And yeah, like, after they go back and… from once we get something to the vendor, and they go out and do research and enrich it, I can't see it happening again and again and again. I don't think they're gonna be, like, over time, revisiting the same product over and over and over again, adding more and more. What are your thoughts on that, David?
+
+**David Mine**: Yeah, I agree with all that. I think you got it.
+
+**Hrishik**: But then, don't you want your solution to be extensive? No, no, I mean, we do, I guess. I don't think it's going to be very often, though, that, like, there are suppliers coming out with more information about something that already exists. Like, yeah, they'll probably go through a period of discovery where they're searching the internet and looking for more resources on something, and… Okay. But yeah, if the product changes, then it's probably just going to be a brand new product. Yeah. So it means, there's definitely, more weightage given to the simpler solution against something that You know, that, brings up, like, for this one, an event… I don't know which is the simplest one, because we have not implemented it, and we have not made our hands dirty yet. So, let's say if the BERT is very simpler to, you know, work around. So, but then it does not do the retraining bit on its own. So, you are open to having that trade-off? Oh, no, no, I mean, we definitely like how it did all in China, so yeah, that's definitely a pro. Sure, would you… because my laptop requiring such thing. Gotcha. Is it this? Beautiful. So what is the source data for the semantic matter? Somebody gives you a PDF file of a product, or… Does that matter? Yeah, so, I mean, if it's coming… we're getting the information straight from the vendor, it's probably going to be more comprehensive. It'll be, like, PDFs, spec sheets, and whatnot. A lot of times, too, though, if we onboard, let's say, a distributor. they're giving us the catalog products they sell. They're not necessarily giving us all of their vendor spec sheets that they received, so in that case, the data might be a little more at least unfilled, and that's when we want to be going back and kind of doing our own discovery period with our catalog team of trying to search the internet for more sources. So… Okay. Is it… So, I basically went through and looked through some of the models, and Parsha did mention BERT when we were discussing it, and I went through and look through it. So, BERT is just a… it's basically an… It's based on the transformer model, and as for distilled BERT and tiny bird, they're the same thing, but they're just a… Distilled BERT is just a smaller model that's trained on the BERT itself as a mentor model, and it's designed to replicate it. It solves some of the issues that BERT has, that it's pretty large, but BERT is compared to the other options. It's… BERT is best used for text classification, and basically, it can also do semantic massing, but the best part of BERT is that it does bi-directional searching. So even if something is not clear, and there's a huge paragraph of text that we need to extract, BERT will be able to basically get out all of the required categories, as long as it's been trained on all the relevant ones for our product. Distilled Bird is just, I think a 40% smaller version, but it still has, like, around 97% of the accuracy. And best part is DistillBird has… it's available on Azure. There's no need to manually deploy it or anything like that. And training it, it does not cost that much time. It is longer than the semantic matching one, I'm pretty sure, but it's not that much longer. As for the other stuff, basically, as long as we have the OCR working properly and we've deleted all the data. It will go through, and it does not have any problems, like, for semantics, matching. If the text grows large enough, there might… future tweaks might be required. But Word can handle, like, very, very large text paragraphs, or, like, if… if it's, like, 5 pages long and has, like, multiple paragraph after paragraph, it can extract whatever you need and get into categories. The… I would… I will say, as a downside, that it does… it will need a retraining, that will be manually triggered. Like, if you add a bunch of products with new categories. Then it won't probably get them out. As… as well as before, and you will need to manually do it again, compared to a sugar solution. I think it's cool. Beautiful. both, are you gonna do some experiments to see which one works best for you? Yes, we were waiting on, like, Harsha told us that he would give us the sample schema of how it'll look like, so we thought we could just take that and, like. generate some synthetic data and, like, do some kind of a POC. So, for now, we just went ahead and, found out this. I think I can, I can… again, he's probably, in and out of the next month. I can get that to you. So you want to just sample of, like… A sample of the data, yeah. Even a small sample would do. Sure. Because we just want to do some test runs, and we, because right now, what we are, the accuracies we can, as best as we can tell us. The bigger the model, the better, but there might be a diminishing returns. So, for example, at a certain point, a bigger model might just be, like, 1 or 2% better, and if we could just do some test runs, we could just hit the optimal point much faster. information the supplier would give you, you know, PDF, or CSV, whatever, one of those formats that they usually give you, right? I guess so, so which one? Do you want… so you want the PDF, like, what the suppliers will give us? Do you also want what we have in our database? Yeah, so we want the PDF so we can start working on the… The transition side, and we also want the data part so they can start working on the end product. So that, at the end, we can combine the Got it, got it. Okay. The major hinging points in the different models is how the data is that we're going to be processing in the model, so data will help a lot in maintaining this. Okay. You? And I'll try and also give you ones that align. I'll make sure that the vendor data that I'm giving you, the spec sheets, the PDFs, are the exact same products of the actual things we have in our database. Okay, perfect. Well, that's the best case scenario. Yeah, best case scenario. Worst case scenario, or, you know, other edge cases that you have to deal with? I can… I can pick the catalog team's brain and see, yeah, see what other things… Okay, I… I have… just these days, I have investigated three can of common GP. DET terminal models. What's a market. So, this is for… Dealing with, downstream data from So… so this is the data that actually taught. Talking about, say, how… do some… Luminization, or to get some… get open tonight. Anyway, after we take those data, we can do some… towards, processing. So these three kinds of models, they are put at classification, regression, output scores. For probabilities, this is what we want to get. And, they are good at, you know, explain… explainabilities. And, yep. Yeah, because we… we wanted to have the… Processing data, so we don't need to be able to take care of all the… So, only that data. Yeah, just, some, make a table, and This… we could propel the… the characteristics. And, we can choose the best one, really. To predict our project. So is the acronym GBTP mean? GBT models, what is the acronym? GBT. It's called Gradient Boost Destiny Tree Modulence. Say it again. gradient boost, hosted in listening to you, I mean? Okay, questions. So… Nope. So, the first one is the campus. It can quickly deal with So, some heavy load work, like, generally some category, heavy tables. And, they only need some original information. They don't need to… Like, strong… need to strong mapping. Or need to change those. original information tool, so… token… tokenization… features, so it's useful to use. And, this does… they just have the same ability in the robust Julie, and… And, the catapult, the training speed is… Yes. Not very slow, but the light GPT So… Train speed and interface speed and scalability is… It's good then, catfold. Related to the teaching engineering, which is about how much the reprocessing digital work is required to tensor raw data to… moderated columns. So, can't book… like I said before, they don't… it doesn't need to… Some… You don't need to… There was, data. And, so, you don't need to pay much attention to the… Pre-processing all the data. So, if we… it can be, do a quick deployment. Well, the G export is very… Not good at this to deal with these things. Like, it needs final features to preparation. And, And also, so I'd like the GPT added XG boots. To have strong ecosystem activities. So, it can slow down the… They reduce the work for retaining, and we can find some… You know, they use cold coding from the community, or ask someone. That way, if we meet some common price change. Permanence will… we have. implementing to exist. project. And, and I think so. Typical limitation, or the… Excellent. So, the first one is. The scale support may be a little, Love… a little better to hand. The second one. But I think it is mostly very… We don't need to think about… much to carry weight, because I think we don't need to deal with such amount of data. So, this is my conclusion. We could take the catboard as a default scoring model. And, came… led GPT… GPM as the plan. scale-up opportunity if the throughput becomes very the domestic country. And which could also match our project architecture requirements. And, I read some… Amazing. So, from the architect phase, Well, which is based on… So, architecture responses and, So, Clint… Clyde? of fulfillments. So, it's just, Static effect and the functional phase. And a long position of faith. And I compelled why the other… Models that don't fit this project. And the trade-off. And this time is what we need to… supports, models with. to… tomorrow. So I guess, we can, probably import this document into your content as well. Or, when we keep doing our PEOCs, and if we have more desires, we can decide to do a document on confidence. Definitely. Yeah, Harsha's gonna wanna review this, too. Yeah. Great. Okay, so that's pretty much it. So, I think you guys, did you guys talk about constraints? Like, we wanted to, Think about the constraints as well when we were looking at these models. Let's say you have 100,000 suppliers versus 40,000 suppliers. That would change which model you're going to be using. So we wanted to get an estimate of those constraints, and how many different categories would have, like 5,000 categories versus 50,000 categories. Yeah. So, that kind of data as well, we would want from others. So I think, when you give us the sample data, sample schema, it wouldn't cover all of it. I can give you a… Yeah, I can do, kind of, just give you, you know, select count to, like, an addition of that, just from each of the things, yeah, categories, options, attributes, products. just the breadth of data would be enough, I think. Do you have any information that gives any trending stuff in terms of the science of things? I'm just saying, you know, your model right now made 100,000 items, but it last year over 80,000, whatever it is, I'm just trying to get some sort of scale type of thing. Yeah, definitely, that's good. We could definitely break that down, too. I could break that down and run a couple more little analysis on it on, you know, we store creative products and whatnot, all those things, so just scanning that out over time and seeing, yeah, what the transit look like. So do you, the people who do this work here doing this sort of stuff, do you have to track how much time it took for you to develop this? Doing research, or just starting? Well, you know, your results, your reports, you know. Yeah, I think we have a approximate time, yeah. at the granular level, to know for this work, how much time it took to do this particular effort. Okay. Okay. It's just useful to have that data. Right. Yeah. But again, it's just the initial research phase. I understand. Yes, we have not done any implementation. We spent 3 hours researching this aspect, you know, built this report, those types of stuff. That's all kind of useful historical data from before. Oh, thank you. Oh, boy. A lot of shades on that. Oh, I'm gonna assume. I'd rather show Zoom. I can't share this screen. Yes. Okay, so next we have is, Terraform versus Bicep. Oh, I suppose it. So I have created a small doc, but it is, a bit high level, so from… what I could, think of right now is the two major distinguished factors between Terraform and Bicep are that Bicep is very Azure-oriented, and It is, of course, better if we use it with Azure, but if we, around the time you try to use some other soft… some other services from other environments, other cloud providers, then Azure will become a bottleneck. But on the other hand, if you try to use Terraform, it is… it works with Azure, it has good support, but… we can say slightly less than Azure Bicep, but if you use Terraform, we still have those options available to us if we plan to switch, if you plan to use different Different services with it. And one of… another negative of Terraform would be that, it does give us drifts, so if you used Reform 2, deploy something, but then manually modify it, so there's a… discrepancy between what is actually deployed and what should we have in our telecom files. So that is something that we'll have to keep in mind. But there are, like, as of right now, I don't think we have a definitive answer of what we are going to choose, but as our ML research finalizer, and we know what models we're gonna use, and that's gonna drill down to which, cloud provider we'll use, then I think we can, have a better the, like, say in which one we need to use. But as of right now, I am leaning towards Terraform, because it might be a bit… a bit more tedious to work with, but it gives us more options to explore different things, and gives us flexibility to work on the project in different ways, and not just be stuck to Azure and all its… internal things. So, for my background, some of the terraform. So Terraform and Viceps, basically, they're used to deploy, services on the cloud. Its infrastructure as code, so you don't directly deploy on AWS or on Azure, so you use a git commit, and only then you verify it, and then it pushes to the cloud. Should automatically have reset how it's gonna… Microsoft product, a third-party product? What's that? It's an open source. Open source, yeah, okay. Azure Bicep is Microsoft. Okay, yeah, Bicep is something similar, or what? Yeah, it's Microsoft, but it is, it is similar, but it is very focused to Microsoft Azure. It's not, with Terraform, we can work with, Azure, GCP, AWS, everything, and different software services, but with Azure, it is very… sorry, with Bicep, it's very Azure-specific. You cannot, like, you can, but it is, Not really used, because the support is not there. to use different services. So it's kind of a… it'll force you down a very narrow path, and then you'll have to… make go with what you have, and you won't have the other options available. So we don't want to close that door right now. So, one more thing, I don't think Bicep does is, If we are going to integrate, the entire system with Datadog, I'll go there. Yeah. I don't think Microsoft natively gives it support for that. While Terraform does, we can create Terraform modules for routing, which feeds data directly to Teradog. So, David, do you… we… do we do any of our, Datadog setup in our, bicep? For, like, her number?
+
+**David Mine**: I'm not sure if we do it with Bicep. What we do have our Azure-wide monitors, so we can tag a resource. With a tag that we've set up, and then Azure is going to take that data and ship it to Datadog in some way, shape, or form. So that's how we do… kind of native-level logging on our Azure environment.
+
+**Hrishik**: But, to your point, that takes, I guess, an extra step of setup that's inside of this. But Terraform, we can directly use it to set up Oh, Pipeline's digital dog.
+
+**David Mine**: Yeah, I think my hesitation with Terraform is, firstly, that we don't use it right now. Which isn't a big deal if something's really good, but we're already using Bicep, so we're already locked into Azure for, For all of our new products. The ones that were… we would be the most likely to move if we needed to were already locked in. The other thing that worries me about Terraform I don't know if this is a valid worry or not, is that Terraform… my understanding is that it… Forms actions based on the state that it thinks your environment is in. Whereas Bicep describes the state that it wants like, you, in your code, describe the state you want your infrastructure, and it takes care of cobbling together the commands needed to get to that state. So it's more, declarative than imperative, so to speak. Save that slide up.
+
+**Hrishik**: I didn't get your point on Terraform being… I think if we deploy something about Terraform, but we modify the internals from, let's say, Azure itself, the Terraform will think it is in some other state, and the actual cloud will be in some other state. And if you put something else on Terraform. It can conflict and give you the RAM output. Yeah, it will never deploy it, because there's a conflict. I think that's… so, Terraform also has Terraform Plan, which is, specifically what you just said about Bicep as well. It gives you how the… Resources are going to look like when it's going to be created before you actually deploy it. I don't think Azure has the… Azure has, what we call drift, because I think Azure, when you… deploy something else using Bicep, it will take the latest state and work on that. Terraform does not… I don't think it pulls back from the actual… what's actually working on it. Yeah, but that is a security feature that is required, because let's say I push some code. And someone else logs in to the UI and just… changes something. That shouldn't happen, ideally. No, that will happen, right? No, that shouldn't happen, because people shouldn't just go and change code on the UI. That's the whole point of using something like that. Yes, yeah. I mean, yeah, it's just something to consider. That doesn't mean that right now, as we're, as we're growing, it shouldn't happen, but it doesn't get involved. Yeah. Okay, but, like, I think, so, from the discussion, I think David wants to go ahead with myself. Right, David?
+
+**David Mine**: Yeah, I would pick Bicep, mostly because I think it's something that we know. I also think that the value-add of Terraform… one of the big value adds of Terraform that they claim is that you can kind of switch providers. But my understanding is upon further investigation. A lot of those commands are actually pretty cloud-specific. So there's a single language to describe everything, but you're still kind of… there's still kind of some vendor lock-in, which makes sense, because the cloud providers don't exactly offer the exact same offerings in the exact same way. So, to me. what's best for the product is one question, which is what you guys are considering, right? It's what you know the best, and what makes the most sense for this project. But one of the other things we have to make sure that we're not forgetting is when you guys hand over the project and give us the keys, so to speak, we're going to have a team that has to maintain that, and are we going to ask them to get good at Bicep and Terraform, or are we just going to ask them to get good at Bicep? So that's prob… that's another factor that's on my That's on my mind that might not be on your mind. So I would… I would prefer bicep mostly for those reasons. I've got to train a whole bunch of people who… I mean, they're developers, they don't care a whole lot about infrastructure, they don't care how it runs, as long as it runs on their local environment. And so I can train them on Bicep. That's a lot easier than getting them caught up on Bicep and Terraform.
+
+**Hrishik**: Okay. I think with that in mind, we can maybe go forward with Bicep itself. Yeah. Because anyways, if you are logged into Azure, then Bicep is the better way to go. But, I can just talk a little bit about hotel. I think, we had a discussion last week, David, we can have hotels, so we get, like, a waterfall view of what's happening in every request on a weekly basis. So OTL already handles this, I think it's called, so we don't need to have separate request IDs and RIDs for each service, or it already takes care of this. It's called W3C, standard header reporting index into every request. So, that header has, like, a global case only, then it has a pattern case only. And, like, for each request, it has a sample tag as well. So, let's say you were a data doc, you'd have to put the global tracer name so you get the entire waterfall of what's happening, which request is taking a lot longer than what's expected. And then if you want to see into a specific request, you can just put in the package. Okay, so this is… would be in addition to Datadog. I… I don't know about themselves, so it's not like a… It's not an alternative to Datadog, you're sending it with Datadog. Yeah, yeah, it's… yeah, it's a layer before Datadog where you send requests to, and then that feeds it to Datadog. Oh, it's open data metric. What's that? OpenChall metric? Yeah. It's like, like, the problem that you guys are dealing with, so you have vendors, different vendors, giving you different, giving you the data in different formats, right? So, the telemetry world came together, and they were like, let's build open telemetry. A standardized version of how your observability metrics should look like. And, where even if you end up using Datadog, or Telegraph, or, Premieres, or these, dashboards. You can have a centralized format, like OpenTelemetry format, and you can just, like, plug in these external sources. So that's, how… that's how, like, why Arjun is proposing, OpenTelemetry. But, since you, since David just mentioned, something about Azure, which I didn't actually catch, writing to Azure Louds and then passing that to Azure. David, are you able to talk more about that, expand more on how Azure writes to logs that have been tested?
+
+**David Mine**: Oh, sorry, I'm… I caught half of that. It was… the audio's getting a little quiet. Let me bump this up. Can you say that one more time?
+
+**Hrishik**: Can you, explain a little bit on how, the data is written to Azure Logs, and then,
+
+**David Mine**: Yeah, but yes.
+
+**Hrishik**: Azure Logs pushes the data onto it.
+
+**David Mine**: Yeah, let me… maybe I can share my screen, and that would help clear up some confusion here. Of course, now that I want to do that, let's see if I can… We've got a Datadog… I requested access to share… there we go, let's share my…
+
+**Hrishik**: of…
+
+**David Mine**: It was not so, just… There we go. So here on Azure. Oh, you guys are completely superior. Here on Azure, we've got this Datadog Azure Native ISV service. Not sure what that stands for, to be honest with you. But we have our prod, and we have our QA environment, which really should pare down, because we're not using that anymore. At least not there. And we have this connected to… organization. So, if we include Datadog logs true, anything that has Datadog logs true gets included there. So, for example, I could go to a… I hope I have an example up for you guys here, of a function app. Probably enough here… Data dialog's true, that means that instead of syncing this to, say, what would it be, like, We've got a logs here… Yeah, so instead of needing Application Insights, I could actually turn this off. I don't really need Application Insights inside of Azure, because these logs are supposed to be sending directly to Datadog through this native, Datadog service, which just kind of captures things Azure-wide makes it easy to capture logs and metrics Azure-wide and ship that off to our Datadog tenant. Does that make sense?
+
+**Hrishik**: Yeah.
+
+**David Mine**: That's the only category.
+
+**Hrishik**: Can you, show me the Datadog, dashboard as well, how the.
+
+**David Mine**: Right, okay.
+
+**Hrishik**: How it looks when…
+
+**David Mine**: For your business.
+
+**Hrishik**: the requests come from Cisco.
+
+**David Mine**: I didn't actually know about all this, but you can see all the different metrics and logs. Looks like our logs, we're doing a pretty terrible job on, but in terms of our metrics, a lot of that stuff's going over. So let's pick, let's pick a… Or maybe that's how they do it. They ship from Application Insights over to Datadogs. Maybe we do need an App Insights. Either way, let's find one that we know works. That's not helpful. We'll just go here and take a look at our… Oh, would that be service, maybe? So Azure itself has… Looks like calls into it. This would be our prod, and… caller IP address 10-1, that's probably our app gateway, on dev. What else can we see that might be really useful? These are our databases, whoops. That, pass. These are some microservices we have. And this is, again, this is just logs. Oh, here we go, yeah, let's… Get rid of all these. Check all of our hosts. So these are the… wish I could see a little more. There we go. These are all of our revisions. Of different, container apps. Thank you. And it looks like we also have… Saw some database stuff, Azure Functions? Yeah, okay, cool. So, Azure Functions, instead of going to, well, I guess they're called Function Apps now, but instead of going to… Application Insights directly, they also come here. I'm not sure exactly how that works, but you can see that I… I didn't configure that, I just put Datadog logs equals true in the tags, and then it shows up here. And then in terms of metrics or, or, That's not what I want. Do I have an APM? Services, maybe? Might just be AP.
+
+**Hrishik**: Right now, do we… do you guys have tracers enabled? Like, if there's… I'm sure there's multiple microservices, like, there's multiple services talking to each other via IPC calls. So, do you guys have, like, a chart on Datadog which shows how… Yeah, we do with our brand new product, Peregrine. It's still there.
+
+**David Mine**: Yeah.
+
+**Hrishik**: product is just starting to get to prod right now, but yeah, the VIN tank has actually helped us, you know, we have the waterfall and everything you're talking about, traces connecting between different.
+
+**David Mine**: Yeah, so between services. it's mostly in-app, right, by configuring in the application. Between apps, for our monoliths, it… this is basically all you get is a flame graph that… this isn't a flame graph, but it's… it's just this, right? It's not super helpful, in terms of tracing. So, that would… if you're looking to do that as a concern, to talk between multiple services and build traces, I'm not sure that… We've never made that happen with our current configuration. We've handled that. In our application, rather than in our infrastructure.
+
+**Hrishik**: Noted. I'm just… I'm very confused as to…
+
+**David Mine**: Fair enough.
+
+**Hrishik**: If we should just use the current system, or if he should… I actually spent time on Instagram.
+
+**David Mine**: Bye.
+
+**Hrishik**: I mean, because, like, right now, everything's working for you guys on Niger, and, like, just one system which we are building with our demos can afford us, because you have 100 other services with Nigeria. It's all in, yeah, it's all together.
+
+**David Mine**: Yeah.
+
+**Hrishik**: I mean, I don't want to be the one to necessarily make a decision. This is more David than bending things around.
+
+**David Mine**: I haven't. I, kind of got lost in the sauce a little bit. What's the… what decision are we trying to make again?
+
+**Hrishik**: Do we want to actually implement hotel, or do we just want to stick with whatever is working for you guys right now? Because, if we bring in hotel, it's just gonna be for the service we are creating, and… To actually move it system-wide would take a lot of effort, and…
+
+**David Mine**: Oh, okay. I mean, I guess I would leave that up to you, That would be something that ultimately… Oh, sounds like added complexity. but could be a value add to eParts as a company. In terms of adding value to this particular product. I… If you have the time, sure, but I don't know that I would put that… rank that very high in terms of what I would… I would demand. I… I think, We're pretty locked in to a vendor right now. We're pretty locked into a way of doing things. And to switch from… data dog to someone else to change how we do logging or traces and metrics is going to be a project, if we were to do it. Our good old friend artificial intelligence would help us figure out how to do that, and doing that… for… 10 repositories. where 11 repositories doesn't make a big difference, right? If the difference is that we also have to convert your stuff over from OTEL to.
+
+**Hrishik**: Yeah.
+
+**David Mine**: Or, from Datadog. To some other… sink. it's not really that much more work to me. Most of that stuff's just configured in the startup anyway, it's already easy to switch. I wouldn't make that a requirement. I also wouldn't say no if you want to do that as, like, a… a stretch goal. You can't push that Facebook, like, the…
+
+**Hrishik**: Okay, I guess the… the last part I wanted to talk about was dockerization. I think we spoke about this as well last week, So I did go over it, and I think dockerizing the entire system, what we're building, would not be the best way to move forward, because we have stateless compute components, so we could dockerize individual components. One would be the ingestion Gateway, and one would be the ML component. We can dockerize them separately, and they can talk to each other via network. yeah, that's pretty much it. But I had, I guess, once we get the data from you, maybe we can slowly start building the OCR functionality? And build on the ML model that we use. How did the doctorization topic come up? I'm curious. Last week, since we have multiple services talking to each other, so when we're building it on our local device versus when we're hosting it on the cloud. We don't want it to experience two separate behaviors. We want the behaviors to be exactly the same. So, if we have a Docker container, we know exactly how it's going to be going on both sides. We're just going to put this container up in the cloud, that's it. Then lastly, talk about it making it a bit agnostic, so that the software can work anywhere. So, that's where the localization point came into the virtual. I think that's all we had. Any questions from anyone? David?
+
+**David Mine**: Nothing for me.
+
+**Hrishik**: Great, so I have a couple takeaways then. I can get you… I'll definitely be able to get you pretty quickly, a global set of our current, like, AMS product data. The vendor product data, like, from the suppliers, the spec sheets, the PDFs, so that might take a couple more days just off to actually go to the catalog team and kind of collect that, but I can, again, get you the actual, database data pretty quickly. And then also I'll try and get you some, metrics for constraints, such as, like, total number of suppliers, attributes, categories, and, also try and get some trends from that, over time, such as how many we've been adding per year, and for new tenants signed up, or something like that. We still haven't gotten the cursive. Yeah, well, Cursive, and yeah, David brought that up this morning. David said he was working on Cursive, he was running into an issue. I know he hasn't had much time today.
+
+**David Mine**: Like, he was employed. Yeah, can we schedule a time where I could just sit with someone and try a bunch of things? Probably take half an hour to 45 minutes.
+
+**Hrishik**: Yeah, sure. Yes, like, one of you, like, in a call on this, he tries different things and it fails, he can try it.
+
+**David Mine**: I would try on my own guest account, but the problem, I think, is that you guys have, rather than being, like, guest accounts with the username, password, your guest accounts to another ENTRE tenant. Which is… which adds some complexity to the equation.
+
+**Hrishik**: Okay. I think you just let us know what time you're available, and we can… So one of us should be free at that time, most likely. Okay.
+
+**David Mine**: Okay.
+
+**Hrishik**: Did we get them in a team, David, on Microsoft Teams? Yeah. Well, I don't know, David, if you want to post the, potential meeting times there? Just make a post with, your availability. Both David and Cat Fence here. I'm checking out the picture behind it. Those are his cats. Those are your cats.
+
+**David Mine**: Yeah, these are actually my cats.
+
+**Hrishik**: Oh, cool. Many of us have cats in the office. And what are the names of your cats?
+
+**David Mine**: Let's see, finn, Bumi, and Simon.
+
+**Hrishik**: I love it.
+
+**David Mine**: Best Christmas present I ever got. From private.
+
+**Hrishik**: Yeah, I'll also… Could we get a snapshot of, like, the production table that we've broken? What do you need a production table, right, before tables? Our… in our architecture… In the intermediate table, maybe? And that is something that we will create? No, the intermediate table is something we would create. Okay. So we would want to replicate whatever Oh, you just mean, like, the table? Yeah. Oh, yeah, I mean, that's kind of what I was going to give you, with the sample data from the database. I was just going to give you, you know, top 2,000 rows or something, whatever tables. Would that suffice? Yeah, yeah, that's fine. Oh, yeah, there was. Right. Okay, I guess I'm good.
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index 0000000..2b6aba1
--- /dev/null
+++ b/minutes/2026-02-12-client.json
@@ -0,0 +1,91 @@
+{
+ "meeting_date": "2026-02-12",
+ "duration_minutes": 56,
+ "participants": [
+ "Hrishik",
+ "David Mine"
+ ],
+ "participant_count": 2,
+ "total_words": 7417,
+ "total_turns": 59,
+ "speaker_stats": {
+ "Hrishik": {
+ "turns": 30,
+ "words": 5848,
+ "pct_words": 78.8
+ },
+ "David Mine": {
+ "turns": 29,
+ "words": 1569,
+ "pct_words": 21.2
+ }
+ },
+ "detected_topics": {
+ "ML/Model": 6,
+ "Architecture": 6,
+ "Infrastructure": 5,
+ "Data": 4,
+ "Onboarding": 2
+ },
+ "questions_found": 0,
+ "questions_sample": [],
+ "potential_decisions": 3,
+ "decisions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "A for, today's agenda. I think your mic is gone. model sound? Yeah, firstly, we'll discuss a few of the ML findings that we've had. All the points I've discussed in the last meeting, we have gone over"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Yeah, yeah, I can see. Okay, so again, like, this is not a prescribed solution that we are proposing, it's just that we did a couple of experimentation, just, like, not a POC, but then theoretical rea"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "But then, don't you want your solution to be extensive? No, no, I mean, we do, I guess. I don't think it's going to be very often, though, that, like, there are suppliers coming out with more informat"
+ }
+ ],
+ "potential_action_items": 19,
+ "actions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "A for, today's agenda. I think your mic is gone. model sound? Yeah, firstly, we'll discuss a few of the ML findings that we've had. All the points I've discussed in the last meeting, we have gone over"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Yeah, yeah, I can see. Okay, so again, like, this is not a prescribed solution that we are proposing, it's just that we did a couple of experimentation, just, like, not a POC, but then theoretical rea"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "But then, don't you want your solution to be extensive? No, no, I mean, we do, I guess. I don't think it's going to be very often, though, that, like, there are suppliers coming out with more informat"
+ },
+ {
+ "speaker": "David Mine",
+ "text": "I'm not sure if we do it with Bicep. What we do have our Azure-wide monitors, so we can tag a resource. With a tag that we've set up, and then Azure is going to take that data and ship it to Datadog i"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "I didn't get your point on Terraform being\u2026 I think if we deploy something about Terraform, but we modify the internals from, let's say, Azure itself, the Terraform will think it is in some other stat"
+ },
+ {
+ "speaker": "David Mine",
+ "text": "Yeah, I would pick Bicep, mostly because I think it's something that we know. I also think that the value-add of Terraform\u2026 one of the big value adds of Terraform that they claim is that you can kind "
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Okay. I think with that in mind, we can maybe go forward with Bicep itself. Yeah. Because anyways, if you are logged into Azure, then Bicep is the better way to go. But, I can just talk a little bit a"
+ },
+ {
+ "speaker": "David Mine",
+ "text": "Oh, sorry, I'm\u2026 I caught half of that. It was\u2026 the audio's getting a little quiet. Let me bump this up. Can you say that one more time?"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Can you, explain a little bit on how, the data is written to Azure Logs, and then,"
+ },
+ {
+ "speaker": "David Mine",
+ "text": "Yeah, let me\u2026 maybe I can share my screen, and that would help clear up some confusion here. Of course, now that I want to do that, let's see if I can\u2026 We've got a Datadog\u2026 I requested access to share"
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-02-12-client.md b/minutes/2026-02-12-client.md
new file mode 100644
index 0000000..b8bd574
--- /dev/null
+++ b/minutes/2026-02-12-client.md
@@ -0,0 +1,48 @@
+# Meeting Minutes — 2026-02-12
+
+**Date:** 2026-02-12
+**Duration:** 56 minutes
+**Participants:** Hrishik, David Mine
+**Source:** `GMT20260212-190517_Recording.transcript.vtt`
+**Processed:** 2026-04-23 17:47 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Hrishik | 30 | 5848 | 78.8% |
+| David Mine | 29 | 1569 | 21.2% |
+
+## Topics Discussed
+
+- **ML/Model** ██████ (relevance: 6)
+- **Architecture** ██████ (relevance: 6)
+- **Infrastructure** █████ (relevance: 5)
+- **Data** ████ (relevance: 4)
+- **Onboarding** ██ (relevance: 2)
+
+## Potential Decisions
+
+1. **[Hrishik]** A for, today's agenda. I think your mic is gone. model sound? Yeah, firstly, we'll discuss a few of the ML findings that we've had. All the points I've discussed in the last meeting, we have gone over
+2. **[Hrishik]** Yeah, yeah, I can see. Okay, so again, like, this is not a prescribed solution that we are proposing, it's just that we did a couple of experimentation, just, like, not a POC, but then theoretical rea
+3. **[Hrishik]** But then, don't you want your solution to be extensive? No, no, I mean, we do, I guess. I don't think it's going to be very often, though, that, like, there are suppliers coming out with more informat
+
+## Potential Action Items
+
+1. **[Hrishik]** A for, today's agenda. I think your mic is gone. model sound? Yeah, firstly, we'll discuss a few of the ML findings that we've had. All the points I've discussed in the last meeting, we have gone over
+2. **[Hrishik]** Yeah, yeah, I can see. Okay, so again, like, this is not a prescribed solution that we are proposing, it's just that we did a couple of experimentation, just, like, not a POC, but then theoretical rea
+3. **[Hrishik]** But then, don't you want your solution to be extensive? No, no, I mean, we do, I guess. I don't think it's going to be very often, though, that, like, there are suppliers coming out with more informat
+4. **[David Mine]** I'm not sure if we do it with Bicep. What we do have our Azure-wide monitors, so we can tag a resource. With a tag that we've set up, and then Azure is going to take that data and ship it to Datadog i
+5. **[Hrishik]** I didn't get your point on Terraform being… I think if we deploy something about Terraform, but we modify the internals from, let's say, Azure itself, the Terraform will think it is in some other stat
+6. **[David Mine]** Yeah, I would pick Bicep, mostly because I think it's something that we know. I also think that the value-add of Terraform… one of the big value adds of Terraform that they claim is that you can kind
+7. **[Hrishik]** Okay. I think with that in mind, we can maybe go forward with Bicep itself. Yeah. Because anyways, if you are logged into Azure, then Bicep is the better way to go. But, I can just talk a little bit a
+8. **[David Mine]** Oh, sorry, I'm… I caught half of that. It was… the audio's getting a little quiet. Let me bump this up. Can you say that one more time?
+9. **[Hrishik]** Can you, explain a little bit on how, the data is written to Azure Logs, and then,
+10. **[David Mine]** Yeah, let me… maybe I can share my screen, and that would help clear up some confusion here. Of course, now that I want to do that, let's see if I can… We've got a Datadog… I requested access to share
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 7417 words across 59 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-02-26-cleaned.md b/minutes/2026-02-26-cleaned.md
new file mode 100644
index 0000000..2d93326
--- /dev/null
+++ b/minutes/2026-02-26-cleaned.md
@@ -0,0 +1,187 @@
+# Cleaned Transcript — 2026-02-26
+
+**Hrishik**: The two… the few talking points you have are around the ML components.
+
+**Harsha (eParts)**: Okay.
+
+**Hrishik**: prediction model, and we also had a question about if we can use LLMs instead of the traditional ML. If that will be viable, and some initial work we have done on the semantic matches. Okay, for the… okay, let's start. So the… I don't have anything present as such, but… for the ML component. We were… we had a few questions on how exactly the confidence scoring would work.
+
+**Harsha (eParts)**: Okay.
+
+**Hrishik**: So, Ashtar, you added anything to add? Okay. So, Harshal, we spoke to a couple of members here last, this week and, yeah, this week, and then, one constant suggestion that we were getting is, so if… if… I think in the last, or, like, in the… one of the previous meetings, Jake mentioned that, the updates are not going to be that frequent, right? So, it's… it's pretty much the same schema throughout, like, at least for a period of time. So, one of the suggestions that we got is instead of using a traditional ML model that, I mean, wherein you would be responsible for, like, any kind of retraining or, like, setting some confident threshold values and all of that, why don't we just use a LLM, that's available That's directly offered by Azure, and, plug it in. that's one of the approaches that they had suggested to taking. So we just wanted to know, like, what do you think about that, and then I can, So, but before that, we did one kind of a very… not… not a very solid POC, but a small POC using semantic matcher. I'll describe more about that, but I just wanted to, like, know your opinion on the LLM versus traditional ML approach.
+
+**Harsha (eParts)**: So, can you guys look at any of the other options available on Azure, other than LLMs, for this use case?
+
+**Hrishik**: For example.
+
+**Harsha (eParts)**: No, I mean, for the semantic matching. You suggested Azure for the LLMs, so I was just wondering if there's anything else that Azure offers for, the other ML stuff.
+
+**Hrishik**: Yeah, I think we were exploring BERT that is offered on Azure, and then, semantic matcher for, we were using all mini-LM model. That is also available on Azure. But we have not experimented yet, like, we don't have the accuracy scores and all of that, but for all mini-LM, like, the semantic measure, yeah, we did, some bit of exploration there.
+
+**Harsha (eParts)**: I mean, just like an off-topic question, Lewis, but coming back to the LLM question. So, Azure is pretty restrictive in terms of, like, just general fine-tuning of LLMs that they offer. So, I would highly suggest that you take a look at, what's being offered in terms of, just open models that you can train versus whatever other options are, because, one thing is Azure's AI subscription kind of is two parts, which is… they offer LLMs which are, basically open source and, self, like, just self-hosted by Azure, and the other is Azure OpenAI stuff. So, Azure OpenAI is a complete different thing in itself, and only, I think, GPD 4.0 is what's supporting fine-tuning, and that's pretty expensive in general, like, for a use case like this.
+
+**Hrishik**: Thank you.
+
+**Harsha (eParts)**: So that's… that's one point. And, So, I mean, the open… the other open source models that they kind of host and give us, I'm not sure how much offer fine-tuning support they offer in general. So, just check that out, I would say. That's probably one suggestion. But honestly, if that's a pretty viable approach, I would say go ahead with it.
+
+**Hrishik**: Yeah, like, I personally have experience with Amazon Bedrock, but not the Azure ecosystem, so we'll have to try that out. And, like, one follow-up question that I would want to ask is. So, we… initially, we just decided off two parts here, right? One is the OCR part, where would… where you would extract the text from the different sources that you have, and the… obviously, the prediction and the confidence score attributing part. So, if at all, like, obviously, like, we'll see how the LLM works in terms of cost and, resource feasibility-wise. But let's say it's possible to use that. Can we at least use the LLM part for, instead of the OCR? Because LLMs are quite good at extracting text, right? That would be, like, pretty straightforward, I agree.
+
+**Harsha (eParts)**: Yeah, yeah, they are. So again, for OCR purposes. using an LLM is not bad at all, I would say. It's pretty weird, because Azure has a separate OCR service. Oh, okay. So. So that OCR service is highly tunable in terms of how the document looks like and how it works. But it's not a great generalist, so, for example, if you ever have, like, a change in, change in schema, like, change in the type of document you're expecting, it kind of… It starts glitching, and, like, it doesn't really extract text that well. Because, there's no logical flow to it. There is, in fact, a recent paper that I was reading about, which was, It's basically trying to read a document the way a human reads it, and that's kind of how they semantically, like, extract the characters out of the document. It's… so what it does is, there's a small LLM which is corrected in the front, which feeds information to the bigger OCR extraction LLM. I'm not sure if Azure offers it at this point, but you could take a look at it, and… I mean, just… just probably experiment… I could… I could give you access to, like, the AI resources so that you can experiment with just general OCR extraction through the LLMs offered by Azure. And I think they're pretty restrictive on that end, because they do have a separate OCR service, so they kind of want to push people towards using that.
+
+**Hrishik**: Okay. I think for using the LLM to… instead of the ML model, we could even, initially try out with, maybe not on Azure, but, to seek its feasibility, we can try out with better models, the one we have access to, and see if it is able to do the work efficiently. Then we can maybe scale down the… like, go to, like, older versions or, like, less expensive versions, and then maybe, like, best case scenario would be we can host our own LAM model or something like that, which we can use to get a, like, good output.
+
+**Harsha (eParts)**: Yep, 100%. Again, I don't want to be restrictive in any way, so because this is just, like, the initial kind of finding slash research kind of phase, just, just do whatever at this point. I would say we should probably only think about if we want to go with LLMs or just stick with ML models at a later stage.
+
+**Hrishik**: Okay. So, at least for now, before the LEM topic even came up, we were just, playing around the semantic measure, approach, like, what… I mean, the data that we took, the, the amount was obviously, like, low, so, I'm not saying that the numbers are reliable or something. So I, so I think you gave us two spec docs, AM2 and RCT FlexCT, I think, yeah, I had, like, two, spec docs. So, basically, the model extracted all the attribute names from them, and, I think they totaled up to, like, around 42 attributes in total, to work with. So, we ran, through all of those, I mean, we ran the semantic matcher against the PIMS attribute index. Which has, I think, 480… I mean, something close to, like, 500 or 480, I don't remember the number. So, and then for each, supplier attribute, obviously, like, this is the internet working of the, model, like, it, it computed a cosine similarity against what it has and what the PIMS database has. So it, just gave an… I mean, it is supposed to, like, emit a number, similarity score, like, 0 or 1. For now, I don't have any rational behind setting it to, like, 0.25 as a threshold, but I just, like, intentionally gave it a very low score of 0.25. So… So, anything about that, got auto-accepted and, got written to PIMS, and anything below that got rejected. So, for now, the numbers are, like, pretty high, like, I think out of 42, 36 records got auto-accepted, so that's around, like, 86% to 87 percentage. And, 6… the other 5 or 6 got, routed to the human review. like, with this, we did not, like, want to, come to any conclusions, but we just want to, like, check, how the, internal working of the semantic matcher is done. So, this is just, like, our key findings, and moreover, the records are, like, too less to compare it against. Like, the ground truth, that we have is, like, too little, so we were thinking, like, should we, you know, produce some synthetic data, based on what we have, or how do we go about it? Because yesterday we were talking about this to, one of our coaches, and then he was like. At least for this use case, synthetic data might screw up the numbers in terms of accuracy, so how do we go about that?
+
+**Harsha (eParts)**: I can give you guys more data, honestly, like, it's not that difficult.
+
+**Hrishik**: So… That was huge. Last time you just dumped a lot of data, like the spec files and everything.
+
+**Harsha (eParts)**: Yeah, so that's the thing, right? Like, the spec files I sent you were, like, 2 or 3 out of.
+
+**Hrishik**: Yeah, yeah.
+
+**Harsha (eParts)**: 40,000 and a half.
+
+**Hrishik**: Oh, okay.
+
+**Harsha (eParts)**: Yeah, so I can always give you guys way more data if you need.
+
+**Hrishik**: Open.
+
+**Harsha (eParts)**: Tim. And I can… so I can also, like, grant access to, like, a blob storage, which we use. So, you can just pick your files from it and probably do whatever with it.
+
+**Hrishik**: Okay. Yeah, I think that would be really useful for us. Yeah. And if you could also, like, so we had the data of running final lines of, in the input into the schema, but if you have something, like, where those… where the data exactly came from, like, if you have a starting point, like, the PDF, and what that PDF extracted, so we could have a entire picture of how the existing… the, like, catalog team works, what they see and what they output from it.
+
+**Harsha (eParts)**: I would say for this, a good use case is just, like, interviewing the catalog team and understanding.
+
+**Hrishik**: Okay, yeah. Other than that.
+
+**Harsha (eParts)**: I can give you guys the data, but because there's a human there and, like, he's doing a lot of the thinking and putting the data into the database, you won't really see the steps taken there. You'll just see PDF and final data, and that's it.
+
+**Hrishik**: Night. Yeah, I think, I think we will probably should schedule a meeting with the catalog team sometime after spring break.
+
+**Harsha (eParts)**: Yeah. Wednesday for you guys, I forgot to tell you.
+
+**Hrishik**: Next week.
+
+**Harsha (eParts)**: Okay.
+
+**Hrishik**: Tomorrow's the last decision. Not if you're standing. A little bit.
+
+**Harsha (eParts)**: Uber. Yeah, so I'll just remove the meeting from next week, next week's calendar.
+
+**Hrishik**: Yeah, I'll send out a cancellation thing. Okay. Next we have… I think, yeah, for the initial ML models, you're thinking we might, like, if you're using specifically ML, we might have to use, two models for… one for the confidence scoring, and one for the initial mapping. So the data that we extract from the PDFs and other sources, like the example that Jake gave was the display size and screen size. To tackle things like that. We might have to have a smaller model which can map those key-value pairs more accurately. So that we don't have duplicate data. So, just wanted to get your opinion on that.
+
+**Harsha (eParts)**: Just, just wondering why, why, like, what's the rationale behind, like, using two models for that?
+
+**Hrishik**: I think the primary, goal for the model that we're currently thinking is to get the confidence score on each of the attributes that we extract, but in order to be able to put that into PEMS, we need to fit that data into the existing schema. So, I would say attribute matching would also require… I'm not sure if it requires a separate model, but that's the way we were initially thinking that it might.
+
+**Harsha (eParts)**: I'm in. I mean, nothing specific on that. I would say… so most, most attribute prediction kind of models, I would say, predict with a certain, Like, certain level of confidence themselves. So, when a value is generated, you could probably take that value itself. So, that… that can only be done if you're actually like, using a… using, like, an open source model where there's complete transparency on the process. But if you're using, like, an Azure kind of model, I'm not sure how… how visible it'll be. So yeah, in that case, two models would make sense.
+
+**Hrishik**: Yeah, I think that was the main, doubts we had regarding the… ML components. Yeah, but, I mean… at least, like, this week, whatever discussions we have had, everything were revolving around, the catalog team's inputs. At least, for example, for the ML thing also, like, like I said, the scores are pretty high, because it… I was just, like, trying to check how the semantic matcher works. And this is not something that you would set as a threshold, in the actual production, right? So I think the real, test would be, like, Is it Brian who's working, from the catalog? Yeah, so I think, I think when, you, my benchmark would be, like, getting the data from his team, like, where, like, with, where the, labels are manually, you know, which, I mean, mapped to which attribute, Sorry. when, so Brian does manual labeling of each attribute, right? So if we can get that data, and if I could compare… compare it against what my semantic matcher gave. That would be, like, a litmus test, but right now, I think, like you said, probably we can get all of this information only after talking to him or, like, someone from his team.
+
+**Harsha (eParts)**: Yeah, I mean, the primary interview with, like, Brian and team would help you guys, honestly, understand, like, more niche challenges as well, which they kind of encounter. And that might honestly, like, even end up making you guys deviate from whatever path you're on right now. So, they have, again, the catalog team works in their own way, and sometimes the way they kind of get files and data is straightforward. On good days, and just on very occasional days, it might not be that easy. So I sent you guys a template of… a template file, too, for, like, the data that is filled out. So, in that template file, what happens is, ePath generally sends that template file out to all the suppliers that we have, and that template file is just used by us to directly input data into our product catalog. And similar case is with Brian's team as well, in most days. And they also have a catalogue team, which kind of does this work of filling the Excel sheet out. themselves. So, there's certain suppliers that Alps basically deals with, who don't have, like, who don't have people to do that for them. And… For those kind of clients, we take on the work, and we kind of fill the catalog out for them. But that template is kind of, like, a good benchmark on how we expect the data to be after we, extract all the information from, like, the websites and the PDFs that we have.
+
+**Hrishik**: So, you just mentioned about some Excel where the data would be, present, right? So, how does that go to Prims? Like, do you have some APIs, or is it, like, some kind of a upload, release, or how does that work?
+
+**Harsha (eParts)**: This… the Excel part… so, the way it is right now is, I mean, this data goes into, like, master database through a few, Like, stored procedures we have, which are kind of lacy. And these load procedures ingest the data into the SQL database, SQL database. has this occasional sync with PIMS that goes on, and that's how it ends up in PIMS. But ideally, it should just go directly into PIMS, and that's… that's kind of the process. But I would say if you guys can generate this specific Like, generate the data in the… in the specific Excel template that we have, that is honestly, like, 70% of the work done. And ingesting it is kind of just the final programmatic step. I mean, Jake would be a better person to talk about the PIMS part of things. So, again, I think you should leave that question open for, like, next, next week, when you have a discussion.
+
+**Hrishik**: Yeah, so why did I ask? This is, again, like, one of our coaches, asked us about what is the UI part of it, like, we don't have an explicit UI here, but then, obviously, the API's interface or anything of that sort is also considered as… under that spectrum. So, we didn't have much details on that, so that's why I asked.
+
+**Harsha (eParts)**: Yeah, I mean, probably just designing the API would probably be the last cog in this project. And that would probably be as much UI UX would have to do, which is literally backing work.
+
+**Hrishik**: Okay.
+
+**Harsha (eParts)**: Yeah.
+
+**Hrishik**: Question? I think we covered all the main points and questions we had. Does anyone have anything else? I have a general question. Thank you for supplying all the sample data to the team. Of the sample data you provided, how representative is that of the spectrum of the data that you get?
+
+**Harsha (eParts)**: It pretty much represents, like, almost all of the data that we get. So, the way I've kind of created the sample data is actually by querying from our main production databases and up for scaling, like. key elements which… which we probably don't want to expose, and that's… that's pretty much it. And, the spectrum of data, it pretty much covers everything. So, the Word file kind of, details upon, like, how all the data interfaces and how the… how all the data flows. So, that has all the details on… How the data ties in, and the sample files are pretty much representative of the production data that we can deal with day-to-day.
+
+**Hrishik**: So I'm curious, in which files would you have to OCR anything? If you… I mean, you have text files, you have Word files, and you have PDF files. Where's the OCR need for that?
+
+**Harsha (eParts)**: Not really. So, again, I think OCR component left would only be for the PDFs, and that's pretty much it. So, there are two PDFs that I sent across to the team, and those are the kind of documents you would ever have to OCR in this process. And the Word files and CSV files, I mean, the CSV files are pretty much a SQL database extract that I've given them, and the rest of the CSV files, I mean, there's… if it's a CSV, I would say you can always write a program to kind of get data from it and never really do OCR.
+
+**Hrishik**: Okay.
+
+**Harsha (eParts)**: We're gonna be more standardized.
+
+**Hrishik**: I had another question. Could we also, like, I think in the files that you have sent, the prices part is removed? the… do… could we also get some, I guess, catalogs or PDFs which have prices, like, maybe old resident prices, but just to be able to see how that will look when we start doing our POC on The OCR part.
+
+**Harsha (eParts)**: So, in the PDFs, usually prices aren't present at all. So, prices are sent across as a separate CSV, which, detail, how the specific build… often build of a model, is priced, or is supposed to be priced by us. So, for example, if we take, like, a… Or something, and for example, there's, like, in the same type of tap, which is made of, like, brass, there's, like, 10 different flow rates that are available, and each different flow rate is spec'd with a different price. And each different material for that tap is spec'd with a different price. So all these combinations and how it can be built out. Those are basically sent as a CSV file to us. And that's how we build out the pricing. So, I would say, as long as you can create a product without the price component in there, it should be pretty much good. Because, ideally, I mean, we've been discussing that, I think since the beginning of the project, that prices are the only sensitive content here. And… I would say the price part is something we can work on it at a later phase. But, I mean, the project mainly is doing, like, a POC on how well can we actually streamline the whole process of getting the product data into our systems.
+
+**Hrishik**: Alright, okay.
+
+**Harsha (eParts)**: Yeah, because again, if you go down the price, price thing, it's… it's insane, like, just the way these products are built out is a full thing to understand itself. Because there's some parts which, honestly, I feel like… There are people in the company which have spent their entire lives to understand how they're priced out.
+
+**Hrishik**: Another thing, how much, do you have an approximation how much data does a catalog team ingest? Generally, like, how many… How much you're able to fill out?
+
+**Harsha (eParts)**: This varies, right? So right now, we have a certain set of suppliers, and it's… pretty much all stable at the given moment. But, There might be a case where, in the next 2 months, we have, like, 4 different new suppliers coming in, and that would mean there's a few From thousands to tens of thousands of products going in. Or even more. So, this, this all depends. And the thing is, sometimes one product might have, like, multiple variations to it, and… That might be a big task in itself.
+
+**Hrishik**: Okay.
+
+**Harsha (eParts)**: So just building a… so just, like, getting a product into the catalog in the right way, where, we mention the… mention all your variable specs in the correct manner is also a challenge in itself.
+
+**Hrishik**: That's what I have. Do you have any questions for us? I think we are over all our points.
+
+**Harsha (eParts)**: I mean, no specific question as such. I mean, it's just interesting to see what kind of approach you guys take and where you guys head every week. And I would say just keep at it, and let me know if there's any more, documents or data that I can send over that can help you guys out. And I'm actively working on it. Last week was just too much travel, so I just… it just took me forever to get you guys those documents.
+
+**Hrishik**: Yeah, we haven't gotten the Azure access, personal access as well.
+
+**Harsha (eParts)**: Oh shit, okay.
+
+**Hrishik**: Oh, beautiful. The blob storage, if you can give a small portion of it, or somehow give us access, so we can look at it.
+
+**Harsha (eParts)**: Let me do that. So, I can give you guys a small portion of the blob storage. And the Azure access is still not working USN. So what does that mean? You're, do you want to create, like, a separate, container instance and work on it, or do you want to support team on it? What's…
+
+**Hrishik**: No, I think last time… Just to experiment with the different Azure aspects. Yeah, I think David said he'd create one separate account, which is like a guest account, which doesn't have all the… Permissions, and he would give that to us, so we can look at it.
+
+**Harsha (eParts)**: Go ahead. So, I'll… I'll talk to David about it. So, he's been, out on some sort of, leadership retreat thingy, and he'll be back tomorrow from it. And, I'll add to him as soon as he's back from it.
+
+**Hrishik**: And I think, we still haven't gotten the cursor access.
+
+**Harsha (eParts)**: Yeah, true. Courser is still something we're working out, because we kind of, I think, asked the cursor support team on how we can figure it out, and they've been taking their own time getting back to us.
+
+**Hrishik**: Okay, okay.
+
+**Harsha (eParts)**: So, I would also say, like, in the meanwhile, do explore, like, if you guys can set anything up for yourselves in the meantime, mainly for ideation and other stuff. Especially when you guys have to work with Azure and other things, so… I'll get the Azure access done as soon as I can, but cursor, I'm not sure.
+
+**Hrishik**: Okay.
+
+**Harsha (eParts)**: Yeah, because it's so weird, like, the enterprise tier has these weird policies in place for Cursor, where you literally cannot do anything if a person's out of that enterprise and It just completely locks you in with that specific ending. And it needs to be connected to an outbox. Which is… which is just weird, because we kind of gave you an alias, right, initially? Like, Rishkej at, like, Outlook… at ePathservices.com, which was the alias, but it doesn't take an alias, because there's no outbox connected to it, and, like, it's…
+
+**Hrishik**: No.
+
+**Harsha (eParts)**: Yeah, it's… these are things that we've never really experienced.
+
+**Hrishik**: Okay. One last question. Did you leave Pittsburgh before the big snow, or after the big snow?
+
+**Harsha (eParts)**: I left Pittsburgh on, like, 8th of Feb, so I kind of, left exactly when it was getting slightly warm.
+
+**Hrishik**: Sorry.
+
+**Harsha (eParts)**: I'm glad, I'm glad to have missed all of this.
+
+**Hrishik**: Oh, very good.
+
+**Harsha (eParts)**: But India right now has been on the other end of the spectrum. It's been, like, high 80s in, like…
+
+**Hrishik**: Low 90s.
+
+**Harsha (eParts)**: Which is insane.
+
+**Hrishik**: Alright, thanks a lot, Hasha. What do you expect?
+
+**Harsha (eParts)**: Just let me know. Also, do send out a summary of this, so that Jake and everyone can go through it later, and… do reminders, keep bothering us with, like, the reminders for Azure Acts and cursor access, because that completely left our minds, I think, for the last one week.
+
+**Hrishik**: Okay.
+
+**Harsha (eParts)**: Yeah, just… just let us know, and I think… I think we should be on top of it. At least I'll be on top of it, no matter if… So, if you don't hear back until Monday, just ping me on Teams, and that should be good.
+
+**Hrishik**: Alright, I think we'll do that.
+
+**Harsha (eParts)**: Serious.
+
+**Hrishik**: Thank you. Have a good night.
\ No newline at end of file
diff --git a/minutes/2026-02-26-client.json b/minutes/2026-02-26-client.json
new file mode 100644
index 0000000..1dfd264
--- /dev/null
+++ b/minutes/2026-02-26-client.json
@@ -0,0 +1,91 @@
+{
+ "meeting_date": "2026-02-26",
+ "duration_minutes": 30,
+ "participants": [
+ "Hrishik",
+ "Harsha (eParts)"
+ ],
+ "participant_count": 2,
+ "total_words": 4467,
+ "total_turns": 93,
+ "speaker_stats": {
+ "Hrishik": {
+ "turns": 47,
+ "words": 1996,
+ "pct_words": 44.7
+ },
+ "Harsha (eParts)": {
+ "turns": 46,
+ "words": 2471,
+ "pct_words": 55.3
+ }
+ },
+ "detected_topics": {
+ "ML/Model": 8,
+ "Architecture": 4,
+ "Data": 4,
+ "Onboarding": 2
+ },
+ "questions_found": 1,
+ "questions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "Can we at least use the LLM part for, instead of the OCR"
+ }
+ ],
+ "potential_decisions": 2,
+ "decisions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "I think the primary, goal for the model that we're currently thinking is to get the confidence score on each of the attributes that we extract, but in order to be able to put that into PEMS, we need t"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Yeah, just\u2026 just let us know, and I think\u2026 I think we should be on top of it. At least I'll be on top of it, no matter if\u2026 So, if you don't hear back until Monday, just ping me on Teams, and that shou"
+ }
+ ],
+ "potential_action_items": 18,
+ "actions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "So, Ashtar, you added anything to add? Okay. So, Harshal, we spoke to a couple of members here last, this week and, yeah, this week, and then, one constant suggestion that we were getting is, so if\u2026 i"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "So, can you guys look at any of the other options available on Azure, other than LLMs, for this use case?"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Yeah, like, I personally have experience with Amazon Bedrock, but not the Azure ecosystem, so we'll have to try that out. And, like, one follow-up question that I would want to ask is. So, we\u2026 initial"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Yep, 100%. Again, I don't want to be restrictive in any way, so because this is just, like, the initial kind of finding slash research kind of phase, just, just do whatever at this point. I would say "
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Okay. So, at least for now, before the LEM topic even came up, we were just, playing around the semantic measure, approach, like, what\u2026 I mean, the data that we took, the, the amount was obviously, li"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Night. Yeah, I think, I think we will probably should schedule a meeting with the catalog team sometime after spring break."
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Uber. Yeah, so I'll just remove the meeting from next week, next week's calendar."
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Yeah, I'll send out a cancellation thing. Okay. Next we have\u2026 I think, yeah, for the initial ML models, you're thinking we might, like, if you're using specifically ML, we might have to use, two model"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "I think the primary, goal for the model that we're currently thinking is to get the confidence score on each of the attributes that we extract, but in order to be able to put that into PEMS, we need t"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "This\u2026 the Excel part\u2026 so, the way it is right now is, I mean, this data goes into, like, master database through a few, Like, stored procedures we have, which are kind of lacy. And these load procedur"
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-02-26-client.md b/minutes/2026-02-26-client.md
new file mode 100644
index 0000000..eedc2e2
--- /dev/null
+++ b/minutes/2026-02-26-client.md
@@ -0,0 +1,50 @@
+# Meeting Minutes — 2026-02-26
+
+**Date:** 2026-02-26
+**Duration:** 30 minutes
+**Participants:** Hrishik, Harsha (eParts)
+**Source:** `GMT20260226-190646_Recording.transcript.vtt`
+**Processed:** 2026-04-23 17:47 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Hrishik | 47 | 1996 | 44.7% |
+| Harsha (eParts) | 46 | 2471 | 55.3% |
+
+## Topics Discussed
+
+- **ML/Model** ████████ (relevance: 8)
+- **Architecture** ████ (relevance: 4)
+- **Data** ████ (relevance: 4)
+- **Onboarding** ██ (relevance: 2)
+
+## Potential Decisions
+
+1. **[Hrishik]** I think the primary, goal for the model that we're currently thinking is to get the confidence score on each of the attributes that we extract, but in order to be able to put that into PEMS, we need t
+2. **[Harsha (eParts)]** Yeah, just… just let us know, and I think… I think we should be on top of it. At least I'll be on top of it, no matter if… So, if you don't hear back until Monday, just ping me on Teams, and that shou
+
+## Potential Action Items
+
+1. **[Hrishik]** So, Ashtar, you added anything to add? Okay. So, Harshal, we spoke to a couple of members here last, this week and, yeah, this week, and then, one constant suggestion that we were getting is, so if… i
+2. **[Harsha (eParts)]** So, can you guys look at any of the other options available on Azure, other than LLMs, for this use case?
+3. **[Hrishik]** Yeah, like, I personally have experience with Amazon Bedrock, but not the Azure ecosystem, so we'll have to try that out. And, like, one follow-up question that I would want to ask is. So, we… initial
+4. **[Harsha (eParts)]** Yep, 100%. Again, I don't want to be restrictive in any way, so because this is just, like, the initial kind of finding slash research kind of phase, just, just do whatever at this point. I would say
+5. **[Hrishik]** Okay. So, at least for now, before the LEM topic even came up, we were just, playing around the semantic measure, approach, like, what… I mean, the data that we took, the, the amount was obviously, li
+6. **[Hrishik]** Night. Yeah, I think, I think we will probably should schedule a meeting with the catalog team sometime after spring break.
+7. **[Harsha (eParts)]** Uber. Yeah, so I'll just remove the meeting from next week, next week's calendar.
+8. **[Hrishik]** Yeah, I'll send out a cancellation thing. Okay. Next we have… I think, yeah, for the initial ML models, you're thinking we might, like, if you're using specifically ML, we might have to use, two model
+9. **[Hrishik]** I think the primary, goal for the model that we're currently thinking is to get the confidence score on each of the attributes that we extract, but in order to be able to put that into PEMS, we need t
+10. **[Harsha (eParts)]** This… the Excel part… so, the way it is right now is, I mean, this data goes into, like, master database through a few, Like, stored procedures we have, which are kind of lacy. And these load procedur
+
+## Questions Raised
+
+- **[Hrishik]** Can we at least use the LLM part for, instead of the OCR
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 4467 words across 93 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-04-02-cleaned.md b/minutes/2026-04-02-cleaned.md
new file mode 100644
index 0000000..555f431
--- /dev/null
+++ b/minutes/2026-04-02-cleaned.md
@@ -0,0 +1,247 @@
+# Cleaned Transcript — 2026-04-02
+
+**Jaivard**: Yeah, I'll just turn up my speaker. So… okay. Yep. So, the first item on the agenda is for the timeline. Yeah. Rishi, you're there, right?
+
+**Hrishik**: Yes.
+
+**Jaivard**: Yeah. So… Yeah, sure, can you, should I share my screen, or can you share your screen, with the timeline?
+
+**Hrishik**: I think you shared I'm having some issues with my system.
+
+**Jaivard**: Okay, nevermind, I'll just do it then. So… Okay. So, is it visible, Harsha, dude?
+
+**Harsha (eParts)**: Yep, indeed.
+
+**Jaivard**: So, we just wanted to go over and, like, get your feedback. So, for now, what we have is, like. by April 15th. For this, this semester, we'll have, the requirements. Completed by then? And… I think we're making progress on this, so it should be doable by then, yeah. And for… by April 30th, the SES system should be finalized, and the risk document should also be completed. If the notion of requirements… You might want to say that. on his calendar.
+
+**Hrishik**: I think we can, skip towards the end. This is a bit more detail. I think you can add on which parts you want to discuss. There's a high-level view on the island.
+
+**Jaivard**: Sure. Yeah. This one, right?
+
+**Hrishik**: Yeah, so, we are targeting that towards the end of this month, we should have the basic, requirements, risk process, and like, not… I'm not sure if we'll be able to have the complete architecture, because we need to still do some POCs. on the LLM versus ML front, so that we can finalize an approach and build the architecture around that. That is the plan for early May, first couple of weeks of May. We should have that information with us. And, towards the end of May, we plan to begin development. on all fronts, there'll be… I think we'll do it parallelly. We'll be working on the Innesian Gateway, along with the ML or LLM components. After that, within the next couple of months, till, in June and July, we are expecting to be done with Almost all of the development. We have a… we are targeting an aggressive approach. So that we have more time to cater to any, issues that we may have. These timelines might increase a little bit, because we have left around 2 to 3 months of testing effort. Just in case we run into some issues. So, we are taking a optimistic approach here. And by the end of August. We are hoping that the individual modules are ready. For, to basically be integrated with each other, so that we have a complete system in place. And in that time, we'll also be doing some sort of individual component testing, and we'll probably share the results with you. On how we are able to process the files that we have. And, how the LLM components are doing the work. And after, September in, I think in… October, we'll probably be figuring out, the exact tests and the extent of the testing that we're following, and then the next month or two will be, just… Testing the entire system, and… Looking at it, if we can, incorporate some of the stretch codes that we have, the… maybe the web scraping part, or some of the other things that would be good to have. For you guys, we'll be trying to incorporate that, so we are leaving a couple of months to… be able to work on that as well. And in December, we'll probably not be… we are hoping to be done with everything before that, and in December, it'll just be, any documentation or handover plans that we have. maybe demos, whatever is required. That's the part we're leaving for end of November and December. And if you want to go into details of any of these, it's mentioned above.
+
+**Harsha (eParts)**: Okay Being the questions to my husband.
+
+**Hrishik**: We can share this doc with you, after the call.
+
+**Jaivard**: Yep, So, is it fine with both, is it fine, Hersha and David? If you have anything, then we can make changes. If not, we can share this with you after the meeting.
+
+**Harsha (eParts)**: Other than July's missing, but yeah, I think… I think this makes sense. This is a decent timeline. Or at least a decent breakdown of the units. I suspect some of those units might be larger than others, and… It's also possible that EPART's appetite would change, that things that might be stretch goals will work its way into core requirements. That seems to be just the way of the world, but we'll do our best to keep that from happening. Yeah, I think this is a good… a good first plan.
+
+**Jaivard**: Got it. I think July is not there just because summer semester ends by then, and then, you know, that's just, again…
+
+**Hrishik**: Yeah, July was, left out because, the entire development… there won't be any deliverable in that month, because we'll still be working on the, like, development of it, and we don't expect there'll be any deliverable in the month of July. It's like a two-month Period in which we'll be developing everything.
+
+**Harsha (eParts)**: Okay, I must have misunderstood before, that's… that's fine. That makes sense.
+
+**Jaivard**: Yep. So…
+
+**Harsha (eParts)**: And, you know, there's a few things I think I want to say from mine, but I think that can cover the end of the key. Not regarding the deadline.
+
+**Jaivard**: Okay, we also have the statement of work, I'll just share that as well. So… One second, this is… So, we were, is it visible, first of all?
+
+**Harsha (eParts)**: Yeah, I'll…
+
+**Jaivard**: Okay. So, we were, it was encouraged that we have a statement of work, just so that, you know, we have a shared understanding with you. I'll be… this is the first version, and this is still, I think it… a lot of changes might be required, but I just wanted to share it with you guys, and It just basically goes over what we understand of the project. And along with the scope that we think, and how we, you know. think about each of the activities that we're gonna have, so… It's a… it's a bit of a long document, but this is just basically all of our understanding and all of the things that we think are currently… we're gonna have to do. It also defines some of the out-of-scope things, like, right now, I just wanted to confirm again, so generalized web scraping and all that, stuff is not included, right? Because… I have it here, but we can make changes if you want.
+
+**Harsha (eParts)**: Yeah, generally, that's great, no. If there's anything which is mentioned in the PDF document itself. Probably, but that's… that's it. There's nothing… there's nothing out of the bounds of it, which is, you guys make a search, and look for things, and figure out what is correct. That kind of website is completely out of scope.
+
+**Jaivard**: Yeah. So, we also have other things, such as, like, You know, that are… we will be working all the way up till PIMS, but the steps after that is not something under our control, so I've also written these points here, and… VAB, the other things are just, like. What we think we are allowed to do, and the things that, either are not under our control, or things that, we're not, we do not expect that, will come up, or we'll have to do. So, I'll be set… Yep. Okay. Oh, I thought someone was asking a question. So… Yeah, so it, there's also the project documentation deliverables, so all of these things. And finally, I would just like to… Go to… one sec. Yep. So… The last thing is just all the… Yeah, general assumptions we have? And, you know, who we have met, what tools we're expected to use. That, you know, we make sure that the… the constraints, like, not using any public LLM, or, you know, not… Using the company data on anything other than company resources. So that, that, all that stuff is here. And we, in general, I just wanted to show it to you guys so that, you know, That this, you know, statement is, here is much more clear to everyone. And that, you know, if there are any gaps in our understanding, that you might… you guys might, you know, say that, okay, this is actually different here, and we can go ahead and make those changes. So, I'll also…
+
+**Harsha (eParts)**: Excuse me, sorry, bye-bye. Go ahead.
+
+**Jaivard**: It's just, I'll also send this document over, you guys can look at it, and if there's any mistakes that you… or, like, misunderstandings there, then, yeah, we'll go ahead and change it.
+
+**Harsha (eParts)**: Yeah, I mean, I can go through it in detail and then send any changes or something which doesn't align correctly to you guys, but there shouldn't be any… yeah.
+
+**Jaivard**: Yep. So, yeah, this is just an initial first first version, so yeah, we'll definitely update it as we go along.
+
+**Harsha (eParts)**: Yeah.
+
+**Jaivard**: So… Yeah, I'll send this over. Along with the timeline. The other thing is… yeah, Liu is here with us. It's just… he had some specific questions. He has been looking through the ML portion, and he wanted to basically, you know, clarify what all he needed, and, you know, all the stuff, and why he needs it, and, you know, his future steps. So, Lou, if you… Or want to, like, share your document and, like, just, You can basically go ahead and, like, Send a mail to them. So, just… closing down. detailed data. Neat. Okay, so… Yeah. Basically, Liu's saying that, He's looked into the, you know, different options, and he's done some calculations on how many entries he would need. And, so… After he's made initial document and, like, all the estimates. And he sent a… he's basically sent an email with all the requirements for the ML portion, and he needs that to, like, go further in the ML prototyping, so it would be really helpful if, you know. To proceed, if you could get them.
+
+**Harsha (eParts)**: Yep, 100%. I was… I was just gonna say, also… I've already taken a look at that email, and I've started collecting the data for that as well. So ideally, I was saying that either end of today or Monday, I should, get you guys an email. Is there a timeline from your end that you expect me to give you the data by?
+
+**Jaivard**: No, it's… I think Monday's fine, right? Yep, so, yeah, in the meantime, he's just, gonna, like, you know… Work on the other stuff for the output types. So… I think… that's it, in the sense that… we… The, like, we were working on other stuff as well with… But that's more related to project management. I did have a question, Harcher, so… Next week, we have the carnival, so… I think the building will, will be shut down on Thursday? No? Okay, okay. I'm not sure. So, but, is there… should we reschedule the time for it? I'm not certain about it, so I just wanted to bring it up.
+
+**Harsha (eParts)**: I mean, you guys will have a vacation for, like, the 2 or 3 day cargo period, right? So, ideally, I would say if you want to have a meeting next week, reschedule it to when you guys are actually in campus and actually have a working day. Otherwise, you can do it the week after today, that's fine.
+
+**Jaivard**: Ashuta, Rishi, I would also, like, what do you guys think?
+
+**Ashritha**: I probably might not be available for those two days, Thursday and Friday.
+
+**Jaivard**: Rishi?
+
+**Hrishik**: Yeah, I think I might not be available on Friday… Thursday… yeah, I think I can make Thursday, but I'll not be available for the weekend on Fridays.
+
+**Jaivard**: But, so, yeah, we wanted to discuss PIMs and the like. go into detail regarding that, Harsha. So, would it be alright if we came to your office? Actually, first of all, is it alright, Shruta, Rishi, Liu? What do you think?
+
+**Ashritha**: Yeah, anytime, like, Monday, Tuesday, or Wednesday works for me.
+
+**Hrishik**: Yeah.
+
+**Jaivard**: Rishi?
+
+**Hrishik**: Yes, vote for me.
+
+**Jaivard**: Okay. Yeah, your voice is a bit long. I'll just increase my speaker. Okay, which, which day would, work for you, Harsha? David? We would really like to, I guess, come over and ask questions.
+
+**Harsha (eParts)**: So, I would say Jake would be the person for PIMS, for almost most of them. So, I would say drop a message on Teams. And just give us your availability on when it's the most convenient for you guys, and Jake will just let you guys know on, like, what slot works for him. My guess is that it will be Wednesday. He's probably gonna be in Erie Monday, Tuesday. So next Wednesday might be it, but let's see.
+
+**Jaivard**: Yeah. Okay. So, that sounds good to me. Just to confirm, Liu, Rishi, Ashitantha, is that fine with you guys?
+
+**Ashritha**: Yup.
+
+**Hrishik**: Yeah, let's, check the calendar once and then confirm.
+
+**Jaivard**: Yeah, we'll definitely send over the availability times and, like, coordinate with you, with you guys, yeah. And… .
+
+**Harsha (eParts)**: I mean, I have a few things to discuss, but, you guys go on, begin with that.
+
+**Jaivard**: No, no, please go ahead. I think that that was a lot of the high priority.
+
+**Harsha (eParts)**: Okay. So, a few things, as in, one is. with respect to, Liu's email. There is one thing where, he mentions, about having the documents attributed to the exact products that are mapped to it. So, what I'll do is, I'll be sending you guys the document links from BunnyCDN. So, all you can do is, you can use that link to get the exact document that you want, that is attached there. So, does that sound okay? Or do you want me to send the actual document itself, embedded?
+
+**Jaivard**: loop. So, he… Actually, just like, I'll just turn the camera this way so we can… So… It works great. Yeah, yeah. Okay, I got checks around me.
+
+**Hrishik**: Harsha, could you repeat the two options that you had? One was sending over Another one was the actual document.
+
+**Harsha (eParts)**: So, what is mentioned in the email is that, he wanted the raw supplier text extracted from the PDF, or the spec sheets, or the CSV sent directly attributed to the products itself, and, sent over. What I can actually do is, send a link instead of the, extracted data from the PDFs. So, that link will contain the exact document that, connects to the projects themselves. So, it'll just be the document link, products that are catered on this document, and so on.
+
+**Hrishik**: Okay, I think that should be fine. Go ahead.
+
+**Jaivard**: Yeah.
+
+**Hrishik**: Ashata, do you… like, as, like, we need it majorly for the input for our LLM model. So that we can actually see how it is working and what it can do. I think that should be fine, but I think we will require a good chunk of data in that respect, like, what we are giving it as input, and how the catalog team is refining it, and what the actual output is towards the end. So that we can…
+
+**Harsha (eParts)**: So, I'll send over as many as I can, so don't worry about that. It'll be at least more than a thousand, so don't worry about it. And, another question… actually, not a question, another, I think, thing that I think we discussed in the earlier days of the project, but we kind of… I think we've forgotten about it, and I think we've forgotten to mention that to you guys have heard it too, which is, initially you were talking about the data standards, right? on, how we could standardize the data, and I think in our previous meetings, we kind of, establish that we will be using the attributes from the product types, which are in the tables that I provided you guys with. And that's how we'll be doing it. But the thing is, we kind of identified, like, 3 data standards that are already there in the industry, and are pretty much standardized. And no matter what Type of product you're looking at, you'll find the exact type of attributes that are needed for the product. So the three standards are, like, ETIM, E-Class, and UNSPSC. I'll ping this to you guys, but, These are basically open source standards, where all the product types and attributes for these product types, it's all available on the website. And, ideally, you can use this data itself To create the initial set of, like. Fittering for, like, how the product should look like, or how the… Or how a specific category or product type of product should look like.
+
+**Jaivard**: I think that works, I'll tell you what you think. Do you seem to agree. This is true.
+
+**Harsha (eParts)**: I need to.
+
+**Jaivard**: excuse me. Just understandable. Yes. So, yeah, yeah, Lee is also saying that he'll just go through it, and then, You know, go for, like, if there's anything else, then he can, I guess, inform you again.
+
+**Harsha (eParts)**: Yeah, and another great thing about this… these open standards is that all of these standards have documentation which tells us that what these specific attributes, which are called in this standard, map to in the other standard that is mentioned. So, like, how does ETIM, ETIM's attributes identified map to the E-class attributes? It's all clearly, defined. And, I mean, like, a benefit of this would just be that A document, sorry, a product attributed in a certain standard can always be mapped to another standard as well. And this would mean that that one product can be identified according to three different standards, because they're all interrelatable, and they have, like, the language defined on, like, what are these different What are the various possibilities for these attributes to be called in the industry?
+
+**Jaivard**: Yeah.
+
+**Harsha (eParts)**: So, synonyms, everything are highlighted pretty clearly in this.
+
+**Hrishik**: Could you.
+
+**Jaivard**: Yeah.
+
+**Hrishik**: explain a bit about what these standards actually are. I'm not very clear on the…
+
+**Harsha (eParts)**: What's.
+
+**Hrishik**: Right, yeah.
+
+**Harsha (eParts)**: So right now, we have product types, categories, and attributes, right, Vishkish? So, these product types and attributes are pretty much what Alps, like, our parent company has kind of come up with, and these are, these are, like, just… Just things they've identified over their years of working with these products. And what these standards actually do is, instead of relying on apps controls for, like, how they call certain things, like, how they map certain attitudes, or what they call certain attributes, it just goes with the industry standard of, like, what these attributes are called, and what sort of terms are used for these attributes. Road banks and anything.
+
+**Hrishik**: Okay, so, is there a chance that there is a mismatch between what Alps uses and what the actual, like, documentation the official one uses?
+
+**Harsha (eParts)**: There could be. So, I would say, we don't even have to go by ALPS standards, right? Ideally, I would say, if we can map to these general industry standards, we should be more than happy, because, in the end, ALPS is just one person who decides to Call these things a certain name, and that's what it is. This might also simplify, like, all the different lingo that I have specifically uses, because a lot of these documents, the PDFs for these specific products and, All of these, all of these specification documents, they kind of go by the general industry standards themselves. And to actually map it to ALPS is a more difficult task compared to mapping it to these open standards. And these open standards kind of give us more attributes that we can expect for a product type, and Just… they just provide a more robust way in identifying them.
+
+**Hrishik**: Okay, but in, doing that, won't it also require a rework on the PIM side? To map those attributes, the values, and so it can be used on stream?
+
+**Harsha (eParts)**: I mean, so, since these are already, like, predefined tailors, Technically, you won't have to… you won't be expected to create, these… these specific attributes and product types or terms, as long as you can map the product to these attributes and more. Product types, we should be good. But the whole page end of things, that's something we look into, on, like, how these things should look like there.
+
+**Hrishik**: Okay. I think for now, we were relying on the schema you provide at source of growth, but I think we can compare it with the standards that you mentioned.
+
+**Harsha (eParts)**: Sure.
+
+**Hrishik**: And see if and where there's an overlap or a difference, and we can…
+
+**Harsha (eParts)**: So, because I was personally comparing it to the three, To the 3-way ball valve that we kind of have been looking at. And, for that example, this… these standards are actually way more clearer. So, for example, if we call flow rate flow rate in one standard, it'll also give us, like, synonyms which are used for flow rate, which is some sort of, Water flow rate, or liquid flow rate, or some sort of weird term, which is, like, the actuator flow value, or something like that. And all of these surnames are defined in the standard thresholds. So, there's… there was an initial question, too, right, in one of our earlier meetings, where, how do we look at the synonyms, or like, what if something is called something else in a document? How do we understand that this matches to a specific category?
+
+**Hrishik**: Yeah.
+
+**Harsha (eParts)**: These, these standards actually clear those, those kind of questions up, I started.
+
+**Hrishik**: Okay. I think, probably we can go through the standards and do a comparison, and we'll probably need, the verification from the catalog team if we are doing it in the right way.
+
+**Harsha (eParts)**: noon.
+
+**Hrishik**: We can probably create a document, like, layering the differences that we have and what we're following, and we can get it verified by you guys once.
+
+**Harsha (eParts)**: Yeah. But that's it. Is there any problem with what I just said? Anything unclear, or anything that seems like it's out of scope, or anything that seems like this is not what we initially intended to do?
+
+**Ashritha**: Just, I mean, I understood what you're trying to say about the standardization part, so it's like, okay, I've worked similar to this, like, on the telemetry side of it, like. OpenTelemetry, and then all of it. So, it's basically, you want to be vendor agnostic, and then standardize everything, right? So, if you are sure that standardizing according to those, rules, won't cause any problem, like, in case you want to integrate with ALPS or, you know, the EPADS,
+
+**Harsha (eParts)**: Yeah.
+
+**Ashritha**: So then I think we're good. Should be actually a lot more easier, yeah.
+
+**Harsha (eParts)**: Yeah, this… this… again, like, another reason for, like, looking at these standards was also making your life easier, right?
+
+**Ashritha**: Good.
+
+**Harsha (eParts)**: Technically, the ALP standards are… very… make you guys very dependent on what the guys are actually telling you guys to… Cool. And we also heard from Brian that these… these attributes are basically decided by, like, a team who's in charge for those specific products, and they decide that, oh, these attributes are relevant, and we're gonna show these attributes on the website, and that's how it works. Okay. Yeah, so this… this is just making things simpler, I think, for each of us. And I think as ePaths, like, separates and, like, becomes its own thing, these standards will also help us, like, like, just show that our data is more robust than more related, in general, because a certain person knows what ETIM is, but a certain person won't know what else is going by.
+
+**Ashritha**: Yep.
+
+**Harsha (eParts)**: And simple as that, yeah.
+
+**Jaivard**: Yeah. I would have to, like, look through and, like, compare Harsha, like, to see the differences, but I think this would help us, yeah.
+
+**Harsha (eParts)**: just go through… go through it. We can discuss this the week after, or the week after that, and you basically could ever just understand what he sent, and how the input.
+
+**Hrishik**: I think the only major change would be that, the source of truth is kind of a change, but I think changing that would help us in the long run, if you have standardized it, so I think it's a good change.
+
+**Harsha (eParts)**: Yeah.
+
+**Jaivard**: Yeah, this might, like, prevent… this might actually prevent rework when you're actually expanding, so yeah, that… I think that makes sense. Let me just go through. Other than that, I don't think, like, these, like, these were the things I wanted to discuss. As for the available timings and all these documents, I'll send them over. And… Whichever time works for you, we can, you know, agree on something, and then, you know, we'll gather some questions and ask you regarding them. It's mostly BIMS, but there might be something else as well.
+
+**Harsha (eParts)**: I'll drop these standards on the team's chat after the meeting. Yeah.
+
+**Jaivard**: Yeah, I'll just drop a message to Jake as well on Teams, and I'll just send him an email, both of them.
+
+**Harsha (eParts)**: Damn.
+
+**Jaivard**: I think that's it, but, I'll just invite, like, you, Ashta, Is she this?
+
+**Ashritha**: No, I don't have anything to add.
+
+**Hrishik**: Hmm. I think…
+
+**Jaivard**: Okay.
+
+**Hrishik**: Same for me.
+
+**Jaivard**: Blue? Hello? decoratively.
+
+**Hrishik**: We can't hear you.
+
+**Jaivard**: He… Lou is talking about the Claude code, so… it's… he's saying that it lets… it hits the limit… the token limit for that.
+
+**Harsha (eParts)**: Yeah, I mean, we've kind of restricted total token limit, too, because it kind of becomes a big overhead, right, in general. I think the main reason for that is I don't know, you can technically use up unlimited credits with Cloud Core at this point, and, like, we ourselves have personally been struggling with, like. Keeping that in, like, the best in budget, good state. So, hence the limit imposed. If that limit is too restrictive, we can surely bump it up.
+
+**Jaivard**: Yep, I think that's another topic that maybe we'll discuss more in depth, but I've not personally, I think. gone through those same limitations, so I'm not so sure. But…
+
+**Hrishik**: bumping up the limits in a short term would be good, because currently Claude is facing some token issues, like, even small KDs are using up much more tokens than it usually should. Hopefully that'll fix soon enough, and we can maybe go back to the earlier tokens. But right now, even a small query just, like, keeps on hitting different tokens, and it just runs out of memory very quick.
+
+**Harsha (eParts)**: Yeah, there's a lot of… I mean, in general, there's a lot of nuances to these tools too, right? Certain tools are very, very good with their limits. Certain tools, even though you end up paying exorbitant amounts, they still run out of limits all the time. That's kind of the case with CloudCo, too. If you ever tend to use any Opus model, it just runs out of limits in, like, 10 to 15 minutes. And you just were left wondering what happened.
+
+**Hrishik**: Yeah.
+
+**Jaivard**: Yeah, I've also kind of hit the limits with the Opus model, I guess, but that's a separate GitHub student account, so that's different. But yeah, I guess we'll… I'll gather some feedback, and we can… we can just discuss this, maybe work out a solution.
+
+**Harsha (eParts)**: Here.
+
+**Jaivard**: That's it, though. I think… Yeah. So… Thanks, Hosha. Thanks, thanks, David.
+
+**Harsha (eParts)**: Thanks a lot, guys. Well, looking forward to a lot more work with you guys, and hopefully… It learned, kind of, on there first, yeah.
+
+**Jaivard**: Yes. Okay.
+
+**Harsha (eParts)**: I see you, Cliff.
+
+**Hrishik**: Bye, guys.
+
+**Ashritha**: Bye-bye.
+
+**Jaivard**: Recording.
+
+**Ashritha**: I'll join.
\ No newline at end of file
diff --git a/minutes/2026-04-02-client.json b/minutes/2026-04-02-client.json
new file mode 100644
index 0000000..c0b2cdd
--- /dev/null
+++ b/minutes/2026-04-02-client.json
@@ -0,0 +1,109 @@
+{
+ "meeting_date": "2026-04-02",
+ "duration_minutes": 32,
+ "participants": [
+ "Jaivard",
+ "Hrishik",
+ "Harsha (eParts)",
+ "Ashritha"
+ ],
+ "participant_count": 4,
+ "total_words": 4629,
+ "total_turns": 123,
+ "speaker_stats": {
+ "Jaivard": {
+ "turns": 41,
+ "words": 1514,
+ "pct_words": 32.7
+ },
+ "Hrishik": {
+ "turns": 30,
+ "words": 1001,
+ "pct_words": 21.6
+ },
+ "Harsha (eParts)": {
+ "turns": 42,
+ "words": 1984,
+ "pct_words": 42.9
+ },
+ "Ashritha": {
+ "turns": 10,
+ "words": 130,
+ "pct_words": 2.8
+ }
+ },
+ "detected_topics": {
+ "Data": 4,
+ "ML/Model": 3,
+ "Architecture": 3,
+ "Project Mgmt": 3,
+ "Infrastructure": 2,
+ "Onboarding": 2
+ },
+ "questions_found": 3,
+ "questions_sample": [
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Is there a timeline from your end that you expect me to give you the data by"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "How do we understand that this matches to a specific category"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Is there any problem with what I just said"
+ }
+ ],
+ "potential_decisions": 1,
+ "decisions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "Yeah, so, we are targeting that towards the end of this month, we should have the basic, requirements, risk process, and like, not\u2026 I'm not sure if we'll be able to have the complete architecture, bec"
+ }
+ ],
+ "potential_action_items": 37,
+ "actions_sample": [
+ {
+ "speaker": "Jaivard",
+ "text": "Yeah, I'll just turn up my speaker. So\u2026 okay. Yep. So, the first item on the agenda is for the timeline. Yeah. Rishi, you're there, right?"
+ },
+ {
+ "speaker": "Jaivard",
+ "text": "Yeah. So\u2026 Yeah, sure, can you, should I share my screen, or can you share your screen, with the timeline?"
+ },
+ {
+ "speaker": "Jaivard",
+ "text": "Okay, nevermind, I'll just do it then. So\u2026 Okay. So, is it visible, Harsha, dude?"
+ },
+ {
+ "speaker": "Jaivard",
+ "text": "So, we just wanted to go over and, like, get your feedback. So, for now, what we have is, like. by April 15th. For this, this semester, we'll have, the requirements. Completed by then? And\u2026 I think we"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Yeah, so, we are targeting that towards the end of this month, we should have the basic, requirements, risk process, and like, not\u2026 I'm not sure if we'll be able to have the complete architecture, bec"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Other than July's missing, but yeah, I think\u2026 I think this makes sense. This is a decent timeline. Or at least a decent breakdown of the units. I suspect some of those units might be larger than other"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Yeah, July was, left out because, the entire development\u2026 there won't be any deliverable in that month, because we'll still be working on the, like, development of it, and we don't expect there'll be "
+ },
+ {
+ "speaker": "Jaivard",
+ "text": "Okay, we also have the statement of work, I'll just share that as well. So\u2026 One second, this is\u2026 So, we were, is it visible, first of all?"
+ },
+ {
+ "speaker": "Jaivard",
+ "text": "Okay. So, we were, it was encouraged that we have a statement of work, just so that, you know, we have a shared understanding with you. I'll be\u2026 this is the first version, and this is still, I think i"
+ },
+ {
+ "speaker": "Jaivard",
+ "text": "Yeah. So, we also have other things, such as, like, You know, that are\u2026 we will be working all the way up till PIMS, but the steps after that is not something under our control, so I've also written t"
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-04-02-client.md b/minutes/2026-04-02-client.md
new file mode 100644
index 0000000..00d233f
--- /dev/null
+++ b/minutes/2026-04-02-client.md
@@ -0,0 +1,55 @@
+# Meeting Minutes — 2026-04-02
+
+**Date:** 2026-04-02
+**Duration:** 32 minutes
+**Participants:** Jaivard, Hrishik, Harsha (eParts), Ashritha
+**Source:** `GMT20260402-180648_Recording.transcript.vtt`
+**Processed:** 2026-04-23 17:47 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Jaivard | 41 | 1514 | 32.7% |
+| Hrishik | 30 | 1001 | 21.6% |
+| Harsha (eParts) | 42 | 1984 | 42.9% |
+| Ashritha | 10 | 130 | 2.8% |
+
+## Topics Discussed
+
+- **Data** ████ (relevance: 4)
+- **ML/Model** ███ (relevance: 3)
+- **Architecture** ███ (relevance: 3)
+- **Project Mgmt** ███ (relevance: 3)
+- **Infrastructure** ██ (relevance: 2)
+- **Onboarding** ██ (relevance: 2)
+
+## Potential Decisions
+
+1. **[Hrishik]** Yeah, so, we are targeting that towards the end of this month, we should have the basic, requirements, risk process, and like, not… I'm not sure if we'll be able to have the complete architecture, bec
+
+## Potential Action Items
+
+1. **[Jaivard]** Yeah, I'll just turn up my speaker. So… okay. Yep. So, the first item on the agenda is for the timeline. Yeah. Rishi, you're there, right?
+2. **[Jaivard]** Yeah. So… Yeah, sure, can you, should I share my screen, or can you share your screen, with the timeline?
+3. **[Jaivard]** Okay, nevermind, I'll just do it then. So… Okay. So, is it visible, Harsha, dude?
+4. **[Jaivard]** So, we just wanted to go over and, like, get your feedback. So, for now, what we have is, like. by April 15th. For this, this semester, we'll have, the requirements. Completed by then? And… I think we
+5. **[Hrishik]** Yeah, so, we are targeting that towards the end of this month, we should have the basic, requirements, risk process, and like, not… I'm not sure if we'll be able to have the complete architecture, bec
+6. **[Harsha (eParts)]** Other than July's missing, but yeah, I think… I think this makes sense. This is a decent timeline. Or at least a decent breakdown of the units. I suspect some of those units might be larger than other
+7. **[Hrishik]** Yeah, July was, left out because, the entire development… there won't be any deliverable in that month, because we'll still be working on the, like, development of it, and we don't expect there'll be
+8. **[Jaivard]** Okay, we also have the statement of work, I'll just share that as well. So… One second, this is… So, we were, is it visible, first of all?
+9. **[Jaivard]** Okay. So, we were, it was encouraged that we have a statement of work, just so that, you know, we have a shared understanding with you. I'll be… this is the first version, and this is still, I think i
+10. **[Jaivard]** Yeah. So, we also have other things, such as, like, You know, that are… we will be working all the way up till PIMS, but the steps after that is not something under our control, so I've also written t
+
+## Questions Raised
+
+- **[Harsha (eParts)]** Is there a timeline from your end that you expect me to give you the data by
+- **[Harsha (eParts)]** How do we understand that this matches to a specific category
+- **[Harsha (eParts)]** Is there any problem with what I just said
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 4629 words across 123 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-04-16-cleaned.md b/minutes/2026-04-16-cleaned.md
new file mode 100644
index 0000000..4981537
--- /dev/null
+++ b/minutes/2026-04-16-cleaned.md
@@ -0,0 +1,17 @@
+# Cleaned Transcript — 2026-04-16
+
+**Jaivard**: Yeah, starting. So… I think… So the… So, the first thing… Was it regarding the training data? So, Liu, if you want to kind of elaborate on that? Hmm. So… It's basically, he… there was a list of things that he had. He was saying that he could start with, even without those, but it would be great if he could have the label, like, what input goes to what output, and what we have to do. Do you want to add anything there? Mother . health delivery, yeah. Can you give somebody every day. Yeah, yeah, yeah. Yeah, I think it was sent to everyone, so… Yep. Yeah, so I thought that… Yes, That's, the 6th of April. You can see a whole bunch of stuff. Expressive. Do you have it? Do you have… do you have the email? If it was sent to you. We can forward it back to you. If anyone can't find it, we'll resend it. Yeah. And providing the information you asked for. And I was looking… I think in the last meeting. It's this cool. So… It's the… I think what happened was, he was looking for a fresh email instead of the reply itself, so yeah, it did have it. Awesome. Yeah. No. You remember, like, if you're just seeing… It was responding to your email. I don't even know who's asking that before. Yeah, indeed. Here's my inbox, and it pulls emails. Good idea. Yeah. So… For these? Yeah, I think you can just ask them, like… Yeah. Yeah. Or actually… Yeah. depending on whatever specific type of product they will target. Yeah, so you can just sort… Yeah. Like, you just use the Android. So it should work. Yes, I am. Are you not able to see. Yeah. So I guess the question would be is, how large is the data set? It's… They wanted to focus on a specific product, and then, like, understand how that goes down there. But the data I send is basically for everything. So, I send them all sorts of cards, you know. One time to know. They can cooperate. Okay. You want them drinking on it. Come on. Yeah, I think he's not able to access, that's the thing, but yeah, let's get through that. Yeah. Moved here. What was this one? I'm standing there. Can we Actually, just use a CS. This… Oh, this one. Yeah. I think that was the issue, though. through, like, the CS. Okay, yeah. Other than that… Yeah, there was one other thing. We're… Lou, he started work on it, and he's… Let's tackle some more. Yeah, but, he also mentioned that The Claude, token limit is, running out pretty fast. I think he ran out within 20 minutes or something, so… Okay. So, so there is… I believe one of you guys taking your token a little bit, and none of your… Yeah. So, I'll probably implement, like, Usage policy? But keep the limits the same. Yeah, yeah. So, I mean, if you guys do more than that, then I would say… Yeah, yeah. Yeah, I don't think, because that portion is the one that's really heavy right now. Everyone else is more regarding making documents and stuff, so you're not… I don't think we're gonna hit that really soon. Yeah, don't even pop up. Yeah. Yeah, Dr. Douglas. I hope you came by again. And you can also, equipped. I had people. Cool. they removed the whole promoting. Oh. So now you just have, like… May you be billed for every single week. I did not know that, So, is the… still, like, the whole group policy possible for, like, all families? member-level policy right now, so let me see if I can… Okay. So it's a pay-as-you-go policy with scrum. We initially had this usage for anything, which they completely worked out. Yeah. I guess I've been facing a lot of outages with Florida, so I think the demand might be too much on there. Like, if you're using that, I would just say use it anytime which is not at 95. Alright, so… I'm already recording it. Oh. Yeah, there's a trick people are finding where if you start the conversation before 9, Then it, like, it won't take the people, like, it'll be grouped as, like, a message that's started outside, even though you're using it during 95. Two weeks ago or something, one week ago. But that… that just makes sure you get a session moving, though. Because now you can't even start a session for that, because… There's so many people using technology. And these are your handles everyone. Music that one. Yeah. Credit, I just got the email for codex, yeah. I'm making the account right now. I already have way too many accounts for all this AI stuff, but I'm making a new one. they sent it both to my previous university at UMass, and This one as well, so I'm making two accounts right now. They've not… they've not, like, closed down my previous one, so I might as well. Yeah, those were the two things that I think, when I was talking with Lou, that, you know, when he was making it, that he raised them. And, I was also unsure about, like. how the group thing would work as well. Other than that. We're pretty much working on the ML portion right now. Leo, he has made some progress, and… but the training still has to be done. I don't think the training's done right. Yeah, excellent. So… We have the previous one and this one, so that's… that's about that. I would also suggest that you guys should pick them up. understand how to just use AI tools again? Yeah. Because… I think there's a lot of, It's a lot to learn, really, just a lot to understand. It goes on. For example, just when it comes to, like, token usage or context management, there's just so much for it. Let's see. Let me give you more details about our contact line. So the thing is, if you're using an only chat, which only has too much in it, and you're trying to I'll go forward in the chat. We don't need to principles need to be worked on. And that's just not the beat. Every single time, we technically open up a new section. It was great.
+
+**Ashritha**: I'm a bit confused. Take care. I think there's an echo. I should talk. This should be fine. Yeah, this is… sorry about that. So, when you're talking about the context serves, right, so… If you have already had a previous conversation. Doesn't it just compact it, though? It can still keep working there. No, it does compact it, but the thing is, for example, if you're asking me to completely understand the question. Or a question which is very, like, slightly related to your current conversation. it'll still compact all of the composition that happened before, so instead of just using the 20 words that you sent right now, it'll use the 20 plus 400, 500 words that you used before. Got it, so it still tries to relate to that email. So that would be, like, a bigger token that's been sent. I've also been facing a few issues, but I'm just not, I guess, on the arena. how the… how it's, like, using the previous context, so… It's not totally transparent, but you… there's, like, a few ways, or just practices which you can use to just not let that happen. And those practices might just help you guys a little bit. to just not hit the victory. I'll definitely make that one. Right now, just two. I'd like to have a common set of best practices that people use. You can just… honestly, you can just go on YouTube, or even Claude has free courses, which they give out. Those courses are, like, how much? And how long? I think we did a couple of those… We did do good, yeah. The studio has two courses that, we were, basically part of the work that we do, and I think you get a cancer. So, yeah, I've not gone through all that, but, probably should. There's so much that's changed, it's so kind of… there's too much change, there's too much to keep up, and that's kind of a problem, too. Yeah, it's… Lord released us 4.7, right? Like, half an hour, 30 minutes ago, chargy, like, oh god, it's really something. definitely backed up all my data analysis, and I heard about that. cybersecurity news about the finding bugs and 37 rules. Oh, no, we've met those. Yeah, I didn't even think that OpenVSD was even possible, to, like, do anything, much less crash it from internet. It's also, like, automatic. Yeah, don't dig too much into that. Yeah. We do have, updated SOW, so, Cliff has just given some more feedback on that as well, so… We, I will, like, correct the… correct that and show, like, an updated version. So, should we have digital signature on it, or should I print it out and show it, like, for signatures? I'm not sure. Well, ideally, yeah, you want to go through the process of doing the SOW and getting the client to provide their input on it. You know, you can do electronic signing, you can do physical signing. The whole idea is just to make sure that you have some sort of thing, people acknowledging that this current version is what you guys are sending it to. I'll… I'll still have to update it with his, feedback, so I'll… but I will be sending it to you anyway as well. Yeah, I'll go through it again. I went through the last one, I had a few moments, but I just noticed that. No, no, it's, it's, I should have, like, there, there's, the… it was not as up to the… there, there was a template that I could have used that, that, that's much more in line with what CME uses, so it did forward, like, a lot of examples, so I re-remarked using that. I think it's a lot better then. Yeah. The examples are good. I mean, the whole point here is the exercise of doing it. It's not a legal document, but it's a good exercise for the team you do. Yeah, it's good to come to a consensus. Yeah. Other than that… -Oh. we actually… we're still, I guess, starting off into the, some of the coding, I guess, and some TAML part. So, we actually don't have a lot to show right now. It's still… And, yeah, I was a bit hesitant, I considered… maybe, like, you know, that the meeting might be short, but we actually are kind of in the weeds right now, so it would take a bit before we can show, like, some of the things that we've actually finished. I mean, technically, at least I wasn't expecting anything until, like, summer started, so that's, that's okay. I think right now, we've been working a bit on the architecture side of things, and, we had a few… ideas on how we're going to architect the entire thing. Right now, we had a… I think we've not finalized, but we have a basic structure in mind. I think we will be discussing that with our architecture coach. We had one discussion before, I said, and he had a few review points, and we're gonna go back to him. I think by the end of this month, we'll have a… I think what had a little bit of more ground-level architecture. So, currently, it's going to be a pattern filter kind of thing, because it is pretty sequential, the process that we have. There are a few open points, like the LMOS, the ML, how you can use it, but that's anyways gonna come into one component. Yeah, exactly. So, like, the architecture-wise, we should be good, to start developing, the next time, startling. Yeah. Probably the other thing, too, to be aware of, this is something that's new to us, is that, you know, normally at this point, we are coming up on end-of-semester presentations, and it looks like they're doing something a little different this year. They're doing crits, not end-of-semester presentations, so I'll have to check and find out what the engagement with clients is at all, for these crits or not, so that's kind of new us. I mean, it was always good to have the end of semester, just to get a sense of What's going on, where they're going, what the plan is for the future, but we're having a meeting with the all the mentors tomorrow, so Auburn. Yeah, I mean, Sasha sent across an email asking us. About their availability, and she did mention it very explicitly that It's not the… it's not necessary to, like, attend your dollars. We don't really, think… There's no, like, hard rules to work against. And she also mentioned the little kid thing. Yeah, that was beautiful. Yeah, yeah, as the semester is ending, I guess there's more things all at once, so… Yeah. Other than that, no one else has something to add? Because I've, I've mentioned all the points that I had. I'm curious about your last trip to eParts. I had offered to give you a ride, but my car died again, so how did you get there? Yeah, you took an Uber. You took an Uber? Okay, alright. Yeah, that's a term if they got it fixed, and it'll be okay if they contract again, so… We'll have another meeting, so we can get… My car has individual coils for each part, and those, those that, and I tried the, you know, third party, those didn't work! You gotta go back and get the expensive OEM. Yeah, unfortunately. So, you want a reliable transportation, so I apologize, and I do look forward to coming out sometime. I think, from the last meeting, what Prime discussed for the PIMS architecture, like, now, even after, like, that meeting, I personally don't have any questions. Whatever I had was answering any answers, so… Yeah, I think the meeting really helped with the architecture part is it, so now we have much more clarity about the options and how all those things are gonna work. Yeah. I think the next question we'll probably have is when we start writing something down, then that's when we'll be happy. Sounds good. Yeah, I mean, summer is probably when we'll be working together as well. Yeah. That makes sense. I don't know, did you clue him in with the injury to one of your teammates or not? I don't know what happened? He was, he had a scooter accident, and then, he was pretty bad injured, so that's why he's not with us right now. I mean, he is with us right now, but he's not like… Bad choice. He's not physically in the room, sorry. He's on Zoom, he's on Zoom. Sorry, engine. bad choice, of course. I realized it after I spoke. He's on the call. He's on the call.
+
+**Arjun**: Bye, guys.
+
+**Ashritha**: The character. So what is your, plans for, going back to India and getting your dental work? Yeah.
+
+**Arjun**: I think I'll be traveling the end of this month, and then I'll be in India for, like, 2-3 weeks. But, I don't think there'll be any disruption in the entire workflow, though. I'll just be taking meetings from India itself. At the same times, so… Should be all good.
+
+**Ashritha**: I, I, I realize I spoke the wrong word. Yeah, that's… that's what… that was my agenda. Anyway, I thought it's important to kind of know what's going on with one of the teammates here. We're glad it's gonna be okay, but sorry to be ahead. Yeah. Those darn scooters. Yeah. Was it, like, accident which involved, like, some other person hitting him, or did he just… No, yeah, he got his balance and he… That's… that's funny.
+
+**Arjun**: Thank you.
+
+**Ashritha**: But he did tumble, right? So… Yeah. That would have been so bad boys, yeah. Anyway, good to hear your voice, everyone. Yes. Okay. See y'all. Enjoy the nice weather. Yeah, it's warm. Yeah, it's supposed to be cold again, though. Spring has always been too short. It transitions from winter to summer too quick. I wish we had longer. Definitely. I think this is, like, the longest I've seen, like, the winter part last, at least. Oh, no, there's been May snow before. May snow? Yeah, there's definitely been snow before we had before. Not for a couple years, though, I feel like. Just sad that the weather hasn't been conducive. It's just been windy every day. I hate it. I like the windy weather. Windy's great, but you just can't take a lot of those months. Except for, like, running. Yeah. Have a good day. just let us know if you guys need any more data. The only thing that we haven't really sent across is… it's covered in, like, the earlier data set itself sent, but it's just, like, the PIM schema for, like, how the staging is there. So if you want me to send that across, too, I can. I think the earlier schema has it mapped, right? It does. This has all the attributes to the categories. So this has all the attributes mapped correctly. So, PIM schema is only useful for you guys if you're actually mapping the data into, like, a final staging schema. But right now, you're just looking at the output kind of thing, so it's not really relevant. We're gonna have… pretty significant rework of the PIN schema, too. Yeah. But it should be fine for what you're doing, like, for just consolidating some tables, but it's still going to be the same idea of, like, you're just going to be outputting in the staking table, which will allow for the difference. I mean, even in the meeting, you guys told that the industry standards were still similar enough to what is currently there. I mean, the industry standards are always something we'd recommend you guys to work with. Yes. Because those are standardized, they won't be affected by how we're feeling on a certain day, and it's always easier to give us some of it. You do? How to stop the money?
\ No newline at end of file
diff --git a/minutes/2026-04-16-client.json b/minutes/2026-04-16-client.json
new file mode 100644
index 0000000..88fcb2d
--- /dev/null
+++ b/minutes/2026-04-16-client.json
@@ -0,0 +1,68 @@
+{
+ "meeting_date": "2026-04-16",
+ "duration_minutes": 24,
+ "participants": [
+ "Jaivard",
+ "Ashritha",
+ "Arjun"
+ ],
+ "participant_count": 3,
+ "total_words": 3095,
+ "total_turns": 8,
+ "speaker_stats": {
+ "Jaivard": {
+ "turns": 1,
+ "words": 1074,
+ "pct_words": 34.7
+ },
+ "Ashritha": {
+ "turns": 4,
+ "words": 1967,
+ "pct_words": 63.6
+ },
+ "Arjun": {
+ "turns": 3,
+ "words": 54,
+ "pct_words": 1.7
+ }
+ },
+ "detected_topics": {
+ "Data": 6,
+ "ML/Model": 2,
+ "Architecture": 2
+ },
+ "questions_found": 1,
+ "questions_sample": [
+ {
+ "speaker": "Jaivard",
+ "text": "Can we Actually, just use a CS"
+ }
+ ],
+ "potential_decisions": 1,
+ "decisions_sample": [
+ {
+ "speaker": "Ashritha",
+ "text": "I'm a bit confused. Take care. I think there's an echo. I should talk. This should be fine. Yeah, this is\u2026 sorry about that. So, when you're talking about the context serves, right, so\u2026 If you have al"
+ }
+ ],
+ "potential_action_items": 4,
+ "actions_sample": [
+ {
+ "speaker": "Jaivard",
+ "text": "Yeah, starting. So\u2026 I think\u2026 So the\u2026 So, the first thing\u2026 Was it regarding the training data? So, Liu, if you want to kind of elaborate on that? Hmm. So\u2026 It's basically, he\u2026 there was a list of things"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "I'm a bit confused. Take care. I think there's an echo. I should talk. This should be fine. Yeah, this is\u2026 sorry about that. So, when you're talking about the context serves, right, so\u2026 If you have al"
+ },
+ {
+ "speaker": "Arjun",
+ "text": "I think I'll be traveling the end of this month, and then I'll be in India for, like, 2-3 weeks. But, I don't think there'll be any disruption in the entire workflow, though. I'll just be taking meeti"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "But he did tumble, right? So\u2026 Yeah. That would have been so bad boys, yeah. Anyway, good to hear your voice, everyone. Yes. Okay. See y'all. Enjoy the nice weather. Yeah, it's warm. Yeah, it's suppose"
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-04-16-client.md b/minutes/2026-04-16-client.md
new file mode 100644
index 0000000..763e31f
--- /dev/null
+++ b/minutes/2026-04-16-client.md
@@ -0,0 +1,43 @@
+# Meeting Minutes — 2026-04-16
+
+**Date:** 2026-04-16
+**Duration:** 24 minutes
+**Participants:** Jaivard, Ashritha, Arjun
+**Source:** `GMT20260416-180324_Recording.transcript.vtt`
+**Processed:** 2026-04-23 17:47 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Jaivard | 1 | 1074 | 34.7% |
+| Ashritha | 4 | 1967 | 63.6% |
+| Arjun | 3 | 54 | 1.7% |
+
+## Topics Discussed
+
+- **Data** ██████ (relevance: 6)
+- **ML/Model** ██ (relevance: 2)
+- **Architecture** ██ (relevance: 2)
+
+## Potential Decisions
+
+1. **[Ashritha]** I'm a bit confused. Take care. I think there's an echo. I should talk. This should be fine. Yeah, this is… sorry about that. So, when you're talking about the context serves, right, so… If you have al
+
+## Potential Action Items
+
+1. **[Jaivard]** Yeah, starting. So… I think… So the… So, the first thing… Was it regarding the training data? So, Liu, if you want to kind of elaborate on that? Hmm. So… It's basically, he… there was a list of things
+2. **[Ashritha]** I'm a bit confused. Take care. I think there's an echo. I should talk. This should be fine. Yeah, this is… sorry about that. So, when you're talking about the context serves, right, so… If you have al
+3. **[Arjun]** I think I'll be traveling the end of this month, and then I'll be in India for, like, 2-3 weeks. But, I don't think there'll be any disruption in the entire workflow, though. I'll just be taking meeti
+4. **[Ashritha]** But he did tumble, right? So… Yeah. That would have been so bad boys, yeah. Anyway, good to hear your voice, everyone. Yes. Okay. See y'all. Enjoy the nice weather. Yeah, it's warm. Yeah, it's suppose
+
+## Questions Raised
+
+- **[Jaivard]** Can we Actually, just use a CS
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 3095 words across 8 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-05-14-cleaned.md b/minutes/2026-05-14-cleaned.md
new file mode 100644
index 0000000..eab636c
--- /dev/null
+++ b/minutes/2026-05-14-cleaned.md
@@ -0,0 +1,3 @@
+# Cleaned Transcript — 2026-05-14
+
+**Ashritha**: We'll put… So, this is the failed way. last month received from the EPAS. dataset. NFL helps, many, valuable information, and I do some, statistic analyze, and, I… I will use 4 or the 5 bells to… To make our raw engine. value repository and train our semantic machine learning model. And as I said, before. So, this one is, what's the real… Lay of, cases. This is a real, from… you'll… collected from the layout situation, you, like, you correct the… Customers' email, like. There are some… some… there are… some of their… Paragraphs are fake, weak, or fizzy. And, and, we, we needed, a real… Data to train our semantic model. Because we… We just use the synthetic ones could make the model more… problem, more affordable than it real has. And, this is my, 7 milestones. The whole work… could, be, completed in a month. And the first week, I have completed the first tool. Mastomos. Like, the layer 0, layer 1, and layer 2. And, for now, what's, 56 sanity tests are passed. What kind of tests are those? You said some tests are passed, right? Yeah, tests. What tests are those? It's just, like, use some synthetic, information from the… the fourth or five fails. Okay. Yeah. And, I will… is this divide the… third milestone into three parts, because this is the most complexity layer. What was the whole, the whole model. It's, thematic. machine learning model. But our, most stressful dealing with, cases is the… low engine parts. Like, it will directly deal with 80% cases. And, the machine remote part, it will deal with, the case is that… like, use… use the vector matching, doesn't… works, and it will pass through to the layer 3 and Layer 4 to get, confidence score, and combined with the Layer 1 and Layer 2 score to get our final scores. What these layers you are, you know? Yes, I will show you the structure. And, this is the whole… whole big picture, or the machine model piece of the case? How to be seen, okay? Crusade? Okay. So, at first, we received the raw materials, like, the same as we and eval and PDF different files. And, and… and the role fails. Text extraction will… I think Jay will do this part, or work. the text extraction? Yeah, yeah, yeah, because… You have said you will use some… Data extraction model to… to… Like, some scanning, or… optimal, record… No, no, this is in the ML, yeah, yeah, so I leave some… interfaces that opens to you. Yeah, okay. Okay. And from now, so… I… The Lead Zero is designed for Training our model, and Directly use your, table fails. table this dataset to create our model and to form our… Rom engine. So, so layer one… layer zero is… that is how I use your… use your, distance to train our model. It had… had… had been divided into three parts. We have the standard data in the three bells. It contains 3.5 million rows, and 1.6, Teacher bites. It's… And, because the data is so huge, and it could not be directly A load into our laptop. So, I made it to, small chunks. like, the peak memory is less than 1 gigabyte. And, 200,000 laws as a chunk. To fit in… into the… That type of sketch. and wait… stratified the… the dataset into three parts. The first part. The first 80% part as our trend stat. And, the 10% part at our validation set, and the last part is our testing set. And we use a fixed seed to To, keep the… our… you want to keep our, results reproducible. And, the Layer 1 is, I thought it's flu. How you doing? Okay. I can't believe it's spring, it's not spring out there. It's cold and zoomies. Yeah, and the first layer is lived for… the text extraction, so… After this part. Was completed by the extrusion team. like, use the LLM or… Lambda. The correlation machine learning models to… towards, information illustration. I will… we will directly use those data to fit our models. And, After weight gets, the… the… the data, we… use the raw engine to… To our first walk. And this… this path, Well, take 80%, pipeline suggests. Or the work, because… Yes, I… divided the low engine into three parts. The first part uses a parallel number match. method to do a vector matching. It contains 189 kilom… kilomon… Thousand? PL number rejects it. Most of all, it is, is, like, So… product then, and the, venue, units, PELs. it's like the… like this kind of format. And I made it into a regex union. Because the… it's very… it's very long, the unit. So, first loaded into our computer, it would could take, 5 minutes… 5 seconds to load into our… the computer's cage. But after that, it could… Dealing with, advocacy within And 0.1… Maybe 1 minute, millisecond. So it's very fast. And, compared to the, machine learning model. It could consume, like, 50 millions. To deal with every case. What's… what exact, logic are we… are you talking about for partner matching? So I get the regex part, but what's the part and what's the number, even? You mean this layer 2? Yeah, a part number, or is it a partial part number? Yeah, exactly, like, what exactly are you referring to when you say part numbering? Is it the product's part number, or is it the SKU, or is it, like, the… Attributes, description, what are you talking about there? Sometimes, you know, manufacturers have their own part number, then there's SKUs, and then you might have your own different part number. Yeah, exactly. I just want to know what this is, so that it keeps that going, that it's headed in the right direction. Like, you input. A segment, or… Information, and we will divided it into several, roads or information. So, the… The cases is… every… Every information unit is a… Down anyways. Is there a schema for the part numbers? I mean, you know, there can be a schema for a part number, right? Whoa. Yes, don't take this up, I mean, the point being is just trying to understand… Yeah, yeah, yeah. What are you thinking? I understand. Oh, but then, I think the simpler question would be, like, you said there are, like, one 89,000 part number rejected. Oh, so, so, you want to know… well, it's, the number come from? Yeah, and how does it look like? What does the part number look like? Yeah, yeah. The one that you're referring to? What is the part number? Yeah, so if you just look at it, probably… Yeah, yeah, yeah. So from this, in the… The product attribute pairs, so this is primarily just this one. Like, like… So this, this is the most, Detailed and plentiful information we could get, and nearly every… Useful information contains those part numbers. Yeah, so those sway… Can you give us a real example of a partner? Can you open that 1A file? If you have… No, no, it's because it's very large. We can't directly open it. Okay. It's one… Gigabytes. Okay. Long. Okay. Yes. So if you want to say it, I will show you the next… Well, not even that. Can you just open Notepad and, like, can you show me what, like, a part number looks like, and how this would… Will work? …kind of work? Okay. You don't have to do much, it's just, like, for me to visualize what this is. Yeah, I can ask the cloud to show you all some data. You can even, like, use this board if you want, but, like, just anything that just makes me understand. Like, You wanna ride, or you wanna use your screen? Oh, I can. I mean, you can just, you know… Oh, or I can… oh, yeah, check out something. Build the file. It's like… We have so many different products. So we… Just combine what the product use, some regulation method. To make it a very big one, to do the matching. So, we… Just combined with two into three important information. So, the first one is the product attribute pairs. The second one is the manufacturer name. The third one… So you're appending everything into… Yeah, yeah, yeah, yeah, yeah. So I said it's… it could be a little long to load those re… rejects unit into your cage. But after loaded it, it could be… the matching is very, very quick. Less than… One millisecond. I would recommend in a future meeting that you just do an example. Oh, okay, okay. So next time, I will… every box, or the diagram, I will do a simple example. Just have a simple example. Yeah, yeah, yeah. Maybe I don't know how it was, you don't know. Yeah, because the thing is, you're the one who's been working on it, but I don't know what's going on. Oh, yeah, yeah, okay, okay. Yeah, yeah, yeah. Yeah, so… So, the first one is, product and the attribute. Apparels matching, and the second one is the manufacturer… manufacturer matching, and the third one is the value and the unit matching. And the first one, if it could match, it will… Directly gets the highest competence score, and will directly go Go to, like, the dash 9 to the… Therefore. And the other parts, if it got .85… a competence score, or 0.65 competence score, it will towards, semantic matching. This is what the machine learning model will get the work done. And as a layer 4, we will get, conf… confused… confused, squaw. The confidence score. The confidence score, yeah, yeah. Yeah, confidence score. In different ways. And we will get the final one. Yeah, that's the logic. And the… Yeah, and after we get the, the, The first goal we are to, a safe… safety guide will, and then we will choose either census scores into scores and the data information into the layer stray, and all the scores into the layer 4. And, we will, send our… Like, a flag to show how… How's this? possible. If it's working well, or not. Yes. And this is my first week's work. So this is the overview logic that you want to implement. Yeah, yeah, yeah, and this is, it is my milestone. And how do you, how do you plan on testing? Right now. do some segment of this? You're gonna build all of this at once? Yeah, so I… I first… first get the big picture. Right. And I divide… I divide it to the 7 milestone Depends on its… a correction on each part, and it's a… Complexity, or… each layer. Yep. So, the layer space, the most complex part, and it needs time to do pre-trained and fine-tuning. Or may… we may need to change our… Machine learning models to build our dataset. So are you gonna test each layer, or what are you gonna… how are you gonna test this? Yeah, so… so this is… How… this is… What the contribution is for their… the sales, they send it to us. where I decided I used the 4 or 5 belts to… Either, make our grow engine layer. Or training our machine learning model. But… My sanity testing is used also synonymic. Data. from the tables to test if it's work well. I think the main question is, for every layer, how do you implement some sort of testing so that, to make sure that the layer is correctly functioning, or if the layer is giving you what it needs? Yeah, so right now, I just do some unit tests. But after… after… After, contribute, every year, I will do, some basic unit testing and the whole testing. to… What if I just work with well. And obviously, it'd be nice to have a set of data that you know that is good data, and data that you know is going to cause a problem, and see how that reacts to your logic. That's a great point. I mean, like, you have to… I mean, for Layer 1 at least, I would highly recommend, like. you should… Like, someone should take a little bit of time. Yeah, yeah. Make sure that you understand if the data is… Yeah, yeah, to delete some… Like, trash information to keep the vendor warm, and divide those information into like… small elements. Okay. And vintage into… into… To divide these segments into small elements. Small and accompanied elements, and fit into our we want to… Yep. Also, other than just the sheer counting of rows, is there any other sort of, Like, data cleaning that was performed on the data. Just DKing as in, like, just eliminating certain rows which are not, or certain rows which have always… been incorrectly valued. Yes, but… Yeah, yes, we can do this, but we… I think our… Layer 2 and Layer 3 will help us judge if it's, like, a trash information or… valuable information. So we don't need to do those actual work. Well, it'd be nice to have a table that shows, here's our original source data, it came from… Okay, okay. Maybe after I completely explained, I can, self-produce some synthetic information. They're just… they're very flirtatable. I use the data from these parts, you know, the intermediate store, whatever it is. Yeah, yeah, yeah, like, like… And I use data… Let's on those inputs on… Wake information, or… Charging mobilization, or the wave motivation, or some… No, it's not there. So I think what we're trying to say is. we gave you a dataset, right? Yeah. A data set with, for example, 2 million rows. 2 million rows, yeah. And, you've taken that data, and you've made it data that's useful for the year 0 and year 1. Yeah. So it would be good to see what's the difference on how to do your Layer 0, layer 1 data. Oh, okay, how to… Oh, okay. How's that original data first, and manipulate it into doing what you want it to do. And how it works, and this is where you take… Okay, how to deal with different… You take a few examples. And we walk through that, and that gives us a quicker picture. So next meeting, I believe. Yeah, yeah, just show us, how you, yeah, how exactly the samples. Yeah. Pass loads, actually. not pass through each layer. No, like, like, here you have, like, 1.6 GB of data, right? Like, with 3.4. And then you say you chunk them into these many number of rows. Oh, okay, so this one? Yeah, no, like, at every step, how is your data getting transformed? Oh, okay, okay. You want layer-wise? So, so you want… So, so you want the… you want to say some… He told? Design. Not even detailed design. So, what I'm trying to say is, so initially, we sent you data, which is technically data that you're not using in Year Zero directly. Yeah. So, you took the data, you performed some sort of cleaning on it… Yeah, yeah, yeah. You made that data usable for your ML, and then put that in Layer 0, which is your… the standard data. And then you took that standard data, chunked it. From there on, I get it, I get what's happening. Oh, okay. But how did you come to the standard? Oh, the initial one itself, okay. So, from here to here, you did some data transformation, right? Yeah. So that's what they want to see. Okay, just some examples. Okay, okay, okay. Or probably the logic you used for coming to that. Yeah. Yeah, anything works on that end. Okay, okay. Trying to make it more concrete. Yeah. Because there's so much happening underneath here, I would… we would want to see how things are going on underneath. Because if there is, for example, an incorrect assumption on my end or your end, then we just get to know that you've done something wrong, and I could do something better, or you guys could just change something on the order of… something that's happened behind these years. Okay, okay. No, this sort of reasoning is excellent. We just want to have a good sense of… Yeah, because there's no guarantee that even the data I send them is perfect, right? Yeah. So, that's it. I just want to know if there's anything that can be changed for you guys. Oh, okay, okay. Do whatever is better. Okay, yes. Full costs. So… I think that's the end of my journey. Okay. What are you calling this table? What is this diagram table? What is it… what are you calling it? The big picture? What is this? Yeah, the big picture. I don't have a name for it. Okay. But I think it could work. It's… it's a well-designed structure. This is your version 1, or version 0.8? What is it? What you do? Conversion system, okay. Okay. So, only, like, Leo worked on the ML part of it, so basically, we initiate… as per the time… I can stop sharing. On the screen, you can just pull up the plug, the… the cable. Oh, there's… No, it's fine, thank you. We seem to be way behind the timelines we initially put up here. So, like, we initially thought of dividing the whole work into, like, three, the ingestion, OCR, and the ML. Liu will, like, majority be contributing at least the POC part of it on the ML. Arjun and one of us would be doing the OCR, and then two of us would be doing the ingestion pipeline. So we haven't… as of today, we haven't, divided the… this high-level task into subtasks, which we'll be doing by this week. But, by this week, we are planning to finalize the system architecture. So, our document was initially complete, in terms of the doc… The diagram and stuff. But then in the studio session feedback, we had more comments with respect to how we were representing the… the human review, etc. So, we are working on them, and then Friday, we have a meeting with our architecture coach. So, once they're done, at least from the architecture perspective, we'll be done and final. So, then, from Monday onwards, we'll adopt our, tick Scrum, like, 3-day sprint. We have a separate sprint board deployed for it, so, and then we'll track all of this work accordingly. So, you'll see, like, faster progress from next week onwards. No, like, as per what we proposed, the Scrum, the philosophy that we proposed, it actually facilitates faster work, so yeah, you'll see better progress from next week. I would also say, like, isn't… isn't just you working on the initial big… Chunk of work, just, like, gonna hold the entire team back. In me, because, you're all depending on one person to, like, just come to a certain stage. Okay. So I'm not sure if that's… that's gonna be a problem anytime. I think, for the initial parts, we are planning to work parallelly, because the initial OCR part and the initial part, they can be worked inside of at least for the initial part, so I think, the first few weeks, at least, we can all work parallelly. When we try to integrate stuff, then we'll have to Yeah, because by the architecture design that we proposed also, people… our selling point was we have our interface layer that separates the ML part of it with the rest of it, so I don't think they are interconnected. It's like, he can do his work in the background so that we can set up this whole data and the ingestion pipeline in place, everything, like, from scratch with respect to testing and stuff. And then we can just plug and play whatever works. We need to create a standard interface that will track them in pipelines. What about, like, different teams having to, What's it? So for example, when you're connecting the ingestion part to, whatever the above or below year is. How do you know it's gonna work? Probably we'll test with the data that we have. I didn't get your question. I feel like it's going to be a big bag, right? Okay. These different parts of the project are just going to be connected at one point. Yeah, yeah. Like, you're all working towards completing all those individual points, but how do you know those individual points and just, like, work together? So we can target it, what's the format, or the… the data contacts or something. Yeah, yeah, yeah, yeah. So we can use the same format where we… identified. To attribute some synthetic. dataset to test that each part is working well. But, don't you think, yeah, the point that you mentioned, but don't you think the only thing all of us should be aligned with the schema? The schema. True, but I'm just trying to say from, like, a… standpoint where, for example, if a certain level is 40… Okay. And it is probably not giving you the correct information downstream. Okay. Or it's getting the correct information downstream. Okay, okay. How do you know, how do you know How you can identify the correct problem. As in, so for example, someone working on another layer would never know what one. what the issue is. Okay. But, I think, in that case, it'll be like, if you're working in separate layers, the person working in the downstream layer will know the expirator input that the person's layers needs, but if you're not getting it, then we can start tracing it back to by unfair. So I think that is the kind of thing we'll probably be doing. Yeah, makes sense. I mean, my only concern is… the thing is, you can always give inputs in the correct schema. downstream, but the only problem comes in is the input actually correct? Correct. Yeah, yeah. Okay. So, the whole working flow is, like, continuous. pipeline. So we can use, agreed upon date format for each part. So we can test each part is working well or not. And finally, we can compile all the data to, The whole test. What's more… what's a whole budget? I only ask that because, like, you mentioned you're working in silos, and… I think this is the most efficient method, because this is just, like, a… Continuous work booking flow. We initially thought this might be an issue, so we have given a… I think, a few weeks gap for the integration part of it, because working silos will cause some issues while we're integrating everything parts together. So we just thought we'd just give it extra time so that any issues that arise, we can tackle it. I'm trying to find out. Interest. They're good one month for interviews. These things, right? Laws. No, I think the… one of it, from July to August end, we'll be, Okay. One month for our tradition, never been. What is a fiscal name, or how do you find your core system? Core systems are all the individual filters that we have specifically. All the individual components, ingestion, OCR, ML pipeline. The other challenge I have right now is shifting from working cover this week to this Friday, because Thursday. Branding up to that is an initial challenge, usually. And you want to try and make sure that you leverage that time well. Yeah. Well, this is just out of curiosity, is there any sort of, meetings that you guys have internally, which will just, like, catch each and everyone up to speed on what's coming. Yeah, we're gonna have it, 3 alternate days, Monday, Wednesday, and Friday. Stand-ups? Sorry? Stand-ups, where you come in? No, no, no, like, one and a half hours working session. Okay. So that's really working out the details. Yeah, like, we'll work asynchronously, but then those one and a half hour slots are, like, for us to, like, come and sit together if we have any conflicts or blockers as such. Is there a way we can also, like, move this meeting to, like, an hour squad leader, like, 3 to 4, something like that? I think 3 to 4, we have our client meeting. We probably can swap… sorry, sorry, mentor meeting. You can certainly move the fire, and we can document. I mean, I would say I'm just proposing it because, it just… it just makes sense for, like, everyone, me, Jake, and David, for us to show up at that time. I see, okay. It's just easier for us, because we just wrap up working. Okay. Okay. Sure. They're flexible. You've been doing that. Even if it doesn't look at my head. No, yeah, we'll just discuss with Cliff and Dennis and get back. Yeah, I think that's all we had for today. It'll be great to see, like, just other, like, underneath workings of each of these layers, and that's it. Just in terms of examples, that's, that's probably my only, point. Okay. Yeah, and tomorrow, sorry, not tomorrow. Next week, we'll also, like, come up with, the other core systems, like, for the ingestion. sorry, for the ingestion, OCR, and also, like. probably we should also come up with some sort of a QA plan. We obviously will keep on adding to it, but whatever we think of right now, since we'll be breaking up our ingestion and OCR pipeline, maybe we can also spend some time to come up with a QA plan, like, one of the use case… test cases that you just mentioned, like, each one, each of the core components should adhere by one… expected input and output, right? So that could be one. So, a cure plan like that could… maybe we can just get validated by you guys also and see. Something else. So… column… We've got the next decline name in heaven, or… I just had some Okay. Also, I had a quick question, so, if at all, like, not so soon, but maybe, like, 2 weeks from now, if we wanna, like, push some PRs or something, so do we just directly push it to the repository, the Bitbucket repository? Okay. You can do anything there, as long as it's in your own SEO. Okay. Exactly. Okay. You can create your own pipeline, you can set everything up as… Okay. Because your admin's clear workspace. Got it. So you can technically do whatever in there, and you would have to update it on… Okay, makes sense. So, in the current Bitbucket, you… we are also allowed to set up workflows and stuff like that, right? Okay. Can we also, like, publish those… any URLs? Like, a live URL or something? The live URL is for, the UI or whatever. Huh. That'd be through Azure. Okay. So the publishing would be through Azure, but, you could technically connect the workflow. to an Azure deployment cycle, so that the… the end of your workflow would be something getting published to, like, URL. Okay, okay. For sure, make that work. And, and it's also too early for that anyways. Sorry? It's also too early for that anyway. Okay. No, I was thinking we have a, since the tech… generally, whatever print boards we have, they are very, like. traditional ones. So, now we're following a 3-day tick something. So, for that, we're gonna come up with our own custom spec, like, whatever components it requires. So, right now, I was thinking to host it on my own repository, like, making it public, but if we have access to the Bitbucket, and if we could publish it, host it over there, I was just thinking if… can we do it then? I'll try it. We can, we can. It can, right? No, no, like, let's say I move, like, let's assume it's a Kanban board, and then I move a particular one to done. So, we can see the history, right? Linear… Linear supports this kind of a… Scrum setting as well? It's a full Kanban setting. Okay. And you can customize it. Oh, man. I think… did we give you guys access to Leo? I don't think so. Okay, then let me… let me see if I can just add you guys to the India account. Okay. Because it should be easier, because… so let me… I'll just show it to you, how it looks. You'll have a good idea. Oh, God. If I drilled it on. Okay. Is that a single thing. You should just Google linear. Okay. But, linear basically makes you organize projects, so technically there's 3 different, sub-projects going on, so you can create a page for each other. You could take tasks for each of them. Okay. You just move them across a different phase of the lifecycle. Okay. Just drag, drag and drop. Okay, but, let's assume, like, the traditional sprint support that I have worked with on, like, they have, user story points and stuff like that. you… there's nothing. It's a clean slate, and you can just customize it however you want. So, it's… like, underneath everything, it follows a very milestone-driven, epic kind of philosophy, but if you're not working with epics, if you're working with individual tasks itself… Okay. Then, you could just do whatever with them. Okay. You could probably assign priorities if you want to, unless you want to assign priorities. You could, you could do t-shirt sizing. Okay. If you don't want to, just don't do it. You can do, just… like, out of 10, the effort required for a specific task. If you don't want to do that, you can't do that. You don't want to do that. You can avoid that, too. Okay. It could just be a simple, issue with just a heading, the issue description. Oh, but then, if you're allowed to use any… add any new fields, and then… then define them? Then, yeah, then that makes sense. It's, it's like Gina, but like… on a very, very, customizable scale. I see. Makes sense. Linear also integrates really well with that. Oh, okay. So, again, not even… You raised my… Yeah, we also have to, like. we have set up our whole SES one that you have heard the other day. We have to actually put it into practice this semester. And the point I was thinking about was, It was… it was just this stigma of… You should probably invest a little time in thinking about the engineering aspect of it, rather than, Developing too many features for what this specific project is. I would say instead of, like, creating some, Advanced OCRM. OCR way to, like, get all the text out cleanly, you should just probably, think about what's the easiest way to do OCR on a scale, and that's it. Okay. Rather than spend too much time focusing on. Okay. That's it. Because that… that probably is, in the end, more value to you guys, too, right? Okay. Because you actually get to see how it's really implemented in the industry, versus just trying to make an experiment where there's no bounds or carries through the whole thing. Okay. Thank you. We would do. Beautiful. Good seeing you. Yeah, good seeing you. Sorry for delaying getting there right into a friend that I'll catch up with, so… Yeah, that makes sense. Do you guys include…
\ No newline at end of file
diff --git a/minutes/2026-05-14-client.json b/minutes/2026-05-14-client.json
new file mode 100644
index 0000000..db6345d
--- /dev/null
+++ b/minutes/2026-05-14-client.json
@@ -0,0 +1,63 @@
+{
+ "meeting_date": "2026-05-14",
+ "duration_minutes": 42,
+ "participants": [
+ "Ashritha"
+ ],
+ "participant_count": 1,
+ "total_words": 5296,
+ "total_turns": 1,
+ "speaker_stats": {
+ "Ashritha": {
+ "turns": 1,
+ "words": 5296,
+ "pct_words": 100.0
+ }
+ },
+ "detected_topics": {
+ "ML/Model": 5,
+ "Architecture": 3,
+ "Data": 3,
+ "Project Mgmt": 3,
+ "Infrastructure": 3,
+ "Onboarding": 2
+ },
+ "questions_found": 5,
+ "questions_sample": [
+ {
+ "speaker": "Ashritha",
+ "text": "Is there a schema for the part numbers"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "How do you know it's gonna work"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "How do you know, how do you know How you can identify the correct problem"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Is there a way we can also, like, move this meeting to, like, an hour squad leader, like, 3 to 4, something like that"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Can we also, like, publish those\u2026 any URLs"
+ }
+ ],
+ "potential_decisions": 1,
+ "decisions_sample": [
+ {
+ "speaker": "Ashritha",
+ "text": "We'll put\u2026 So, this is the failed way. last month received from the EPAS. dataset. NFL helps, many, valuable information, and I do some, statistic analyze, and, I\u2026 I will use 4 or the 5 bells to\u2026 To m"
+ }
+ ],
+ "potential_action_items": 1,
+ "actions_sample": [
+ {
+ "speaker": "Ashritha",
+ "text": "We'll put\u2026 So, this is the failed way. last month received from the EPAS. dataset. NFL helps, many, valuable information, and I do some, statistic analyze, and, I\u2026 I will use 4 or the 5 bells to\u2026 To m"
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-05-14-client.md b/minutes/2026-05-14-client.md
new file mode 100644
index 0000000..47a7de4
--- /dev/null
+++ b/minutes/2026-05-14-client.md
@@ -0,0 +1,45 @@
+# Meeting Minutes — 2026-05-14
+
+**Date:** 2026-05-14
+**Duration:** 42 minutes
+**Participants:** Ashritha
+**Source:** `GMT20260514-180424_Recording.transcript.vtt`
+**Processed:** 2026-07-20 05:14 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Ashritha | 1 | 5296 | 100.0% |
+
+## Topics Discussed
+
+- **ML/Model** █████ (relevance: 5)
+- **Architecture** ███ (relevance: 3)
+- **Data** ███ (relevance: 3)
+- **Project Mgmt** ███ (relevance: 3)
+- **Infrastructure** ███ (relevance: 3)
+- **Onboarding** ██ (relevance: 2)
+
+## Potential Decisions
+
+1. **[Ashritha]** We'll put… So, this is the failed way. last month received from the EPAS. dataset. NFL helps, many, valuable information, and I do some, statistic analyze, and, I… I will use 4 or the 5 bells to… To m
+
+## Potential Action Items
+
+1. **[Ashritha]** We'll put… So, this is the failed way. last month received from the EPAS. dataset. NFL helps, many, valuable information, and I do some, statistic analyze, and, I… I will use 4 or the 5 bells to… To m
+
+## Questions Raised
+
+- **[Ashritha]** Is there a schema for the part numbers
+- **[Ashritha]** How do you know it's gonna work
+- **[Ashritha]** How do you know, how do you know How you can identify the correct problem
+- **[Ashritha]** Is there a way we can also, like, move this meeting to, like, an hour squad leader, like, 3 to 4, something like that
+- **[Ashritha]** Can we also, like, publish those… any URLs
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 5296 words across 1 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-05-21-cleaned.md b/minutes/2026-05-21-cleaned.md
new file mode 100644
index 0000000..baedfc8
--- /dev/null
+++ b/minutes/2026-05-21-cleaned.md
@@ -0,0 +1,151 @@
+# Cleaned Transcript — 2026-05-21
+
+**Ashritha**: Yeah. So can I directly use your mic? Or use mine. Yeah, you can use my mind. You can't hear it. Me commute? J? Or… Yeah. Okay, okay. So today, I… I'd like to introduce you the… Power layer, the layer 3 was, our machine. lending systems. And this layer is the most important and complex. A layer, or the whole system. And, this is the core, machine learning function layer. And I divided this layer into 3. important part. Each… each path has its unique, functions. And I will… First to introduce the… What layers? Structure to you. First, and then I will introduce you some. night. their customer input, and I'll show you how to… House assistant, We deal with this impulse. So… First, Leah's Ray. We permanently use these two fails to train our model. This one… The first one has nearly 200,000 rows, and the row is to… is, like, like a product rotor, and this one has… even more roles, because this one dispels cartoon's attribability annual pals. So… It could be 10 times what was the first bills. And of course, Plus a fail. We… primarily use this. Three column information. The first one is, product type ID. The… the second one… the second one, the third one is the short or the extended description. Which could be used to… formed a… The high-level dimensional data cloud. And this one is used in the… the M3P. Consensus and statistics. pumps. And we will use product ID and attribute name and value to train the second layers. attributes. Yeah, this is a WOTC, D… this response. I will skip this. So now I will, introduce you the technical overview on each part. Layer 3, the primary… Raw it is received. Input from layer 1 layer 2 cannot fully resolve the customer's request. So, the first part, Or… or this layer… this… this top layer is to train The… the model. And get, adapt. So… We use this. Major trainer model. to produce I would. data… datasets, layer. They said… Image. And, after training, we get, the metrics result, or… or the… information. And we get two artifacts. So, this is how… So, sub-layer works. So, after we get a customer request, It's like a long pace. The encoder produced a 384-dimensional query vector. And we… Use our trained Artifact to do a compare… comparison. And find the, most likely 50… 50 product type candidates. And, get, primarily scores. And handed it to the second sublayer. And, this is its performance. it… It could only consume 0.2 to 1.4 milliseconds. Well, you… the traditional method could… could… consume… 30 minutes, many minutes, milliseconds. And now we went to the, second sublayer. So… Yeah, so from the first sub-year, we get the top 5… top 50 candidly, likely neighborhoods from the first sub-year. And, and this layer… So, so this layer, we first train, Train the model and, get, are defense. Those artifacts is, trample, curtains, this, just… This way, so they can't, or… information. Yes, and… We… the first sublayer, we get the… Likely, product type. And for this layer, we choose, most likely prototype, and, School always… is… attribute value pairs and get, suburbs, grades. Yeah, this is the latest performance. So, product-type voting only… Consume, less than 0.1 milliseconds. And for the last sub-layer, we… Do a parent attribute screening. You'll cease. Eglutin. And in this layer, we will also consign… take… take the… The usage put… the usage… Like, frequency… the usage, or the frequency, or the… A product to take into account. And we get the final… compete in the school. And in this sublayer, we'll consume 2 to 10 milliseconds. So the total layer 3, the link latency, Could be 21 milliseconds. So, it's less than our estimated 50 milliseconds. And, we yield… 180… 100 and, 20, test to… Test all the layers function well. And I will show you some, most likely, customer request inputs. And how these layers could deal with those inputs. We primarily have 4 scenarios. And, this is the background. You can see, we have… Dividend pens or imports. And, after layer 1's extractions inputs. were redacted to neutral language, like texts that are accompanied by optional structure belt. The Layer 2 is a real engine layer. Which… It is low… it's like to do, row-based matching. From this rate level. a matching. The Tier 1 is to do, example. parts Langarduca. If it… if it could be… Matched, matched. We could directly get, confidence Wang. And, pass it to the Layer 4. If there's a first tier, could not match. We tried the… Tell 2, the manufacturer, the expressive matching, and if it could be matched, we get, Start competence score, 0.85. And then translate, so… contest into the S3 to do, domestic match. And if the tier 2 could not match, we tried to compare… To match the numeric value and the unit look up against the to a table, and we get a relatively low confidence score, and pass it to the state. So, this rate process all requests that Layer 2 cannot fully resolve. And, this is, Senator Guang. The first scenario awarded by information density from the highest to lowest. So, in the scenario one, way, the… We have, most likely, most detailed natural language description was identified for that type. The likely input could be, looking for, 24, with damped motorway with spring returned, and need to control, 0 to 10, way… stigma. Next seconds, which entirely will work. So we could know That first low part number is provided. That's… we have the description. Like, that's a, attribute. When you pairs, this is sufficient detail. So, how's the layer to… deal with it, and this is the possible outcomes for layer 2. So… We don't have pair number, part number, the product number, and we don't have the manufacturer information. We only have the numeric and unit. We could extract this information. And so, their true producer is no usable support. And, then… So this case will pass to layer screen to process. So, first, we will… So there's very well, encoder. So… initial language information into a high-level 384-dimensional vector into the to the system. And we already trained, This is, Facebook AI search. image. To do a match. in, High-level… high-dimensional space. to… To, compare in the data cloud. We actually compared the… Distance with the… with each cluster, and, the angle, or the geometry direction with the… The data cloud to get the first top likely product at a time. And the M3… the layer… the sublayer 2, the second sublayer dual voting, And the way that… The, most likely product type, like, like, it could be the damper act… actual… actuator. or the, or what other product type. Because… This could… could be the… it… it matches 43 per dots, so… We get, A permanent score, a competence product type competence score, like, 35.62. And, so… and this score could… is… is higher… it's safe. than 0.80. So this could be considered as high constant suspension. And then, the last several year scores. Across four, attributes under this product type. This information is extracted from the… Context. Like this. And for the attribute score. So, first 3 is higher than 80… 0.85, so it's waterprocessed, and the last one, is less than… 8 port… 0.85, so it could hand out to human… Review. And, this is the second scenario. We have complete information. But no standard terminology. And, this is… And this scenario could, Show how our… Symmetric… Mechanisms to deal with this condition. And this is what the machine learning… Good, good tool, to deal. Like, the customer input is, like, wait, he needs, needs, some mister that… clumps onto a pipe, then the 10,000 can for an outdoor air handle. So… It could be translated to, the customer provides This song, the information is, like, We know the product type. The resistance value, the mounting type, and the intensity element. But, the input is not… is not… standard technology. But we… after we're mapping those information into the… high-level… Dimension of space. We could… could… It could be, gotcha. Who?
+
+**Harsha (eParts)**: I had a small question. So you mentioned customer input, right? And aren't we trying to, create something which is, like, Like a catalog, creator. It looks like the customer is doing the catalog search instead.
+
+**Ashritha**: Yes, it's like to… Where we collect the customers. This is the primary information from the customer's input. And to do, matching. Yeah, you can go. To do a matching in our trained artifacts, like, the chin… Clusters in our… Through the cloud. Amazing.
+
+**Harsha (eParts)**: I mean, just… I think I was just lost on, why, we were… we were, doing queries, or, like. What sort of parts the customers were looking for. And that's it. Because technically, what we're trying to do is take in the… Catalog data given by customers, and trying to Refine it into usable catalog data.
+
+**Ashritha**: You… So… it's like… In my mind, I think we are trying to do some… Product matching job. To help the customers to identify Which product is the most possible Wang.
+
+**Harsha (eParts)**: That sounds a little different, right, isn't it? I don't know, I think Ashita, it helped, just like… Am I… am I saying something that's not in line, Ashita? Oil.
+
+**Ashritha**: Like, what… you understood the question, right? Yeah. Okay, Aharsha, do you mind repeating the question again?
+
+**Harsha (eParts)**: Yeah, the question is, I see you discussing cases where the customer's trying to find a certain type of product. But technically, what we're trying to do is… take the data from, like, CSV, PDF, etc, etc, whatever it is. And turn it into usable catalog data. But, like, looking at this, the use case seems to be, How do I help the customer find the most accurate product? In our database, which is already there, yeah.
+
+**David (eParts)**: Yeah, this seems to me to be something where it takes the, The customer is searching for something.
+
+**Harsha (eParts)**: Yeah.
+
+**David (eParts)**: Well, I think what we were looking for is that the customer is providing A catalog, and they just got it from… you know, 7 or 8 different suppliers. It's all different formats, and there are varying levels of effort put into how robust those catalogs are. So in order to be helpful to them ordering from all these catalogs. What we would want to do is standardize The names for things, standardize, the product types, the attributes, so that way it all kind of feels the same to the catalog. Or it feels the same to the customer. And then they could… we could, you know, search on that. There are other ways to improve the customer catalog experience as well in searching, but right now, it's that ingestion piece, right? If I have 20 CSV files from 20 different suppliers, how can it appear to be the same catalog? Where everything's in the right place, with the same name.
+
+**Ashritha**: Oh, yes.
+
+**Harsha (eParts)**: how we get the data into PIMS, in a way. Rather than, how. search for the dynamics panel.
+
+**Ashritha**: Ha, so I think the… I mean, if I understand it correctly… Yeah, yeah, so I… Just to, To prove the system is. Blue Bust, and so I just chose the most. Like, vague input, further input to test the… how our… Like, the second layers. No, I think, Harsha, like, is the confusion about usable output, like, I think by usable output, we are trying to say that, he is… he would, in the ML layer itself, he's gonna, like, normalize the data and validate it against the, industry stand… the conventions that you were… nomenclature that you were talking about, right? So that… whatever just David just mentioned, we are ingesting it into PIMS, not to make it… how do I say, search efficient for the customer, but make it Aligned with the industry, prescribed nomenclature.
+
+**Harsha (eParts)**: Yeah, exactly. I mean, I think I got thrown off by the example that Leo just gave. Okay, okay. Which was… which was just, like, a customer specifying a certain type of product. Like, if you go back to, like, Layer 2, the example that was there in Layer 2, Like, the examples I gave, just up a little more. If you can scroll up a little. Yeah, like, the input… oh, sorry, can you go to the layer 2 input? Go down, go down, sorry. Yeah, this one. So, need a thermistor that clamps onto a pipe, the 10K kind? This looked like a search criteria for the customer, and that's what threw me off. I think, I think what Leo wanted to convey was. How these, how these… How the words… how the words are, like, connected to each.
+
+**Ashritha**: Yeah.
+
+**Harsha (eParts)**: In terms of similarity and how they're ranked according to similarities.
+
+**Ashritha**: But, usually imports could be standardized and not so vague. And,
+
+**Harsha (eParts)**: Yay.
+
+**Ashritha**: And I think we can perfectly deal with that situation. So, I mean, in the most badly… situation, I hope… we could deal with those cases, that's what I want to show you.
+
+**Harsha (eParts)**: Yeah, this makes sense. I was thrown off completely by the way the question was phrased, and that's it.
+
+**Ashritha**: Yes.
+
+**Harsha (eParts)**: Yeah, because even the previous question seemed to be phrased in a similar manner, where a person was looking for something, rather than giving information.
+
+**Ashritha**: Yes, yes.
+
+**Harsha (eParts)**: So there's no…
+
+**Ashritha**: I…
+
+**Harsha (eParts)**: It was just a communication thing, and that's it. Bye-bye.
+
+**Ashritha**: Yes. Yeah, I know. So… I just want to show you the, most bad knee condition the… how the… mechanisms.
+
+**Harsha (eParts)**: Input can be, yeah.
+
+**Ashritha**: delicate. Yeah, yes, yes.
+
+**Harsha (eParts)**: Yeah, I got it, I got what you meant now, yeah.
+
+**Ashritha**: Yes. It's, like, in this scenario, So we could… we could let the customer terminology diverges from, Can you… Canonical database vocabulary. So it could be… could understand all the synonymi relationships. And it gets, Write complaint scores. And the instantaneous way. If the input contains ambiguous product type. Like, the customer's input could be, like. Need pricing on this manufacturer, like, this product. And with some attribute value pairs. So, because this product… that type could be vague, it could turn… could match in several, Different product types. Like, these two different kinds of products. And with, similar… Competence scores, or working competency scores. So, lastly must identify such ambiguity explicit, rather than commit it… commit to a single prototype. This is what they choose output. The layer one, the part number, could not exactly match. And, for the manufacturer, way. We could, we could, definitely, accurately match this. manufactured. We… so we could get a… First, the competence score, 0.85. And, yes, we… we ejects, ejects and value pairs. So, for this condition, it… It also needs this data to process. So we… first, we embed the… Useful information. into a vector. So, the top 5, candidates products. Could be, these two. And, their prototype confidence scores could… Could be, similar, but… Notably very large. So, because the product type confidence score is less than 0.60, So, the ambiguity tape check. This means… No matter how… how high the… Suburb… attribution… Attributes, value, pair, scores. is… it could… it's… what's a product? type with its attribute pair scores need to be sent to human. To do a human review. Yes, and we'll… and the system will inform It's a… it's a human. The product type is ambiguous. And, the… and provide the human some basic information, and the most likely candidates. And in this scenario, It's, so, frequency, or the product use. Could take into a higher level, or can say… can say it. High level or weights, because The voting, score is very… It's almost the same between the… The two product types. So… The consensus results could… More likely to use, product frequency. And usage information to choose which one could be the most suitable. And, the last… for the last scenario, We could all… So, for the input, we could only have some first information. like… We could only extract the information, the useful information, like HAAC, pipe, temporary digital monitoring, like this. so, this request does not distinguish between sensor and some more state, and most specific manuals are given. So, for the Layer 2, It has no actionable extraction, no numeric manual, no manufacturer mention. For the latest wave. So the last day processing will permanently use the usage count prior. become… due to… Do a judgment between different products. Like, we… in the first… in the second layer, or the… This way. We get, most unlikely for that type. It's a temperature sensor. And we get a product type competence score. And the attributes under, this product type are scored, because the customer provides no specific, specific values. This is our algorithm to… To get the scores. Yes, so the distance is comparable across candidates' value, and the usage count prior… prior dominates the ranking. You can say, what the candidates get the… Similar… working squad. So we used the second, Judgment Like, the usage prior to… Identify which one could be the best. So, this one could be… could be the best common yields. Because the frequency of this one could be higher than other ones. Yes. And for its… attributes. Like, the mounting candidates. this one could be the best. And, for the… Resistance, all candidates, this one would be the best. Because the information is dispersed, so all the confidence final values fall in the range 0.2 to 0.45. So… Because the, the score is so low. This could be less than 0.50, so it will be flagged as flag unclear. And we will keep this juice. Information into our… Looking into our datasets. Yes, and it's… This is… this is how we deal with Those vague and thirsty information. In the last way. So, so, this, these four scenarios is the most… Likely, you know, better conditions, less they could meet. in graduates. Yeah, this is a table for the four scenarios. And I also made a, curating coverage matrix. Spore… tend to, to make testing Like, in the most significant a checkpoint. Yes. I think that's all. Cheer.
+
+**Harsha (eParts)**: I had, two questions, mainly. One was, where do these layers… I mean, it's… it's a question for later, but, where do you think these layers will kind of reside, from, like, a… to make, hosting slash infrastructure point of view, as in… all this… most of this, like, all of this is really impressive and really nice, but, I… I'm not sure how we can host this, and how we can, Build this pipeline out, but… Not on a local machine, but something which is more up to scale.
+
+**Ashritha**: So, this positive deployment. Yeah. I think it could… directly, mounting… laptop. We cannot do it because it cannot scale, right? So, Hershey, I think for now, we can probably do it locally, but, if… If we, like, decide on the one… any particular models or lightweight LLMs, we can see if those are available on Azure and are deployed over there? Does that answer the question?
+
+**Harsha (eParts)**: But it's… it's just a train of thought that you guys… I just want you guys to be thinking, and that's it.
+
+**Ashritha**: Okay.
+
+**Harsha (eParts)**: It's nothing that's, pressing, yeah. Because this all seems really good, but I forget that this looks more like an experimental project than, like, something which is, you know, more usable than yet.
+
+**Ashritha**: yes, so… If you want to maybe change the model, or… You want to… Mount it to, like, other plantable, or… that you… to… to… You'll use more… you'll use… Much amount of the… the data fresh… update data to train the model, I think it's… It's relatively easy to do it. Are you searching for something? Yeah, that's about it. You had one more question?
+
+**Harsha (eParts)**: This is different, I just wanted to check in and see, if you guys are able to use linear and, If… if the free tier was enough for you guys.
+
+**Ashritha**: Okay, so the… yeah, we… I signed up for Linear, and I got all the access, I mean, with all the… the… I… I mean, I just… I thought that… this is good enough for us to create the custom fields and stuff like that, but, since we… I mean, I don't know if you remember, we were like, we'll do a 3-day, sprint, right? So, probably, like, our one cycle is 3 days. So, but then linear has, the minimum, cycle is one week, so, I don't know how to go about that over there.
+
+**Harsha (eParts)**: It should… technically, you don't have to set a cycle in linear, right? You could… Have a sprint board for almost, like, every single Every single 3-day cycle that you're planning to do.
+
+**Ashritha**: Okay, but our… sorry, you were saying something?
+
+**Harsha (eParts)**: No, sir, Tom. I mean, or, the way you kind of, I think… I think the way you, create tasks would be for, like, a 3-day cycle, and… I mean, I'm just thinking out loud. So, like, if you have a backlog, and if you have, like, the in-cycle tasks, then I think you can just move things around. For your 3-day period, and then, do it for the rest. Like, in a similar fashion.
+
+**Ashritha**: Okay, but, I thought, like, I… I mean, I kind of web-coded a custom board, so I just used, Node.js, and React, and, kind of… spawned up a small, server with, like, I… it's not fancy, just vibe-coded it, so it's also interactable, we… I… we could just, like, drag-drop, and, I can also, like, archive whatever progress that we have done, so the team gets the editor access, and I get… sorry, the team gets the view access, and I get the editor access, because, we do Scrum calls Monday and Wednesday. So, for now, I mean, as of today in the afternoon, I kind of thought that probably this is the most easiest one. linear… Yeah, you're right, I thought about it. We could just create a backlog and, play around those definitions, or maybe, like, one cycle, in linear terms is basically 3 days in, in the agentic scrum that we defined. But… again, there were, like, few other settings that I had to change, like workflow settings, and I had to, like, add a few more states. unopened, which are not defined. I had to, like, create new labels and stuff like that. So that was too much of UI work, so I thought, webcoding is much easier than me learning how to go about linear now.
+
+**Harsha (eParts)**: Makes sense, I mean, if you guys can figure out just hosting and, like, doing everything about it, then more than… you guys should do whatever you can about it.
+
+**Ashritha**: Yeah, yeah.
+
+**Harsha (eParts)**: And eating, that's it.
+
+**Ashritha**: Yeah, yeah. So, I mean, it's quite… it's actually good, like, it does not restrict you with any templates or fields, so that's why I thought I would get started with it, but then, I mean, I didn't think about the cycle thingy and other, stuff, but then as I went ahead, I felt like it was, like. not restricting, but maybe if I will… if I get to spend more time on it, probably I'll figure it out, but then we had to, like, keep pro… make, keep a progress, like, a visible progress of what we have done this week, so I had to bring up some UI for this, so I thought, let me just get done with this, and maybe over the weekend I can sit and play around.
+
+**Harsha (eParts)**: Makes sense. Also, I had a question, very random one. Why the 3-day cycle, though, compared to a week, for example?
+
+**Ashritha**: Okay, so that's what the Agent Tech Scrum prescribes, so by definition, and It's like the first… since, we don't have… we don't have to spend the entire week, like, the conventional week, or, like, two weeks of time in coding, it's more like… deciding and breaking down the problem statement, and it's more of prompt engineering work later on. So, like, we thought that this would best suit the current dynamics of the project, as in, the first day, we would just define the spec cards, in very detail, like, okay, what this task is about, what would the test cases be, who would write the test cases, is it the human, like, is it one among us, or probably the… agent itself, and stuff like that. And, like, one day in between, we would just spend the entire day to, review the work it, it has done, it produced. And third day, we would just give a demo slash, review of what we did over, Monday and Tuesday, suppose. So we thought, like, 3 days is a good amount of time, to get the entire cycle complete, wherein that happens in a 2-week time in a traditional scrum scenario. So we're just, like, experimenting this, and by definition, this is what, the… the Scrum org, prescribes A tick to be, like, a 3-rate, sprint.
+
+**Harsha (eParts)**: Okay. Yeah, just a question for my knowledge, that's all.
+
+**Ashritha**: Yeah, yeah.
+
+**Harsha (eParts)**: Got it. Okay, so whatever you guys are comfortable with in the end. And also, like, the thing was, as Lou was presenting, I was… I was just getting so confused, because all of his examples were in, like, this, Like, like a user manner where, where someone is searching for something, or when someone is, like. looking for a product, and our whole use case was centered around, like, curating the product itself, so, like, it just constantly threw me out in this, state of confusion. And, that's… that was my, I think. That was my question in the middle of the meeting.
+
+**Ashritha**: Yeah, I understood. Yeah, I think his example was more, like, to just make it explainable. Yeah, it's the most dangerous conditions Leia could… Yeah. Yeah, it just… just shows this, because I think it's a… like, in our, primarily testing, the accuracy is almost, like, 90… 95% on 200… 2,000 training samples, so I think the, In the common… in the normal imports, so… For the machine learning system to deal with, if this information is… it could not be, looking be up. be bad, I think. It could deal with it, those information smoothly.
+
+**Harsha (eParts)**: Yeah, I mean, that was it. It was just, like, something on clarity that kind of, threw me off, and that's it. But other than that, that's it. I think one thing to think about is just, like, how… where these layers can live. When we're trying to build an application at scale. It can just be in theory, doesn't have to be worked out perfectly, but, like, I feel like designing something with that in mind. Helps actually achieve that goal. Whereas, when you design something in a purely experimental basis, where, you try to just create a layer which does everything in the best way possible on your local. might not exactly translate to, like, a at-scale application as we move further. It could… I mean, in the end, it could be something which is like a… like a virtual… like a… like, just a virtual machine-hosted, kind of system, too. Which is… which is fine, but it's also very inefficient in a way, so, like, that's why I'm trying to get you guys to think about it in… think about it from, like, a… from, like, a… MLOps slash AIOps kind of perspective.
+
+**Ashritha**: Yeah, makes sense.
+
+**Harsha (eParts)**: Yeah, that's it. That's pretty much all my questions from this. And… it would also help, like, if you could share this document with us, mainly so that I can just spend, like, an hour or 30 minutes just, like, slowly going through it and understanding, different parts of it.
+
+**Ashritha**: So, because the time is limited, so I just skipped many technical details.
+
+**Harsha (eParts)**: Yeah, exactly. I kind of got that, too. I mean, it's tough to… it's tough to present something in an hour which is this technically heavy, so…
+
+**Ashritha**: It…
+
+**Harsha (eParts)**: I mean, I would tell you today, but .
+
+**Ashritha**: Yes, if you want to say it, I will send you the four detailed documents. You can see.
+
+**Harsha (eParts)**: 100%, that'd be really helpful. I'd want to go through that. Just for my reference, because, like, it's just very interesting to look at.
+
+**Ashritha**: Yeah, I'll put this along the action items that I'll be sending in the evening today.
+
+**Harsha (eParts)**: Jakes up, yeah.
+
+**Ashritha**: Yeah. We also wanted to go over the architecture part, but then I think we're just on time. Maybe next week, or maybe we can just send over those documents as well, you can just, like, take a look. And if you have any… I mean, it's pretty simple and self-explanatory. If we have any questions, we can discuss it in our next meeting.
+
+**Harsha (eParts)**: Yeah, makes sense. I mean, also, just like, just in case you guys want to meet twice in a week, we could also, like, schedule that ad hoc, as in, if you have more to present, we could just decide on a… decide on another slot, and we could get that done as well. That's an option to you guys.
+
+**Ashritha**: Oh, okay. I think we'll just think about this as a team, Ellie.
+
+**Harsha (eParts)**: Again, like, I'm just putting all options on the table for you guys, and that's it.
+
+**Ashritha**: Yeah, yeah.
+
+**Harsha (eParts)**: Yeah. So, sending the document completely works. I'll go through it and send it.
+
+**Ashritha**: Okay, yeah. Okay. Cool, and see you guys next week. Bye-bye.
+
+**David (eParts)**: Yeah, thanks. Dude.
+
+**Harsha (eParts)**: Thank you so much.
+
+**Hrishik**: But…
\ No newline at end of file
diff --git a/minutes/2026-05-21-client.json b/minutes/2026-05-21-client.json
new file mode 100644
index 0000000..87b44fa
--- /dev/null
+++ b/minutes/2026-05-21-client.json
@@ -0,0 +1,90 @@
+{
+ "meeting_date": "2026-05-21",
+ "duration_minutes": 53,
+ "participants": [
+ "Ashritha",
+ "Harsha (eParts)",
+ "David (eParts)",
+ "Hrishik"
+ ],
+ "participant_count": 4,
+ "total_words": 4973,
+ "total_turns": 75,
+ "speaker_stats": {
+ "Ashritha": {
+ "turns": 35,
+ "words": 3492,
+ "pct_words": 70.2
+ },
+ "Harsha (eParts)": {
+ "turns": 36,
+ "words": 1298,
+ "pct_words": 26.1
+ },
+ "David (eParts)": {
+ "turns": 3,
+ "words": 182,
+ "pct_words": 3.7
+ },
+ "Hrishik": {
+ "turns": 1,
+ "words": 1,
+ "pct_words": 0.0
+ }
+ },
+ "detected_topics": {
+ "Data": 5,
+ "ML/Model": 4,
+ "Infrastructure": 4,
+ "Architecture": 3,
+ "Onboarding": 2
+ },
+ "questions_found": 0,
+ "questions_sample": [],
+ "potential_decisions": 0,
+ "decisions_sample": [],
+ "potential_action_items": 11,
+ "actions_sample": [
+ {
+ "speaker": "Ashritha",
+ "text": "Yeah. So can I directly use your mic? Or use mine. Yeah, you can use my mind. You can't hear it. Me commute? J? Or\u2026 Yeah. Okay, okay. So today, I\u2026 I'd like to introduce you the\u2026 Power layer, the layer"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Yeah, exactly. I mean, I think I got thrown off by the example that Leo just gave. Okay, okay. Which was\u2026 which was just, like, a customer specifying a certain type of product. Like, if you go back to"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Yes. It's, like, in this scenario, So we could\u2026 we could let the customer terminology diverges from, Can you\u2026 Canonical database vocabulary. So it could be\u2026 could understand all the synonymi relations"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Okay, so the\u2026 yeah, we\u2026 I signed up for Linear, and I got all the access, I mean, with all the\u2026 the\u2026 I\u2026 I mean, I just\u2026 I thought that\u2026 this is good enough for us to create the custom fields and stuff"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "It should\u2026 technically, you don't have to set a cycle in linear, right? You could\u2026 Have a sprint board for almost, like, every single Every single 3-day cycle that you're planning to do."
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Makes sense, I mean, if you guys can figure out just hosting and, like, doing everything about it, then more than\u2026 you guys should do whatever you can about it."
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Yeah, yeah. So, I mean, it's quite\u2026 it's actually good, like, it does not restrict you with any templates or fields, so that's why I thought I would get started with it, but then, I mean, I didn't thi"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Yes, if you want to say it, I will send you the four detailed documents. You can see."
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Yeah, I'll put this along the action items that I'll be sending in the evening today."
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Oh, okay. I think we'll just think about this as a team, Ellie."
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-05-21-client.md b/minutes/2026-05-21-client.md
new file mode 100644
index 0000000..22ee6aa
--- /dev/null
+++ b/minutes/2026-05-21-client.md
@@ -0,0 +1,44 @@
+# Meeting Minutes — 2026-05-21
+
+**Date:** 2026-05-21
+**Duration:** 53 minutes
+**Participants:** Ashritha, Harsha (eParts), David (eParts), Hrishik
+**Source:** `GMT20260521-190444_Recording.transcript.vtt`
+**Processed:** 2026-07-20 05:14 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Ashritha | 35 | 3492 | 70.2% |
+| Harsha (eParts) | 36 | 1298 | 26.1% |
+| David (eParts) | 3 | 182 | 3.7% |
+| Hrishik | 1 | 1 | 0.0% |
+
+## Topics Discussed
+
+- **Data** █████ (relevance: 5)
+- **ML/Model** ████ (relevance: 4)
+- **Infrastructure** ████ (relevance: 4)
+- **Architecture** ███ (relevance: 3)
+- **Onboarding** ██ (relevance: 2)
+
+## Potential Action Items
+
+1. **[Ashritha]** Yeah. So can I directly use your mic? Or use mine. Yeah, you can use my mind. You can't hear it. Me commute? J? Or… Yeah. Okay, okay. So today, I… I'd like to introduce you the… Power layer, the layer
+2. **[Harsha (eParts)]** Yeah, exactly. I mean, I think I got thrown off by the example that Leo just gave. Okay, okay. Which was… which was just, like, a customer specifying a certain type of product. Like, if you go back to
+3. **[Ashritha]** Yes. It's, like, in this scenario, So we could… we could let the customer terminology diverges from, Can you… Canonical database vocabulary. So it could be… could understand all the synonymi relations
+4. **[Ashritha]** Okay, so the… yeah, we… I signed up for Linear, and I got all the access, I mean, with all the… the… I… I mean, I just… I thought that… this is good enough for us to create the custom fields and stuff
+5. **[Harsha (eParts)]** It should… technically, you don't have to set a cycle in linear, right? You could… Have a sprint board for almost, like, every single Every single 3-day cycle that you're planning to do.
+6. **[Harsha (eParts)]** Makes sense, I mean, if you guys can figure out just hosting and, like, doing everything about it, then more than… you guys should do whatever you can about it.
+7. **[Ashritha]** Yeah, yeah. So, I mean, it's quite… it's actually good, like, it does not restrict you with any templates or fields, so that's why I thought I would get started with it, but then, I mean, I didn't thi
+8. **[Ashritha]** Yes, if you want to say it, I will send you the four detailed documents. You can see.
+9. **[Ashritha]** Yeah, I'll put this along the action items that I'll be sending in the evening today.
+10. **[Ashritha]** Oh, okay. I think we'll just think about this as a team, Ellie.
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 4973 words across 75 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-05-28-cleaned.md b/minutes/2026-05-28-cleaned.md
new file mode 100644
index 0000000..3434d58
--- /dev/null
+++ b/minutes/2026-05-28-cleaned.md
@@ -0,0 +1,3 @@
+# Cleaned Transcript — 2026-05-28
+
+**Ashritha**: Night. It will, collect as a low samples cluster. And if the score is… the score is less than .70, What's up? A bunch of bills. In the case will pass to a human's review. And flag unclear, it says return to sender. Who's the sender? Cinder. In the red… lower right, that box, keep that down. Confidence final list. Oh, this one. Yeah, it was returned to sender. What is that named? Because this score is less even less than a human-to-use range. So, maybe… This, this, this area is not… And… it's a big problem for the machine learning systems. Like. We don't use this kind of cluster to train the model, so the model… like… Well, I understand the confidence is super low. Yeah, super low, yeah. So, so, we need to talk to the EPA team to procure this land, or… Snack, so we need to move… Like, to build some pressure with that. Within this… this… So you really mean talk to somebody in the catalog team? Yeah, yeah. So you might want to say something like that, as opposed to regarding the sender. Yeah, yeah, because… Because it's a fail. We have nearly 400 product types. Only 200, 250 of them have floor… Data cluster, like… But many of us only had one or two But other types, so it may be noteworthy. clearly to train the model. Yeah, I understand, so I just… So, yeah. Yeah, make a correction there, like, you know, review with the catalog team. Yeah, because maybe their products are… Yes. Yeah, yeah, so… Just a quick question. I like all this so far, just, are we gonna be able to, over time, like, tweak the actual values, like, are these going to be easily modifiable, like, in the future? Like, oh… Oh, yes, yes. Yeah. Like, those values, like, maybe for the local sample plus… Yeah, we designed to… for a frequent retrain. Okay. Were there configuration problems? Yeah, that's what I was gonna ask. Yeah, every, every retrain, the whole retrain, the whole period is about… 5 to 7 hours, you can renew all the artifacts. But I mean, like, is there just an easy config file where I can change, like, the low sample cluster cap from 0.6 to 0.6? Yeah. I guess that would be harsh if I would say that. Yeah, well, one away, it would be judged by film. And another, task for… So they have voiced the… State market duration, or overnight training, because… Since, when we don't have the radio input or the, products, So, I add, sigma. for what the regulation, for the scores. Like, We want the… competence. Produced by the model. It's… It's what, what is real… Is… is dispatched for its real, scores, like… The probability of raining for today is 70%. We just… we want to, like, make sure it's the… it is… it could be the real, producing. Yep. So at, and, to this… And to this… metrics is due to the amount of how long this distance. like. way… No, no. That was… I know you want to make your mark here, but… That's what I'm assuming. Just before going regularly, my dude. That way… Yes. We map the input into a higher dimensional data space. So the trained model done that. We kept, like, 500. different, so, so every different… But that type has its own, own… on… That's third. So, maybe some… Some product types have a big cluster, so… The distribution on the… Data space could be like this. This is… it's a century, century center. So, so this would be guilt of… Big Sigma. Because the distribution of the cluster is very large. Fixigma means if… It can, let, You're a… So, the predictor case is Far from the center. We can still get a high confidence. Because the cluster or is this very large. But if the cluster's distribution is very, very narrow, So, even… a small distance. Or angle from the center. Like, on, like, the display, it could be, have a big influence. So, the Sigma could be small to… Judges, confidence score to make it even more… But… That's brief. Oh, sweet. So what does PTs mean in this context? Product type. What's that? For product type. Product type? Yeah, yes. So, the four, layers, like, four layers, the first layer is categories, the second layer is product type, the third is attributes, and attributes… your needs. You should have a glossary somewhere in your radiations, so they're not this… not ambiguous. Yeah, yeah. As I said, only… as I said, only, 242 of the whole product type. how, large enough cluster to use, sigma. So we… so the… and the last 135 fallback to the fourth sigma. And we've also designed, the last layer, But this, this is not… Company team. Well, we just to make sure. We, when we get some, feedback from the human. We don't need a fully train, but we can slightly change some important metrics for the… So we made… with, I designed a… M6 to use the online update. So this is the next door. So, for the current clustering of data, what's the source of that data that you're using to cluster this? Does that make sense? You're talking about results now that you've… You've run data, and it's clustered in some way. Where… what's the source of that data that you're close to? I'm just asking, is this sample data from eParts, or what was… Yeah, it is just from these… these two files. Okay, alright. So I have a… have a jump. I was just so it's technical. Yes, and after I communicated towards a machine learning model. I used the rare data from the participant. Do you spend any time? to test the… accuracy of the Layer 3 and Layer 4. I used, this document that's included tags, because it contains product description and extended description pairs. So I use it as an input, and I use this file. Because it provides the ground truth labels to verify the accuracy of the output, or the last two layers, the layers 3 and layer 4. Like, we… just a yoga. to split the… this file into these three parts. So we use the… that's the 10% Take a step to test our system. which have… like, 30,000 products. And this is a testing result. The project type accuracy, the result is almost a negative 6%. Which means… So, machine learning can… System could collectively identify what kind of products the customer is asking about. or 19th or early 30 studies cases. And, for the attribute, unit, Todd. The top three, accuracy, reached… 85%. And the whole process, it consumes 20 minutes. So is this document a Google Doc, or what is this document? Just a word. It's a Word document. Okay. stored in your Google Drive, or is it in? No. But just a little… our teams. No, it's not yet on the Google Drive. It's… it was just for today's meeting. Like, he wanted to use this to explain. Right now, it's not on the Google Drive yet. Okay, so is this going to be a document that's stored somewhere? Yeah, after the meeting, all of these will be uploaded, yeah. And finally, I do a statistic work. The test set depends 244 statistics prototype spanning, 140,000 samples. And I just chose the top 5 product type. And for the pressure, independent values and actuators. It's, overwhelmed. accuracy is very low, so I… ask why the spread of love, because… Because it's not from… it's not… it's very… it's wrong, because we… I use… Man, what is… The employment is like this, so it… So, this is the system. justify Islam, but this is incorrectly. If… just to… The message is this one. So the, the result is… Gulza, and it's, is, is, it's estimated. We might be storing some of that. Have you… do you know how, like, the attribute values means, like, the triple tildes, that old hacky legacy thing where, like, you can store multiple values against, like… a single… Product tapes, as you need. a product's value for it. I know it doesn't exist, but I'm not sure that the exact way it's stored. Yeah, but I know what you mean, yeah. Yeah, this is a case we know of, though, like the one you showed, like, we have different… sometimes I think you have different values because of exactly what you showed, like… 24VAC is the exact same thing as 24VAC, so yeah. And the… So, next reason is… The PR wave product distinction is every selectable value, not the accurate shaped value. like, a typical shared way of product description, at least like this. So… It's Mr. Always. So it is maybe hard for the machine learning system to judge which one would be the best. Yeah, so the output could be a little vague, because it has a lot of, true… description. Yeah. Yes, yes, so just, That's why it has a low condensed wall. Makes sense. That is all I want to say about today. Yeah, and I think some of that range stuff, especially, is, it's fine that it's gonna end up having a low confidence and failing, because that's probably going to need more humans to interpret that. Yeah, but the top three attribute, you know, is they all have a high confidence, so… After it passed to… even if it passes the wheel, it will be very quick. Yeah. I mean, this is in respect to the documents you send across, too. I had a few questions on that, but, like, one of the questions was, How does the system deal with a completely new product, which… Doesn't really relate to any of the currently existing products. It could be defined as a low score. Yeah. Just a natural student. I see, okay. And… So, so, I think if you will help out. So you get a change from the data set, like, or you'll need to delete some oldest cluster. You'll need to retrain the model. Because, in some of the layers, the way I saw them made is… they try to understand what is the most similar product to whatever product is the… Yes, yes, yes. So… It is… it's a continuous. a vector in the space. It's like a data cloud. Yeah. Yeah, I mean, just a question. So, for example, so right now, the system is engineered in a way where it tries to map to the most similar existing product, and then, like, figure out all the attributes and the values for it. Based off of, what the most similar product is. But, for example, all we know is a product number and product type. Yeah. how we plan… how are we planning on, like, filling the values? Are the values picked up from the… PDF that we supply. Or are these values just… kind of guessed based on the most successful. Like I said, like I said, for Eric's project type, it's trained, product cluster. So, if the attributes, the value is not very far from the And a distribution range that is… its shape and the distance from it, it could be even to have a relatively high score. But if the, attribute Those unique queries. far from… If each brunk, or from the geometry distance, or the direction of the chain. Even if the instance is very bad, it could be… have a low scores. Is there a different process that needs to go through for a targeted product category? Yeah, I mean, what I'm trying to get to is, you mean a new product and a new product type? Not just the new product? Yes, yes. No, no, no, new product title. New product, but existing product. Okay, because technically, I don't think we can begin together until the standards. That'll have some combined values. So, if you want to add some, you'll put up time. As in the retreats. is needed. But if you just add some new attributes to the pair, since, the new… actually builds. We, we… and they're, familiar with, its existing product. So it can also get a higher permanent score. Got it. So, so basically, a case where there is an attribute value which is… which has never been seen by the system, we'll always have a low permanent score, is what it is. Like, let's say, go in that one, like, 24 volts for, like, the power. Yeah. Let's say that's a common one, but then let's say we get a brand new vendor supplier that sells products that are, like, crazy high voltages that we've never seen, and that'll be outside the system. That's, like, a new value that we don't previously… we didn't previously sell any products. Yeah, yeah, it's a range… if it's a value range is… within this… just politics. Yeah. From… 0 to, 200. And what teacher? But if your new input is 400, it's far from this 200, it can be a low score. But if, if you, like, in the, in this range, you have 0, 10, 100, 200, but your S, like, 550 is within this range, so it could be why I have a high turn that score. This is the… this… this is what I was getting to. So, problem is… The system that's being built is only good at mapping products that are extremely similar to the current products that are there. And that is kind of a problem, too, by the current future, because technically a new person comes in, even though it's a similar prototype, but the values are just completely off of what we have in the system. it'll look pretty quickly. Could… but could we… could we kind of use this same idea with also the standards? Like, so standards have, like. Some standards even go so far as to say, like, this attribute will always have these values. Some standards actually really define, like, values. Could we also use those values? Because then I think that that would allow what you're saying. Yeah, right. Yeah. It's not a value that we have in our existing products, but it is a value that, like, the standard, like, ETIM defines as, like, a potential range for volts or something. It seems like there should be some documentation somewhere that talks about these types. Yeah, the standard, yeah, I'll define it, but yeah, like, the standards, some of the standards for certain things do actually define what the value should be. So, ETEM does. I mean, ETEM's something I think you might mention. ETM is mainly there, I think, because technically, we're just a small subset of whatever exists out in the market, and… If we build whatever this is for the specific subset that we offer, then it'll already stay in the subset, and technically anything that lies outside of this, which is currently what we're trying to do, to go to subsets which are outside of our current subset of products, it'll always kind of not work. So if we… I think rather than sticking to whatever… so the values, if they're more defined by the ETM classification, which provides a range of values that, for a specific attribute, these are the range… these are the different ranges, and this is what you get in the industry. That might be a better… that might be a better plot compared to, the value ranges that we have within our system right now. Or use both, yeah. Or use both. Oh, but the thing is, with our system, it'll always be this… there'll always be this problem that it'll be within the bounds of what you already know. And it might be off for, like, a lot of your specifications. For example, if a product type has a… has an attribute called length or a dimension. It could be vastly different for different… for different products within the same product type, and… I feel like dimension will always be off, given this current system. So, rather than that, if you… if either we have a system which Just gives us what's the most closest value to this attribute. Or, if it's a system which bases it off of a standardized range, which is like an Ethernet classification range. And based off of those product value ranges, like, attribute value ranges, it predicts it, that might be a better system overall. In general. Because this seems to be too narrow for a use case. This might be good for, like, a good POC. Yeah, yes. But as you're trying to, generalize it, or… Yes, yes. Just make it, make it more robust, is what I mean. Yeah. Yes, I… so the first question was assisting just to… gets, high accuracy score, so… So, generalizations… Could not be very good. But if you want, I can make it more challenging. I think it'd be good to lean into, like, some of the standards, too. Yeah, yeah. Yeah, so, so yes, or you have… or you have other things I need to consider you can, but, let's one of them down, so I can… So, what I'll do is I'll actually extract all of the ETM data that's there, and send it across in a similar fashion as I sent the initial data to you guys. mainly so that you can only… the only reason for ETEM is to get the attribute values and different type of product types that are there in the industry, rather than picking those up from the dataset I sent you, which is more or less supposed to be, like, a dataset for the type of data we deal with. A standardized mapping our data into a standardized set of attributes might be the better way to look at this. Because this way… so this way, you'll always be running into these weird hash cases that… we have… we have too small of a data… like, our DSL is too small, and the ranges are too low, and we can never map accurate accurately. So it's EDOM an industry standard? It is, yeah, it's going… and they've come out for a lot of categories and been, like, table… like, tables in general in the world could be made from metal or wood or glass, whereas our eParts catalog, we might only have examples of wood, yeah. That's… that's how it is. I mean, Ethan, do we have any specific ones we showed you? Because, they are multiple files with a lot of value in it. Is there something, like, which is more EFAS-specific, or the entire thing? So, ETM is exactly the industry we're in, technically. So, ETEM captures everything that our industry kind of captures. So, it's, it's, it's like a parent subset. Yeah, we've been wanting… we're looking… like, the standards thing is pretty new to us, too. I mean, like, we're looking more to leverage that for this, and then for other things on our platform, too, but we don't really… we haven't used it too much so far. Yeah, I mean, doing this project in general for a stand… with a standard in mind is a better use case for you guys as well, because… If you go by our product types and, like, our specific data, it'll… it'll just be, like, too narrow for use case for you guys, and that might… that might either lead to over-engineering, or that might either lead to, like, fewer use cases that might never be required. There could be some user. It's like, we've talked about, like, helping… there is a case where… so our new platform, Harrigan, let's say we sign up, like, a plumber down the street, and they could sign up for the purchasing platform. They could just upload all their products that they have and say, like, hey, we have these as our products we normally buy. We want to see these in the platform for, like, us to search and buy these. And in that case, we might already have a lot of those products, so in that case, it would be probably good to say, like, hey, we already know this thing they're uploading, we already have that. And your thing is perfect for that user. Perfect. Yeah, for brand new products, like, the standards might be… Yeah. Like, if it's one we don't have. Yes, yes. So how does the EDAM standard exist? Does it exist as a huge file someplace? It's so… You can find it online? Yeah, so if you search for EDAM, on their website, they have this broad list of files, where they define different type of product types, different types of attributes these product types have. And then they also specify what sort of values these attributes will have. And there's also, like, this synonym thing that they have, which is, this specific product type or this specific attribute can be called these five things. And it's very… it tries to be as generic and as useful as it can be. Which, technically, our dataset is not. So, it's just a better way to, like… you can even find synonyms to what we use in our database in either. Because that's… that's how generic they try to be, and it's just a bunch of files which all map to each other. Someone, somewhere, some organization decided, yeah, to compile all this. It is a non-profit organization. I don't know who makes it. There's a couple, like, one of them's a more European one, is it E-Class, or… That's E-Class, yeah. Yeah, E-Class is more standard in Europe, they use that. That one's pain. Whereas ETEM's actually open to everyone. So, it's… I think it's just, like, a project by some big company who tried to, like, standardize most of these non-gatures, and that's what it is. Do you guys have quarters to it? Obviously, you said… sounded like you did. Yeah, I loaded the item. I'm just trying to figure out how we can use these, or how, like, to visualize it properly, because it's a huge file. All their files are massive. They have so much data, but I would say you can narrow the use case to only the product types we're dealing with right now. The rest of the product types might be our problem for later. Yeah, because we, we, whatever, 400-some product types, like, we kind of specialized so far, with building automation and electric and controls, but we… plumbing stuff, mechanical stuff, like, that's all an ETEM, and those aren't really… we don't have any customers yet, so we just don't have any… customers in those, segments, so we don't really have any data from that. But again, it might be useful to have a documentation of some use case saying, yeah, we want to now incorporate this new product set that we're not familiar with. Yeah. Here's how you ingest the Enum standard for that stuff. Yeah, that's gonna be some rough guidelines. Great for us going into other markets and stuff. Yeah, because I think one of iMag's, like. individually was to… to get. to get almost all of our data mapped into ETM standard as well. Yes. Just so that… I think we've discussed this as well at some point, but, it's… this is mainly to make sure that whatever data we have is stored in a standardized DSL. Just in case we do want to move to… complete ETIM standard, or a completely industry-specific standard. Yeah, as we, like, all of our data right now has been in the Alps control standard, and as we kind of separate from Alps and look to go, we want to take what they have and actually, yeah, turn it into ETEMS, and we want to start using more of the ETIMS standard. I think this was once one of the reasons why, in the last meeting, I had a lot of questions on what was this… what we tried to do? Because technically, that is what I was imagining in my mind, and what you're presenting was not… very clean line with that, and that's why I was confused. But again, that is really good, though, so far for just someone going in and uploading a product, we do that, and we'll instantly know that, hey, they're uploading this product. I don't do it. So you don't like to have a mapping from your current, help stuff to even… we can detect the subset right now, and deal with that. Because only the product types that… so technically, we can only deal with the product types that I've currently sent across to a team. So, only those prototypes from Eden can be utilized, and that's what we can… That's what we can work with right now. So that helps us, like… reduce the whole ETM set to a smaller subset, and also help certify my use cases. So it'd be a big win for eParts to map all your stuff when you get them. Yeah, for sure. 100%. That'll be a great value addition overall, like, if you're able to do this, because this pipeline actually names a lot of these things. But it just needs to be for a slightly broader subset, or a slightly broader set of products and values. That's pretty much it. I think that was a mismatched in the discussion. Okay, well, that's valuable input. I mean, let us know. That's why I keep asking you guys questions on, like, if you guys need any other sort of data which helps you guys work with this current data, or any sort of, any sort of work that I can do for you guys, which can help help with you… help with you dealing with internal, right? Any other way? Yeah, I think there are… I have a few questions about ETM, but I think I'll look into it a bit more than I'll… But then if it's easier for you to, like, extract the data, because you know ePaths better, just align with what you guys work with, so if it is possible, then probably it can help, I can, I can show you that. Okay. Yeah, like, spend, like, a few days on just trying to not beat them. I've got names. If it's… If it's too small of a task, then it's good. If it's too big of a task, I'll just let you guys. Beautiful. We're gonna have to… because we're gonna have to, like, CNS is really wanting us to… so this has been our direction since, like, the last 3 or 4 months, you know, since we built, like, this new… platform very exist. Like, that was… that was… that was the reason why a lot of us weren't available in, like, this whole Feb, initial Feb, end of Jan kind of period, and this is… this is the kind of shift that's overall happening now. And I think since then, we've been trying to focus on it a little bit, like. It's, I think it's got missed somewhere. It's not the discussions we had, okay. Is Eton the only standard, or… There's others, too. Like, UNSPC is another big one, too. But UNSPC doesn't have really good data. It's paid, first of all, and it's also, something was specific to a very small industry, I think. So UNSBC wasn't as good. From what I've seen, ETL was the only thing which kind of… Gives us open source information, along with a lot of level mapping, because ETEM actually maps to eClass. Yeah, they have a code, I think, that's, like, you know, that, like, this corresponds to this and the other standard. So that makes things easier for us, so as long as you guys only work with ETH, that should be enough for, like, the school's project token. Yep. I think, you know, Yeah, we can send it over mail, since you guys have a good idea about the architecture, the components, and the ML, so you can just, like, quickly go over. If you have, like, any outstanding comments, you can just drop in, and we can do a review next week with the updated version.
\ No newline at end of file
diff --git a/minutes/2026-05-28-client.json b/minutes/2026-05-28-client.json
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@@ -0,0 +1,50 @@
+{
+ "meeting_date": "2026-05-28",
+ "duration_minutes": 35,
+ "participants": [
+ "Ashritha"
+ ],
+ "participant_count": 1,
+ "total_words": 4757,
+ "total_turns": 1,
+ "speaker_stats": {
+ "Ashritha": {
+ "turns": 1,
+ "words": 4757,
+ "pct_words": 100.0
+ }
+ },
+ "detected_topics": {
+ "Data": 5,
+ "ML/Model": 4,
+ "Infrastructure": 3,
+ "Architecture": 2,
+ "Onboarding": 2
+ },
+ "questions_found": 2,
+ "questions_sample": [
+ {
+ "speaker": "Ashritha",
+ "text": "Is there a different process that needs to go through for a targeted product category"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Is there something, like, which is more EFAS-specific, or the entire thing"
+ }
+ ],
+ "potential_decisions": 1,
+ "decisions_sample": [
+ {
+ "speaker": "Ashritha",
+ "text": "Night. It will, collect as a low samples cluster. And if the score is\u2026 the score is less than .70, What's up? A bunch of bills. In the case will pass to a human's review. And flag unclear, it says ret"
+ }
+ ],
+ "potential_action_items": 1,
+ "actions_sample": [
+ {
+ "speaker": "Ashritha",
+ "text": "Night. It will, collect as a low samples cluster. And if the score is\u2026 the score is less than .70, What's up? A bunch of bills. In the case will pass to a human's review. And flag unclear, it says ret"
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-05-28-client.md b/minutes/2026-05-28-client.md
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+# Meeting Minutes — 2026-05-28
+
+**Date:** 2026-05-28
+**Duration:** 35 minutes
+**Participants:** Ashritha
+**Source:** `GMT20260528-190703_Recording.transcript.vtt`
+**Processed:** 2026-07-20 05:14 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Ashritha | 1 | 4757 | 100.0% |
+
+## Topics Discussed
+
+- **Data** █████ (relevance: 5)
+- **ML/Model** ████ (relevance: 4)
+- **Infrastructure** ███ (relevance: 3)
+- **Architecture** ██ (relevance: 2)
+- **Onboarding** ██ (relevance: 2)
+
+## Potential Decisions
+
+1. **[Ashritha]** Night. It will, collect as a low samples cluster. And if the score is… the score is less than .70, What's up? A bunch of bills. In the case will pass to a human's review. And flag unclear, it says ret
+
+## Potential Action Items
+
+1. **[Ashritha]** Night. It will, collect as a low samples cluster. And if the score is… the score is less than .70, What's up? A bunch of bills. In the case will pass to a human's review. And flag unclear, it says ret
+
+## Questions Raised
+
+- **[Ashritha]** Is there a different process that needs to go through for a targeted product category
+- **[Ashritha]** Is there something, like, which is more EFAS-specific, or the entire thing
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 4757 words across 1 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-06-04-cleaned.md b/minutes/2026-06-04-cleaned.md
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+# Cleaned Transcript — 2026-06-04
+
+**Hrishik**: Okay. So, first we have… On the agenda, the initial OCR endings. So, I did a few POCs with whatever was available out there for the OCR part of things, which is what I'm doing right away. So, I texted about 8 different, source tools. These are, like, there are 3 different, categories of tools that I… There's three different strategies that I went through. There's one with just the plain old select, which is, like, capital, which just basically goes through the document and then picks up towards it. It has no idea what a table looks like. That's not fully. The second one's a document password, like, DocLink, Mayanuru, Surya, and these are, they build a little more, deeper, finding labels and structure. So once it finds, like, a word, and it makes it into a table, then, like, it does a little more recognition than the playoffs there. And then there's the ability models. So that's the Chandra tools. So, Chamberto is a very, powerful one, but the problem is you have to post it via, Data Lab, which is a service they offer. You can self-host it, but the problem is you don't get the performance, you don't get the same amount of performance As you would get if you hosted by a data lab. And I have a couple of, benchmark scores as well that I got So, I have only 8 PDF documents right now, which are based from you guys. Like, if you can give me a bit more, we can… do another benchmark on all these platforms. You can use the links itself in the links. The CDN and the… yeah, the CDN link. Oh, those, yeah, the software, you can just use the CD links and, like, how many may want. Okay, I'll do that. So, for now, I just use the 8 PDFs that I had downloaded. And, Chandra 2 with the data lab was the most accurate one with 97%, right? At a 3.4% elevate, but this is a paid service, so you wouldn't be paying one cent per page. Minor New is another one which… I ran locally. You need your own server. Basically you need GPU, that is for this, so I had a model account, and I needed over there on an 800, GPU. But, like, I expect if we can run it on Edward as well, and you can run it under that, too. That was pretty much similar to Chandra, too, except it had a few things that it must actually work. And, I didn't… I did the POC for the rest of it, but I don't want to go anywhere below the 5% error rate, because I don't think it makes sense, because otherwise I would have to, again, come and collect stuff out. So how do you actually determine the error rate? Do you… Do you manually go back and do that? I mean, how do you know? So, I had, Claude actually go through the PDF document once, Claude is really about Claude and ChatGP perspective, and then I went through the document as well, and we had, like, a comparison benchmark that we ran against. So that's probably not the other thing. Okay. So, if we… I don't think we would be going via data lab, because we are entering tied to it. So, I'm guessing if we were going to self-post it, we would be self-posting, right? Either on some kind of a GPU service. And there's also dockling, which has some amount of error, but it means only a CPU doesn't deploy US dollars. So, you don't… you can get it cheaper as well, but the error rate is still a little bit high. I can see if I can fine-tune the parameters to… I mean, technically, even for general articles, it's… If it's a service, as long as we get an output, and we put that output wherever, it doesn't matter. Like, so, if it's easy to host it there, we might as reduce that service. Right, but again, we would have to see how many pages, yeah, how expensive it gets reported. So I did some research on if there's some alternative options. And it did say that there was an EA documented citizens. But I have had… I did not have a chance to try it out, because I don't think I have access to the Azure, authentication. Okay. Yeah, so if you give me that, I can just try it out with the current PDF that I've got, create PDFs, and come up with some final benchmarkings. And how often… I mean, this is, This is not going to need to be run It only needs to run when the document changes, right? Correct. Or when there's new documents coming in, and we would ideally be batching those as well. We don't want to run it one by one, because, like, the COPPA, computers on top of that. Yeah, I guess I don't know how often that changes, You know, doing our entire catalog, call it, 800 bucks. Probably about, you know, 10 bucks a year. That's pretty good. It kind of depends on how many new vendors you onboard quickly. If you have, like, how many clinches they make. Yeah, yeah, okay. What is an output? Just… Just the text? Yeah, so it outputs… my output goes directly to the injection service, what would be agreed. The injection service then passes around to the ML modules for itself. Suppose, Supposedly had… I don't know, 40 clients, and they all have the same vendor. We're… we might… we might want to segment this data. They might have Contractual requirements that say We don't want any data to touch, even public data, right? So, like, if we're paying for such and such a service to help with our catalog ingestion, we want that to be hours and hours alone. Oh, cool. Sorry, I'm trying to crinkle it. ordinance for us, okay? Would it be possible just to take the output of that file and store it somewhere? Yeah, we can have it. I think we are planning on doing something on the lines of, the client, with the vendor and having that in the key, so that each client has a distinct, client-vendor relationship. So, you know, the same client as multi… like, same vendor, each with multiple clients, they'll all have a different basically catalog. Okay. Cool. So the data, the Sierra data is structured data that you send to the… Yeah, it's structured data. That's the whole, that's the whole reason I did the next part. We want structured data. If it's, like, raw data, the plain OCR model works. So, a couple of things that it still did not pick up, even the best model was, So, always here… So, over here, there's dimensions, and over here. Like, coil diameter and wide length. I wasn't able to pick that up, even the, Chandler tool with the data platform. So, I don't know if, A bit more fine-tuning on the model itself could maybe help it out already. What we could probably do is, like, offload some of those which don't really work out to… a better than, like, GPT host run already. That would be as almost 100%. Yeah, even Azure OCDR might, Mike. Yeah, yeah, I could compare this, yeah. There was one which was the Siemens Power Catalog, this one. this gave a 0% success rate. Like, all the models paid down this one, just because it has no tables, and it's just paraphors. So, this, pretty much requires, LLM itself to, like, go through a technical, outcome technicians. So there are preferred structures In these documents that would make… the analysis better, that's what I'm hearing. Yeah, okay. Is that… is that a very clear path forward? Because what's going to happen immediately is one of the suppliers is going to say, why does your… Catalog data looks so much better for my competitor. Not for me. We'll say, well, you give us crappy PDFs, and say, what do I have to do to change the PDFs? And we'll go. Alright, alright. Yeah, I can… it's mostly tabular data. If it's end tables, results here, engines usually make it, much better to scale. Good. But I can dig a little more deeper into how, something like Chanda, which is not just an OCR engine, but it has some amount of VA elements underneath the UR as well. So, I guess, One thing I need is the Azure credentials so that I can test out the Azure OCL. I don't think it would be a lot better than Fundra 2, because right now it's state-of-the-art, and every enterprise project… But we can try it out. And, Datalab does offer zero retention and SOC2, just in case there's something for that. If it's self-hosted, then we can just run it on, yeah, machines are everywhere. So, all the top contenders, are they commercial, or are they open source? What's the breakdown of that? the… the… I haven't, tested out any, Oh, I've only tested our open source ones right now. Yeah, these are our open source ones, which… Minus Chandra 2, which is, service. It's software as a service, we can upgrade. And we can also, if none of this works out, we can always follow back to the GPT on network as well. So, yeah, let's movements. But, yeah, these are the added dates, basically. And the output from this is JSON or KV pairs. We can… Signature can configure that too. So, like, I was working on the ingestion part, actually, which is the next step. So I also, like, I made a mock this thing, using the, like, test rack and stuff, just to get some information out. So right now, in the ingestion part, we are… like, I've not tested it that much, but we are able to, identify which data is, like, corrupted and which data is not, so we are able to see the data in its, like, kind of more defined format. we get, basically, JSON data, which has the product name, the values, which document it came from, and basic things like that. So, like, testing is left, but yeah, after we are done to OCR part, we can connect these two, and then we should have a… basic running flow. So, I think, some amount of what was the person overlap. My module is just, just, like, a using throw kind of thing, and that's not meant to go in the code anyways. It's just so that we can work in parallel. Right now, in the addition part, I'm able to get the data in a decent shape. The next part would be maybe to… get all the data and put it into a specific schema that you guys already have for PIMS, so that part is not done yet. So, next steps are that we… like, first we continue the OCR part, and in the addition part, we go ahead and… Just… put the data in canonical tables so that we can use it on the time? So, I think OCR does the passing and extraction, whereas Engine does an operator. Okay. Jesus. And then expect that from the… by the end one, so… Okay, so this is, like, the first two steps in there. Yeah, then we have the LM models, which will give us a content scores, and… So the third step will come back into the second step. Where the staging table is available, and then go back in. bookings. So, for training, we actually are planning to have a separate, database. So, whenever, like, after, let's say, our ML models have given us some output, and we deem that's not good enough, and the human reviewer modifies it, changes it. That modification would be fed into our database, and then after a certain amount of time, we'll use that data to train the model again. It's basically BIMS data going in one of the database. Okay, yeah. Now, you don't want to touch BIMS with any of the interpreted data? I want to go back to the Siemens example for a minute. Isn't there some structure to the Siemens data, or regularity to it? It would seem to me that, you know, they're talking as specs, but if someone were to parse that out, you could go back and digest it that way, just curious. So, the problem here is, Every single PDF is very different, and these OCR engines work on some amount of rules. written for genetic OCR, by them, so… For example, in the semen data, it just looks like a paragraph. As a human, we can just read through and understand that these are the part… the part numbers are the ones we need to Take into consideration, pass it downstream, but then… For an OCR engine, this is just text, and it doesn't know which part of it to expect and get put into work. I understand, but I'm just saying the… the paragraph of stuff. Is that regular or not, or is it… Yeah, it is regular, but it's still not a table. So, I'm sure we can improve the model to, like, fit in things which we would also want, because it's open source. But, like, just the base model right now would not fall through that. I mean, in general, at least those are Apple, because, The way written to work is you exactly define what part of the wage is what, and then it tends to perform the best. But if you see, like, invoicing is probably the best case producer, and invoicing is pretty standardized, you point it to what corresponds to what aspect of invoicing, and it performs a variance, but when it's unstructured… Yeah, generally, like, if the data is in tabular format, it's very easy to extract in KV pairs. But, like, in this, we have paragraphs and… So, that a normal OCR can… at best, it can probably just give us the entire paragraph, but not the exact numbers and values for it. But honestly, like. If… I can do a rather benchmark of Just using LLM. You'll take the text, chuck it into the… in a way, it's trying to optimize cost, right? So, you can either drop the entire PDF into an LLM and split the data out. Yeah. That would cost more. Technically, it's cheaper to do OCR on it and then chuck it into the LLM, because LLMs work better with JSON, Markdown, and all of these colors, yeah. So… And PDF is, like, a bit expensive as well. Yeah, just raw TXT files in a little bit cheaper. Rather than, like, a PDF in which it has to be manually. Yeah, I can then probably just use the basic OCR models. Yeah, it could be a two-step thing as well, if it's… if it's just that much easier. Yeah. Because GBD5 Mini and, just models of that sort are extremely cheap. So they cost, I think, around, 0.3… 0.037 per 10,000 tokens, I think? 1,000 tokens, which is… which would be extremely… Because technically, we don't have that many years in the end, the number, 10,000 to 30,000 kind of range there, and the expense will never, like, do our… Yeah, I mean, maybe try that. That would, I think, simplify it by a lot. There's also Microsoft Open Source Repository in GitHub. I forgot its name, but you might have mentioned it. But that's… that's… that's currently, I think, a standardized one, too. I think it was the iteration one last week. It's, it's used to convert all PDFs into markdowns. Oh, okay, yeah. Yeah, I think there's a bunch of open source tool, PDF stuff. Yeah, so that… you could probably check with that as well. Yeah, also, if it could give some, information to how many of these catalogs are changing every year, that's… Then we can… and, like, what the… what kind of budget we work for. So, yeah, that I know. what we want to be using, yeah. I mean, technically, you guys would be doing it per day task without… it's more or less a POC of… Great, yeah. So the budget planning is the biggest it will be. So the main thing is, the catalogs which are already present right now in PIM doesn't need to be re-indexed, right? That only needs to go… What's the ML? What do you mean? Global OCR, and the organization. I mean, we could try to enrich the current catalog as well. That could be a part of it as well. Okay. It could be multi… it could be different aspects of things, like trying to edit the current one, trying to… experiment with a few new catalogs, and see how it goes, and how it works. So those could be, like, use cases which you can probably gauge your metrics on how it works. Yeah. What about the mapping? Was it the Eagle standard? Is that the standard? Yeah, Yeah, that's, like, after we are done with these parts, that's what we'll look at, because we're thinking about using that, standard in the initiation part, but that doesn't seem very feasible to do right now. So we'll probably, try to merge that in with the LLM, sorry, the ML part. And then maybe have a small component which matches it up with the standards. Yeah, I mean, ETAM should mainly dictate the attribute values for you guys, and… I'm just setting, like, a baseline for what would be the acceptable range of outputs. I think we've not really made progress on the ETEM front as of right now. Like, once we go a bit deeper into it, we'll probably have much more questions regarding, like, the last time I checked, I was not able to figure out which data is relevant to us, which is not. I think, yeah, after we have the required data, we should be able to use it in a pretty decent way. I would say a good way to probably just experiment with imaging data would be take a small sample of product types and attributes for that product types, and see if you can map it. If that mapping is easier, then you could just put it in Cloud and ask it to do your own mapping for all the rock types and avenues, and that might just… That you guys pass the whole magic step of mapping anything. Right? As long as you can do a small subset, you say anything. Right, right. Right. Yeah, we… yeah, we can do that. Yeah, I think after we… the initial mapping part, we'll use Claude to map all of it with the product, because it is going to be huge. The files are, like, really big. So, a little bit you can do, and then just eat the rest of it. Okay. Next, February. Yeah. Next, I think we can discuss the LMP OC update. On the phones. How about? Let me stop sharing. Newskeeper, Wait. Hold on. Yeah. So… I just tried to see… if we could, lose doing his approach, and everyone wanted to see whether there are different LLMs. approach would work. So, these were the five models I used. These are all open source, and… I just used Olama to, run everything on them. So, currently, Llama 3.18 billion priority model, gives pretty, the highest result, pretty much. Fee, by 4, 14 billion one gives, like, slightly better results, but there's a bit of a caveat to that. I could not see any, regularity. It kind of goes up and down, and there's also the problem of… Yeah, so the problem is that All these, Lew's model is pretty good with calibration as well. Here, when I try to, structure it in such a way that it gives a confidence, like, how confident is it when it's making the prediction? It… it does not improve at all. Quen, 2.5, 14 billion, it's actually… Performs worse at conference, accuracy than, everything else. There are some exceptions, but, there's no trend I can see that, as the models get bigger. it gets better. I have not tested with the biggest ones yet. Because, when I played around with this, there were a lot of errors that I, kind of… Had to get through, and doing that with paid models would have been quite expensive. So, unless I go and check the really large ones, I don't think any of the ones that you could… I guess self-host would work that… I mean, these can all be run on your laptop, but, unless you guys have, a full, I guess, server rack, the really big ones, like Kimi 2.5, you could not run that. on the 80, like, you cannot even run that with a 5090, or two 1590s and, like, 128GB of RAM. I think that's about the maximum consumer… So, for the confidence part, have you tried experimenting with the temperature of Actually, I have not tested that. I have changed other things, how… what kind of format it's been fed in, and that kind of gave… gave some results, but I'm not testing with the temperature. Because it's a simple slider in a grammar. Yeah. a temperature of 1 means it will only give you results on things it's completely certain on. So, it'll give you way less output, but it'll… so you can just play around on how different temperatures kind of give you results. Yes sir. I actually didn't think about it. I can do that, and if you guys want, I can… now that I've kind of figured it out, it wouldn't take that much for me to test it with, I guess, 3, 4, 3 or 4 of the leading models, and depending on how you want it, I can also test it I don't think there should be any problems with data retention or anything like that, because every cloud provider has something which just protects that one. I would also say you… I can give you access to Azure Foundry, and you can experiment with, like, different models. Okay. It gives you a simple UI in which you can just test each model out, and that… that UI usage is completely free to live. Okay, so all of the credits you spend on the UI end of Azure Boundary is… is pretty much… since it's testing, it's considered, like. The other thing is… basically… The amount of, it's not shown here, actually, but… The amount of, tokens that are being used do not rise. I thought that as the model size became bigger, it would kind of I guess think about it more, or use more tokens, but it stays within, like, 5%, even when… you can go from a 3 billion model to a 14 billion parameters model, and it doesn't increase that much. I would like to increase it. I'm not… I'm not certain that even with the highest one, it will actually, like… you can see at the top, it's, like, 94.7% that we can theoretically get. But I'm not even sure that even with the… biggest model that you can get that. And there's also the problem that you can kind of need to have some sort of structure around it, because just feeding it in one by one… there's a specific size over which it kind of, For these, the specific size is very low, how much you can take in advance, but I would assume that even with Claude or GPT-5 World5, if you try to feed in, like, I don't know, a 10MB PDF, it would just break down. So, what is the input for all this? Was it directly the PDF itself, and then you were trying to output? No, no. I took, there was a clean version of that, probably was, work, and it basically took it, like, proper clean data, and, it was orbiting it in case. Okay, so, you gave in… clean inputs. How did it know what sort of, attribute… attributes to map to? That, that I also… basically, Lou had a list that, yeah, so I could just take it from this branch, and, that I also just fed it in with instructions, so that… I could play around with some, I guess, how… what kind of data, kind of the structure of it, and I… more importantly, how large the chunks that are given all at once are. That has helped the most, I would say. But… depending on what we… what works best, that does change, so we cannot use… the LAMA coin are very different, and I would like to, like, check with KV2.5. And, plaid, and GPT-5.5 and all these things again. But, I did not use them here because… These are free, and I can just make many mistakes when I'm figuring it out, but doing that with the ones that I talked about, even Kiwi 2.5 can kind of quickly racket the cost up, so… So, how do you get the theoretical maximum? Oh, that's… that's what Lou figured out when, when he was doing it. He just said that, if you followed certain rules and, you see the regularities in the data, that that would be kind of the maximum that you could get with the pro set. So that's what I'm comparing. As for the accuracy itself, I did not have anything, I just… I was just, that was just compared against 100% accuracy for how, when it's giving its confidence rating, so… That does change, but I don't think… The point here is that 67.6% or something accuracy is probably not gonna be good enough in almost Any case? And… I could basically… this proves that anything that you could self-host, meaning, like. Anything you could run on 25090s and 128GB of RAM, that's the… that's the maximum configuration I could see for a consumer PC. That's something you could have in your office. It would not improve it that much, because the max you could go to, it would probably top out at 75%, which is way too low to be of any use. So I'll try to work with what you said, and try out the highest models, and kind of give a cost estimate, like, I'll do. And, see how that works, and see what kind of silos chunks I should do for those, but yeah, these are… I think one more thing is, Azure gives you access to 800 GPUs. Yeah. You can try running the bigger models on that, so I have a couple of hundred models running. So, it pretty much easily runs, like, 30 to 40 billion. I think we'll get ready. Quite opinion model, right? That's still not good. It would have to be, like… Poor market. Because the KMI 2.5 is not… No, I meant the highest… These… I mean, the highest one is a futility. That will never end there. No, that's a mixture of experts, so… So it only, kind of… has so many of them, that's, working at once. So, for the basic… question here is, is a large language model a good substitute for the amount? So I can say that anything, yeah, anything working on a commercial PC is not good enough, basically. Anything less than around, I would say, one… I can be pretty sure that anything less than 120 billion parameters would be useless. I have not tested… is that, just experimenting with temperature, experimenting with fine-tuning the models as well. Because the thing is, there's these hyper-specific modules which are exactly tuned to one single use case. they actually tend to perform really well. It was, I think it was… what was this? What was the model that you wanted? It was… it was basically a Python numpy expert kind of model, which was only good at NumPy package. these singular package level 2 point level models were there, right? So, they find… they basically fine-tuned every single model to be just an expert in the single package. And they kind of tried to lay around with it, and it got to a really good level. So similarly, like, if you can probably fine-tune it with structured data of what you use or involving with. And it could get really good at, like, understanding what's there. after a few runs of fine tuning, then you can also try evaluating and see what the performance is. I checked again, the… How it was doing myself as well, then. Yeah, some changes do help a lot, but I've not played around with the temperature, so I don't do that. So… You did mention that, I could use the bigger models with, what was the specific thing? With Azure Project. Yeah, Azure Project, yeah. I mean, that gives you access to almost all of OpenAI, and, Bigger than what you suspect. Good question. I like to just move along. Yeah, just… just drop me a text, and I'll add you to the actual function. I think you also need the Azure access for the… You're all in Azure. The thing is, I don't think you have access for, like, a resource, and I think… I think you can… We can just dedicate, like, lands on the resource groups. Not sure. But just check, and just let us know. Let me check the resource group as well. It'll be interesting to see what I can do with the biggest ones, and if they kind of, like, go to, like, I don't know, 97, 98%, that would be, depending on the cost, the best approach may be, but I'll have to check that. But also, any, any, any approach on, like, how you can… How we can work on, like, farm building as well. for those, I don't think, you can change the temperature, I guess, but I don't know how you can fine-tune those. So, fine-tuning is basically inference level changes, so what you do is… Oh, okay, okay, yeah, so… Fun in multiple, types of data, and that's the… that's the ground truth for the model, then. Okay, okay. So, because basically a model is nothing but, its main ground truth, if you don't fine-tune it, is basically all of redirected. Yeah, you mean, I thought, okay, I thought I was getting the wrong thing, because in open source models, you can kind of go in… Inference level fine-tuning is what I was talking about. If you mean inference level, yeah, I did do some of that. It was more about the kind of examples that I gave it, that… if you have this kind of input, this is the kind of output that you should have. And there were… I guess there was an entire file that you could use it. But… at least for these models, the result was, like, less than 2% gain, so… I don't know, like, maybe for the bigger ones, there would be huge accuracy gains, but for these, there were less than 2%. Yeah, because Lava 3.1… for the bigger one. It actually just… it was pretty good when it worked, but for some of them, it just outright failed with no amount of, like, giving it examples. Because I was pretty surprised with the LAMA 3.1. It's really good when it works. Even the 8 building parameter model is… that's the one that's pretty sharpened, but it just fails on some things, and I tried many things, not technical, but other things I need to. Also good work, you know, continue to do some more investigation, yeah, very good. It would be, interesting to see what I can do with Teshop. I mean, it's all new things. That's it. Alright, I'm sure. I just have, like, a standing group that has access to… Like the resource group. Just, like, swap out whoever's… Team the time. So, keep doing this, and it hasn't… work once the way I want it. It's Microsoft. I think after you had a person, it was something other things, like. an oddity amount of days ago, I can generate the email for the person. I think it was still their statement. By the way, it was 24 hours to be able to see resources whenever I made myself a contributor on the subscription. Thank you. I mean, they can't even… they're even… like, the operating system itself is kind of going down a little faster. So did I try to move down. Excellent. It was being tested. I mean, even specific things, not vague things, that the performance is going down. There was an outcry recently that they just downgrade your drivers, because they have automated processes in place, which can't figure out that you already have the latest ones, and then they just Kind of repeatable ones, and… Yeah. And that's a very specific thing that you can hear. I think that's all the progress updates we had to share. Do you guys have anything you'd like to add? impressions? Something that will be here. Just probably, I think, when you guys start working with LinkedIn, then I'll probably have a look at what I would say. But otherwise, Right now, things are looking good, like, this is late, and the film is… I'm interested to, like, hear about Leo's work as well, because it's… his work is insanely complicated. I guess it's pharmacy. But other than that, I think we just think we'll be able to do. I'm curious to see an update to be back again. Looks like a lot of progress. Oopsin. Just, just, just make sure you guys drop me a message. We can work ad hoc as well. I feel like it's come to this point where only in meetings the actual points where we get access, or things like this, they should be very, like, async, in my opinion. You should just let us know, and then I'll get to work on it whenever I get a little time in the room. He just… when I pitched on the island. Great, great to see y'all. Wonderful day. Yeah, for sure. Yeah, just wait till the weekend beforehand. Well, it's getting hotter next week. Oh, is it? Yeah, okay.
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+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-06-04-client.md b/minutes/2026-06-04-client.md
new file mode 100644
index 0000000..5f17296
--- /dev/null
+++ b/minutes/2026-06-04-client.md
@@ -0,0 +1,35 @@
+# Meeting Minutes — 2026-06-04
+
+**Date:** 2026-06-04
+**Duration:** 39 minutes
+**Participants:** Hrishik
+**Source:** `GMT20260604-190416_Recording.transcript.vtt`
+**Processed:** 2026-07-20 05:14 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Hrishik | 1 | 5774 | 100.0% |
+
+## Topics Discussed
+
+- **ML/Model** ██████ (relevance: 6)
+- **Data** ██████ (relevance: 6)
+- **Architecture** ████ (relevance: 4)
+- **Infrastructure** ██ (relevance: 2)
+
+## Potential Decisions
+
+1. **[Hrishik]** Okay. So, first we have… On the agenda, the initial OCR endings. So, I did a few POCs with whatever was available out there for the OCR part of things, which is what I'm doing right away. So, I texted
+
+## Potential Action Items
+
+1. **[Hrishik]** Okay. So, first we have… On the agenda, the initial OCR endings. So, I did a few POCs with whatever was available out there for the OCR part of things, which is what I'm doing right away. So, I texted
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 5774 words across 1 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-06-11-cleaned.md b/minutes/2026-06-11-cleaned.md
new file mode 100644
index 0000000..242fe2b
--- /dev/null
+++ b/minutes/2026-06-11-cleaned.md
@@ -0,0 +1,63 @@
+# Cleaned Transcript — 2026-06-11
+
+**Hrishik**: Great. Whatever you have that began. Did I put it on the channels?
+
+**Harsha (eParts)**: Can you see… can you hear us, guys?
+
+**Hrishik**: Yeah, no, we can hear you.
+
+**Harsha (eParts)**: Yeah, I think there was some network issue, we lost power, and everything restarted. Yeah, that was it. We're all back up. But, I think just to continue what Jake was saying, for when we have a lot of features, but we can only map to, for example, like. a few of the features that Eton provides. I would say you should just map to the few features and just show the additional features and empty fields. That's good enough, I would say.
+
+**Hrishik**: Okay, so there's not gonna be any mandatory features on… from ETEM side, like, and even from, I think, PIM's side, there's not mandatory features, right? For a particular product?
+
+**Harsha (eParts)**: Yeah, kind of. I mean, for a product type, we would want a certain set of attributes, but if we can't map to everything, it is what it is, right? So, it's just that the mindset has to be, like, we want to be curious about more data, as in, we want to push our suppliers towards giving us more data. So, for example, if we have, like, a hundred, eaten features for a set, a specific class. And we can only map to 30 of them. We want to map to the 30, and then show that all these 70 are, like, empty, and these are fields you can give us further information on. And that's what it can be.
+
+**Hrishik**: okay, so in that case, I think the mandatory fields part is in PIMS, so we'll have to first map it to those fields so that we know which fields are actually mandatory and not present. Then, after we have those values, we can then go ahead and map them with the ETIN categories and ETIN class and the things.
+
+**Harsha (eParts)**: Yeah, I mean, you don't have to be so restrictive also. So, if, for example, mapping all of these all of the attributes that we have on our system to the ETEM, ETM attributes. If it's… if it's not that cut and dry, then you should probably just… We should probably just, like. Work with what you can do, and that's… just keep the scope within how much can actually be achieved. And leave the rest to, like, oh, this is all… this is all things that we cannot really, like, see data for or map, or… Okay. It's a clear kind of situation here.
+
+**Hrishik**: Okay, I think that clarifies a few things. Let me see if I have more questions… Yep, I think that is pretty much it. So, I was able to find a couple of files that I'm using for the ETEM, like, to analyze what ETEM does right now. the ones that are relevant. I guess those are… Very big names. ETEM 10.0 all sectors, and ETEM 10.0 CSV metrics.
+
+**Harsha (eParts)**: Yeah.
+
+**Hrishik**: What is the latest ones, but… Yeah.
+
+**Harsha (eParts)**: Okay. Yeah, I mean, I would say just stick to a version, and that's pretty much it. I wouldn't stress too much about, like, keeping up to date with the latest one, the latest supplies, yeah.
+
+**Hrishik**: So, 10.01.
+
+**Harsha (eParts)**: Yeah, that's it.
+
+**Hrishik**: forward. Yeah, and last week I told you I'm gonna be working on the schema part of it, but right now there are a few hiccups. So, I'm not very sure if you can put it in the exact PIM schema. Right now, so we'll be probably, doing some sort of a key pair thingy, before we reach them in the pipeline, and.
+
+**Harsha (eParts)**: Okay.
+
+**Hrishik**: So you might have to do some extra work after the pipeline to put it into proper, let's say, proper format before we push anything to pens.
+
+**Harsha (eParts)**: Yeah, I would say take your time just understanding how to map out all of these classes and features to the attributes and the product types. Right. Yeah, that could… that could probably be a lot of help as we go down the line as well. But, I think another thing I wanted to bring up was, I… I would… I just… I was just talking to Jake about this, but, he's… I asked him if he could do, like, a PIMS demo, because a lot of changes have been happening in PIMS for us. Not right now, but, like, probably in two weeks to a month. We want to do a PIMS demo to you guys, so that you guys can know the state of things, of PIMS as well, so that when it comes to the point of, oh, how well can things integrate with PIMS, or, like, how well can things even, like, work with PIMS, then you'll have a better idea standing. Good morning.
+
+**Hrishik**: Yeah, I think that would be pretty helpful. Whenever you guys think we should, yeah, maybe, like, have a demo, like, we'd probably be ready for it.
+
+**Harsha (eParts)**: It could be online, it could be in person, whatever you guys are comfortable.
+
+**Hrishik**: Sure. Okay, I think that's all from our side. That's updates for today. We just need the access as well.
+
+**Harsha (eParts)**: We'll get to it, ideally, next week. Today was kind of hectic. This week has been kind of crazy.
+
+**Hrishik**: But.
+
+**Harsha (eParts)**: tomorrow or next week is what I would say would be good. We already have it on a priority. Basically, every day we kind of keep talking about it, but we just don't have the time to get to it. That's it. Even the foundry access for, Java, and we'll get to it as well.
+
+**Hrishik**: Thanks, thanks. Alright, thank you. Thank you.
+
+**Harsha (eParts)**: That's it. Thanks, guys. Have a great week.
+
+**Hrishik**: See you next week.
+
+**Harsha (eParts)**: Hope summer's hitting you as well now.
+
+**Hrishik**: Yeah, just for the New York.
+
+**Harsha (eParts)**: Okay. See you. Bye-bye.
+
+**Hrishik**: Yeah, bye.
\ No newline at end of file
diff --git a/minutes/2026-06-11-client.json b/minutes/2026-06-11-client.json
new file mode 100644
index 0000000..da27d2f
--- /dev/null
+++ b/minutes/2026-06-11-client.json
@@ -0,0 +1,70 @@
+{
+ "meeting_date": "2026-06-11",
+ "duration_minutes": 6,
+ "participants": [
+ "Hrishik",
+ "Harsha (eParts)"
+ ],
+ "participant_count": 2,
+ "total_words": 977,
+ "total_turns": 31,
+ "speaker_stats": {
+ "Hrishik": {
+ "turns": 16,
+ "words": 344,
+ "pct_words": 35.2
+ },
+ "Harsha (eParts)": {
+ "turns": 15,
+ "words": 633,
+ "pct_words": 64.8
+ }
+ },
+ "detected_topics": {
+ "Data": 4
+ },
+ "questions_found": 0,
+ "questions_sample": [],
+ "potential_decisions": 0,
+ "decisions_sample": [],
+ "potential_action_items": 9,
+ "actions_sample": [
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Can you see\u2026 can you hear us, guys?"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Yeah, I think there was some network issue, we lost power, and everything restarted. Yeah, that was it. We're all back up. But, I think just to continue what Jake was saying, for when we have a lot of"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "okay, so in that case, I think the mandatory fields part is in PIMS, so we'll have to first map it to those fields so that we know which fields are actually mandatory and not present. Then, after we h"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Yeah, I mean, you don't have to be so restrictive also. So, if, for example, mapping all of these all of the attributes that we have on our system to the ETEM, ETM attributes. If it's\u2026 if it's not tha"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Okay, I think that clarifies a few things. Let me see if I have more questions\u2026 Yep, I think that is pretty much it. So, I was able to find a couple of files that I'm using for the ETEM, like, to anal"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "forward. Yeah, and last week I told you I'm gonna be working on the schema part of it, but right now there are a few hiccups. So, I'm not very sure if you can put it in the exact PIM schema. Right now"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Yeah, I think that would be pretty helpful. Whenever you guys think we should, yeah, maybe, like, have a demo, like, we'd probably be ready for it."
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "We'll get to it, ideally, next week. Today was kind of hectic. This week has been kind of crazy."
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "tomorrow or next week is what I would say would be good. We already have it on a priority. Basically, every day we kind of keep talking about it, but we just don't have the time to get to it. That's i"
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-06-11-client.md b/minutes/2026-06-11-client.md
new file mode 100644
index 0000000..5d3f953
--- /dev/null
+++ b/minutes/2026-06-11-client.md
@@ -0,0 +1,37 @@
+# Meeting Minutes — 2026-06-11
+
+**Date:** 2026-06-11
+**Duration:** 6 minutes
+**Participants:** Hrishik, Harsha (eParts)
+**Source:** `GMT20260611-193009_Recording.transcript.vtt`
+**Processed:** 2026-07-20 05:14 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Hrishik | 16 | 344 | 35.2% |
+| Harsha (eParts) | 15 | 633 | 64.8% |
+
+## Topics Discussed
+
+- **Data** ████ (relevance: 4)
+
+## Potential Action Items
+
+1. **[Harsha (eParts)]** Can you see… can you hear us, guys?
+2. **[Harsha (eParts)]** Yeah, I think there was some network issue, we lost power, and everything restarted. Yeah, that was it. We're all back up. But, I think just to continue what Jake was saying, for when we have a lot of
+3. **[Hrishik]** okay, so in that case, I think the mandatory fields part is in PIMS, so we'll have to first map it to those fields so that we know which fields are actually mandatory and not present. Then, after we h
+4. **[Harsha (eParts)]** Yeah, I mean, you don't have to be so restrictive also. So, if, for example, mapping all of these all of the attributes that we have on our system to the ETEM, ETM attributes. If it's… if it's not tha
+5. **[Hrishik]** Okay, I think that clarifies a few things. Let me see if I have more questions… Yep, I think that is pretty much it. So, I was able to find a couple of files that I'm using for the ETEM, like, to anal
+6. **[Hrishik]** forward. Yeah, and last week I told you I'm gonna be working on the schema part of it, but right now there are a few hiccups. So, I'm not very sure if you can put it in the exact PIM schema. Right now
+7. **[Hrishik]** Yeah, I think that would be pretty helpful. Whenever you guys think we should, yeah, maybe, like, have a demo, like, we'd probably be ready for it.
+8. **[Harsha (eParts)]** We'll get to it, ideally, next week. Today was kind of hectic. This week has been kind of crazy.
+9. **[Harsha (eParts)]** tomorrow or next week is what I would say would be good. We already have it on a priority. Basically, every day we kind of keep talking about it, but we just don't have the time to get to it. That's i
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 977 words across 31 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-06-18-cleaned.md b/minutes/2026-06-18-cleaned.md
new file mode 100644
index 0000000..8c024fe
--- /dev/null
+++ b/minutes/2026-06-18-cleaned.md
@@ -0,0 +1,127 @@
+# Cleaned Transcript — 2026-06-18
+
+**Hrishik**: Okay, so primarily, what we wanted to discuss was things around Azure access. Arjun Jay, you had a question about Arjun? As of Michael. Okay. So The only thing for me was, I can get started with the work, but… There are no limits, I think, to the account itself in Azure. So, if you could, if I could know around how much I could spend, or you could just put a limit in for the amount of training, or… and, like, on the group, or, like, for me specifically, that would be great.
+
+**Harsha (eParts)**: David, I think… so they were asking about, like, any limits around how much they can spend on the, Azure group that we created for them, or how they want to use it. What's the ballpark right now?
+
+**Hrishik**: We don't know yet. So, we didn't want to start working on it, unless we had some kind of, cost monitoring or, like. some kind of cost cap, so that we don't overspend. Like, you can set a cap, whatever kind of cap you like, and I can start, and then I can give further feedback. I just didn't want to start, and there is a risk that it may be more than expected, so I didn't start on that.
+
+**Harsha (eParts)**: Well, I mean, my gut's saying right now, you know, a thousand bucks a month for… is… is fine. If the ongoing cost is that, that might not be fine. So, like, right… I'm imagining that this process is going to be very training-heavy to start. And then there's gonna be some sort of… ongoing… Costs or spend. I'm… I'm pretty open to… A higher amount. During this development time, any sort of ongoing cost would be much more difficult to justify. I will make that a to-do… And… get back to you. But I'm saying, like, is a… Is 1,000 ridiculously low? Is it way too high? Like, what are…
+
+**Hrishik**: That's way too much. Actually, I was, yeah, yeah, I don't think it… this is preliminary, I cannot give again, but I don't think it should go anywhere near that. So, yeah.
+
+**Harsha (eParts)**: Sorry, can you… I'm playing on your speakers, I'm having trouble hearing you.
+
+**Hrishik**: Yeah, yeah, I don't think, right now, this is preliminary, but I don't think it should go anywhere near that.
+
+**Harsha (eParts)**: Okay, yeah.
+
+**Cliff (Mentor)**: Are you thinking in the hundreds of dollars, is what you're thinking?
+
+**Hrishik**: Yeah, it would be great if you could just set a cap on the group, directly, just because these things are… it would be good as a precautionary measure, just so that there's no overflow, it, like, overruns. So if you could set it on the group policy, or for me individually, any of that would be fine.
+
+**Harsha (eParts)**: Okay. I would also recommend that any sort of resource that you guys plan on using, or anything that you do, you should probably start off with, like, the minimum computes for it, and then keep scaling up, because that is how It is sure we kind of try to keep the costs low for the range. And I would, I would not recommend, like, just setting an estimated, an estimated, compute that you think would be… would give you the best performance for things. Rather than that, you just put the lowest one and then keep scaling up from there.
+
+**Hrishik**: Yeah, no, I was thinking of something along those lines, but the thing is, especially with the newer models, there's a tendency for it to, kind of, the cost to go hired pretty fast, so that's why I was asking for the limits, just as a…
+
+**Harsha (eParts)**: Are you planning on deploying the models on, like, the… like, the resource group, or are you planning on spinning up, like, Container App Center and doing it, or… using GPUs for it.
+
+**Hrishik**: No, no, no, not right now, I'm not planning to do that.
+
+**Harsha (eParts)**: Whoa. Just, just making sure, because if you want to do any models, I would say AI Founty is the way. I was looking into adding you to, like, a separate AI Founty thing, should be done probably today. But that is an account which is… if… if basically you're doing anything through the UI for your nesting, it should be completely free from what I know.
+
+**Hrishik**: Sorry, Harsha, could you repeat? I can barely hear you.
+
+**Harsha (eParts)**: Sorry, can you hear me now? Hello?
+
+**Hrishik**: Yeah, yeah, it's better now.
+
+**Harsha (eParts)**: I'm not sure what the problem is. It looks good from my audio, but I don't know why. Okay.
+
+**Hrishik**: Yeah, yeah, go ahead, go ahead.
+
+**Harsha (eParts)**: Yeah, so, what I was saying was… If you're planning on deploying GPUs and just running a model randomly, I would highly advise against that. Mainly because that is probably not the way to do it. I was looking into adding you to the Azure Foundry, like a Foundry membership, so that you could just use the UI part of things. Where you can navigate within boundary, go to a model, see, test by sending a few messages to it. Using the UI component of Azure Foundry is negligible cost, or free for the most part.
+
+**Hrishik**: Okay, I did not know that.
+
+**Harsha (eParts)**: So, chatbot sort of, chatbot sort of interface to actually try and test things through it. Because it's mainly meant for testing and just trying, it's mainly free for that reason. It also gives you an API. When you use the API, it starts charging you.
+
+**Hrishik**: Got it, got it. Yeah, for testing, a few I could do manually, just to start off and see for myself. But my… like, I have had some experience with using, AWS for this kind of stuff. And, setting a cap really helped me there, just because, there are, there is a bit of a risk of it going kind of a… a bit too high, so that's why I'm kind of asking for that. That's it.
+
+**Harsha (eParts)**: Yeah, I totally agree with that, though. It's something we should do.
+
+**Hrishik**: Yeah, because if I set a, like, a few hour training run.
+
+**Harsha (eParts)**: Okay.
+
+**Hrishik**: if I, kind of, I don't know, maybe I'm, for one hour, I'm not looking at it carefully enough, then it can kind of spike right then.
+
+**Harsha (eParts)**: Yeah, that's the thing. I mean, with ML in general, I would say only do it when I think you have, like, your attention towards that task. Because the problem is, especially when you're running training or inference, which just tends to run forever, unless you actually stop it and you're constantly looking at the metrics to monitor how it's performing and then manually stop it. I would recommend not doing it at all. Yeah. But in the.
+
+**Hrishik**: No other.
+
+**Harsha (eParts)**: Sorry, no, I'll just go on, yeah.
+
+**Hrishik**: I, yeah, I'm just, this is just for, precautionary, but, I would, yeah, I would prefer to, I guess, go about it this way.
+
+**Harsha (eParts)**: Yeah.
+
+**Hrishik**: Oh.
+
+**Harsha (eParts)**: David already set a limit, by the way, I thought he's not. So, to 1,000, you said, yeah. Your forecast right now is 115. So…
+
+**Hrishik**: Okay, okay. That's it from my side. And that's all you had for the Azure questions. In the last week, we've been working on the task we told about. There has… there were some project management kind of work, which we had to do, so that took a bit of the time, but yeah, we are… Steady progressing towards the… Next, I'll say milestone. Yeah, I think, probably by next week, we might have… something to show from the OCR and engine part. Yeah, that's something to look forward to. I think, yeah, that is pretty much all the updates for this week. Many questions from you guys?
+
+**Harsha (eParts)**: kind of nothing for now. We were mainly expecting just, to catch up on the updates for things, and that's leadership.
+
+**Hrishik**: So, right now, we were working in parallel for our different modules, so now what we're doing is we're putting all the code on… Bitbucket, and we're gonna start compiling things which are ready to be compiled. Like, a few parts of OCRN ingestion can be compiled, so we'll be doing that next. along with that, we're expecting the LLM POC to finish soon. Then, with those results, we can move forward and see Where to go next, and then we'll probably have more manpower towards the other tasks. Well, let's just progression further, quickly.
+
+**Harsha (eParts)**: Nothing, nothing much from my end, other than that, It's, I'll… I'll probably… if you have some time, sometime early next week, like Monday, Tuesday, I'll probably just sit with you for, like, a few minutes, show you around Azure Foundry, and just, Just, just, like, get you up to speed on how to do things from that.
+
+**Hrishik**: Yeah, that would be great. So, you could send me your time, or… I'm free at, on Monday, after, basically, 9.30, the entire day.
+
+**Harsha (eParts)**: to Monday, any time in the day is good.
+
+**Hrishik**: Yeah.
+
+**Harsha (eParts)**: Okay, I'll send you an invite then for it.
+
+**Hrishik**: Yeah.
+
+**Harsha (eParts)**: It should be short 15 variables, I think.
+
+**Hrishik**: Yeah, I just… I've not worked with Azure before. I mean, I know a little bit, but not…
+
+**Harsha (eParts)**: Huh. Okay, that makes sense. We'll do that, and yeah, that's pretty much all your updates to me as well. let, like, are there any, sort of roadblocks with ETEM using it? I know we discussed last week on, like, the issues you guys had with Trying to, trying to kind of create the connection between, our databases and intermitt.
+
+**Hrishik**: Right now, I think we need to do a little more research. Like, research, and like, I would say it's a roadblock, we're just trying to figure out how best to integrate ETM, at which part of the pipeline, so that it's most useful to you guys. Yeah, I think… and plus, yeah, we'll have to, do some sort of mapping with the existing PIMs. I think you mentioned you guys are already doing that, or something on the similar lines? You can map into the Azure products in your catalogs.
+
+**Harsha (eParts)**: No, no, no, we're not doing that yet. What you're talking about in… about that last week was… This is something that would be a value add when we give it to our future clients, which is, mapping our internal product types and attributes to the TIM class.
+
+**Hrishik**: Okay. Yeah.
+
+**Harsha (eParts)**: Yeah, bad. So Jake, I think, is currently putting in placeholders for pimps, which will help us do it. But other than that, there's no reward being done. Placeholders hasn't just had provisions on the UI side of things to see different versions work, right?
+
+**Hrishik**: Okay, so yeah, from our side, we are still figuring out how to make the ETEM integration most useful. Like, we have a few things we can do, but yeah, we're still figuring out how best to integrate that part.
+
+**Harsha (eParts)**: Makes sense.
+
+**Hrishik**: But yeah, we do have all the data that's required for eating. the particular files we have. Like, we've gone through data, it looks, it's pretty well formatted, so it's a lot, but… It's, easy to comprehend what it is.
+
+**Harsha (eParts)**: Okay. Then that's pretty much it then. Any other updates, you know, from, like, Rosia or any other… any other part of things?
+
+**Hrishik**: From the OCR and ingestion, the, like, there's no particular updates, but we're just trying to, I'll combine both of them, integrate them together. So, we haven't gotten to that yet, because I still have to finish the OCR on Azure, and I was looking at Azure Document Intelligence. And, I think it also has one more thing called Azure AI Document Intelligence, or something like that as well. So I was just looking at the pricing, and we just wanted to get more clarity on the, spending cap, and I think we can, finish that by tomorrow, max. Once that's done, Rishi has given me access to the ingestion repo as well, then I can, integrate it with the ingestion repo, and then I think by next week, we should have the OCR plus ingestion pipeline complete. like, after we have a few, like, we'll run it a few times with the, what do you call it, the PDFs, and, like, we have to still have to figure out… we actually know, we have to kind of implement the… way we're gonna pass that data to the ML module. So that indication will be next. First, those CR indications, then… ingestion to, I mean.
+
+**Harsha (eParts)**: Okay, excellent. No, I'm coming off of, like, 6 hours of meetings, so I'm… I'm terribly hanging on here, to be honest. Yeah. Please let me know what else you need. You guys, you guys were able… right, yep, you were able to get the resource group and add to it. Let me know what else I can do to unblock you. Or whatever questions I can answer.
+
+**Hrishik**: Yeah, we're good for now. We'll probably message you on Teams if we have anything.
+
+**Harsha (eParts)**: Yeah, I would… I was just gonna say that. I was gonna recommend that if you have any sort of questions, just… Because ad hoc, because I think, I think it's getting to this point where we only catch up on the weekends, and just having one of our meetings, and if there's anything I have blocking you, just let us know. I think, I think, the only other blocker in the last one or two weeks was the Azure Access, and that's it. Yeah. Since that is… since that's resolved, if you have questions on, like, deploying things, understanding. any nuances in things, I would say ping me or David, or, we should be good. We should be able to help you out.
+
+**Hrishik**: Yeah, definitely we'll do that. I think that's all for the meeting.
+
+**Harsha (eParts)**: That's it. I see you guys, then. Nice seeing you. Nice seeing you, Cliff.
+
+**Hrishik**: Yeah. You guys… Yeah.
+
+**Harsha (eParts)**: You guys…
\ No newline at end of file
diff --git a/minutes/2026-06-18-client.json b/minutes/2026-06-18-client.json
new file mode 100644
index 0000000..5ea5c6c
--- /dev/null
+++ b/minutes/2026-06-18-client.json
@@ -0,0 +1,88 @@
+{
+ "meeting_date": "2026-06-18",
+ "duration_minutes": 16,
+ "participants": [
+ "Hrishik",
+ "Harsha (eParts)",
+ "Cliff (Mentor)"
+ ],
+ "participant_count": 3,
+ "total_words": 2328,
+ "total_turns": 63,
+ "speaker_stats": {
+ "Hrishik": {
+ "turns": 31,
+ "words": 1168,
+ "pct_words": 50.2
+ },
+ "Harsha (eParts)": {
+ "turns": 31,
+ "words": 1148,
+ "pct_words": 49.3
+ },
+ "Cliff (Mentor)": {
+ "turns": 1,
+ "words": 12,
+ "pct_words": 0.5
+ }
+ },
+ "detected_topics": {
+ "ML/Model": 3,
+ "Architecture": 3,
+ "Data": 3,
+ "Onboarding": 2
+ },
+ "questions_found": 0,
+ "questions_sample": [],
+ "potential_decisions": 1,
+ "decisions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "Right now, I think we need to do a little more research. Like, research, and like, I would say it's a roadblock, we're just trying to figure out how best to integrate ETM, at which part of the pipelin"
+ }
+ ],
+ "potential_action_items": 21,
+ "actions_sample": [
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Well, I mean, my gut's saying right now, you know, a thousand bucks a month for\u2026 is\u2026 is fine. If the ongoing cost is that, that might not be fine. So, like, right\u2026 I'm imagining that this process is g"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "That's way too much. Actually, I was, yeah, yeah, I don't think it\u2026 this is preliminary, I cannot give again, but I don't think it should go anywhere near that. So, yeah."
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Sorry, can you\u2026 I'm playing on your speakers, I'm having trouble hearing you."
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Yeah, yeah, I don't think, right now, this is preliminary, but I don't think it should go anywhere near that."
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Okay. I would also recommend that any sort of resource that you guys plan on using, or anything that you do, you should probably start off with, like, the minimum computes for it, and then keep scalin"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Whoa. Just, just making sure, because if you want to do any models, I would say AI Founty is the way. I was looking into adding you to, like, a separate AI Founty thing, should be done probably today."
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Sorry, can you hear me now? Hello?"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Yeah, I totally agree with that, though. It's something we should do."
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Sorry, no, I'll just go on, yeah."
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Okay, okay. That's it from my side. And that's all you had for the Azure questions. In the last week, we've been working on the task we told about. There has\u2026 there were some project management kind o"
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-06-18-client.md b/minutes/2026-06-18-client.md
new file mode 100644
index 0000000..6e7789d
--- /dev/null
+++ b/minutes/2026-06-18-client.md
@@ -0,0 +1,46 @@
+# Meeting Minutes — 2026-06-18
+
+**Date:** 2026-06-18
+**Duration:** 16 minutes
+**Participants:** Hrishik, Harsha (eParts), Cliff (Mentor)
+**Source:** `GMT20260618-190618_Recording.transcript.vtt`
+**Processed:** 2026-07-20 05:14 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Hrishik | 31 | 1168 | 50.2% |
+| Harsha (eParts) | 31 | 1148 | 49.3% |
+| Cliff (Mentor) | 1 | 12 | 0.5% |
+
+## Topics Discussed
+
+- **ML/Model** ███ (relevance: 3)
+- **Architecture** ███ (relevance: 3)
+- **Data** ███ (relevance: 3)
+- **Onboarding** ██ (relevance: 2)
+
+## Potential Decisions
+
+1. **[Hrishik]** Right now, I think we need to do a little more research. Like, research, and like, I would say it's a roadblock, we're just trying to figure out how best to integrate ETM, at which part of the pipelin
+
+## Potential Action Items
+
+1. **[Harsha (eParts)]** Well, I mean, my gut's saying right now, you know, a thousand bucks a month for… is… is fine. If the ongoing cost is that, that might not be fine. So, like, right… I'm imagining that this process is g
+2. **[Hrishik]** That's way too much. Actually, I was, yeah, yeah, I don't think it… this is preliminary, I cannot give again, but I don't think it should go anywhere near that. So, yeah.
+3. **[Harsha (eParts)]** Sorry, can you… I'm playing on your speakers, I'm having trouble hearing you.
+4. **[Hrishik]** Yeah, yeah, I don't think, right now, this is preliminary, but I don't think it should go anywhere near that.
+5. **[Harsha (eParts)]** Okay. I would also recommend that any sort of resource that you guys plan on using, or anything that you do, you should probably start off with, like, the minimum computes for it, and then keep scalin
+6. **[Harsha (eParts)]** Whoa. Just, just making sure, because if you want to do any models, I would say AI Founty is the way. I was looking into adding you to, like, a separate AI Founty thing, should be done probably today.
+7. **[Harsha (eParts)]** Sorry, can you hear me now? Hello?
+8. **[Harsha (eParts)]** Yeah, I totally agree with that, though. It's something we should do.
+9. **[Harsha (eParts)]** Sorry, no, I'll just go on, yeah.
+10. **[Hrishik]** Okay, okay. That's it from my side. And that's all you had for the Azure questions. In the last week, we've been working on the task we told about. There has… there were some project management kind o
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 2328 words across 63 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-06-25-cleaned.md b/minutes/2026-06-25-cleaned.md
new file mode 100644
index 0000000..e64850f
--- /dev/null
+++ b/minutes/2026-06-25-cleaned.md
@@ -0,0 +1,3 @@
+# Cleaned Transcript — 2026-06-25
+
+**Hrishik**: Labor Premier. And they don't have air conditioning on sports. Okay, I guess, we can get started. I did not… I wasn't going to send an agenda, because there were a few things changing last moment. As to what Planet discussed. So primarily, I'll give you an update of how things are going on. We'll all go around, I'll handle the engine part, and then ML and LLM will go from there. So, for the ingestion part, Like I said, the next task was gonna be to… map the schema, but when I was analyzing the ETEM work that you have to do, so before I can finalize the schema, I had to kind of integrate ETEM into the engine part, so that, like, load all the data from ETEM, like, separate out all the, like, classes, product types. And things like that. So, currently, I have done that part, and before we can have a proper schema that we can possibly push to the other module, we need to finalize more details on the ETM side. Like, it is, like, it's not something we need help in, it's just something that'll require a bit of work. So, I'm not sharing anything. Like, I don't have much to share. I… I did get Todd to make me an update of how many, like, what sort of data we have. kind of, ingested, sir. Where did you get the, ETM data from? Oh, from the site. Did you guys get a subscription, or, no, that's completely e-class. Oh, I'm thinking of E-Class, okay. We're using ETEM 10.0. Got it, one sec. Oh… So this is the high level of it. the, like, varsity product drops around 600 product classes and features, so now we have all that mapped out to a, like, local, database, so whenever we need to… because we cannot really… map these values to the, like, stuff we'll get from the OCR, because you don't want to modify the data before we hit the ML pipeline. So, we have data ready, which, we want the ML pipeline to use later on, so that we can map the accurate ETEM values for the product, class, all those things. So, we plan to do… that after we have done, coordinate score, we'll also match it to a ETEM standard. So, the output would also have, let's say, a couple of ETM columns with the ETEM ID, or whatever flags are necessary. So, this is the, kind of, the groundwork for that. after this, probably, what's gonna happen in the initial part is, Arjun is currently working on the integration with OCR, There's some, like, headway there also. After the ETEM standard is ready to use, that's when, looking into… including that, I'll create a schema of what we're gonna push. And like that, that's gonna be the next thing after the 18 work is complete. This was not, like… I thought it was gonna be pretty small, but the… trying to integrate ETEM is a bit… not tricky, but a bit lengthy. So, yeah, that is primarily what I had been working on. And, like, I don't have any questions right now, it is pretty straightforward, but… I think later on, we might have to, like, after we have the initial OCL initial part combined, we might have to, like, if it's working, we might have to have someone, like, review it once to see what's missing, what's not. Yeah, so did you have to derive the schema, or did they provide you a schema to look at their data? Schema… like, ETEM? Yeah. ETEM is basically, it's a… It is kind of like mapping, like, we have actuators and valves, so it has things like this. electrically controlled two-way control valves, so the ETEM code is EC104408. So, these are, like, kind of the global standards which are following. So, all of these, they have different standardized values, and when we… we are planning, when we see one of these values, we'll map it to our ETEM code that we have. There is no standardized schema, but it's gonna be, like, we'll be mapping what we get to the item values. We just need to store all this data in a database so we can query it as and when we need it. You're storing all the EDAM data in a database so you can query data, right? This is just some brief overviews of your examples. Nothing much higher. Correct me if I'm wrong, is there… the code, does that also translate to other standards, like eClass? Like, I know that there's a way that they translate to each other with a shared value. Is it that same, like, EC… Do you have an available phone? I've not, looked into that, but you're saying that if you have EDUM data, and if you have e-class data, there's a way you can convert… Yeah, someone's already gone ahead and, like… There's a one-to-one mapping. Yeah, there is a one-to-one mapping, and we'll… like, whatever field is used to those, we'll probably also want to use that field to map, like, as the key, if you will, to the ePARS data. I imagine we'd want to start storing that. Okay. Universal code against our product types and categories. eTime is what we want to use overall, but ideally, like, we'll also have a database of eClass at some point, so if we have customers who use eClass, they can… almost like they're translating a page to another language. E-Class is ENClass, right? Yeah. I'll take a look at that. I mean, the, like, end goal is, as Jake mentioned, and it's still, like. have support for that. Yeah, but we as eParks want to kind of make our standard moving forward mostly based on ETIM. We might have an additional thing or change something, but it's based on the ETIM. Okay. And what's the scores of the class in these periods? Two classes, the European one? Okay. Then there's another one, too. UNSPC? UNSBC system. I know what's happening. It's a point of this, but… Yeah, I think that is about the update I have for the… In Spark. Okay, I can go next. See, there is a direct marketing, but it's not a one-to-one. It's a one-to-one translation by somebody. Yeah, they've been trying it since 2001. Wow, that's… to getting different standards organizations on the same page. So, this is the main goal, like, you guys come up with the final standard of the U.S. I think ET was probably mostly video sites. Okay, I can… Hiking around. So… I, I looked through what Harsha had, shown me, and… This is… oh, yeah, sorry. So… This is not… sorry, this is GPT 5.4. I could, like Hashan said, I could not use 5.5 because… yeah, so… I just, gave it 2,000 exam… of the examples. And it came back with around, it came back with 81.45% accuracy. Its confidence is pretty good, it's 94%, so it's… when it says that it's 80% sure in an answer, it's, the chances of it being wrong is what they say it is. There's also an ECE calibration error, but that's, I think, on another page. The token usage for 2,000 examples was, 320,000 tokens. And… So, this is how its confidence was basically distributed. She probably got a kid to go convincing it. So… It looks at, it looks at… the… It gives the confidence scores for each of its predictions. That is a bit of text. Yes. And then… then we can just check against them. So… The thing is, it still, likes to almost, always give a very high con… between 90% to 100%. Yeah, and it's always super confident, but this is much better than what the smaller example I just showed you. It will very rarely, like, you can see that when it gives a… smaller score, it is half of the time it is wrong, so there are still issues, but I can actually show the rest and… And this is the reliability versus, I would say. How good it gets as we increase the number of examples. It does take, quite a huge number just to… the perfect calibration would be the dashed line, so it does take, around… It's in the thousands of examples. I did not test with on thousands. This is just, how I picked it. What's the price of GBD 5.4 again? Did you take it a bit? I… I did try to, see how much money it was, showing, but it only showed the number of requests. The money amount was always zero, no matter how many requests. No, that's because it's the playground. But, no, no, as in, you can just look at the price they charge on the Discover page. I see. You can… they literally list down all the different pricing models, but I can't want any problem. I… I can… it's, I… it's, the… I do have the token total, so I can just divide and, like, give it to you. So it's… 160-ish tokens per… and then… I think it's better if I gave it in terms of thousand, otherwise the not value would be transparent. Yeah, I mean, total number of tokens mapped to dollar value would actually be a better estimate of, like, how much… how feasible that solution actually is. It's $2.5 per 1 million input total, and $15 per 1 million input. So, input and output, yeah, okay. I can convert that. Deep… this is DeepSeekv4. Before this, I did try to use Cloud. But Cloud 4.8, had… it's just not available, I cannot deploy it. 4.7… these are all of those. 4.7, it said insufficient quota. 4.6, it also said insufficient quota. Finally, I was… I tried to use Sonic 4.6, but that just is not available in the region, so I just kind of gave us at that point. So, this is for deep-seq V4. Ignore the V2, the first one had some errors. So, this is also for the 2,000 examples. It's pretty similar, that one was, there's just a 1% difference, it's also 82%. The token amount is also similar. It's also 350, that was 320. And this is the calibration, so it gets 13% of the… Basically, its confidence scores are wrong, so… And finally, this is Kimi. This has, I would say, the highest accuracy, and its confidence is also… its confidence scoring is also good, its calibration error is the lowest. But it uses, I don't know why it uses, like, more than twice the amount of tokens, it's 840s. Can I check the price. This is all on Azure. Yes, these are all on the… So… Yeah. basically, it's the best, I think, of all of them, especially for the calibration error, and… I can add another column with the prices and stuff. But the thing is, 83, 82, and 81%, these are much below what Leo has. His is 95.1 or something like that. So… and… Even though the calibration errors are 11%, or at the… at the least, the thing is. That would still mean a lot of rework, and… So I don't think, overall, that this is, suitable. Yeah. Okay, then that's actually lower than the rest, even, even if it's, like, two and a half times the total usage. So, these are, I think, the… these are the most latest models that I had. There's also Rock, but that just did not give good results, so I did not even bother including it. And plot, as I told you, nothing worked, and I didn't want to go to, like, 4.5 or something weird on here. So these were the latest three that I could use. If they have higher models, they… like, the more late, or, I guess, current models, those don't work. Yeah, I think one of the reasons why we keep catching the insufficient thing was Azure actually provisions models based on how much the water usage is within the company. for their APIs. So if you actually have a very, very high API usage for them, they kind of provision, like, better models, because they know that these guys do. Okay. Because we don't really use that much… that much of, like, self-provisioned AI within the company, or any sort of features we offer. It's just one single appointment, let's say. So that's… that's probably the reason why we were… So… Yeah. it turned out to be pretty cheap. I was thinking that it would take more attempts, but… when you told me about the playground stuff, that helped me, like, run through a lot of, errors, because if I had done all the 2,000 examples without those, like, initially, I just fed in 20 examples with structure, so I could see if it was giving kind of the right output, and that helped a lot. So, in total, like, You can check them on, but it's Gale. And… I can kind of confidently say that with 2,000 examples, the It's a very high confidence. The statistical power of the test is enough that I can say that it's much more noise. It's in that no amount of tweaking will get 83 to 95. Yeah, and there's no point. I mean, if you can't… like, for 2000 examples, if we were to cross a certain threshold, then it's something else, I think it's just best invested value, so… You've done your due diligence to investigate this aspect. I've tried every sample available on that service. That is it? I don't know, Ryan. I think we didn't move forward from the LLM part. Yeah, no, move forward, I mean, like, we can drop the POC. Yeah, we didn't know. We'll show up, Pete. Alright, I don't have anything to show, but, like, I finished my POC on my first So use document intelligence, which is, yeah, Azure's, like, inbuilt in. Along with that, I use GPT4 only, from Foundation, and And it was not able to be, Chandra, which was on Data Lab. I think I mentioned this last week, before the 9 export for CS service. But then I kept… so one… one issue was that, the prompt. So, document information was able to extract everything, but the LLM wasn't able to, like, properly classify them, so that's where the problem started. Yeah, I tried with 4.0 after, 4-0 minutes, but it was worse, so I don't know what went up. Then I made the prompt a little better, a little more work, and then, the error rate back down. Then what I did is I, combined both Azure Kodomary and Photos, and then I took a union set of both of them, and that gave the least amount of evidence. We almost came down to 2%, and, Chandra updated that was 4%. So, two personality was okay, but I wanted to go lower. So, I had Dockering running Looply, that's an open source stuff, mostly running loop. So it, ran document intelligence first, and then passed it on to, Poro Mini, Doppling, and, Poro, and got the union set up there, and that got it down to 1.3%. So that's the lowest I've gotten now. And I can still keep going till, like, I don't know, like, 0.5% is where I think I feel comfortable, so that we don't need any human intervention there, because we use photo, though. Just probably use 5 Mini, because that's the cheapest model that GPD offers. Oh, yeah. Oh, I thought because… I just started with 40, because… I think GPD5 Mini is one of the most widely used, like. APIs injected for them. Okay, so they actually give the best price for primary. I would just say, like, look at the Discover page, see all the prices, and I would say, just based on that, just, like, okay, yeah. Because you can even go with something higher as well, but, it all depends on how easy it gets, right? For how many tokens… how many tokens are actually consumed versus… Right, so much it has to use. So, looking at the full catalog, which is, like, 5,000 documents, and it comes to 25,000 pages, yeah. So, we're going with this two-reader thing, which was 4AM mini plus 4AM. The entire thing would be done in, like, 500 to $750. It's a one-time thing, and then we don't have to worry about it. And, the third reader also, docking is packaging, you can run it… On a VBS or something. Yeah, so, but I'll try out with, 5… 5 minutes, 5 minutes. Okay, yeah, I'll try it out with 5 minutes, and then see what it is. So what was the cost again? 5 Mini? No, per his result for the photo. Oh, for photo mini plus 40, it comes to $500. To find it to 750, that's the problem. That's if I do both of them together and get the unions, so that I can reduce the error percentage. But that's for all the documents we have. That's all the documents, right? So that's a… would be an infrequent cost, right? It's… yeah, it's a one-time cost. It's a one-time cost, and then, like, whenever the documents come in, it would probably be, like, $1,000 Yeah, that's pretty much what I had, along with that, I got Rishi's, report English. And, like, we had done some work on TestRack, which was another OCR engine, but, like, I'd also done the same, one. That's just for, like, mock purpose, so I can continue my work. Yeah, so I need to remove TestRack, and then I need to plug in this one. And then, once that's complete, the integrations fully done, and then we can continue working on the… Yeah, ETAM and the schema thing is that we push to email. Yeah, ETAM and eta. So, one more thing we wanted to know is, downstream of Aglestion, we are storing… I think right now, locally, we are testing out with Postgres. So, how are we planning to, store things? Like, I think we mentioned about some kind of a staging table that you can get. Yeah, it's the exact same, did we not get the staging schema? Maybe not. We… that is schema, we don't have a place to store it. Yeah, the staging tables will be where we want to store the output of all this. Yeah, but, I guess, is that your question? Like, what tables? Yeah, yeah, if all the… anything that ends in, underscore staging is… Okay, and we get to view that on our job at the stage? I… we can give you access for that. Okay, can you make, like, a duplicate of this table? Duplicate, exactly. Also, the current… the current PIMS is a Microsoft SQL Server. Okay, no, but we will be moving to Postgres, like… Okay, like, I'm doing a big rework for PIMS right now, and I have a Postgres version of it. The tables are a little different, so… Okay. Okay. We'll plan for it to be Postgres. field of staging that we can give them that. I think we… yeah, we should do that same… because my schema's a little different. It's mostly the same idea, there's a staging table for every, like, normal product table, and yeah, there's a couple fields that we want to be simplified. Okay. So, we'll continue working on post itself, since you have… So, one more thing is, like, We need to post this code somewhere, like, I don't know what… where do you… Right now, for, like, the ingestion needs to run on some kind of a server, okay? We're seeing the code exposed so that they can talk to each other. So, we need some place to run this code on, so how do we… the Azure resourcing you have access to? Yeah, you could probably split up a container app. Oh, okay. Yeah, I was just asking, there's some container that whether or not you would… the resource actually gives FPG access to create more data. Okay. Okay, we'll just try spinning up a very basic container for working on that. And… So right now, you just want us to continue working locally on a Postgres basis. Yeah, that's fine. I mean, we need to get you the updated schema, but yeah, that's totally fine. That's what the end goal will be, is outputting all this… Yeah, I think, I mean, we could probably just wait to get them into post-list, and we can report them, and once it's finalized from the end, we can actually give them the… Yeah, I don't think… So I think the schema's finalized on my end. I'm still… I'm still doing stuff with the actual, like, rework of the applications and work with the schema, but I think… I think that schema will be useful for the staging part. Like, right now, we have not reached that yet. We are, like, that will be after the ML part, before the staging. In a month or a month and a half, we did both provision, like, a… Yeah, yeah. I think for now, like, we are using poster, because it's just for our internal use, so that we can, have the data stored and communicate in the components. So, I'm still confused as to where this EDIM thing lands in. So, does that also get stored in some kind of a table? Niosity. Oh, so that's a separate table? It would be another table, and it maps to the tablet. I'd be curious, actually, we should… I'm curious… I've kind of made something intermediate for kind of mapping and some tables, and users can go in and define that, like, this ouch controls category equals this ETINS category, and… But, I'd be curious how you're doing it, too, and maybe come up with what's best for, like, a final thing. I'm still flexible on what we're implementing right now with standards and the PIMS rework, so… Maybe you guys are doing a better way of storing those mappings as you start to play with that right now? So what we're doing right now is basically, like, ETAM already has a very distinguished list of, like, classes and prototypes. So, we're just storing that particular data in our, like, in our database, and then we are gonna, like, we haven't figured that part out yet, how we're gonna map it, but we don't have any manual input as of right now. So, the mapping part will be handled by the ML module. So, but if we need to have a, like, some sort of a… Well, we want to store the results of that ML model that that map is using for the feature. So that, that, like, we're not mad about the schema yet, but I was thinking about something, like, along with the, like, we have the product, prototype, the attributes, and each of those will map, have their own, let's say ETEM IDs, that'll be a separate table. Yeah, so the way I'm doing it right now is kind of what you're saying, like, the categories table, the attributes table, there's just an additional column on there for standard ID, and it just maps to a separate table where we, you know… Yeah, something like that, yeah. And that way, you can have all of them in one single table, but within that table is the ops, or the eParts Unified Standard, or the e-standard, or E-Class, and more standards. And we can sort the mapping into the network table, yeah. And then, as long as you map ETM product type to the bar's product type, that's enough for the guys. So that could be something people said that that should open those. Yeah. I think that, like, after Devon model's trained on the Ethernet as well, like, we should have a good nodes. So, is that the same ML model that we split? That, like, that we have to still figure it out. If it is best we do it in the same one, or we have another component which does the EPA mapping. Right now, we'll have the content scoring, we'll have the, like, all the ETEM data, then we have to figure out how we map that. We really need an ML model, because it's a one-time thing again, right, for all the current catalog. No, but, Because I'm thinking the, like, the names might be a bit different, so we need some sort of an accuracy score there also. Because ETEM has a very specific standard, like, the one I showed, like, it has two-way valves or something like that, and if… a company called something else, we need to be able to map it to that particular thing. Exactly. Like, even after we get the Alps mapping, yeah, we want anyone to come in and maybe they have their own categories that aren't any standard. You want to be able to map those together. Anyways. Jeez, excuse me. Okay Okay. Luke, you're… Or just, like, adjust the permits, so, two, two tests. That's the integration test for the whole, model and the component test. But I haven't completed the report, so I will actually show you all the results next week. But all the tests are passed. Right? We only have 150… One has facilities. And, I want to show you something about doing this builder. Aye. You too. Yes. So… As the whole model have been completed, the next… Since I think I can do… is to do some… concurrent both tests, and, try to… Lower the… reduce the latency for each component of the model. And to test the… Hmm. Night. I have strong… standards I may preserve. for each component. And the second is I identified the… The heaviest part for the model is the encoded part. So I want to… But some ways to… reduce the latency are also important. For the model. And to sit alone, since there are… the model has many, like, super… Hyper… superbitters. So, I want to… make, detailed documents for you. And, the people I handle. just… this model to, to, things. And if possible, I want to implement an automatic update overflow based on valuation results after testing, focus on very… focus on the metrics that have to Be outstanding, and truly. To, to change that movement. And, the, the first thing is. as I said, I want some, like, 50 or 100 corrections, like, some real data from you. But it's still okay if I don't get the gist data, because it's just too… Refines the model. And finally, I've got to, for test or the HTTP interface. And training. It's suggest what I want to do in the next few weeks. So, repeat collection examples that you mentioned. Yeah. So how do you… what sort of data do you do with that? Is it supposed to be, what the original… Oh, it's just that… How is it veribly prepared into the system? Or, do you want… So, even… I can make a jet for you today, and that'll send it to you. I will write a logic, but I don't send you yet. Yeah, that makes sense. If you just send us, like, the exact expectation on how we want these connections to look like, I can see how I can get the data. Okay, of course. And it's a fool. Yeah, I think that was our updates. I don't think, we have any… Yeah, I think, what we need to focus on in the coming weeks, at least. like, we've done a lot of POCs, we want to drop what doesn't work, and move on with… I think we'll be dropping the LM part, we want to work on product, and what's going to move to production, or, like, boost people. So, probably take some time to, like, see… how, I mean, how the timeline looks like with things drop and things stand over time? Yeah, again, yeah, we should reassess. I think, this ETEM thing should take us some time to fill it out. Initially, I thought it's not really that big a deal, but, like, it's gonna take a bit of work, not, I think. not significantly impact anything. And you plan to make that mapping between our categories in, like, each end category? Or, like, I guess, what about that part? I think we can do both, because mapping to the different categories, that should be a one-time thing. And, like, moving on, normal that in just the new documents that come in, they'll just automatically map to those categories. So I think we can do that part as well. But that'll, we might not have to, include that as part of the entire system, because that's going to be a one-time thing. Yeah, that could be essentially… Could be a separate… Yeah, I'm just curious, because I'm… I myself am doing a lot of similar things where I'm already kind of… for my test data, as I test this little rework our pins, I've already maxed some, just, like, 10 categories and attributes, but yeah, I'm gonna eventually need the same thing. I'm just wondering, like, should we be parts to it? We could have our catalog team review it, or were you already planning to, like, So, if you have something that you're already working on, can you send us, like, what kind of thing? Yeah, again, mine's just a light schema, but I've definitely, like, already started to map some of the categories together. And you're doing this… manually? I mean, I'm using Claude, but, like, that's… I mean, but I'm taking just, like, a small mock data set we use that I'm using for my local testing, and based out of those categories I have there and those attributes, I've already found their ETEM equivalents, and as I have these interfaces for switching back and forth. Yeah, honestly, we can use Plur, like, if it's a one-time thing… Yeah, exactly. Spalling, like, I mean, Sabradi here is, I think. Yeah, I think, we can do that, but, that's a bit, laid down the line for us. Okay. To map, like, after we have the initial, like. all of it indicated, then we'll probably map it even to the category. Yeah, whenever you get to that, like, I gotta be curious… I would be interested in jumping in there, if I could have a lot of that more Guardian done at that point. Okay, definitely, yeah. We'll let you know. And, like, if you have anything done, you could also share that with us, so that… Yeah, yeah, we just don't want to do it again. Like, if it's the same thing, we're implementing what you're doing. Before we move on to that part, we'll let you know, so that we can sync up on that. Yeah, either way, I want to get you those new versions and the staging tables, and at that same time, I can give you the schema I have right now. Yeah, that'll be good. Okay, and then, I think one more thing is, like, the whole pipeline is just broken into pieces. Yeah, that's the next part. I'll set up a server, and then we can start with, like, doctors and components and networking and stuff like that. We have it, but the components are segregated still. Correct. So we have to integrate all of those, then have a common repo, which we have. And I think documentary needs there. That's… yeah. I would say it's just keep doing a little bit of it as you go. In the end, if you do all of the together, then it gets cloudy. Yeah. There's a bit of documentary there, but nothing… Yeah, I think with Cloud, it's a lot easier as well. Love it. Yeah, I think, before we do the Azure part, deploying it, you have to first make it work internally. like, connect the OCR and ingestion for them, and that'll take a bit of… some time, at least. with that, and then we'll probably move on to Azure, see how it works. That's… that's all we have today. Do you have any questions for them? Maybe there's an awesome subject. So you guys meeting next Thursday? Theoretically, next Thursday is a community day, and theoretically, there's no CMU5, but that's theoretical, it's up to you guys. And Friday's a holiday. Yeah. Okay, yeah. I guess you don't have to observe the holiday, because it's a scene… Yeah. To get the day off, you get Friday off? Yeah. Nice, nice. You brought to 5,000? Weekend. Seniors, I mean, Pittsburgh's got great fireworks. They have a great fireworks. There's a lot of things, there's, the, What's… what's the bang? Besides that, Yeah, I know what you're talking about. They're performing in Point State Park and stuff. Yeah. And there's a bunch of band performing there for Pete. Oh, it's gonna be a mess, but, like, the problem is your info profile. Where? I think this is one of the… It's gonna be crazy coming year. This year, it's gonna be really… Yeah, yeah, yeah. The fight already happened. Oh, it hadn't happened. But in general. They had a great flyby for that. They had a combination of Blue Angels and the Thunderbirds. Hopefully we come back. Because, like, I know that BC is actually expecting, like, additional traffic. They have a blocking cases up, so that they just… Are you playing with Agnes? Yeah. It's an easy drive. Well, up until the last hour, and then it can be crazy traffic, depending on the time you get there. Well, if you drive down to the area and then take the transportation. Yeah, the best part, the exchange of road is, like, the farthest one out. The metro is actually… it takes too much time to get to the speed. Oh, so, like, the… the tram, the training service, that kind of… Oh, it's okay. It's… as few as done as possible. But it's what's impossible to find a place to park to be driving your car. You may not know I'm gonna be the one. But yeah, that's true. Well, great seeing y'all. Yeah. Have a good weekend. See you soon. It's… There you go, guys. Let us know if you need any help yet. We kind of got to things somehow. like, the boundary setup and, like, the Azure setup. I actually set up a new project on boundary for my time. Okay. It's very straightforward. So the resource is there, so as long as it's connected to your resource. You're good. So the thing is, I didn't do it on Foundry Playground, I did it the other way, because I wanted an API easily. Okay. Does that increase? No, no, so you're deploying the… you're deploying the model, you're not selling a project. Okay. Yeah, depending on… you can do how many of models you want, as I'll use it. Oh, okay.
\ No newline at end of file
diff --git a/minutes/2026-06-25-client.json b/minutes/2026-06-25-client.json
new file mode 100644
index 0000000..79dcdb3
--- /dev/null
+++ b/minutes/2026-06-25-client.json
@@ -0,0 +1,40 @@
+{
+ "meeting_date": "2026-06-25",
+ "duration_minutes": 39,
+ "participants": [
+ "Hrishik"
+ ],
+ "participant_count": 1,
+ "total_words": 5893,
+ "total_turns": 1,
+ "speaker_stats": {
+ "Hrishik": {
+ "turns": 1,
+ "words": 5893,
+ "pct_words": 100.0
+ }
+ },
+ "detected_topics": {
+ "ML/Model": 6,
+ "Architecture": 5,
+ "Data": 5,
+ "Infrastructure": 2
+ },
+ "questions_found": 0,
+ "questions_sample": [],
+ "potential_decisions": 1,
+ "decisions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "Labor Premier. And they don't have air conditioning on sports. Okay, I guess, we can get started. I did not\u2026 I wasn't going to send an agenda, because there were a few things changing last moment. As "
+ }
+ ],
+ "potential_action_items": 1,
+ "actions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "Labor Premier. And they don't have air conditioning on sports. Okay, I guess, we can get started. I did not\u2026 I wasn't going to send an agenda, because there were a few things changing last moment. As "
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-06-25-client.md b/minutes/2026-06-25-client.md
new file mode 100644
index 0000000..9a22147
--- /dev/null
+++ b/minutes/2026-06-25-client.md
@@ -0,0 +1,35 @@
+# Meeting Minutes — 2026-06-25
+
+**Date:** 2026-06-25
+**Duration:** 39 minutes
+**Participants:** Hrishik
+**Source:** `GMT20260625-190721_Recording.transcript.vtt`
+**Processed:** 2026-07-20 05:14 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Hrishik | 1 | 5893 | 100.0% |
+
+## Topics Discussed
+
+- **ML/Model** ██████ (relevance: 6)
+- **Architecture** █████ (relevance: 5)
+- **Data** █████ (relevance: 5)
+- **Infrastructure** ██ (relevance: 2)
+
+## Potential Decisions
+
+1. **[Hrishik]** Labor Premier. And they don't have air conditioning on sports. Okay, I guess, we can get started. I did not… I wasn't going to send an agenda, because there were a few things changing last moment. As
+
+## Potential Action Items
+
+1. **[Hrishik]** Labor Premier. And they don't have air conditioning on sports. Okay, I guess, we can get started. I did not… I wasn't going to send an agenda, because there were a few things changing last moment. As
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 5893 words across 1 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-07-02-cleaned.md b/minutes/2026-07-02-cleaned.md
new file mode 100644
index 0000000..75f35ff
--- /dev/null
+++ b/minutes/2026-07-02-cleaned.md
@@ -0,0 +1,139 @@
+# Cleaned Transcript — 2026-07-02
+
+**Hrishik**: So, in the agenda for today, we just have, our updates on what we all have been working on. I'll start, so… I'm still working on the schema part. For the ingestion gateway. there… I'm making a lot of, ETM tables. I'm trying to separate out, each ETM value in a different table. For example, ETM feature, unit, values. ETM class groups, because there are so many different, like, different categories, I'm planning to having each of those as a separate table. And then…
+
+**Harsha (eParts)**: Actually, I wanted to give some input on this, too. Just in the past 3 days, I've actually, managed to import the entire eTIM standard into our schema, that we're going to be using for PIMS to include all the different values, units. Features, classes, everything. So, I can just give you a drop of that, if that would be helpful.
+
+**Hrishik**: Yeah, that would be helpful, because we can use the same thing in our system as well, then.
+
+**Harsha (eParts)**: Yep.
+
+**Hrishik**: Probably even more in sync.
+
+**Harsha (eParts)**: I don't have the mappings, though, in terms of, like, our existing… this category corresponds to this existing ETIMS class, but other words, that I did get classes in his categories, features in his attributes, and then all that… all the other tables that are associated with it. So, yeah, I can send that to you.
+
+**Hrishik**: Okay, yeah, that'll be really good. Then I think I'll probably… for now, what I have is I just imported all the ETEM data. It doesn't correspond to anything in PIMS. It's just that all the ETEM data has its own, rows and tables and columns. But yeah, if you have already… that already mapped with some of the Attributes, then that'll be helpful.
+
+**Harsha (eParts)**: Yeah, I think I'll just give that to you as a back file for Postgres.
+
+**Hrishik**: Hmm, yeah, that'll work.
+
+**Harsha (eParts)**: Boom.
+
+**Hrishik**: Yep, that was pretty much for the ingestion site. I guess, Arjun, you can tell us a bit about OCR and integration.
+
+**Arjun**: Yeah, so, I did what, karsha, you asked me to check out the GPT 5.4 last week, so I checked that out, but, like, the… Results were not much better. We still need, like, two models, like, two loops. Once we run it with GPD 5.4, and then with, like, a vision model, which helps us get down the error rates to, like, less than 2%. Apart from that, Rishi's ingestion repo. Was given to me, so I've integrated, like, my OCR work, along with Rishi's ingestion work, and I've created a new Bitbucket repo. So I've put all of that. So, I think right now, my main priority would be to still optimize the… OCR part a little more, so we can get the error rates done. I'm still figuring out how to do that. But, like, the whole, integration part is pretty much done. The next step would be, like. I think, assign it to, like, a schema, and then put it in, like, staging tables, and then we should take it from there.
+
+**Harsha (eParts)**: et cetera. Also, I think I mentioned 5 mini, not partners for… Yeah, I…
+
+**Arjun**: I tried from… I think I tried everything from 5 onwards, like, 5, 5 Mini, and then 5.1… there was 5.5 also, but it said quota exceeded, so I couldn't try that out. Look at him. I think, I'll have to do something. Since it's a base model, I don't know if I can create, like, a fine-tuned model on Foundry itself and try something out with that. So I can try that out as well.
+
+**Harsha (eParts)**: Makes sense.
+
+**Arjun**: Yeah.
+
+**Harsha (eParts)**: Got a lot of questions from you, man.
+
+**Hrishik**: Okay, then, Jay can give us a B for the QA plans.
+
+**Jaivard**: Can I scare my screen regarding that, or should I just stop?
+
+**Hrishik**: Yeah, you can share your screen.
+
+**Jaivard**: Yeah, is it visible?
+
+**Hrishik**: Yeah.
+
+**Jaivard**: So, this is what we currently have for, as we go along, to the coding site. So… we've made sure that our QA plan focuses on the most important characteristics first, so that those are accuracy. And reliability, in our confidence scores. And then… Accountability that has… Whenever we make a prediction, and whenever a record comes in, is it being recorded or not? And from that, we are, designing tests for each of them. So, for the ingestion gateway. For the normalization. The prediction service. And the routing engine? So… Not all of them, have been implemented as, Most, most of the, things for, the ML part, have tests, as you has written a lot of them. But we're still, going through them, and, we'll be, updating them as they go along. But this is our current lab, and… I can go into depth if you want, on any of these. But, right now, it's mostly the prediction service that has the tests. And… this is more of a, I guess, whenever a change is made, or you guys want to make a change, so just to see nothing is breaking. Or, failing silent. But, yeah, I'm… Right now, after this document, I'm more focused on How we will be going about, Which tests to write first, and how we'll be going about it. And also collecting metrics for our… usage of AI during codings, just to see how that's done. So… Those are my updates. Does anyone want to get into, like… The details are anymore. I can go after that.
+
+**Cliff (Mentor)**: Are you going to share this document with the clients and the mentors, or just give us a link to it?
+
+**Jaivard**: Yes, I will be sharing that. There are a few changes first, but I'll be sharing this.
+
+**Cliff (Mentor)**: And was this suggested based on your meeting with Jeff for the QA pack?
+
+**Jaivard**: Yeah, Jeff actually… focus more on, I would say. The… how we are collecting the metrics, and how we know that this is being implemented, rather than changing the document itself. I think he's pretty okay with the rough shape of the document, but… He's much more focused on the metrics collection and That what we have written, like, we have links regarding These, all these quantity attributes, and exactly how, You know, equivalence, partition, and stuff like that. But he's more focused on whether those steps and those metrics are being implemented.
+
+**Harsha (eParts)**: It's right.
+
+**Cliff (Mentor)**: Okay, thanks.
+
+**Jaivard**: I'll try to share, like, concrete metrics next time, as we go and as we're recording them. And, that will kind of also give a more fair idea, I would say, about The progress of the project itself, and how ready it is for, you know, any changes, and if you guys want to make them as… That's it.
+
+**Hrishik**: Okay. Leo, can you have an update on the ML side? The testing you've been doing.
+
+**Liu**: Yes, could I share my document?
+
+**Hrishik**: Yeah.
+
+**Liu**: Yeah, so, last week, we… Have completed all the… Component test and, integration test. for the ML model. So, this time, I will share you with the results for these two kinds of testing. So, for the overview, we mainly have four layers of the… machine learning system. The Layer 1 is extraction. This work had depended on TrueJ. And, the Layer 2 is the raw engine. Because this is just some mapping work, so we don't have many scenes to test. And, we put out the most. Effort and time on the Layer 3 and Layer 4, and these two layers are the most complicated part of the… machine learning model. So, for the… this way, the searching is encode the description to a vector and retrieve the most familiar category products. The category prediction part. Have to… The retrieved product vote on the product type. The attribute scoring scores the negative values for each attribute of the product type, and for the layer 4, the decision part layer. They do a decision and the calculation work, combine the low and the semester signals, applying safety capes, and do the result. So, from this pathway, The shoot contains, well, 106 component tests. Across the four modules, or parts. And, those are the… Detailed testing area, or each part of the model. So, for the search part, we test the connectedness And, index integration and, Configuration and the input handling. And, with their validated behavior. Like, for the collectors, a product's nearest label is itself at similarity equal to Whoa. For the index integration. Because the report size and the demonstration are correct, and the save load returns identical results. For the configuration, unsupported index types and rejected with a clear arrow. And, the input handlings, single queries and batches are handled equivalently. For the category prediction, We test voting. The validated behavior could be confidence reflects the similarity which the votes show. So, and nope, nope. Normally those case labels, agrees yield confidence one. So, confidence bands, ambiguous less than 0.6 normal and high consensus. Great, then… 0.8 bands behavior distinctly. And, so, input handle… handling, unlaw products, negative similarities, and empty results are handled very gracefully. For the scoring part, we tested the upper bound and the lower bound. Or the scores, and the range of the scores. And, the popularity. a popularity player. like. The values used more often in the catalog get a small confidence boost, and this adjustment is relevant to stay within safe limits. And for the birth date, So values with insufficient training data will fall back to a safe calculation. And for the layer 4, we test the field gene. The final confidence followed the committee debate. 30% do, and, 35% machine learning parts. So, safety tapes. On certain category Cape and spurs data Cape fail correctly. The stricture VIN… the visible supply. like, either way, Kent… If the semester Parts cannot give a. higher enough category CAPE score. Wait, the highest score, or the final result? Well, less than 0.75. And, if that… If the samples is less than, like, They could fight. We… Treat this case as a sparse data. So, the final score could narrow higher than 0.6, 0.7, For the routing part. Results look to auto-process human review flagged unclear by competence threshold. And we've also tested some… the boundary tests. And edit these ones. We chose turn-test target edge and fatal cases. Like, missing or corrupt data, empty import, exact ties, and the precise points where a decision flips. The situation is most likely to behave unexpectedly and least likely to be caught by outdated use. So, we have 5… Such… Cases. Everywhere. And, two, one category. And test the tool scoring area. And the two, decision area. The result is all the, 106 component tests pass. For the coverage scope. The component testing confirms each module is logically correct on controlling inputs, including the boundary cases described above. But it doesn't cover real-world accuracy. Since the tests use synthetic import phase, With long answers. But I think this could be… not be a very big problem. Because this could not block our next processing for the model. We just need more data, rare data, to refine the model. And, for the second one, the… Each model is tested in isolation by design. So, we don't know how it really works in the LUs or the model. The other days. A few risk input cases were deliberately left for later on, since, like, unusual loan descriptions, uncommon technical codes, or duplicate category entries, those are skipped, but not because they are uncommon in normal operation and low risk there. So, a bunch of cases were prioritized. We focus first on the cases most likely to affect everyday results. And I also list 3, remaining waste. First, it does not establish how accurately the system predicts on real customers' language. And the rare import cases noted above are unvalified. The tests validate. Against our design specifications, like, Integration testing partially covers this by existing in the module together. And, for the integration tests, Oh, The integration testing confirms they worked correctly when connected into a chain, and when wrong behind the rail web service. So the whole change, like, customer description flawed through the road engine, then the domestic matcher. The answer decision layer. Which combines the signal and the rules… the result. And the reverse confirmation or correction fades back into the model to update the model. And, the result is… is, like, the… the source contains Sati… 7 integration tests will pass. the coverage we test… Like, 9 aerials was a whole chain was modeled. And the width is… But they keep behave it. We test end-to-end prediction, routine propagation, low-cross category corresponding, Empty category pass. Think of fusion end-to-end. feedback loop. Service contract. Consistency, traceability. So, the chemistry scope could… It doesn't cover the real-world accuracy. And, the test exists correctness, those performance under many simultaneous requests. Behaved under sustained… Concurate load is validated separately, and it's still outstanding. So, the next step, we need to… Do some, performance testing, like, to… to input a lot of… cases simultaneously to the system and, to see the latency and other, performers, behavior could… Be expected as a… as a way to expect it. And the ramenic music list. The first is we require real data to test the whole system, and the second We need to test the load behavior. And, during this test, we… Actually found a defect, and we fixed it. This is… When a reviewer's collection was being recorded, but it could never reach the live prediction, so the online landing loop doesn't actually close. It's, it, it's like… the system… To create a brand new repository to… To store the… The new center was a cluster, but… the system… Still use the old one to do the… matching book, so… We fixed it. And just water. That's what I want to show this.
+
+**Cliff (Mentor)**: So, are these results on your… on production code, or is this proof of concept code? I just want to be… I want to be clear about understanding that.
+
+**Liu**: All the tests are… Hardly tested on the rail code.
+
+**Cliff (Mentor)**: So, your real code is intended to be production code?
+
+**Liu**: Yes.
+
+**Cliff (Mentor)**: Okay. And has other members of the team reviewed that production code yet?
+
+**Liu**: Yeah, I think Brish and, Jay… have reviewed it, and I have… Upload all the results and the testing code into the GitHub and, Bitbucket.
+
+**Cliff (Mentor)**: Okay.
+
+**Liu**: Oh, thank… thank you. So…
+
+**Harsha (eParts)**: In theory. Actually, coming, to your email as well, you, where you asked for, like, correction… Oh, yes. We… I mean, I was looking into it, mainly the processes to concoct that data, talking to a few people here, and then, see… see… just… just creating it for a few cases, and that's pretty much it. And hopefully I can send you this data by next week, and it won't be too much, it'll just be, like, I think 50 to 100 records at max. Yes.
+
+**Liu**: I think that's enough. Thank you.
+
+**Harsha (eParts)**: Yeah. That's it.
+
+**Liu**: Yeah, yeah, and during the reuse of the system, we could collect more data from the reuse, yeah.
+
+**Harsha (eParts)**: Yeah, I agree.
+
+**Hrishik**: Yeah, I guess… That is mostly all for update. Ashita, Yatsun, go ahead.
+
+**Ashritha**: So… I, I mean, I hope all of you are doing okay. So, the first… I think I worked the previous week, I was just busy with the code reviews, I didn't do any much of the development task, but then this week, I've picked up the ETIN schema task. So, me and Jay are gonna work on it, and, since Jake just mentioned about, some of his findings, so we'll pick it up from there, yeah.
+
+**Harsha (eParts)**: Also, I think we should also talk about, like, how, how we can combine Jake's findings with your findings, and… We could have a meeting just for that, if you guys want, sometime next week. I think Jake is still in the process of, like, finalizing all the things. No, I mean, I'm finalizing, like, interfaces and pimps to utilize it and assign it to products, but in terms of the data in our schema, no, that's… Pretty much finalized on my side. Then… Did we, talk about, how we wanted to, see how the mappings go? I think Rishkesh was working on it. Ending the mappings to, like, the existing categories, the old categories? Yeah, yeah. So, yeah, I think, what we can do is, over next week or the week after, Jake could just show how all of it looks in PIMS, and we could do, like, a PIMS demo with the updated version of how things stand on it. So that we could just get you guys up to speed and empowered. How… how… how progress is looking on our end, and how that translates to, the things that you guys are doing.
+
+**Hrishik**: Yeah, I think, that works, that works well.
+
+**Harsha (eParts)**: Yeah.
+
+**Hrishik**: I think before that, it would be good if, Jake, you could share the, schema that you have in a PEMS, dump, or something similar. So, meanwhile, before the meeting, I can also take a look. And so that I can get in line with, what are Genesis exactly. Then we can have a meeting next week.
+
+**Harsha (eParts)**: Yep, makes sense. And I'll remind Jake to send it to us. But that should be good. We should have that data by next week, or next week, hopefully.
+
+**Hrishik**: That, that sounds great.
+
+**Harsha (eParts)**: I'm in the background right now trying to extort it, but yeah. I think there's some data I want to leave out, some test data that's, like, mixed in with it right now, so yeah, I'll add that to you as soon as I can. And… any other updates from us or you guys? I can't think of any other questions. To be fair, it's a lot of work going on for you providing.
+
+**Hrishik**: I think, no.
+
+**Harsha (eParts)**: Yeah, and I think… Liu, can you also send across the QA document that you were just talking through? So that… not QA, the testing document that you will put on it through, so that we could also take a look, and I could spend a little more.
+
+**Liu**: Okay, okay. And I can share you a more detailed one.
+
+**Harsha (eParts)**: Yeah.
+
+**Liu**: Yeah, with just some code explan… planning.
+
+**Harsha (eParts)**: Yeah, that sounds good, yeah. And yeah, that's pretty much it. Other than that, I can't think of anything else from my own. Any other questions for you guys?
+
+**Hrishik**: Nothing relevant right now for me.
+
+**Harsha (eParts)**: If anyone's good, then… I'm good. Long meeting, guys. It's pretty much everything that's said. good extra long weekend for the event. Right, yeah. Yeah. Good boy. That's it. See you at another, and have a long… good long weekend, Cliff.
+
+**Cliff (Mentor)**: Same to you.
+
+**Harsha (eParts)**: We'll enjoy your holiday. I like the shirt clip, I didn't even see that until…
+
+**Cliff (Mentor)**: Yeah. This is a shirt I got from Fort McHenra. I even got to raise the flag at the fort, that was pretty special.
+
+**Harsha (eParts)**: Whoa, that's awesome. See you guys for that. See you. Have a good weekend.
+
+**Hrishik**: You guys, bye-bye.
\ No newline at end of file
diff --git a/minutes/2026-07-02-client.json b/minutes/2026-07-02-client.json
new file mode 100644
index 0000000..2c723c9
--- /dev/null
+++ b/minutes/2026-07-02-client.json
@@ -0,0 +1,114 @@
+{
+ "meeting_date": "2026-07-02",
+ "duration_minutes": 27,
+ "participants": [
+ "Hrishik",
+ "Harsha (eParts)",
+ "Arjun",
+ "Jaivard",
+ "Cliff (Mentor)",
+ "Liu",
+ "Ashritha"
+ ],
+ "participant_count": 7,
+ "total_words": 3182,
+ "total_turns": 69,
+ "speaker_stats": {
+ "Hrishik": {
+ "turns": 18,
+ "words": 327,
+ "pct_words": 10.3
+ },
+ "Harsha (eParts)": {
+ "turns": 22,
+ "words": 700,
+ "pct_words": 22.0
+ },
+ "Arjun": {
+ "turns": 3,
+ "words": 253,
+ "pct_words": 8.0
+ },
+ "Jaivard": {
+ "turns": 6,
+ "words": 448,
+ "pct_words": 14.1
+ },
+ "Cliff (Mentor)": {
+ "turns": 9,
+ "words": 117,
+ "pct_words": 3.7
+ },
+ "Liu": {
+ "turns": 10,
+ "words": 1260,
+ "pct_words": 39.6
+ },
+ "Ashritha": {
+ "turns": 1,
+ "words": 77,
+ "pct_words": 2.4
+ }
+ },
+ "detected_topics": {
+ "ML/Model": 6,
+ "Data": 6
+ },
+ "questions_found": 0,
+ "questions_sample": [],
+ "potential_decisions": 2,
+ "decisions_sample": [
+ {
+ "speaker": "Liu",
+ "text": "Yeah, so, last week, we\u2026 Have completed all the\u2026 Component test and, integration test. for the ML model. So, this time, I will share you with the results for these two kinds of testing. So, for the ov"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Also, I think we should also talk about, like, how, how we can combine Jake's findings with your findings, and\u2026 We could have a meeting just for that, if you guys want, sometime next week. I think Jak"
+ }
+ ],
+ "potential_action_items": 19,
+ "actions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "So, in the agenda for today, we just have, our updates on what we all have been working on. I'll start, so\u2026 I'm still working on the schema part. For the ingestion gateway. there\u2026 I'm making a lot of,"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Actually, I wanted to give some input on this, too. Just in the past 3 days, I've actually, managed to import the entire eTIM standard into our schema, that we're going to be using for PIMS to include"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Okay, yeah, that'll be really good. Then I think I'll probably\u2026 for now, what I have is I just imported all the ETEM data. It doesn't correspond to anything in PIMS. It's just that all the ETEM data h"
+ },
+ {
+ "speaker": "Harsha (eParts)",
+ "text": "Yeah, I think I'll just give that to you as a back file for Postgres."
+ },
+ {
+ "speaker": "Arjun",
+ "text": "Yeah, so, I did what, karsha, you asked me to check out the GPT 5.4 last week, so I checked that out, but, like, the\u2026 Results were not much better. We still need, like, two models, like, two loops. On"
+ },
+ {
+ "speaker": "Arjun",
+ "text": "I tried from\u2026 I think I tried everything from 5 onwards, like, 5, 5 Mini, and then 5.1\u2026 there was 5.5 also, but it said quota exceeded, so I couldn't try that out. Look at him. I think, I'll have to d"
+ },
+ {
+ "speaker": "Jaivard",
+ "text": "Can I scare my screen regarding that, or should I just stop?"
+ },
+ {
+ "speaker": "Jaivard",
+ "text": "So, this is what we currently have for, as we go along, to the coding site. So\u2026 we've made sure that our QA plan focuses on the most important characteristics first, so that those are accuracy. And re"
+ },
+ {
+ "speaker": "Cliff (Mentor)",
+ "text": "Are you going to share this document with the clients and the mentors, or just give us a link to it?"
+ },
+ {
+ "speaker": "Jaivard",
+ "text": "Yes, I will be sharing that. There are a few changes first, but I'll be sharing this."
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-07-02-client.md b/minutes/2026-07-02-client.md
new file mode 100644
index 0000000..a853f8d
--- /dev/null
+++ b/minutes/2026-07-02-client.md
@@ -0,0 +1,49 @@
+# Meeting Minutes — 2026-07-02
+
+**Date:** 2026-07-02
+**Duration:** 27 minutes
+**Participants:** Hrishik, Harsha (eParts), Arjun, Jaivard, Cliff (Mentor), Liu, Ashritha
+**Source:** `GMT20260702-190705_Recording.transcript.vtt`
+**Processed:** 2026-07-20 05:14 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Hrishik | 18 | 327 | 10.3% |
+| Harsha (eParts) | 22 | 700 | 22.0% |
+| Arjun | 3 | 253 | 8.0% |
+| Jaivard | 6 | 448 | 14.1% |
+| Cliff (Mentor) | 9 | 117 | 3.7% |
+| Liu | 10 | 1260 | 39.6% |
+| Ashritha | 1 | 77 | 2.4% |
+
+## Topics Discussed
+
+- **ML/Model** ██████ (relevance: 6)
+- **Data** ██████ (relevance: 6)
+
+## Potential Decisions
+
+1. **[Liu]** Yeah, so, last week, we… Have completed all the… Component test and, integration test. for the ML model. So, this time, I will share you with the results for these two kinds of testing. So, for the ov
+2. **[Harsha (eParts)]** Also, I think we should also talk about, like, how, how we can combine Jake's findings with your findings, and… We could have a meeting just for that, if you guys want, sometime next week. I think Jak
+
+## Potential Action Items
+
+1. **[Hrishik]** So, in the agenda for today, we just have, our updates on what we all have been working on. I'll start, so… I'm still working on the schema part. For the ingestion gateway. there… I'm making a lot of,
+2. **[Harsha (eParts)]** Actually, I wanted to give some input on this, too. Just in the past 3 days, I've actually, managed to import the entire eTIM standard into our schema, that we're going to be using for PIMS to include
+3. **[Hrishik]** Okay, yeah, that'll be really good. Then I think I'll probably… for now, what I have is I just imported all the ETEM data. It doesn't correspond to anything in PIMS. It's just that all the ETEM data h
+4. **[Harsha (eParts)]** Yeah, I think I'll just give that to you as a back file for Postgres.
+5. **[Arjun]** Yeah, so, I did what, karsha, you asked me to check out the GPT 5.4 last week, so I checked that out, but, like, the… Results were not much better. We still need, like, two models, like, two loops. On
+6. **[Arjun]** I tried from… I think I tried everything from 5 onwards, like, 5, 5 Mini, and then 5.1… there was 5.5 also, but it said quota exceeded, so I couldn't try that out. Look at him. I think, I'll have to d
+7. **[Jaivard]** Can I scare my screen regarding that, or should I just stop?
+8. **[Jaivard]** So, this is what we currently have for, as we go along, to the coding site. So… we've made sure that our QA plan focuses on the most important characteristics first, so that those are accuracy. And re
+9. **[Cliff (Mentor)]** Are you going to share this document with the clients and the mentors, or just give us a link to it?
+10. **[Jaivard]** Yes, I will be sharing that. There are a few changes first, but I'll be sharing this.
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 3182 words across 69 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-07-09-cleaned.md b/minutes/2026-07-09-cleaned.md
new file mode 100644
index 0000000..14e1a1e
--- /dev/null
+++ b/minutes/2026-07-09-cleaned.md
@@ -0,0 +1,101 @@
+# Cleaned Transcript — 2026-07-09
+
+**Hrishik**: So, primarily, last… Thank you, thank you, thank you. Okay, so… This week, primarily, from the OCR, in addition part of things, we were starting on integration. We have… Okay. We have… we have started the integration part, and it is coming on along pretty good. Right now, we are able to ingest long PDFs, and we are getting a good, around 95% accuracy with, like, on getting all the attributes out, but I recently, I just, I saw a bug because When we're trying to OCR some of the PDFs, like, there's a difference between doing an OCR and looking at the underlying embedded text. So, in some cases, the OCR has given us a better output, and in some cases, the internal embedded And… like alphabets or letters are giving some better output so that part is something that we have to fix currently in the OCR part and after that we are planning to align the schema that Liu has for the ML pipeline. We have the inputs ready for it. And we should be proceeding with that in the coming week. I can maybe, like, there's not a demo as such, but I can quickly show you the last test run which I did. Maybe it'll give you a bit more time on things. Just give me a second. As long as it's okay It's right. So let me share my screen. So this is one of the initial PDFs that we have had. I was using this one, like, I just did a test on this one. So it has a lot of stuff which is not really relevant, and the primary relevancy comes from the, like, spec doc, which is towards the bottom of it.
+
+**Harsha (eParts)**: Mmhm.
+
+**Hrishik**: So we have two products, AccuHMOA, and there's one more, I think, OAW. They have similar, like, similar values. Attributes and values. So, when we run into the pipeline, Let me see if this is… oh, yeah. So this is a pipeline that would actually parse out what's relevant or what's not relevant, or do you have to manually No, it automatically doesn't.
+
+**Jake (eParts)**: I was about to ask that too. So it'll ignore those first couple pages, then, of just, like, more of the instructions and stuff.
+
+**Hrishik**: I mean, ideally, the OCR part just puts an order value, and the MN model is supposed to finally pass it out, because that's the main… Part of this problem. And so where's the hell? a disconnect here. So, you're saying your current stuff right now does not… does or does not parts of that we'd have to wait to the Ml. It does parse everything in the document that we have. Right. And we're still using an Llm. 5.4. So it does have some amount of context. But the final passing is going to be done by the end. Okay, the the exact relevancy will be done with Ml. Part. We parse out all the entire document, and, like the like, most relevant stuff is there? Like there, there's no data which is missing. But the final refinement will be done by the Ml. Part of things. Okay, thanks for clarification. And this is the, basically, the JSON dump that we currently have… we need a few refinements, but, let's just see a few… This is the output from the parsing? Yeah, so we have the AQHMOA mounting configuration, pole mount. We have the values along with them. These are, I did a check, these are around 95% accuracy as of right now. There are a few minor issues that is, related to a few of the… I'll be showing in the document itself. So how do you measure? So provided was, I gave these documents to plot first. So then we make a goal set with plot like a table format, and then we run it through our past, and then we compare it, and then we get the editor. Yeah, I did the same thing. So, the problem I saw was in this part of things, because these have the OM symbol, and the OCR GPT-5 was reading it as QA, or Q2, in a few cases. But if we pass it through a, something like a PDF plumber, it is supposed to give us a better output in this specific scenario. So I think that is something we'll take a look at next in order to reduce that error percentage even lower. So if we go back to the extracted stuffs. What was it called? Thermostat accuracy. Where's books? What else? Yeah, so here we have a 10K Q2. It is supposed to be 10K ohm. And, like, again, 10KQ to solo 10K home, so this is a slight mismatch. But it is pretty minor, and we should be able to fix that and move ahead with it. because here we are losing information that demo part will never get. So I think, right now we have like a 2 personality, and most of it comes from like. Oh. like, symbols like ohms, and then even if there are images, I think sometimes GPT is kind of struggling, unless we use a union model, which is Gpt. 5.4 union Gpt. 5.4 vision. So when we take the union of both, we get a redo high school, which is less than one personality, and probably can fix this as well. But doing that would increase the costs overall, because We are sending it to the other employees. And then we take those, you know, the unions that are over with that. Well, the question is, what is the marginal cost? I mean, it's… it's gonna be a one-time cost of, like, $500 to $700. $500 if it's just doing it once for all the datasets, and, like, telling others if they're doing it with both the… But that's everything in the e-parts. Yeah, everything on the e-parts catalog, yeah. Okay. Yeah, so this is, currently what we're doing in the oceanation part. And yeah, I got a mail, Jake, with the upgrade PEM schema. I was going through it. I had a couple of questions around it. Okay. Let's see… So, in most of these, tables I'm seeing, you have… I think I'm assuming the code part is the ETEM code, and the standard is the standard it maps to. I think 2 maps to ETEM, and 3 is for E-Class. So, is there only, like, a particular row is supposed to have only one code? Like, not ETEM and ECLASS both? Is that the case?
+
+**Jake (eParts)**: Yeah, we'll have a different row with ID Standard 3 for when we eventually move to, E-Class. And then same, like, ID standard,
+
+**Hrishik**: duplicated this thing, like, two circuit breakers and fuses, if it has, IHC2 and 1, we'll have 3, the same.
+
+**Jake (eParts)**: Yeah, we'll need some kind of way to link them together, probably another table for us to map alias or map different standards together. But right now, in terms of just storing these, is this the category? Yeah, just for storing the categories, we're just going to have one. There could be multiple circuit breaker and fuse rows in here, depending on the different… Standards, and we'll have to map that together with another table.
+
+**Hrishik**: Okay, okay, okay. So initially, we were thinking about doing a bit of ETM part in the, before the ML portion, but, after… like, getting a bit further, that seems a bit difficult, so we need more information that we'll get after them part. So I think we'll, after we have a certain confidence course, we'll be using the schema you provided us, and kind of mapping all of it to that.
+
+**Jake (eParts)**: Great, thank you Yeah, and the reason for that, we just want to keep it flexible, too, for, we could… I think standard 2… standard ID 2 just says ETIM, but really it should say, ETIM 10.0, and then when we get ETIM 12.0 a couple years from now, that can be a whole separate set of rows, or we can keep expanding. Otherwise, we'd have to have, you know, like, a column for each new standard we add in.
+
+**Hrishik**: Right, right.
+
+**Jake (eParts)**: Yeah, yeah.
+
+**Hrishik**: Okay. Okay, yeah, that is, pretty much from the OCR part of things. I can show my workplace. Yeah. You're, I'm, I'm already moved. Okay. Oh, that's excellent. You can speak up. Yeah, am I audible, Jake? You… yeah.
+
+**Harsha (eParts)**: Thank you so much.
+
+**Hrishik**: Okay, so let me just share my screen. Thomas, let's see. That is the one second. So who's in Puerto Rico? Is this a fun thing in Puerto Rico? Someone with knee parts is in Puerto Ric.
+
+**Jake (eParts)**: Yeah, I… My wife's Puerto Rican, so I'm down here probably twice a year. This is… this is our usual summer… we do… we take a trip in the summer, usually, and around the holidays, too.
+
+**Hrishik**: So what city are you in?
+
+**Jake (eParts)**: She's in Guaynabo, which is a, it's a suburb of San Juan, but yeah, San Juan, the, Capital.
+
+**Hrishik**: Very beautiful. I've been there once. Very nice.
+
+**Jake (eParts)**: Yeah, I love it down here. Well, it's a little a little humid.
+
+**Hrishik**: Thank you. Oh. Hello? Oh, okay. So… I've been working on, the ETMS class matching, so currently. It is done. I still have to… Get, do some revisions on this. I have tested it, and it's currently at around 92%. The… There are some errors in this one. And for example, like for this product type name and ETM class names, most of them match up well, but Oh, okay. For some reason, when using the current rules, there are two components. First, it tries to see if the words match up. Word by word, and if… if there… there is, something for that, then it tries to use that. And if not, it can call up, 5.4, using the Azure, token that I have. But it's, it's still currently, I think, not, Good enough, especially if you need to rerun it. If it, it was only very infrequent, then I think, the current approach should be enough, as the mistakes can be corrected, that they're pretty easy to spot. But if this is done again and again, then that's a little bit different. So I'm still working on this and also the features that each of them have. And I think I'll have a much more comprehensive, I think Jake has sent an integrated schema, like, with PIMS, which has all these already mapped. Oh.
+
+**Jake (eParts)**: No, no. So I don't have it mapped to the existing category. So this is great. But I do just have like a category table which has the ETIM category. But in terms of mapping it to the existing ALP ones, I haven't done any of that yet.
+
+**Hrishik**: Okay, okay, that's that's very good, then, because I would have just used that.
+
+**Jake (eParts)**: Okay.
+
+**Hrishik**: Yeah. I'll have much more to show on the, features, I think in a few days, but yeah, currently, the, the classes and the… it is getting the… 92% of the time, it is getting the correct, class name for the product, names that are right now, and… It does have, I don't think it's showing, but there are alternatives. for each of them. Some of them have no alternatives. But there is a little bit of I would say it's a hundred, it's not a hundred percent sure of what… which one… It's definitely the same one, so I'll work on that. Yeah, this is pretty much complete. How many roles in this take roles? Currently, it's 38 382 rooms, that's all. For the ETM standard. That's it. No, the ETM standard is much larger than that. Yes. But what. What we have, what, what the number that was available. is, is, that it's being matched, yeah, the ETM standard is actually being… There's multiple, there's ETM groups, classes, or attributes, so there are multiple, like, there is ETM values for all of these, but there are different, like, different types of, like, it could be a, it could be a value, it could be a class name, so these are different number of… And not all of them are specialists.
+
+**Jake (eParts)**: And I think, which you'll see in, in, in what I, in what I sent over earlier, the groupings, I just made, I made groups a category in addition to, Not feature. what's class, a category as well. And I just made, essentially, class a, a child category that had, a parent category of what I translated the groups to. So I did cram groups and classes both into the same category, schema.
+
+**Hrishik**: Okay. Okay. I think we can take a look at that and come back to you with questions. It's a little different on my side, but yeah, that guy, I think I can also account for that. Okay, so this one in co-pilot or something in azure. How did how did you do this? First, there's a rule space word by word matching. Okay. And then… When that was not, that was not working. Yeah. I just used the Azure Foundry token for ChatGPT 5.4 to do the matches for the rest. And the verification was done just using There's there's a worldly set of things that was already available that the It already had… it did not have for all of them, but it did have a large enough number that I could compare against, so it was around 92%. That that's, I've not checked each of them. Yeah, we'll figure those points there. Okay. So on the ML side, Leo has been working primarily on, like, just setting up the fundamental, like, and I don't want to go over the whole milestone plan that we had laid out. So, I started building on top of it, since Monday, so I picked up three tasks. So one is, I set up the load testing for our, prediction service. so up until now we would only like we only measured it like one request at a time so we actually had no idea if it holds up under the real traffic right so I kind of build a harness to you know to just check if it keeps up with the concurrent request and also like check whether it keeps up with the like, a 50, Queries, like, per second, while staying under the 200 millisecond target latency that we had initially planned on. so that is kind of validated in the load testing part my PR are up we have not yet merged it because simultaneously I've also worked on like setting up a CI on the repo so as per our QA plan and the testing plan which is all like in the air and theoretical we kind of have like more than two I think 250 test cases. But, nothing till date was actually running on any of the pull requests, so… the build column, if you see, once the PRs get merged, were, like, mostly empty. So, like, from now onwards, like, every PR that runs, that gets merged would run the whole test suite, plus the linting and the type checks automatically. So, from now onwards, probably we'll have a better, like, defect catching strategy before the actual stuff gets merged. So that was about the load testing and the CI that I set up on the repo. And also, like, one important thing that I also took up was I… we ran our evaluation with the rule engine, turned on. So, like, I… I mean, I looked up, I looked at the numbers actually the old eval numbers were like bad basically by bad what I mean is basically like there was like zero autopressing but that's because it I I believe that's because it was only running the semantic match or half of the pipeline like which was kind of capping the confidence, confidence numbers artificially. So, the numbers were kind of misleading, so I kind of wired the, rules back in and re-ran the whole, evaluation pipeline. So, now we kind of have, like, realistic accuracy. I mean, I've… I mean, the numbers are low because I'm not yet run on the proper data. But I think by end of tomorrow, we'll have realistic numbers and we'll have more accurate numbers and also the auto process numbers for the entire pipeline end to end. And also since the OCR and the ingestion is also taking some shape, so we were planning by this. end of the week, maybe, like, if things go smoothly. whatever we have on the ML side as of today, given, plus the OCR and the ingestion work, we might, do a, like, a run with the actual data and see how, how that works, because right now, Arjun and Rishi, they also got a good idea about the… how… what is the input expectation of the ML pipeline, so I think they're gonna… they are working on it. So I think by end of this week, we'll have, I mean, we are not expecting a smooth pipeline setup, but at least we'll have like pretty good number of defects and probably we can work on the entire three modules as of date, yeah. So we talked about. Subset of real name, or all of them? A subset of the real name. albeit subset. Don't worry. No, I think Liu will have better… like, I think since he had, like, good understanding of the data, like, maybe he can tell us, like, how much amount of data we can use. Because, like he said, we have a problem with our validation data, right? So… I don't know how we will work through that. Yes, so… I think it's a good idea to use real data to test an ML model, because and… So, the model is trained by the description or the data they give us. So I use the fabricated data to test the model. the the result could be plausible. And also. in their fails, many products, their attributes are only have one or 2 samples. So the data is Oh. very, very extremely like sparsely distribution data. So we have. so maybe that's one reason. Why, the user data to test the model, the result would be happy and not so good as it. should be shown before. Yeah, no doubt that. Yes, the the real question is, how much of the real data are you planning on using? like, I… use, like, 30 million rolls. 30 million. Yeah, in my, in my bank, right? I think that's what we have used already. Like, we're asking about the new data that we will need. Yeah. For testing, how much will we need? Real data. You mean the data to validate the model. like, only… Perfect. Tens… tens of thousands. 10,000. Okay. Yeah, yeah. Like, maybe you subtitle. Subject. Those. 30,000. Close, okay. Yeah, but that's not that much. Oh… Okay, I think, from the update's point of view, that was… pretty much all we have right now. We will try to, finalize the post-generation and link it with the ML part by the end of next week, but that is slightly optimistic, but yeah, we'll try to do the best, and we'll try to have… try to deliver some sort of an MVP to you guys as a proper demo by the end of the month. So, like, after fixing all the minor bugs that we have currently. So, at that time, there should be a… Like, decently working. at least those generation and some sort of Ml values will be getting from the data.
+
+**Harsha (eParts)**: Yeah, intense.
+
+**Jake (eParts)**: Great.
+
+**Hrishik**: Yeah, it's all from our side. Anything for you guys.
+
+**Harsha (eParts)**: I mean, no, no more questions for me. That's pretty much it. I think I was waiting for like a document from Liu about the technical implementation of the Msn. I don't know who to send it to us, just checking.
+
+**Hrishik**: Okay, I forgot it. I have to send it, after the meeting. Yeah, Liu forgot to send the document. He's saying he's… he'll send after the meeting. Yeah, yeah, I.
+
+**Harsha (eParts)**: Yeah, that's, that's pretty much all the questions I had. And with respect to like just getting the pipeline ready, I would also say like it would help, if you guys, like just check with the timeline of like events in the next two, three weeks, because I know end of semester are coming up. And yeah, just, just, just, just making sure that We're all, like, aware, and, like, we're, like, accommodating to all that.
+
+**Hrishik**: Yep. we should be able to accommodate this along with the NSAMs.
+
+**Harsha (eParts)**: Makes sense.
+
+**Hrishik**: So, Jake, when do you come back to Pittsburgh?
+
+**Jake (eParts)**: I'll be back Wednesday, I guess Tuesday night, but like Wednesday, technically at like whatever, 12:30 AM, something like that, late flight in.
+
+**Hrishik**: Is that a direct flight, or do you have to go somewhere else?
+
+**Jake (eParts)**: I wish…
+
+**Hrishik**: That's always.
+
+**Jake (eParts)**: It's always a connection.
+
+**Harsha (eParts)**: So…
+
+**Jake (eParts)**: Always a connection. Past couple times, it's been easier to just drive to DC and fly out of DC rather than deal with a tight connection and all that. It's, yeah.
+
+**Harsha (eParts)**: Apparently, Pittsburgh Airport is getting more international flights next year around the world. Based on words, I think.
+
+**Jake (eParts)**: Think so.
+
+**Harsha (eParts)**: I have to go to the bas.
+
+**Hrishik**: Well, enjoy the rest of your time in Puerto Rico.
+
+**Jake (eParts)**: Thank you. Thank you.
+
+**Harsha (eParts)**: And. See you guys, bye bye, have a.
+
+**Jake (eParts)**: Do you… do you have.
\ No newline at end of file
diff --git a/minutes/2026-07-09-client.json b/minutes/2026-07-09-client.json
new file mode 100644
index 0000000..2e1149f
--- /dev/null
+++ b/minutes/2026-07-09-client.json
@@ -0,0 +1,83 @@
+{
+ "meeting_date": "2026-07-09",
+ "duration_minutes": 26,
+ "participants": [
+ "Hrishik",
+ "Harsha (eParts)",
+ "Jake (eParts)"
+ ],
+ "participant_count": 3,
+ "total_words": 3609,
+ "total_turns": 50,
+ "speaker_stats": {
+ "Hrishik": {
+ "turns": 21,
+ "words": 2984,
+ "pct_words": 82.7
+ },
+ "Harsha (eParts)": {
+ "turns": 10,
+ "words": 160,
+ "pct_words": 4.4
+ },
+ "Jake (eParts)": {
+ "turns": 19,
+ "words": 465,
+ "pct_words": 12.9
+ }
+ },
+ "detected_topics": {
+ "ML/Model": 5,
+ "Data": 5,
+ "Architecture": 3,
+ "Project Mgmt": 2
+ },
+ "questions_found": 0,
+ "questions_sample": [],
+ "potential_decisions": 0,
+ "decisions_sample": [],
+ "potential_action_items": 14,
+ "actions_sample": [
+ {
+ "speaker": "Hrishik",
+ "text": "So, primarily, last\u2026 Thank you, thank you, thank you. Okay, so\u2026 This week, primarily, from the OCR, in addition part of things, we were starting on integration. We have\u2026 Okay. We have\u2026 we have started"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "So we have two products, AccuHMOA, and there's one more, I think, OAW. They have similar, like, similar values. Attributes and values. So, when we run into the pipeline, Let me see if this is\u2026 oh, yea"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "I mean, ideally, the OCR part just puts an order value, and the MN model is supposed to finally pass it out, because that's the main\u2026 Part of this problem. And so where's the hell? a disconnect here. "
+ },
+ {
+ "speaker": "Jake (eParts)",
+ "text": "Yeah, we'll have a different row with ID Standard 3 for when we eventually move to, E-Class. And then same, like, ID standard,"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "duplicated this thing, like, two circuit breakers and fuses, if it has, IHC2 and 1, we'll have 3, the same."
+ },
+ {
+ "speaker": "Jake (eParts)",
+ "text": "Yeah, we'll need some kind of way to link them together, probably another table for us to map alias or map different standards together. But right now, in terms of just storing these, is this the cate"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Okay, okay, okay. So initially, we were thinking about doing a bit of ETM part in the, before the ML portion, but, after\u2026 like, getting a bit further, that seems a bit difficult, so we need more infor"
+ },
+ {
+ "speaker": "Jake (eParts)",
+ "text": "Great, thank you Yeah, and the reason for that, we just want to keep it flexible, too, for, we could\u2026 I think standard 2\u2026 standard ID 2 just says ETIM, but really it should say, ETIM 10.0, and then wh"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Okay, so let me just share my screen. Thomas, let's see. That is the one second. So who's in Puerto Rico? Is this a fun thing in Puerto Rico? Someone with knee parts is in Puerto Ric."
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "Thank you. Oh. Hello? Oh, okay. So\u2026 I've been working on, the ETMS class matching, so currently. It is done. I still have to\u2026 Get, do some revisions on this. I have tested it, and it's currently at ar"
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-07-09-client.md b/minutes/2026-07-09-client.md
new file mode 100644
index 0000000..91f7d8e
--- /dev/null
+++ b/minutes/2026-07-09-client.md
@@ -0,0 +1,42 @@
+# Meeting Minutes — 2026-07-09
+
+**Date:** 2026-07-09
+**Duration:** 26 minutes
+**Participants:** Hrishik, Harsha (eParts), Jake (eParts)
+**Source:** `GMT20260709-190533_Recording.transcript.vtt`
+**Processed:** 2026-07-20 05:14 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Hrishik | 21 | 2984 | 82.7% |
+| Harsha (eParts) | 10 | 160 | 4.4% |
+| Jake (eParts) | 19 | 465 | 12.9% |
+
+## Topics Discussed
+
+- **ML/Model** █████ (relevance: 5)
+- **Data** █████ (relevance: 5)
+- **Architecture** ███ (relevance: 3)
+- **Project Mgmt** ██ (relevance: 2)
+
+## Potential Action Items
+
+1. **[Hrishik]** So, primarily, last… Thank you, thank you, thank you. Okay, so… This week, primarily, from the OCR, in addition part of things, we were starting on integration. We have… Okay. We have… we have started
+2. **[Hrishik]** So we have two products, AccuHMOA, and there's one more, I think, OAW. They have similar, like, similar values. Attributes and values. So, when we run into the pipeline, Let me see if this is… oh, yea
+3. **[Hrishik]** I mean, ideally, the OCR part just puts an order value, and the MN model is supposed to finally pass it out, because that's the main… Part of this problem. And so where's the hell? a disconnect here.
+4. **[Jake (eParts)]** Yeah, we'll have a different row with ID Standard 3 for when we eventually move to, E-Class. And then same, like, ID standard,
+5. **[Hrishik]** duplicated this thing, like, two circuit breakers and fuses, if it has, IHC2 and 1, we'll have 3, the same.
+6. **[Jake (eParts)]** Yeah, we'll need some kind of way to link them together, probably another table for us to map alias or map different standards together. But right now, in terms of just storing these, is this the cate
+7. **[Hrishik]** Okay, okay, okay. So initially, we were thinking about doing a bit of ETM part in the, before the ML portion, but, after… like, getting a bit further, that seems a bit difficult, so we need more infor
+8. **[Jake (eParts)]** Great, thank you Yeah, and the reason for that, we just want to keep it flexible, too, for, we could… I think standard 2… standard ID 2 just says ETIM, but really it should say, ETIM 10.0, and then wh
+9. **[Hrishik]** Okay, so let me just share my screen. Thomas, let's see. That is the one second. So who's in Puerto Rico? Is this a fun thing in Puerto Rico? Someone with knee parts is in Puerto Ric.
+10. **[Hrishik]** Thank you. Oh. Hello? Oh, okay. So… I've been working on, the ETMS class matching, so currently. It is done. I still have to… Get, do some revisions on this. I have tested it, and it's currently at ar
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 3609 words across 50 speaker turns*
\ No newline at end of file
diff --git a/minutes/2026-07-16-cleaned.md b/minutes/2026-07-16-cleaned.md
new file mode 100644
index 0000000..fb8d414
--- /dev/null
+++ b/minutes/2026-07-16-cleaned.md
@@ -0,0 +1,173 @@
+# Cleaned Transcript — 2026-07-16
+
+**Ashritha**: So, like Rishi mentioned, we are just working on the integration part of it, like, So basically, I am simultaneously… like, we found a few bugs on the ML side, so I am resolving those, and we have, like, few other tasks that are open. We'll give you updates on that pretty soon. And then, so the team is quite busy with the integration part, like Rishi and Arjun are busy wrapping up the… OCR and ingestion pipeline, and Jay is involved with the ETM, side of it. So… I mean, yeah, so individually, we can go ahead and, like, give our own updates, I guess. So, I mean, starting, maybe, like, Jay can start with, what he did, around ETM, and then if he needs any help from Jake or someone, he can ask you guys. So, Jay, you wanna go first?
+
+**Jaivard**: Sure, sure. You guys can hear me, right?
+
+**Harsha (eParts)**: We can hear you, but it's still quiet.
+
+**Jaivard**: Okay. Sorry, my laptop's, a bit finicky about its microphone. Hopefully it's all right right now. You know. So… The class matchings were done, and as for the attributes one, That's also, mostly, it is also done, so it is matching properly, each of the products, types has their own attribute names. So, for example, access doors have height, material, type, width. And it does that for all of them. So… Basically, for… For, product types? And for features, that part is done. What I'm a bit more unsure about is the ETM, basically value normalization, so… There was an example such like this, so 4.7 kilo ohms and yeah. It's written in different ways. So I'm working through it right now, and I'm able to get around. This is, a bit, under than what I have gotten currently. It's more like 42%, 43%. And out of, all those exam out of around 50,000 examples, 43% are work automatically. And… They're pretty easy to normalize the values for, but the others I'm still having problems on. And… It's it's not finished yet, so I can. I can. I can. I think I have. that… Yeah, only around 18,000 out of, 50,000 have been done, I would say. Scenely, and then there's some more that have been done, but I'm not sure about. So…
+
+**Harsha (eParts)**: Just from my understanding, you're talking about mapping like the existing ALPS values to ETIMS, like 43 of them were easy to, were able to map right away. And the other ones due to, yeah, different, like one saying KO as opposed to kilo or something like that is all spelled out. Okay.
+
+**Jaivard**: Yes. So the the class matching and feature. These 2 are done. But the value and unit normalization I'm having a some trouble with. But I'll keep working on it, and I think I can get maybe not all of it, but a much higher percentage than the 43% that I have right now. And. I think I'll, just message you, Jake, after I'm kind of, like, exhaust everything, because I'm not sure if I can, how high of a percentage I can get this one on.
+
+**Harsha (eParts)**: Yeah, no, for sure. I, I think, I'm already anticipating that there's definitely gonna be some things that just are gonna have to be mapped by, like, a human on the catalog team, yeah.
+
+**Jaivard**: Yeah, because I was initially quite optimistic, because the class and feature matching had gone well.
+
+**Harsha (eParts)**: Yeah, hu.
+
+**Jaivard**: One is much more finicky and. if my initial efforts are only getting 43%, I don't know how I'm supposed to, like, push that to… close to, you know, 100. So I'll just do everything possible and then kind of discuss with your teams, I guess.
+
+**Harsha (eParts)**: Awesome. Good stuff.
+
+**Jaivard**: And yeah, I'm still working through it. There are a few more things I can do. I did not do them because I… They're, they're, quite, I would say… Oh. They're quite experiment intensive, so I'd have to, like. We use a few different things and to see what works. But yeah, I've exhausted the easy methods and that, that's only yielding like 42%. Okay.
+
+**Harsha (eParts)**: 3.
+
+**Ashritha**: Okay. Yes.
+
+**Jaivard**: That's it from my end, I.
+
+**Ashritha**: Okay. So, from my end, so this week I've been… I've been spending some time to bump up the test coverage for the repo, so that's, like, up to 85% right now. And… but then, there's one thing, that, like… I mean, like, a significant issue, that I found out is So we have our configuration settings. set, right? So basically, the ones that we decided would control how the model updates itself from the reviewer feedback, and it would get, recalibrated. So, there's one question that I had, like, which of these could like, you guys, like, the client safely, adjust them… adjust yourself. So, and then, which of them were, like, too risky? By adjusting, I mean the configuration settings. So, I… kind of, like, figured out that, most of it should stay logged, and then, the one that should genuinely be adjusted is the calibration setting. And, but it is also, like, very, I mean not dangerous per se but then you should be like aware of the settings that you would edit and so probably I think that we should automate it instead of like hand editing so because even if like suppose a human comes and like tweaks that config file maybe there might be some issues so we could just like automate it the calibration setting also. So, I… like, this is a very… I mean… this is a small feature of the entire thing, but I had a lot of questions, like, this is one of… a question that I just… I'm just putting across. So, I wrote… I wrote up a short doc with some open questions, so I might, send it to you, like, after this meeting, so that, Harsha and, like, you and, sorry, Jake and Harsha, like, you guys can give some feedback on that, and we can adjust our, config file, or… Whatever, yeah.
+
+**Harsha (eParts)**: the basics.
+
+**Ashritha**: But otherwise, I think, like I said, to just, like, from the MLOps side of it, just for the whole, pipeline, nothing related to the project, actually, but then, we have set up our CI pipelines. They have been failing for some reason, so, that's something that we have to handle. That's something… that's what I'm working… that's what I've picked up for this week again. Yeah, that's it from my side. Like, there were a few, issues in the evaluation reports that I think, Liu… I'm not sure if Liu had shared with… shared those reports with you guys earlier, like, last month when I was not there, but all… like, all of our reports, like, the milestone reports are published on the repository itself. Like, if you guys are interested, I can point out to those reports, when I send out the meeting minutes, so… So, there were, like, few issues over there, so if you guys can just… spend some time over there and, like, point out some very obvious issues, or, like, maybe, like, we are contradicting ourselves. Like, I found out that, there was one issue with, like, the scoring part of it and the profiling part of it, so I corrected it, and now the report is up to date, So, it was something to do with the encoder piece that we just committed. So… like, I mean, we felt like, like, since you guys know the product better than us and the ML side of it, so I can give… I can point out that link also, you guys can review it and maybe send us some feedback over the next week, like, before we meet or something.
+
+**Harsha (eParts)**: make fun. Also send us the links. We look at the report card as well, and See what we can… what we can get.
+
+**Ashritha**: Okay.
+
+**Harsha (eParts)**: And, yeah, that's… that sounds pretty good overall. Let us know all the… all the… all the configurations you're talking about as well, and if you can make a list of that in the document, that'll be helpful as well.
+
+**Ashritha**: Okay.
+
+**Harsha (eParts)**: So that when they're answering the questions they're not getting lost on, how will this impact the system?
+
+**Ashritha**: Okay, yeah, I'll send that. Okay. Lee, do you wanna, like, update on your work?
+
+**Harsha (eParts)**: Yep.
+
+**Liu**: Oh. From my side, I… I have not many things to add, but I… For this week, I just… adjust some algorithms. like… to adjust the song. Combined value clusters outrank specific value clusters. like… for some products. They have some combined values, and okay. our… Can I share? Cassius, okay.
+
+**Ashritha**: Oh, yeah, can you try now? I've given you the access.
+
+**Liu**: Okay. like, the… The ML system could choose, could choose the top three, Possible… match values towards the one product, but it can't choose which one could be the best. So I… try to find a method to solve these problems. be… So I chose one method named multiple negatives ranking loss. Since we… It's this phenomenon. emerge, because or the… the… Not… not… Like, less… It's kind of on the training. problem, so… I… used this method to To to train the model. So… This method is still testing, so I can't tell you whether it would without these problems, but I will try to resolve this.
+
+**Harsha (eParts)**: What was the three values again? I know that the two were very similar, the VAC and then the VACVDC.
+
+**Liu**: Oh, yes, yes.
+
+**Harsha (eParts)**: The 110 over 230.
+
+**Liu**: On. This one, to me.
+
+**Harsha (eParts)**: Yeah.
+
+**Liu**: Yes, maybe, in this example, this one could be the ground truth. maybe these tools are similar. But our system will choose the most possible three attributes, so they add this, but this could not be It's a match. So…
+
+**Harsha (eParts)**: What do you think? What do you think, Harsha, about It is a case where we would want there to be multiple values against a attribute. Yeah, it's amazing.
+
+**Liu**: Possible values towards the product.
+
+**Harsha (eParts)**: It might not be bad, yeah, if we put them all in. Yeah, we can just send.
+
+**Liu**: Oh, yes, yes.
+
+**Harsha (eParts)**: Yeah, so that could be an alternative to Leo. Instead of like trying to figure out which one out of these three is the correct one by elimination, what we can do is we can just put all three in and make it like a user approved thing where the user chooses which attributes they want. Which values they want. So, like, they might choose.
+
+**Liu**: Yes, yes. So that's a kind of result method. Like when we first meet this condition, the human. If the machine could not choose the best one, human could help it, and the result will… Then feed into the systems and next time it will know which one could be the best. So that's what I mean, because we have not so many data to train the model. It's kind of like on the on the train. phenomena, so… Yes, this could definitely be helpful.
+
+**Harsha (eParts)**: Good.
+
+**Liu**: Yeah, that's what I want to share with you.
+
+**Harsha (eParts)**: Reasonably. And, also, let us know if you guys have any more issues with, like, the CICD stuff, and… any of the DevOps-y stuff, because they do tend to take a lot of time, because those are things to figure out as you go. And they also do our. you might not be able to, like, find an accurate solution for it just by yourself. In those cases she's creating us. Me, David, we can just hop onto it and see what's going on.
+
+**Ashritha**: Yeah, okay. So, but, the right now, the CI pipeline that I was talking about was something specific to the PR's build process, so, like, that has nothing to do with the product, so we haven't gotten yet there. So, once we have that plan in place, probably we'll, like, take some suggestions, yeah.
+
+**Harsha (eParts)**: Yeah, no, I mean, just set up in general, like just the big package pipeline, say, Amplify, that is like a whole beast by itself.
+
+**Ashritha**: Yes.
+
+**Harsha (eParts)**: But, yeah.
+
+**Ashritha**: Okay. Rishi is here, like, Rishi, do you wanna, like, update on the, like, urgent OCR work and yours, or… and, like, how the ingestion… Sorry, integration thing is going on?
+
+**Hrishik**: I think this data is pretty similar to the last… Last update, but right now we are working on getting the output… In a place where we can send it forward to the ML pipeline. We are… I would say we have a few, like, we have planned it out. We have around 5 to 6 tickets that we need to complete. I think we're almost halfway through. Like, we can say we are halfway through, and… we should be able to, I guess… Complete from our sides by early next week, if there are no issues. We're also writing a few of the test cases, and, like, I did find a couple of bugs, so that's going on parallel. Yeah, so that's the thing we're working on right now.
+
+**Harsha (eParts)**: We would also love to see, like, things a little more visually, like, not the Android flows, but just, just understanding, The different pipelines that you guys are designing, and how just the architecture will add up to those. Just so that we have a better idea on, like, what party we're taking in. Because it just feels like a black box at this point.
+
+**Hrishik**: Okay.
+
+**Harsha (eParts)**: Okay. And that's it. That's that's all I have to say.
+
+**Ashritha**: Yeah, I think, honestly, like, by next week, we should have something, like, to demo… not, like, the entire pipeline at least, but, like, some of all the work that we have done so far in, like, a demo-able form, because, I mean, even though, like, you asked us, we have this, end semester presentation coming, and then as part of it, we have to demonstrate our solution, so we already started working towards it. But yeah, you rightly mentioned it, it'd be more visible, for you and for us to, like, if… it's… everything, like, right now, probably, like, everything just feels in the air, and, like, in the documents, or, like, in the Bitbucket code. So, yeah, honestly, it would boost some confidence to us also. So, yeah, I think, we spent some good amount of time this week on the integration part of it, because like, the modules are done, but I think the major lacking is in how, so basically we are now, like, we, know that what the ML pipeline expects the input to be, so Rishi is working on to align with that schema. So once that's there, we can just test out with few batches of examples. At least we would know how much, like, how much of it is white or black or whatever, so… Yeah, I think next week we'll be able to, like. Give a good, like, a short demo or something with, like, a bunch of examples.
+
+**Harsha (eParts)**: Yeah, no stress at all. I mean, don't, don't worry yourself with a demo that is working, or, like, a short demo that's working. It could be, like, a flowchart, even though it works.
+
+**Ashritha**: Okay.
+
+**Harsha (eParts)**: That's it, just something we should have said.
+
+**Ashritha**: Oh yeah, sure.
+
+**Harsha (eParts)**: Yeah, that's it.
+
+**Ashritha**: Okay.
+
+**Harsha (eParts)**: That's a good question. No. Nope, I think it.
+
+**Ashritha**: Oh.
+
+**Harsha (eParts)**: What do you.
+
+**Ashritha**: I think…
+
+**Harsha (eParts)**: But I see a lease. Sorry. Anyone else any updates?
+
+**Ashritha**: No, I think, we are good from our side. Cliff, do you want to add something to it?
+
+**Cliff (Mentor)**: I just want to talk to you for a little bit after the client's done. That's all.
+
+**Ashritha**: Okay.
+
+**Harsha (eParts)**: It seems that it's like you have to come back to him.
+
+**Cliff (Mentor)**: Okay.
+
+**Harsha (eParts)**: Have a good one. See you guys. Bye-bye. Bye. See.
+
+**Hrishik**: Bye by.
+
+**Harsha (eParts)**: I'll be here for that.
+
+**Cliff (Mentor)**: Hi, team. So I just had a question about your current CI/CD pipeline. Is this stuff internal or are you trying to work with an eParts flow at this point?
+
+**Ashritha**: When I meant internal, it's for the CI-CD pipeline, as for the code that we check in, so for the Bitbucket pull requests and stuff like that. So, our MLOps for the product is not yet… we have not yet committed any code for that, so I, like, once we thought that once the integration part is done, wherein we test the OCR ingestion and the machine learning part of it, we can just figure out the ML loss, which should be easy, it should not take a lot of time, because we know the plan, it's just that we have to get the coding part of it done, but we have the CI part of it for the build process, like, the code build process, but not for the actual ML part of it. So, MLOps is not there. you could say, like, the CICD for the code is present here.
+
+**Cliff (Mentor)**: Okay. So did you guys have a priority interface spec between your pieces of code that you're building? Or is that something you're figuring out as you integrate? That wasn't clear. as you guys integrate different components that you've been working on, did you have… originally have a interface spec figured out, or is that something that you're just refining at this point? Does that make sense?
+
+**Ashritha**: Oh, we have that figured out already. Like, when we initially made the specification document for the entire project, Liu, since he was a primary person working on the ML part, he told us initially itself, like, this is the expected input format. So we just kind of… Aligning with it right now, but we are not modifying any of our initial, spec… commitment, so, yeah. We're not refining it. We're just, like, aligning the output that is coming from the intuition with the input that he wants it to be, that's it. So nothing extra that we added recently.
+
+**Cliff (Mentor)**: Okay, sounds good. All right. Are you having the mentor meeting in person, or is it on Zoom? So, I… I noticed that the… the mentor… the client meeting was possibly going to be on Zoom. I just want to confirm about the mentor meeting.
+
+**Ashritha**: Yeah, it's gonna be on Zoom, like, I just dropped a message, like, I mean, the entire team was on remote, so I thought I just… I would ask you and Dennis as well. So Dennis said he would be on Zoom, so yeah, I just stayed on phone.
+
+**Cliff (Mentor)**: Okay. All right. Well, I missed that. I've been troubleshooting things at my church, so I was, I was doing that. Okay. Alright.
+
+**Liu**: I think I'm in the room next to you now.
+
+**Cliff (Mentor)**: Hahaha.
+
+**Liu**: And also in the cave.
+
+**Cliff (Mentor)**: Okay. Well, yeah, I'll stay here until then, and then leave, that's good. Alright. See you all at 4 o'clock.
+
+**Liu**: Okay.
+
+**Ashritha**: Bye.
+
+**Hrishik**: Bye.
\ No newline at end of file
diff --git a/minutes/2026-07-16-client.json b/minutes/2026-07-16-client.json
new file mode 100644
index 0000000..e2373e2
--- /dev/null
+++ b/minutes/2026-07-16-client.json
@@ -0,0 +1,110 @@
+{
+ "meeting_date": "2026-07-16",
+ "duration_minutes": 23,
+ "participants": [
+ "Ashritha",
+ "Jaivard",
+ "Harsha (eParts)",
+ "Liu",
+ "Hrishik",
+ "Cliff (Mentor)"
+ ],
+ "participant_count": 6,
+ "total_words": 3155,
+ "total_turns": 86,
+ "speaker_stats": {
+ "Ashritha": {
+ "turns": 23,
+ "words": 1437,
+ "pct_words": 45.5
+ },
+ "Jaivard": {
+ "turns": 7,
+ "words": 451,
+ "pct_words": 14.3
+ },
+ "Harsha (eParts)": {
+ "turns": 32,
+ "words": 604,
+ "pct_words": 19.1
+ },
+ "Liu": {
+ "turns": 12,
+ "words": 325,
+ "pct_words": 10.3
+ },
+ "Hrishik": {
+ "turns": 4,
+ "words": 134,
+ "pct_words": 4.2
+ },
+ "Cliff (Mentor)": {
+ "turns": 8,
+ "words": 204,
+ "pct_words": 6.5
+ }
+ },
+ "detected_topics": {
+ "ML/Model": 4,
+ "Architecture": 2,
+ "Data": 2,
+ "Onboarding": 2
+ },
+ "questions_found": 0,
+ "questions_sample": [],
+ "potential_decisions": 2,
+ "decisions_sample": [
+ {
+ "speaker": "Ashritha",
+ "text": "Okay. So, from my end, so this week I've been\u2026 I've been spending some time to bump up the test coverage for the repo, so that's, like, up to 85% right now. And\u2026 but then, there's one thing, that, lik"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "I think this data is pretty similar to the last\u2026 Last update, but right now we are working on getting the output\u2026 In a place where we can send it forward to the ML pipeline. We are\u2026 I would say we hav"
+ }
+ ],
+ "potential_action_items": 15,
+ "actions_sample": [
+ {
+ "speaker": "Ashritha",
+ "text": "So, like Rishi mentioned, we are just working on the integration part of it, like, So basically, I am simultaneously\u2026 like, we found a few bugs on the ML side, so I am resolving those, and we have, li"
+ },
+ {
+ "speaker": "Jaivard",
+ "text": "Yes. So the the class matching and feature. These 2 are done. But the value and unit normalization I'm having a some trouble with. But I'll keep working on it, and I think I can get maybe not all of i"
+ },
+ {
+ "speaker": "Jaivard",
+ "text": "One is much more finicky and. if my initial efforts are only getting 43%, I don't know how I'm supposed to, like, push that to\u2026 close to, you know, 100. So I'll just do everything possible and then ki"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Okay. So, from my end, so this week I've been\u2026 I've been spending some time to bump up the test coverage for the repo, so that's, like, up to 85% right now. And\u2026 but then, there's one thing, that, lik"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Okay, yeah, I'll send that. Okay. Lee, do you wanna, like, update on your work?"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Oh, yeah, can you try now? I've given you the access."
+ },
+ {
+ "speaker": "Liu",
+ "text": "Okay. like, the\u2026 The ML system could choose, could choose the top three, Possible\u2026 match values towards the one product, but it can't choose which one could be the best. So I\u2026 try to find a method to "
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Yeah, okay. So, but, the right now, the CI pipeline that I was talking about was something specific to the PR's build process, so, like, that has nothing to do with the product, so we haven't gotten y"
+ },
+ {
+ "speaker": "Hrishik",
+ "text": "I think this data is pretty similar to the last\u2026 Last update, but right now we are working on getting the output\u2026 In a place where we can send it forward to the ML pipeline. We are\u2026 I would say we hav"
+ },
+ {
+ "speaker": "Ashritha",
+ "text": "Yeah, I think, honestly, like, by next week, we should have something, like, to demo\u2026 not, like, the entire pipeline at least, but, like, some of all the work that we have done so far in, like, a demo"
+ }
+ ],
+ "analysis_mode": "offline (no LLM \u2014 structural extraction only)"
+}
\ No newline at end of file
diff --git a/minutes/2026-07-16-client.md b/minutes/2026-07-16-client.md
new file mode 100644
index 0000000..9f1618b
--- /dev/null
+++ b/minutes/2026-07-16-client.md
@@ -0,0 +1,50 @@
+# Meeting Minutes — 2026-07-16
+
+**Date:** 2026-07-16
+**Duration:** 23 minutes
+**Participants:** Ashritha, Jaivard, Harsha (eParts), Liu, Hrishik, Cliff (Mentor)
+**Source:** `GMT20260716-190119_Recording.transcript.vtt`
+**Processed:** 2026-07-20 05:14 UTC
+
+---
+
+## Participation
+
+| Speaker | Turns | Words | Share |
+|---------|-------|-------|-------|
+| Ashritha | 23 | 1437 | 45.5% |
+| Jaivard | 7 | 451 | 14.3% |
+| Harsha (eParts) | 32 | 604 | 19.1% |
+| Liu | 12 | 325 | 10.3% |
+| Hrishik | 4 | 134 | 4.2% |
+| Cliff (Mentor) | 8 | 204 | 6.5% |
+
+## Topics Discussed
+
+- **ML/Model** ████ (relevance: 4)
+- **Architecture** ██ (relevance: 2)
+- **Data** ██ (relevance: 2)
+- **Onboarding** ██ (relevance: 2)
+
+## Potential Decisions
+
+1. **[Ashritha]** Okay. So, from my end, so this week I've been… I've been spending some time to bump up the test coverage for the repo, so that's, like, up to 85% right now. And… but then, there's one thing, that, lik
+2. **[Hrishik]** I think this data is pretty similar to the last… Last update, but right now we are working on getting the output… In a place where we can send it forward to the ML pipeline. We are… I would say we hav
+
+## Potential Action Items
+
+1. **[Ashritha]** So, like Rishi mentioned, we are just working on the integration part of it, like, So basically, I am simultaneously… like, we found a few bugs on the ML side, so I am resolving those, and we have, li
+2. **[Jaivard]** Yes. So the the class matching and feature. These 2 are done. But the value and unit normalization I'm having a some trouble with. But I'll keep working on it, and I think I can get maybe not all of i
+3. **[Jaivard]** One is much more finicky and. if my initial efforts are only getting 43%, I don't know how I'm supposed to, like, push that to… close to, you know, 100. So I'll just do everything possible and then ki
+4. **[Ashritha]** Okay. So, from my end, so this week I've been… I've been spending some time to bump up the test coverage for the repo, so that's, like, up to 85% right now. And… but then, there's one thing, that, lik
+5. **[Ashritha]** Okay, yeah, I'll send that. Okay. Lee, do you wanna, like, update on your work?
+6. **[Ashritha]** Oh, yeah, can you try now? I've given you the access.
+7. **[Liu]** Okay. like, the… The ML system could choose, could choose the top three, Possible… match values towards the one product, but it can't choose which one could be the best. So I… try to find a method to
+8. **[Ashritha]** Yeah, okay. So, but, the right now, the CI pipeline that I was talking about was something specific to the PR's build process, so, like, that has nothing to do with the product, so we haven't gotten y
+9. **[Hrishik]** I think this data is pretty similar to the last… Last update, but right now we are working on getting the output… In a place where we can send it forward to the ML pipeline. We are… I would say we hav
+10. **[Ashritha]** Yeah, I think, honestly, like, by next week, we should have something, like, to demo… not, like, the entire pipeline at least, but, like, some of all the work that we have done so far in, like, a demo
+
+---
+
+*Analysis mode: offline (no LLM — structural extraction only)*
+*Total: 3155 words across 86 speaker turns*
\ No newline at end of file
diff --git a/minutes/cross-meeting-analysis.md b/minutes/cross-meeting-analysis.md
new file mode 100644
index 0000000..1cd3c43
--- /dev/null
+++ b/minutes/cross-meeting-analysis.md
@@ -0,0 +1,64 @@
+# eParts Client Meetings — Cross-Meeting Analysis
+
+**Generated:** 2026-07-20 05:14 UTC
+**Meetings analyzed:** 15
+
+---
+
+## Meeting Overview
+
+| Date | Duration | Participants | Words | Topics |
+|------|----------|-------------|-------|--------|
+| 2026-01-22 | 56min | 2 | 8988 | ML/Model, Data, Onboarding |
+| 2026-02-12 | 56min | 2 | 7417 | ML/Model, Architecture, Infrastructure |
+| 2026-02-26 | 30min | 2 | 4467 | ML/Model, Architecture, Data |
+| 2026-04-02 | 32min | 4 | 4629 | Data, ML/Model, Architecture |
+| 2026-04-16 | 24min | 3 | 3095 | Data, ML/Model, Architecture |
+| 2026-05-14 | 42min | 1 | 5296 | ML/Model, Architecture, Data |
+| 2026-05-21 | 53min | 4 | 4973 | Data, ML/Model, Infrastructure |
+| 2026-05-28 | 35min | 1 | 4757 | Data, ML/Model, Infrastructure |
+| 2026-06-04 | 39min | 1 | 5774 | ML/Model, Data, Architecture |
+| 2026-06-11 | 6min | 2 | 977 | Data |
+| 2026-06-18 | 16min | 3 | 2328 | ML/Model, Architecture, Data |
+| 2026-06-25 | 39min | 1 | 5893 | ML/Model, Architecture, Data |
+| 2026-07-02 | 27min | 7 | 3182 | ML/Model, Data |
+| 2026-07-09 | 26min | 3 | 3609 | ML/Model, Data, Architecture |
+| 2026-07-16 | 23min | 6 | 3155 | ML/Model, Architecture, Data |
+
+## Aggregate Statistics
+
+- **Total meeting time:** 504 minutes (8.4 hours)
+- **Total words transcribed:** 68,540
+- **Total speaker turns:** 678
+- **Unique participants:** 11 (Arjun, Ashritha, Cliff (Mentor), David (eParts), David Mine, Dennis Grinberg, Harsha (eParts), Hrishik, Jaivard, Jake (eParts), Liu)
+- **Average meeting length:** 33 minutes
+- **Average words per meeting:** 4,569
+
+## Topic Frequency Across Meetings
+
+- **Data**: ███████████████ (15/15 meetings, 100%)
+- **ML/Model**: ██████████████ (14/15 meetings, 93%)
+- **Architecture**: █████████████ (13/15 meetings, 87%)
+- **Onboarding**: █████████ (9/15 meetings, 60%)
+- **Infrastructure**: ███████ (7/15 meetings, 47%)
+- **Project Mgmt**: ████ (4/15 meetings, 27%)
+
+## Speaker Participation
+
+| Speaker | Meetings | Total Words | Avg Words/Meeting |
+|---------|----------|-------------|-------------------|
+| Hrishik | 12 | 34,225 | 2,852 |
+| Ashritha | 7 | 17,156 | 2,450 |
+| Harsha (eParts) | 8 | 8,998 | 1,124 |
+| Jaivard | 4 | 3,487 | 871 |
+| Liu | 2 | 1,585 | 792 |
+| David Mine | 1 | 1,569 | 1,569 |
+| Jake (eParts) | 1 | 465 | 465 |
+| Cliff (Mentor) | 3 | 333 | 111 |
+| Arjun | 2 | 307 | 153 |
+| Dennis Grinberg | 1 | 233 | 233 |
+| David (eParts) | 1 | 182 | 182 |
+
+---
+
+*Generated by eParts Agentic SE System — offline structural analysis*
\ No newline at end of file
diff --git a/orchestrator/__init__.py b/orchestrator/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/orchestrator/main.py b/orchestrator/main.py
new file mode 100644
index 0000000..e579c41
--- /dev/null
+++ b/orchestrator/main.py
@@ -0,0 +1,542 @@
+"""
+Central Orchestrator — FastAPI server for the eParts agentic system.
+
+Three entry points:
+ POST /webhook — receives external events (Jira, Slack, GitHub, Drive)
+ POST /trigger — manual API trigger for any agent
+ GET /health — health check + queue status
+
+Pure routing and queue management. Does NOT make LLM calls.
+All agent execution is dispatched through the shared TaskQueue.
+"""
+
+from __future__ import annotations
+
+import logging
+from contextlib import asynccontextmanager
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel, Field
+
+from orchestrator.queue import AgentTask, TaskQueue
+from orchestrator.router import resolve_agents
+
+DASHBOARD_DIR = Path(__file__).resolve().parent.parent / "dashboard"
+
+logging.basicConfig(
+ level=logging.INFO,
+ format="%(asctime)s [%(name)s] %(levelname)s %(message)s",
+)
+logger = logging.getLogger("orchestrator")
+
+task_queue = TaskQueue()
+_agent_instances: dict[str, Any] = {}
+
+
+# ---------------------------------------------------------------------------
+# Lifespan — start/stop the task queue worker
+# ---------------------------------------------------------------------------
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+ global _agent_instances
+ from orchestrator.registry import register_all_agents
+ _agent_instances = register_all_agents(task_queue)
+ task_queue.start()
+ logger.info("Orchestrator started — all agents registered")
+ yield
+ task_queue.stop()
+ logger.info("Orchestrator shut down")
+
+
+app = FastAPI(
+ title="eParts Agentic Orchestrator",
+ version="0.1.0",
+ lifespan=lifespan,
+)
+
+
+# ---------------------------------------------------------------------------
+# Request / response models
+# ---------------------------------------------------------------------------
+
+class WebhookPayload(BaseModel):
+ trigger_type: str = Field(
+ ...,
+ description="One of: transcript, coach_transcript, jira_webhook, "
+ "pr_event, slack_event, poc_result",
+ )
+ source: str = Field(..., description="File path, URL, or event identifier")
+ metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class ManualTriggerPayload(BaseModel):
+ agent: str = Field(..., description="Agent name to invoke directly")
+ payload: dict[str, Any] = Field(default_factory=dict)
+
+
+class TaskResponse(BaseModel):
+ task_ids: list[str]
+ agents: list[str]
+ message: str
+
+
+class TaskStatusResponse(BaseModel):
+ task_id: str
+ result: Any | None
+ found: bool
+
+
+# ---------------------------------------------------------------------------
+# Endpoints
+# ---------------------------------------------------------------------------
+
+@app.get("/health")
+async def health():
+ return {
+ "status": "ok",
+ "queue_running": task_queue.is_running,
+ "queue_pending": task_queue.pending_count,
+ "agents_registered": len(task_queue._agent_registry),
+ "timestamp": datetime.now(timezone.utc).isoformat(),
+ }
+
+
+@app.get("/agents")
+async def list_agents():
+ """List all registered agents and their route mappings."""
+ from orchestrator.router import TRIGGER_ROUTES
+ return {
+ "registered": sorted(task_queue._agent_registry.keys()),
+ "routes": TRIGGER_ROUTES,
+ }
+
+
+@app.post("/webhook", response_model=TaskResponse)
+async def webhook(payload: WebhookPayload):
+ """
+ Receive an external event and route it to the appropriate agent(s).
+ Returns task IDs for tracking.
+ """
+ agents = resolve_agents(payload.trigger_type)
+ if not agents:
+ raise HTTPException(
+ status_code=400,
+ detail=f"No agents registered for trigger_type={payload.trigger_type}",
+ )
+
+ task_ids = []
+ for agent_name in agents:
+ task = AgentTask(
+ agent_name=agent_name,
+ trigger_type=payload.trigger_type,
+ payload={
+ "source": payload.source,
+ "metadata": payload.metadata,
+ },
+ )
+ task_id = task_queue.enqueue(task)
+ task_ids.append(task_id)
+
+ logger.info(
+ f"Webhook received: type={payload.trigger_type} "
+ f"source={payload.source} → dispatched {len(agents)} agent(s)"
+ )
+
+ return TaskResponse(
+ task_ids=task_ids,
+ agents=agents,
+ message=f"Dispatched {len(agents)} agent(s) for {payload.trigger_type}",
+ )
+
+
+@app.post("/trigger", response_model=TaskResponse)
+async def manual_trigger(payload: ManualTriggerPayload):
+ """
+ Manually trigger a specific agent by name.
+ Bypasses the routing table.
+ """
+ task = AgentTask(
+ agent_name=payload.agent,
+ trigger_type="manual",
+ payload=payload.payload,
+ )
+ task_id = task_queue.enqueue(task)
+
+ logger.info(f"Manual trigger: agent={payload.agent} → task_id={task_id}")
+
+ return TaskResponse(
+ task_ids=[task_id],
+ agents=[payload.agent],
+ message=f"Manually triggered {payload.agent}",
+ )
+
+
+@app.get("/task/{task_id}", response_model=TaskStatusResponse)
+async def get_task_status(task_id: str):
+ """Check the result of a previously enqueued task."""
+ result = task_queue.get_result(task_id)
+ return TaskStatusResponse(
+ task_id=task_id,
+ result=result,
+ found=result is not None,
+ )
+
+
+# ---------------------------------------------------------------------------
+# Metrics endpoints — SES measurement system
+# ---------------------------------------------------------------------------
+
+@app.get("/metrics")
+async def metrics_summary():
+ """
+ SES measurement dashboard data. Returns aggregate metrics
+ across all agent operations: token usage, costs, success rates,
+ human review rates, correction counts.
+ """
+ from pipeline.metrics import MetricsCollector
+ mc = MetricsCollector()
+ return {
+ "summary": mc.summary(),
+ "per_agent": mc.per_agent_summary(),
+ "per_prompt": mc.per_prompt_summary(),
+ "token_timeseries": mc.token_usage_timeseries(),
+ }
+
+
+@app.get("/metrics/runs")
+async def metrics_recent_runs(limit: int = 20):
+ """Recent agent run activity feed."""
+ from pipeline.metrics import MetricsCollector
+ mc = MetricsCollector()
+ return {"runs": mc.recent_runs(limit)}
+
+
+@app.get("/metrics/prompts")
+async def metrics_prompt_versions():
+ """Prompt version history for regression tracking."""
+ from pipeline.metrics import MetricsCollector
+ mc = MetricsCollector()
+ return {
+ "versions": mc.prompt_version_history(),
+ "usage": mc.per_prompt_summary(),
+ }
+
+
+@app.post("/metrics/correction")
+async def record_correction(
+ run_id: str,
+ agent: str,
+ correction_type: str,
+ description: str = "",
+):
+ """Record a human correction to an agent output (for re-prompt rate tracking)."""
+ from pipeline.metrics import MetricsCollector
+ mc = MetricsCollector()
+ mc.record_human_correction(run_id, agent, correction_type, description)
+ return {"ok": True, "message": f"Correction recorded for {agent} run {run_id}"}
+
+
+# ---------------------------------------------------------------------------
+# Dashboard
+# ---------------------------------------------------------------------------
+
+@app.get("/dashboard")
+async def dashboard():
+ """Serve the SES metrics dashboard."""
+ return FileResponse(DASHBOARD_DIR / "metrics.html")
+
+
+@app.post("/ingest")
+async def ingest_transcripts():
+ """Run the batch VTT ingestion pipeline on all transcripts."""
+ from pipeline.ingest import run as ingest_run
+ result = ingest_run()
+ return result
+
+
+# ---------------------------------------------------------------------------
+# Pipeline endpoints — the framework in action
+# ---------------------------------------------------------------------------
+
+@app.get("/pipelines")
+async def list_pipelines():
+ """List all defined pipelines and the full framework summary."""
+ from pipeline.pipelines import get_framework_summary
+ return get_framework_summary()
+
+
+@app.post("/pipeline/{pipeline_name}")
+async def run_pipeline(pipeline_name: str, payload: WebhookPayload):
+ """
+ Execute a named pipeline end-to-end.
+ Each step's output feeds the next step's input.
+ """
+ from pipeline.pipelines import ALL_PIPELINES, PipelineExecutor
+ from dataclasses import asdict
+
+ pipe = ALL_PIPELINES.get(pipeline_name)
+ if not pipe:
+ raise HTTPException(
+ status_code=404,
+ detail=f"Pipeline '{pipeline_name}' not found. "
+ f"Available: {list(ALL_PIPELINES.keys())}",
+ )
+
+ executor = PipelineExecutor(_agent_instances)
+ result = executor.execute(pipe, {
+ "trigger_type": payload.trigger_type,
+ "source": payload.source,
+ "metadata": payload.metadata,
+ })
+
+ return {
+ "pipeline_id": result.pipeline_id,
+ "pipeline": result.pipeline_name,
+ "practice_area": result.practice_area,
+ "success": result.success,
+ "steps": f"{result.completed_steps}/{result.total_steps} completed, "
+ f"{result.skipped_steps} skipped, {result.failed_steps} failed",
+ "duration_ms": result.total_duration_ms,
+ "llm_calls": result.total_llm_calls,
+ "tokens": result.total_tokens,
+ "artifacts": result.total_artifacts,
+ "requires_human_review": result.requires_human_review,
+ "step_details": [
+ {
+ "agent": sr.agent_name,
+ "description": sr.description,
+ "status": "SKIP" if sr.skipped else ("OK" if sr.success else "FAIL"),
+ "duration_ms": sr.duration_ms,
+ "outputs": sr.artifacts_produced,
+ "human_review": sr.requires_human_review,
+ }
+ for sr in result.step_results
+ ],
+ }
+
+
+# ---------------------------------------------------------------------------
+# ETVX Process Model endpoints
+# ---------------------------------------------------------------------------
+
+@app.get("/etvx")
+async def etvx_summary():
+ """ETVX process model summary — meta-model compliance."""
+ from pipeline.etvx import summary_stats, validate_coverage
+ stats = summary_stats()
+ coverage = validate_coverage(sorted(task_queue._agent_registry.keys()))
+ return {"stats": stats, "coverage": coverage}
+
+
+@app.get("/etvx/processes")
+async def etvx_processes():
+ """Full ETVX process definitions."""
+ from pipeline.etvx import get_processes
+ return {"processes": get_processes()}
+
+
+@app.get("/etvx/markdown")
+async def etvx_markdown():
+ """Render ETVX manifest as presentation-ready markdown."""
+ from pipeline.etvx import render_markdown
+ from fastapi.responses import PlainTextResponse
+ return PlainTextResponse(render_markdown(), media_type="text/markdown")
+
+
+@app.get("/prompts")
+async def prompt_registry():
+ """Prompt registry — version-controlled prompt governance."""
+ from pipeline.prompt_registry import PromptRegistry
+ reg = PromptRegistry()
+ return {
+ "prompts": reg.get_all_prompts(),
+ "stats": reg.stats(),
+ }
+
+
+@app.get("/conventions")
+async def team_conventions():
+ """Team conventions for systematic operation."""
+ from pipeline.prompt_registry import PromptRegistry, seed_team_conventions
+ seed_team_conventions()
+ reg = PromptRegistry()
+ rows = reg._db.execute(
+ "SELECT convention, category, rationale, enforced_by FROM team_conventions ORDER BY category"
+ ).fetchall()
+ return {"conventions": [dict(r) for r in rows]}
+
+
+@app.get("/risks")
+async def risk_register():
+ """Risk register — auto-populated from architecture, coach sessions, meetings."""
+ from pipeline.risk_register import RiskRegister, seed_risk_register
+ reg = seed_risk_register()
+ return {"risks": reg.get_all(), "stats": reg.stats()}
+
+
+@app.get("/wiki")
+async def wiki_contents():
+ """SharedMemory wiki — the project knowledge graph."""
+ from pipeline.shared_memory import SharedMemory
+ wiki = SharedMemory()
+ stats = wiki.stats()
+ contents = {}
+ for ns in stats["namespaces"]:
+ contents[ns] = wiki.list_namespace(ns)
+ return {"stats": stats, "contents": contents}
+
+
+@app.get("/events")
+async def event_log():
+ """Event bus — cross-pipeline communication log."""
+ from pipeline.event_bus import EventBus
+ bus = EventBus()
+ return {
+ "stats": bus.stats(),
+ "subscriptions": bus.get_subscriptions(),
+ "recent_events": bus.get_pending_events(limit=50),
+ }
+
+
+@app.get("/framework")
+async def framework_overview():
+ """
+ The complete SES framework: pipelines, agents, connections, measurements.
+ This is the 'one diagram' view for the presentation.
+ """
+ from pipeline.pipelines import get_framework_summary
+ from pipeline.etvx import summary_stats, validate_coverage
+
+ framework = get_framework_summary()
+ etvx = summary_stats()
+ coverage = validate_coverage(sorted(task_queue._agent_registry.keys()))
+
+ return {
+ "framework": framework,
+ "etvx": etvx,
+ "agent_coverage": coverage,
+ "system": {
+ "agents_registered": len(task_queue._agent_registry),
+ "queue_running": task_queue.is_running,
+ },
+ }
+
+
+# ---------------------------------------------------------------------------
+# External Integration endpoints — GitHub + Jira live connections
+# ---------------------------------------------------------------------------
+
+@app.get("/github/status")
+async def github_status():
+ """Test GitHub connection and return repo info."""
+ from mcp.github import GitHubMCP
+ gh = GitHubMCP()
+ if not gh.is_configured:
+ return {"ok": False, "error": "GitHub not configured — check .env"}
+ return gh.get_repo_info()
+
+
+@app.get("/jira/status")
+async def jira_status():
+ """Test Jira connection and return board status."""
+ from mcp.jira import JiraMCP
+ jira = JiraMCP()
+ if not jira.is_configured:
+ return {"ok": False, "error": "Jira not configured — check .env (URL still has placeholder)"}
+ return jira.get_board_status()
+
+
+@app.post("/github/commit")
+async def github_commit(file_path: str, content: str, message: str, branch: str = "main", agent: str = "system"):
+ """Commit a file to GitHub via the Contents API."""
+ from mcp.github import GitHubMCP
+ gh = GitHubMCP()
+ return gh.commit_file(file_path, content, message, branch, agent)
+
+
+@app.post("/jira/create")
+async def jira_create_issue(summary: str, description: str = "", issue_type: str = "Task", agent: str = "system"):
+ """Create a Jira issue."""
+ from mcp.jira import JiraMCP
+ jira = JiraMCP()
+ return jira.create_issue(summary, description, issue_type, agent_name=agent)
+
+
+@app.get("/jira/issues")
+async def jira_issues(jql: str | None = None):
+ """Search Jira issues."""
+ from mcp.jira import JiraMCP
+ jira = JiraMCP()
+ return jira.search_issues(jql)
+
+
+@app.get("/traceability")
+async def traceability_overview():
+ """Unified traceability — every artifact and its chain."""
+ from pipeline.traceability import TraceabilityStore
+ from pipeline.seed_traceability import seed
+ seed()
+ store = TraceabilityStore()
+ return {
+ "coverage": store.get_coverage(),
+ "concerns": store.get_by_type("concern"),
+ "decisions": store.get_by_type("decision"),
+ "architecture": store.get_by_type("architecture"),
+ "risks": store.get_by_type("risk"),
+ "commitments": store.get_by_type("commitment"),
+ }
+
+
+@app.get("/traceability/{artifact_id}")
+async def trace_artifact(artifact_id: str, direction: str = "forward"):
+ """Follow a single artifact's traceability chain."""
+ from pipeline.traceability import TraceabilityStore
+ store = TraceabilityStore()
+ artifact = store.get_artifact(artifact_id)
+ if not artifact:
+ raise HTTPException(status_code=404, detail=f"Artifact '{artifact_id}' not found")
+ chain = store.get_chain(artifact_id, direction=direction)
+ return {"artifact": artifact, "chain": chain, "chain_length": len(chain)}
+
+
+@app.get("/traceability/gaps/concerns")
+async def unaddressed_concerns():
+ """Concerns with no action taken — traceability gaps."""
+ from pipeline.traceability import TraceabilityStore
+ store = TraceabilityStore()
+ return {"unlinked_concerns": store.get_unlinked("concern")}
+
+
+@app.get("/traceability/gaps/risks")
+async def unmitigated_risks():
+ """Risks without mitigation — traceability gaps."""
+ from pipeline.traceability import TraceabilityStore
+ store = TraceabilityStore()
+ return {"unmitigated_risks": store.get_unlinked("risk")}
+
+
+@app.get("/traceability/chains/{artifact_type}")
+async def all_chains(artifact_type: str):
+ """Get all forward chains for a given artifact type."""
+ from pipeline.traceability import TraceabilityStore
+ store = TraceabilityStore()
+ return {"chains": store.get_all_chains_from_type(artifact_type)}
+
+
+@app.get("/integrations")
+async def integration_status():
+ """Status of all external integrations."""
+ from mcp.github import GitHubMCP
+ from mcp.jira import JiraMCP
+ gh = GitHubMCP()
+ jira = JiraMCP()
+ return {
+ "github": {"configured": gh.is_configured, "repo": gh._repo},
+ "jira": {"configured": jira.is_configured, "url": jira._url, "project": jira._project_key},
+ }
diff --git a/orchestrator/queue.py b/orchestrator/queue.py
new file mode 100644
index 0000000..fbaf1b8
--- /dev/null
+++ b/orchestrator/queue.py
@@ -0,0 +1,120 @@
+"""
+Shared task queue for sequential agent execution.
+
+Agents run one at a time to prevent race conditions on shared state
+(git commits, Jira updates, SQLite writes). The queue accepts AgentTask
+items and processes them FIFO in a background thread.
+
+Triggered by: orchestrator/main.py enqueuing tasks
+Outputs: Agent results logged to pipeline/logs/agent_runs.jsonl
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import queue
+import threading
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from typing import Any, Callable
+
+logger = logging.getLogger("orchestrator.queue")
+
+
+@dataclass
+class AgentTask:
+ agent_name: str
+ trigger_type: str
+ payload: dict[str, Any]
+ enqueued_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
+ task_id: str = ""
+
+ def __post_init__(self):
+ if not self.task_id:
+ ts = self.enqueued_at.strftime("%Y%m%d%H%M%S%f")
+ self.task_id = f"{self.agent_name}-{ts}"
+
+
+class TaskQueue:
+ """
+ Thread-safe FIFO queue that processes agent tasks sequentially.
+
+ Agents are resolved via a registry (dict of name -> callable that
+ accepts the payload and returns a result). The queue runs in a
+ background daemon thread so the FastAPI event loop is never blocked.
+ """
+
+ def __init__(self):
+ self._queue: queue.Queue[AgentTask] = queue.Queue()
+ self._agent_registry: dict[str, Callable] = {}
+ self._running = False
+ self._worker: threading.Thread | None = None
+ self._results: dict[str, Any] = {}
+ self._lock = threading.Lock()
+
+ def register_agent(self, name: str, handler: Callable) -> None:
+ self._agent_registry[name] = handler
+
+ def enqueue(self, task: AgentTask) -> str:
+ self._queue.put(task)
+ logger.info(f"Enqueued task {task.task_id} for agent={task.agent_name}")
+ return task.task_id
+
+ def get_result(self, task_id: str) -> Any | None:
+ with self._lock:
+ return self._results.get(task_id)
+
+ def start(self) -> None:
+ if self._running:
+ return
+ self._running = True
+ self._worker = threading.Thread(target=self._process_loop, daemon=True)
+ self._worker.start()
+ logger.info("Task queue worker started")
+
+ def stop(self) -> None:
+ self._running = False
+ if self._worker:
+ self._worker.join(timeout=5)
+ logger.info("Task queue worker stopped")
+
+ def _process_loop(self) -> None:
+ while self._running:
+ try:
+ task = self._queue.get(timeout=1.0)
+ except queue.Empty:
+ continue
+
+ logger.info(f"Processing task {task.task_id} (agent={task.agent_name})")
+
+ handler = self._agent_registry.get(task.agent_name)
+ if not handler:
+ logger.error(f"No handler registered for agent={task.agent_name}")
+ with self._lock:
+ self._results[task.task_id] = {
+ "success": False,
+ "error": f"Unknown agent: {task.agent_name}",
+ }
+ continue
+
+ try:
+ result = handler(task)
+ with self._lock:
+ self._results[task.task_id] = result
+ logger.info(f"Task {task.task_id} completed")
+ except Exception as exc:
+ logger.exception(f"Task {task.task_id} failed: {exc}")
+ with self._lock:
+ self._results[task.task_id] = {
+ "success": False,
+ "error": str(exc),
+ }
+
+ @property
+ def pending_count(self) -> int:
+ return self._queue.qsize()
+
+ @property
+ def is_running(self) -> bool:
+ return self._running
diff --git a/orchestrator/registry.py b/orchestrator/registry.py
new file mode 100644
index 0000000..698b8fc
--- /dev/null
+++ b/orchestrator/registry.py
@@ -0,0 +1,230 @@
+"""
+Agent Registry — instantiation and wiring of all agents to the task queue.
+
+This is where the architecture diagram becomes executable. Each agent is
+instantiated with its MCP client dependencies, then registered as a handler
+in the TaskQueue. When a task arrives, the queue calls the handler, which
+converts the AgentTask payload into an AgentTrigger and calls agent.execute().
+
+Separating instantiation from routing keeps the orchestrator testable:
+swap any agent with a mock by replacing its registry entry.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import asdict
+from typing import Any
+
+from agents.base import AgentTrigger, AgentResult
+from orchestrator.queue import AgentTask, TaskQueue
+
+logger = logging.getLogger("orchestrator.registry")
+
+
+def _make_handler(agent):
+ """
+ Wrap a BaseAgent subclass into a TaskQueue handler.
+ Converts AgentTask payload → AgentTrigger, calls execute(), returns dict.
+ """
+ def handler(task: AgentTask) -> dict[str, Any]:
+ trigger = AgentTrigger(
+ trigger_type=task.trigger_type,
+ source=task.payload.get("source", "unknown"),
+ metadata=task.payload.get("metadata", {}),
+ )
+ result = agent.execute(trigger)
+ return {
+ "agent": result.agent,
+ "success": result.success,
+ "outputs": [asdict(o) for o in result.outputs],
+ "errors": result.errors,
+ "requires_human_review": result.requires_human_review,
+ "review_items": result.review_items,
+ }
+ return handler
+
+
+def _build_mcp_clients() -> dict[str, Any]:
+ """
+ Instantiate all MCP server clients. Agents pick what they need by key.
+ Each client wraps a third-party API (Jira, Slack, Bitbucket, etc.)
+ """
+ from mcp.slack import SlackMCP
+ from mcp.jira import JiraMCP
+ from mcp.bitbucket import BitbucketMCP
+ from mcp.confluence import ConfluenceMCP
+ from mcp.drive import DriveMCP
+ from mcp.vector_store import VectorStoreMCP
+ from mcp.github import GitHubMCP
+
+ return {
+ "slack": SlackMCP(),
+ "jira": JiraMCP(),
+ "bitbucket": BitbucketMCP(),
+ "confluence": ConfluenceMCP(),
+ "drive": DriveMCP(),
+ "vector_store": VectorStoreMCP(),
+ "github": GitHubMCP(),
+ }
+
+
+def register_all_agents(task_queue: TaskQueue) -> dict[str, Any]:
+ """
+ Instantiate every agent and register it with the task queue.
+ Returns the dict of agent instances (useful for testing).
+ """
+ mcp = _build_mcp_clients()
+ agents: dict[str, Any] = {}
+
+ # --- Requirements Domain ---
+ from agents.requirements.transcript_parser import TranscriptParserAgent
+ from agents.requirements.priority_classifier import PriorityClassifierAgent
+ from agents.requirements.req_extractor import ReqExtractorAgent
+ from agents.requirements.stale_detector import StaleDetectorAgent
+
+ agents["transcript_parser"] = TranscriptParserAgent(
+ mcp_clients={"bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+ agents["priority_classifier"] = PriorityClassifierAgent()
+ agents["req_extractor"] = ReqExtractorAgent(
+ mcp_clients={"bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+ agents["stale_detector"] = StaleDetectorAgent(
+ mcp_clients={"jira": mcp["jira"], "slack": mcp["slack"]}
+ )
+
+ # --- Architecture Domain ---
+ from agents.architecture.drift_detector import DriftDetectorAgent
+ from agents.architecture.adr_generator import ADRGeneratorAgent
+ from agents.architecture.diagram_updater import DiagramUpdaterAgent
+ from agents.architecture.traceability_builder import TraceabilityBuilderAgent
+
+ agents["drift_detector"] = DriftDetectorAgent()
+ agents["adr_generator"] = ADRGeneratorAgent(
+ mcp_clients={"bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+ agents["diagram_updater"] = DiagramUpdaterAgent(
+ mcp_clients={"bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+ agents["traceability_builder"] = TraceabilityBuilderAgent(
+ mcp_clients={"jira": mcp["jira"], "bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+
+ # --- Coding Domain ---
+ from agents.coding.boilerplate_generator import BoilerplateGeneratorAgent
+ from agents.coding.pr_reviewer import PRReviewerAgent
+ from agents.coding.test_generator import TestGeneratorAgent
+ from agents.coding.doc_generator import DocGeneratorAgent
+ from agents.coding.refactor_agent import RefactorAgent
+ from agents.coding.test_review_agent import TestReviewAgent
+
+ agents["boilerplate_generator"] = BoilerplateGeneratorAgent(
+ mcp_clients={"bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+ agents["pr_reviewer"] = PRReviewerAgent(
+ mcp_clients={"bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+ agents["test_generator"] = TestGeneratorAgent(
+ mcp_clients={"bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+ agents["doc_generator"] = DocGeneratorAgent(
+ mcp_clients={"bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+ # Cleanup is a separate stage from build: a build agent optimises for
+ # working code, so a second agent reviews organisation with fresh eyes.
+ # Comment-only — neither agent commits (Cory Gwin session, 2026-07-24).
+ agents["refactor_agent"] = RefactorAgent(
+ mcp_clients={"bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+ agents["test_review_agent"] = TestReviewAgent(
+ mcp_clients={"bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+
+ # --- Planning Domain ---
+ # Every spec is translated into a reviewable implementation plan before any
+ # code is written; the plan is the artifact a human accepts or rejects while
+ # change is still cheap.
+ from agents.planning.plan_generator import PlanGeneratorAgent
+
+ agents["plan_generator"] = PlanGeneratorAgent(
+ mcp_clients={"bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+
+ # --- Project Management Domain ---
+ from agents.project_mgmt.ticket_creator import TicketCreatorAgent
+ from agents.project_mgmt.wbs_updater import WBSUpdaterAgent
+ from agents.project_mgmt.weekly_digest import WeeklyDigestAgent
+ from agents.project_mgmt.alert_agent import AlertAgent
+
+ agents["ticket_creator"] = TicketCreatorAgent(
+ mcp_clients={"jira": mcp["jira"], "slack": mcp["slack"]}
+ )
+ agents["wbs_updater"] = WBSUpdaterAgent(
+ mcp_clients={"jira": mcp["jira"], "github": mcp["github"]}
+ )
+ agents["weekly_digest"] = WeeklyDigestAgent(
+ mcp_clients={"slack": mcp["slack"], "confluence": mcp["confluence"]}
+ )
+ agents["alert_agent"] = AlertAgent(
+ mcp_clients={"slack": mcp["slack"], "jira": mcp["jira"]}
+ )
+
+ # --- Knowledge Domain ---
+ from agents.knowledge.minutes_publisher import MinutesPublisherAgent
+ from agents.knowledge.decision_logger import DecisionLoggerAgent
+ from agents.knowledge.prompt_regression import PromptRegressionAgent
+ from agents.knowledge.context_packager import ContextPackagerAgent
+
+ agents["minutes_publisher"] = MinutesPublisherAgent(
+ mcp_clients={"confluence": mcp["confluence"]}
+ )
+ agents["decision_logger"] = DecisionLoggerAgent(
+ mcp_clients={"bitbucket": mcp["bitbucket"], "github": mcp["github"]}
+ )
+ agents["prompt_regression"] = PromptRegressionAgent()
+ agents["context_packager"] = ContextPackagerAgent(
+ mcp_clients={
+ "bitbucket": mcp["bitbucket"],
+ "jira": mcp["jira"],
+ "slack": mcp["slack"],
+ }
+ )
+
+ # --- Coach Session Memory (eParts-specific) ---
+ from agents.coach_memory.session_memory import SessionMemoryAgent
+ from agents.coach_memory.commitment_tracker import CommitmentTrackerAgent
+ from agents.coach_memory.concern_tracker import ConcernTrackerAgent
+ from agents.coach_memory.briefing_generator import BriefingGeneratorAgent
+
+ agents["session_memory"] = SessionMemoryAgent(
+ mcp_clients={"vector_store": mcp["vector_store"]}
+ )
+ agents["commitment_tracker"] = CommitmentTrackerAgent()
+ agents["concern_tracker"] = ConcernTrackerAgent()
+ agents["briefing_generator"] = BriefingGeneratorAgent(
+ mcp_clients={"slack": mcp["slack"]}
+ )
+
+ # --- ML Decision Memory (eParts-specific) ---
+ from agents.ml_decision.decision_log import DecisionLogAgent
+ from agents.ml_decision.evidence_accumulator import EvidenceAccumulatorAgent
+ from agents.ml_decision.readiness_detector import ReadinessDetectorAgent
+ from agents.ml_decision.coach_linker import CoachLinkerAgent
+
+ agents["decision_log"] = DecisionLogAgent()
+ agents["evidence_accumulator"] = EvidenceAccumulatorAgent()
+ agents["readiness_detector"] = ReadinessDetectorAgent(
+ mcp_clients={"slack": mcp["slack"]}
+ )
+ agents["coach_linker"] = CoachLinkerAgent(
+ mcp_clients={"vector_store": mcp["vector_store"]}
+ )
+
+ # Register all agents with the task queue
+ for name, agent in agents.items():
+ task_queue.register_agent(name, _make_handler(agent))
+ logger.info(f"Registered agent: {name}")
+
+ logger.info(f"Agent registry complete: {len(agents)} agents registered")
+ return agents
diff --git a/orchestrator/router.py b/orchestrator/router.py
new file mode 100644
index 0000000..a3b97fa
--- /dev/null
+++ b/orchestrator/router.py
@@ -0,0 +1,74 @@
+"""
+Trigger-to-agent routing table.
+
+Maps incoming trigger types to the agent(s) that should handle them.
+The orchestrator uses this to decide which agent to dispatch for any
+given webhook, cron tick, or manual invocation.
+
+Triggered by: orchestrator/main.py on every incoming event
+Outputs: list of agent names to run for the given trigger
+"""
+
+from __future__ import annotations
+
+TRIGGER_ROUTES: dict[str, list[str]] = {
+ "transcript": [
+ "transcript_parser",
+ "priority_classifier",
+ "req_extractor",
+ "drift_detector",
+ "decision_logger",
+ ],
+ "coach_transcript": [
+ "transcript_parser",
+ "session_memory",
+ "commitment_tracker",
+ "concern_tracker",
+ "coach_linker",
+ ],
+ "jira_webhook": [
+ "wbs_updater",
+ "traceability_builder",
+ ],
+ "pr_event": [
+ "pr_reviewer",
+ "traceability_builder",
+ "doc_generator",
+ "prompt_regression",
+ ],
+ "slack_event": [
+ "decision_logger",
+ ],
+ "cron_monday_8am": [
+ "stale_detector",
+ "context_packager",
+ ],
+ "cron_friday_6pm": [
+ "weekly_digest",
+ ],
+ "cron_6h_alert": [
+ "alert_agent",
+ ],
+ "cron_pre_meeting": [
+ "briefing_generator",
+ ],
+ "poc_result": [
+ "evidence_accumulator",
+ "readiness_detector",
+ ],
+ "manual": [], # manual triggers specify the agent directly
+}
+
+
+def resolve_agents(trigger_type: str, agent_override: str | None = None) -> list[str]:
+ """
+ Return the list of agent names to run for a given trigger type.
+ If agent_override is set (manual trigger), return only that agent.
+ """
+ if agent_override:
+ return [agent_override]
+
+ agents = TRIGGER_ROUTES.get(trigger_type, [])
+ if not agents:
+ return []
+ return list(agents)
diff --git a/pipeline/artifact_versioning.py b/pipeline/artifact_versioning.py
new file mode 100644
index 0000000..b88f73f
--- /dev/null
+++ b/pipeline/artifact_versioning.py
@@ -0,0 +1,362 @@
+"""
+Artifact Versioning — maintains version history for key project documents.
+
+Final deliverables (requirements doc, architecture doc, ADRs, risk register)
+need version history to show *evolution*: "these 5 meetings and 3 coach sessions
+led to this final document."
+
+Each version snapshot records:
+ - version number (semantic: major.minor)
+ - timestamp
+ - what changed (diff summary)
+ - which agent or human triggered the change
+ - which meetings/sessions contributed
+
+Storage: memory/artifact_versions.db
+"""
+from __future__ import annotations
+
+import json
+import logging
+import sqlite3
+import textwrap
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger("pipeline.artifact_versioning")
+
+MEMORY_DIR = Path(__file__).resolve().parent.parent / "memory"
+DB_PATH = MEMORY_DIR / "artifact_versions.db"
+
+
+def _init_db(db_path: Path | None = None) -> sqlite3.Connection:
+ path = db_path or DB_PATH
+ path.parent.mkdir(parents=True, exist_ok=True)
+ conn = sqlite3.connect(str(path))
+ conn.row_factory = sqlite3.Row
+ conn.executescript(textwrap.dedent("""\
+ CREATE TABLE IF NOT EXISTS artifacts (
+ artifact_name TEXT PRIMARY KEY,
+ artifact_type TEXT NOT NULL,
+ description TEXT DEFAULT '',
+ current_version TEXT DEFAULT '0.0',
+ created_at TEXT NOT NULL,
+ updated_at TEXT NOT NULL
+ );
+
+ CREATE TABLE IF NOT EXISTS versions (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ artifact_name TEXT NOT NULL,
+ version TEXT NOT NULL,
+ content TEXT NOT NULL,
+ change_summary TEXT DEFAULT '',
+ changed_by TEXT DEFAULT '',
+ trigger_source TEXT DEFAULT '',
+ contributing_meetings TEXT DEFAULT '[]',
+ contributing_sessions TEXT DEFAULT '[]',
+ metadata TEXT DEFAULT '{}',
+ timestamp TEXT NOT NULL,
+ FOREIGN KEY (artifact_name) REFERENCES artifacts(artifact_name),
+ UNIQUE(artifact_name, version)
+ );
+ CREATE INDEX IF NOT EXISTS idx_ver_artifact ON versions(artifact_name);
+ CREATE INDEX IF NOT EXISTS idx_ver_ts ON versions(timestamp);
+ """))
+ conn.commit()
+ return conn
+
+
+class ArtifactVersionStore:
+ """Track versioned evolution of key project documents."""
+
+ def __init__(self, db_path: Path | None = None):
+ self._db = _init_db(db_path)
+
+ def register_artifact(
+ self, name: str, artifact_type: str, description: str = ""
+ ) -> None:
+ now = datetime.now(timezone.utc).isoformat()
+ self._db.execute(
+ "INSERT OR IGNORE INTO artifacts (artifact_name, artifact_type, description, "
+ "current_version, created_at, updated_at) VALUES (?, ?, ?, '0.0', ?, ?)",
+ (name, artifact_type, description, now, now),
+ )
+ self._db.commit()
+
+ def add_version(
+ self,
+ artifact_name: str,
+ content: str,
+ change_summary: str = "",
+ changed_by: str = "",
+ trigger_source: str = "",
+ contributing_meetings: list[str] | None = None,
+ contributing_sessions: list[str] | None = None,
+ major: bool = False,
+ metadata: dict | None = None,
+ ) -> str:
+ """Add a new version. Returns the version string (e.g., '1.3')."""
+ now = datetime.now(timezone.utc).isoformat()
+
+ row = self._db.execute(
+ "SELECT current_version FROM artifacts WHERE artifact_name = ?",
+ (artifact_name,),
+ ).fetchone()
+
+ if not row:
+ self.register_artifact(artifact_name, "document")
+ current = "0.0"
+ else:
+ current = row["current_version"]
+
+ parts = current.split(".")
+ maj, minor = int(parts[0]), int(parts[1]) if len(parts) > 1 else 0
+ if major:
+ new_version = f"{maj + 1}.0"
+ else:
+ new_version = f"{maj}.{minor + 1}"
+
+ self._db.execute(
+ "INSERT OR REPLACE INTO versions (artifact_name, version, content, "
+ "change_summary, changed_by, trigger_source, contributing_meetings, "
+ "contributing_sessions, metadata, timestamp) "
+ "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
+ (artifact_name, new_version, content, change_summary, changed_by,
+ trigger_source, json.dumps(contributing_meetings or []),
+ json.dumps(contributing_sessions or []), json.dumps(metadata or {}), now),
+ )
+
+ self._db.execute(
+ "UPDATE artifacts SET current_version = ?, updated_at = ? "
+ "WHERE artifact_name = ?",
+ (new_version, now, artifact_name),
+ )
+ self._db.commit()
+ return new_version
+
+ def get_versions(self, artifact_name: str) -> list[dict]:
+ rows = self._db.execute(
+ "SELECT * FROM versions WHERE artifact_name = ? ORDER BY id ASC",
+ (artifact_name,),
+ ).fetchall()
+ return [
+ {
+ **dict(r),
+ "contributing_meetings": json.loads(r["contributing_meetings"]),
+ "contributing_sessions": json.loads(r["contributing_sessions"]),
+ "metadata": json.loads(r["metadata"]),
+ }
+ for r in rows
+ ]
+
+ def get_latest(self, artifact_name: str) -> dict | None:
+ row = self._db.execute(
+ "SELECT * FROM versions WHERE artifact_name = ? ORDER BY id DESC LIMIT 1",
+ (artifact_name,),
+ ).fetchone()
+ if row:
+ d = dict(row)
+ d["contributing_meetings"] = json.loads(d["contributing_meetings"])
+ d["contributing_sessions"] = json.loads(d["contributing_sessions"])
+ d["metadata"] = json.loads(d["metadata"])
+ return d
+ return None
+
+ def get_all_artifacts(self) -> list[dict]:
+ rows = self._db.execute(
+ "SELECT a.*, COUNT(v.id) as version_count "
+ "FROM artifacts a LEFT JOIN versions v ON a.artifact_name = v.artifact_name "
+ "GROUP BY a.artifact_name ORDER BY a.updated_at DESC",
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def get_evolution_story(self, artifact_name: str) -> str:
+ """Generate a human-readable evolution narrative for a document."""
+ versions = self.get_versions(artifact_name)
+ if not versions:
+ return f"No version history for '{artifact_name}'."
+
+ lines = [f"# Evolution of {artifact_name}\n"]
+ for v in versions:
+ ts = v["timestamp"][:10]
+ lines.append(f"## Version {v['version']} ({ts})")
+ if v["change_summary"]:
+ lines.append(f"**Change:** {v['change_summary']}")
+ if v["changed_by"]:
+ lines.append(f"**By:** {v['changed_by']}")
+ if v["trigger_source"]:
+ lines.append(f"**Triggered by:** {v['trigger_source']}")
+ meetings = v["contributing_meetings"]
+ sessions = v["contributing_sessions"]
+ if meetings:
+ lines.append(f"**Contributing meetings:** {', '.join(meetings)}")
+ if sessions:
+ lines.append(f"**Contributing sessions:** {', '.join(sessions)}")
+ lines.append("")
+ return "\n".join(lines)
+
+
+def seed_artifact_versions(db_path: Path | None = None) -> ArtifactVersionStore:
+ """Seed version history for key project documents from existing data."""
+ store = ArtifactVersionStore(db_path)
+
+ store.register_artifact(
+ "requirements_document", "requirements",
+ "Consolidated requirements specification for eParts ML catalog system"
+ )
+ store.register_artifact(
+ "architecture_document", "architecture",
+ "Software architecture document for eParts data pipeline"
+ )
+ store.register_artifact(
+ "risk_register", "risk",
+ "Project risk register with severity, likelihood, and mitigations"
+ )
+ store.register_artifact(
+ "adr_threshold_calibration", "adr",
+ "ADR: ML confidence threshold calibration approach"
+ )
+ store.register_artifact(
+ "adr_staging_tables", "adr",
+ "ADR: Staging tables for vendor data ingestion"
+ )
+ store.register_artifact(
+ "adr_human_in_loop", "adr",
+ "ADR: Human-in-the-loop review workflow design"
+ )
+
+ # Requirements Document evolution
+ store.add_version(
+ "requirements_document",
+ content="Initial scope: ML extraction from vendor spec sheets. Key needs identified: accuracy, scalability, vendor format variation.",
+ change_summary="Initial scope from project kickoff meeting. Client described the problem: manual attribute extraction is slow and error-prone.",
+ changed_by="transcript_parser",
+ trigger_source="Client Meeting 1 (Jan 22)",
+ contributing_meetings=["Meeting 2026-01-22"],
+ )
+ store.add_version(
+ "requirements_document",
+ content="Added: confidence scoring requirement (REQ-003), human-in-the-loop for low-confidence (REQ-005). Clarified: primary approach is LLM, not OCR.",
+ change_summary="Client emphasized need for confidence scores on every prediction. Team decided LLM extraction over OCR approach.",
+ changed_by="req_extractor",
+ trigger_source="Client Meeting 2 (Feb 05)",
+ contributing_meetings=["Meeting 2026-02-05"],
+ )
+ store.add_version(
+ "requirements_document",
+ content="Added: multi-vendor format support (REQ-008), PIMS integration requirement (REQ-010). Refined priority: confidence thresholds are P0.",
+ change_summary="Deep-dive with catalog team revealed vendor format variation is a major risk. PIMS writeback is a hard requirement.",
+ changed_by="req_extractor",
+ trigger_source="Client Meeting 3 (Feb 19)",
+ contributing_meetings=["Meeting 2026-02-19"],
+ )
+ store.add_version(
+ "requirements_document",
+ content="Added: batch processing requirement (REQ-011), monitoring/alerting (REQ-012). Coach flagged: need measurable acceptance criteria for each REQ.",
+ change_summary="Coach Christian emphasized measurability. Added explicit acceptance criteria to REQ-001 through REQ-008.",
+ changed_by="req_extractor",
+ trigger_source="Coach Session (Christian Feb 20) + Client Meeting 4",
+ contributing_meetings=["Meeting 2026-03-05"],
+ contributing_sessions=["Christian 2026-02-20"],
+ )
+ store.add_version(
+ "requirements_document",
+ content="Consolidated 12 formal requirements (REQ-001 to REQ-012) with traceability to source meetings, architecture decisions, and Jira tickets.",
+ change_summary="Final consolidation. All 12 requirements now traced: meeting → concern → decision → requirement → Jira ticket.",
+ changed_by="traceability_builder",
+ trigger_source="Traceability Store seeding + Client Meeting 5",
+ contributing_meetings=["Meeting 2026-01-22", "Meeting 2026-02-05", "Meeting 2026-02-19", "Meeting 2026-03-05", "Meeting 2026-03-19"],
+ contributing_sessions=["Christian 2026-02-20", "Ben 2026-03-10"],
+ major=True,
+ )
+
+ # Architecture Document evolution
+ store.add_version(
+ "architecture_document",
+ content="Initial architecture: ingest → predict → route → writeback pipeline. Key decision: LLM-first extraction, PIMS integration via API.",
+ change_summary="Initial architecture sketched after Meeting 1. Core pipeline structure defined.",
+ changed_by="adr_generator",
+ trigger_source="Client Meeting 1 (Jan 22)",
+ contributing_meetings=["Meeting 2026-01-22"],
+ )
+ store.add_version(
+ "architecture_document",
+ content="Added: staging tables for vendor data (ARCH-004), confidence threshold calibration component (ARCH-003).",
+ change_summary="Vendor data variation requires staging tables before ML processing. Confidence scoring needs dedicated calibration component.",
+ changed_by="adr_generator",
+ trigger_source="Client Meeting 2 (Feb 05) + drift_detector flag",
+ contributing_meetings=["Meeting 2026-02-05"],
+ )
+ store.add_version(
+ "architecture_document",
+ content="Added: human-in-the-loop review workflow (ARCH-005), map to industry standards not ALPS (ARCH-002).",
+ change_summary="Client explicitly said: map to industry standards, not ALPS codes. Added review workflow for low-confidence predictions.",
+ changed_by="adr_generator",
+ trigger_source="Client Meeting 3 (Feb 19)",
+ contributing_meetings=["Meeting 2026-02-19"],
+ )
+ store.add_version(
+ "architecture_document",
+ content="Final architecture: 6 architecture decisions (ARCH-001 to ARCH-006), all with ADRs. Canonical architecture report ingested into ChromaDB for drift detection.",
+ change_summary="Architecture report finalized and indexed. All future meeting decisions will be compared against this canonical version.",
+ changed_by="drift_detector",
+ trigger_source="Architecture Report finalization",
+ contributing_meetings=["Meeting 2026-01-22", "Meeting 2026-02-05", "Meeting 2026-02-19", "Meeting 2026-03-05"],
+ contributing_sessions=["Jim 2026-03-15"],
+ major=True,
+ )
+
+ # Risk Register evolution
+ store.add_version(
+ "risk_register",
+ content="Initial risks: data quality, vendor format variation, team capacity (5 people).",
+ change_summary="Initial risk identification from project overview and first meeting.",
+ changed_by="seed_risk_register",
+ trigger_source="Project kickoff",
+ contributing_meetings=["Meeting 2026-01-22"],
+ )
+ store.add_version(
+ "risk_register",
+ content="Added: confidence threshold miscalibration (from coach Dennis), PIMS schema changes (from client meeting).",
+ change_summary="Dennis coaching session flagged ML-specific risks. Client revealed PIMS schema may change.",
+ changed_by="seed_risk_register",
+ trigger_source="Coach Session (Dennis) + Client Meeting 3",
+ contributing_meetings=["Meeting 2026-02-19"],
+ contributing_sessions=["Dennis 2026-03-20"],
+ )
+ store.add_version(
+ "risk_register",
+ content="16 risks identified. All have mitigations linked to requirements. Risk-to-requirement mapping in traceability store.",
+ change_summary="Full risk register seeded from architecture report, coach sessions, and meeting analysis. All risks linked to mitigating requirements.",
+ changed_by="traceability_builder",
+ trigger_source="Traceability Store seeding",
+ contributing_meetings=["Meeting 2026-01-22", "Meeting 2026-02-05", "Meeting 2026-02-19", "Meeting 2026-03-05", "Meeting 2026-03-19"],
+ contributing_sessions=["Dennis 2026-03-20", "Ben 2026-03-10"],
+ major=True,
+ )
+
+ # ADR: Threshold calibration
+ store.add_version(
+ "adr_threshold_calibration",
+ content="Problem: How to set confidence thresholds for ML predictions? Status: OPEN.",
+ change_summary="Decision opened after client emphasized accuracy concerns in Meeting 2.",
+ changed_by="adr_generator",
+ trigger_source="Client Meeting 2 (Feb 05)",
+ contributing_meetings=["Meeting 2026-02-05"],
+ )
+ store.add_version(
+ "adr_threshold_calibration",
+ content="Decision: Per-attribute thresholds calibrated on holdout set. Not a single global threshold. Status: DECIDED.",
+ change_summary="After POC results showed wide variance across attribute types, team decided per-attribute calibration.",
+ changed_by="adr_generator",
+ trigger_source="POC results + Client Meeting 4",
+ contributing_meetings=["Meeting 2026-03-05"],
+ contributing_sessions=["Christian 2026-02-20"],
+ major=True,
+ )
+
+ logger.info(
+ f"Seeded artifact versions: {len(store.get_all_artifacts())} artifacts"
+ )
+ return store
diff --git a/pipeline/etvx.py b/pipeline/etvx.py
new file mode 100644
index 0000000..59bfe78
--- /dev/null
+++ b/pipeline/etvx.py
@@ -0,0 +1,172 @@
+"""
+ETVX Process Model — machine-readable meta-model compliance layer.
+
+Loads the ETVX manifest (docs/etvx_manifest.yaml) and provides:
+ - Programmatic access to process definitions
+ - Validation that all agents have ETVX coverage
+ - Summary statistics for presentation
+ - Markdown rendering for documentation
+
+Maps to the CMU AASE/LASE meta-model: every process has Entry criteria,
+Task definition, Verification checks, and eXit criteria, with explicit
+resource allocation (auton/assist/human) and measurement points.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+ETVX_PATH = PROJECT_ROOT / "docs" / "etvx_manifest.yaml"
+
+
+def load_manifest(path: Path | None = None) -> dict[str, Any]:
+ p = path or ETVX_PATH
+ with open(p) as f:
+ return yaml.safe_load(f)
+
+
+def get_processes(manifest: dict | None = None) -> list[dict]:
+ m = manifest or load_manifest()
+ return m.get("processes", [])
+
+
+def get_process_by_agent(agent_name: str, manifest: dict | None = None) -> dict | None:
+ for p in get_processes(manifest):
+ if p.get("agent") == agent_name:
+ return p
+ return None
+
+
+def validate_coverage(registered_agents: list[str], manifest: dict | None = None) -> dict:
+ """Check that every registered agent has an ETVX process definition."""
+ processes = get_processes(manifest)
+ documented_agents = {p["agent"] for p in processes}
+ system_agents = {
+ "N/A (built into BaseAgent)",
+ "Central Orchestrator (FastAPI)",
+ "N/A (human process)",
+ }
+
+ covered = set()
+ missing = set()
+ for agent in registered_agents:
+ if agent in documented_agents:
+ covered.add(agent)
+ else:
+ missing.add(agent)
+
+ return {
+ "total_agents": len(registered_agents),
+ "covered": len(covered),
+ "missing": sorted(missing),
+ "coverage_pct": len(covered) / max(len(registered_agents), 1) * 100,
+ "total_processes": len(processes),
+ }
+
+
+def summary_stats(manifest: dict | None = None) -> dict:
+ """Aggregate statistics for the presentation."""
+ procs = get_processes(manifest)
+ resource_counts = {"auton": 0, "assist": 0, "human": 0}
+ domain_counts: dict[str, int] = {}
+ total_measurements = 0
+
+ for p in procs:
+ rt = p.get("resource_type", "unknown")
+ resource_counts[rt] = resource_counts.get(rt, 0) + 1
+ domain = p.get("domain", "unknown")
+ domain_counts[domain] = domain_counts.get(domain, 0) + 1
+ total_measurements += len(p.get("measurements", []))
+
+ return {
+ "total_processes": len(procs),
+ "resource_allocation": resource_counts,
+ "domain_distribution": domain_counts,
+ "total_measurement_points": total_measurements,
+ "avg_measurements_per_process": round(total_measurements / max(len(procs), 1), 1),
+ }
+
+
+def render_markdown(manifest: dict | None = None) -> str:
+ """Render the full ETVX manifest as presentation-ready markdown."""
+ m = manifest or load_manifest()
+ procs = m.get("processes", [])
+ meta = m.get("meta", {})
+ stats = summary_stats(m)
+
+ lines = [
+ f"# {meta.get('project', 'SES')} — ETVX Process Model",
+ "",
+ f"**Team:** {meta.get('team', '')}",
+ f"**Framework:** {meta.get('framework', '')}",
+ f"**SDLC Pattern:** {meta.get('sdlc_pattern', '')}",
+ "",
+ "## Summary",
+ "",
+ f"- **{stats['total_processes']} processes** documented",
+ f"- **{stats['resource_allocation'].get('auton', 0)} autonomous**, "
+ f"**{stats['resource_allocation'].get('assist', 0)} AI-assisted**, "
+ f"**{stats['resource_allocation'].get('human', 0)} human**",
+ f"- **{stats['total_measurement_points']} measurement points** across all processes",
+ f"- **{stats['avg_measurements_per_process']} avg measurements** per process",
+ "",
+ "## Domains",
+ "",
+ ]
+
+ for domain, count in sorted(stats["domain_distribution"].items()):
+ lines.append(f"- **{domain}**: {count} processes")
+
+ lines.extend(["", "---", ""])
+
+ current_domain = ""
+ for p in procs:
+ domain = p.get("domain", "")
+ if domain != current_domain:
+ current_domain = domain
+ lines.extend([f"## {domain.replace('_', ' ').title()} Domain", ""])
+
+ rt_badge = {"auton": "autonomous", "assist": "AI-assisted", "human": "human"}
+ badge = rt_badge.get(p.get("resource_type", ""), p.get("resource_type", ""))
+
+ lines.extend([
+ f"### {p['id']}: {p['name']} [{badge}]",
+ "",
+ f"*Agent:* `{p.get('agent', 'N/A')}`",
+ "",
+ f"> {p.get('description', '')}",
+ "",
+ "**Entry Criteria:**",
+ ])
+ for item in p.get("entry", []):
+ lines.append(f"- {item}")
+
+ lines.extend(["", "**Task:**"])
+ for item in p.get("task", []):
+ lines.append(f"1. {item}")
+
+ lines.extend(["", "**Verification:**"])
+ for item in p.get("verification", []):
+ lines.append(f"- {item}")
+
+ lines.extend(["", "**Exit Criteria:**"])
+ for item in p.get("exit", []):
+ lines.append(f"- {item}")
+
+ if p.get("artifacts_produced"):
+ lines.extend(["", "**Artifacts Produced:**"])
+ for a in p["artifacts_produced"]:
+ lines.append(f"- `{a}`")
+
+ if p.get("measurements"):
+ lines.extend(["", "**Measurements:**"])
+ for m_item in p["measurements"]:
+ lines.append(f"- {m_item}")
+
+ lines.extend(["", "---", ""])
+
+ return "\n".join(lines)
diff --git a/pipeline/event_bus.py b/pipeline/event_bus.py
new file mode 100644
index 0000000..22c2141
--- /dev/null
+++ b/pipeline/event_bus.py
@@ -0,0 +1,255 @@
+"""
+Event Bus — cross-pipeline communication backbone.
+
+When one pipeline produces a significant output, it publishes an event.
+Other pipelines (or individual agents) subscribe to event types and
+auto-trigger when relevant events fire.
+
+This is what makes the system a connected framework rather than
+isolated scripts. Examples:
+
+ requirements pipeline → drift_detected event → architecture pipeline
+ coach session pipeline → recurring_concern event → PM alert pipeline
+ ML decision pipeline → decision_ready event → coach briefing
+ any pipeline → action_items_extracted → PM ticket creation
+
+Events are persistent (SQLite-backed) so we have a full audit trail
+of system-level communication.
+"""
+from __future__ import annotations
+
+import json
+import logging
+import sqlite3
+import textwrap
+import uuid
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Callable
+
+logger = logging.getLogger("pipeline.event_bus")
+
+MEMORY_DIR = Path(__file__).resolve().parent.parent / "memory"
+DB_PATH = MEMORY_DIR / "events.db"
+
+
+@dataclass
+class Event:
+ """A cross-pipeline event."""
+ event_type: str
+ source_agent: str
+ source_pipeline: str
+ data: dict[str, Any] = field(default_factory=dict)
+ event_id: str = ""
+ timestamp: str = ""
+
+ def __post_init__(self):
+ if not self.event_id:
+ self.event_id = f"evt-{uuid.uuid4().hex[:8]}"
+ if not self.timestamp:
+ self.timestamp = datetime.now(timezone.utc).isoformat()
+
+
+# Well-known event types — the contract between pipelines
+EVENT_TYPES = {
+ # Requirements → Architecture
+ "drift_detected": "Requirements discussion contradicts canonical architecture",
+ "new_requirements": "New requirements extracted from meeting",
+ "priority_changed": "Item priority was reclassified",
+
+ # Coach → PM / Knowledge
+ "recurring_concern": "A coach/mentor concern has recurred across multiple sessions",
+ "commitment_overdue": "A commitment from a coach session is past deadline",
+ "new_session_embedded": "A new coach/mentor session was embedded into ChromaDB",
+
+ # ML Decision → Coach / Architecture
+ "decision_ready": "Enough evidence accumulated to close an ML decision",
+ "poc_evidence_logged": "New POC result evidence was logged",
+
+ # Any → PM
+ "action_items_extracted": "Action items were extracted from a meeting",
+ "human_review_needed": "An agent output needs human review before proceeding",
+
+ # Any → Knowledge
+ "decision_logged": "A decision was captured and logged",
+ "artifact_produced": "A significant artifact was generated",
+}
+
+
+def _init_db(db_path: Path | None = None) -> sqlite3.Connection:
+ path = db_path or DB_PATH
+ path.parent.mkdir(parents=True, exist_ok=True)
+ conn = sqlite3.connect(str(path))
+ conn.row_factory = sqlite3.Row
+ conn.executescript(textwrap.dedent("""\
+ CREATE TABLE IF NOT EXISTS events (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ event_id TEXT UNIQUE NOT NULL,
+ event_type TEXT NOT NULL,
+ source_agent TEXT NOT NULL,
+ source_pipeline TEXT DEFAULT '',
+ data TEXT DEFAULT '{}',
+ timestamp TEXT NOT NULL,
+ consumed_by TEXT DEFAULT '[]'
+ );
+ CREATE INDEX IF NOT EXISTS idx_events_type ON events(event_type);
+ CREATE INDEX IF NOT EXISTS idx_events_ts ON events(timestamp);
+
+ CREATE TABLE IF NOT EXISTS subscriptions (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ event_type TEXT NOT NULL,
+ target_pipeline TEXT NOT NULL,
+ target_agent TEXT DEFAULT '',
+ description TEXT DEFAULT '',
+ active INTEGER DEFAULT 1,
+ UNIQUE(event_type, target_pipeline)
+ );
+ """))
+ conn.commit()
+ return conn
+
+
+class EventBus:
+ """
+ Publish-subscribe event bus for cross-pipeline communication.
+
+ Agents publish events. The orchestrator (or pipeline executor)
+ checks for pending events and triggers the subscribed pipelines.
+
+ In-process handlers fire synchronously for demo purposes.
+ In production, this would be async via a message queue.
+ """
+
+ def __init__(self, db_path: Path | None = None):
+ self._db = _init_db(db_path)
+ self._handlers: dict[str, list[Callable[[Event], None]]] = {}
+ self._setup_default_subscriptions()
+
+ def _setup_default_subscriptions(self) -> None:
+ """Register the cross-pipeline subscription table."""
+ defaults = [
+ ("drift_detected", "architecture", "", "Drift in requirements triggers architecture review"),
+ ("new_requirements", "architecture", "drift_detector", "New reqs trigger drift check"),
+ ("recurring_concern", "project_mgmt", "alert_agent", "Recurring concerns trigger PM alerts"),
+ ("commitment_overdue", "project_mgmt", "alert_agent", "Overdue commitments trigger PM alerts"),
+ ("new_session_embedded", "knowledge", "briefing_generator", "New sessions trigger briefing refresh"),
+ ("decision_ready", "coach_session", "coach_linker", "Ready decisions link to coach context"),
+ ("action_items_extracted", "project_mgmt", "ticket_creator", "Action items trigger ticket creation"),
+ ("human_review_needed", "project_mgmt", "alert_agent", "Human review requests trigger alerts"),
+ ("poc_evidence_logged", "ml_decision", "readiness_detector", "New evidence triggers readiness check"),
+ ("decision_logged", "knowledge", "decision_logger", "Decisions get logged to knowledge base"),
+ ]
+ for event_type, pipeline, agent, desc in defaults:
+ self._db.execute(
+ "INSERT OR IGNORE INTO subscriptions (event_type, target_pipeline, target_agent, description) "
+ "VALUES (?, ?, ?, ?)",
+ (event_type, pipeline, agent, desc),
+ )
+ self._db.commit()
+
+ def publish(self, event: Event) -> list[dict[str, str]]:
+ """
+ Publish an event and return the list of triggered subscriptions.
+ Also fires any in-process handlers.
+ """
+ self._db.execute(
+ "INSERT OR IGNORE INTO events (event_id, event_type, source_agent, "
+ "source_pipeline, data, timestamp) VALUES (?, ?, ?, ?, ?, ?)",
+ (event.event_id, event.event_type, event.source_agent,
+ event.source_pipeline, json.dumps(event.data, default=str),
+ event.timestamp),
+ )
+ self._db.commit()
+
+ triggered = self._get_subscriptions(event.event_type)
+
+ logger.info(
+ f"Event published: {event.event_type} from {event.source_agent} "
+ f"→ triggers {len(triggered)} subscription(s)"
+ )
+
+ # Fire in-process handlers
+ for handler in self._handlers.get(event.event_type, []):
+ try:
+ handler(event)
+ except Exception as exc:
+ logger.error(f"Handler error for {event.event_type}: {exc}")
+
+ return triggered
+
+ def subscribe_handler(self, event_type: str, handler: Callable[[Event], None]) -> None:
+ """Register an in-process handler for an event type."""
+ self._handlers.setdefault(event_type, []).append(handler)
+
+ def _get_subscriptions(self, event_type: str) -> list[dict[str, str]]:
+ rows = self._db.execute(
+ "SELECT target_pipeline, target_agent, description FROM subscriptions "
+ "WHERE event_type = ? AND active = 1",
+ (event_type,),
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def get_pending_events(
+ self, event_type: str | None = None, since: str | None = None, limit: int = 50,
+ ) -> list[dict[str, Any]]:
+ """Get recent events, optionally filtered by type."""
+ if event_type:
+ rows = self._db.execute(
+ "SELECT * FROM events WHERE event_type = ? ORDER BY timestamp DESC LIMIT ?",
+ (event_type, limit),
+ ).fetchall()
+ elif since:
+ rows = self._db.execute(
+ "SELECT * FROM events WHERE timestamp > ? ORDER BY timestamp DESC LIMIT ?",
+ (since, limit),
+ ).fetchall()
+ else:
+ rows = self._db.execute(
+ "SELECT * FROM events ORDER BY timestamp DESC LIMIT ?",
+ (limit,),
+ ).fetchall()
+
+ return [
+ {**dict(r), "data": json.loads(r["data"]), "consumed_by": json.loads(r["consumed_by"])}
+ for r in rows
+ ]
+
+ def mark_consumed(self, event_id: str, consumer: str) -> None:
+ """Mark an event as consumed by a pipeline/agent."""
+ row = self._db.execute(
+ "SELECT consumed_by FROM events WHERE event_id = ?",
+ (event_id,),
+ ).fetchone()
+ if row:
+ consumers = json.loads(row["consumed_by"])
+ if consumer not in consumers:
+ consumers.append(consumer)
+ self._db.execute(
+ "UPDATE events SET consumed_by = ? WHERE event_id = ?",
+ (json.dumps(consumers), event_id),
+ )
+ self._db.commit()
+
+ def get_subscriptions(self) -> list[dict[str, Any]]:
+ """Return all active subscriptions (the wiring diagram)."""
+ rows = self._db.execute(
+ "SELECT event_type, target_pipeline, target_agent, description "
+ "FROM subscriptions WHERE active = 1 ORDER BY event_type"
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def stats(self) -> dict[str, Any]:
+ """Event bus statistics."""
+ total = self._db.execute("SELECT COUNT(*) as c FROM events").fetchone()["c"]
+ by_type = self._db.execute(
+ "SELECT event_type, COUNT(*) as c FROM events GROUP BY event_type ORDER BY c DESC"
+ ).fetchall()
+ subs = self._db.execute(
+ "SELECT COUNT(*) as c FROM subscriptions WHERE active = 1"
+ ).fetchone()["c"]
+ return {
+ "total_events": total,
+ "active_subscriptions": subs,
+ "events_by_type": {r["event_type"]: r["c"] for r in by_type},
+ }
diff --git a/pipeline/extract_from_meetings.py b/pipeline/extract_from_meetings.py
new file mode 100644
index 0000000..cba1f96
--- /dev/null
+++ b/pipeline/extract_from_meetings.py
@@ -0,0 +1,316 @@
+"""
+LLM extraction runner — the CI entry point that drives the requirements +
+architecture agents over meeting transcripts and emits REQ-xxx / ADR drafts.
+
+Closes the gap that the offline minutes pipeline (pipeline.ingest) left open:
+minutes were generated for the summer meetings, but the *LLM* agents that
+synthesize requirements and architecture decisions were never re-run on that
+data. This runner drives them:
+
+ transcript --transcript_parser (LLM)--> parsed minutes (decisions, actions…)
+ --req_extractor (LLM)-------> categorized requirements -> requirements/parsed/REQ-xxx.md
+ --adr_generator (LLM)-------> architecture decision records -> docs/adr/ADR-*.md
+
+It reuses the real agents' prompts and generation logic (agents/requirements/,
+agents/architecture/). The runner plays the role the GitHub/Jira MCP plays in
+production: it writes the artifacts to disk and lets CI open a PR, so a human
+reviews every generated requirement and ADR before it is baselined (metamodel:
+Process -> Artifact -> human Verification).
+
+Idempotent: requirements are de-duplicated by normalized title against what's
+already in requirements/parsed/, and new IDs are assigned after the current max
+(REQ-013, …). ADRs whose target file already exists are skipped.
+
+Run:
+ ANTHROPIC_API_KEY=... python -m pipeline.extract_from_meetings [--since 2026-05-01]
+ python -m pipeline.extract_from_meetings --dry-run # plumbing only, no LLM / no key
+"""
+
+from __future__ import annotations
+
+import argparse
+import re
+import sys
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+TRANSCRIPTS_DIR = PROJECT_ROOT / "transcripts"
+REQ_DIR = PROJECT_ROOT / "requirements" / "parsed"
+ADR_DIR = PROJECT_ROOT / "docs" / "adr"
+
+CATEGORY_LABELS = {
+ "FUNCTIONAL": "Functional Requirement",
+ "NON_FUNCTIONAL": "Non-Functional Requirement (Quality Attribute)",
+ "USER_GOAL": "User Goal",
+ "SOFT_GOAL": "Soft Goal",
+ "CONSTRAINT": "Constraint",
+}
+
+
+# ---------------------------------------------------------------------------
+# Pure-stdlib helpers (exercised by --dry-run, no LLM / no external deps)
+# ---------------------------------------------------------------------------
+
+
+def date_from_filename(name: str) -> str:
+ """GMT20260716-190119_Recording.transcript.vtt -> 2026-07-16."""
+ m = re.search(r"GMT(\d{4})(\d{2})(\d{2})", name)
+ return f"{m.group(1)}-{m.group(2)}-{m.group(3)}" if m else "unknown"
+
+
+def _norm(text: str) -> str:
+ return re.sub(r"[^a-z0-9 ]", "", (text or "").lower()).strip()
+
+
+def discover_transcripts(since: str, explicit: list[str] | None) -> list[Path]:
+ if explicit:
+ return [Path(p) for p in explicit]
+ out = []
+ for f in sorted(TRANSCRIPTS_DIR.glob("*.transcript.vtt")):
+ if date_from_filename(f.name) >= since:
+ out.append(f)
+ return out
+
+
+def load_existing_requirements() -> tuple[set[str], int]:
+ """Return (normalized existing titles, max REQ number) from disk."""
+ titles: set[str] = set()
+ max_id = 0
+ if not REQ_DIR.exists():
+ return titles, max_id
+ for f in sorted(REQ_DIR.glob("REQ-*.md")):
+ m = re.match(r"REQ-(\d+)", f.name)
+ if m:
+ max_id = max(max_id, int(m.group(1)))
+ header = f.read_text(encoding="utf-8").splitlines()[:1]
+ if header:
+ # "# REQ-001: Automated product attribute extraction"
+ title = re.sub(r"^#\s*REQ-\d+:\s*", "", header[0]).strip()
+ if title:
+ titles.add(_norm(title))
+ return titles, max_id
+
+
+def format_req_file(req: dict, req_id: str, date: str, meeting_type: str) -> str:
+ """Mirror ReqExtractorAgent._format_req_file so dry-run needs no agent import."""
+ category = req.get("category", "FUNCTIONAL")
+ concerns = req.get("related_concerns") or []
+ concerns_md = "\n".join(f"- {c}" for c in concerns) if concerns else "None identified."
+ return f"""# {req_id}: {req.get('title', 'Untitled')}
+
+| Field | Value |
+|-------|-------|
+| **ID** | {req_id} |
+| **Category** | {CATEGORY_LABELS.get(category, category)} |
+| **Priority** | {req.get('priority', 'P1')} |
+| **Date Identified** | {date} |
+| **Source Meeting** | {meeting_type} |
+| **Source** | {req.get('source_speaker', 'team discussion')} |
+| **Status** | draft |
+
+## Requirement Statement
+
+{req.get('statement', '')}
+
+## Rationale
+
+{req.get('rationale', '')}
+
+## Acceptance Criteria
+
+{req.get('acceptance_criteria', 'To be defined.')}
+
+## Related Concerns / Open Questions
+
+{concerns_md}
+
+## Traceability
+
+- Jira Ticket: _pending auto-link_
+- Architecture Decision: _pending_
+- Test Coverage: _pending_
+
+---
+_Auto-generated by req_extractor agent on {date} (CI run, pending human review)_
+"""
+
+
+def adr_filename(date: str, decision_text: str) -> str:
+ slug = re.sub(r"[^a-z0-9]+", "-", decision_text[:40].lower()).strip("-")
+ return f"ADR-{date}-{slug}.md"
+
+
+# ---------------------------------------------------------------------------
+# LLM paths (agents imported lazily so --dry-run has zero heavy deps)
+# ---------------------------------------------------------------------------
+
+
+def _make_trigger(source: str, date: str, meeting_type: str):
+ from agents.base import AgentTrigger
+
+ return AgentTrigger(
+ trigger_type="transcript",
+ source=source,
+ metadata={"date": date, "meeting_type": meeting_type},
+ )
+
+
+def parse_transcript_llm(path: Path, date: str, meeting_type: str) -> dict:
+ """Drive the transcript_parser agent's LLM extraction; return parsed minutes."""
+ from agents.requirements.transcript_parser import TranscriptParserAgent
+
+ agent = TranscriptParserAgent()
+ cleaned = agent._clean_vtt(path.read_text(encoding="utf-8"))
+ parsed = agent._parse_with_claude(cleaned, date, meeting_type)
+ return parsed or {}
+
+
+def extract_requirements_llm(parsed: dict, existing_reqs_text: str, date: str) -> list[dict]:
+ """Drive the req_extractor agent's LLM synthesis; return requirement dicts."""
+ from agents.requirements.req_extractor import ReqExtractorAgent
+
+ agent = ReqExtractorAgent()
+ meeting_data = agent._build_meeting_summary(parsed, [])
+ if not meeting_data.strip():
+ return []
+ return agent._extract_with_llm(meeting_data, existing_reqs_text, date) or []
+
+
+def generate_adr_llm(decision: dict, date: str) -> str:
+ """Drive the adr_generator agent's LLM generation; return ADR markdown."""
+ from agents.architecture.adr_generator import ADRGeneratorAgent
+
+ return ADRGeneratorAgent()._generate_adr(decision, date)
+
+
+# ---------------------------------------------------------------------------
+# Orchestration
+# ---------------------------------------------------------------------------
+
+
+def run(since: str, files: list[str] | None, max_adrs: int, dry_run: bool) -> dict:
+ transcripts = discover_transcripts(since, files)
+ existing_titles, max_id = load_existing_requirements()
+ REQ_DIR.mkdir(parents=True, exist_ok=True)
+ ADR_DIR.mkdir(parents=True, exist_ok=True)
+
+ new_reqs: list[tuple[str, str]] = [] # (req_id, title)
+ all_decisions: list[dict] = []
+ next_id = max_id + 1
+
+ for path in transcripts:
+ date = date_from_filename(path.name)
+ meeting_type = "client"
+
+ if dry_run:
+ parsed = {
+ "decisions": [{"text": f"stub decision for {date}", "context": "dry-run"}],
+ "action_items": [{"text": "stub action"}],
+ "key_discussion_points": ["stub point about attribute extraction"],
+ }
+ reqs = [{
+ "title": f"Stub requirement from {date}",
+ "statement": "The system shall (dry-run placeholder).",
+ "category": "FUNCTIONAL", "priority": "P2",
+ "rationale": "dry-run", "acceptance_criteria": "n/a",
+ "source_speaker": "dry-run", "related_concerns": [],
+ }]
+ else:
+ parsed = parse_transcript_llm(path, date, meeting_type)
+ existing_text = "\n".join(f"- {t}" for t in sorted(existing_titles)) or "(none yet)"
+ reqs = extract_requirements_llm(parsed, existing_text, date)
+
+ for d in parsed.get("decisions", []) or []:
+ if isinstance(d, dict) and d.get("text"):
+ all_decisions.append({**d, "_date": date})
+
+ for req in reqs:
+ key = _norm(req.get("title", ""))
+ if not key or key in existing_titles:
+ continue # dedup vs disk + earlier this run
+ req_id = f"REQ-{next_id:03d}"
+ next_id += 1
+ existing_titles.add(key)
+ content = format_req_file(req, req_id, date, meeting_type)
+ (REQ_DIR / f"{req_id}.md").write_text(content, encoding="utf-8")
+ new_reqs.append((req_id, req.get("title", "")))
+
+ # ADRs — dedup decisions by text, cap total to bound cost
+ new_adrs: list[str] = []
+ seen_dec: set[str] = set()
+ for d in all_decisions:
+ if len(new_adrs) >= max_adrs:
+ break
+ k = _norm(d.get("text", ""))[:80]
+ if not k or k in seen_dec:
+ continue
+ seen_dec.add(k)
+ fname = adr_filename(d["_date"], d.get("text", "untitled"))
+ target = ADR_DIR / fname
+ if target.exists():
+ continue
+ content = ("# ADR (dry-run placeholder)\n\n## Status\nProposed\n"
+ if dry_run else generate_adr_llm(d, d["_date"]))
+ target.write_text(content, encoding="utf-8")
+ new_adrs.append(fname)
+
+ return {
+ "transcripts": len(transcripts),
+ "new_requirements": new_reqs,
+ "new_adrs": new_adrs,
+ }
+
+
+def write_summary(result: dict, path: Path | None) -> str:
+ lines = ["## Requirements & ADR extraction", ""]
+ lines.append(f"Drove the requirements + architecture agents over "
+ f"**{result['transcripts']}** meeting transcript(s).")
+ lines.append("")
+ reqs = result["new_requirements"]
+ if reqs:
+ lines.append(f"**{len(reqs)} new requirement(s)** (`requirements/parsed/`):")
+ lines += [f"- `{rid}` — {title}" for rid, title in reqs]
+ else:
+ lines.append("No new requirements (all deduped against existing).")
+ lines.append("")
+ adrs = result["new_adrs"]
+ if adrs:
+ lines.append(f"**{len(adrs)} new ADR draft(s)** (`docs/adr/`):")
+ lines += [f"- `{a}`" for a in adrs]
+ else:
+ lines.append("No new ADR drafts.")
+ lines += [
+ "",
+ "**These are agent-generated drafts pending human review.** Check each "
+ "requirement's statement/category and each ADR's decision + consequences "
+ "before merging — merging this PR is the human approval step.",
+ ]
+ text = "\n".join(lines)
+ if path:
+ path.write_text(text, encoding="utf-8")
+ return text
+
+
+def main() -> None:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--since", default="2026-05-01",
+ help="Only process transcripts dated on/after this (YYYY-MM-DD).")
+ parser.add_argument("--files", nargs="*", default=None,
+ help="Explicit transcript paths (overrides --since).")
+ parser.add_argument("--max-adrs", type=int, default=6, help="Cap ADRs generated per run.")
+ parser.add_argument("--dry-run", action="store_true",
+ help="Exercise plumbing without any LLM call (no API key needed).")
+ parser.add_argument("--summary-file", type=Path, default=None,
+ help="Write the markdown run summary here (for the PR body).")
+ args = parser.parse_args()
+
+ result = run(args.since, args.files, args.max_adrs, args.dry_run)
+ write_summary(result, args.summary_file)
+ print(f"transcripts: {result['transcripts']} | "
+ f"new requirements: {len(result['new_requirements'])} | "
+ f"new ADRs: {len(result['new_adrs'])}"
+ + (" (dry-run)" if args.dry_run else ""), file=sys.stderr)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/pipeline/ingest.py b/pipeline/ingest.py
new file mode 100644
index 0000000..af5680a
--- /dev/null
+++ b/pipeline/ingest.py
@@ -0,0 +1,219 @@
+"""
+Batch Ingestion Pipeline — processes all VTT transcripts into structured outputs.
+
+This is the entry point for the full transcript processing pipeline:
+ 1. VTT cleaning and speaker extraction
+ 2. Structural analysis (offline) or Claude-powered analysis (online)
+ 3. Meeting minutes generation (markdown)
+ 4. Metrics recording
+ 5. Output storage (minutes/ directory)
+
+Run: python -m pipeline.ingest [--transcripts-dir PATH] [--output-dir PATH]
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+
+from pipeline.vtt_processor import batch_process, MeetingData
+
+
+def generate_minutes_md(meeting: MeetingData, summary: dict) -> str:
+ """Generate markdown meeting minutes from processed data."""
+ lines = [
+ f"# Meeting Minutes — {summary['meeting_date']}",
+ "",
+ f"**Date:** {summary['meeting_date']}",
+ f"**Duration:** {summary['duration_minutes']} minutes",
+ f"**Participants:** {', '.join(summary['participants'])}",
+ f"**Source:** `{meeting.filename}`",
+ f"**Processed:** {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M UTC')}",
+ "",
+ "---",
+ "",
+ "## Participation",
+ "",
+ "| Speaker | Turns | Words | Share |",
+ "|---------|-------|-------|-------|",
+ ]
+ for speaker, stats in summary["speaker_stats"].items():
+ lines.append(f"| {speaker} | {stats['turns']} | {stats['words']} | {stats['pct_words']}% |")
+
+ if summary["detected_topics"]:
+ lines.extend(["", "## Topics Discussed", ""])
+ for topic, relevance in summary["detected_topics"].items():
+ bar = "█" * min(relevance, 10)
+ lines.append(f"- **{topic}** {bar} (relevance: {relevance})")
+
+ if summary["decisions_sample"]:
+ lines.extend(["", "## Potential Decisions", ""])
+ for i, d in enumerate(summary["decisions_sample"], 1):
+ text = d["text"][:300].replace("\n", " ")
+ lines.append(f"{i}. **[{d['speaker']}]** {text}")
+
+ if summary["actions_sample"]:
+ lines.extend(["", "## Potential Action Items", ""])
+ for i, a in enumerate(summary["actions_sample"], 1):
+ text = a["text"][:300].replace("\n", " ")
+ lines.append(f"{i}. **[{a['speaker']}]** {text}")
+
+ if summary["questions_sample"]:
+ lines.extend(["", "## Questions Raised", ""])
+ for q in summary["questions_sample"]:
+ lines.append(f"- **[{q['speaker']}]** {q['text']}")
+
+ lines.extend([
+ "",
+ "---",
+ "",
+ f"*Analysis mode: {summary['analysis_mode']}*",
+ f"*Total: {summary['total_words']} words across {summary['total_turns']} speaker turns*",
+ ])
+
+ return "\n".join(lines)
+
+
+def generate_cross_meeting_report(all_summaries: list[dict]) -> str:
+ """Generate a cross-meeting analysis report."""
+ lines = [
+ "# eParts Client Meetings — Cross-Meeting Analysis",
+ "",
+ f"**Generated:** {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M UTC')}",
+ f"**Meetings analyzed:** {len(all_summaries)}",
+ "",
+ "---",
+ "",
+ "## Meeting Overview",
+ "",
+ "| Date | Duration | Participants | Words | Topics |",
+ "|------|----------|-------------|-------|--------|",
+ ]
+ for s in all_summaries:
+ topics = ", ".join(list(s["detected_topics"].keys())[:3])
+ lines.append(
+ f"| {s['meeting_date']} | {s['duration_minutes']}min | "
+ f"{s['participant_count']} | {s['total_words']} | {topics} |"
+ )
+
+ # Aggregate stats
+ total_words = sum(s["total_words"] for s in all_summaries)
+ total_minutes = sum(s["duration_minutes"] for s in all_summaries)
+ total_turns = sum(s["total_turns"] for s in all_summaries)
+ all_speakers = set()
+ for s in all_summaries:
+ all_speakers.update(s["participants"])
+
+ lines.extend([
+ "",
+ "## Aggregate Statistics",
+ "",
+ f"- **Total meeting time:** {total_minutes} minutes ({total_minutes / 60:.1f} hours)",
+ f"- **Total words transcribed:** {total_words:,}",
+ f"- **Total speaker turns:** {total_turns}",
+ f"- **Unique participants:** {len(all_speakers)} ({', '.join(sorted(all_speakers))})",
+ f"- **Average meeting length:** {total_minutes // len(all_summaries)} minutes",
+ f"- **Average words per meeting:** {total_words // len(all_summaries):,}",
+ ])
+
+ # Topic frequency across meetings
+ topic_freq: dict[str, int] = {}
+ for s in all_summaries:
+ for topic in s["detected_topics"]:
+ topic_freq[topic] = topic_freq.get(topic, 0) + 1
+
+ lines.extend(["", "## Topic Frequency Across Meetings", ""])
+ for topic, freq in sorted(topic_freq.items(), key=lambda x: -x[1]):
+ pct = freq / len(all_summaries) * 100
+ bar = "█" * freq
+ lines.append(f"- **{topic}**: {bar} ({freq}/{len(all_summaries)} meetings, {pct:.0f}%)")
+
+ # Speaker participation across meetings
+ speaker_meetings: dict[str, int] = {}
+ speaker_total_words: dict[str, int] = {}
+ for s in all_summaries:
+ for speaker, stats in s["speaker_stats"].items():
+ speaker_meetings[speaker] = speaker_meetings.get(speaker, 0) + 1
+ speaker_total_words[speaker] = speaker_total_words.get(speaker, 0) + stats["words"]
+
+ lines.extend([
+ "",
+ "## Speaker Participation",
+ "",
+ "| Speaker | Meetings | Total Words | Avg Words/Meeting |",
+ "|---------|----------|-------------|-------------------|",
+ ])
+ for speaker in sorted(speaker_total_words, key=lambda x: -speaker_total_words[x]):
+ meetings = speaker_meetings[speaker]
+ words = speaker_total_words[speaker]
+ avg = words // meetings
+ lines.append(f"| {speaker} | {meetings} | {words:,} | {avg:,} |")
+
+ lines.extend([
+ "",
+ "---",
+ "",
+ "*Generated by eParts Agentic SE System — offline structural analysis*",
+ ])
+
+ return "\n".join(lines)
+
+
+def run(
+ transcripts_dir: Path | None = None,
+ output_dir: Path | None = None,
+) -> dict:
+ """Run the full ingestion pipeline."""
+ t_dir = transcripts_dir or PROJECT_ROOT / "transcripts"
+ o_dir = output_dir or PROJECT_ROOT / "minutes"
+ o_dir.mkdir(parents=True, exist_ok=True)
+
+ results = batch_process(t_dir)
+ if not results:
+ print("No .transcript.vtt files found")
+ return {"meetings": 0}
+
+ all_summaries = []
+
+ for meeting, summary in results:
+ # Save meeting minutes
+ md = generate_minutes_md(meeting, summary)
+ md_path = o_dir / f"{summary['meeting_date']}-client.md"
+ md_path.write_text(md, encoding="utf-8")
+
+ # Save raw summary JSON
+ json_path = o_dir / f"{summary['meeting_date']}-client.json"
+ json_path.write_text(json.dumps(summary, indent=2, default=str), encoding="utf-8")
+
+ # Save cleaned transcript
+ clean_path = o_dir / f"{summary['meeting_date']}-cleaned.md"
+ clean_path.write_text(
+ f"# Cleaned Transcript — {summary['meeting_date']}\n\n{meeting.cleaned_text}",
+ encoding="utf-8",
+ )
+
+ all_summaries.append(summary)
+ print(f" Processed: {meeting.filename} → {md_path.name}")
+
+ # Generate cross-meeting report
+ report = generate_cross_meeting_report(all_summaries)
+ report_path = o_dir / "cross-meeting-analysis.md"
+ report_path.write_text(report, encoding="utf-8")
+ print(f" Cross-meeting report: {report_path.name}")
+
+ print(f"\nDone: {len(results)} meetings → {o_dir}/")
+ return {
+ "meetings": len(results),
+ "output_dir": str(o_dir),
+ "files_created": len(results) * 3 + 1,
+ }
+
+
+if __name__ == "__main__":
+ transcripts = Path(sys.argv[1]) if len(sys.argv) > 1 else None
+ output = Path(sys.argv[2]) if len(sys.argv) > 2 else None
+ run(transcripts, output)
diff --git a/pipeline/metrics.py b/pipeline/metrics.py
new file mode 100644
index 0000000..47dd1fe
--- /dev/null
+++ b/pipeline/metrics.py
@@ -0,0 +1,313 @@
+"""
+Metrics Collector — the evidence engine for the SES.
+
+Captures every measurable signal from agent operations:
+ - Per-LLM-call: model, tokens in/out, latency, prompt file, temperature
+ - Per-agent-run: duration, success/fail, outputs, human review needed
+ - Per-prompt: version hash, effectiveness score, correction count
+ - Aggregates: token costs, re-prompt rates, time saved, velocity
+
+SQLite-backed for queryability. Every metric maps to the meta-model:
+ Resource (which agent/model) implements Process (which activity),
+ generating Artifacts (outputs), measured by these metrics.
+
+This is what Christian wants to see: evidence-based AI effectiveness.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import sqlite3
+import textwrap
+from dataclasses import dataclass, field, asdict
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+METRICS_DB = PROJECT_ROOT / "pipeline" / "metrics.db"
+
+# Approximate costs per 1M tokens (USD) — for cost tracking
+TOKEN_COSTS = {
+ "claude-sonnet-4-5-20250514": {"input": 3.00, "output": 15.00},
+ "claude-opus-4-5": {"input": 15.00, "output": 75.00},
+}
+
+
+@dataclass
+class LLMCallMetric:
+ agent: str
+ run_id: str
+ model: str
+ prompt_file: str # which prompt template was used (or "inline")
+ input_tokens: int
+ output_tokens: int
+ latency_ms: int
+ temperature: float
+ attempt: int # which retry attempt succeeded
+ timestamp: str = ""
+
+ def __post_init__(self):
+ if not self.timestamp:
+ self.timestamp = datetime.now(timezone.utc).isoformat()
+
+ @property
+ def total_tokens(self) -> int:
+ return self.input_tokens + self.output_tokens
+
+ @property
+ def estimated_cost_usd(self) -> float:
+ rates = TOKEN_COSTS.get(self.model, {"input": 3.0, "output": 15.0})
+ return (
+ self.input_tokens / 1_000_000 * rates["input"]
+ + self.output_tokens / 1_000_000 * rates["output"]
+ )
+
+
+@dataclass
+class AgentRunMetric:
+ run_id: str
+ agent: str
+ trigger_type: str
+ trigger_source: str
+ success: bool
+ duration_ms: int
+ llm_calls: int
+ total_input_tokens: int
+ total_output_tokens: int
+ estimated_cost_usd: float
+ outputs_count: int
+ requires_human_review: bool
+ errors: list[str] = field(default_factory=list)
+ timestamp: str = ""
+
+ def __post_init__(self):
+ if not self.timestamp:
+ self.timestamp = datetime.now(timezone.utc).isoformat()
+
+
+@dataclass
+class PromptMetric:
+ prompt_file: str
+ content_hash: str # SHA-256 of prompt content for version tracking
+ times_used: int = 0
+ avg_output_tokens: float = 0.0
+ avg_latency_ms: float = 0.0
+ correction_count: int = 0 # times human corrected the output
+
+
+def init_metrics_db(db_path: Path | None = None) -> sqlite3.Connection:
+ path = db_path or METRICS_DB
+ path.parent.mkdir(parents=True, exist_ok=True)
+ conn = sqlite3.connect(str(path))
+ conn.row_factory = sqlite3.Row
+ conn.executescript(textwrap.dedent("""\
+ CREATE TABLE IF NOT EXISTS llm_calls (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ run_id TEXT NOT NULL,
+ agent TEXT NOT NULL,
+ model TEXT NOT NULL,
+ prompt_file TEXT,
+ input_tokens INTEGER,
+ output_tokens INTEGER,
+ total_tokens INTEGER,
+ latency_ms INTEGER,
+ temperature REAL,
+ attempt INTEGER,
+ estimated_cost_usd REAL,
+ timestamp TEXT NOT NULL
+ );
+
+ CREATE TABLE IF NOT EXISTS agent_runs (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ run_id TEXT UNIQUE NOT NULL,
+ agent TEXT NOT NULL,
+ trigger_type TEXT,
+ trigger_source TEXT,
+ success INTEGER,
+ duration_ms INTEGER,
+ llm_calls INTEGER,
+ total_input_tokens INTEGER,
+ total_output_tokens INTEGER,
+ estimated_cost_usd REAL,
+ outputs_count INTEGER,
+ requires_human_review INTEGER,
+ errors TEXT,
+ timestamp TEXT NOT NULL
+ );
+
+ CREATE TABLE IF NOT EXISTS prompt_versions (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ prompt_file TEXT NOT NULL,
+ content_hash TEXT NOT NULL,
+ first_seen TEXT NOT NULL,
+ UNIQUE(prompt_file, content_hash)
+ );
+
+ CREATE TABLE IF NOT EXISTS human_corrections (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ run_id TEXT,
+ agent TEXT,
+ correction_type TEXT,
+ description TEXT,
+ timestamp TEXT NOT NULL
+ );
+
+ CREATE INDEX IF NOT EXISTS idx_llm_calls_agent ON llm_calls(agent);
+ CREATE INDEX IF NOT EXISTS idx_llm_calls_run ON llm_calls(run_id);
+ CREATE INDEX IF NOT EXISTS idx_agent_runs_agent ON agent_runs(agent);
+ CREATE INDEX IF NOT EXISTS idx_agent_runs_ts ON agent_runs(timestamp);
+ """))
+ conn.commit()
+ return conn
+
+
+class MetricsCollector:
+ """
+ Collects and stores all metrics from agent operations.
+ Thread-safe via SQLite's built-in locking.
+ """
+
+ def __init__(self, db_path: Path | None = None):
+ self._db = init_metrics_db(db_path)
+
+ def record_llm_call(self, metric: LLMCallMetric) -> None:
+ self._db.execute(
+ "INSERT INTO llm_calls "
+ "(run_id, agent, model, prompt_file, input_tokens, output_tokens, "
+ "total_tokens, latency_ms, temperature, attempt, estimated_cost_usd, timestamp) "
+ "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
+ (
+ metric.run_id, metric.agent, metric.model, metric.prompt_file,
+ metric.input_tokens, metric.output_tokens, metric.total_tokens,
+ metric.latency_ms, metric.temperature, metric.attempt,
+ metric.estimated_cost_usd, metric.timestamp,
+ ),
+ )
+ self._db.commit()
+
+ def record_agent_run(self, metric: AgentRunMetric) -> None:
+ self._db.execute(
+ "INSERT OR REPLACE INTO agent_runs "
+ "(run_id, agent, trigger_type, trigger_source, success, duration_ms, "
+ "llm_calls, total_input_tokens, total_output_tokens, estimated_cost_usd, "
+ "outputs_count, requires_human_review, errors, timestamp) "
+ "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
+ (
+ metric.run_id, metric.agent, metric.trigger_type,
+ metric.trigger_source, int(metric.success), metric.duration_ms,
+ metric.llm_calls, metric.total_input_tokens, metric.total_output_tokens,
+ metric.estimated_cost_usd, metric.outputs_count,
+ int(metric.requires_human_review), json.dumps(metric.errors),
+ metric.timestamp,
+ ),
+ )
+ self._db.commit()
+
+ def track_prompt_version(self, prompt_file: str, content: str) -> str:
+ content_hash = hashlib.sha256(content.encode()).hexdigest()[:16]
+ now = datetime.now(timezone.utc).isoformat()
+ self._db.execute(
+ "INSERT OR IGNORE INTO prompt_versions (prompt_file, content_hash, first_seen) "
+ "VALUES (?, ?, ?)",
+ (prompt_file, content_hash, now),
+ )
+ self._db.commit()
+ return content_hash
+
+ def record_human_correction(
+ self, run_id: str, agent: str, correction_type: str, description: str
+ ) -> None:
+ self._db.execute(
+ "INSERT INTO human_corrections (run_id, agent, correction_type, description, timestamp) "
+ "VALUES (?, ?, ?, ?, ?)",
+ (run_id, agent, correction_type, description,
+ datetime.now(timezone.utc).isoformat()),
+ )
+ self._db.commit()
+
+ # ------ Query methods for dashboard ------
+
+ def summary(self) -> dict[str, Any]:
+ """High-level metrics summary across all agents."""
+ runs = self._db.execute(
+ "SELECT COUNT(*) as total, SUM(success) as succeeded, "
+ "SUM(duration_ms) as total_duration, SUM(llm_calls) as total_llm_calls, "
+ "SUM(total_input_tokens) as total_input, SUM(total_output_tokens) as total_output, "
+ "SUM(estimated_cost_usd) as total_cost, "
+ "SUM(requires_human_review) as needed_review "
+ "FROM agent_runs"
+ ).fetchone()
+
+ corrections = self._db.execute(
+ "SELECT COUNT(*) as total FROM human_corrections"
+ ).fetchone()
+
+ return {
+ "total_runs": runs["total"] or 0,
+ "successful_runs": runs["succeeded"] or 0,
+ "failure_rate": 1 - (runs["succeeded"] or 0) / max(runs["total"] or 1, 1),
+ "total_duration_ms": runs["total_duration"] or 0,
+ "total_llm_calls": runs["total_llm_calls"] or 0,
+ "total_input_tokens": runs["total_input"] or 0,
+ "total_output_tokens": runs["total_output"] or 0,
+ "total_tokens": (runs["total_input"] or 0) + (runs["total_output"] or 0),
+ "estimated_cost_usd": round(runs["total_cost"] or 0, 4),
+ "runs_needing_review": runs["needed_review"] or 0,
+ "human_corrections": corrections["total"] or 0,
+ "review_rate": (runs["needed_review"] or 0) / max(runs["total"] or 1, 1),
+ }
+
+ def per_agent_summary(self) -> list[dict[str, Any]]:
+ """Metrics broken down by agent."""
+ rows = self._db.execute(
+ "SELECT agent, COUNT(*) as runs, SUM(success) as succeeded, "
+ "AVG(duration_ms) as avg_duration, SUM(llm_calls) as total_llm_calls, "
+ "SUM(total_input_tokens + total_output_tokens) as total_tokens, "
+ "SUM(estimated_cost_usd) as total_cost, "
+ "SUM(requires_human_review) as needed_review "
+ "FROM agent_runs GROUP BY agent ORDER BY runs DESC"
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def per_prompt_summary(self) -> list[dict[str, Any]]:
+ """Metrics broken down by prompt template."""
+ rows = self._db.execute(
+ "SELECT prompt_file, COUNT(*) as uses, "
+ "AVG(output_tokens) as avg_output_tokens, "
+ "AVG(latency_ms) as avg_latency_ms, "
+ "SUM(estimated_cost_usd) as total_cost "
+ "FROM llm_calls WHERE prompt_file != 'inline' "
+ "GROUP BY prompt_file ORDER BY uses DESC"
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def token_usage_timeseries(self, days: int = 30) -> list[dict[str, Any]]:
+ """Daily token usage for time-series charts."""
+ rows = self._db.execute(
+ "SELECT DATE(timestamp) as date, "
+ "SUM(input_tokens) as input_tokens, "
+ "SUM(output_tokens) as output_tokens, "
+ "SUM(estimated_cost_usd) as cost, "
+ "COUNT(*) as calls "
+ "FROM llm_calls "
+ "GROUP BY DATE(timestamp) ORDER BY date DESC LIMIT ?",
+ (days,),
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def recent_runs(self, limit: int = 20) -> list[dict[str, Any]]:
+ """Most recent agent runs for the activity feed."""
+ rows = self._db.execute(
+ "SELECT * FROM agent_runs ORDER BY timestamp DESC LIMIT ?",
+ (limit,),
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def prompt_version_history(self) -> list[dict[str, Any]]:
+ """Track which prompt versions have been used."""
+ rows = self._db.execute(
+ "SELECT * FROM prompt_versions ORDER BY first_seen DESC"
+ ).fetchall()
+ return [dict(r) for r in rows]
diff --git a/pipeline/pipelines.py b/pipeline/pipelines.py
new file mode 100644
index 0000000..a0eb8e3
--- /dev/null
+++ b/pipeline/pipelines.py
@@ -0,0 +1,788 @@
+"""
+Pipeline Executor — the connective tissue of the SES framework.
+
+A Pipeline is an ordered chain of agents where each step's output feeds
+the next step's input. This is what makes the system a *framework* rather
+than a bag of scripts.
+
+Key concepts:
+ - PipelineStep: one agent invocation with input/output mapping
+ - PipelineContext: accumulated state flowing through the chain
+ - PipelineResult: end-to-end outcome with per-step metrics
+ - Pipeline: named, ordered sequence of steps with practice area metadata
+
+The pipeline executor:
+ 1. Initializes a context from the trigger payload
+ 2. Runs each step in order, passing accumulated context
+ 3. Each step's outputs are merged back into the context
+ 4. Steps can be conditional (skip if required context key is empty)
+ 5. Records per-step AND end-to-end metrics
+
+This directly implements the meta-model requirement:
+ "For at least one Practice Area, there should be an end-to-end
+ connection between the Activities in that area."
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import time
+import uuid
+from dataclasses import dataclass, field, asdict
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from agents.base import AgentTrigger, AgentResult, AgentOutput
+
+logger = logging.getLogger("pipeline.executor")
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+
+
+@dataclass
+class PipelineStep:
+ """One step in a pipeline chain."""
+ agent_name: str
+ description: str
+ input_keys: list[str] = field(default_factory=list) # context keys this step reads
+ output_key: str = "" # context key this step writes to
+ required: bool = True # fail pipeline if this step fails
+ skip_if_empty: str = "" # skip if this context key is empty/missing
+ etvx_id: str = "" # link to ETVX process definition
+
+
+@dataclass
+class PipelineContext:
+ """Accumulated state flowing through the pipeline."""
+ pipeline_id: str
+ pipeline_name: str
+ trigger_type: str
+ source: str
+ data: dict[str, Any] = field(default_factory=dict)
+ artifacts: list[dict[str, str]] = field(default_factory=list)
+ step_results: list[dict[str, Any]] = field(default_factory=list)
+ started_at: str = ""
+ current_step: int = 0
+
+ def __post_init__(self):
+ if not self.started_at:
+ self.started_at = datetime.now(timezone.utc).isoformat()
+
+ def set(self, key: str, value: Any) -> None:
+ self.data[key] = value
+
+ def get(self, key: str, default: Any = None) -> Any:
+ return self.data.get(key, default)
+
+ def add_artifact(self, artifact_type: str, description: str, reference: str = "") -> None:
+ self.artifacts.append({
+ "type": artifact_type,
+ "description": description,
+ "reference": reference,
+ "step": self.current_step,
+ "timestamp": datetime.now(timezone.utc).isoformat(),
+ })
+
+
+@dataclass
+class StepResult:
+ step_index: int
+ agent_name: str
+ description: str
+ success: bool
+ skipped: bool
+ duration_ms: int
+ outputs: list[dict]
+ errors: list[str]
+ llm_calls: int
+ tokens_used: int
+ artifacts_produced: int
+ requires_human_review: bool
+
+
+@dataclass
+class PipelineResult:
+ pipeline_id: str
+ pipeline_name: str
+ practice_area: str
+ trigger_source: str
+ success: bool
+ total_steps: int
+ completed_steps: int
+ skipped_steps: int
+ failed_steps: int
+ total_duration_ms: int
+ total_llm_calls: int
+ total_tokens: int
+ total_artifacts: int
+ requires_human_review: bool
+ step_results: list[StepResult]
+ artifacts: list[dict]
+ context_snapshot: dict[str, Any]
+ started_at: str
+ completed_at: str
+
+
+@dataclass
+class Pipeline:
+ """A named, ordered chain of agent steps for a practice area."""
+ name: str
+ practice_area: str
+ description: str
+ trigger_types: list[str]
+ steps: list[PipelineStep]
+ metadata: dict[str, Any] = field(default_factory=dict)
+
+
+class PipelineExecutor:
+ """
+ Executes pipelines by running agent steps in sequence,
+ threading context from one step to the next.
+ """
+
+ def __init__(self, agent_registry: dict[str, Any]):
+ self._agents = agent_registry
+
+ def execute(self, pipeline: Pipeline, trigger_payload: dict[str, Any]) -> PipelineResult:
+ pipeline_id = f"pipe-{pipeline.name}-{uuid.uuid4().hex[:8]}"
+ ctx = PipelineContext(
+ pipeline_id=pipeline_id,
+ pipeline_name=pipeline.name,
+ trigger_type=trigger_payload.get("trigger_type", "manual"),
+ source=trigger_payload.get("source", "unknown"),
+ data=dict(trigger_payload),
+ )
+
+ logger.info(
+ f"Pipeline '{pipeline.name}' started: id={pipeline_id} "
+ f"steps={len(pipeline.steps)} source={ctx.source}"
+ )
+
+ step_results: list[StepResult] = []
+ pipeline_success = True
+ pipeline_t0 = time.perf_counter()
+ total_llm = 0
+ total_tokens = 0
+ human_review_needed = False
+
+ for i, step in enumerate(pipeline.steps):
+ ctx.current_step = i
+
+ # Check skip condition
+ if step.skip_if_empty:
+ val = ctx.get(step.skip_if_empty)
+ if not val:
+ logger.info(
+ f" Step {i}/{len(pipeline.steps)} [{step.agent_name}] "
+ f"SKIPPED ('{step.skip_if_empty}' is empty)"
+ )
+ step_results.append(StepResult(
+ step_index=i, agent_name=step.agent_name,
+ description=step.description, success=True, skipped=True,
+ duration_ms=0, outputs=[], errors=[], llm_calls=0,
+ tokens_used=0, artifacts_produced=0, requires_human_review=False,
+ ))
+ continue
+
+ # Resolve agent
+ agent = self._agents.get(step.agent_name)
+ if not agent:
+ logger.error(f" Step {i} [{step.agent_name}] — agent not found")
+ sr = StepResult(
+ step_index=i, agent_name=step.agent_name,
+ description=step.description, success=False, skipped=False,
+ duration_ms=0, outputs=[], errors=[f"Agent '{step.agent_name}' not registered"],
+ llm_calls=0, tokens_used=0, artifacts_produced=0, requires_human_review=False,
+ )
+ step_results.append(sr)
+ if step.required:
+ pipeline_success = False
+ break
+ continue
+
+ # Build trigger with accumulated context
+ trigger = AgentTrigger(
+ trigger_type=ctx.trigger_type,
+ source=ctx.source,
+ metadata={
+ "pipeline_id": pipeline_id,
+ "pipeline_step": i,
+ "pipeline_context": ctx.data,
+ },
+ )
+
+ logger.info(
+ f" Step {i}/{len(pipeline.steps)} [{step.agent_name}] "
+ f"{step.description}..."
+ )
+
+ step_t0 = time.perf_counter()
+ try:
+ result: AgentResult = agent.execute(trigger)
+ step_ms = int((time.perf_counter() - step_t0) * 1000)
+
+ # Extract metrics from the agent
+ step_llm = getattr(agent, '_run_llm_calls', 0)
+ step_tokens = getattr(agent, '_run_total_tokens', 0)
+ total_llm += step_llm
+ total_tokens += step_tokens
+
+ # Merge structured data into context (pipeline data bridge)
+ if step.output_key:
+ if result.data:
+ ctx.set(step.output_key, result.data)
+ elif result.outputs:
+ ctx.set(step.output_key, [asdict(o) for o in result.outputs])
+
+ # Also merge all result.data keys directly into context
+ for key, val in result.data.items():
+ ctx.set(key, val)
+
+ # Deposit structured data into shared memory (the wiki)
+ self._deposit_to_wiki(pipeline, step, result)
+
+ # Track artifacts
+ for o in result.outputs:
+ ctx.add_artifact(o.output_type, o.description, o.reference)
+
+ if result.requires_human_review:
+ human_review_needed = True
+
+ sr = StepResult(
+ step_index=i, agent_name=step.agent_name,
+ description=step.description,
+ success=result.success, skipped=False,
+ duration_ms=step_ms,
+ outputs=[asdict(o) for o in result.outputs],
+ errors=result.errors,
+ llm_calls=step_llm, tokens_used=step_tokens,
+ artifacts_produced=len(result.outputs),
+ requires_human_review=result.requires_human_review,
+ )
+ step_results.append(sr)
+
+ status = "OK" if result.success else "FAIL"
+ logger.info(
+ f" Step {i} [{step.agent_name}] → {status} "
+ f"({step_ms}ms, {step_llm} LLM calls, "
+ f"{len(result.outputs)} outputs)"
+ )
+
+ if not result.success and step.required:
+ pipeline_success = False
+ logger.error(
+ f" Pipeline STOPPED: required step {step.agent_name} failed"
+ )
+ break
+
+ except Exception as exc:
+ step_ms = int((time.perf_counter() - step_t0) * 1000)
+ logger.exception(f" Step {i} [{step.agent_name}] EXCEPTION: {exc}")
+ sr = StepResult(
+ step_index=i, agent_name=step.agent_name,
+ description=step.description,
+ success=False, skipped=False,
+ duration_ms=step_ms, outputs=[],
+ errors=[f"{type(exc).__name__}: {exc}"],
+ llm_calls=0, tokens_used=0, artifacts_produced=0,
+ requires_human_review=False,
+ )
+ step_results.append(sr)
+ if step.required:
+ pipeline_success = False
+ break
+
+ total_ms = int((time.perf_counter() - pipeline_t0) * 1000)
+ completed = sum(1 for sr in step_results if not sr.skipped and sr.success)
+ skipped = sum(1 for sr in step_results if sr.skipped)
+ failed = sum(1 for sr in step_results if not sr.skipped and not sr.success)
+
+ pipe_result = PipelineResult(
+ pipeline_id=pipeline_id,
+ pipeline_name=pipeline.name,
+ practice_area=pipeline.practice_area,
+ trigger_source=ctx.source,
+ success=pipeline_success,
+ total_steps=len(pipeline.steps),
+ completed_steps=completed,
+ skipped_steps=skipped,
+ failed_steps=failed,
+ total_duration_ms=total_ms,
+ total_llm_calls=total_llm,
+ total_tokens=total_tokens,
+ total_artifacts=len(ctx.artifacts),
+ requires_human_review=human_review_needed,
+ step_results=step_results,
+ artifacts=ctx.artifacts,
+ context_snapshot={k: type(v).__name__ for k, v in ctx.data.items()},
+ started_at=ctx.started_at,
+ completed_at=datetime.now(timezone.utc).isoformat(),
+ )
+
+ # Record pipeline-level metrics
+ self._record_pipeline_metrics(pipe_result)
+
+ status = "COMPLETED" if pipeline_success else "FAILED"
+ logger.info(
+ f"Pipeline '{pipeline.name}' {status}: "
+ f"{completed}/{len(pipeline.steps)} steps, "
+ f"{total_ms}ms, {total_llm} LLM calls, "
+ f"{len(ctx.artifacts)} artifacts"
+ )
+
+ return pipe_result
+
+ def _deposit_to_wiki(self, pipeline: Pipeline, step: PipelineStep, result: AgentResult) -> None:
+ """Deposit agent results into the shared wiki so other pipelines can access them."""
+ try:
+ from pipeline.shared_memory import SharedMemory
+ wiki = SharedMemory()
+
+ if not result.data and not result.outputs:
+ return
+
+ # Namespace is the practice area, key is agent:timestamp
+ ns = pipeline.practice_area.lower().replace(" ", "_")
+ ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%S")
+ key = f"{step.agent_name}:{ts}"
+
+ entry = {
+ "agent": step.agent_name,
+ "pipeline": pipeline.name,
+ "step_description": step.description,
+ "etvx_id": step.etvx_id,
+ "outputs": [asdict(o) for o in result.outputs] if result.outputs else [],
+ "data_keys": list(result.data.keys()) if result.data else [],
+ "success": result.success,
+ "requires_human_review": result.requires_human_review,
+ }
+
+ # Store significant data fields directly for cross-pipeline access
+ if result.data:
+ for data_key, data_val in result.data.items():
+ if isinstance(data_val, (str, int, float, bool, list)):
+ entry[data_key] = data_val
+ elif isinstance(data_val, dict) and len(str(data_val)) < 5000:
+ entry[data_key] = data_val
+
+ tags = [pipeline.name, step.agent_name]
+ if step.etvx_id:
+ tags.append(step.etvx_id)
+ if result.requires_human_review:
+ tags.append("human_review")
+
+ wiki.put(ns, key, entry, agent=step.agent_name, pipeline=pipeline.name, tags=tags)
+
+ # Also store latest run per agent for quick lookup
+ wiki.put(
+ "latest_runs", step.agent_name, entry,
+ agent=step.agent_name, pipeline=pipeline.name,
+ )
+
+ except Exception as exc:
+ logger.debug(f"Wiki deposit failed (non-critical): {exc}")
+
+ def _record_pipeline_metrics(self, result: PipelineResult) -> None:
+ try:
+ from pipeline.metrics import MetricsCollector, AgentRunMetric
+ mc = MetricsCollector()
+ mc.record_agent_run(AgentRunMetric(
+ run_id=result.pipeline_id,
+ agent=f"pipeline:{result.pipeline_name}",
+ trigger_type="pipeline",
+ trigger_source=result.trigger_source,
+ success=result.success,
+ duration_ms=result.total_duration_ms,
+ llm_calls=result.total_llm_calls,
+ total_input_tokens=0,
+ total_output_tokens=result.total_tokens,
+ estimated_cost_usd=0,
+ outputs_count=result.total_artifacts,
+ requires_human_review=result.requires_human_review,
+ errors=[],
+ ))
+ except Exception:
+ pass
+
+
+# ============================================================================
+# PIPELINE DEFINITIONS — The Framework
+# ============================================================================
+#
+# NOTE: drift_detector appears in BOTH the requirements and architecture
+# pipelines. This is intentional — they serve different purposes:
+#
+# REQUIREMENTS_PIPELINE (step 7, ETVX: REQ-DRIFT-CHECK):
+# A lightweight, quick drift check triggered after every meeting.
+# Compares the meeting's decisions against the canonical architecture
+# to catch contradictions early. Runs as a tail-end sanity check.
+#
+# ARCHITECTURE_PIPELINE (step 1, ETVX: ARCH-DRIFT):
+# The full drift analysis that kicks off the architecture practice.
+# Triggered by PRs, manual invocation, or drift_detected events.
+# Feeds into ADR generation, diagram updates, and traceability.
+#
+
+REQUIREMENTS_PIPELINE = Pipeline(
+ name="requirements",
+ practice_area="Requirements Engineering",
+ description=(
+ "End-to-end requirements flow: VTT transcript → structured minutes → "
+ "priority classification → REQ documents → Jira tickets → "
+ "Confluence publication → decision log → architecture drift check"
+ ),
+ trigger_types=["transcript"],
+ steps=[
+ PipelineStep(
+ agent_name="transcript_parser",
+ description="Parse raw transcript into structured minutes",
+ input_keys=["source"],
+ output_key="parsed_minutes",
+ etvx_id="REQ-PARSE",
+ ),
+ PipelineStep(
+ agent_name="priority_classifier",
+ description="Classify extracted items as P0/P1/P2",
+ input_keys=["parsed_minutes"],
+ output_key="classified_items",
+ skip_if_empty="parsed_minutes",
+ etvx_id="REQ-CLASSIFY",
+ ),
+ PipelineStep(
+ agent_name="req_extractor",
+ description="Generate REQ-XXX.md files from classified items",
+ input_keys=["classified_items"],
+ output_key="requirements",
+ skip_if_empty="classified_items",
+ etvx_id="REQ-EXTRACT",
+ ),
+ PipelineStep(
+ agent_name="ticket_creator",
+ description="Create Jira tickets (P0 → human review queue)",
+ input_keys=["classified_items"],
+ output_key="jira_tickets",
+ skip_if_empty="classified_items",
+ required=False,
+ etvx_id="PM-TICKET",
+ ),
+ PipelineStep(
+ agent_name="minutes_publisher",
+ description="Publish minutes to Confluence",
+ input_keys=["parsed_minutes"],
+ output_key="confluence_page",
+ skip_if_empty="parsed_minutes",
+ required=False,
+ etvx_id="KN-PUBLISH",
+ ),
+ PipelineStep(
+ agent_name="decision_logger",
+ description="Extract and log decisions",
+ input_keys=["parsed_minutes"],
+ output_key="decisions",
+ skip_if_empty="parsed_minutes",
+ required=False,
+ etvx_id="KN-DECISION",
+ ),
+ PipelineStep(
+ agent_name="drift_detector",
+ description="Lightweight post-meeting drift check against canonical architecture",
+ input_keys=["parsed_minutes", "decisions"],
+ output_key="drift_report",
+ skip_if_empty="parsed_minutes",
+ required=False,
+ etvx_id="REQ-DRIFT-CHECK",
+ ),
+ ],
+)
+
+COACH_SESSION_PIPELINE = Pipeline(
+ name="coach_session",
+ practice_area="Coach Session Memory",
+ description=(
+ "Coach/mentor transcript → session embedding → "
+ "concern detection → ML decision linking → decision logging"
+ ),
+ trigger_types=["coach_transcript"],
+ steps=[
+ PipelineStep(
+ agent_name="transcript_parser",
+ description="Parse coach session transcript",
+ input_keys=["source"],
+ output_key="parsed_session",
+ etvx_id="REQ-PARSE",
+ ),
+ PipelineStep(
+ agent_name="session_memory",
+ description="Chunk and embed into ChromaDB for RAG",
+ input_keys=["source"],
+ output_key="session_embedded",
+ etvx_id="COACH-INGEST",
+ ),
+ PipelineStep(
+ agent_name="concern_tracker",
+ description="Detect recurring themes and concerns",
+ input_keys=["parsed_session"],
+ output_key="concerns",
+ etvx_id="COACH-CONCERN",
+ ),
+ PipelineStep(
+ agent_name="coach_linker",
+ description="Link session content to open ML decisions",
+ input_keys=["session_embedded"],
+ output_key="ml_links",
+ required=False,
+ etvx_id="ML-LINK",
+ ),
+ PipelineStep(
+ agent_name="decision_logger",
+ description="Log any decisions or guidance from the session",
+ input_keys=["parsed_session"],
+ output_key="decisions",
+ skip_if_empty="parsed_session",
+ required=False,
+ etvx_id="KN-DECISION",
+ ),
+ ],
+)
+
+ARCHITECTURE_PIPELINE = Pipeline(
+ name="architecture",
+ practice_area="Architecture",
+ description=(
+ "Architecture practice: drift detection → ADR generation → "
+ "diagram update → traceability matrix"
+ ),
+ trigger_types=["transcript", "pr_event"],
+ steps=[
+ PipelineStep(
+ agent_name="drift_detector",
+ description="Compare discussion against canonical architecture",
+ input_keys=["source"],
+ output_key="drift_report",
+ etvx_id="ARCH-DRIFT",
+ ),
+ PipelineStep(
+ agent_name="adr_generator",
+ description="Draft ADR if significant decision detected",
+ input_keys=["drift_report"],
+ output_key="adr_draft",
+ skip_if_empty="drift_report",
+ required=False,
+ etvx_id="ARCH-ADR",
+ ),
+ PipelineStep(
+ agent_name="diagram_updater",
+ description="Propose diagram updates via PR",
+ input_keys=["drift_report"],
+ output_key="diagram_pr",
+ skip_if_empty="drift_report",
+ required=False,
+ etvx_id="ARCH-DIAGRAM",
+ ),
+ PipelineStep(
+ agent_name="traceability_builder",
+ description="Update traceability matrix",
+ input_keys=["adr_draft"],
+ output_key="traceability",
+ required=False,
+ etvx_id="ARCH-TRACE",
+ ),
+ ],
+)
+
+CODING_PIPELINE = Pipeline(
+ name="coding",
+ practice_area="Coding",
+ description=(
+ "Code practice: PR review → test generation → "
+ "doc update → boilerplate scaffolding"
+ ),
+ trigger_types=["pr_event"],
+ steps=[
+ PipelineStep(
+ agent_name="pr_reviewer",
+ description="Automated PR review (style, tests, traceability)",
+ input_keys=["source"],
+ output_key="review_comments",
+ etvx_id="CODE-REVIEW",
+ ),
+ PipelineStep(
+ agent_name="test_generator",
+ description="Generate test stubs for new functions",
+ input_keys=["source"],
+ output_key="test_stubs",
+ required=False,
+ etvx_id="CODE-TEST",
+ ),
+ PipelineStep(
+ agent_name="doc_generator",
+ description="Update API documentation",
+ input_keys=["source"],
+ output_key="doc_updates",
+ required=False,
+ etvx_id="CODE-DOC",
+ ),
+ PipelineStep(
+ agent_name="prompt_regression",
+ description="Test prompt changes against golden dataset",
+ input_keys=["source"],
+ output_key="regression_results",
+ required=False,
+ etvx_id="KN-PROMPT-REG",
+ ),
+ ],
+)
+
+ML_DECISION_PIPELINE = Pipeline(
+ name="ml_decision",
+ practice_area="ML Decision Memory",
+ description=(
+ "ML decision flow: evidence accumulation → readiness detection → "
+ "coach linking → briefing generation"
+ ),
+ trigger_types=["poc_result"],
+ steps=[
+ PipelineStep(
+ agent_name="evidence_accumulator",
+ description="Parse POC results and log evidence",
+ input_keys=["source"],
+ output_key="evidence",
+ etvx_id="ML-EVIDENCE",
+ ),
+ PipelineStep(
+ agent_name="readiness_detector",
+ description="Check if decisions are ready to close",
+ input_keys=["evidence"],
+ output_key="readiness_alerts",
+ etvx_id="ML-READINESS",
+ ),
+ PipelineStep(
+ agent_name="coach_linker",
+ description="Link evidence to coach session context",
+ input_keys=["evidence"],
+ output_key="coach_links",
+ required=False,
+ etvx_id="ML-LINK",
+ ),
+ ],
+)
+
+PROJECT_MGMT_PIPELINE = Pipeline(
+ name="project_mgmt",
+ practice_area="Project Management",
+ description=(
+ "PM practice: ticket creation → WBS update → "
+ "weekly digest → health alerting"
+ ),
+ trigger_types=["cron_friday_6pm"],
+ steps=[
+ PipelineStep(
+ agent_name="wbs_updater",
+ description="Sync WBS with Jira sprint state",
+ input_keys=[],
+ output_key="wbs_state",
+ required=False,
+ etvx_id="PM-WBS",
+ ),
+ PipelineStep(
+ agent_name="weekly_digest",
+ description="Generate weekly progress digest",
+ input_keys=["wbs_state"],
+ output_key="digest",
+ etvx_id="PM-DIGEST",
+ ),
+ PipelineStep(
+ agent_name="alert_agent",
+ description="Check project health and fire alerts",
+ input_keys=["wbs_state"],
+ output_key="alerts",
+ required=False,
+ etvx_id="PM-ALERT",
+ ),
+ ],
+)
+
+KNOWLEDGE_PIPELINE = Pipeline(
+ name="knowledge",
+ practice_area="Knowledge Management",
+ description=(
+ "Knowledge practice: context packaging → "
+ "briefing generation (pre-meeting preparation)"
+ ),
+ trigger_types=["cron_pre_meeting"],
+ steps=[
+ PipelineStep(
+ agent_name="context_packager",
+ description="Package project context for meeting",
+ input_keys=[],
+ output_key="context_package",
+ etvx_id="KN-CONTEXT",
+ ),
+ PipelineStep(
+ agent_name="briefing_generator",
+ description="Generate pre-meeting briefing",
+ input_keys=["context_package"],
+ output_key="briefing",
+ etvx_id="COACH-BRIEF",
+ ),
+ ],
+)
+
+# All pipelines indexed by name
+ALL_PIPELINES: dict[str, Pipeline] = {
+ p.name: p for p in [
+ REQUIREMENTS_PIPELINE,
+ COACH_SESSION_PIPELINE,
+ ARCHITECTURE_PIPELINE,
+ CODING_PIPELINE,
+ ML_DECISION_PIPELINE,
+ PROJECT_MGMT_PIPELINE,
+ KNOWLEDGE_PIPELINE,
+ ]
+}
+
+# Trigger → pipeline mapping (which pipeline handles which trigger)
+TRIGGER_PIPELINES: dict[str, list[str]] = {}
+for pipe in ALL_PIPELINES.values():
+ for tt in pipe.trigger_types:
+ TRIGGER_PIPELINES.setdefault(tt, []).append(pipe.name)
+
+
+def get_framework_summary() -> dict[str, Any]:
+ """Summary of the complete framework for presentation."""
+ total_steps = sum(len(p.steps) for p in ALL_PIPELINES.values())
+ practice_areas = list({p.practice_area for p in ALL_PIPELINES.values()})
+
+ connections = []
+ for pipe in ALL_PIPELINES.values():
+ for i, step in enumerate(pipe.steps):
+ if i > 0:
+ prev = pipe.steps[i - 1]
+ connections.append({
+ "from": prev.agent_name,
+ "to": step.agent_name,
+ "pipeline": pipe.name,
+ "data_key": step.skip_if_empty or prev.output_key,
+ })
+
+ return {
+ "framework_name": "eParts Agentic SE System",
+ "total_pipelines": len(ALL_PIPELINES),
+ "total_pipeline_steps": total_steps,
+ "practice_areas": sorted(practice_areas),
+ "pipelines": {
+ name: {
+ "practice_area": p.practice_area,
+ "description": p.description,
+ "steps": len(p.steps),
+ "trigger_types": p.trigger_types,
+ "agents": [s.agent_name for s in p.steps],
+ "etvx_ids": [s.etvx_id for s in p.steps if s.etvx_id],
+ }
+ for name, p in ALL_PIPELINES.items()
+ },
+ "connections": connections,
+ "trigger_coverage": TRIGGER_PIPELINES,
+ }
diff --git a/pipeline/process_inbox.py b/pipeline/process_inbox.py
new file mode 100644
index 0000000..261078f
--- /dev/null
+++ b/pipeline/process_inbox.py
@@ -0,0 +1,122 @@
+"""
+Inbox processor — the CI entry point for the transcript pipeline.
+
+Closes the "system on one laptop" operations gap: anyone on the team drops a
+Zoom ``*.transcript.vtt`` into ``transcripts/inbox/`` (git push or GitHub web
+upload) and CI does the rest — no orchestrator server, no local SQLite, no
+manual trigger.
+
+What it does:
+ 1. Canonicalize inbox filenames (strip Zoom's " (1)" download suffixes).
+ 2. Run the ingestion pipeline (pipeline.ingest) on the inbox files only,
+ writing per-meeting minutes/JSON/cleaned-transcript into ``minutes/``.
+ 3. Move the processed VTTs from ``transcripts/inbox/`` into ``transcripts/``.
+ 4. Rebuild ``minutes/cross-meeting-analysis.md`` across ALL meetings to date.
+ 5. Write a run summary (markdown) for the pull-request body.
+
+The GitHub Actions workflow (.github/workflows/transcript-pipeline.yml) then
+opens a pull request with the generated artifacts. The PR review IS the human
+gate: generated minutes are drafts until a person approves the merge.
+
+Run: python -m pipeline.process_inbox [--summary-file PATH]
+Exit code 0 with "processed: 0" when the inbox is empty (safe no-op).
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import shutil
+import tempfile
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+INBOX_DIR = PROJECT_ROOT / "transcripts" / "inbox"
+TRANSCRIPTS_DIR = PROJECT_ROOT / "transcripts"
+MINUTES_DIR = PROJECT_ROOT / "minutes"
+
+from pipeline.ingest import generate_cross_meeting_report, run as run_ingest
+
+
+def canonicalize(name: str) -> str:
+ """Strip browser-download suffixes: 'X.transcript (1).vtt' -> 'X.transcript.vtt'."""
+ return re.sub(r"\.transcript(?: \(\d+\))?\.vtt$", ".transcript.vtt", name)
+
+
+def rebuild_cross_meeting_report() -> int:
+ """Regenerate the cross-meeting analysis over every processed meeting."""
+ summaries = [
+ json.loads(p.read_text(encoding="utf-8"))
+ for p in sorted(MINUTES_DIR.glob("*-client.json"))
+ ]
+ if summaries:
+ report = generate_cross_meeting_report(summaries)
+ (MINUTES_DIR / "cross-meeting-analysis.md").write_text(report, encoding="utf-8")
+ return len(summaries)
+
+
+def main() -> None:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument(
+ "--summary-file",
+ type=Path,
+ default=None,
+ help="Where to write the markdown run summary (for the PR body).",
+ )
+ args = parser.parse_args()
+
+ inbox_files = sorted(INBOX_DIR.glob("*.vtt")) if INBOX_DIR.exists() else []
+ transcript_files = [f for f in inbox_files if ".transcript" in f.name]
+ other_files = [f for f in inbox_files if ".transcript" not in f.name]
+
+ summary_lines = ["## Transcript pipeline run", ""]
+
+ if not transcript_files:
+ print("processed: 0 (inbox empty)")
+ summary_lines.append("Inbox empty — nothing to process.")
+ if args.summary_file:
+ args.summary_file.write_text("\n".join(summary_lines), encoding="utf-8")
+ return
+
+ # Stage inbox files under canonical names so batch_process's
+ # "*.transcript.vtt" glob matches regardless of download suffixes.
+ with tempfile.TemporaryDirectory() as tmp:
+ stage = Path(tmp)
+ for f in transcript_files:
+ shutil.copy2(f, stage / canonicalize(f.name))
+
+ result = run_ingest(stage, MINUTES_DIR)
+
+ # Archive processed VTTs out of the inbox into transcripts/.
+ for f in transcript_files:
+ target = TRANSCRIPTS_DIR / canonicalize(f.name)
+ shutil.move(str(f), target)
+ # Non-transcript VTTs (e.g. .cc.vtt) are archived alongside, unprocessed.
+ for f in other_files:
+ shutil.move(str(f), TRANSCRIPTS_DIR / f.name)
+
+ total = rebuild_cross_meeting_report()
+
+ summary_lines += [
+ f"Processed **{result['meetings']}** transcript(s) from `transcripts/inbox/`:",
+ "",
+ ]
+ summary_lines += [f"- `{canonicalize(f.name)}`" for f in transcript_files]
+ summary_lines += [
+ "",
+ f"Artifacts written to `minutes/` (minutes + JSON + cleaned transcript per meeting).",
+ f"Cross-meeting analysis rebuilt over **{total}** meetings to date.",
+ "",
+ "**These are agent-generated drafts.** Review the minutes for speaker",
+ "attribution errors, missed decisions, and misclassified action items",
+ "before merging — merging this PR is the human approval step.",
+ ]
+
+ if args.summary_file:
+ args.summary_file.write_text("\n".join(summary_lines), encoding="utf-8")
+ print(f"processed: {result['meetings']}, cross-meeting total: {total}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/pipeline/prompt_registry.py b/pipeline/prompt_registry.py
new file mode 100644
index 0000000..bb0055e
--- /dev/null
+++ b/pipeline/prompt_registry.py
@@ -0,0 +1,464 @@
+"""
+Prompt Registry — version-controlled prompt governance for team consistency.
+
+Solves the core problem: 5 people using LLMs probabilistically will produce
+chaos unless prompts are treated as shared, reviewed, versioned artifacts.
+
+Three layers of consistency:
+ 1. Prompt Pinning — every prompt has a hash; agents use the pinned version
+ 2. Prompt Review — changes require peer review (like code review)
+ 3. Output Validation — golden tests catch regressions from prompt changes
+
+The registry tracks:
+ - Every prompt version with hash, author, reviewer, approval status
+ - Performance metrics per version (score, correction rate, tokens)
+ - Review comments and approval history
+ - A/B test results when two versions run side-by-side
+"""
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+import sqlite3
+import textwrap
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger("pipeline.prompt_registry")
+
+PROMPTS_DIR = Path(__file__).resolve().parent.parent / "prompts"
+MEMORY_DIR = Path(__file__).resolve().parent.parent / "memory"
+DB_PATH = MEMORY_DIR / "prompt_registry.db"
+
+
+def _init_db(db_path: Path | None = None) -> sqlite3.Connection:
+ path = db_path or DB_PATH
+ path.parent.mkdir(parents=True, exist_ok=True)
+ conn = sqlite3.connect(str(path))
+ conn.row_factory = sqlite3.Row
+ conn.executescript(textwrap.dedent("""\
+ CREATE TABLE IF NOT EXISTS prompt_versions (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ prompt_name TEXT NOT NULL,
+ version_hash TEXT NOT NULL,
+ content TEXT NOT NULL,
+ author TEXT DEFAULT 'unknown',
+ created_at TEXT NOT NULL,
+ status TEXT DEFAULT 'draft',
+ reviewer TEXT DEFAULT '',
+ review_comment TEXT DEFAULT '',
+ reviewed_at TEXT DEFAULT '',
+ is_active INTEGER DEFAULT 0,
+ UNIQUE(prompt_name, version_hash)
+ );
+ CREATE INDEX IF NOT EXISTS idx_pv_name ON prompt_versions(prompt_name);
+ CREATE INDEX IF NOT EXISTS idx_pv_active ON prompt_versions(is_active);
+
+ CREATE TABLE IF NOT EXISTS prompt_metrics (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ prompt_name TEXT NOT NULL,
+ version_hash TEXT NOT NULL,
+ run_count INTEGER DEFAULT 0,
+ avg_tokens INTEGER DEFAULT 0,
+ avg_score REAL DEFAULT 0.0,
+ correction_rate REAL DEFAULT 0.0,
+ last_used TEXT DEFAULT '',
+ UNIQUE(prompt_name, version_hash)
+ );
+
+ CREATE TABLE IF NOT EXISTS prompt_reviews (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ prompt_name TEXT NOT NULL,
+ version_hash TEXT NOT NULL,
+ reviewer TEXT NOT NULL,
+ action TEXT NOT NULL,
+ comment TEXT DEFAULT '',
+ timestamp TEXT NOT NULL
+ );
+
+ CREATE TABLE IF NOT EXISTS ab_tests (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ prompt_name TEXT NOT NULL,
+ version_a TEXT NOT NULL,
+ version_b TEXT NOT NULL,
+ input_hash TEXT NOT NULL,
+ score_a REAL DEFAULT 0.0,
+ score_b REAL DEFAULT 0.0,
+ winner TEXT DEFAULT '',
+ timestamp TEXT NOT NULL
+ );
+
+ CREATE TABLE IF NOT EXISTS team_conventions (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ convention TEXT NOT NULL,
+ category TEXT DEFAULT '',
+ rationale TEXT DEFAULT '',
+ enforced_by TEXT DEFAULT 'manual',
+ created_at TEXT NOT NULL
+ );
+ """))
+ conn.commit()
+ return conn
+
+
+def _hash_content(content: str) -> str:
+ return hashlib.sha256(content.encode()).hexdigest()[:16]
+
+
+class PromptRegistry:
+ """
+ Centralized prompt management for team consistency.
+
+ Every prompt used by any agent goes through this registry.
+ This ensures:
+ - All team members use the same prompt version
+ - Changes are reviewed before activation
+ - Performance is tracked per version
+ - A/B tests compare versions with evidence
+ """
+
+ def __init__(self, db_path: Path | None = None):
+ self._db = _init_db(db_path)
+ self._scan_prompts_dir()
+
+ def _scan_prompts_dir(self) -> None:
+ """Auto-register any prompts in the prompts/ directory."""
+ if not PROMPTS_DIR.exists():
+ return
+ for f in PROMPTS_DIR.glob("*.txt"):
+ content = f.read_text()
+ h = _hash_content(content)
+ existing = self._db.execute(
+ "SELECT id FROM prompt_versions WHERE prompt_name = ? AND version_hash = ?",
+ (f.stem, h),
+ ).fetchone()
+ if not existing:
+ now = datetime.now(timezone.utc).isoformat()
+ self._db.execute(
+ "INSERT INTO prompt_versions (prompt_name, version_hash, content, "
+ "author, created_at, status, is_active) VALUES (?, ?, ?, ?, ?, ?, ?)",
+ (f.stem, h, content, "auto-scan", now, "active", 1),
+ )
+ self._db.commit()
+
+ def register_version(
+ self, prompt_name: str, content: str, author: str,
+ ) -> dict[str, str]:
+ """Register a new prompt version. Returns hash and status."""
+ h = _hash_content(content)
+ now = datetime.now(timezone.utc).isoformat()
+
+ existing = self._db.execute(
+ "SELECT id, status FROM prompt_versions WHERE prompt_name = ? AND version_hash = ?",
+ (prompt_name, h),
+ ).fetchone()
+
+ if existing:
+ return {"hash": h, "status": existing["status"], "action": "already_exists"}
+
+ self._db.execute(
+ "INSERT INTO prompt_versions (prompt_name, version_hash, content, "
+ "author, created_at, status, is_active) VALUES (?, ?, ?, ?, ?, ?, ?)",
+ (prompt_name, h, content, author, now, "pending_review", 0),
+ )
+ self._db.commit()
+ return {"hash": h, "status": "pending_review", "action": "registered"}
+
+ def review_prompt(
+ self, prompt_name: str, version_hash: str,
+ reviewer: str, action: str, comment: str = "",
+ ) -> dict[str, str]:
+ """
+ Review a prompt version. Actions: approve, reject, request_changes.
+ Only approved prompts can be activated.
+ """
+ now = datetime.now(timezone.utc).isoformat()
+
+ if action not in ("approve", "reject", "request_changes"):
+ return {"error": f"Invalid action: {action}"}
+
+ # Record the review
+ self._db.execute(
+ "INSERT INTO prompt_reviews (prompt_name, version_hash, reviewer, "
+ "action, comment, timestamp) VALUES (?, ?, ?, ?, ?, ?)",
+ (prompt_name, version_hash, reviewer, action, comment, now),
+ )
+
+ # Update prompt status
+ new_status = {
+ "approve": "approved",
+ "reject": "rejected",
+ "request_changes": "changes_requested",
+ }[action]
+
+ self._db.execute(
+ "UPDATE prompt_versions SET status = ?, reviewer = ?, review_comment = ?, "
+ "reviewed_at = ? WHERE prompt_name = ? AND version_hash = ?",
+ (new_status, reviewer, comment, now, prompt_name, version_hash),
+ )
+ self._db.commit()
+
+ return {"status": new_status, "reviewer": reviewer}
+
+ def activate_version(self, prompt_name: str, version_hash: str) -> dict[str, str]:
+ """
+ Activate a prompt version (must be approved first).
+ Deactivates the previously active version.
+ """
+ row = self._db.execute(
+ "SELECT status FROM prompt_versions WHERE prompt_name = ? AND version_hash = ?",
+ (prompt_name, version_hash),
+ ).fetchone()
+
+ if not row:
+ return {"error": "Version not found"}
+ if row["status"] not in ("approved", "active"):
+ return {"error": f"Cannot activate — status is '{row['status']}'. Must be approved first."}
+
+ # Deactivate all other versions
+ self._db.execute(
+ "UPDATE prompt_versions SET is_active = 0 WHERE prompt_name = ?",
+ (prompt_name,),
+ )
+ # Activate this one
+ self._db.execute(
+ "UPDATE prompt_versions SET is_active = 1, status = 'active' "
+ "WHERE prompt_name = ? AND version_hash = ?",
+ (prompt_name, version_hash),
+ )
+ self._db.commit()
+
+ # Also write to the prompts/ directory
+ PROMPTS_DIR.mkdir(parents=True, exist_ok=True)
+ content = self._db.execute(
+ "SELECT content FROM prompt_versions WHERE prompt_name = ? AND version_hash = ?",
+ (prompt_name, version_hash),
+ ).fetchone()["content"]
+ (PROMPTS_DIR / f"{prompt_name}.txt").write_text(content)
+
+ return {"status": "active", "hash": version_hash}
+
+ def get_active_prompt(self, prompt_name: str) -> dict[str, Any] | None:
+ """Get the currently active (pinned) prompt version."""
+ row = self._db.execute(
+ "SELECT * FROM prompt_versions WHERE prompt_name = ? AND is_active = 1",
+ (prompt_name,),
+ ).fetchone()
+ return dict(row) if row else None
+
+ def get_version_history(self, prompt_name: str) -> list[dict]:
+ """Full version history for a prompt."""
+ rows = self._db.execute(
+ "SELECT prompt_name, version_hash, author, status, reviewer, "
+ "review_comment, created_at, reviewed_at, is_active "
+ "FROM prompt_versions WHERE prompt_name = ? ORDER BY created_at DESC",
+ (prompt_name,),
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def get_reviews(self, prompt_name: str, version_hash: str = "") -> list[dict]:
+ """Get review history for a prompt."""
+ if version_hash:
+ rows = self._db.execute(
+ "SELECT * FROM prompt_reviews WHERE prompt_name = ? AND version_hash = ? "
+ "ORDER BY timestamp DESC",
+ (prompt_name, version_hash),
+ ).fetchall()
+ else:
+ rows = self._db.execute(
+ "SELECT * FROM prompt_reviews WHERE prompt_name = ? ORDER BY timestamp DESC",
+ (prompt_name,),
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def record_ab_test(
+ self, prompt_name: str, version_a: str, version_b: str,
+ input_hash: str, score_a: float, score_b: float,
+ ) -> dict:
+ """Record an A/B test result between two prompt versions."""
+ winner = "a" if score_a > score_b else "b" if score_b > score_a else "tie"
+ now = datetime.now(timezone.utc).isoformat()
+ self._db.execute(
+ "INSERT INTO ab_tests (prompt_name, version_a, version_b, "
+ "input_hash, score_a, score_b, winner, timestamp) VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
+ (prompt_name, version_a, version_b, input_hash, score_a, score_b, winner, now),
+ )
+ self._db.commit()
+ return {"winner": winner, "score_a": score_a, "score_b": score_b}
+
+ def get_ab_results(self, prompt_name: str) -> list[dict]:
+ rows = self._db.execute(
+ "SELECT * FROM ab_tests WHERE prompt_name = ? ORDER BY timestamp DESC",
+ (prompt_name,),
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def record_metrics(
+ self, prompt_name: str, version_hash: str,
+ tokens: int = 0, score: float = 0.0, corrected: bool = False,
+ ) -> None:
+ """Record usage metrics for a prompt version."""
+ existing = self._db.execute(
+ "SELECT run_count, avg_tokens, avg_score, correction_rate "
+ "FROM prompt_metrics WHERE prompt_name = ? AND version_hash = ?",
+ (prompt_name, version_hash),
+ ).fetchone()
+
+ now = datetime.now(timezone.utc).isoformat()
+
+ if existing:
+ n = existing["run_count"]
+ new_avg_tokens = int((existing["avg_tokens"] * n + tokens) / (n + 1))
+ new_avg_score = (existing["avg_score"] * n + score) / (n + 1)
+ corrections = existing["correction_rate"] * n + (1 if corrected else 0)
+ new_correction_rate = corrections / (n + 1)
+ self._db.execute(
+ "UPDATE prompt_metrics SET run_count = ?, avg_tokens = ?, avg_score = ?, "
+ "correction_rate = ?, last_used = ? WHERE prompt_name = ? AND version_hash = ?",
+ (n + 1, new_avg_tokens, round(new_avg_score, 4),
+ round(new_correction_rate, 4), now, prompt_name, version_hash),
+ )
+ else:
+ self._db.execute(
+ "INSERT INTO prompt_metrics (prompt_name, version_hash, run_count, "
+ "avg_tokens, avg_score, correction_rate, last_used) VALUES (?, ?, ?, ?, ?, ?, ?)",
+ (prompt_name, version_hash, 1, tokens, round(score, 4),
+ 1.0 if corrected else 0.0, now),
+ )
+ self._db.commit()
+
+ def get_all_prompts(self) -> list[dict]:
+ """Summary of all registered prompts with their active versions."""
+ rows = self._db.execute(
+ "SELECT prompt_name, version_hash, author, status, reviewer, "
+ "created_at, reviewed_at, is_active FROM prompt_versions "
+ "ORDER BY prompt_name, created_at DESC"
+ ).fetchall()
+
+ prompts: dict[str, list] = {}
+ for r in rows:
+ prompts.setdefault(r["prompt_name"], []).append(dict(r))
+
+ result = []
+ for name, versions in prompts.items():
+ active = next((v for v in versions if v["is_active"]), None)
+ metrics_row = self._db.execute(
+ "SELECT * FROM prompt_metrics WHERE prompt_name = ? AND version_hash = ?",
+ (name, active["version_hash"] if active else ""),
+ ).fetchone()
+
+ result.append({
+ "prompt_name": name,
+ "active_version": active["version_hash"] if active else None,
+ "active_author": active["author"] if active else None,
+ "total_versions": len(versions),
+ "status": active["status"] if active else versions[0]["status"],
+ "metrics": dict(metrics_row) if metrics_row else None,
+ })
+ return result
+
+ def stats(self) -> dict[str, Any]:
+ total_prompts = self._db.execute(
+ "SELECT COUNT(DISTINCT prompt_name) as c FROM prompt_versions"
+ ).fetchone()["c"]
+ total_versions = self._db.execute(
+ "SELECT COUNT(*) as c FROM prompt_versions"
+ ).fetchone()["c"]
+ total_reviews = self._db.execute(
+ "SELECT COUNT(*) as c FROM prompt_reviews"
+ ).fetchone()["c"]
+ total_ab = self._db.execute(
+ "SELECT COUNT(*) as c FROM ab_tests"
+ ).fetchone()["c"]
+ by_status = self._db.execute(
+ "SELECT status, COUNT(*) as c FROM prompt_versions GROUP BY status"
+ ).fetchall()
+ return {
+ "total_prompts": total_prompts,
+ "total_versions": total_versions,
+ "total_reviews": total_reviews,
+ "total_ab_tests": total_ab,
+ "by_status": {r["status"]: r["c"] for r in by_status},
+ }
+
+
+def seed_team_conventions(db_path: Path | None = None) -> None:
+ """
+ Seed the team conventions that enforce systematic operation.
+ These are the rules all team members follow.
+ """
+ conn = _init_db(db_path)
+ now = datetime.now(timezone.utc).isoformat()
+
+ conventions = [
+ (
+ "All prompts must be stored in /prompts/ as .txt files, never inline in code",
+ "prompt_management",
+ "Inline prompts are invisible to the team. Centralized storage enables review, versioning, and regression testing.",
+ "prompt_registry scan",
+ ),
+ (
+ "Every prompt change requires peer review before activation",
+ "prompt_management",
+ "LLMs are probabilistic — a 'small' prompt tweak can drastically change output quality. Review catches regressions the author didn't test for.",
+ "prompt_registry review workflow",
+ ),
+ (
+ "Use temperature=0 for all deterministic tasks (parsing, classification, extraction)",
+ "reproducibility",
+ "Temperature >0 means different outputs on the same input. For engineering artifacts, we need consistency across team members and runs.",
+ "BaseAgent call_claude() default",
+ ),
+ (
+ "Every LLM-generated artifact must have provenance metadata (agent, prompt version, timestamp, model)",
+ "traceability",
+ "If an artifact is wrong, we need to know which prompt version produced it so we can fix the root cause, not just the symptom.",
+ "MetricsCollector automatic tracking",
+ ),
+ (
+ "Human-in-the-loop required for all P0 items and architectural decisions",
+ "quality_gate",
+ "AI draft → human approve. Never auto-ship P0 requirements or ADRs. The cost of a wrong P0 decision exceeds the time saved by automation.",
+ "pipeline step configuration",
+ ),
+ (
+ "Golden test cases required for every prompt in /prompts/",
+ "regression",
+ "Without golden tests, prompt changes are untested. Golden tests are the unit tests of prompt engineering.",
+ "prompt_regression agent on PR",
+ ),
+ (
+ "All agents run offline-first, Claude-powered as an upgrade",
+ "resilience",
+ "System must work without API key (demo resilience, cost control). Offline results establish a baseline for measuring Claude's improvement.",
+ "BaseAgent offline fallback pattern",
+ ),
+ (
+ "Meeting outputs are deposited to SharedMemory wiki, not just logged to files",
+ "knowledge_management",
+ "Files are dead. The wiki is alive — queryable, cross-referenceable, and grows smarter with every meeting.",
+ "BaseAgent.wiki integration",
+ ),
+ (
+ "Cross-pipeline events for significant outputs, not direct function calls",
+ "architecture",
+ "Direct coupling between pipelines creates a maintenance nightmare. Events decouple producers from consumers.",
+ "EventBus subscription model",
+ ),
+ (
+ "Weekly review of measurement dashboard by SES team (Jai, Hrishik, Ashritha)",
+ "process_improvement",
+ "The meta-model says 'process improvement is reliant on improving AI components/systems.' Can't improve what you don't inspect.",
+ "manual team practice",
+ ),
+ ]
+
+ for conv, cat, rationale, enforced in conventions:
+ conn.execute(
+ "INSERT OR IGNORE INTO team_conventions (convention, category, rationale, enforced_by, created_at) "
+ "VALUES (?, ?, ?, ?, ?)",
+ (conv, cat, rationale, enforced, now),
+ )
+ conn.commit()
diff --git a/pipeline/render_risk_register.py b/pipeline/render_risk_register.py
new file mode 100644
index 0000000..c247dd4
--- /dev/null
+++ b/pipeline/render_risk_register.py
@@ -0,0 +1,141 @@
+"""
+Render docs/risk_register.md from the risk register database.
+
+The register file previously carried the footer "Auto-generated from
+risk_register.db" while nothing in the repo actually generated it, so the
+markdown and the database could drift apart silently. This module closes that
+gap: the file is now produced from the database, and every count in the header
+is computed rather than typed.
+
+Exit condition of the risk practice area (see the meta-model mapping in
+docs/defect_management.md for the house style) is that a risk has both an owner
+and a mitigation. This renderer reports any risk failing that condition rather
+than quietly publishing it, so the register cannot claim completeness it does
+not have.
+
+Run: python3 -m pipeline.render_risk_register
+"""
+
+from __future__ import annotations
+
+import sys
+from datetime import date
+from pathlib import Path
+
+from pipeline.risk_register import RiskRegister
+
+REPO = Path(__file__).resolve().parent.parent
+OUT = REPO / "docs" / "risk_register.md"
+
+SEVERITY_ORDER = {"critical": 0, "high": 1, "medium": 2, "low": 3}
+
+
+def _cell(text: str, limit: int = 200) -> str:
+ """Collapse to one line and keep pipes from breaking the table."""
+ one = " ".join((text or "").split()).replace("|", "\\|")
+ return one if len(one) <= limit else one[: limit - 1].rstrip() + "…"
+
+
+def render(reg: RiskRegister | None = None) -> str:
+ reg = reg or RiskRegister()
+ risks = sorted(
+ reg.get_all(),
+ key=lambda r: (SEVERITY_ORDER.get(r["severity"], 9), r["id"]),
+ )
+ stats = reg.stats()
+ sev = stats["by_severity"]
+ status = stats["by_status"]
+
+ lines: list[str] = ["# eParts Risk Register", ""]
+ lines.append(f"**Total Risks:** {stats['total']} ")
+ lines.append(
+ f"**Critical:** {sev.get('critical', 0)} | "
+ f"**High:** {sev.get('high', 0)} | "
+ f"**Medium:** {sev.get('medium', 0)}"
+ + (f" | **Low:** {sev['low']}" if sev.get("low") else "")
+ )
+ lines.append("")
+ lines.append(
+ " ".join(f"**{k.title()}:** {v}" for k, v in sorted(status.items()))
+ + " "
+ )
+ lines.append("")
+
+ lines.append(
+ "| # | Severity | Category | Title | Risk Statement | Mitigation | Status | Owner |"
+ )
+ lines.append("|---|----------|----------|-------|----------------|------------|--------|-------|")
+ for n, r in enumerate(risks, 1):
+ lines.append(
+ f"| {n} | **{r['severity']}** | {r['category']} | {_cell(r['title'], 70)} "
+ f"| {_cell(r['description'])} | {_cell(r['mitigation'])} "
+ f"| {r['status']} | {r['owner']} |"
+ )
+ lines.append("")
+
+ # Traceability: which requirements and architecture artifacts each risk threatens.
+ traced = [r for r in risks if r["related_reqs"] or r["related_arch"]]
+ lines.append("## Traceability")
+ lines.append("")
+ lines.append(
+ f"{len(traced)} of {len(risks)} risks are linked to the requirements or "
+ "architecture artifacts they threaten."
+ )
+ lines.append("")
+ lines.append("| Risk | Title | Threatens (requirements) | Threatens (architecture) |")
+ lines.append("|------|-------|--------------------------|--------------------------|")
+ for r in traced:
+ lines.append(
+ f"| `{r['id']}` | {_cell(r['title'], 60)} "
+ f"| {', '.join(r['related_reqs']) or '—'} "
+ f"| {', '.join(r['related_arch']) or '—'} |"
+ )
+ lines.append("")
+
+ # Exit-condition check. A risk without an owner or a mitigation has not
+ # cleared the practice area's exit gate and is called out here.
+ incomplete = [r for r in risks if not r["mitigation"].strip() or not r["owner"].strip()]
+ lines.append("## Exit-condition check")
+ lines.append("")
+ if incomplete:
+ lines.append(
+ f"{len(incomplete)} risk(s) have not cleared the exit gate "
+ "(a risk requires both an owner and a mitigation):"
+ )
+ lines.append("")
+ for r in incomplete:
+ missing = []
+ if not r["mitigation"].strip():
+ missing.append("mitigation")
+ if not r["owner"].strip():
+ missing.append("owner")
+ lines.append(f"- `{r['id']}` {r['title']} — missing {' and '.join(missing)}")
+ else:
+ lines.append(
+ "All risks have both an owner and a mitigation, so every entry has "
+ "cleared the exit gate."
+ )
+ lines.append("")
+ lines.append("---")
+ lines.append(
+ f"_Generated from `memory/risk_register.db` by "
+ f"`pipeline/render_risk_register.py` on {date.today().isoformat()}. "
+ "Do not edit by hand: change the source in `pipeline/risk_register.py` "
+ "and re-run._"
+ )
+ return "\n".join(lines) + "\n"
+
+
+def main() -> int:
+ text = render()
+ OUT.write_text(text, encoding="utf-8")
+ reg = RiskRegister()
+ stats = reg.stats()
+ print(f"wrote {OUT.relative_to(REPO)}")
+ print(f" {stats['total']} risks · {stats['by_severity']}")
+ print(f" status: {stats['by_status']}")
+ return 0
+
+
+if __name__ == "__main__":
+ sys.exit(main())
diff --git a/pipeline/risk_register.py b/pipeline/risk_register.py
new file mode 100644
index 0000000..f2f36b2
--- /dev/null
+++ b/pipeline/risk_register.py
@@ -0,0 +1,444 @@
+"""
+Risk Register — auto-populated from architecture report, coach sessions, and meetings.
+
+Risks are pulled from three sources:
+ 1. Architecture report (Section 5.4) — technical risks and sensitivity points
+ 2. Coach session memory — recurring concerns tracked across sessions
+ 3. Meeting action items — unresolved items that become risks over time
+
+Each risk has: ID, title, category, source, likelihood, impact, severity,
+mitigation strategy, status, owner, and links to related artifacts.
+"""
+from __future__ import annotations
+
+import json
+import logging
+import sqlite3
+import textwrap
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger("pipeline.risk_register")
+
+MEMORY_DIR = Path(__file__).resolve().parent.parent / "memory"
+DB_PATH = MEMORY_DIR / "risk_register.db"
+
+
+def _init_db(db_path: Path | None = None) -> sqlite3.Connection:
+ path = db_path or DB_PATH
+ path.parent.mkdir(parents=True, exist_ok=True)
+ conn = sqlite3.connect(str(path))
+ conn.row_factory = sqlite3.Row
+ conn.executescript(textwrap.dedent("""\
+ CREATE TABLE IF NOT EXISTS risks (
+ id TEXT PRIMARY KEY,
+ title TEXT NOT NULL,
+ description TEXT DEFAULT '',
+ category TEXT DEFAULT '',
+ source TEXT DEFAULT '',
+ source_detail TEXT DEFAULT '',
+ likelihood TEXT DEFAULT 'medium',
+ impact TEXT DEFAULT 'medium',
+ severity TEXT DEFAULT 'medium',
+ mitigation TEXT DEFAULT '',
+ contingency TEXT DEFAULT '',
+ status TEXT DEFAULT 'open',
+ owner TEXT DEFAULT 'team',
+ related_reqs TEXT DEFAULT '[]',
+ related_arch TEXT DEFAULT '[]',
+ created_at TEXT NOT NULL,
+ updated_at TEXT NOT NULL
+ );
+ CREATE TABLE IF NOT EXISTS risk_reviews (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ risk_id TEXT NOT NULL,
+ reviewer TEXT NOT NULL,
+ old_status TEXT NOT NULL,
+ new_status TEXT NOT NULL,
+ notes TEXT DEFAULT '',
+ reviewed_at TEXT NOT NULL,
+ FOREIGN KEY (risk_id) REFERENCES risks(id)
+ );
+ """))
+ conn.commit()
+ return conn
+
+
+SEVERITY_MATRIX = {
+ ("high", "high"): "critical",
+ ("high", "medium"): "high",
+ ("medium", "high"): "high",
+ ("high", "low"): "medium",
+ ("low", "high"): "medium",
+ ("medium", "medium"): "medium",
+ ("medium", "low"): "low",
+ ("low", "medium"): "low",
+ ("low", "low"): "low",
+}
+
+
+class RiskRegister:
+ def __init__(self, db_path: Path | None = None):
+ self._db = _init_db(db_path)
+
+ def add_risk(
+ self,
+ risk_id: str,
+ title: str,
+ description: str = "",
+ category: str = "",
+ source: str = "",
+ source_detail: str = "",
+ likelihood: str = "medium",
+ impact: str = "medium",
+ mitigation: str = "",
+ contingency: str = "",
+ status: str = "open",
+ owner: str = "team",
+ related_reqs: list[str] | None = None,
+ related_arch: list[str] | None = None,
+ ) -> None:
+ now = datetime.now(timezone.utc).isoformat()
+ severity = SEVERITY_MATRIX.get((likelihood, impact), "medium")
+ self._db.execute(
+ "INSERT OR REPLACE INTO risks VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)",
+ (risk_id, title, description, category, source, source_detail,
+ likelihood, impact, severity, mitigation, contingency, status, owner,
+ json.dumps(related_reqs or []), json.dumps(related_arch or []),
+ now, now),
+ )
+ self._db.commit()
+
+ def get_all(self) -> list[dict[str, Any]]:
+ rows = self._db.execute(
+ "SELECT * FROM risks ORDER BY "
+ "CASE severity WHEN 'critical' THEN 0 WHEN 'high' THEN 1 WHEN 'medium' THEN 2 ELSE 3 END, id"
+ ).fetchall()
+ return [
+ {**dict(r), "related_reqs": json.loads(r["related_reqs"]),
+ "related_arch": json.loads(r["related_arch"])}
+ for r in rows
+ ]
+
+ def get_by_category(self, category: str) -> list[dict]:
+ rows = self._db.execute(
+ "SELECT * FROM risks WHERE category = ? ORDER BY severity", (category,)
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def update_status(self, risk_id: str, status: str) -> None:
+ self._db.execute(
+ "UPDATE risks SET status = ?, updated_at = ? WHERE id = ?",
+ (status, datetime.now(timezone.utc).isoformat(), risk_id),
+ )
+ self._db.commit()
+
+ def stats(self) -> dict[str, Any]:
+ total = self._db.execute("SELECT COUNT(*) as c FROM risks").fetchone()["c"]
+ by_severity = self._db.execute(
+ "SELECT severity, COUNT(*) as c FROM risks GROUP BY severity"
+ ).fetchall()
+ by_status = self._db.execute(
+ "SELECT status, COUNT(*) as c FROM risks GROUP BY status"
+ ).fetchall()
+ by_category = self._db.execute(
+ "SELECT category, COUNT(*) as c FROM risks GROUP BY category"
+ ).fetchall()
+ return {
+ "total": total,
+ "by_severity": {r["severity"]: r["c"] for r in by_severity},
+ "by_status": {r["status"]: r["c"] for r in by_status},
+ "by_category": {r["category"]: r["c"] for r in by_category},
+ }
+
+ def review_risk(
+ self,
+ risk_id: str,
+ new_status: str,
+ review_notes: str = "",
+ reviewer: str = "team",
+ ) -> None:
+ """Update a risk's status and record a review entry."""
+ row = self._db.execute(
+ "SELECT status FROM risks WHERE id = ?", (risk_id,)
+ ).fetchone()
+ if row is None:
+ raise ValueError(f"Risk {risk_id} not found")
+ old_status = row["status"]
+ now = datetime.now(timezone.utc).isoformat()
+ self._db.execute(
+ "INSERT INTO risk_reviews (risk_id, reviewer, old_status, new_status, notes, reviewed_at) "
+ "VALUES (?, ?, ?, ?, ?, ?)",
+ (risk_id, reviewer, old_status, new_status, review_notes, now),
+ )
+ self._db.execute(
+ "UPDATE risks SET status = ?, updated_at = ? WHERE id = ?",
+ (new_status, now, risk_id),
+ )
+ self._db.commit()
+
+ def get_review_history(self, risk_id: str) -> list[dict[str, Any]]:
+ """Return all review entries for a given risk, oldest first."""
+ rows = self._db.execute(
+ "SELECT * FROM risk_reviews WHERE risk_id = ? ORDER BY reviewed_at",
+ (risk_id,),
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def due_for_review(self, days: int = 14) -> list[dict[str, Any]]:
+ """Return risks whose updated_at is older than *days* days."""
+ cutoff = datetime.now(timezone.utc).isoformat()
+ rows = self._db.execute(
+ "SELECT * FROM risks "
+ "WHERE julianday(?) - julianday(updated_at) > ? "
+ "ORDER BY updated_at",
+ (cutoff, days),
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+
+def seed_risk_register(reg: RiskRegister | None = None) -> RiskRegister:
+ """
+ Seed the risk register from all known sources:
+ architecture report, coach sessions, project management doc.
+ """
+ if reg is None:
+ reg = RiskRegister()
+
+ # === Architecture Report Risks (Section 5.4) ===
+ reg.add_risk(
+ "RISK-ARCH-01", "Confidence threshold miscalibration",
+ description="IF the 0.85 confidence threshold is not calibrated with empirical data "
+ "THEN the review queue either overwhelms the catalog team (threshold too high) "
+ "or lets incorrect data into PIMS (threshold too low) "
+ "RESULTING IN no labor savings or data quality degradation.",
+ category="technical", source="architecture_report", source_detail="Section 5.4 Risk 1",
+ likelihood="high", impact="high",
+ mitigation="Refinement 1: Run prototype on >=200 labeled submissions, compute precision-recall curves",
+ contingency="Improve model or renegotiate accuracy target with Harsha",
+ related_reqs=["QA-1", "FR-4"], related_arch=["AD-4", "routing"],
+ )
+ reg.add_risk(
+ "RISK-ARCH-02", "Insufficient training data (<200 labeled examples)",
+ description="IF fewer than 200 labeled examples are available for training "
+ "THEN the embedding layer will be undertrained and the hybrid approach "
+ "falls back to pure rules with limited coverage (~40-60%) "
+ "RESULTING IN degraded prediction accuracy and increased manual review burden.",
+ category="technical", source="architecture_report", source_detail="Section 5.4 Risk 2",
+ likelihood="medium", impact="high",
+ mitigation="Secure labeled data from eParts; augment with synthetic examples if needed",
+ contingency="Fall back to pure rule engine (ADR-1 Alt A trigger)",
+ related_reqs=["FR-3"], related_arch=["ADR-1", "prediction"],
+ )
+ reg.add_risk(
+ "RISK-ARCH-03", "PIMS staging schema incompatibility (P1-C pending)",
+ description="IF Jake does not deliver the P1-C schema or staging tables use incompatible columns "
+ "THEN the writeback mechanism requires redesign "
+ "RESULTING IN schedule delays and potential data integration failures.",
+ category="technical", source="architecture_report", source_detail="Section 5.4 Risk 3",
+ likelihood="medium", impact="high",
+ mitigation="Refinement 4: Map P1-C columns to canonical schema; integration-test 10 sample records",
+ contingency="Team-owned buffer table if schema incompatible",
+ related_reqs=["FR-6"], related_arch=["AD-3", "AD-5", "writeback"],
+ )
+
+ # Architecture sensitivity points
+ reg.add_risk(
+ "RISK-ARCH-04", "Alpha weighting sensitivity in hybrid scoring",
+ description="IF small tuning errors occur in alpha weighting (currently 0.7) "
+ "THEN routing behavior changes disproportionately, suppressing the more accurate signal source "
+ "RESULTING IN misrouted items and unreliable confidence scores.",
+ category="technical", source="architecture_report", source_detail="Section 5.4 Sensitivity 1",
+ likelihood="medium", impact="medium",
+ mitigation="Refinement 3: Sweep alpha 0.3-0.9; measure ECE, precision, coverage",
+ related_reqs=["QA-1"], related_arch=["ADR-1"],
+ )
+ reg.add_risk(
+ "RISK-ARCH-05", "Attribute correlation invalidates per-attribute routing",
+ description="IF correlated attributes are reviewed independently "
+ "THEN inconsistent records are produced when cross-attribute errors exceed 30% "
+ "RESULTING IN need to switch from per-attribute to per-record routing, requiring redesign.",
+ category="technical", source="architecture_report", source_detail="Section 5.4 Sensitivity 2",
+ likelihood="low", impact="high",
+ mitigation="Refinement 2: Pairwise mutual information analysis on labeled data",
+ contingency="Switch to per-record routing (Section 4.2 Alt B)",
+ related_reqs=["QA-1", "FR-4"], related_arch=["routing"],
+ )
+ reg.add_risk(
+ "RISK-ARCH-06", "Catalog team capacity vs review volume",
+ description="IF per-attribute routing still produces too many review items "
+ "THEN the 1.5 + 3 FTE catalog team cannot handle the volume "
+ "RESULTING IN no labor savings and failure of the core value proposition.",
+ category="business", source="architecture_report", source_detail="Section 5.4 Sensitivity 3",
+ likelihood="medium", impact="high",
+ mitigation="Measure actual review volume in prototype; adjust threshold iteratively",
+ contingency="Lower threshold (accepting more accuracy risk) or invest in reviewer tooling",
+ related_reqs=["QA-1"], related_arch=["routing", "review"],
+ )
+
+ # Unresolved items from architecture
+ reg.add_risk(
+ "RISK-ARCH-07", "Drift detection metrics and baselines undefined",
+ description="IF baseline metrics, alert thresholds, and feedback loops are not defined before deployment "
+ "THEN model drift will go undetected "
+ "RESULTING IN silent accuracy degradation and no trigger for retraining.",
+ category="technical", source="architecture_report", source_detail="Section 5.4 Unresolved 1",
+ likelihood="high", impact="medium",
+ mitigation="Define baseline metrics before prototype; SES measurement system can track these",
+ related_reqs=["QA-5"], related_arch=["observability"],
+ )
+ reg.add_risk(
+ "RISK-ARCH-08", "Human review interface design not decided",
+ description="IF the reviewer walkthrough reveals that tabular export is insufficient for review tasks "
+ "THEN a custom review UI must be added to scope "
+ "RESULTING IN scope expansion, additional development effort, and potential schedule delays.",
+ category="ux", source="architecture_report", source_detail="Section 5.4 Unresolved 3",
+ likelihood="medium", impact="medium",
+ mitigation="Refinement 5: Present 30 sample items to Brian/Dewey; measure time and accuracy",
+ contingency="Renegotiate custom UI scope if needed",
+ related_reqs=["FR-5"], related_arch=["review"],
+ )
+
+ reg.add_risk(
+ "RISK-ARCH-09", "ETIM release pin leaves the catalog progressively stale",
+ description="IF the client's suppliers begin publishing against ETIM 11.0 while the platform "
+ "remains pinned to release 10.0 EI (constraint C-4) "
+ "THEN new classes, features and values are unavailable to the matcher "
+ "RESULTING IN affected products falling to ETIM Other handling or manual review, "
+ "and the catalog drifting further from the standard over time.",
+ category="dependency", source="architecture_report",
+ source_detail="ADR-020 Consequences: deliberately accepted, revisit before production transition",
+ likelihood="medium", impact="medium",
+ mitigation="Release pinned explicitly as constraint C-4 rather than left unspecified. Every ETIM "
+ "reference row, the interpretation table and the PIMS writeback key all carry "
+ "etim_release_id (ADR-013/014/017), so each published value names the release it was "
+ "matched under and provenance survives. The loader rejects an archive whose release "
+ "does not match the declared one, which is what stops an 11.0 archive being loaded "
+ "into a 10.0-pinned system by accident.",
+ contingency="Un-pinning is a change request against C-4, not a gap to fill quietly. The governed "
+ "upgrade path (load releases side by side, diff, re-match affected products through a "
+ "review queue) is the shape that work would take.",
+ status="mitigating",
+ related_reqs=["FR-10", "HLR-6", "FR-9"],
+ related_arch=["ADR-020", "ADR-013", "ADR-014", "ADR-017", "C-4"],
+ )
+
+ # === Coach Session Concerns ===
+ reg.add_risk(
+ "RISK-COACH-01", "Data access delay blocking ML development",
+ description="IF client data is not available for model training "
+ "THEN ML development stalls and the team cannot validate the hybrid approach "
+ "RESULTING IN schedule delays and inability to meet prototype milestones.",
+ category="schedule", source="coach_sessions", source_detail="Cory, Dennis, Ben sessions",
+ likelihood="high", impact="high",
+ mitigation="Data received ~Feb 22; team started basic model tests. Continue pressing for complete dataset.",
+ status="mitigating",
+ related_reqs=["REQ-DATA"],
+ )
+ reg.add_risk(
+ "RISK-COACH-02", "Scope creep risk",
+ description="IF the team expands beyond valves/actuators scope before the core pipeline is validated "
+ "THEN development effort is diluted across unvalidated categories "
+ "RESULTING IN an incomplete core pipeline and missed delivery deadlines.",
+ category="scope", source="coach_sessions", source_detail="Dennis mentor session",
+ likelihood="medium", impact="medium",
+ mitigation="Strict phase scoping; architecture designed for category extension without structural change",
+ related_reqs=["QA-3"],
+ )
+ reg.add_risk(
+ "RISK-COACH-03", "Azure tool constraints",
+ description="IF Azure platform limitations (GPU availability, service quotas) conflict with architecture needs "
+ "THEN design decisions must be reworked for the constrained environment "
+ "RESULTING IN reduced model performance or additional engineering workarounds.",
+ category="technical", source="coach_sessions", source_detail="Cory SES session",
+ likelihood="medium", impact="medium",
+ mitigation="Single App Service deployment chosen to minimize Azure operational complexity",
+ related_arch=["AD-6"],
+ )
+ reg.add_risk(
+ "RISK-COACH-04", "Measurement validity for AI effectiveness",
+ description="IF AI effectiveness is not measured with rigorous before/after comparisons "
+ "THEN the team cannot demonstrate genuine AI-driven improvement "
+ "RESULTING IN weak capstone evaluation and inability to justify the AI approach.",
+ category="measurement", source="coach_sessions", source_detail="Christian AI coach session",
+ likelihood="medium", impact="high",
+ mitigation="SES measurement system tracks tokens, cost, latency, human review rate, correction rate per agent. "
+ "Prompt regression testing validates quality over time.",
+ related_reqs=["REQ-SES"],
+ )
+ reg.add_risk(
+ "RISK-COACH-05", "Model selection uncertainty",
+ description="IF the ML model selection remains unresolved and the hybrid approach (ADR-1) is not validated "
+ "THEN the prediction pipeline lacks a stable foundation "
+ "RESULTING IN rework risk and delayed confidence in system accuracy.",
+ category="technical", source="coach_sessions", source_detail="Christian, Cory sessions",
+ likelihood="medium", impact="high",
+ mitigation="ADR-1 hybrid approach with clear trigger conditions for switching to pure ML or pure rules",
+ related_arch=["ADR-1"],
+ )
+
+ # === Project Management Risks ===
+ reg.add_risk(
+ "RISK-PM-01", "Capstone timeline constraint",
+ description="IF the 5-person team cannot prototype within the Spring-Fall 2026 semester "
+ "THEN operational complexity exceeds team capacity "
+ "RESULTING IN incomplete deliverables and a failed capstone milestone.",
+ category="schedule", source="project_constraints",
+ likelihood="low", impact="high",
+ mitigation="Architecture favors simplicity (single App Service, internal interfaces). "
+ "SES agents automate repetitive tasks to free team capacity.",
+ )
+ reg.add_risk(
+ "RISK-PM-02", "Integration dependency on Jake (PIMS schema)",
+ description="IF Jake does not deliver the P1-C staging table schema on time "
+ "THEN the writeback mechanism cannot be implemented against the real target "
+ "RESULTING IN critical-path schedule slip and potential redesign of the integration layer.",
+ category="dependency", source="architecture_report", source_detail="Constraint table, Refinement 4",
+ likelihood="high", impact="medium",
+ mitigation="Refinement 4 scheduled; team-owned buffer table as fallback",
+ owner="Hrishik",
+ related_arch=["AD-3", "AD-5"],
+ )
+ reg.add_risk(
+ "RISK-PM-03", "Knowledge loss from manual processes",
+ description="IF meeting decisions, action items, and rationale are captured manually "
+ "THEN information is lost or inconsistently documented across artifacts "
+ "RESULTING IN duplicated effort, contradictory decisions, and knowledge gaps.",
+ category="process", source="ses_design",
+ likelihood="medium", impact="medium",
+ mitigation="Agentic SE system auto-captures decisions, action items, and commitments from meetings. "
+ "SharedMemory wiki maintains persistent project knowledge.",
+ status="mitigating",
+ )
+
+ # === Health & Team Risks ===
+ reg.add_risk(
+ "RISK-H1", "Team burnout from capstone + coursework overlap",
+ description="IF team members are overloaded with concurrent capstone and coursework demands "
+ "THEN productivity and code quality decline as fatigue accumulates "
+ "RESULTING IN missed deadlines, increased defect rates, and potential team attrition.",
+ category="health", source="team_assessment",
+ likelihood="high", impact="high",
+ mitigation="Establish sustainable sprint cadence; enforce work-hour limits; rotate intensive tasks across members.",
+ )
+ reg.add_risk(
+ "RISK-H2", "Single point of failure — key person unavailable",
+ description="IF a key team member becomes unavailable (illness, emergency, dropout) "
+ "THEN critical knowledge and in-progress work are inaccessible "
+ "RESULTING IN blocked deliverables and schedule delays until knowledge is reconstructed.",
+ category="team", source="team_assessment",
+ likelihood="high", impact="high",
+ mitigation="Cross-train on all subsystems; maintain pair-programming rotation; document decisions in SharedMemory.",
+ )
+ reg.add_risk(
+ "RISK-H3", "Communication gaps between distributed team members",
+ description="IF distributed team members have infrequent or asynchronous-only communication "
+ "THEN misalignments on requirements, design, and priorities go undetected "
+ "RESULTING IN integration conflicts, rework, and divergent implementations.",
+ category="team", source="team_assessment",
+ likelihood="medium", impact="medium",
+ mitigation="Weekly sync meetings; shared Slack channel for async updates; meeting summaries auto-generated by SES.",
+ )
+
+ return reg
diff --git a/pipeline/seed_traceability.py b/pipeline/seed_traceability.py
new file mode 100644
index 0000000..664e710
--- /dev/null
+++ b/pipeline/seed_traceability.py
@@ -0,0 +1,759 @@
+"""
+Seed the traceability store from all existing data sources.
+
+Pulls from:
+ 1. Client meeting JSONs (concerns, decisions, action items, participants)
+ 2. Coach session DB (commitments, concerns)
+ 3. Jira (live tickets)
+ 4. Risk register
+ 5. Architecture report (key decisions)
+ 6. EventBus (logged events)
+
+Run: python -m pipeline.seed_traceability
+"""
+from __future__ import annotations
+
+import json
+import logging
+import os
+import sqlite3
+from glob import glob
+from pathlib import Path
+
+logging.basicConfig(level=logging.INFO, format="%(message)s")
+logger = logging.getLogger(__name__)
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+
+
+def seed(force: bool = False) -> dict:
+ from pipeline.traceability import TraceabilityStore
+
+ store = TraceabilityStore()
+
+ if not force:
+ existing = store.stats()
+ if existing["total_artifacts"] > 20:
+ logger.info(f"Traceability already seeded ({existing['total_artifacts']} artifacts). Use force=True to reseed.")
+ return existing
+
+ counts = {"meetings": 0, "concerns": 0, "decisions": 0, "action_items": 0,
+ "commitments": 0, "risks": 0, "jira_tickets": 0, "architecture": 0,
+ "requirements": 0, "links": 0}
+
+ # =========================================================================
+ # 1. CLIENT MEETINGS — meetings, concerns, decisions, action items
+ # =========================================================================
+ for f in sorted(glob(str(PROJECT_ROOT / "minutes" / "*-client.json"))):
+ with open(f) as fh:
+ data = json.load(fh)
+
+ meeting_date = data.get("meeting_date", "unknown")
+ participants = data.get("participants", [])
+
+ # Create meeting artifact
+ meeting_id = store.add_artifact(
+ artifact_type="meeting",
+ title=f"Client Meeting {meeting_date}",
+ description=f"{data.get('duration_minutes', 0)} min, {data.get('total_words', 0)} words, {data.get('participant_count', 0)} participants",
+ status="done",
+ source_meeting=meeting_date,
+ artifact_id=f"MTG-{meeting_date}",
+ metadata={"participants": participants, "topics": data.get("detected_topics", {})},
+ )
+ counts["meetings"] += 1
+
+ # Extract concerns from detected topics and questions
+ for q in data.get("questions_sample", []):
+ speaker = q.get("speaker", "unknown")
+ text = q.get("text", "")[:200]
+ if not text:
+ continue
+ cid = store.add_artifact(
+ artifact_type="concern",
+ title=text[:100],
+ description=text,
+ source_meeting=meeting_date,
+ source_speaker=speaker,
+ source_quote=text[:300],
+ owner=speaker,
+ )
+ store.link(cid, meeting_id, "RAISED_IN", f"Raised by {speaker} in {meeting_date} meeting")
+ counts["concerns"] += 1
+ counts["links"] += 1
+
+ # Extract decisions
+ for d in data.get("decisions_sample", []):
+ speaker = d.get("speaker", "unknown")
+ text = d.get("text", "")[:200]
+ if not text:
+ continue
+ did = store.add_artifact(
+ artifact_type="decision",
+ title=text[:100],
+ description=text,
+ status="done",
+ source_meeting=meeting_date,
+ source_speaker=speaker,
+ source_quote=text[:300],
+ )
+ store.link(did, meeting_id, "RAISED_IN", f"Decided in {meeting_date} meeting")
+ counts["decisions"] += 1
+ counts["links"] += 1
+
+ # Extract action items
+ for a in data.get("actions_sample", []):
+ speaker = a.get("speaker", "unknown")
+ text = a.get("text", "")[:200]
+ if not text or len(text) < 20:
+ continue
+ aid = store.add_artifact(
+ artifact_type="action_item",
+ title=text[:100],
+ description=text,
+ source_meeting=meeting_date,
+ source_speaker=speaker,
+ source_quote=text[:300],
+ owner=speaker,
+ )
+ store.link(aid, meeting_id, "RAISED_IN", f"Action from {meeting_date} meeting")
+ counts["action_items"] += 1
+ counts["links"] += 1
+
+ # =========================================================================
+ # 2. COACH SESSIONS — commitments and concerns
+ # =========================================================================
+ coach_db_path = PROJECT_ROOT / "memory" / "coach_sessions.db"
+ if coach_db_path.exists():
+ cdb = sqlite3.connect(str(coach_db_path))
+ cdb.row_factory = sqlite3.Row
+
+ # Sessions
+ for sess in cdb.execute("SELECT * FROM sessions").fetchall():
+ sess = dict(sess)
+ sid = store.add_artifact(
+ artifact_type="coach_session",
+ title=f"Coach Session {sess['date']} ({sess['session_type']})",
+ description=f"Participants: {sess['participants']}",
+ status="done",
+ source_meeting=sess["date"],
+ artifact_id=f"COACH-{sess['session_id'][:8]}",
+ metadata={"session_id": sess["session_id"], "type": sess["session_type"]},
+ )
+ counts["meetings"] += 1
+
+ # Commitments
+ for c in cdb.execute(
+ "SELECT c.*, s.date FROM commitments c JOIN sessions s ON c.session_id = s.session_id"
+ ).fetchall():
+ c = dict(c)
+ text = c.get("commitment_text", "")
+ if not text:
+ continue
+ cid = store.add_artifact(
+ artifact_type="commitment",
+ title=text[:100],
+ description=text,
+ status=c.get("status", "open"),
+ source_meeting=c.get("date", ""),
+ owner=c.get("owner", "Team"),
+ metadata={"deadline": c.get("deadline", ""), "evidence": c.get("evidence_link", "")},
+ )
+ session_aid = f"COACH-{c['session_id'][:8]}"
+ store.link(cid, session_aid, "RAISED_IN", f"Commitment from coach session {c.get('date', '')}")
+ counts["commitments"] += 1
+ counts["links"] += 1
+
+ # Concerns from coach
+ for con in cdb.execute("SELECT co.*, s.date FROM concerns co JOIN sessions s ON co.session_id = s.session_id").fetchall():
+ con = dict(con)
+ text = con.get("concern_text", "")
+ if not text:
+ continue
+ coid = store.add_artifact(
+ artifact_type="concern",
+ title=text[:100],
+ description=text,
+ source_meeting=con.get("date", ""),
+ source_speaker=con.get("raised_by", "coach"),
+ metadata={"theme": con.get("theme", ""), "times_raised": con.get("times_raised", 1)},
+ )
+ session_aid = f"COACH-{con['session_id'][:8]}"
+ store.link(coid, session_aid, "RAISED_IN", f"Coach concern from {con.get('date', '')}")
+ counts["concerns"] += 1
+ counts["links"] += 1
+
+ cdb.close()
+
+ # =========================================================================
+ # 3. JIRA TICKETS
+ # =========================================================================
+ try:
+ from dotenv import load_dotenv
+ load_dotenv(PROJECT_ROOT / ".env")
+ except Exception:
+ pass
+
+ try:
+ from mcp.jira import JiraMCP
+ jira = JiraMCP()
+ if jira.is_configured:
+ result = jira.search_issues(max_results=50)
+ if result.get("ok"):
+ for iss in result.get("issues", []):
+ labels = iss.get("labels", [])
+ jid = store.add_artifact(
+ artifact_type="jira_ticket",
+ title=iss.get("summary", ""),
+ status=_map_jira_status(iss.get("status", "")),
+ owner=iss.get("assignee", ""),
+ jira_key=iss.get("key", ""),
+ priority=iss.get("priority", ""),
+ artifact_id=iss.get("key", ""),
+ metadata={"labels": labels},
+ )
+ counts["jira_tickets"] += 1
+
+ # Link AI-generated tickets to their source meetings
+ for label in labels:
+ if label.startswith("meeting-"):
+ meeting_date = label.replace("meeting-", "")
+ meeting_aid = f"MTG-{meeting_date}"
+ store.link(jid, meeting_aid, "RAISED_IN", f"Created from {meeting_date} meeting")
+ counts["links"] += 1
+ except Exception as e:
+ logger.warning(f"Jira seeding skipped: {e}")
+
+ # =========================================================================
+ # 4. RISK REGISTER
+ # =========================================================================
+ try:
+ from pipeline.risk_register import RiskRegister, seed_risk_register
+ reg = seed_risk_register()
+ for risk in reg.get_all():
+ rid = store.add_artifact(
+ artifact_type="risk",
+ title=risk.get("title", ""),
+ description=risk.get("description", ""),
+ status="open" if risk.get("status", "open") == "open" else "done",
+ priority=f"L{risk.get('likelihood', '?')}/I{risk.get('impact', '?')}",
+ artifact_id=risk.get("risk_id", ""),
+ metadata={
+ "category": risk.get("category", ""),
+ "mitigation": risk.get("mitigation", ""),
+ "severity": risk.get("severity", 0),
+ "source": risk.get("source", ""),
+ },
+ )
+ counts["risks"] += 1
+ except Exception as e:
+ logger.warning(f"Risk register seeding skipped: {e}")
+
+ # =========================================================================
+ # 5. KEY ARCHITECTURE DECISIONS (manually curated from meeting data)
+ # =========================================================================
+ arch_decisions = [
+ {
+ "id": "ARCH-001", "title": "Use Bicep over Terraform for Azure IaC",
+ "description": "David recommended Bicep due to team familiarity. Terraform considered but rejected.",
+ "meeting": "2026-02-12", "speaker": "David Mine", "status": "done",
+ },
+ {
+ "id": "ARCH-002", "title": "Map to industry standards instead of ALPS-specific attributes",
+ "description": "Harsha recommended general industry standards over ALPS naming for product attributes. Simplifies extraction from vendor spec sheets.",
+ "meeting": "2026-04-02", "speaker": "Harsha (eParts)", "status": "done",
+ },
+ {
+ "id": "ARCH-003", "title": "ML confidence scoring for attribute prediction",
+ "description": "Use ML confidence scores for predicted product attributes. Low-confidence items go to human review queue.",
+ "meeting": "2026-01-22", "speaker": "Hrishik", "status": "open",
+ },
+ {
+ "id": "ARCH-004", "title": "Staging tables as Git-diff model for data review",
+ "description": "eParts uses staging tables as a diff view for catalog team to review. ML pipeline outputs to staging, humans approve to production.",
+ "meeting": "2026-01-22", "speaker": "David Mine", "status": "done",
+ },
+ {
+ "id": "ARCH-005", "title": "Human-in-the-loop for all AI-generated data",
+ "description": "All AI-predicted attributes must pass through human review before entering production catalog. No fully automated path to production.",
+ "meeting": "2026-01-22", "speaker": "Dennis Grinberg", "status": "done",
+ },
+ {
+ "id": "ARCH-006", "title": "Agent-Augmented Iterative SDLC (bespoke)",
+ "description": "Custom SDLC instead of Scrum/RUP. Practice areas map to agent pipelines. AI handles repeatable 80%, humans own judgment 20%.",
+ "meeting": "internal", "speaker": "Team", "status": "done",
+ },
+ ]
+
+ for ad in arch_decisions:
+ aid = store.add_artifact(
+ artifact_type="architecture",
+ title=ad["title"],
+ description=ad["description"],
+ status=ad["status"],
+ source_meeting=ad["meeting"],
+ source_speaker=ad["speaker"],
+ artifact_id=ad["id"],
+ )
+ if ad["meeting"] != "internal":
+ store.link(aid, f"MTG-{ad['meeting']}", "RAISED_IN", f"Architecture decision from {ad['meeting']}")
+ counts["links"] += 1
+ counts["architecture"] += 1
+
+ # =========================================================================
+ # 6. REQUIREMENTS — derived from architecture report and meeting decisions
+ # =========================================================================
+ requirements = [
+ {"id": "REQ-001", "title": "Extract product attributes from vendor spec sheets",
+ "description": "System must parse PDF/CSV vendor documents and extract structured attributes (dimensions, material, voltage, etc.)",
+ "meeting": "2026-01-22", "priority": "P0", "arch": ["ARCH-002", "ARCH-003"]},
+ {"id": "REQ-002", "title": "Map extracted attributes to industry-standard taxonomy",
+ "description": "Use general industry standards instead of ALPS-specific naming. Decided based on Harsha's recommendation (April 2 meeting).",
+ "meeting": "2026-04-02", "priority": "P0", "arch": ["ARCH-002"]},
+ {"id": "REQ-003", "title": "ML confidence scoring on every predicted attribute",
+ "description": "Each predicted attribute value must carry a confidence score. Low-confidence items routed to human review queue.",
+ "meeting": "2026-01-22", "priority": "P0", "arch": ["ARCH-003", "ARCH-005"]},
+ {"id": "REQ-004", "title": "Human review queue for AI-generated catalog data",
+ "description": "All AI predictions must pass through human review before entering production. No fully automated path to production catalog.",
+ "meeting": "2026-01-22", "priority": "P0", "arch": ["ARCH-005", "ARCH-004"]},
+ {"id": "REQ-005", "title": "Staging table diff model for catalog review workflow",
+ "description": "ML pipeline outputs to staging tables. Catalog team reviews diffs (like Git) and approves to production.",
+ "meeting": "2026-01-22", "priority": "P0", "arch": ["ARCH-004"]},
+ {"id": "REQ-006", "title": "Azure infrastructure with Bicep IaC",
+ "description": "Deploy on Azure. Use Bicep (not Terraform) for infrastructure-as-code based on team familiarity.",
+ "meeting": "2026-02-12", "priority": "P0", "arch": ["ARCH-001"]},
+ {"id": "REQ-007", "title": "Azure Log Analytics for monitoring and observability",
+ "description": "Use Azure Log Analytics for system monitoring. Track ML model performance, API latency, data ingestion metrics.",
+ "meeting": "2026-02-12", "priority": "P1", "arch": ["ARCH-001"]},
+ {"id": "REQ-008", "title": "Support multiple vendor document formats",
+ "description": "Handle PDFs, CSVs, and other formats from different vendors. OCR capability for scanned documents.",
+ "meeting": "2026-02-26", "priority": "P0", "arch": ["ARCH-002", "ARCH-003"]},
+ {"id": "REQ-009", "title": "POC demonstrating end-to-end attribute extraction",
+ "description": "Build proof-of-concept showing: vendor doc → extraction → confidence scoring → human review → catalog update.",
+ "meeting": "2026-04-02", "priority": "P0", "arch": ["ARCH-003", "ARCH-004", "ARCH-005"]},
+ {"id": "REQ-010", "title": "Statement of Work signed by end of April",
+ "description": "Finalize and sign SoW with client feedback on deliverables and timeline.",
+ "meeting": "2026-04-02", "priority": "P0", "arch": []},
+ {"id": "REQ-011", "title": "Team AI usage policy and best practices guide",
+ "description": "Implement Claude token usage policy. Create shared best practices guide for consistent AI use across team.",
+ "meeting": "2026-04-16", "priority": "P1", "arch": ["ARCH-006"]},
+ {"id": "REQ-012", "title": "Formal ADRs for all architecture decisions",
+ "description": "Document architecture decisions as formal ADRs. Coach feedback emphasized this for traceability.",
+ "meeting": "coach", "priority": "P1", "arch": ["ARCH-001", "ARCH-002", "ARCH-003", "ARCH-004", "ARCH-005"]},
+ ]
+
+ for req in requirements:
+ rid = store.add_artifact(
+ artifact_type="requirement",
+ title=req["title"],
+ description=req["description"],
+ status="open",
+ source_meeting=req["meeting"],
+ priority=req["priority"],
+ artifact_id=req["id"],
+ )
+ if req["meeting"] not in ("coach", "internal"):
+ store.link(rid, f"MTG-{req['meeting']}", "RAISED_IN", f"Derived from {req['meeting']} meeting")
+ counts["links"] += 1
+ for arch_id in req.get("arch", []):
+ store.link(rid, arch_id, "DECIDED_BY", f"Requirement shaped by {arch_id}")
+ counts["links"] += 1
+ counts["requirements"] = counts.get("requirements", 0) + 1
+
+ # =========================================================================
+ # 7. CROSS-LINKS — connect concerns → decisions → requirements → jira → risks
+ # =========================================================================
+ _build_cross_links(store, counts)
+
+ logger.info(f"\n=== Traceability Seeded ===")
+ for k, v in counts.items():
+ logger.info(f" {k}: {v}")
+
+ final = store.stats()
+ logger.info(f"\n Total artifacts: {final['total_artifacts']}")
+ logger.info(f" Total links: {final['total_links']}")
+ logger.info(f" Coverage: {final['coverage_pct']}%")
+
+ return final
+
+
+def _build_cross_links(store, counts: dict) -> None:
+ """
+ Build real cross-links between artifacts using three strategies:
+ 1. LABEL-BASED — Jira ticket labels map to domains/architecture decisions
+ 2. EXPLICIT CURATED — known relationships from project knowledge
+ 3. THEMATIC — domain keyword groups for fuzzy matching
+ """
+ concerns = store.get_by_type("concern")
+ decisions = store.get_by_type("decision")
+ action_items = store.get_by_type("action_item")
+ arch_items = store.get_by_type("architecture")
+ risks = store.get_by_type("risk")
+ jira_tickets = store.get_by_type("jira_ticket")
+ commitments = store.get_by_type("commitment")
+ requirements = store.get_by_type("requirement")
+
+ # Domain keyword groups — shared across all linking strategies
+ DOMAIN_KEYWORDS = {
+ "ml_data": {"ml", "model", "confidence", "training", "predict", "attribute",
+ "extraction", "vendor", "catalog", "data", "schema", "accuracy",
+ "llm", "ai", "ocr", "classification"},
+ "architecture": {"architecture", "adr", "decision", "bicep", "terraform",
+ "diagram", "tradeoff", "analysis", "design", "pattern"},
+ "infrastructure": {"azure", "deploy", "monitor", "log", "analytics",
+ "infrastructure", "pipeline", "bicep", "iac"},
+ "requirements": {"requirement", "specification", "sow", "scope", "deliverable",
+ "finalize", "document", "timeline"},
+ "process": {"sprint", "agile", "sdlc", "process", "risk", "management",
+ "project", "board", "presentation", "critique"},
+ "review": {"review", "approval", "staging", "human", "loop", "queue",
+ "diff", "catalog"},
+ }
+
+ def _get_domains(text: str, labels: list | None = None) -> set[str]:
+ """Classify text into domain groups."""
+ text_lower = text.lower()
+ words = set(text_lower.split())
+ matched = set()
+ for domain, keywords in DOMAIN_KEYWORDS.items():
+ if words & keywords:
+ matched.add(domain)
+ if labels:
+ label_set = {l.lower() for l in labels}
+ if label_set & {"architecture", "decision"}:
+ matched.add("architecture")
+ if label_set & {"ml", "research"}:
+ matched.add("ml_data")
+ if label_set & {"infrastructure", "milestone"}:
+ matched.add("infrastructure")
+ if label_set & {"requirements"}:
+ matched.add("requirements")
+ if label_set & {"ai-tooling", "best-practices"}:
+ matched.add("process")
+ if label_set & {"measurement"}:
+ matched.add("ml_data")
+ matched.add("review")
+ if label_set & {"coach-feedback"}:
+ matched.add("process")
+ matched.add("architecture")
+ return matched
+
+ # Map architecture decisions to domains
+ arch_domain_map = {
+ "ARCH-001": {"infrastructure"},
+ "ARCH-002": {"ml_data", "requirements"},
+ "ARCH-003": {"ml_data", "review"},
+ "ARCH-004": {"review", "ml_data"},
+ "ARCH-005": {"review", "ml_data"},
+ "ARCH-006": {"process"},
+ }
+
+ # Map requirements to domains
+ req_domain_map = {
+ "REQ-001": {"ml_data"},
+ "REQ-002": {"ml_data", "requirements"},
+ "REQ-003": {"ml_data", "review"},
+ "REQ-004": {"review"},
+ "REQ-005": {"review", "ml_data"},
+ "REQ-006": {"infrastructure"},
+ "REQ-007": {"infrastructure"},
+ "REQ-008": {"ml_data"},
+ "REQ-009": {"ml_data", "review"},
+ "REQ-010": {"requirements", "process"},
+ "REQ-011": {"process"},
+ "REQ-012": {"architecture"},
+ }
+
+ # =====================================================================
+ # 1. CONCERNS → ARCHITECTURE (ADDRESSES)
+ # =====================================================================
+ for concern in concerns:
+ c_domains = _get_domains(concern["title"] + " " + concern.get("description", ""))
+ for arch in arch_items:
+ a_domains = arch_domain_map.get(arch["id"], set())
+ if c_domains & a_domains:
+ store.link(arch["id"], concern["id"], "ADDRESSES",
+ f"Architecture decision addresses concern (shared domains: {c_domains & a_domains})")
+ counts["links"] += 1
+
+ # =====================================================================
+ # 2. CONCERNS → DECISIONS (BECAME) — same meeting
+ # =====================================================================
+ for concern in concerns:
+ c_meeting = concern.get("source_meeting", "")
+ if not c_meeting:
+ continue
+ c_domains = _get_domains(concern["title"])
+ for decision in decisions:
+ if decision.get("source_meeting") != c_meeting:
+ continue
+ d_domains = _get_domains(decision["title"])
+ if c_domains & d_domains:
+ store.link(concern["id"], decision["id"], "BECAME",
+ "Concern led to this decision in the same meeting")
+ counts["links"] += 1
+
+ # =====================================================================
+ # 3. CONCERNS → REQUIREMENTS (BECAME)
+ # =====================================================================
+ for concern in concerns:
+ c_domains = _get_domains(concern["title"] + " " + concern.get("description", ""))
+ for req in requirements:
+ r_domains = req_domain_map.get(req["id"], set())
+ if c_domains & r_domains:
+ store.link(concern["id"], req["id"], "BECAME",
+ "Concern shaped this requirement")
+ counts["links"] += 1
+
+ # =====================================================================
+ # 4. DECISIONS → ARCHITECTURE (DECIDED_BY)
+ # =====================================================================
+ for decision in decisions:
+ d_domains = _get_domains(decision["title"] + " " + decision.get("description", ""))
+ for arch in arch_items:
+ a_domains = arch_domain_map.get(arch["id"], set())
+ if d_domains & a_domains:
+ store.link(decision["id"], arch["id"], "DECIDED_BY",
+ "Decision influenced by architecture choice")
+ counts["links"] += 1
+
+ # =====================================================================
+ # 5. DECISIONS → ACTION ITEMS (TRIGGERED) — same meeting
+ # =====================================================================
+ for decision in decisions:
+ d_meeting = decision.get("source_meeting", "")
+ if not d_meeting:
+ continue
+ d_domains = _get_domains(decision["title"])
+ for ai in action_items:
+ if ai.get("source_meeting") != d_meeting:
+ continue
+ ai_domains = _get_domains(ai["title"])
+ if d_domains & ai_domains:
+ store.link(ai["id"], decision["id"], "TRIGGERED",
+ "Action item triggered by this decision")
+ counts["links"] += 1
+
+ # =====================================================================
+ # 6. REQUIREMENTS → RISKS (requirement failure creates risk)
+ # =====================================================================
+ for req in requirements:
+ r_domains = req_domain_map.get(req["id"], set())
+ req_blob = (req["title"] + " " + req.get("description", "")).lower()
+ for risk in risks:
+ risk_blob = (risk["title"] + " " + risk.get("description", "")).lower()
+ risk_domains = _get_domains(risk_blob)
+ if r_domains & risk_domains:
+ store.link(req["id"], risk["id"], "MITIGATES",
+ "Fulfilling this requirement mitigates the risk")
+ counts["links"] += 1
+
+ # =====================================================================
+ # 7. JIRA TICKETS — label-based and domain-based linking
+ # =====================================================================
+
+ # Explicit Jira → Architecture decision mapping from labels
+ label_to_arch = {
+ "architecture": ["ARCH-001", "ARCH-002", "ARCH-003", "ARCH-004", "ARCH-005"],
+ "decision": ["ARCH-002", "ARCH-003"],
+ "ML": ["ARCH-003"],
+ "infrastructure": ["ARCH-001"],
+ "measurement": ["ARCH-003"],
+ "AI-tooling": ["ARCH-006"],
+ "best-practices": ["ARCH-006"],
+ }
+
+ # Explicit Jira → Requirement mapping from labels
+ label_to_req = {
+ "architecture": ["REQ-012"],
+ "requirements": ["REQ-001", "REQ-002"],
+ "ML": ["REQ-001", "REQ-003", "REQ-008"],
+ "infrastructure": ["REQ-006", "REQ-007"],
+ "measurement": ["REQ-003", "REQ-009"],
+ "AI-tooling": ["REQ-011"],
+ "best-practices": ["REQ-011"],
+ "coach-feedback": ["REQ-012"],
+ "SES": ["REQ-011", "REQ-012"],
+ "project-management": ["REQ-010"],
+ "milestone": ["REQ-009", "REQ-010"],
+ "onboarding": [],
+ "research": ["REQ-001", "REQ-008"],
+ }
+
+ # Explicit Jira key → architecture mapping for manually created tickets
+ manual_ticket_map = {
+ "EPARTS-14": {"arch": ["ARCH-001", "ARCH-002"], "req": ["REQ-009"], "desc": "Context diagram"},
+ "EPARTS-15": {"arch": ["ARCH-006"], "req": ["REQ-010"], "desc": "PM principles"},
+ "EPARTS-33": {"arch": ["ARCH-006"], "req": [], "desc": "Jira automation"},
+ "EPARTS-34": {"arch": [], "req": ["REQ-010"], "desc": "Risk management", "risks": True},
+ "EPARTS-35": {"arch": ["ARCH-006"], "req": ["REQ-011"], "desc": "SES overhaul"},
+ "EPARTS-36": {"arch": [], "req": ["REQ-001", "REQ-002"], "desc": "Requirements doc"},
+ "EPARTS-37": {"arch": [], "req": [], "desc": "Presentation plan"},
+ "EPARTS-38": {"arch": [], "req": [], "desc": "Slides context"},
+ "EPARTS-39": {"arch": [], "req": ["REQ-001", "REQ-002", "REQ-003"], "desc": "Requirements gathering"},
+ "EPARTS-40": {"arch": ["ARCH-001", "ARCH-002", "ARCH-003"], "req": ["REQ-012"], "desc": "Architecture slides"},
+ "EPARTS-41": {"arch": ["ARCH-004", "ARCH-005"], "req": ["REQ-012"], "desc": "Architecture slides"},
+ "EPARTS-42": {"arch": [], "req": ["REQ-010"], "desc": "Risk & PM slides", "risks": True},
+ "EPARTS-43": {"arch": [], "req": [], "desc": "Presentation plan"},
+ "EPARTS-44": {"arch": ["ARCH-006"], "req": [], "desc": "Process: internal rehearsal"},
+ "EPARTS-45": {"arch": ["ARCH-006"], "req": [], "desc": "Process: final rehearsal"},
+ "EPARTS-46": {"arch": [], "req": [], "desc": "Mentor meeting"},
+ "EPARTS-47": {"arch": [], "req": [], "desc": "Mentor meeting"},
+ "EPARTS-48": {"arch": ["ARCH-006"], "req": ["REQ-010"], "desc": "Semester roadmap"},
+ "EPARTS-49": {"arch": ["ARCH-001"], "req": ["REQ-012"], "desc": "Diagram review"},
+ "EPARTS-50": {"arch": [], "req": ["REQ-010"], "desc": "Timeline"},
+ "EPARTS-51": {"arch": ["ARCH-006"], "req": ["REQ-011"], "desc": "Automation processes"},
+ "EPARTS-52": {"arch": [], "req": [], "desc": "Board update"},
+ "EPARTS-53": {"arch": ["ARCH-006"], "req": ["REQ-011"], "desc": "Tool access for AI workflow"},
+ "EPARTS-54": {"arch": ["ARCH-006"], "req": ["REQ-011"], "desc": "Tool access for AI workflow"},
+ "EPARTS-55": {"arch": ["ARCH-001", "ARCH-002", "ARCH-003", "ARCH-004", "ARCH-005"], "req": ["REQ-012"], "desc": "Architecture design"},
+ "EPARTS-56": {"arch": ["ARCH-006"], "req": [], "desc": "Process: meeting preparation"},
+ "EPARTS-57": {"arch": ["ARCH-006"], "req": ["REQ-011"], "desc": "Agentic system tasks"},
+ "EPARTS-58": {"arch": [], "req": ["REQ-001", "REQ-002"], "desc": "Requirements review"},
+ "EPARTS-59": {"arch": [], "req": ["REQ-010"], "desc": "Timeline update"},
+ "EPARTS-60": {"arch": ["ARCH-003"], "req": ["REQ-003", "REQ-009"], "desc": "ML check-in"},
+ "EPARTS-61": {"arch": ["ARCH-006"], "req": [], "desc": "Process: critique preparation"},
+ "EPARTS-62": {"arch": ["ARCH-001", "ARCH-002", "ARCH-003"], "req": ["REQ-012"], "desc": "Architecture diagram"},
+ "EPARTS-63": {"arch": ["ARCH-001", "ARCH-002", "ARCH-003", "ARCH-004", "ARCH-005"], "req": ["REQ-012"], "desc": "Architecture tradeoffs"},
+ "EPARTS-64": {"arch": ["ARCH-001", "ARCH-002", "ARCH-003", "ARCH-004", "ARCH-005"], "req": ["REQ-012"], "desc": "Writing ADRs"},
+ }
+
+ for ticket in jira_tickets:
+ t_blob = (ticket["title"] + " " + ticket.get("description", "")).lower()
+ labels = (ticket.get("metadata") or {}).get("labels", [])
+ jira_key = ticket.get("jira_key", "")
+ t_domains = _get_domains(t_blob, labels)
+
+ linked_arch = set()
+ linked_req = set()
+
+ # Strategy A: manual ticket map for known tickets
+ if jira_key in manual_ticket_map:
+ mapping = manual_ticket_map[jira_key]
+ for arch_id in mapping.get("arch", []):
+ if arch_id not in linked_arch:
+ store.link(ticket["id"], arch_id, "IMPLEMENTS",
+ f"{jira_key} implements {arch_id}")
+ counts["links"] += 1
+ linked_arch.add(arch_id)
+ for req_id in mapping.get("req", []):
+ if req_id not in linked_req:
+ store.link(ticket["id"], req_id, "IMPLEMENTS",
+ f"{jira_key} implements {req_id}")
+ counts["links"] += 1
+ linked_req.add(req_id)
+ if mapping.get("risks"):
+ for risk in risks:
+ store.link(ticket["id"], risk["id"], "MITIGATES",
+ f"{jira_key} risk management work mitigates risks")
+ counts["links"] += 1
+
+ # Strategy B: label-based linking for AI-generated tickets
+ for label in labels:
+ for arch_id in label_to_arch.get(label, []):
+ if arch_id not in linked_arch:
+ store.link(ticket["id"], arch_id, "IMPLEMENTS",
+ f"Label '{label}' maps to {arch_id}")
+ counts["links"] += 1
+ linked_arch.add(arch_id)
+ for req_id in label_to_req.get(label, []):
+ if req_id not in linked_req:
+ store.link(ticket["id"], req_id, "IMPLEMENTS",
+ f"Label '{label}' maps to {req_id}")
+ counts["links"] += 1
+ linked_req.add(req_id)
+
+ # Strategy C: domain-based linking (fallback for unlinked tickets)
+ if not linked_arch:
+ for arch in arch_items:
+ a_domains = arch_domain_map.get(arch["id"], set())
+ if t_domains & a_domains and arch["id"] not in linked_arch:
+ store.link(ticket["id"], arch["id"], "IMPLEMENTS",
+ f"Domain match: {t_domains & a_domains}")
+ counts["links"] += 1
+ linked_arch.add(arch["id"])
+
+ if not linked_req:
+ for req in requirements:
+ r_domains = req_domain_map.get(req["id"], set())
+ if t_domains & r_domains and req["id"] not in linked_req:
+ store.link(ticket["id"], req["id"], "IMPLEMENTS",
+ f"Domain match: {t_domains & r_domains}")
+ counts["links"] += 1
+ linked_req.add(req["id"])
+
+ # Link tickets to risks they mitigate
+ for risk in risks:
+ risk_blob = (risk["title"] + " " + risk.get("description", "")).lower()
+ risk_domains = _get_domains(risk_blob)
+ if t_domains & risk_domains:
+ store.link(ticket["id"], risk["id"], "MITIGATES",
+ f"Ticket addresses risk domain: {t_domains & risk_domains}")
+ counts["links"] += 1
+
+ # Link tickets to commitments
+ for commitment in commitments:
+ cm_blob = (commitment["title"] + " " + commitment.get("description", "")).lower()
+ cm_words = {w for w in cm_blob.split() if len(w) > 3}
+ t_words = {w for w in t_blob.split() if len(w) > 3}
+ if len(cm_words & t_words) >= 3:
+ store.link(ticket["id"], commitment["id"], "IMPLEMENTS",
+ "Ticket fulfills this commitment")
+ counts["links"] += 1
+
+ # Link tickets to action items from same meeting (via meeting label)
+ ticket_meetings = {l.replace("meeting-", "") for l in labels if l.startswith("meeting-")}
+ if ticket_meetings:
+ for ai in action_items:
+ if ai.get("source_meeting") in ticket_meetings:
+ ai_domains = _get_domains(ai["title"])
+ if t_domains & ai_domains:
+ store.link(ticket["id"], ai["id"], "IMPLEMENTS",
+ f"Same meeting + shared domain")
+ counts["links"] += 1
+
+ # =====================================================================
+ # 8. RISK → ARCHITECTURE mitigations
+ # =====================================================================
+ for risk in risks:
+ r_blob = (risk["title"] + " " + risk.get("description", "")).lower()
+ mitigation = ((risk.get("metadata") or {}).get("mitigation", "")).lower()
+ r_domains = _get_domains(r_blob + " " + mitigation)
+
+ for arch in arch_items:
+ a_domains = arch_domain_map.get(arch["id"], set())
+ if r_domains & a_domains:
+ store.link(arch["id"], risk["id"], "MITIGATES",
+ f"Architecture mitigates risk (domains: {r_domains & a_domains})")
+ counts["links"] += 1
+
+ # =====================================================================
+ # 9. COMMITMENTS → ARCHITECTURE + REQUIREMENTS
+ # =====================================================================
+ for commitment in commitments:
+ cm_domains = _get_domains(commitment["title"] + " " + commitment.get("description", ""))
+ for arch in arch_items:
+ a_domains = arch_domain_map.get(arch["id"], set())
+ if cm_domains & a_domains:
+ store.link(arch["id"], commitment["id"], "IMPLEMENTS",
+ "Architecture fulfills commitment")
+ counts["links"] += 1
+ for req in requirements:
+ r_domains = req_domain_map.get(req["id"], set())
+ if cm_domains & r_domains:
+ store.link(commitment["id"], req["id"], "BECAME",
+ "Commitment shaped this requirement")
+ counts["links"] += 1
+
+
+def _map_jira_status(status: str) -> str:
+ s = status.lower()
+ if s == "done":
+ return "done"
+ elif s in ("in progress", "in review"):
+ return "in_progress"
+ return "open"
+
+
+if __name__ == "__main__":
+ seed(force=True)
diff --git a/pipeline/shared_memory.py b/pipeline/shared_memory.py
new file mode 100644
index 0000000..85b214f
--- /dev/null
+++ b/pipeline/shared_memory.py
@@ -0,0 +1,257 @@
+"""
+Shared Memory — the persistent "project wiki" all agents read and write.
+
+This is the Karpathy wiki pattern: instead of agents producing isolated outputs,
+every agent enriches a shared, structured knowledge store. Over time this store
+becomes the team's accumulated intelligence.
+
+Namespaces:
+ requirements/ — extracted requirements, priorities, staleness
+ architecture/ — ADRs, drift reports, canonical component list
+ decisions/ — all logged decisions with context and status
+ risks/ — known risks, current status, mitigation evidence
+ commitments/ — coach/mentor commitments with delivery tracking
+ concerns/ — recurring themes from coach sessions
+ ml_decisions/ — ML model evaluations, evidence, readiness state
+ meetings/ — meeting summaries, action items, cross-meeting analysis
+ metrics/ — aggregate SES performance indicators
+
+Each entry has: namespace, key, value (JSON), source_agent, source_pipeline,
+timestamp, and optional tags for cross-referencing.
+"""
+from __future__ import annotations
+
+import json
+import logging
+import sqlite3
+import textwrap
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger("pipeline.shared_memory")
+
+MEMORY_DIR = Path(__file__).resolve().parent.parent / "memory"
+DB_PATH = MEMORY_DIR / "shared_memory.db"
+
+
+def _init_db(db_path: Path | None = None) -> sqlite3.Connection:
+ path = db_path or DB_PATH
+ path.parent.mkdir(parents=True, exist_ok=True)
+ conn = sqlite3.connect(str(path))
+ conn.row_factory = sqlite3.Row
+ conn.executescript(textwrap.dedent("""\
+ CREATE TABLE IF NOT EXISTS wiki (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ namespace TEXT NOT NULL,
+ key TEXT NOT NULL,
+ value TEXT NOT NULL,
+ source_agent TEXT DEFAULT '',
+ source_pipeline TEXT DEFAULT '',
+ tags TEXT DEFAULT '[]',
+ created_at TEXT NOT NULL,
+ updated_at TEXT NOT NULL,
+ UNIQUE(namespace, key)
+ );
+ CREATE INDEX IF NOT EXISTS idx_wiki_ns ON wiki(namespace);
+ CREATE INDEX IF NOT EXISTS idx_wiki_ns_key ON wiki(namespace, key);
+
+ CREATE TABLE IF NOT EXISTS wiki_log (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ namespace TEXT NOT NULL,
+ key TEXT NOT NULL,
+ action TEXT NOT NULL,
+ old_value TEXT,
+ new_value TEXT,
+ agent TEXT DEFAULT '',
+ pipeline TEXT DEFAULT '',
+ timestamp TEXT NOT NULL
+ );
+ CREATE INDEX IF NOT EXISTS idx_log_ns ON wiki_log(namespace);
+ """))
+ conn.commit()
+ return conn
+
+
+class SharedMemory:
+ """
+ The project wiki — a namespaced key-value store where agents
+ deposit and query structured knowledge.
+
+ Every write is logged, creating an audit trail of how the
+ project's knowledge evolved over time.
+ """
+
+ def __init__(self, db_path: Path | None = None):
+ self._db = _init_db(db_path)
+
+ def put(
+ self,
+ namespace: str,
+ key: str,
+ value: Any,
+ agent: str = "",
+ pipeline: str = "",
+ tags: list[str] | None = None,
+ ) -> None:
+ """Write or update a wiki entry. Logs the change."""
+ now = datetime.now(timezone.utc).isoformat()
+ val_json = json.dumps(value, default=str)
+ tags_json = json.dumps(tags or [])
+
+ existing = self._db.execute(
+ "SELECT value FROM wiki WHERE namespace = ? AND key = ?",
+ (namespace, key),
+ ).fetchone()
+
+ if existing:
+ old_val = existing["value"]
+ self._db.execute(
+ "UPDATE wiki SET value = ?, source_agent = ?, source_pipeline = ?, "
+ "tags = ?, updated_at = ? WHERE namespace = ? AND key = ?",
+ (val_json, agent, pipeline, tags_json, now, namespace, key),
+ )
+ self._log(namespace, key, "update", old_val, val_json, agent, pipeline)
+ else:
+ self._db.execute(
+ "INSERT INTO wiki (namespace, key, value, source_agent, source_pipeline, "
+ "tags, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
+ (namespace, key, val_json, agent, pipeline, tags_json, now, now),
+ )
+ self._log(namespace, key, "create", None, val_json, agent, pipeline)
+
+ self._db.commit()
+
+ def get(self, namespace: str, key: str, default: Any = None) -> Any:
+ """Read a single wiki entry."""
+ row = self._db.execute(
+ "SELECT value FROM wiki WHERE namespace = ? AND key = ?",
+ (namespace, key),
+ ).fetchone()
+ if row:
+ return json.loads(row["value"])
+ return default
+
+ def list_keys(self, namespace: str) -> list[str]:
+ """List all keys in a namespace."""
+ rows = self._db.execute(
+ "SELECT key FROM wiki WHERE namespace = ? ORDER BY key",
+ (namespace,),
+ ).fetchall()
+ return [r["key"] for r in rows]
+
+ def list_namespace(self, namespace: str) -> list[dict[str, Any]]:
+ """Return all entries in a namespace with metadata."""
+ rows = self._db.execute(
+ "SELECT key, value, source_agent, source_pipeline, tags, updated_at "
+ "FROM wiki WHERE namespace = ? ORDER BY updated_at DESC",
+ (namespace,),
+ ).fetchall()
+ return [
+ {
+ "key": r["key"],
+ "value": json.loads(r["value"]),
+ "source_agent": r["source_agent"],
+ "source_pipeline": r["source_pipeline"],
+ "tags": json.loads(r["tags"]),
+ "updated_at": r["updated_at"],
+ }
+ for r in rows
+ ]
+
+ def search(self, query: str, namespace: str | None = None) -> list[dict[str, Any]]:
+ """Full-text search across wiki entries."""
+ if namespace:
+ rows = self._db.execute(
+ "SELECT namespace, key, value, source_agent, updated_at FROM wiki "
+ "WHERE namespace = ? AND (key LIKE ? OR value LIKE ?) "
+ "ORDER BY updated_at DESC",
+ (namespace, f"%{query}%", f"%{query}%"),
+ ).fetchall()
+ else:
+ rows = self._db.execute(
+ "SELECT namespace, key, value, source_agent, updated_at FROM wiki "
+ "WHERE key LIKE ? OR value LIKE ? ORDER BY updated_at DESC",
+ (f"%{query}%", f"%{query}%"),
+ ).fetchall()
+
+ return [
+ {
+ "namespace": r["namespace"],
+ "key": r["key"],
+ "value": json.loads(r["value"]),
+ "source_agent": r["source_agent"],
+ "updated_at": r["updated_at"],
+ }
+ for r in rows
+ ]
+
+ def find_by_tags(self, tags: list[str]) -> list[dict[str, Any]]:
+ """Find wiki entries that have any of the given tags."""
+ results = []
+ for tag in tags:
+ rows = self._db.execute(
+ "SELECT namespace, key, value, tags, source_agent, updated_at FROM wiki "
+ "WHERE tags LIKE ?",
+ (f'%"{tag}"%',),
+ ).fetchall()
+ for r in rows:
+ results.append({
+ "namespace": r["namespace"],
+ "key": r["key"],
+ "value": json.loads(r["value"]),
+ "tags": json.loads(r["tags"]),
+ "source_agent": r["source_agent"],
+ "updated_at": r["updated_at"],
+ })
+ return results
+
+ def delete(self, namespace: str, key: str, agent: str = "") -> bool:
+ old = self._db.execute(
+ "SELECT value FROM wiki WHERE namespace = ? AND key = ?",
+ (namespace, key),
+ ).fetchone()
+ if not old:
+ return False
+ self._db.execute(
+ "DELETE FROM wiki WHERE namespace = ? AND key = ?",
+ (namespace, key),
+ )
+ self._log(namespace, key, "delete", old["value"], None, agent, "")
+ self._db.commit()
+ return True
+
+ def get_history(self, namespace: str, key: str, limit: int = 20) -> list[dict]:
+ """Get the change log for a specific entry."""
+ rows = self._db.execute(
+ "SELECT * FROM wiki_log WHERE namespace = ? AND key = ? "
+ "ORDER BY timestamp DESC LIMIT ?",
+ (namespace, key, limit),
+ ).fetchall()
+ return [dict(r) for r in rows]
+
+ def stats(self) -> dict[str, Any]:
+ """Aggregate stats across all namespaces."""
+ rows = self._db.execute(
+ "SELECT namespace, COUNT(*) as entries FROM wiki GROUP BY namespace ORDER BY namespace"
+ ).fetchall()
+ ns_stats = {r["namespace"]: r["entries"] for r in rows}
+ total = sum(ns_stats.values())
+ log_count = self._db.execute("SELECT COUNT(*) as c FROM wiki_log").fetchone()["c"]
+ return {
+ "total_entries": total,
+ "total_changes": log_count,
+ "namespaces": ns_stats,
+ }
+
+ def _log(
+ self, namespace: str, key: str, action: str,
+ old_value: str | None, new_value: str | None,
+ agent: str, pipeline: str,
+ ) -> None:
+ self._db.execute(
+ "INSERT INTO wiki_log (namespace, key, action, old_value, new_value, "
+ "agent, pipeline, timestamp) VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
+ (namespace, key, action, old_value, new_value, agent, pipeline,
+ datetime.now(timezone.utc).isoformat()),
+ )
diff --git a/pipeline/traceability.py b/pipeline/traceability.py
new file mode 100644
index 0000000..90ae547
--- /dev/null
+++ b/pipeline/traceability.py
@@ -0,0 +1,317 @@
+"""
+Unified Traceability Store — the single source of truth for product lifecycle.
+
+Every artifact that matters for shipping the product gets an entry here.
+Each entry can link to other entries, forming chains:
+
+ Client concern → Requirement → Decision → Architecture → Jira → PR → Test → Risk mitigated
+
+This is what lets you pick any client concern and trace it all the way
+to code — or pick any risk and see what's mitigating it.
+
+Schema:
+ artifacts — every traceable item (concern, decision, requirement, risk, etc.)
+ links — directed edges between artifacts (concern BECAME requirement, etc.)
+
+Link types:
+ BECAME — concern became a requirement
+ DECIDED_BY — requirement decided by a decision
+ IMPLEMENTS — Jira ticket implements a requirement
+ MITIGATES — action mitigates a risk
+ ADDRESSES — decision addresses a concern
+ RAISED_IN — artifact was raised in a meeting/session
+ ASSIGNED_TO — artifact assigned to a person
+ TRIGGERED — one artifact triggered creation of another
+ VERIFIED_BY — artifact verified by test/review
+"""
+from __future__ import annotations
+
+import json
+import logging
+import sqlite3
+import textwrap
+import uuid
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger("pipeline.traceability")
+
+MEMORY_DIR = Path(__file__).resolve().parent.parent / "memory"
+DB_PATH = MEMORY_DIR / "traceability.db"
+
+ARTIFACT_TYPES = [
+ "concern",
+ "decision",
+ "requirement",
+ "risk",
+ "action_item",
+ "commitment",
+ "architecture",
+ "jira_ticket",
+ "pull_request",
+ "test",
+ "meeting",
+ "coach_session",
+ "adr",
+]
+
+LINK_TYPES = [
+ "BECAME",
+ "DECIDED_BY",
+ "IMPLEMENTS",
+ "MITIGATES",
+ "ADDRESSES",
+ "RAISED_IN",
+ "ASSIGNED_TO",
+ "TRIGGERED",
+ "VERIFIED_BY",
+ "SUPERSEDES",
+ "DEPENDS_ON",
+ "RELATES_TO",
+]
+
+STATUS_VALUES = ["open", "in_progress", "done", "superseded", "wont_fix"]
+
+
+def _init_db(db_path: Path | None = None) -> sqlite3.Connection:
+ path = db_path or DB_PATH
+ path.parent.mkdir(parents=True, exist_ok=True)
+ conn = sqlite3.connect(str(path))
+ conn.row_factory = sqlite3.Row
+ conn.executescript(textwrap.dedent("""\
+ CREATE TABLE IF NOT EXISTS artifacts (
+ id TEXT PRIMARY KEY,
+ artifact_type TEXT NOT NULL,
+ title TEXT NOT NULL,
+ description TEXT DEFAULT '',
+ status TEXT DEFAULT 'open',
+ source_meeting TEXT DEFAULT '',
+ source_speaker TEXT DEFAULT '',
+ source_timestamp TEXT DEFAULT '',
+ source_quote TEXT DEFAULT '',
+ owner TEXT DEFAULT '',
+ jira_key TEXT DEFAULT '',
+ pr_number TEXT DEFAULT '',
+ priority TEXT DEFAULT '',
+ created_at TEXT NOT NULL,
+ updated_at TEXT NOT NULL,
+ metadata TEXT DEFAULT '{}'
+ );
+ CREATE INDEX IF NOT EXISTS idx_art_type ON artifacts(artifact_type);
+ CREATE INDEX IF NOT EXISTS idx_art_status ON artifacts(status);
+ CREATE INDEX IF NOT EXISTS idx_art_jira ON artifacts(jira_key);
+ CREATE INDEX IF NOT EXISTS idx_art_meeting ON artifacts(source_meeting);
+
+ CREATE TABLE IF NOT EXISTS links (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ source_id TEXT NOT NULL,
+ target_id TEXT NOT NULL,
+ link_type TEXT NOT NULL,
+ description TEXT DEFAULT '',
+ created_at TEXT NOT NULL,
+ FOREIGN KEY (source_id) REFERENCES artifacts(id),
+ FOREIGN KEY (target_id) REFERENCES artifacts(id),
+ UNIQUE(source_id, target_id, link_type)
+ );
+ CREATE INDEX IF NOT EXISTS idx_link_source ON links(source_id);
+ CREATE INDEX IF NOT EXISTS idx_link_target ON links(target_id);
+ CREATE INDEX IF NOT EXISTS idx_link_type ON links(link_type);
+ """))
+ conn.commit()
+ return conn
+
+
+class TraceabilityStore:
+ def __init__(self, db_path: Path | None = None):
+ self._db = _init_db(db_path)
+
+ def add_artifact(
+ self,
+ artifact_type: str,
+ title: str,
+ description: str = "",
+ status: str = "open",
+ source_meeting: str = "",
+ source_speaker: str = "",
+ source_timestamp: str = "",
+ source_quote: str = "",
+ owner: str = "",
+ jira_key: str = "",
+ pr_number: str = "",
+ priority: str = "",
+ artifact_id: str = "",
+ metadata: dict | None = None,
+ ) -> str:
+ prefix = artifact_type[:3].upper()
+ aid = artifact_id or f"{prefix}-{uuid.uuid4().hex[:6]}"
+ now = datetime.now(timezone.utc).isoformat()
+
+ self._db.execute(
+ "INSERT OR REPLACE INTO artifacts "
+ "(id, artifact_type, title, description, status, source_meeting, "
+ "source_speaker, source_timestamp, source_quote, owner, jira_key, "
+ "pr_number, priority, created_at, updated_at, metadata) "
+ "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, "
+ "COALESCE((SELECT created_at FROM artifacts WHERE id = ?), ?), ?, ?)",
+ (aid, artifact_type, title, description, status, source_meeting,
+ source_speaker, source_timestamp, source_quote, owner, jira_key,
+ pr_number, priority, aid, now, now, json.dumps(metadata or {})),
+ )
+ self._db.commit()
+ return aid
+
+ def link(
+ self,
+ source_id: str,
+ target_id: str,
+ link_type: str,
+ description: str = "",
+ ) -> None:
+ now = datetime.now(timezone.utc).isoformat()
+ self._db.execute(
+ "INSERT OR IGNORE INTO links (source_id, target_id, link_type, description, created_at) "
+ "VALUES (?, ?, ?, ?, ?)",
+ (source_id, target_id, link_type, description, now),
+ )
+ self._db.commit()
+
+ def get_artifact(self, artifact_id: str) -> dict | None:
+ row = self._db.execute(
+ "SELECT * FROM artifacts WHERE id = ?", (artifact_id,)
+ ).fetchone()
+ if row:
+ d = dict(row)
+ d["metadata"] = json.loads(d["metadata"])
+ return d
+ return None
+
+ def get_by_type(self, artifact_type: str) -> list[dict]:
+ rows = self._db.execute(
+ "SELECT * FROM artifacts WHERE artifact_type = ? ORDER BY created_at",
+ (artifact_type,),
+ ).fetchall()
+ return [{**dict(r), "metadata": json.loads(r["metadata"])} for r in rows]
+
+ def get_chain(self, artifact_id: str, direction: str = "forward") -> list[dict]:
+ """
+ Follow the traceability chain from an artifact.
+ direction='forward': follow outgoing links (concern → what it became)
+ direction='backward': follow incoming links (jira ticket → where it came from)
+ """
+ visited = set()
+ chain = []
+ self._walk_chain(artifact_id, direction, visited, chain, depth=0)
+ return chain
+
+ def _walk_chain(
+ self, artifact_id: str, direction: str,
+ visited: set, chain: list, depth: int,
+ ) -> None:
+ if artifact_id in visited or depth > 10:
+ return
+ visited.add(artifact_id)
+
+ artifact = self.get_artifact(artifact_id)
+ if not artifact:
+ return
+
+ if direction == "forward":
+ links = self._db.execute(
+ "SELECT * FROM links WHERE source_id = ?", (artifact_id,)
+ ).fetchall()
+ else:
+ links = self._db.execute(
+ "SELECT * FROM links WHERE target_id = ?", (artifact_id,)
+ ).fetchall()
+
+ link_list = [dict(l) for l in links]
+
+ chain.append({
+ "depth": depth,
+ "artifact": artifact,
+ "links": link_list,
+ })
+
+ for link in links:
+ next_id = link["target_id"] if direction == "forward" else link["source_id"]
+ self._walk_chain(next_id, direction, visited, chain, depth + 1)
+
+ def get_unlinked(self, artifact_type: str | None = None) -> list[dict]:
+ """Find artifacts with no outgoing links — potential gaps in traceability."""
+ query = """
+ SELECT a.* FROM artifacts a
+ LEFT JOIN links l ON a.id = l.source_id
+ WHERE l.id IS NULL
+ """
+ if artifact_type:
+ query += " AND a.artifact_type = ?"
+ rows = self._db.execute(query, (artifact_type,)).fetchall()
+ else:
+ rows = self._db.execute(query).fetchall()
+ return [{**dict(r), "metadata": json.loads(r["metadata"])} for r in rows]
+
+ def get_coverage(self) -> dict[str, Any]:
+ """Traceability coverage report — what percentage of artifacts are linked."""
+ total = self._db.execute("SELECT COUNT(*) as c FROM artifacts").fetchone()["c"]
+ by_type = self._db.execute(
+ "SELECT artifact_type, COUNT(*) as c FROM artifacts GROUP BY artifact_type ORDER BY c DESC"
+ ).fetchall()
+ linked = self._db.execute(
+ "SELECT COUNT(DISTINCT source_id) + COUNT(DISTINCT target_id) as c FROM links"
+ ).fetchone()["c"]
+ by_link_type = self._db.execute(
+ "SELECT link_type, COUNT(*) as c FROM links GROUP BY link_type ORDER BY c DESC"
+ ).fetchall()
+ by_status = self._db.execute(
+ "SELECT status, COUNT(*) as c FROM artifacts GROUP BY status ORDER BY c DESC"
+ ).fetchall()
+
+ unlinked_concerns = self._db.execute(
+ "SELECT COUNT(*) as c FROM artifacts a "
+ "LEFT JOIN links l ON a.id = l.source_id "
+ "WHERE a.artifact_type = 'concern' AND l.id IS NULL"
+ ).fetchone()["c"]
+ total_concerns = self._db.execute(
+ "SELECT COUNT(*) as c FROM artifacts WHERE artifact_type = 'concern'"
+ ).fetchone()["c"]
+
+ unlinked_risks = self._db.execute(
+ "SELECT COUNT(*) as c FROM artifacts a "
+ "LEFT JOIN links l ON a.id = l.target_id AND l.link_type = 'MITIGATES' "
+ "WHERE a.artifact_type = 'risk' AND l.id IS NULL"
+ ).fetchone()["c"]
+ total_risks = self._db.execute(
+ "SELECT COUNT(*) as c FROM artifacts WHERE artifact_type = 'risk'"
+ ).fetchone()["c"]
+
+ return {
+ "total_artifacts": total,
+ "by_type": {r["artifact_type"]: r["c"] for r in by_type},
+ "total_links": self._db.execute("SELECT COUNT(*) as c FROM links").fetchone()["c"],
+ "by_link_type": {r["link_type"]: r["c"] for r in by_link_type},
+ "by_status": {r["status"]: r["c"] for r in by_status},
+ "linked_artifacts": linked,
+ "coverage_pct": round(linked / total * 100, 1) if total > 0 else 0,
+ "concerns_without_action": unlinked_concerns,
+ "total_concerns": total_concerns,
+ "risks_without_mitigation": unlinked_risks,
+ "total_risks": total_risks,
+ }
+
+ def get_all_chains_from_type(self, artifact_type: str) -> list[dict]:
+ """Get full forward chains for all artifacts of a given type."""
+ artifacts = self.get_by_type(artifact_type)
+ results = []
+ for a in artifacts:
+ chain = self.get_chain(a["id"], direction="forward")
+ results.append({
+ "root": a,
+ "chain": chain,
+ "chain_length": len(chain),
+ })
+ return results
+
+ def stats(self) -> dict[str, Any]:
+ return self.get_coverage()
diff --git a/pipeline/vtt_processor.py b/pipeline/vtt_processor.py
new file mode 100644
index 0000000..34db3f2
--- /dev/null
+++ b/pipeline/vtt_processor.py
@@ -0,0 +1,342 @@
+"""
+VTT Processor — cleans Zoom auto-transcripts into structured meeting data.
+
+Handles the real-world messiness of Zoom VTT files:
+ - Speaker identification from email addresses
+ - Timestamp stripping and turn merging
+ - Filler removal and text cleaning
+ - Speaker turn consolidation (adjacent lines from same speaker)
+ - Meeting metadata extraction (date, duration, participants)
+
+Two output modes:
+ - Offline: structural extraction without LLM (speaker stats, turn counts, topics)
+ - Online: full Claude-powered extraction (decisions, action items, requirements)
+"""
+
+from __future__ import annotations
+
+import json
+import re
+from dataclasses import dataclass, field, asdict
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+# Known team member mapping (CMU email → display name)
+SPEAKER_MAP = {
+ "hrishikb@andrew.cmu.edu": "Hrishik",
+ "jaivards@andrew.cmu.edu": "Jaivard",
+ "arjunnai@andrew.cmu.edu": "Arjun",
+ "zhelianl@andrew.cmu.edu": "Liu",
+ "JakeMonroe": "Jake (eParts)",
+ "Clifford Huff": "Cliff (Mentor)",
+ "Ashritha": "Ashritha",
+ "Harsha Tummala": "Harsha (eParts)",
+ "Cory Gwin": "Cory (Coach)",
+ "Dennis Grinberg": "Dennis (Mentor)",
+ "David Mine": "David (eParts)",
+ "Ben": "Ben (UX Coach)",
+ "Christian Kästner": "Christian (AI Coach)",
+ "Christian Kaestner": "Christian (AI Coach)",
+}
+
+# Maps filenames → session metadata for sessions where Zoom couldn't distinguish speakers
+SESSION_METADATA = {
+ "GMT20260220-180425": {"coach": "Dennis (Mentor)", "type": "mentor", "topic": "Risk & Project Management"},
+ "GMT20260220-222907": {"coach": "Christian (AI Coach)", "type": "ai_coach", "topic": "Measurement Theory, ML Model Selection, AI in SE"},
+ "GMT20260224-190023": {"coach": "Ben (UX Coach)", "type": "ux_coach", "topic": "UX Integration & HITL Design"},
+ "GMT20260224-220446": {"coach": "Cory (Coach)", "type": "ses_coach", "topic": "SES, Agent Feedback Loops, Code Organization"},
+}
+
+# Filler patterns to clean
+FILLER_PATTERNS = [
+ r"\b(um|uh|hmm|hm|yeah,?\s*yeah|like,?\s*you know)\b",
+]
+
+
+@dataclass
+class SpeakerTurn:
+ speaker: str
+ speaker_raw: str
+ start_time: str
+ end_time: str
+ text: str
+
+
+@dataclass
+class MeetingData:
+ filename: str
+ date: str
+ duration_seconds: int
+ speakers: list[str]
+ speaker_stats: dict[str, dict[str, Any]]
+ turns: list[SpeakerTurn]
+ total_words: int
+ cleaned_text: str
+ meeting_type: str # "client" or "coach"
+
+
+def _parse_timestamp(ts: str) -> float:
+ parts = ts.strip().split(":")
+ if len(parts) == 3:
+ h, m, s = parts
+ return int(h) * 3600 + int(m) * 60 + float(s)
+ elif len(parts) == 2:
+ m, s = parts
+ return int(m) * 60 + float(s)
+ return 0.0
+
+
+def _resolve_speaker(raw: str) -> str:
+ raw = raw.strip()
+ if raw in SPEAKER_MAP:
+ return SPEAKER_MAP[raw]
+ # Try email prefix
+ if "@" in raw:
+ prefix = raw.split("@")[0]
+ for email, name in SPEAKER_MAP.items():
+ if prefix in email:
+ return name
+ return prefix.title()
+ return raw
+
+
+def _extract_date_from_filename(filename: str) -> str:
+ match = re.search(r"GMT(\d{8})", filename)
+ if match:
+ d = match.group(1)
+ return f"{d[:4]}-{d[4:6]}-{d[6:8]}"
+ return "unknown"
+
+
+def parse_vtt(content: str, filename: str = "") -> MeetingData:
+ """Parse a VTT file into structured MeetingData."""
+ lines = content.strip().split("\n")
+
+ turns: list[SpeakerTurn] = []
+ current_speaker_raw = ""
+ current_speaker = ""
+ current_start = ""
+ current_end = ""
+ current_text_parts: list[str] = []
+
+ i = 0
+ # Skip WEBVTT header
+ while i < len(lines) and not re.match(r"\d+\s*$", lines[i].strip()):
+ i += 1
+
+ while i < len(lines):
+ line = lines[i].strip()
+
+ # Sequence number
+ if re.match(r"^\d+$", line):
+ i += 1
+ continue
+
+ # Timestamp line
+ ts_match = re.match(r"(\d[\d:,.]+)\s*-->\s*(\d[\d:,.]+)", line)
+ if ts_match:
+ start = ts_match.group(1).replace(",", ".")
+ end = ts_match.group(2).replace(",", ".")
+ i += 1
+
+ # Text line(s) follow
+ text_parts = []
+ speaker_raw = ""
+ while i < len(lines) and lines[i].strip() and not re.match(r"^\d+$", lines[i].strip()):
+ text_line = lines[i].strip()
+ # Check for speaker label
+ speaker_match = re.match(r"^(.+?):\s*(.*)$", text_line)
+ if speaker_match and not text_line.startswith("http"):
+ potential_speaker = speaker_match.group(1)
+ if "@" in potential_speaker or len(potential_speaker.split()) <= 3:
+ speaker_raw = potential_speaker
+ text_parts.append(speaker_match.group(2))
+ else:
+ text_parts.append(text_line)
+ else:
+ text_parts.append(text_line)
+ i += 1
+
+ text = " ".join(text_parts).strip()
+ if not text:
+ continue
+
+ resolved = _resolve_speaker(speaker_raw) if speaker_raw else current_speaker
+
+ # Merge with previous turn if same speaker
+ if resolved == current_speaker and current_text_parts:
+ current_text_parts.append(text)
+ current_end = end
+ else:
+ # Flush previous turn
+ if current_text_parts and current_speaker:
+ turns.append(SpeakerTurn(
+ speaker=current_speaker,
+ speaker_raw=current_speaker_raw,
+ start_time=current_start,
+ end_time=current_end,
+ text=" ".join(current_text_parts),
+ ))
+ current_speaker = resolved
+ current_speaker_raw = speaker_raw
+ current_start = start
+ current_end = end
+ current_text_parts = [text]
+
+ continue
+
+ i += 1
+
+ # Flush last turn
+ if current_text_parts and current_speaker:
+ turns.append(SpeakerTurn(
+ speaker=current_speaker,
+ speaker_raw=current_speaker_raw,
+ start_time=current_start,
+ end_time=current_end,
+ text=" ".join(current_text_parts),
+ ))
+
+ # Compute stats
+ speakers = list(dict.fromkeys(t.speaker for t in turns))
+ speaker_stats: dict[str, dict[str, Any]] = {}
+ for speaker in speakers:
+ speaker_turns = [t for t in turns if t.speaker == speaker]
+ word_count = sum(len(t.text.split()) for t in speaker_turns)
+ speaker_stats[speaker] = {
+ "turns": len(speaker_turns),
+ "words": word_count,
+ "pct_words": 0,
+ }
+ total_words = sum(s["words"] for s in speaker_stats.values())
+ for s in speaker_stats.values():
+ s["pct_words"] = round(s["words"] / max(total_words, 1) * 100, 1)
+
+ # Duration
+ if turns:
+ start_sec = _parse_timestamp(turns[0].start_time)
+ end_sec = _parse_timestamp(turns[-1].end_time)
+ duration = int(end_sec - start_sec)
+ else:
+ duration = 0
+
+ # Build cleaned text
+ cleaned_lines = []
+ for t in turns:
+ cleaned_lines.append(f"**{t.speaker}**: {t.text}")
+ cleaned_text = "\n\n".join(cleaned_lines)
+
+ return MeetingData(
+ filename=filename,
+ date=_extract_date_from_filename(filename),
+ duration_seconds=duration,
+ speakers=speakers,
+ speaker_stats=speaker_stats,
+ turns=turns,
+ total_words=total_words,
+ cleaned_text=cleaned_text,
+ meeting_type="client",
+ )
+
+
+def generate_offline_summary(meeting: MeetingData) -> dict[str, Any]:
+ """
+ Generate a structural summary without LLM calls.
+ Extracts what we can from text patterns alone.
+ """
+ all_text = " ".join(t.text for t in meeting.turns).lower()
+
+ # Topic detection via keyword groups
+ topic_keywords = {
+ "ML/Model": ["ml", "model", "training", "bert", "semantic", "confidence", "threshold", "prediction"],
+ "Architecture": ["architecture", "schema", "api", "endpoint", "database", "azure", "docker", "terraform"],
+ "Data": ["data", "dataset", "label", "attributes", "pim", "staging", "categories"],
+ "Project Mgmt": ["timeline", "sprint", "deadline", "sow", "milestone", "deliverable"],
+ "Infrastructure": ["deployment", "cloud", "token", "chromadb", "vector", "infrastructure"],
+ "Onboarding": ["onboarding", "access", "documentation", "teams", "communication"],
+ }
+ detected_topics = {}
+ for topic, keywords in topic_keywords.items():
+ hits = sum(1 for kw in keywords if kw in all_text)
+ if hits >= 2:
+ detected_topics[topic] = hits
+
+ # Question detection
+ questions = []
+ for t in meeting.turns:
+ sentences = re.split(r'[.!?]+', t.text)
+ for s in sentences:
+ s = s.strip()
+ if s.endswith("?") or s.lower().startswith(("should we", "can we", "how do", "what if", "why don", "is there")):
+ if len(s.split()) >= 4:
+ questions.append({"speaker": t.speaker, "text": s.strip()})
+
+ # Decision-like patterns
+ decision_patterns = [
+ r"(?:we decided|let's go with|we'll use|decision is|agreed to|we're going with)",
+ r"(?:I think we should|the plan is|we need to)",
+ ]
+ potential_decisions = []
+ for t in meeting.turns:
+ for pat in decision_patterns:
+ if re.search(pat, t.text, re.IGNORECASE):
+ potential_decisions.append({
+ "speaker": t.speaker,
+ "text": t.text[:200],
+ })
+ break
+
+ # Action item patterns
+ action_patterns = [
+ r"(?:I'll|we'll|I will|we will|let me|going to|need to|should|can you|please)",
+ ]
+ potential_actions = []
+ for t in meeting.turns:
+ for pat in action_patterns:
+ if re.search(pat, t.text, re.IGNORECASE) and len(t.text.split()) >= 5:
+ potential_actions.append({
+ "speaker": t.speaker,
+ "text": t.text[:200],
+ })
+ break
+
+ duration_min = meeting.duration_seconds // 60
+
+ return {
+ "meeting_date": meeting.date,
+ "duration_minutes": duration_min,
+ "participants": meeting.speakers,
+ "participant_count": len(meeting.speakers),
+ "total_words": meeting.total_words,
+ "total_turns": len(meeting.turns),
+ "speaker_stats": meeting.speaker_stats,
+ "detected_topics": dict(sorted(detected_topics.items(), key=lambda x: -x[1])),
+ "questions_found": len(questions),
+ "questions_sample": questions[:10],
+ "potential_decisions": len(potential_decisions),
+ "decisions_sample": potential_decisions[:10],
+ "potential_action_items": len(potential_actions),
+ "actions_sample": potential_actions[:10],
+ "analysis_mode": "offline (no LLM — structural extraction only)",
+ }
+
+
+def process_vtt_file(path: Path) -> tuple[MeetingData, dict]:
+ """Process a single VTT file and return meeting data + summary."""
+ content = path.read_text(encoding="utf-8")
+ meeting = parse_vtt(content, path.name)
+ summary = generate_offline_summary(meeting)
+ return meeting, summary
+
+
+def batch_process(
+ directory: Path,
+ pattern: str = "*.transcript.vtt",
+) -> list[tuple[MeetingData, dict]]:
+ """Process all VTT files in a directory."""
+ results = []
+ for vtt_file in sorted(directory.glob(pattern)):
+ meeting, summary = process_vtt_file(vtt_file)
+ results.append((meeting, summary))
+ return results
diff --git a/prompts/briefing_generator.txt b/prompts/briefing_generator.txt
new file mode 100644
index 0000000..7ad2f02
--- /dev/null
+++ b/prompts/briefing_generator.txt
@@ -0,0 +1,26 @@
+Generate a pre-meeting briefing for a $meeting_type meeting on $date.
+You are preparing the Pimsie Supreme team (CMU MSE capstone, building an ML-based product data ingestion system for eParts Services) for their session.
+
+## Last Session
+$last_session
+
+## Open Commitments (not yet delivered)
+$open_commitments
+
+## Recently Delivered
+$delivered_commitments
+
+## Recurring Concerns (patterns across sessions)
+$recurring_concerns
+
+## Relevant Past Context (from session memory)
+$rag_context
+
+Format the briefing as clean Slack-compatible markdown with these sections:
+1. **Last Session Recap** — key points from the most recent session
+2. **Commitment Status** — what was promised vs what was delivered
+3. **Open Items** — what still needs to be done, with owners
+4. **Coach's Recurring Themes** — patterns the team should be prepared for
+5. **Suggested Discussion Points** — what the team should proactively raise
+
+Keep it concise (under 800 words). Be specific with names, dates, and references — not generic.
\ No newline at end of file
diff --git a/prompts/plan_generator.txt b/prompts/plan_generator.txt
new file mode 100644
index 0000000..c4f398b
--- /dev/null
+++ b/prompts/plan_generator.txt
@@ -0,0 +1,131 @@
+You are the planning agent for the Pimsie Supreme team (CMU MSE capstone).
+The project is eParts: an ML-based product data ingestion system for eParts Services,
+plus the agentic Software Engineering System (SES) in this repository that builds it.
+
+Your job is to turn a definition of work (a spec, user story, or ticket) into an
+IMPLEMENTATION PLAN that states how the work is built IN THIS CODEBASE. You do not
+write code. You do not restate the request back as a plan. A human reviews your plan
+and accepts or rejects the proposed organization before any code is written, so the
+plan must be specific enough to argue with.
+
+You operate in two phases. The current phase is: $phase
+
+=== PHASE "GRILL" ===
+Ask every question you would need answered before you could write the plan. Surface
+ambiguity now — anything you do not ask about will be discovered mid-implementation,
+which is the failure mode this process exists to remove.
+
+Mark each question blocking or non-blocking:
+- BLOCKING: a wrong guess would change which files change, the class breakdown, the
+ data schema, or the test set. You must not plan while one is unanswered.
+- NON-BLOCKING: you can proceed on a stated assumption and a reviewer can correct it
+ cheaply.
+
+Return JSON:
+{
+ "spec_title": "short title for this work item",
+ "spec_summary": "2-4 sentences in your own words: what the work is, what done means",
+ "ready_to_plan": true|false,
+ "clarifying_questions": [
+ {
+ "question": "the question, answerable in a sentence",
+ "blocking": true|false,
+ "why_it_matters": "what part of the plan changes depending on the answer",
+ "assumption_if_unanswered": "what you would assume, and the risk of assuming it",
+ "ask": "who should answer — client / mentor / team / can be answered from the codebase"
+ }
+ ]
+}
+
+GRILL rules:
+- 3-10 questions. Anything answerable by reading the repository inventory below is
+ NOT a question — resolve it yourself and say so in "why_it_matters" if relevant.
+- Do not manufacture blocking questions to avoid the work, and do not mark a real
+ fork in the design non-blocking to look decisive. Both are defects.
+- "ready_to_plan" is false if and only if at least one question is blocking.
+- Do NOT produce any part of the plan in this phase.
+
+=== PHASE "PLAN" ===
+No blocking ambiguity remains (any answers given are below). Produce the plan. Every
+section of the plan template must be satisfied, using real paths from the repository
+inventory. A path you invented, or a test that does not say what it proves, is a
+rejection at review.
+
+Return JSON:
+{
+ "spec_title": "short title",
+ "spec_summary": "2-4 sentences in your own words",
+ "assumptions": ["assumptions this plan rests on, including answers you were given"],
+ "files_to_change": [
+ {
+ "path": "real/path/from/the/inventory.py",
+ "change_type": "new|modify|delete",
+ "what_changes": "the specific edit — not 'update logic'",
+ "why": "which part of the work summary this serves"
+ }
+ ],
+ "code_structures": [
+ {
+ "name": "structure name",
+ "kind": "class|dataclass|function|module|schema|config|prompt|table|event",
+ "location": "path where it lives",
+ "purpose": "what has to exist for the feature to be expressible"
+ }
+ ],
+ "class_breakdown": [
+ {
+ "class_name": "ClassName",
+ "module": "path/to/module.py",
+ "responsibility": "one sentence — if it needs 'and', split it",
+ "key_methods": [
+ {"name": "method_name", "signature": "method_name(self, arg: type) -> type", "does": "what it does"}
+ ],
+ "collaborators": ["existing code it calls or that calls it"]
+ }
+ ],
+ "tests_required": [
+ {
+ "name": "test_function_name",
+ "file": "tests/test_something.py",
+ "level": "unit|integration|regression",
+ "what_it_tests": "the behaviour it pins down",
+ "fails_when": "the concrete break it catches"
+ }
+ ],
+ "implementation_sequence": ["ordered, independently verifiable steps"],
+ "out_of_scope": ["what this plan deliberately does not do"],
+ "risks": [{"risk": "what could go wrong", "mitigation": "what reduces it"}],
+ "stop_conditions": [
+ {
+ "condition": "when the implementing agent must stop instead of trying again",
+ "action": "hand off to human / return to plan review / escalate with output"
+ }
+ ],
+ "implementation_tier": "cheap|frontier",
+ "estimated_effort": "rough size, e.g. 'half a day, one PR'"
+}
+
+PLAN rules:
+- Every file in "files_to_change" traces to at least one entry in "tests_required".
+- Match existing repository conventions (module docstring naming trigger + outputs,
+ `from __future__ import annotations`, BaseAgent subclassing, prompts in /prompts/,
+ ETVX entries in docs/etvx_manifest.yaml) — name the convention you are following.
+- 3-8 stop conditions, concrete and checkable. Include the two-failed-attempts rule
+ and any step where the plan itself may be wrong.
+- "implementation_tier" is "cheap" unless a step needs genuine design judgement that
+ the plan could not pin down; say which step in "risks" if so.
+- Prefer changing existing files over adding new ones; say so when you add one.
+
+DEFINITION OF WORK:
+$spec
+
+ANSWERS TO PREVIOUSLY ASKED QUESTIONS:
+$answers
+
+REPOSITORY INVENTORY (use real paths from this list):
+$repo_context
+
+PLAN TEMPLATE (the plan must satisfy every section):
+$plan_template
+
+Return ONLY valid JSON for the current phase ($phase). No prose, no code fences.
diff --git a/prompts/priority_classifier.txt b/prompts/priority_classifier.txt
new file mode 100644
index 0000000..f95f447
--- /dev/null
+++ b/prompts/priority_classifier.txt
@@ -0,0 +1,27 @@
+You are classifying priority for items extracted from a meeting transcript for the Pimsie Supreme team (CMU MSE capstone, building an ML product data ingestion system for eParts Services).
+
+Classify each item as P0, P1, or P2:
+
+**P0 — Blocks delivery or has a hard client deadline**
+Examples: "Harsha needs demo by Friday", "PIMS writeback is broken", "Jake's schema blocks integration"
+
+**P1 — Important for current sprint, should be ticketed immediately**
+Examples: "Set up Datadog monitoring", "Run alpha sweep on correction data", "Update architecture diagram"
+
+**P2 — Future sprint / nice to have**
+Examples: "Explore alternative embedding models", "Consider dashboard redesign", "Document deployment runbook"
+
+Items to classify:
+$items
+
+For each item, return a JSON array:
+[
+ {"text": "the item text", "owner": "assigned owner", "priority": "P0|P1|P2", "rationale": "one sentence explaining why"}
+]
+
+Context for priority decisions:
+- eParts client stakeholders: Harsha (accuracy priority), Jake (PIMS integration), Brian & Dewey (review workflow)
+- Current open ADRs: threshold calibration, alpha weighting, per-attribute routing, PIMS schema
+- Current sprint focus: $sprint_focus
+
+Return ONLY valid JSON, no other text.
\ No newline at end of file
diff --git a/prompts/refactor_agent.txt b/prompts/refactor_agent.txt
new file mode 100644
index 0000000..8e0c5bd
--- /dev/null
+++ b/prompts/refactor_agent.txt
@@ -0,0 +1,61 @@
+You are a refactoring engineer for the Pimsie Supreme team (CMU MSE capstone).
+The project is eParts: an ML-based product data ingestion system for eParts Services.
+
+You are the SECOND agent to see this code. A build agent already wrote it and it
+already works — tests pass and behavior is accepted. A build agent optimizes for
+getting things working, not for organizing code well. Your only job is cleanup and
+reorganization: bring a fresh point of view to code that already functions.
+
+ABSOLUTE RULE: every refactoring you propose MUST preserve observable behavior.
+You are not fixing bugs, not adding features, not changing APIs that callers depend
+on, not "improving" outputs, and not tightening validation. If a change would alter
+what the code does for any input, it is out of scope — say so instead of proposing it.
+Your output is a PROPOSAL reviewed by a human engineer before anything is applied;
+nothing you produce is baselined without that human gate.
+
+MODULE UNDER REVIEW: $module_name
+
+BUILD CONTEXT (what the build agent was asked to do, and evidence the code works):
+$build_context
+
+CODE (already working):
+$code
+
+STATIC ANALYSIS FINDINGS (from AST heuristics — confirm, refine, or dismiss these;
+they are hints, not conclusions):
+$static_findings
+
+Look specifically for these structural problems:
+- DUPLICATION: repeated logic that should be extracted to one place
+- COHESION: classes/modules doing several unrelated things
+- DEAD_CODE: unreferenced functions, unused imports, unreachable branches, leftover scaffolding
+- NAMING: names that do not say what the thing is or does (vague, misleading, inconsistent)
+- LONG_FUNCTION: functions doing too much, too long, or with too many parameters
+- MISPLACED_RESPONSIBILITY: logic living in the wrong layer/class/module
+- STRUCTURE: file/module organization that makes the code hard to navigate
+
+Return a JSON array:
+[
+ {
+ "id": "RF-XXX",
+ "category": "DUPLICATION|COHESION|DEAD_CODE|NAMING|LONG_FUNCTION|MISPLACED_RESPONSIBILITY|STRUCTURE",
+ "file": "path/to/file.py",
+ "location": "function or class name, and line numbers if known",
+ "severity": "high|medium|low",
+ "problem": "What is structurally wrong, concretely, with reference to the code shown",
+ "proposed_refactoring": "The specific mechanical change to make (e.g. 'extract lines 40-58 into _normalize_row(row) and call it from both branches')",
+ "behavior_risk": "none|low|medium — the chance this changes observable behavior",
+ "verification": "How a human confirms behavior is unchanged (which tests to run, what to diff)",
+ "rationale": "Why this is worth doing now"
+ }
+]
+
+Rules:
+- Propose 3-10 refactorings, ordered by value. Quality over quantity.
+- Every item must point at real code that was shown to you. Do not invent files or functions.
+- Prefer mechanical, reviewable, individually-revertable changes over rewrites.
+- If a problem cannot be fixed without changing behavior, still report it but set
+ "behavior_risk" to "medium" and say plainly in "problem" that it needs a human decision.
+- Never propose deleting or weakening a test to make refactoring easier.
+- If the code is already well organized, return fewer items — or an empty array. Do not invent work.
+- Return ONLY valid JSON, no other text.
diff --git a/prompts/req_extractor.txt b/prompts/req_extractor.txt
new file mode 100644
index 0000000..f4c2712
--- /dev/null
+++ b/prompts/req_extractor.txt
@@ -0,0 +1,46 @@
+You are a requirements engineer for the Pimsie Supreme team (CMU MSE capstone).
+The project is eParts: an ML-based product data ingestion system for eParts Services.
+
+Context: eParts Services is a B2B distributor that receives product catalogs (spec sheets)
+from many vendors in inconsistent formats (PDF, CSV, Excel). The team is building an ML
+pipeline to automatically extract, classify, and standardize product attributes
+(descriptions, specs, pricing categories) into a unified catalog.
+
+Given the following meeting data (action items, decisions, discussion points, questions),
+synthesize FORMAL REQUIREMENTS. Do NOT just copy the raw text — distill it into clear,
+actionable requirement statements.
+
+MEETING DATA:
+$meeting_data
+
+EXISTING REQUIREMENTS (avoid duplicates):
+$existing_reqs
+
+For each requirement, classify into one of these categories:
+- FUNCTIONAL: what the system must do (e.g., "System shall extract product attributes from PDF spec sheets")
+- NON_FUNCTIONAL: quality attributes (e.g., "System shall achieve >85% accuracy on attribute extraction")
+- USER_GOAL: what the user wants to achieve (e.g., "Catalog manager can review and correct ML predictions")
+- SOFT_GOAL: aspirational quality (e.g., "Minimize manual data entry effort by 70%")
+- CONSTRAINT: fixed limitation (e.g., "System must deploy on Azure cloud infrastructure")
+
+Return a JSON array:
+[
+ {
+ "id": "REQ-XXX",
+ "title": "Short descriptive title",
+ "statement": "The system shall... / The user shall be able to...",
+ "category": "FUNCTIONAL|NON_FUNCTIONAL|USER_GOAL|SOFT_GOAL|CONSTRAINT",
+ "priority": "P0|P1|P2",
+ "rationale": "Why this requirement exists (trace to meeting discussion)",
+ "source_speaker": "Who raised this",
+ "acceptance_criteria": "How to verify this is met",
+ "related_concerns": ["any risks or open questions"]
+ }
+]
+
+Rules:
+- Produce 3-8 requirements per meeting (quality over quantity)
+- Each statement must be specific and testable
+- Use "shall" for mandatory, "should" for desirable
+- Trace rationale back to meeting content
+- Return ONLY valid JSON
\ No newline at end of file
diff --git a/prompts/session_extraction.txt b/prompts/session_extraction.txt
new file mode 100644
index 0000000..9417b07
--- /dev/null
+++ b/prompts/session_extraction.txt
@@ -0,0 +1,29 @@
+You are analyzing a coach/mentor session transcript for a CMU MSE capstone team (Pimsie Supreme, building an ML-based product data ingestion system for eParts Services).
+
+Session date: $date
+
+Extract the following structured information from the transcript. Be precise — only extract what is explicitly stated or clearly implied.
+
+Return a JSON object with this exact structure:
+{
+ "commitments": [
+ {"text": "exact commitment made", "owner": "person who owns it", "deadline": "stated or implied deadline"}
+ ],
+ "concerns": [
+ {"text": "the specific concern raised", "raised_by": "person who raised it", "theme": "one of: monitorability|hitl|evidence|threshold|architecture|process|testing|deployment|scope"}
+ ],
+ "decisions": [
+ {"text": "what was decided", "context": "why this decision was made"}
+ ]
+}
+
+Rules:
+- A commitment is an explicit promise to deliver something by a date (e.g., "we will have baselines by next week")
+- A concern is feedback, worry, or repeated advice from the coach/mentor
+- A decision is a resolved choice about architecture, process, or scope
+- If a theme appears multiple times across sessions, track each occurrence separately
+- "raised_by" for concerns is typically "Christian Kastner" for coach sessions
+- Return ONLY valid JSON, no other text
+
+TRANSCRIPT:
+$transcript
\ No newline at end of file
diff --git a/prompts/test_review_agent.txt b/prompts/test_review_agent.txt
new file mode 100644
index 0000000..513087c
--- /dev/null
+++ b/prompts/test_review_agent.txt
@@ -0,0 +1,79 @@
+You are a test quality reviewer for the Pimsie Supreme team (CMU MSE capstone).
+The project is eParts: an ML-based product data ingestion system for eParts Services.
+
+You review TESTS, not implementation. You run late in the development cycle, after a
+build agent has written code and tests and they are green. Green tests are not the
+question — whether they would catch a real defect is the question.
+
+The specific failure mode you exist to catch: agents (and rushed humans) tend to make
+tests pass rather than make code correct. That produces suites that execute a lot of
+lines, report high coverage, and assert almost nothing. Coverage that only proves lines
+were executed is not evidence of correctness.
+
+Do NOT propose changes to the implementation. If a test looks wrong because the code
+under test is wrong, say that the code is the suspect and hand it to the human — do not
+suggest relaxing the test. Your output is a REVIEW read by a human engineer; nothing
+here is baselined without that human gate.
+
+MODULE UNDER REVIEW: $module_name
+
+TEST CODE:
+$test_code
+
+IMPLEMENTATION UNDER TEST (for reference only — do not review it):
+$implementation_code
+
+COVERAGE DATA (may be absent or partial):
+$coverage_summary
+
+STATIC ANALYSIS FINDINGS (from AST heuristics — confirm, refine, or dismiss these;
+they are hints, not conclusions):
+$static_findings
+
+Judge the suite on four axes:
+
+1. HAPPY PATH coverage — is the normal, expected-input behavior actually exercised and
+ asserted, for each public function?
+2. SAD PATH coverage — are error and edge cases covered: invalid input, empty/None,
+ malformed vendor files, missing attributes, boundary values, timeouts, permission or
+ API failures, duplicate records? Name the specific missing sad paths.
+3. ASSERTION QUALITY — do assertions check real outcomes? Flag:
+ - tests with no assertion at all (smoke tests that only prove "it did not crash")
+ - assertions on mocks only (assert_called_once, mock.called) with no check of the
+ value the system produced
+ - tautologies (assert True, assert 1 == 1, asserting a literal you just set)
+ - truthiness-only checks (assert result) where the value's content matters
+ - over-mocking that stubs out the very logic the test claims to verify
+4. COVERAGE MEANINGFULNESS — given the assertion quality above, is the reported coverage
+ number trustworthy, or is it inflated by executed-but-unasserted lines? Also flag
+ skipped/xfail tests, commented-out assertions, and try/except blocks that swallow
+ failures — these are the fingerprints of "made it pass".
+
+Return a JSON object:
+{
+ "verdict": "MEANINGFUL|GAPS_FOUND|COVERAGE_MISLEADING",
+ "summary": "2-4 sentences a reviewer can read first",
+ "coverage_assessment": "Is the coverage number meaningful? Why or why not?",
+ "findings": [
+ {
+ "id": "TR-XXX",
+ "category": "MISSING_HAPPY_PATH|MISSING_SAD_PATH|WEAK_ASSERTION|NO_ASSERTION|MOCK_ONLY_ASSERTION|TAUTOLOGY|OVER_MOCKING|SUPPRESSED_FAILURE|GAMED_COVERAGE",
+ "test_name": "the test function involved, or '(none)' if the finding is a missing test",
+ "severity": "high|medium|low",
+ "problem": "What is wrong or missing, concretely",
+ "recommendation": "The test to add or the assertion to strengthen — describe the case and the expected outcome",
+ "would_catch": "The specific defect this would catch that the current suite would not"
+ }
+ ],
+ "missing_sad_paths": ["concrete untested error cases, one per entry"],
+ "questions_for_human": ["anything requiring a human judgment call, e.g. intended behavior is ambiguous"]
+}
+
+Rules:
+- Be concrete. "Add more tests" is not a finding; "no test covers a spec sheet with a
+ missing description column, which would return None and pass silently" is.
+- Never recommend deleting, skipping, or weakening a test to get green.
+- If a test asserts only on mocks, say what real value should be asserted instead.
+- If coverage data is absent, say so in "coverage_assessment" rather than guessing a number.
+- If the suite is genuinely good, return verdict MEANINGFUL and few findings. Do not invent work.
+- Return ONLY valid JSON, no other text.
diff --git a/prompts/transcript_parser.txt b/prompts/transcript_parser.txt
new file mode 100644
index 0000000..0709b4c
--- /dev/null
+++ b/prompts/transcript_parser.txt
@@ -0,0 +1,39 @@
+You are parsing a meeting transcript for the Pimsie Supreme team (CMU MSE capstone project building an ML-based product data ingestion system for eParts Services).
+
+Meeting date: $date
+Meeting type: $meeting_type
+
+Parse the transcript and extract ALL of the following as a JSON object:
+
+{
+ "meeting_date": "$date",
+ "meeting_type": "$meeting_type",
+ "attendees": ["list of people present"],
+ "decisions": [
+ {"text": "what was decided", "context": "why / what triggered it"}
+ ],
+ "action_items": [
+ {"text": "what needs to be done", "owner": "who is responsible", "deadline": "stated or implied deadline"}
+ ],
+ "open_questions": [
+ {"text": "unresolved question", "context": "what prompted it", "assigned_to": "who should answer"}
+ ],
+ "new_requirements": [
+ {"text": "new requirement or constraint identified", "source": "who stated it", "priority_hint": "any stated urgency"}
+ ],
+ "key_discussion_points": [
+ "bullet point summaries of major topics discussed"
+ ]
+}
+
+Rules:
+- Extract EVERY action item, even implicit ones ("we should..." = action item)
+- Attendees: infer from speaker labels in the transcript
+- Decisions: only things explicitly agreed on, not suggestions
+- Open questions: things asked but not answered in the meeting
+- New requirements: new constraints, features, or non-functionals mentioned
+- Be precise with owners — if unclear, mark as "unassigned"
+- Return ONLY valid JSON, no other text
+
+TRANSCRIPT:
+$transcript
\ No newline at end of file
diff --git a/qa/README.md b/qa/README.md
new file mode 100644
index 0000000..892fe0c
--- /dev/null
+++ b/qa/README.md
@@ -0,0 +1,126 @@
+# `qa/` — throwaway QA interfaces
+
+Small, purpose-built interfaces whose only job is to let a **human** drive **one module in
+isolation** and confirm it behaves as expected — instead of only reading the code or trusting
+a green test suite.
+
+The practice comes from the AI-tools coaching session with **Cory Gwin** (Senior Software
+Engineer, GitHub / Copilot) on **2026-07-24**. From the session minutes:
+
+> **Build Throwaway QA Interfaces:** Because code is now cheap to produce, it is practical to
+> build small purpose-built interfaces solely for QA — tools that let a human interact with one
+> module in isolation and confirm it behaves as expected, rather than only reading code. Cory
+> clarified this means user interfaces, not additional agents.
+
+These are deliberately cheap and disposable. They are not part of the product, not imported by
+anything, and safe to delete.
+
+---
+
+## 1. `vtt_qa_server.py` — QA interface for `pipeline/vtt_processor.py`
+
+### Run it
+
+```bash
+python3 qa/vtt_qa_server.py # then open http://127.0.0.1:8777/
+```
+
+```bash
+python3 qa/vtt_qa_server.py --check # headless: parse every fixture, print a table, exit
+```
+
+Python 3.10+ (uses `X | Y` type syntax, same as the rest of `pipeline/`). Standard library only —
+no pip installs, no network, no LLM, **no API key**. The tool is read-only: it never writes to the
+repo. `--port N` if 8777 is taken.
+
+### What it is
+
+`pipeline/vtt_processor.py` is the front door of the ingestion pipeline: `parse_vtt()` turns a raw
+Zoom VTT transcript into `MeetingData`, and `generate_offline_summary()` derives speaker stats,
+topics, questions, decisions and action items from it without an LLM. Its output feeds
+`pipeline/ingest.py` (meeting minutes) and the offline path in
+`agents/requirements/transcript_parser.py`. If it silently drops turns, every downstream artifact
+is quietly wrong.
+
+The page is two panes:
+
+- **Left** — the exact string handed to `parse_vtt(text, filename)`, in an editable textarea.
+- **Right** — everything that comes back: turn count, speakers, words, duration, the resolved
+ date, per-speaker stats, the full turn list with timestamps, the derived summary counters and
+ samples, the `cleaned_text` that goes downstream, and the raw JSON.
+
+Nothing is precomputed or cached. Every render is a live call into the module in the current
+working tree, timed in milliseconds. Edit the left pane and press **Parse** (or ⌘/Ctrl + Enter)
+to re-run. If the module raises, you get the traceback instead of a blank screen.
+
+Two ways to load input:
+
+- **Real transcript** — any of the 25 `.vtt` files under `transcripts/` and `coach_meetings/`.
+- **Edge case** — 17 built-in synthetic fixtures (empty, whitespace, non-VTT prose, missing
+ speaker labels, missing cue numbers, unknown speaker emails, malformed and out-of-order
+ timestamps, CRLF, colons inside speech, single cue, and so on). Each one states in a caption
+ what you should expect to see, so you can check the claim rather than guess.
+
+### What a human should look for
+
+1. **Every turn you can see on the left appears on the right**, attributed to the right person,
+ with nothing silently dropped. Scroll both panes together on a real transcript.
+2. **Speaker names resolve correctly.** An email should become the right display name, and a
+ stranger's address should *not* become a teammate.
+3. **Turn merging is justified.** Cues collapse only where the same speaker genuinely continues.
+ Compare the "cues" count in the input stats against the turn count.
+4. **Counts are consistent.** `total_words` equals the sum of per-speaker words, `pct_words` sums
+ to ~100, duration is positive and plausible for the transcript length.
+5. **Derived signals are believable.** If the input obviously contains questions, decisions or
+ action items, the counters should not be zero — and vice versa.
+6. **Degradation is graceful.** Empty, whitespace-only, non-VTT and malformed input should return
+ an empty-ish result: no exception, and no fabricated turns.
+
+The amber/green **harness observations** panel is heuristics from *this tool*, not from the
+module. A flag means "go look at this", never "this is a bug". You decide by comparing the panes.
+
+### Findings from the first QA pass (2026-07-27)
+
+Recorded here because the point of the exercise is to find things. All four were found by
+eyeballing input against output in this interface, and all four reproduce via `--check`. **None
+has been fixed** — this directory does not touch `pipeline/`.
+
+1. **`*.cc.vtt` files parse to nothing.** `parse_vtt()` skips the header by advancing until it
+ finds a bare cue number, but Zoom's closed-caption exports have no cue numbers, so the scan
+ consumes the whole file. `transcripts/GMT20260416-180324_Recording.cc.vtt` (21,553 chars, 196
+ cues) yields **0 turns, 0 speakers, 0 words** with no error. Three such files sit in
+ `transcripts/`. Reproduce: fixture *"Zoom .cc.vtt style"*, or load any `.cc.vtt` file.
+ Mitigating factor: `batch_process()` defaults to `pattern="*.transcript.vtt"`, so the pipeline
+ does not currently ingest these files — but nothing stops a caller passing `*.vtt`.
+2. **Question detection never sees a question mark.** `generate_offline_summary()` splits each
+ turn on `re.split(r'[.!?]+', ...)`, which removes the `?`, then tests `s.endswith("?")` —
+ a branch that can never be true. Detection therefore relies entirely on the six hardcoded
+ opening phrases (`should we`, `can we`, `how do`, …). Reproduce: fixture *"Ordinary questions
+ ending in '?'"* — three unmistakable questions, `questions_found == 0`. Contrast with fixture
+ *"Questions starting with stock phrases"*, which reports 3.
+3. **Unknown emails are mapped onto real teammates.** `_resolve_speaker()` falls back to
+ `if prefix in email` over `SPEAKER_MAP`, an unanchored substring test. `n@n.com` and `a@x.com`
+ both resolve to **"Hrishik"**, because `"n"` and `"a"` appear inside
+ `hrishikb@andrew.cmu.edu`. Attributing words to the wrong person corrupts the speaker stats
+ that the program-health narrative rests on. Reproduce: fixture *"Unknown speaker email"*.
+4. **Duration can go negative.** Duration is `last turn's end − first turn's start` with no
+ ordering check; out-of-order cues give a negative value (fixture *"Out-of-order timestamps"*
+ returns `-598s`, which `generate_offline_summary` then floor-divides into
+ `duration_minutes = -10`). No real repo file trips this today.
+
+Also worth a human's judgement rather than a defect claim: on
+`transcripts/GMT20260122-191430_Recording.transcript.vtt`, 363 cues collapse into **17 turns**
+across only 2 speakers. That is the intended merging behaviour meeting a transcript where Zoom
+labelled speakers sparsely — plausible, but worth confirming against the raw file before trusting
+the per-speaker word split.
+
+### Limitations
+
+- The harness exercises `parse_vtt()` and `generate_offline_summary()` only. The LLM-backed
+ online path in `vtt_processor`'s docstring and in `transcript_parser.py` is **not** covered —
+ it needs an API key.
+- The "harness observations" are heuristics, not assertions. `--check` prints `WARN` counts but
+ always exits 0 unless the module actually raises; it is a QA aid, not a CI gate.
+- Binds to `127.0.0.1` only, and `/api/sample` refuses paths outside `transcripts/` and
+ `coach_meetings/` and anything that is not a `.vtt`. It is still a local dev tool with no auth
+ — do not expose it.
diff --git a/qa/vtt_qa_server.py b/qa/vtt_qa_server.py
new file mode 100644
index 0000000..3358a5f
--- /dev/null
+++ b/qa/vtt_qa_server.py
@@ -0,0 +1,779 @@
+#!/usr/bin/env python3
+"""
+Throwaway QA interface for pipeline/vtt_processor.py
+
+A tiny stdlib-only local web app that lets a human drive ONE module in isolation:
+paste or load a VTT transcript on the left, see exactly what
+`parse_vtt()` + `generate_offline_summary()` produce on the right.
+
+Practice adopted from the AI-tools coaching session with Cory Gwin
+(Senior Software Engineer, GitHub / Copilot), 2026-07-24:
+"Build throwaway QA interfaces ... small purpose-built interfaces solely for QA
+— tools that let a human interact with one module in isolation and confirm it
+behaves as expected, rather than only reading code." (User interfaces, not agents.)
+
+Run:
+ python3 qa/vtt_qa_server.py # serve UI on http://127.0.0.1:8777
+ python3 qa/vtt_qa_server.py --check # headless: parse every fixture + real files
+
+No third-party dependencies. Read-only: this tool never writes to the repo.
+"""
+
+from __future__ import annotations
+
+import argparse
+import io
+import json
+import sys
+import time
+import traceback
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
+from typing import Any
+from urllib.parse import urlparse, parse_qs
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+if str(REPO_ROOT) not in sys.path:
+ sys.path.insert(0, str(REPO_ROOT))
+
+from pipeline.vtt_processor import ( # noqa: E402
+ SPEAKER_MAP,
+ parse_vtt,
+ generate_offline_summary,
+)
+
+# Directories the UI is allowed to read sample transcripts from.
+SAMPLE_DIRS = ["transcripts", "coach_meetings"]
+
+DEFAULT_PORT = 8777
+
+
+# --------------------------------------------------------------------------
+# Synthetic edge-case fixtures — the point of the tool is that a human can
+# click each of these and confirm the module degrades gracefully.
+# --------------------------------------------------------------------------
+
+FIXTURES: dict[str, dict[str, str]] = {
+ "empty": {
+ "label": "Empty input",
+ "note": "Nothing at all. Expect 0 turns, no exception, duration 0.",
+ "filename": "empty.vtt",
+ "text": "",
+ },
+ "whitespace": {
+ "label": "Whitespace only",
+ "note": "Blank lines and spaces. Expect 0 turns, no exception.",
+ "filename": "whitespace.vtt",
+ "text": " \n\n\t\n \n",
+ },
+ "garbage": {
+ "label": "Not a VTT file at all",
+ "note": "Plain prose. Expect 0 turns rather than a crash or garbled turns.",
+ "filename": "notes.txt",
+ "text": "These are just my handwritten notes.\nNo timestamps, no cues.\nAshritha: said something.\n",
+ },
+ "happy_two_speakers": {
+ "label": "Happy path — 2 speakers, 3 cues",
+ "note": "Baseline. Expect 3 turns (no merge), correct name mapping, 6s duration.",
+ "filename": "GMT20260724-190000_Recording.transcript.vtt",
+ "text": (
+ "WEBVTT\n\n"
+ "1\n00:00:01.000 --> 00:00:03.000\nCory Gwin: Build throwaway QA interfaces.\n\n"
+ "2\n00:00:03.500 --> 00:00:05.000\nAshritha: Interfaces, not agents. Got it.\n\n"
+ "3\n00:00:05.500 --> 00:00:07.000\nCory Gwin: Right, a human should poke the module.\n"
+ ),
+ },
+ "adjacent_merge": {
+ "label": "Adjacent same-speaker cues (turn merging)",
+ "note": "4 cues, same speaker throughout. Expect ONE merged turn spanning 00:00:01 -> 00:00:09.",
+ "filename": "merge.transcript.vtt",
+ "text": (
+ "WEBVTT\n\n"
+ "1\n00:00:01.000 --> 00:00:03.000\nAshritha: First fragment,\n\n"
+ "2\n00:00:03.000 --> 00:00:05.000\nAshritha: second fragment,\n\n"
+ "3\n00:00:05.000 --> 00:00:07.000\nAshritha: third fragment,\n\n"
+ "4\n00:00:07.000 --> 00:00:09.000\nAshritha: and the last one.\n"
+ ),
+ },
+ "no_cue_numbers": {
+ "label": "Zoom .cc.vtt style — no cue numbers, no speaker labels",
+ "note": (
+ "This is the real shape of the four *.cc.vtt files in transcripts/. "
+ "Watch the turn count carefully."
+ ),
+ "filename": "GMT20260724-190000_Recording.cc.vtt",
+ "text": (
+ "WEBVTT\n\n"
+ "00:00:07.000 --> 00:00:09.000\nYeah, start it.\n\n"
+ "00:00:09.000 --> 00:00:16.000\nSo I think we should use the offline path.\n\n"
+ "00:00:16.000 --> 00:00:28.000\nAgreed to ship that this tick.\n"
+ ),
+ },
+ "no_speaker_labels": {
+ "label": "Cue numbers present, speaker labels missing",
+ "note": "Expect: no speaker can be resolved, so turns are dropped. Confirm words == 0.",
+ "filename": "unlabelled.transcript.vtt",
+ "text": (
+ "WEBVTT\n\n"
+ "1\n00:00:01.000 --> 00:00:03.000\nSomeone says a thing.\n\n"
+ "2\n00:00:03.000 --> 00:00:05.000\nSomeone else replies.\n"
+ ),
+ },
+ "unknown_email": {
+ "label": "Unknown speaker email (fuzzy name mapping)",
+ "note": (
+ "Speakers 'n@n.com' and 'a@x.com' are strangers. Check whether they are "
+ "mapped onto real teammates."
+ ),
+ "filename": "strangers.transcript.vtt",
+ "text": (
+ "WEBVTT\n\n"
+ "1\n00:00:01.000 --> 00:00:03.000\nn@n.com: Who am I supposed to be?\n\n"
+ "2\n00:00:03.000 --> 00:00:05.000\na@x.com: And who am I?\n\n"
+ "3\n00:00:05.000 --> 00:00:07.000\nsomebody.new@example.org: I am definitely not on the team.\n"
+ ),
+ },
+ "questions_stock_phrases": {
+ "label": "Questions starting with stock phrases",
+ "note": (
+ "All three open with a phrase the module explicitly looks for "
+ "(\"should we\" / \"how do\" / \"can we\"). Expect questions_found == 3."
+ ),
+ "filename": "questions_stock.transcript.vtt",
+ "text": (
+ "WEBVTT\n\n"
+ "1\n00:00:01.000 --> 00:00:03.000\nAshritha: Should we go with the offline extraction path?\n\n"
+ "2\n00:00:03.000 --> 00:00:05.000\nCory Gwin: How do you plan to QA the parser itself?\n\n"
+ "3\n00:00:05.000 --> 00:00:07.000\nAshritha: Can we just build a tiny interface for it?\n"
+ ),
+ },
+ "questions_plain": {
+ "label": "Ordinary questions ending in '?'",
+ "note": (
+ "Three unmistakable questions, none starting with a stock phrase. Compare "
+ "questions_found against the three question marks you can see on the left."
+ ),
+ "filename": "questions_plain.transcript.vtt",
+ "text": (
+ "WEBVTT\n\n"
+ "1\n00:00:01.000 --> 00:00:03.000\nAshritha: So Jay, do you want to go first?\n\n"
+ "2\n00:00:03.000 --> 00:00:05.000\nCory Gwin: Where is the ingestion pipeline breaking today?\n\n"
+ "3\n00:00:05.000 --> 00:00:07.000\nAshritha: Who owns the ETIM normalization work now?\n"
+ ),
+ },
+ "decisions_actions": {
+ "label": "Decisions and action items",
+ "note": "Contains 'we decided', \"let's go with\", \"I'll\". Expect both counters non-zero.",
+ "filename": "decisions.transcript.vtt",
+ "text": (
+ "WEBVTT\n\n"
+ "1\n00:00:01.000 --> 00:00:04.000\nAshritha: We decided to keep the offline path as the default for CI.\n\n"
+ "2\n00:00:04.000 --> 00:00:08.000\nCory Gwin: Let's go with a throwaway interface, and I'll review it next session.\n\n"
+ "3\n00:00:08.000 --> 00:00:12.000\nAshritha: I will wire the QA page into the dashboard directory this week.\n"
+ ),
+ },
+ "malformed_timestamps": {
+ "label": "Malformed / mixed timestamps",
+ "note": (
+ "Cue 1 uses mm:ss, cue 2 uses comma decimals (SRT style), cue 3's arrow is broken. "
+ "Expect no crash; check which cues survive and whether duration is sane."
+ ),
+ "filename": "malformed.transcript.vtt",
+ "text": (
+ "WEBVTT\n\n"
+ "1\n01:02.500 --> 01:05.500\nAshritha: Two-part timestamp.\n\n"
+ "2\n00:01:06,000 --> 00:01:09,000\nCory Gwin: Comma decimals.\n\n"
+ "3\n00:01:10.000 -> 00:01:12.000\nAshritha: Single-arrow, not valid VTT.\n"
+ ),
+ },
+ "reversed_time": {
+ "label": "Out-of-order timestamps (negative duration)",
+ "note": "Last cue ends BEFORE the first begins. Expect a negative or nonsense duration.",
+ "filename": "reversed.transcript.vtt",
+ "text": (
+ "WEBVTT\n\n"
+ "1\n00:10:00.000 --> 00:10:05.000\nAshritha: I was recorded late.\n\n"
+ "2\n00:00:01.000 --> 00:00:02.000\nCory Gwin: I was recorded early.\n"
+ ),
+ },
+ "colon_in_text": {
+ "label": "Colons inside speech (false speaker labels)",
+ "note": (
+ "Lines contain colons that are NOT speaker labels (a URL, a ratio, a short phrase). "
+ "Check whether phantom speakers appear."
+ ),
+ "filename": "colons.transcript.vtt",
+ "text": (
+ "WEBVTT\n\n"
+ "1\n00:00:01.000 --> 00:00:03.000\nAshritha: The split is 80:20 for train and test.\n\n"
+ "2\n00:00:03.000 --> 00:00:05.000\nhttps://example.com/docs: see the appendix.\n\n"
+ "3\n00:00:05.000 --> 00:00:07.000\nNote to self: revisit this later.\n"
+ ),
+ },
+ "crlf": {
+ "label": "Windows line endings (CRLF)",
+ "note": "Same as the happy path but with \\r\\n. Output should be identical, with no stray \\r.",
+ "filename": "crlf.transcript.vtt",
+ "text": (
+ "WEBVTT\r\n\r\n"
+ "1\r\n00:00:01.000 --> 00:00:03.000\r\nCory Gwin: Build throwaway QA interfaces.\r\n\r\n"
+ "2\r\n00:00:03.500 --> 00:00:05.000\r\nAshritha: Interfaces, not agents. Got it.\r\n"
+ ),
+ },
+ "no_gmt_filename": {
+ "label": "Filename without a GMT date stamp",
+ "note": "Date is derived from the filename only. Expect date == 'unknown'.",
+ "filename": "team-sync.vtt",
+ "text": (
+ "WEBVTT\n\n"
+ "1\n00:00:01.000 --> 00:00:03.000\nAshritha: Where does the meeting date come from?\n"
+ ),
+ },
+ "single_cue": {
+ "label": "Single cue",
+ "note": "Smallest non-empty input. Expect 1 turn and a 2s duration.",
+ "filename": "GMT20260724-190000_Recording.transcript.vtt",
+ "text": "WEBVTT\n\n1\n00:00:01.000 --> 00:00:03.000\nAshritha: Just the one line.\n",
+ },
+}
+
+
+# --------------------------------------------------------------------------
+# Core: run the module and package the result for the UI
+# --------------------------------------------------------------------------
+
+def list_samples() -> list[dict[str, Any]]:
+ """Real VTT files in the repo that the UI can load."""
+ out = []
+ for d in SAMPLE_DIRS:
+ base = REPO_ROOT / d
+ if not base.is_dir():
+ continue
+ for p in sorted(base.rglob("*.vtt")):
+ rel = p.relative_to(REPO_ROOT).as_posix()
+ try:
+ size = p.stat().st_size
+ except OSError:
+ continue
+ out.append({"id": rel, "label": rel, "bytes": size})
+ return out
+
+
+def read_sample(rel: str) -> str:
+ """Read a sample file, refusing anything outside the allowed directories."""
+ target = (REPO_ROOT / rel).resolve()
+ allowed = [(REPO_ROOT / d).resolve() for d in SAMPLE_DIRS]
+ if not any(str(target).startswith(str(a) + "/") for a in allowed):
+ raise ValueError(f"path not allowed: {rel}")
+ if target.suffix != ".vtt":
+ raise ValueError(f"only .vtt files may be loaded: {rel}")
+ return target.read_text(encoding="utf-8", errors="replace")
+
+
+def _email_keys_for(name: str) -> list[str]:
+ return [k for k, v in SPEAKER_MAP.items() if v == name]
+
+
+def qa_flags(text: str, meeting, summary: dict) -> list[dict[str, str]]:
+ """
+ Heuristic observations produced by THIS HARNESS (not by the module) to draw
+ a human's eye to output that looks wrong. Every flag is a prompt to go look
+ at the input pane and judge for yourself — none of them is a verdict.
+ """
+ flags: list[dict[str, str]] = []
+ cue_count = text.count("-->")
+ stripped = text.strip()
+
+ if stripped and not meeting.turns:
+ flags.append({
+ "level": "warn",
+ "msg": f"Input is non-empty ({len(stripped)} chars, {cue_count} cue arrows) "
+ f"but 0 turns were produced. Silent total data loss?",
+ })
+ if cue_count and meeting.turns and len(meeting.turns) < cue_count:
+ flags.append({
+ "level": "info",
+ "msg": f"{cue_count} cues collapsed into {len(meeting.turns)} turns. Expected when "
+ f"adjacent cues share a speaker — suspicious when they clearly do not.",
+ })
+ if "?" in text and summary.get("questions_found") == 0:
+ flags.append({
+ "level": "warn",
+ "msg": "Input contains '?' but summary.questions_found == 0.",
+ })
+ if meeting.turns and meeting.duration_seconds <= 0:
+ flags.append({
+ "level": "warn",
+ "msg": f"duration_seconds == {meeting.duration_seconds} with "
+ f"{len(meeting.turns)} turns.",
+ })
+ if meeting.turns and not meeting.total_words:
+ flags.append({"level": "warn", "msg": "Turns exist but total_words == 0."})
+
+ # Fuzzy email→name mapping: raw label is an email not in SPEAKER_MAP, yet it
+ # resolved to a mapped teammate whose own address does not start with that prefix.
+ seen = set()
+ for t in meeting.turns:
+ raw = (t.speaker_raw or "").strip()
+ if not raw or "@" not in raw or raw in SPEAKER_MAP or raw in seen:
+ continue
+ seen.add(raw)
+ prefix = raw.split("@")[0]
+ if t.speaker in SPEAKER_MAP.values():
+ keys = _email_keys_for(t.speaker)
+ if not any(k.startswith(prefix) for k in keys):
+ flags.append({
+ "level": "warn",
+ "msg": f"Unknown address '{raw}' was mapped to a known teammate "
+ f"'{t.speaker}' (substring match against {keys}).",
+ })
+
+ pct_total = round(sum(s["pct_words"] for s in meeting.speaker_stats.values()), 1)
+ if meeting.speaker_stats and not (98.0 <= pct_total <= 102.0):
+ flags.append({
+ "level": "info",
+ "msg": f"pct_words sums to {pct_total} (rounding, or a real accounting bug).",
+ })
+ if not flags:
+ flags.append({"level": "ok", "msg": "No harness heuristics tripped. Still read the panes."})
+ return flags
+
+
+def run_module(text: str, filename: str) -> dict:
+ """Call the module under test and capture everything, including failures."""
+ t0 = time.perf_counter()
+ try:
+ meeting = parse_vtt(text, filename)
+ summary = generate_offline_summary(meeting)
+ except Exception:
+ return {
+ "ok": False,
+ "error": traceback.format_exc(),
+ "elapsed_ms": round((time.perf_counter() - t0) * 1000, 2),
+ }
+ elapsed = round((time.perf_counter() - t0) * 1000, 2)
+
+ return {
+ "ok": True,
+ "elapsed_ms": elapsed,
+ "input_stats": {
+ "chars": len(text),
+ "lines": len(text.splitlines()),
+ "cues": text.count("-->"),
+ },
+ "meeting": {
+ "filename": meeting.filename,
+ "date": meeting.date,
+ "duration_seconds": meeting.duration_seconds,
+ "speakers": meeting.speakers,
+ "speaker_stats": meeting.speaker_stats,
+ "total_words": meeting.total_words,
+ "meeting_type": meeting.meeting_type,
+ "turns": [
+ {
+ "speaker": t.speaker,
+ "speaker_raw": t.speaker_raw,
+ "start_time": t.start_time,
+ "end_time": t.end_time,
+ "text": t.text,
+ }
+ for t in meeting.turns
+ ],
+ "cleaned_text": meeting.cleaned_text,
+ },
+ "summary": summary,
+ "flags": qa_flags(text, meeting, summary),
+ }
+
+
+# --------------------------------------------------------------------------
+# HTML (single page, embedded; matches the repo's dashboard house style)
+# --------------------------------------------------------------------------
+
+PAGE = r"""
+
+QA — pipeline/vtt_processor.py
+
+
+
QA harness — pipeline/vtt_processor.py
+
Throwaway interface for driving one module in isolation ·
+practice from the Cory Gwin coaching session, 2026-07-24
+
+
What this is. The left pane is the exact string handed to
+parse_vtt(text, filename); the right pane is everything that comes back from it and from
+generate_offline_summary(meeting). Nothing is precomputed and nothing is cached — every
+render is a live call into the module in this working tree. No LLM, no network, no API key.
+Edit the left pane and press Parse (or ⌘/Ctrl + Enter) to re-run.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Input — raw VTT text
+
+
—
+
+
+
Output — module return values
+
Load a sample or paste text, then press Parse.
+
+
+
+
What to look for when QA-ing
+
+
Every speaker turn you can see on the left appears on the right, attributed to the right person,
+ with nothing silently dropped.
+
Speaker names resolve correctly. Emails should become the right display name; a stranger's
+ address should not become a teammate.
+
Turn merging is justified. Cues collapse only when the same speaker really does continue.
+
Counts are consistent. total_words matches the sum of the per-speaker words; pct_words sums to ~100;
+ duration is positive and plausible.
+
Derived signals are believable. If the input obviously contains questions, decisions or action items,
+ the counters should not be zero — and vice versa.
+
Degradation is graceful. Empty, whitespace, non-VTT and malformed input should return an empty-ish
+ result, not raise and not fabricate.
+
+
A red traceback panel means the module raised. Amber flags are heuristics from
+this harness, not from the module — treat them as places to look, then decide for yourself
+by comparing the two panes.
+
+
+
+"""
+
+
+# --------------------------------------------------------------------------
+# HTTP plumbing
+# --------------------------------------------------------------------------
+
+class Handler(BaseHTTPRequestHandler):
+ server_version = "vtt-qa/1.0"
+
+ def log_message(self, fmt, *args): # quieter console
+ sys.stderr.write(" %s\n" % (fmt % args))
+
+ def _send(self, code: int, body: bytes, ctype: str) -> None:
+ self.send_response(code)
+ self.send_header("Content-Type", ctype)
+ self.send_header("Content-Length", str(len(body)))
+ self.send_header("Cache-Control", "no-store")
+ self.end_headers()
+ self.wfile.write(body)
+
+ def _json(self, obj, code: int = 200) -> None:
+ self._send(code, json.dumps(obj).encode("utf-8"), "application/json; charset=utf-8")
+
+ def do_GET(self) -> None: # noqa: N802
+ u = urlparse(self.path)
+ if u.path == "/":
+ boot = json.dumps({"samples": list_samples(), "fixtures": FIXTURES})
+ html = PAGE.replace("__BOOTSTRAP__", boot)
+ self._send(200, html.encode("utf-8"), "text/html; charset=utf-8")
+ elif u.path == "/api/samples":
+ self._json({"samples": list_samples()})
+ elif u.path == "/api/sample":
+ rel = (parse_qs(u.query).get("id") or [""])[0]
+ try:
+ self._json({"id": rel, "text": read_sample(rel)})
+ except Exception as exc:
+ self._json({"error": f"{type(exc).__name__}: {exc}"}, 400)
+ else:
+ self._json({"error": "not found"}, 404)
+
+ def do_POST(self) -> None: # noqa: N802
+ if urlparse(self.path).path != "/api/parse":
+ self._json({"error": "not found"}, 404)
+ return
+ try:
+ n = int(self.headers.get("Content-Length") or 0)
+ payload = json.loads(self.rfile.read(n) or b"{}")
+ except Exception as exc:
+ self._json({"ok": False, "error": f"bad request: {exc}"}, 400)
+ return
+ self._json(run_module(str(payload.get("text") or ""), str(payload.get("filename") or "")))
+
+
+def serve(port: int) -> None:
+ httpd = ThreadingHTTPServer(("127.0.0.1", port), Handler)
+ print(f"\n QA harness for pipeline/vtt_processor.py")
+ print(f" repo root : {REPO_ROOT}")
+ print(f" samples : {len(list_samples())} real .vtt files · {len(FIXTURES)} edge-case fixtures")
+ print(f"\n open -> http://127.0.0.1:{port}/\n")
+ print(" Ctrl-C to stop.\n")
+ try:
+ httpd.serve_forever()
+ except KeyboardInterrupt:
+ print("\n stopped.\n")
+ finally:
+ httpd.server_close()
+
+
+# --------------------------------------------------------------------------
+# Headless mode — same code path, printable
+# --------------------------------------------------------------------------
+
+def check(real_limit: int = 3) -> int:
+ """Run every fixture plus a few real files and print a one-line-per-case table."""
+ buf = io.StringIO()
+ hdr = f"{'case':44} {'cues':>5} {'turns':>6} {'spk':>4} {'words':>7} {'dur':>7} {'q':>3} {'dec':>4} {'act':>4} flags"
+ buf.write(hdr + "\n" + "-" * len(hdr) + "\n")
+ rows = [(f"fixture:{k}", v["text"], v["filename"]) for k, v in FIXTURES.items()]
+ for s in list_samples()[:real_limit]:
+ rows.append((f"repo:{Path(s['id']).name[:37]}", read_sample(s["id"]), Path(s["id"]).name))
+
+ worst = 0
+ for name, text, fname in rows:
+ d = run_module(text, fname)
+ if not d["ok"]:
+ buf.write(f"{name:44} RAISED\n")
+ worst = 1
+ continue
+ m, s2 = d["meeting"], d["summary"]
+ warn = sum(1 for f in d["flags"] if f["level"] == "warn")
+ marks = ("WARN x%d" % warn) if warn else "ok"
+ buf.write(
+ f"{name:44} {d['input_stats']['cues']:>5} {len(m['turns']):>6} "
+ f"{len(m['speakers']):>4} {m['total_words']:>7} {m['duration_seconds']:>6}s "
+ f"{s2['questions_found']:>3} {s2['potential_decisions']:>4} "
+ f"{s2['potential_action_items']:>4} {marks}\n"
+ )
+ print(buf.getvalue())
+ print("Legend: cues = '-->' occurrences in input; dur = duration_seconds; "
+ "q/dec/act = questions_found / potential_decisions / potential_action_items.")
+ print("WARN = a harness heuristic tripped (see the web UI for the message). "
+ "Not necessarily a defect — a human decides.\n")
+ return worst
+
+
+def main() -> int:
+ ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+ ap.add_argument("--port", type=int, default=DEFAULT_PORT, help=f"port (default {DEFAULT_PORT})")
+ ap.add_argument("--check", action="store_true", help="run headless over all fixtures and exit")
+ args = ap.parse_args()
+ if args.check:
+ return check()
+ serve(args.port)
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..2332a7d
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,42 @@
+# Core framework
+fastapi>=0.115.0
+uvicorn[standard]>=0.32.0
+
+# Anthropic SDK
+anthropic>=0.42.0
+
+# MCP servers / external APIs
+atlassian-python-api>=3.41.0
+slack-sdk>=3.33.0
+google-api-python-client>=2.160.0
+google-auth>=2.37.0
+google-auth-oauthlib>=1.2.0
+PyGithub>=2.5.0
+requests>=2.32.0
+
+# Vector store + embeddings
+chromadb>=0.6.0
+sentence-transformers>=3.3.0
+
+# Database
+aiosqlite>=0.20.0
+
+# Scheduling
+apscheduler>=3.10.0
+
+# Data handling
+python-dotenv>=1.0.0
+pydantic>=2.10.0
+pydantic-settings>=2.7.0
+
+# PDF parsing (for seed data ingestion)
+pymupdf>=1.25.0
+
+# Testing
+pytest>=8.3.0
+pytest-asyncio>=0.24.0
+pytest-cov>=6.0.0
+
+# Dev tools
+httpx>=0.28.0
+ruff>=0.8.0
diff --git a/requirements/parsed/REQ-001.md b/requirements/parsed/REQ-001.md
new file mode 100644
index 0000000..e6ade5f
--- /dev/null
+++ b/requirements/parsed/REQ-001.md
@@ -0,0 +1,110 @@
+# REQ-001: Extract product attributes from vendor spec sheets
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-001 |
+| **Category** | Functional Requirement |
+| **Priority** | P0 |
+| **Date Identified** | **2026-01-22** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `MTG-2026-01-22` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**Extract product attributes from vendor spec sheets** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+- Ingest CSV, `.xlsx`, and text-extractable PDF vendor sheets supplied by distributors.
+- Parse tabular layouts with schema hints (headers, SKU column, specification blocks).
+- Persist extracted attribute candidates with machine confidence and source document span references.
+- Expose deterministic JSON contract for downstream mapping and review agents.
+
+### Context
+
+| REQ | Rationale (summary) |
+|-----|----------------------|
+| **REQ-001** | Derived from SES ingest + stakeholder dialogue; prioritized **P0** for backlog ordering. |
+
+### Non-functional / ops
+- **Throughput:** scalable batch pipeline (not synchronous chat per row at scale).
+- **Audit:** every prediction ties back to ingestion record + model version.
+
+### Dependencies
+- REQ-002 (taxonomy mapping)
+- REQ-008 (multi-format coverage)
+- REQ-009 (POC scope)
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `Given a labeled evaluation set drawn from supplier sheets covering ≥3 vendors, extractor F1-meets agreed threshold vs human labels.` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+- Vendor variability in column naming expects canonical mapping (see REQ-002).
+- Legal sensitivity requires redaction rules for certain PDF regions (process, not this REQ).
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `REQ-001` → meeting)
+
+- **`MTG-2026-01-22`** (2026-01-22) — _Client Meeting 2026-01-22_
+- **`MTG-2026-04-02`** (2026-04-02) — _Client Meeting 2026-04-02_
+
+### Concerns linking here (`concern --BECAME--> REQ-001`)
+
+- **`CON-3d2b20`** — Is there… I know before we talked about the most sensitive thing from the vendor
+- **`CON-8262ce`** — Is there a timeline from your end that you expect me to give you the data by
+- **`CON-daffed`** — Can we at least use the LLM part for, instead of the OCR
+
+### Commitments (`commitment --BECAME--> REQ-001`)
+
+- **`COM-2cb319`** — we'll be primarily using a cursor with the client's data to use it to process it
+- **`COM-33b981`** — we'll probably reach out to you with some kind of data we have as well.
+- **`COM-b7a75c`** — we need to finalize how the data is being inputted, how they want the data to be
+- **`COM-d3f704`** — we'll be removing the human part, and only it'll be automatically pushed to the
+- **`COM-fa60d8`** — we'll have some sort of precise rules to see if particular attributes fits the,
+
+### Architecture canon (`REQ-001` --DECIDED_BY--> architecture)
+
+- **`ARCH-002`** — Map to industry standards instead of ALPS-specific attributes
+- **`ARCH-003`** — ML confidence scoring for attribute prediction
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+- **`DEC-e9b865`:** I think the primary, goal for the model that we're currently thinking is to get
+- **`DEC-fab886`:** Yeah, so, we are targeting that towards the end of this month, we should have th
+
+### Risks `REQ-001` participates in mitigating (`REQ-001` --MITIGATES--> risk)
+
+- **`RIS-016cf1`** — Integration dependency on Jake (PIMS schema)
+- **`RIS-11e5db`** — Measurement validity for AI effectiveness
+- **`RIS-50bc62`** — Data access delay blocking ML development
+- **`RIS-5de8fb`** — Catalog team capacity vs review volume
+- **`RIS-8083f1`** — PIMS staging schema incompatibility (P1-C pending)
+- **`RIS-816a97`** — Model selection uncertainty
+- **`RIS-d8a886`** — Insufficient training data (<200 labeled examples)
+- **`RIS-ddd0e1`** — Attribute correlation invalidates per-attribute routing
+- **`RIS-f5c786`** — Confidence threshold miscalibration
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> REQ-001`)
+
+`EPARTS-36`, `EPARTS-39`, `EPARTS-58`, `EPARTS-68`, `EPARTS-72`, `EPARTS-73`, `EPARTS-74`
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
diff --git a/requirements/parsed/REQ-002.md b/requirements/parsed/REQ-002.md
new file mode 100644
index 0000000..7e1c065
--- /dev/null
+++ b/requirements/parsed/REQ-002.md
@@ -0,0 +1,107 @@
+# REQ-002: Map extracted attributes to industry-standard taxonomy
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-002 |
+| **Category** | Functional Requirement |
+| **Priority** | P1 |
+| **Date Identified** | **2026-04-02** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `MTG-2026-04-02` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**Map extracted attributes to industry-standard taxonomy** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+- Maintain canonical attribute dictionary for industrial SKUs (valves, actuators first category).
+- Map vendor-local labels to industry vocabulary with confidence.
+- Support multi-language labels where present in source sheets.
+
+### Context
+
+| REQ | Rationale (summary) |
+|-----|----------------------|
+| **REQ-002** | Derived from SES ingest + stakeholder dialogue; prioritized **P1** for backlog ordering. |
+
+### Non-functional / ops
+- **Versioning:** taxonomy versions must be bumpable without invalidating historical trace rows.
+
+### Dependencies
+- REQ-001 (extraction)
+- REQ-003 (confidence scoring)
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `Given heterogeneous vendor schemas, mapper assigns ≥target coverage of canonical attributes without manual remap per SKU.` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+- Mis-mapping propagates to catalog—requires human review below confidence (REQ-004).
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `REQ-002` → meeting)
+
+- **`MTG-2026-04-02`** (2026-04-02) — _Client Meeting 2026-04-02_
+
+### Concerns linking here (`concern --BECAME--> REQ-002`)
+
+- **`CON-3d2b20`** — Is there… I know before we talked about the most sensitive thing from the vendor
+- **`CON-8262ce`** — Is there a timeline from your end that you expect me to give you the data by
+- **`CON-daffed`** — Can we at least use the LLM part for, instead of the OCR
+
+### Commitments (`commitment --BECAME--> REQ-002`)
+
+- **`COM-2cb319`** — we'll be primarily using a cursor with the client's data to use it to process it
+- **`COM-33b981`** — we'll probably reach out to you with some kind of data we have as well.
+- **`COM-b7a75c`** — we need to finalize how the data is being inputted, how they want the data to be
+- **`COM-d3f704`** — we'll be removing the human part, and only it'll be automatically pushed to the
+- **`COM-fa60d8`** — we'll have some sort of precise rules to see if particular attributes fits the,
+
+### Architecture canon (`REQ-002` --DECIDED_BY--> architecture)
+
+- **`ARCH-002`** — Map to industry standards instead of ALPS-specific attributes
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+- **`DEC-e9b865`:** I think the primary, goal for the model that we're currently thinking is to get
+- **`DEC-fab886`:** Yeah, so, we are targeting that towards the end of this month, we should have th
+
+### Risks `REQ-002` participates in mitigating (`REQ-002` --MITIGATES--> risk)
+
+- **`RIS-016cf1`** — Integration dependency on Jake (PIMS schema)
+- **`RIS-11e5db`** — Measurement validity for AI effectiveness
+- **`RIS-2119e6`** — Capstone timeline constraint
+- **`RIS-50bc62`** — Data access delay blocking ML development
+- **`RIS-5de8fb`** — Catalog team capacity vs review volume
+- **`RIS-603445`** — Scope creep risk
+- **`RIS-8083f1`** — PIMS staging schema incompatibility (P1-C pending)
+- **`RIS-816a97`** — Model selection uncertainty
+- **`RIS-d8a886`** — Insufficient training data (<200 labeled examples)
+- **`RIS-ddd0e1`** — Attribute correlation invalidates per-attribute routing
+- **`RIS-f5c786`** — Confidence threshold miscalibration
+- **`RIS-fc0584`** — Human review interface design not decided
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> REQ-002`)
+
+`EPARTS-36`, `EPARTS-39`, `EPARTS-58`, `EPARTS-68`, `EPARTS-73`, `EPARTS-74`
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
diff --git a/requirements/parsed/REQ-003.md b/requirements/parsed/REQ-003.md
new file mode 100644
index 0000000..b405370
--- /dev/null
+++ b/requirements/parsed/REQ-003.md
@@ -0,0 +1,107 @@
+# REQ-003: ML confidence scoring on every predicted attribute
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-003 |
+| **Category** | Quality Attribute Requirement |
+| **Priority** | P2 |
+| **Date Identified** | **2026-01-22** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `MTG-2026-01-22` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**ML confidence scoring on every predicted attribute** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+- Emit per-attribute scalar or vector confidence after model forward pass.
+- Feed scores into routing policy: auto-accept band, review band, reject band (thresholds ADR-governed).
+- Log score distributions for calibration regression tests.
+
+### Context
+
+| REQ | Rationale (summary) |
+|-----|----------------------|
+| **REQ-003** | Derived from SES ingest + stakeholder dialogue; prioritized **P2** for backlog ordering. |
+
+### Non-functional / ops
+- **Observability:** aggregate calibration metrics exportable to monitoring (REQ-007).
+
+### Dependencies
+- REQ-001 / REQ-002 upstream features
+- REQ-004 (review queue)
+- ADR-001 (threshold calibration)
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `Given batched SKU rows, every attribute prediction ships with numeric confidence usable by routing policy.` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+- Threshold mistakes are top risk class; mitigated by explicit risk records + ADR-001.
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `REQ-003` → meeting)
+
+- **`MTG-2026-01-22`** (2026-01-22) — _Client Meeting 2026-01-22_
+
+### Concerns linking here (`concern --BECAME--> REQ-003`)
+
+- **`CON-3d2b20`** — Is there… I know before we talked about the most sensitive thing from the vendor
+- **`CON-8262ce`** — Is there a timeline from your end that you expect me to give you the data by
+- **`CON-daffed`** — Can we at least use the LLM part for, instead of the OCR
+
+### Commitments (`commitment --BECAME--> REQ-003`)
+
+- **`COM-2cb319`** — we'll be primarily using a cursor with the client's data to use it to process it
+- **`COM-33b981`** — we'll probably reach out to you with some kind of data we have as well.
+- **`COM-b7a75c`** — we need to finalize how the data is being inputted, how they want the data to be
+- **`COM-d3f704`** — we'll be removing the human part, and only it'll be automatically pushed to the
+- **`COM-fa60d8`** — we'll have some sort of precise rules to see if particular attributes fits the,
+
+### Architecture canon (`REQ-003` --DECIDED_BY--> architecture)
+
+- **`ARCH-003`** — ML confidence scoring for attribute prediction
+- **`ARCH-005`** — Human-in-the-loop for all AI-generated data
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+- **`DEC-e9b865`:** I think the primary, goal for the model that we're currently thinking is to get
+- **`DEC-fab886`:** Yeah, so, we are targeting that towards the end of this month, we should have th
+
+### Risks `REQ-003` participates in mitigating (`REQ-003` --MITIGATES--> risk)
+
+- **`RIS-016cf1`** — Integration dependency on Jake (PIMS schema)
+- **`RIS-11e5db`** — Measurement validity for AI effectiveness
+- **`RIS-50bc62`** — Data access delay blocking ML development
+- **`RIS-5de8fb`** — Catalog team capacity vs review volume
+- **`RIS-8083f1`** — PIMS staging schema incompatibility (P1-C pending)
+- **`RIS-816a97`** — Model selection uncertainty
+- **`RIS-d8a886`** — Insufficient training data (<200 labeled examples)
+- **`RIS-ddd0e1`** — Attribute correlation invalidates per-attribute routing
+- **`RIS-f5c786`** — Confidence threshold miscalibration
+- **`RIS-fc0584`** — Human review interface design not decided
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> REQ-003`)
+
+`EPARTS-39`, `EPARTS-47`, `EPARTS-60`, `EPARTS-69`, `EPARTS-72`, `EPARTS-80`
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
diff --git a/requirements/parsed/REQ-004.md b/requirements/parsed/REQ-004.md
new file mode 100644
index 0000000..996122d
--- /dev/null
+++ b/requirements/parsed/REQ-004.md
@@ -0,0 +1,94 @@
+# REQ-004: Human review queue for AI-generated catalog data
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-004 |
+| **Category** | User Goal |
+| **Priority** | P1 |
+| **Date Identified** | **2026-01-22** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `MTG-2026-01-22` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**Human review queue for AI-generated catalog data** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+- Web queue lists pending predictions with source doc diff and model explanation snippet.
+- Actions: accept, edit value, reject with mandatory reason codes.
+- Accepted edits enqueue training-feedback dataset builder (phase 2—not blocking MVP read path).
+
+### Context
+
+| REQ | Rationale (summary) |
+|-----|----------------------|
+| **REQ-004** | Derived from SES ingest + stakeholder dialogue; prioritized **P1** for backlog ordering. |
+
+### Non-functional / ops
+- Target reviewer productivity ≥10 reviewed lines/min sustained (per product goals).
+
+### Dependencies
+- REQ-003 (scores)
+- REQ-005 (staging diff UX alignment)
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `Given routed low-confidence SKU fields, reviewer can accept/modify/reject with audit trace and queue drains without silent loss.` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+- Human bottlenecks if routing too conservative (see risk register linkage).
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `REQ-004` → meeting)
+
+- **`MTG-2026-01-22`** (2026-01-22) — _Client Meeting 2026-01-22_
+
+### Concerns linking here (`concern --BECAME--> REQ-004`)
+
+- _None in trace store for this slice._
+
+### Commitments (`commitment --BECAME--> REQ-004`)
+
+- **`COM-d3f704`** — we'll be removing the human part, and only it'll be automatically pushed to the
+
+### Architecture canon (`REQ-004` --DECIDED_BY--> architecture)
+
+- **`ARCH-004`** — Staging tables as Git-diff model for data review
+- **`ARCH-005`** — Human-in-the-loop for all AI-generated data
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+- _None resolved through concern bridges in ingest._
+
+### Risks `REQ-004` participates in mitigating (`REQ-004` --MITIGATES--> risk)
+
+- **`RIS-016cf1`** — Integration dependency on Jake (PIMS schema)
+- **`RIS-5de8fb`** — Catalog team capacity vs review volume
+- **`RIS-8083f1`** — PIMS staging schema incompatibility (P1-C pending)
+- **`RIS-f5c786`** — Confidence threshold miscalibration
+- **`RIS-fc0584`** — Human review interface design not decided
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> REQ-004`)
+
+`EPARTS-47`
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
diff --git a/requirements/parsed/REQ-005.md b/requirements/parsed/REQ-005.md
new file mode 100644
index 0000000..700597f
--- /dev/null
+++ b/requirements/parsed/REQ-005.md
@@ -0,0 +1,105 @@
+# REQ-005: Staging table diff model for catalog review workflow
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-005 |
+| **Category** | Functional Requirement |
+| **Priority** | P0 |
+| **Date Identified** | **2026-01-22** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `MTG-2026-01-22` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**Staging table diff model for catalog review workflow** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+- Persist proposed catalog rows into staging relation mirroring prod shape.
+- Generate row-level Git-style diffs vs prior approved snapshot per SKU.
+- Support batch approve / batch rollback.
+
+### Context
+
+| REQ | Rationale (summary) |
+|-----|----------------------|
+| **REQ-005** | Derived from SES ingest + stakeholder dialogue; prioritized **P0** for backlog ordering. |
+
+### Non-functional / ops
+_None beyond global platform NFRs._
+
+### Dependencies
+- REQ-004 (review UX)
+- Azure data plane (REQ-006/007)
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `Given sequential catalog revisions, diff engine surfaces row-level deltas with checksum-stable ordering.` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+- Schema churn from PIMS must be isolated—see ARCH-level mitigations.
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `REQ-005` → meeting)
+
+- **`MTG-2026-01-22`** (2026-01-22) — _Client Meeting 2026-01-22_
+
+### Concerns linking here (`concern --BECAME--> REQ-005`)
+
+- **`CON-3d2b20`** — Is there… I know before we talked about the most sensitive thing from the vendor
+- **`CON-8262ce`** — Is there a timeline from your end that you expect me to give you the data by
+- **`CON-daffed`** — Can we at least use the LLM part for, instead of the OCR
+
+### Commitments (`commitment --BECAME--> REQ-005`)
+
+- **`COM-2cb319`** — we'll be primarily using a cursor with the client's data to use it to process it
+- **`COM-33b981`** — we'll probably reach out to you with some kind of data we have as well.
+- **`COM-b7a75c`** — we need to finalize how the data is being inputted, how they want the data to be
+- **`COM-d3f704`** — we'll be removing the human part, and only it'll be automatically pushed to the
+- **`COM-fa60d8`** — we'll have some sort of precise rules to see if particular attributes fits the,
+
+### Architecture canon (`REQ-005` --DECIDED_BY--> architecture)
+
+- **`ARCH-004`** — Staging tables as Git-diff model for data review
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+- **`DEC-e9b865`:** I think the primary, goal for the model that we're currently thinking is to get
+- **`DEC-fab886`:** Yeah, so, we are targeting that towards the end of this month, we should have th
+
+### Risks `REQ-005` participates in mitigating (`REQ-005` --MITIGATES--> risk)
+
+- **`RIS-016cf1`** — Integration dependency on Jake (PIMS schema)
+- **`RIS-11e5db`** — Measurement validity for AI effectiveness
+- **`RIS-50bc62`** — Data access delay blocking ML development
+- **`RIS-5de8fb`** — Catalog team capacity vs review volume
+- **`RIS-8083f1`** — PIMS staging schema incompatibility (P1-C pending)
+- **`RIS-816a97`** — Model selection uncertainty
+- **`RIS-d8a886`** — Insufficient training data (<200 labeled examples)
+- **`RIS-ddd0e1`** — Attribute correlation invalidates per-attribute routing
+- **`RIS-f5c786`** — Confidence threshold miscalibration
+- **`RIS-fc0584`** — Human review interface design not decided
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> REQ-005`)
+
+`EPARTS-47`
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
diff --git a/requirements/parsed/REQ-006.md b/requirements/parsed/REQ-006.md
new file mode 100644
index 0000000..6c83c21
--- /dev/null
+++ b/requirements/parsed/REQ-006.md
@@ -0,0 +1,90 @@
+# REQ-006: Azure infrastructure with Bicep IaC
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-006 |
+| **Category** | Constraint |
+| **Priority** | P0 |
+| **Date Identified** | **2026-02-12** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `MTG-2026-02-12` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**Azure infrastructure with Bicep IaC** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+- Provision application, data, secrets, CI slots via checked-in IaC definitions.
+- Enforce repeatable environment promotion paths (sandbox → staging → prod).
+
+### Context
+
+| REQ | Rationale (summary) |
+|-----|----------------------|
+| **REQ-006** | Derived from SES ingest + stakeholder dialogue; prioritized **P0** for backlog ordering. |
+
+### Non-functional / ops
+**Compliance:** infra changes reviewable via PR with policy-as-code scanners enabled.
+
+### Dependencies
+- REQ-007 (telemetry plane)
+- Bicep module library from architecture slice
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `Given infra PR, IaC yields reproducible sandbox deploy with secret separation and smoke tests wired in CI.` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+_None surfaced in ingest for this REQ._
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `REQ-006` → meeting)
+
+- **`MTG-2026-02-12`** (2026-02-12) — _Client Meeting 2026-02-12_
+
+### Concerns linking here (`concern --BECAME--> REQ-006`)
+
+- _None in trace store for this slice._
+
+### Commitments (`commitment --BECAME--> REQ-006`)
+
+- _None in current slice._
+
+### Architecture canon (`REQ-006` --DECIDED_BY--> architecture)
+
+- **`ARCH-001`** — Use Bicep over Terraform for Azure IaC
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+- _None resolved through concern bridges in ingest._
+
+### Risks `REQ-006` participates in mitigating (`REQ-006` --MITIGATES--> risk)
+
+- **`RIS-603445`** — Scope creep risk
+- **`RIS-c2f026`** — Drift detection metrics and baselines undefined
+- **`RIS-dd2cc8`** — Azure tool constraints
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> REQ-006`)
+
+`EPARTS-66`, `EPARTS-71`, `EPARTS-81`
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
diff --git a/requirements/parsed/REQ-007.md b/requirements/parsed/REQ-007.md
new file mode 100644
index 0000000..c9a1936
--- /dev/null
+++ b/requirements/parsed/REQ-007.md
@@ -0,0 +1,90 @@
+# REQ-007: Azure Log Analytics for monitoring and observability
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-007 |
+| **Category** | Constraint |
+| **Priority** | P0 |
+| **Date Identified** | **2026-02-12** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `MTG-2026-02-12` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**Azure Log Analytics for monitoring and observability** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+- Structured logs shipped to centralized sink with correlation identifiers per ingestion job.
+- Dashboards track latency, OCR failures, routing counts, reviewer throughput.
+
+### Context
+
+| REQ | Rationale (summary) |
+|-----|----------------------|
+| **REQ-007** | Derived from SES ingest + stakeholder dialogue; prioritized **P0** for backlog ordering. |
+
+### Non-functional / ops
+_None beyond global platform NFRs._
+
+### Dependencies
+- REQ-006 (Azure tenancy)
+- OpenTelemetry exporters where applicable
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `Given running services, ingestion + pipeline SLIs observable in shared dashboard within one hop from alert rule.` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+_None surfaced in ingest for this REQ._
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `REQ-007` → meeting)
+
+- **`MTG-2026-02-12`** (2026-02-12) — _Client Meeting 2026-02-12_
+
+### Concerns linking here (`concern --BECAME--> REQ-007`)
+
+- _None in trace store for this slice._
+
+### Commitments (`commitment --BECAME--> REQ-007`)
+
+- _None in current slice._
+
+### Architecture canon (`REQ-007` --DECIDED_BY--> architecture)
+
+- **`ARCH-001`** — Use Bicep over Terraform for Azure IaC
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+- _None resolved through concern bridges in ingest._
+
+### Risks `REQ-007` participates in mitigating (`REQ-007` --MITIGATES--> risk)
+
+- **`RIS-603445`** — Scope creep risk
+- **`RIS-c2f026`** — Drift detection metrics and baselines undefined
+- **`RIS-dd2cc8`** — Azure tool constraints
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> REQ-007`)
+
+`EPARTS-66`, `EPARTS-71`, `EPARTS-81`
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
diff --git a/requirements/parsed/REQ-008.md b/requirements/parsed/REQ-008.md
new file mode 100644
index 0000000..824ece9
--- /dev/null
+++ b/requirements/parsed/REQ-008.md
@@ -0,0 +1,105 @@
+# REQ-008: Support multiple vendor document formats
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-008 |
+| **Category** | Functional Requirement |
+| **Priority** | P0 |
+| **Date Identified** | **2026-01-22** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `MTG-2026-01-22` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**Support multiple vendor document formats** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+- Dispatcher selects parser module by MIME + content sniff (PDF text vs OCR path later).
+- Reject malformed zips outright with actionable error payloads.
+
+### Context
+
+| REQ | Rationale (summary) |
+|-----|----------------------|
+| **REQ-008** | Derived from SES ingest + stakeholder dialogue; prioritized **P0** for backlog ordering. |
+
+### Non-functional / ops
+_None beyond global platform NFRs._
+
+### Dependencies
+- REQ-001 ingestion contract
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `Given enumerated MIME envelopes, ingestion rejects unsupported classes with deterministic error envelopes.` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+_None surfaced in ingest for this REQ._
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `REQ-008` → meeting)
+
+- **`MTG-2026-01-22`** (2026-01-22) — _Client Meeting 2026-01-22_
+- **`MTG-2026-02-26`** (2026-02-26) — _Client Meeting 2026-02-26_
+- **`MTG-2026-04-02`** (2026-04-02) — _Client Meeting 2026-04-02_
+
+### Concerns linking here (`concern --BECAME--> REQ-008`)
+
+- **`CON-3d2b20`** — Is there… I know before we talked about the most sensitive thing from the vendor
+- **`CON-8262ce`** — Is there a timeline from your end that you expect me to give you the data by
+- **`CON-daffed`** — Can we at least use the LLM part for, instead of the OCR
+
+### Commitments (`commitment --BECAME--> REQ-008`)
+
+- **`COM-2cb319`** — we'll be primarily using a cursor with the client's data to use it to process it
+- **`COM-33b981`** — we'll probably reach out to you with some kind of data we have as well.
+- **`COM-b7a75c`** — we need to finalize how the data is being inputted, how they want the data to be
+- **`COM-d3f704`** — we'll be removing the human part, and only it'll be automatically pushed to the
+- **`COM-fa60d8`** — we'll have some sort of precise rules to see if particular attributes fits the,
+
+### Architecture canon (`REQ-008` --DECIDED_BY--> architecture)
+
+- **`ARCH-002`** — Map to industry standards instead of ALPS-specific attributes
+- **`ARCH-003`** — ML confidence scoring for attribute prediction
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+- **`DEC-e9b865`:** I think the primary, goal for the model that we're currently thinking is to get
+- **`DEC-fab886`:** Yeah, so, we are targeting that towards the end of this month, we should have th
+
+### Risks `REQ-008` participates in mitigating (`REQ-008` --MITIGATES--> risk)
+
+- **`RIS-016cf1`** — Integration dependency on Jake (PIMS schema)
+- **`RIS-11e5db`** — Measurement validity for AI effectiveness
+- **`RIS-50bc62`** — Data access delay blocking ML development
+- **`RIS-5de8fb`** — Catalog team capacity vs review volume
+- **`RIS-8083f1`** — PIMS staging schema incompatibility (P1-C pending)
+- **`RIS-816a97`** — Model selection uncertainty
+- **`RIS-d8a886`** — Insufficient training data (<200 labeled examples)
+- **`RIS-ddd0e1`** — Attribute correlation invalidates per-attribute routing
+- **`RIS-f5c786`** — Confidence threshold miscalibration
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> REQ-008`)
+
+`EPARTS-72`
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
diff --git a/requirements/parsed/REQ-009.md b/requirements/parsed/REQ-009.md
new file mode 100644
index 0000000..763f037
--- /dev/null
+++ b/requirements/parsed/REQ-009.md
@@ -0,0 +1,106 @@
+# REQ-009: POC demonstrating end-to-end attribute extraction
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-009 |
+| **Category** | Milestone |
+| **Priority** | P1 |
+| **Date Identified** | **2026-01-22** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `MTG-2026-01-22` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**POC demonstrating end-to-end attribute extraction** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+- Scripted golden-path demo ingest → predict → queue → staged publish within latency budget.
+- Success criteria enumerated in Sprint review rubric—not production cutover.
+
+### Context
+
+| REQ | Rationale (summary) |
+|-----|----------------------|
+| **REQ-009** | Derived from SES ingest + stakeholder dialogue; prioritized **P1** for backlog ordering. |
+
+### Non-functional / ops
+_None beyond global platform NFRs._
+
+### Dependencies
+- REQ-001, REQ-002, REQ-003 core loop
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `Given scripted fixture corpus, POC path completes ingestion→prediction→staging handoff ≤ agreed wall-clock SLA.` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+_None surfaced in ingest for this REQ._
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `REQ-009` → meeting)
+
+- **`MTG-2026-01-22`** (2026-01-22) — _Client Meeting 2026-01-22_
+- **`MTG-2026-04-02`** (2026-04-02) — _Client Meeting 2026-04-02_
+
+### Concerns linking here (`concern --BECAME--> REQ-009`)
+
+- **`CON-3d2b20`** — Is there… I know before we talked about the most sensitive thing from the vendor
+- **`CON-8262ce`** — Is there a timeline from your end that you expect me to give you the data by
+- **`CON-daffed`** — Can we at least use the LLM part for, instead of the OCR
+
+### Commitments (`commitment --BECAME--> REQ-009`)
+
+- **`COM-2cb319`** — we'll be primarily using a cursor with the client's data to use it to process it
+- **`COM-33b981`** — we'll probably reach out to you with some kind of data we have as well.
+- **`COM-b7a75c`** — we need to finalize how the data is being inputted, how they want the data to be
+- **`COM-d3f704`** — we'll be removing the human part, and only it'll be automatically pushed to the
+- **`COM-fa60d8`** — we'll have some sort of precise rules to see if particular attributes fits the,
+
+### Architecture canon (`REQ-009` --DECIDED_BY--> architecture)
+
+- **`ARCH-003`** — ML confidence scoring for attribute prediction
+- **`ARCH-004`** — Staging tables as Git-diff model for data review
+- **`ARCH-005`** — Human-in-the-loop for all AI-generated data
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+- **`DEC-e9b865`:** I think the primary, goal for the model that we're currently thinking is to get
+- **`DEC-fab886`:** Yeah, so, we are targeting that towards the end of this month, we should have th
+
+### Risks `REQ-009` participates in mitigating (`REQ-009` --MITIGATES--> risk)
+
+- **`RIS-016cf1`** — Integration dependency on Jake (PIMS schema)
+- **`RIS-11e5db`** — Measurement validity for AI effectiveness
+- **`RIS-50bc62`** — Data access delay blocking ML development
+- **`RIS-5de8fb`** — Catalog team capacity vs review volume
+- **`RIS-8083f1`** — PIMS staging schema incompatibility (P1-C pending)
+- **`RIS-816a97`** — Model selection uncertainty
+- **`RIS-d8a886`** — Insufficient training data (<200 labeled examples)
+- **`RIS-ddd0e1`** — Attribute correlation invalidates per-attribute routing
+- **`RIS-f5c786`** — Confidence threshold miscalibration
+- **`RIS-fc0584`** — Human review interface design not decided
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> REQ-009`)
+
+`EPARTS-14`, `EPARTS-47`, `EPARTS-60`, `EPARTS-66`, `EPARTS-69`, `EPARTS-74`, `EPARTS-80`, `EPARTS-81`
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
diff --git a/requirements/parsed/REQ-010.md b/requirements/parsed/REQ-010.md
new file mode 100644
index 0000000..35e6922
--- /dev/null
+++ b/requirements/parsed/REQ-010.md
@@ -0,0 +1,94 @@
+# REQ-010: Statement of Work signed by end of April
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-010 |
+| **Category** | Constraint |
+| **Priority** | P1 |
+| **Date Identified** | **2026-04-02** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `MTG-2026-04-02` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**Statement of Work signed by end of April** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+- Executable SoW milestones mapped to EPARTS backlog with explicit sign-off checkpoints.
+- Document client feedback loop SLA for redlines.
+
+### Context
+
+| REQ | Rationale (summary) |
+|-----|----------------------|
+| **REQ-010** | Derived from SES ingest + stakeholder dialogue; prioritized **P1** for backlog ordering. |
+
+### Non-functional / ops
+_None beyond global platform NFRs._
+
+### Dependencies
+- PM artifacts + legal review checklist
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `Given legal template, executable SoW exists with annotated signatures milestones before April close-out window.` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+_None surfaced in ingest for this REQ._
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `REQ-010` → meeting)
+
+- **`MTG-2026-04-02`** (2026-04-02) — _Client Meeting 2026-04-02_
+
+### Concerns linking here (`concern --BECAME--> REQ-010`)
+
+- **`CON-8262ce`** — Is there a timeline from your end that you expect me to give you the data by
+- **`CON-8ec1cb`** — How does your current, the code checking process look like
+
+### Commitments (`commitment --BECAME--> REQ-010`)
+
+- **`COM-2cb319`** — we'll be primarily using a cursor with the client's data to use it to process it
+- **`COM-b7a75c`** — we need to finalize how the data is being inputted, how they want the data to be
+- **`COM-dc8c00`** — we'll do that as well. but happy to focus on… on the risk part.
+
+### Architecture canon (`REQ-010` --DECIDED_BY--> architecture)
+
+- _None in trace store for this slice._
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+- **`DEC-afa506`:** You'd like some metrics to see what the overall process on which it's improved.
+- **`DEC-fab886`:** Yeah, so, we are targeting that towards the end of this month, we should have th
+
+### Risks `REQ-010` participates in mitigating (`REQ-010` --MITIGATES--> risk)
+
+- **`RIS-2119e6`** — Capstone timeline constraint
+- **`RIS-603445`** — Scope creep risk
+- **`RIS-8706ae`** — Knowledge loss from manual processes
+- **`RIS-fc0584`** — Human review interface design not decided
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> REQ-010`)
+
+`EPARTS-15`, `EPARTS-34`, `EPARTS-37`, `EPARTS-38`, `EPARTS-42`, `EPARTS-43`, `EPARTS-46`, `EPARTS-48`, `EPARTS-50`, `EPARTS-52`, `EPARTS-59`, `EPARTS-66`, `EPARTS-74`, `EPARTS-76`, `EPARTS-81`
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
diff --git a/requirements/parsed/REQ-011.md b/requirements/parsed/REQ-011.md
new file mode 100644
index 0000000..a7a3eaf
--- /dev/null
+++ b/requirements/parsed/REQ-011.md
@@ -0,0 +1,89 @@
+# REQ-011: Team AI usage policy and best practices guide
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-011 |
+| **Category** | Process Requirement |
+| **Priority** | P0 |
+| **Date Identified** | **2026-04-16** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `MTG-2026-04-16` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**Team AI usage policy and best practices guide** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+- Covers model usage tiers, forbidden data classes, escalation for suspected secrets in prompts.
+- Must be adopted by Studio + client engineering touchpoints.
+
+### Context
+
+| REQ | Rationale (summary) |
+|-----|----------------------|
+| **REQ-011** | Derived from SES ingest + stakeholder dialogue; prioritized **P0** for backlog ordering. |
+
+### Non-functional / ops
+_None beyond global platform NFRs._
+
+### Dependencies
+- REQ-010 governance threads
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `Given onboarding checklist, engineers acknowledge AI governance doc + violation reporting path quarterly.` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+_None surfaced in ingest for this REQ._
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `REQ-011` → meeting)
+
+- **`MTG-2026-04-16`** (2026-04-16) — _Client Meeting 2026-04-16_
+
+### Concerns linking here (`concern --BECAME--> REQ-011`)
+
+- **`CON-8ec1cb`** — How does your current, the code checking process look like
+
+### Commitments (`commitment --BECAME--> REQ-011`)
+
+- **`COM-2cb319`** — we'll be primarily using a cursor with the client's data to use it to process it
+- **`COM-dc8c00`** — we'll do that as well. but happy to focus on… on the risk part.
+
+### Architecture canon (`REQ-011` --DECIDED_BY--> architecture)
+
+- **`ARCH-006`** — Agent-Augmented Iterative SDLC (bespoke)
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+- **`DEC-afa506`:** You'd like some metrics to see what the overall process on which it's improved.
+
+### Risks `REQ-011` participates in mitigating (`REQ-011` --MITIGATES--> risk)
+
+- **`RIS-603445`** — Scope creep risk
+- **`RIS-8706ae`** — Knowledge loss from manual processes
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> REQ-011`)
+
+`EPARTS-35`, `EPARTS-37`, `EPARTS-38`, `EPARTS-43`, `EPARTS-46`, `EPARTS-51`, `EPARTS-52`, `EPARTS-53`, `EPARTS-54`, `EPARTS-57`, `EPARTS-77`, `EPARTS-78`, `EPARTS-81`
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
diff --git a/requirements/parsed/REQ-012.md b/requirements/parsed/REQ-012.md
new file mode 100644
index 0000000..3faadbe
--- /dev/null
+++ b/requirements/parsed/REQ-012.md
@@ -0,0 +1,96 @@
+# REQ-012: Formal ADRs for all architecture decisions
+
+| Field | Value |
+|-------|-------|
+| **ID** | REQ-012 |
+| **Category** | Process Requirement |
+| **Priority** | P0 |
+| **Date Identified** | **2026-01-22** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `MTG-2026-01-22` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**Formal ADRs for all architecture decisions** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+- Each materially significant architectural choice produces ADR Markdown with status, consequences, rollback.
+- ADRs referenced from SES trace graph and PR templates.
+
+### Context
+
+| REQ | Rationale (summary) |
+|-----|----------------------|
+| **REQ-012** | Derived from SES ingest + stakeholder dialogue; prioritized **P0** for backlog ordering. |
+
+### Non-functional / ops
+_None beyond global platform NFRs._
+
+### Dependencies
+- Architecture pipeline + GitHub MCP
+- Companion docs under `docs/adr/` where overlapping
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `Given ARCH decision events, numbered ADRs exist linked from SES trace graph.` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+_None surfaced in ingest for this REQ._
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `REQ-012` → meeting)
+
+- **`MTG-2026-01-22`** (2026-01-22) — _Client Meeting 2026-01-22_
+- **`MTG-2026-02-12`** (2026-02-12) — _Client Meeting 2026-02-12_
+- **`MTG-2026-04-02`** (2026-04-02) — _Client Meeting 2026-04-02_
+
+### Concerns linking here (`concern --BECAME--> REQ-012`)
+
+- _None in trace store for this slice._
+
+### Commitments (`commitment --BECAME--> REQ-012`)
+
+- _None in current slice._
+
+### Architecture canon (`REQ-012` --DECIDED_BY--> architecture)
+
+- **`ARCH-001`** — Use Bicep over Terraform for Azure IaC
+- **`ARCH-002`** — Map to industry standards instead of ALPS-specific attributes
+- **`ARCH-003`** — ML confidence scoring for attribute prediction
+- **`ARCH-004`** — Staging tables as Git-diff model for data review
+- **`ARCH-005`** — Human-in-the-loop for all AI-generated data
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+- _None resolved through concern bridges in ingest._
+
+### Risks `REQ-012` participates in mitigating (`REQ-012` --MITIGATES--> risk)
+
+- **`RIS-016cf1`** — Integration dependency on Jake (PIMS schema)
+- **`RIS-dd2cc8`** — Azure tool constraints
+- **`RIS-fc0584`** — Human review interface design not decided
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> REQ-012`)
+
+`EPARTS-40`, `EPARTS-41`, `EPARTS-49`, `EPARTS-55`, `EPARTS-62`, `EPARTS-63`, `EPARTS-64`, `EPARTS-70`, `EPARTS-75`, `EPARTS-79`, `EPARTS-80`, `EPARTS-81`
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
diff --git a/requirements/parsed/sync_req_docs.py b/requirements/parsed/sync_req_docs.py
new file mode 100644
index 0000000..39a5f08
--- /dev/null
+++ b/requirements/parsed/sync_req_docs.py
@@ -0,0 +1,388 @@
+#!/usr/bin/env python3
+"""Regenerate requirements/parsed/REQ-*.md from dashboard/traceability_data.json + local elaboration text."""
+from __future__ import annotations
+
+import json
+import re
+from collections import defaultdict
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[2]
+DATA = ROOT / "dashboard" / "traceability_data.json"
+OUT_DIR = ROOT / "requirements" / "parsed"
+
+# Priority carried forward from prior human edit (classifier output)
+PRIORITY = {
+ "REQ-001": "P0",
+ "REQ-002": "P1",
+ "REQ-003": "P2",
+ "REQ-004": "P1",
+ "REQ-005": "P0",
+ "REQ-006": "P0",
+ "REQ-007": "P0",
+ "REQ-008": "P0",
+ "REQ-009": "P1",
+ "REQ-010": "P1",
+ "REQ-011": "P0",
+ "REQ-012": "P0",
+}
+
+CATEGORIES = {
+ "REQ-001": "Functional Requirement",
+ "REQ-002": "Functional Requirement",
+ "REQ-003": "Quality Attribute Requirement",
+ "REQ-004": "User Goal",
+ "REQ-005": "Functional Requirement",
+ "REQ-006": "Constraint",
+ "REQ-007": "Constraint",
+ "REQ-008": "Functional Requirement",
+ "REQ-009": "Milestone",
+ "REQ-010": "Constraint",
+ "REQ-011": "Process Requirement",
+ "REQ-012": "Process Requirement",
+}
+
+ELABORATION: dict[str, dict[str, str]] = {}
+# keyed sections: detailed_behavior, nf, concerns_text, deps
+
+ELABORATION["REQ-001"] = {
+ "detailed_behavior": """- Ingest CSV, `.xlsx`, and text-extractable PDF vendor sheets supplied by distributors.
+- Parse tabular layouts with schema hints (headers, SKU column, specification blocks).
+- Persist extracted attribute candidates with machine confidence and source document span references.
+- Expose deterministic JSON contract for downstream mapping and review agents.""",
+ "nf": """- **Throughput:** scalable batch pipeline (not synchronous chat per row at scale).
+- **Audit:** every prediction ties back to ingestion record + model version.""",
+ "concerns_text": "- Vendor variability in column naming expects canonical mapping (see REQ-002).\n- Legal sensitivity requires redaction rules for certain PDF regions (process, not this REQ).",
+ "deps": "- REQ-002 (taxonomy mapping)\n- REQ-008 (multi-format coverage)\n- REQ-009 (POC scope)",
+}
+ELABORATION["REQ-002"] = {
+ "detailed_behavior": """- Maintain canonical attribute dictionary for industrial SKUs (valves, actuators first category).
+- Map vendor-local labels to industry vocabulary with confidence.
+- Support multi-language labels where present in source sheets.""",
+ "nf": """- **Versioning:** taxonomy versions must be bumpable without invalidating historical trace rows.""",
+ "concerns_text": "- Mis-mapping propagates to catalog—requires human review below confidence (REQ-004).",
+ "deps": "- REQ-001 (extraction)\n- REQ-003 (confidence scoring)",
+}
+ELABORATION["REQ-003"] = {
+ "detailed_behavior": """- Emit per-attribute scalar or vector confidence after model forward pass.
+- Feed scores into routing policy: auto-accept band, review band, reject band (thresholds ADR-governed).
+- Log score distributions for calibration regression tests.""",
+ "nf": """- **Observability:** aggregate calibration metrics exportable to monitoring (REQ-007).""",
+ "concerns_text": "- Threshold mistakes are top risk class; mitigated by explicit risk records + ADR-001.",
+ "deps": "- REQ-001 / REQ-002 upstream features\n- REQ-004 (review queue)\n- ADR-001 (threshold calibration)",
+}
+ELABORATION["REQ-004"] = {
+ "detailed_behavior": """- Web queue lists pending predictions with source doc diff and model explanation snippet.
+- Actions: accept, edit value, reject with mandatory reason codes.
+- Accepted edits enqueue training-feedback dataset builder (phase 2—not blocking MVP read path).""",
+ "nf": """- Target reviewer productivity ≥10 reviewed lines/min sustained (per product goals).""",
+ "concerns_text": "- Human bottlenecks if routing too conservative (see risk register linkage).",
+ "deps": "- REQ-003 (scores)\n- REQ-005 (staging diff UX alignment)",
+}
+ELABORATION["REQ-005"] = {
+ "detailed_behavior": """- Persist proposed catalog rows into staging relation mirroring prod shape.
+- Generate row-level Git-style diffs vs prior approved snapshot per SKU.
+- Support batch approve / batch rollback.""",
+ "nf": "",
+ "concerns_text": "- Schema churn from PIMS must be isolated—see ARCH-level mitigations.",
+ "deps": "- REQ-004 (review UX)\n- Azure data plane (REQ-006/007)",
+}
+ELABORATION["REQ-006"] = {
+ "detailed_behavior": """- Provision application, data, secrets, CI slots via checked-in IaC definitions.
+- Enforce repeatable environment promotion paths (sandbox → staging → prod).""",
+ "nf": "**Compliance:** infra changes reviewable via PR with policy-as-code scanners enabled.",
+ "concerns_text": "",
+ "deps": "- REQ-007 (telemetry plane)\n- Bicep module library from architecture slice",
+}
+ELABORATION["REQ-007"] = {
+ "detailed_behavior": """- Structured logs shipped to centralized sink with correlation identifiers per ingestion job.
+- Dashboards track latency, OCR failures, routing counts, reviewer throughput.""",
+ "nf": "",
+ "concerns_text": "",
+ "deps": "- REQ-006 (Azure tenancy)\n- OpenTelemetry exporters where applicable",
+}
+ELABORATION["REQ-008"] = {
+ "detailed_behavior": """- Dispatcher selects parser module by MIME + content sniff (PDF text vs OCR path later).
+- Reject malformed zips outright with actionable error payloads.""",
+ "nf": "",
+ "concerns_text": "",
+ "deps": "- REQ-001 ingestion contract",
+}
+ELABORATION["REQ-009"] = {
+ "detailed_behavior": """- Scripted golden-path demo ingest → predict → queue → staged publish within latency budget.
+- Success criteria enumerated in Sprint review rubric—not production cutover.""",
+ "nf": "",
+ "concerns_text": "",
+ "deps": "- REQ-001, REQ-002, REQ-003 core loop",
+}
+ELABORATION["REQ-010"] = {
+ "detailed_behavior": """- Executable SoW milestones mapped to EPARTS backlog with explicit sign-off checkpoints.
+- Document client feedback loop SLA for redlines.""",
+ "nf": "",
+ "concerns_text": "",
+ "deps": "- PM artifacts + legal review checklist",
+}
+ELABORATION["REQ-011"] = {
+ "detailed_behavior": """- Covers model usage tiers, forbidden data classes, escalation for suspected secrets in prompts.
+- Must be adopted by Studio + client engineering touchpoints.""",
+ "nf": "",
+ "concerns_text": "",
+ "deps": "- REQ-010 governance threads",
+}
+ELABORATION["REQ-012"] = {
+ "detailed_behavior": """- Each materially significant architectural choice produces ADR Markdown with status, consequences, rollback.
+- ADRs referenced from SES trace graph and PR templates.""",
+ "nf": "",
+ "concerns_text": "",
+ "deps": "- Architecture pipeline + GitHub MCP\n- Companion docs under `docs/adr/` where overlapping",
+}
+
+
+def mtg_dates_from_id(mid: str) -> str | None:
+ if not mid.startswith("MTG-"):
+ return None
+ m = re.match(r"^MTG-(\d{4})-(\d{2})-(\d{2})$", mid)
+ if not m:
+ return None
+ return f"{m.group(1)}-{m.group(2)}-{m.group(3)}"
+
+
+def load_bundle():
+ raw = json.loads(DATA.read_text())
+ nodes = {n["id"]: n for n in raw["nodes"]}
+ edges = raw["edges"]
+ return nodes, edges
+
+
+def gather_links(rid: str, nodes: dict, edges: list) -> dict[str, object]:
+ ins = defaultdict(list)
+ outs = defaultdict(list)
+ for e in edges:
+ if e["target"] == rid:
+ ins[e["type"]].append(e)
+ if e["source"] == rid:
+ outs[e["type"]].append(e)
+
+ mtgs_o = sorted({e["target"] for e in outs.get("RAISED_IN", []) if e["target_type"] == "meeting"})
+ arch_o = sorted({e["target"] for e in outs.get("DECIDED_BY", []) if e["target_type"] == "architecture"})
+ risks_o = sorted({e["target"] for e in outs.get("MITIGATES", []) if e["target_type"] == "risk"})
+ jira_i = sorted({e["source"] for e in ins.get("IMPLEMENTS", []) if e["source_type"] == "jira_ticket"})
+ concerns_i = sorted({e["source"] for e in ins.get("BECAME", []) if e["source_type"] == "concern"})
+ commits_i = sorted({e["source"] for e in ins.get("BECAME", []) if e["source_type"] == "commitment"})
+
+ mtg_dates: list[str] = []
+ display_mtgs: list[tuple[str, str, str]] = []
+
+ def add_meeting(mid: str) -> None:
+ d = nodes.get(mid, {}).get("meeting") or mtg_dates_from_id(mid)
+ if not d:
+ return
+ mtg_dates.append(d)
+ title = nodes.get(mid, {}).get("title") or mid
+ display_mtgs.append((mid, d, title))
+
+ for mid in mtgs_o:
+ add_meeting(mid)
+
+ # Architecture-derived meetings (e.g. REQ linked only via ARCH → RAISED_IN → meeting).
+ for aid in arch_o:
+ for e in edges:
+ if e["source"] != aid:
+ continue
+ if e["type"] != "RAISED_IN" or e["target_type"] != "meeting":
+ continue
+ add_meeting(e["target"])
+
+ by_mid = {mid: (mid, d, title) for mid, d, title in display_mtgs}
+ display_mtgs = sorted(by_mid.values(), key=lambda x: x[1])
+ earliest = min(mtg_dates) if mtg_dates else None
+
+ # decisions via concerns
+ decisions: list[tuple[str, str]] = []
+ for cid in concerns_i:
+ for e in edges:
+ if e["source"] == cid and e["target_type"] == "decision" and e["type"] == "BECAME":
+ did = e["target"]
+ title = nodes.get(did, {}).get("title", "").strip()
+ decisions.append((did, title[:120] + ("…" if len(title) > 120 else "")))
+
+ decisions = sorted(set(decisions))
+
+ return {
+ "meetings": display_mtgs,
+ "meeting_dates": mtg_dates,
+ "date_identified": earliest,
+ "architectures": arch_o,
+ "risks": risks_o,
+ "jira": jira_i,
+ "concerns": concerns_i,
+ "commitments": commits_i,
+ "decisions": decisions,
+ "concern_titles": [(c, nodes.get(c, {}).get("title", c)) for c in concerns_i],
+ "risk_titles": [(r, nodes.get(r, {}).get("title", r)) for r in risks_o],
+ "arch_titles": [(a, nodes.get(a, {}).get("title", a)) for a in arch_o],
+ }
+
+
+def fmt_list_md(label: str, rows: list[tuple[str, str]], maxn: int = 12) -> str:
+ if not rows:
+ return f"- _None in trace store for this slice._"
+ lines = []
+ for k, v in rows[:maxn]:
+ if v and v != k:
+ lines.append(f"- **`{k}`** — {v}")
+ else:
+ lines.append(f"- **`{k}`**")
+ if len(rows) > maxn:
+ lines.append(f"- _… {len(rows) - maxn} additional record(s) in trace export (`traceability_data.json`)._")
+ return "\n".join(lines)
+
+
+def fmt_meetings(rows: list[tuple[str, str, str]]) -> str:
+ if not rows:
+ return "- _No `RAISED_IN` meeting edge; see architecture-derived meetings if listed._"
+ out = []
+ for mid, d, title in rows:
+ out.append(f"- **`{mid}`** ({d}) — _{title}_")
+ return "\n".join(out)
+
+
+def fmt_jira(ids: list[str]) -> str:
+ if not ids:
+ return "- _No `IMPLEMENTS` Jira rows ingested for this requirement._"
+ return ", ".join(f"`{j}`" for j in ids)
+
+
+def build_markdown(rid: str, title: str, nodes: dict, edges: list) -> str:
+ el = ELABORATION.get(rid, {})
+ g = gather_links(rid, nodes, edges)
+ date = g["date_identified"] or "TBD"
+ priority = PRIORITY[rid]
+ category = CATEGORIES[rid]
+
+ primary_mtg = g["meetings"][0] if g["meetings"] else None
+ source_meeting = primary_mtg[0] if primary_mtg else "TBD"
+
+ ac = {
+ "REQ-001": "Given a labeled evaluation set drawn from supplier sheets covering ≥3 vendors, extractor F1-meets agreed threshold vs human labels.",
+ "REQ-002": "Given heterogeneous vendor schemas, mapper assigns ≥target coverage of canonical attributes without manual remap per SKU.",
+ "REQ-003": "Given batched SKU rows, every attribute prediction ships with numeric confidence usable by routing policy.",
+ "REQ-004": "Given routed low-confidence SKU fields, reviewer can accept/modify/reject with audit trace and queue drains without silent loss.",
+ "REQ-005": "Given sequential catalog revisions, diff engine surfaces row-level deltas with checksum-stable ordering.",
+ "REQ-006": "Given infra PR, IaC yields reproducible sandbox deploy with secret separation and smoke tests wired in CI.",
+ "REQ-007": "Given running services, ingestion + pipeline SLIs observable in shared dashboard within one hop from alert rule.",
+ "REQ-008": "Given enumerated MIME envelopes, ingestion rejects unsupported classes with deterministic error envelopes.",
+ "REQ-009": "Given scripted fixture corpus, POC path completes ingestion→prediction→staging handoff ≤ agreed wall-clock SLA.",
+ "REQ-010": "Given legal template, executable SoW exists with annotated signatures milestones before April close-out window.",
+ "REQ-011": "Given onboarding checklist, engineers acknowledge AI governance doc + violation reporting path quarterly.",
+ "REQ-012": "Given ARCH decision events, numbered ADRs exist linked from SES trace graph.",
+ }.get(rid, "Detailed acceptance scripted in QA matrix.")
+
+ rationale = (
+ "| REQ | Rationale (summary) |\n|-----|----------------------|\n"
+ + f"| **{rid}** | Derived from SES ingest + stakeholder dialogue; prioritized **{priority}** for backlog ordering. |\n"
+ )
+
+ nf_block = "### Non-functional / ops\n" + (el["nf"] if el.get("nf") else "_None beyond global platform NFRs._")
+
+ deps_block = "### Dependencies\n" + (el["deps"] if el.get("deps") else "_See trace graph for upstream artifacts._")
+
+ md = f"""# {rid}: {title}
+
+| Field | Value |
+|-------|-------|
+| **ID** | {rid} |
+| **Category** | {category} |
+| **Priority** | {priority} |
+| **Date Identified** | **{date}** (from client meeting ingest / `SharedMemory`-aligned `RAISED_IN` edges; **not** file commit stamp) |
+| **Source Meeting (primary)** | `{source_meeting}` |
+| **Evidence** | `dashboard/traceability_data.json` |
+| **Status** | draft |
+
+## Requirement statement
+
+**{title}** — binds engineering + ML teams to measurable delivery for the eParts catalog program.
+
+## Expanded description
+
+### Behavior
+
+{el.get('detailed_behavior') or '(See requirement statement.)'}
+
+### Context
+
+{rationale.strip()}
+
+{nf_block}
+
+{deps_block}
+
+## Acceptance criteria
+
+1. **Traceability completeness:** Requirement row participates in SES graph with enumerated meetings, architectures, risks, tickets below.
+2. **Delivery:** Implementation satisfies scripted acceptance `{ac}` scoped to POC unless noted otherwise.
+
+## Open questions / concerns
+
+{el.get('concerns_text') or '_None surfaced in ingest for this REQ._'}
+
+## Traceability (populated from SES trace ingest)
+
+_Link types follow SES naming (`RAISED_IN`, `BECAME`, `DECIDED_BY`, `MITIGATES`, `IMPLEMENTS`)._
+
+### Meetings (`RAISED_IN` from `{rid}` → meeting)
+
+{fmt_meetings(g['meetings'])}
+
+### Concerns linking here (`concern --BECAME--> {rid}`)
+
+{fmt_list_md('', g['concern_titles'])}
+
+### Commitments (`commitment --BECAME--> {rid}`)
+
+{fmt_list_md('', [(c, nodes.get(c, {}).get('title','')) for c in g['commitments']]) if g['commitments'] else '- _None in current slice._'}
+
+### Architecture canon (`{rid}` --DECIDED_BY--> architecture)
+
+{fmt_list_md('', g['arch_titles'])}
+
+### Decisions surfaced via bridging concerns (`concern --BECAME--> decision`)
+
+{(chr(10).join(f'- **`{did}`:** {tit if tit else "_(see trace store)_"}' for did, tit in g['decisions']) if g['decisions'] else '- _None resolved through concern bridges in ingest._')}
+
+### Risks `{rid}` participates in mitigating (`{rid}` --MITIGATES--> risk)
+
+{fmt_list_md('', g['risk_titles'])}
+
+### Jira implementation coverage (`jira_ticket --IMPLEMENTS--> {rid}`)
+
+{fmt_jira(g['jira'])}
+
+### Cross-links
+
+| Destination | Repo / dashboard pointer |
+|------------|--------------------------|
+| Trace graph explorer | [`dashboard/intelligence.html`](../../dashboard/intelligence.html) (Traceability tab) |
+| Traceability storyboard | [`dashboard/traceability_story.html`](../../dashboard/traceability_story.html) |
+| ADR corpus (partial overlap) | [`docs/adr/`](../../docs/adr/) |
+
+---
+
+_Requirement body enriched for Studio documentation. Traceability bullets generated from **`dashboard/traceability_data.json`**; regenerate via `python3 requirements/parsed/sync_req_docs.py`. Meeting dates prefer node `meeting` field._
+"""
+ return md
+
+
+def main():
+ nodes, edges = load_bundle()
+ reqs = [(n["id"], n["title"]) for n in nodes.values() if n["type"] == "requirement"]
+ reqs.sort()
+ for rid, title in reqs:
+ md = build_markdown(rid, title, nodes, edges)
+ out = OUT_DIR / f"{rid}.md"
+ out.write_text(md, encoding="utf-8")
+ print(out)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/show_ses_demo.sh b/scripts/show_ses_demo.sh
new file mode 100755
index 0000000..fa80dae
--- /dev/null
+++ b/scripts/show_ses_demo.sh
@@ -0,0 +1,187 @@
+#!/usr/bin/env bash
+# -----------------------------------------------------------------------------
+# eParts SES — High-level pipeline demo for teammates (clone → run)
+#
+# What it does:
+# 1. Verifies Python 3 + installs dependencies if needed (first run)
+# 2. Reminds you about .env for live Jira/GitHub/LLM (optional)
+# 3. Prints a short narrative of the Requirements pipeline
+# 4. Runs demo.py — prefers examples/demo_client_review.transcript.vtt, else transcripts/*.vtt (or SES_DEMO_VTT)
+# 5. Optionally opens local HTML dashboards in your browser (macOS)
+#
+# Usage:
+# chmod +x scripts/show_ses_demo.sh
+# ./scripts/show_ses_demo.sh # interactive (press ENTER once)
+# ./scripts/show_ses_demo.sh --auto # non-interactive (good for screen record)
+# ./scripts/show_ses_demo.sh --step # press ENTER after each agent (live talk track)
+#
+# Env:
+# SES_DEMO_AUTO=1 same as --auto (skip ENTER prompt)
+# SES_DEMO_STEP=1 same as --step (Enter between agents)
+# SES_DEMO_OPEN_DASHBOARDS=0 skip opening browser tabs after demo
+# SES_DEMO_VTT=/abs/path/file.transcript.vtt force which transcript
+# PYTHON override python binary (default: python3)
+
+set -euo pipefail
+
+ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "$ROOT"
+
+PYTHON="${PYTHON:-python3}"
+AUTO_FLAG=0
+STEP_FLAG=0
+SKIP_OPEN=0
+
+for arg in "$@"; do
+ case "$arg" in
+ --auto) AUTO_FLAG=1 ;;
+ --step) STEP_FLAG=1 ;;
+ --no-open|--skip-dashboards) SKIP_OPEN=1 ;;
+ -h|--help)
+ grep '^#' "$ROOT/scripts/show_ses_demo.sh" | head -30 | sed 's/^# \{0,1\}//'
+ exit 0
+ ;;
+ esac
+done
+
+if [[ "$AUTO_FLAG" -eq 1 ]] || [[ "${SES_DEMO_AUTO:-}" =~ ^(1|true|yes)$ ]]; then
+ export SES_DEMO_AUTO=1
+fi
+
+if [[ "$STEP_FLAG" -eq 1 ]] || [[ "${SES_DEMO_STEP:-}" =~ ^(1|true|yes)$ ]]; then
+ export SES_DEMO_STEP=1
+fi
+
+BLUE='\033[94m'
+CYAN='\033[96m'
+GREEN='\033[92m'
+YELLOW='\033[93m'
+DIM='\033[2m'
+RESET='\033[0m'
+BOLD='\033[1m'
+
+echo ""
+echo -e "${CYAN}${BOLD}eParts SES — Pipeline demo (Requirements workstream)${RESET}"
+echo -e "${DIM}Repo: ${ROOT}${RESET}"
+echo ""
+
+need_python() {
+ if ! command -v "$PYTHON" &>/dev/null; then
+ echo -e "${YELLOW}Need Python 3.12+ on PATH as '${PYTHON}'. Install from python.org or use pyenv/uv.${RESET}" >&2
+ exit 1
+ fi
+}
+
+ensure_deps() {
+ if "$PYTHON" -c "import fastapi, chromadb" 2>/dev/null; then
+ echo -e "${GREEN}Python deps OK${RESET}"
+ return 0
+ fi
+ echo -e "${YELLOW}First run: installing dependencies from requirements.txt …${RESET}"
+ "$PYTHON" -m pip install -q -r "$ROOT/requirements.txt"
+ echo -e "${GREEN}Dependencies installed.${RESET}"
+}
+
+ensure_transcript() {
+ if [[ -n "${SES_DEMO_VTT:-}" ]] && [[ ! -f "$SES_DEMO_VTT" ]]; then
+ echo -e "${YELLOW}SES_DEMO_VTT file not found: ${SES_DEMO_VTT}${RESET}" >&2
+ exit 1
+ fi
+ if [[ -n "${SES_DEMO_VTT:-}" ]] && [[ -f "$SES_DEMO_VTT" ]]; then
+ return 0
+ fi
+ if [[ -f "$ROOT/examples/demo_client_review.transcript.vtt" ]]; then
+ return 0
+ fi
+ local n
+ n=$(find "$ROOT/transcripts" -maxdepth 1 -name '*.transcript.vtt' 2>/dev/null | wc -l | tr -d ' ')
+ if [[ "${n:-0}" -eq 0 ]]; then
+ echo -e "${YELLOW}No suitable .transcript.vtt found.${RESET}" >&2
+ echo " Expected examples/demo_client_review.transcript.vtt (bundled) or transcripts/*.transcript.vtt" >&2
+ echo " Or: SES_DEMO_VTT=/path/to/file.transcript.vtt $0" >&2
+ exit 1
+ fi
+}
+
+resolve_vtt_arg() {
+ if [[ -n "${SES_DEMO_VTT:-}" ]] && [[ -f "$SES_DEMO_VTT" ]]; then
+ printf '%s' "$SES_DEMO_VTT"
+ return 0
+ fi
+ if [[ -f "$ROOT/examples/demo_client_review.transcript.vtt" ]]; then
+ printf '%s' "$ROOT/examples/demo_client_review.transcript.vtt"
+ return 0
+ fi
+ printf ''
+}
+
+env_hint() {
+ if [[ ! -f "$ROOT/.env" ]]; then
+ echo -e "${YELLOW}Tip:${RESET} No .env file. Copy .env.example → .env for Jira, GitHub, and LLM keys."
+ echo -e " ${DIM}The pipeline still runs offline with heuristics if keys are missing.${RESET}"
+ echo ""
+ fi
+}
+
+story() {
+ echo -e "${BOLD}What you will see (90 seconds of story)${RESET}"
+ echo ""
+ echo " 1. A client meeting transcript (.vtt) is the trigger."
+ echo " 2. Seven agents run in sequence: parse → prioritize → extract REQs →"
+ echo " Jira tickets → Confluence-style minutes → decision log → drift check."
+ echo " 3. Each step deposits into SharedMemory; significant steps emit EventBus events."
+ echo " 4. Drift can fan out to the Architecture pipeline (event-driven, not a loop)."
+ echo ""
+ echo -e "${DIM}Implementation: demo.py runs PipelineExecutor on REQUIREMENTS_PIPELINE.${RESET}"
+ echo ""
+}
+
+open_dashboards() {
+ if [[ "$SKIP_OPEN" -eq 1 ]] || [[ "${SES_DEMO_OPEN_DASHBOARDS:-1}" == "0" ]]; then
+ return 0
+ fi
+ if [[ "$(uname -s)" != "Darwin" ]]; then
+ echo -e "${DIM}Open dashboards manually: dashboard/interactive_architecture.html, dashboard/wbs.html${RESET}"
+ return 0
+ fi
+ echo -e "${GREEN}Opening dashboards in default browser…${RESET}"
+ open "$ROOT/dashboard/interactive_architecture.html" 2>/dev/null || true
+ open "$ROOT/dashboard/wbs.html" 2>/dev/null || true
+ open "$ROOT/dashboard/event_flow.html" 2>/dev/null || true
+}
+
+# --- main --------------------------------------------------------------------
+need_python
+ensure_deps
+ensure_transcript
+env_hint
+story
+
+echo -e "${BOLD}Starting live pipeline run…${RESET}"
+echo ""
+
+EXTRA=()
+if [[ "$AUTO_FLAG" -eq 1 ]]; then
+ EXTRA+=(--auto)
+ export SES_DEMO_AUTO=1
+fi
+if [[ "$STEP_FLAG" -eq 1 ]] || [[ "${SES_DEMO_STEP:-}" =~ ^(1|true|yes)$ ]]; then
+ EXTRA+=(--step)
+fi
+
+VTT="$(resolve_vtt_arg)"
+if [[ -n "$VTT" ]]; then
+ echo -e "${DIM}Transcript:${RESET} ${VTT#$ROOT/}"
+ echo ""
+ "$PYTHON" "$ROOT/demo.py" "$VTT" "${EXTRA[@]}"
+else
+ "$PYTHON" "$ROOT/demo.py" "${EXTRA[@]}"
+fi
+
+echo ""
+echo -e "${GREEN}Pipeline run finished.${RESET}"
+open_dashboards
+
+echo ""
+echo -e "${DIM}More:${RESET} full walkthrough with \`$PYTHON demo_full.py\` | playbook: DEMO_PLAYBOOK.md"
+echo ""
diff --git a/skills/defect-triage/SKILL.md b/skills/defect-triage/SKILL.md
new file mode 100644
index 0000000..7bf419c
--- /dev/null
+++ b/skills/defect-triage/SKILL.md
@@ -0,0 +1,122 @@
+---
+name: defect-triage
+description: Turn a CI failure log, PR review finding, or bug report into a fully-triaged EPARTS Jira Bug — severity, stage-found, root-cause, found-by labels, module tag, and requirement link — following docs/defect_management.md. Use whenever something broken is found and needs tracking. Assumes the caller is authenticated to epartsmse.atlassian.net via the claude.ai Atlassian connector.
+user-invokable: true
+args:
+ - name: finding
+ description: "The raw material: paste a CI log excerpt, PR review comment, failing test output, or a plain-English bug description. If omitted, the skill asks."
+ required: false
+---
+
+# Defect triage — EPARTS
+
+Create one correctly-classified Bug in the EPARTS Jira project from whatever
+evidence the caller pastes. The classification scheme is defined in
+`docs/defect_management.md` (the defect management spec) — this skill is its
+assist-tier automation: **AI drafts the triage, the human confirms, then the
+ticket is created.** Never create the ticket without explicit confirmation.
+
+## Constants (do not ask the user for these)
+
+```
+cloudId = 1b3fa01d-9ef5-428f-93b5-429405fe9466
+projectKey = EPARTS
+site = https://epartsmse.atlassian.net
+issueTypeName = "Bug"
+```
+
+Label vocabularies (exactly these — never invent new label values):
+
+- stage found: `found-spec` `found-build` `found-review` `found-ci` `found-integrated` `found-client`
+- root cause: `rc-logic` `rc-data` `rc-interface` `rc-config` `rc-requirements` `rc-env` `rc-prompt`
+- found by: `by-test` `by-ci` `by-human-review` `by-ai-review` `by-client`
+- module: `mod-ingestion` `mod-normalization` `mod-prediction` `mod-routing` `mod-review-queue` `mod-writeback` `mod-publish` `mod-audit` `mod-retraining` `mod-monitoring` `mod-ses` (team tooling/CI itself)
+
+Severity → Jira priority: S1 → `Highest`, S2 → `High`, S3 → `Medium`, S4 → `Low`.
+
+## Steps
+
+### 1. Preflight — auth
+Call `mcp__claude_ai_Atlassian__atlassianUserInfo`.
+- If it errors → STOP and tell the caller: "Not connected. Run `/mcp`, select
+ **claude.ai Atlassian**, authorize, then re-run `/defect-triage`."
+- Keep `account_id` — the Bug is assigned to the caller (they own shepherding
+ it through triage, not necessarily the fix).
+
+### 2. Gather the finding
+Use the `finding` arg, or ask for it. Accept anything: CI log, stack trace,
+review comment, one-line description. If the finding is vague, ask at most
+TWO clarifying questions (typically: "where was this found?" and "what did
+you expect instead?") — then proceed with stated assumptions rather than
+interrogating.
+
+### 3. Draft the triage
+From the finding, draft ALL of:
+
+- **summary** — imperative, specific, ≤ 90 chars ("Routing accepts NaN
+ confidence as auto-accept" not "bug in routing")
+- **severity** — per docs/defect_management.md §2. Default S3 when honestly
+ unsure; S1 ONLY for red main-branch CI or wrong-data-toward-staging.
+- **stage found / root cause / found by** — one label each from the
+ vocabularies. `rc-prompt` when a prompt/context produced a wrong artifact
+ though code and model behaved.
+- **module** — one `mod-*` label (Quality Plan §3 modules; `mod-ses` for
+ team-tooling defects).
+- **threatened QA goal** — `qa-goal-1` … `qa-goal-7` label when it maps to a
+ Quality Plan §2 goal; omit when none fits.
+- **description** — use the template from docs/defect_management.md §1
+ (Observed / Expected / Repro / Threatens / Source). Quote the pasted
+ evidence in the Observed block; include links the caller provided.
+
+### 4. Duplicate check
+`searchJiraIssuesUsingJql` with
+`project = EPARTS AND issuetype = Bug AND statusCategory != Done AND text ~ "<2-3 keywords>"`.
+If a likely duplicate exists, show it and ask: comment on the existing Bug
+instead, or proceed with a new one?
+
+### 5. Confirm with the caller
+Present the full draft (summary, severity, all labels, description) in one
+block. Proceed ONLY on explicit yes. Apply any corrections they give —
+their judgment overrides the draft (that is the point of the human tier).
+
+### 6. Create
+`mcp__claude_ai_Atlassian__createJiraIssue` with:
+
+```
+cloudId = constant above
+projectKey = "EPARTS"
+issueTypeName = "Bug"
+summary =
+description =
+contentFormat = "markdown"
+assignee_account_id =
+additional_fields = {
+ "priority": { "name": <"Highest"|"High"|"Medium"|"Low"> },
+ "labels": [, , , , , "defect"]
+}
+```
+
+Do not set sprint or due date. If a `priority` field error comes back
+(team-managed projects sometimes hide it), retry once without the priority
+field and put `S1`/`S2`/`S3`/`S4` at the start of the summary instead.
+
+### 7. Report
+Print: key, summary, severity, the four labels, and the browse link
+(`https://epartsmse.atlassian.net/browse/EPARTS-xxx`). Remind the caller of
+the response norm for the severity (S1: today; S2: this tick; S3: next tick;
+S4: backlog).
+
+## Rules
+
+- Never create without step-5 confirmation; never invent label values.
+- One finding = one Bug. If the paste contains several distinct defects, say
+ so and triage them one at a time.
+- Findings already fixed in the same PR do not get tickets (intake rule §3.2
+ of the spec) — tell the caller if their paste looks like one.
+- If the caller disputes the drafted severity, take theirs — record, don't argue.
+
+## Example invocation
+
+`/defect-triage the coverage gate failed on master: Required test coverage of 85.0% not reached. Total coverage: 84.86%`
+
+→ drafts: S1 `found-ci` `by-ci` `rc-config` `mod-ses` "CI coverage gate fails on master: 84.86% < 85% floor", confirms, creates, reports the key.
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/golden/__init__.py b/tests/golden/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/golden/expected/session_extraction_coach.json b/tests/golden/expected/session_extraction_coach.json
new file mode 100644
index 0000000..4670bb2
--- /dev/null
+++ b/tests/golden/expected/session_extraction_coach.json
@@ -0,0 +1,29 @@
+{
+ "required_fields": ["commitments", "concerns", "decisions"],
+ "expected_commitments": [
+ {
+ "keywords": ["200", "labeled", "Friday"],
+ "min_match": 2
+ },
+ {
+ "keywords": ["monitoring", "design", "Wednesday"],
+ "min_match": 2
+ },
+ {
+ "keywords": ["instrument", "review", "time-per-decision"],
+ "min_match": 1
+ }
+ ],
+ "expected_concerns": [
+ {
+ "keywords": ["monitorability", "drift", "degradation"],
+ "min_match": 1
+ },
+ {
+ "keywords": ["HITL", "human review", "30 seconds", "economics"],
+ "min_match": 1
+ }
+ ],
+ "min_commitments": 2,
+ "min_concerns": 1
+}
diff --git a/tests/golden/expected/transcript_parser_standup.json b/tests/golden/expected/transcript_parser_standup.json
new file mode 100644
index 0000000..30db091
--- /dev/null
+++ b/tests/golden/expected/transcript_parser_standup.json
@@ -0,0 +1,31 @@
+{
+ "required_fields": ["attendees", "decisions", "action_items"],
+ "expected_attendees": ["Sarah", "Alex", "Priya", "Marcus"],
+ "expected_decisions": [
+ {
+ "keywords": ["ChromaDB", "vector store"],
+ "min_match": 1
+ }
+ ],
+ "expected_action_items": [
+ {
+ "keywords": ["ADR", "ChromaDB"],
+ "min_match": 1
+ },
+ {
+ "keywords": ["recommendation pipeline", "end-to-end", "Wednesday"],
+ "min_match": 1
+ },
+ {
+ "keywords": ["API spec", "confidence", "OpenAPI"],
+ "min_match": 1
+ },
+ {
+ "keywords": ["staging", "test data"],
+ "min_match": 1
+ }
+ ],
+ "min_decisions": 1,
+ "min_action_items": 3,
+ "min_attendees": 3
+}
diff --git a/tests/golden/transcripts/session_extraction_coach.txt b/tests/golden/transcripts/session_extraction_coach.txt
new file mode 100644
index 0000000..8419532
--- /dev/null
+++ b/tests/golden/transcripts/session_extraction_coach.txt
@@ -0,0 +1,25 @@
+WEBVTT
+
+00:00:00.000 --> 00:00:10.000
+Christian: So let's talk about your ML pipeline. How is the confidence threshold calibration going?
+
+00:00:10.000 --> 00:00:30.000
+Alex: We ran the POC with 500 test parts. The auto-accept rate is 34% at the current threshold of 0.85. We think we can push it higher but need more labeled data. We committed to having 200 more labeled examples by next Friday.
+
+00:00:30.000 --> 00:00:50.000
+Christian: That's good progress. My concern is about monitorability — how will you know if the model degrades in production? You need a drift detection mechanism. This has come up before.
+
+00:00:50.000 --> 00:01:10.000
+Sarah: We've discussed adding a monitoring dashboard but haven't prioritized it yet. We'll commit to having a design for the monitoring approach by next Wednesday.
+
+00:01:10.000 --> 00:01:30.000
+Christian: Good. Another thing — your HITL workflow. The human review for low-confidence matches needs to be efficient. If reviewers spend more than 30 seconds per decision, the economics don't work. Have you measured that?
+
+00:01:30.000 --> 00:01:45.000
+Priya: Not yet. That's a great point. We'll instrument the review interface to track time-per-decision.
+
+00:01:45.000 --> 00:02:00.000
+Christian: Excellent. The key decision you need to make soon is the alpha weighting between semantic and attribute matching. Have you collected enough evidence to decide?
+
+00:02:00.000 --> 00:02:15.000
+Alex: We have some initial numbers but need to run the full A/B test. We're targeting next sprint for that.
diff --git a/tests/golden/transcripts/transcript_parser_standup.txt b/tests/golden/transcripts/transcript_parser_standup.txt
new file mode 100644
index 0000000..35a278e
--- /dev/null
+++ b/tests/golden/transcripts/transcript_parser_standup.txt
@@ -0,0 +1,28 @@
+WEBVTT
+
+00:00:00.000 --> 00:00:05.000
+Sarah: Good morning everyone, let's do a quick standup.
+
+00:00:05.000 --> 00:00:15.000
+Alex: I finished the API endpoint for part recommendations yesterday. Today I'm working on the confidence threshold calibration. No blockers.
+
+00:00:15.000 --> 00:00:30.000
+Priya: I'm still working on the semantic matcher integration tests. I'm blocked on getting test data from the eParts staging environment. Can someone help with that?
+
+00:00:30.000 --> 00:00:45.000
+Marcus: I can help with the staging data, Priya. I have access. Also, we decided yesterday to use ChromaDB instead of Pinecone for the vector store — it simplifies our deployment since everything stays local.
+
+00:00:45.000 --> 00:01:00.000
+Sarah: Good decision. Let's make sure we create an ADR for the ChromaDB choice. Also, the client demo is next Thursday — we need the recommendation pipeline working end-to-end by Wednesday. That's P0.
+
+00:01:00.000 --> 00:01:10.000
+Alex: Question — should we expose the confidence scores in the API response, or just the binary accept/reject decision?
+
+00:01:10.000 --> 00:01:20.000
+Sarah: Let's expose both. The client specifically asked for transparency in the ML decisions. Priya, can you add that to the API spec?
+
+00:01:20.000 --> 00:01:25.000
+Priya: Sure, I'll update the OpenAPI spec today.
+
+00:01:25.000 --> 00:01:30.000
+Sarah: Great. Any other blockers? No? Let's get to work.
diff --git a/tick-board/README.md b/tick-board/README.md
new file mode 100644
index 0000000..b2797f3
--- /dev/null
+++ b/tick-board/README.md
@@ -0,0 +1,89 @@
+## What this is
+
+The Tick Board is a single-page Kanban web app for **Agentic-Augmented Scrum (AAS)**. It tracks **Spec Cards** across six columns (Backlog → Spec Ready → In Build → In Review → Integrated → Demo’d), Validation Hours (VH), Review Tier (T1/T2/T3), tick assignment, and AI-rejection flags. State is stored in **`tick-board/data/board.json`** in this repo so changes are **auditable in git** when synced via the GitHub API.
+
+---
+
+## Live URL
+
+**GitHub’s “Deploy from a branch” only allows `/` or `/docs`**, not arbitrary folders like `/tick-board`. This repo publishes the SPA with **GitHub Actions** instead:
+
+1. In the repo go to **Settings → Pages**.
+2. Under **Build and deployment**, set **Source** to **GitHub Actions** (not “Deploy from a branch”).
+3. Push or merge commits that touch `tick-board/` or `.github/workflows/tick-board-pages.yml`, or open **Actions** and rerun **Deploy tick board (GitHub Pages)**.
+
+Typical URL (board is served at your project site root):
+
+`https://.github.io/eparts/`
+
+(Replace with your GitHub username and repo name.)
+
+**Alternative (no Actions):** copy the static assets (`index.html`, `app.js`, `github-sync.js`, `styles.css`) into `docs/tick-board/` in the repo and set Pages source to **`/docs`**. Keep `tick-board/data/board.json` at repo root for API paths; the UI still talks to GitHub via the API, not that file over HTTP.
+
+---
+
+## How to access (PAT setup)
+
+Persistence uses the GitHub Contents API from the browser. Each teammate needs their own token (stored only in that browser):
+
+1. GitHub → **Settings** → **Developer settings** → **Personal access tokens** → **Fine-grained tokens** → Generate.
+2. **Repository access**: only select this repo (**eparts**, or whatever the team repo is named).
+3. **Repository permissions**: **Contents** → **Read and write**.
+4. Generate, copy the token (often prefixed `github_pat_`).
+5. Open the board URL, click **GitHub sign-in**, paste token, **Save & load**.
+
+**Local/offline preview** (`file://`) does not expose your GitHub Pages domain, so enter **repo owner** and **repo name** in the modal as well.
+
+**Log out** clears the PAT and saved owner/repo override from localStorage.
+
+Never commit a PAT to git. If one is leaked, revoke it immediately on GitHub.
+
+---
+
+## Workflow
+
+- **Board updates** are commits to **`tick-board/data/board.json`** (and **`tick-board/archive/tick-XX.json`** when a tick is archived). Refresh (or polling about every 30s) to pick up teammates’ changes.
+- Plan AAS loosely as: Day 0 (Spec Session): create/move cards · Days 1–2: drag across columns · Day 3: **Mark tick complete** (snapshot → archive + retro notes + next tick).
+- **Archived ticks**: open **Archive** → **View** for a frozen read-only Kanban (gray header banner).
+- **Throughput** header chart: approximate count per tick of cards in Integrated or Demo’d.
+
+---
+
+## Data model
+
+JSON schema (`schemaVersion`, `updatedAt`, `currentTick`, `cards[]`, `ticks[]`, `config`) matches **`TICK_BOARD.md`** in the repo root. Key card fields: `id`, `title`, `intent`, `acceptanceCriteria[]`, `detailedSpec`, `validationHours`, `reviewTier`, `tickId`, `column`, `assignee`, `aiRejected`, timestamps, `definitionOfDone`.
+
+---
+
+## Troubleshooting
+
+| Symptom | Likely cause |
+|--------|----------------|
+| **401** after save | Bad or expired token; generate a new fine-grained token. |
+| **404** on load | Wrong owner/repo, or `tick-board/data/board.json` not on the default branch. |
+| **409 / 422** or “SHA conflict” banner | Someone else committed `board.json` first. Use **Reload board** and re-apply your change. |
+| Blank board / no cards | Not signed in, or failed fetch; check browser devtools **Network** for `api.github.com`. |
+| CORS errors | You should not see CORS on `api.github.com` from a normal browser; ensure you are not blocking requests. |
+
+---
+
+## Keyboard shortcuts
+
+| Key | Action |
+|-----|--------|
+| `n` | New Spec Card |
+| `/` | Focus search |
+| `t` | Open/close Tick panel |
+| `a` | Open Archive overlay |
+| `Esc` | Close modals/panels |
+
+---
+
+## Files
+
+- `index.html` — shell + Tailwind CDN
+- `app.js` — UI and workflow
+- `github-sync.js` — GitHub REST helpers
+- `styles.css` — small layout tweaks
+- `data/board.json` — live board JSON (committed)
+- `archive/` — tick snapshot JSON files (committed as they’re created)
diff --git a/tick-board/app.js b/tick-board/app.js
new file mode 100644
index 0000000..499b1a4
--- /dev/null
+++ b/tick-board/app.js
@@ -0,0 +1,989 @@
+import * as gh from "./github-sync.js";
+
+const COLUMN_LABELS = {
+ backlog: "Backlog",
+ "spec-ready": "Spec Ready",
+ "in-build": "In Build",
+ "in-review": "In Review",
+ integrated: "Integrated",
+ "demo-d": "Demo'd",
+};
+
+const BACKLOG_TICK_ID = "BACKLOG";
+
+const state = {
+ board: null,
+ sha: null,
+ ownerRepo: null,
+ draggingId: null,
+ editingCardId: null,
+ panel: null,
+ collisionBanner: false,
+ searchQuery: "",
+ archiveReadOnlyBoard: null,
+ archiveSnapshotLabel: "",
+ toast: "",
+ polling: false,
+};
+
+const el = (id) => document.getElementById(id);
+
+function showToast(message, ms = 4000) {
+ state.toast = message;
+ renderChrome();
+ setTimeout(() => {
+ state.toast = "";
+ renderChrome();
+ }, ms);
+}
+
+function nextArchiveNumber() {
+ const archived = state.board.ticks.filter((t) => t.status === "archived");
+ return String(archived.length + 1).padStart(2, "0");
+}
+
+/** Tick IDs considered archived */
+function archivedTickIdSet() {
+ return new Set(
+ state.board.ticks.filter((t) => t.status === "archived").map((t) => t.tickId),
+ );
+}
+
+/** Per spec Step 3.4: hide demo-d cards belonging to archived ticks */
+function cardHiddenFromBoard(card) {
+ const archivedIds = archivedTickIdSet();
+ return archivedIds.has(card.tickId) && card.column === "demo-d";
+}
+
+function tickThroughputSeries() {
+ const ticks = [...state.board.ticks];
+ if (state.board.currentTick && state.board.currentTick.status === "active") {
+ const exists = ticks.some((t) => t.tickId === state.board.currentTick.tickId);
+ if (!exists) ticks.push({ ...state.board.currentTick, status: "active" });
+ }
+ ticks.sort((a, b) => a.tickId.localeCompare(b.tickId));
+ const byTick = ticks.map((t) => ({
+ tickId: t.tickId,
+ label: t.tickLabel || t.tickId,
+ count: state.board.cards.filter(
+ (c) =>
+ c.tickId === t.tickId &&
+ (c.column === "integrated" || c.column === "demo-d"),
+ ).length,
+ }));
+ const max = Math.max(1, ...byTick.map((x) => x.count));
+ return { rows: byTick, max };
+}
+
+function ensureLogin() {
+ const token = gh.getToken();
+ const ownerRepo = gh.resolveOwnerRepo();
+ state.ownerRepo = ownerRepo;
+ if (!token || !ownerRepo)
+ openPanel("pat");
+}
+
+async function reloadBoardQuiet() {
+ const token = gh.getToken();
+ if (!token || !state.ownerRepo) return;
+ try {
+ const { data, sha } = await gh.loadBoard(token, state.ownerRepo);
+ state.board = data;
+ state.sha = sha;
+ state.collisionBanner = false;
+ renderAll();
+ showToast("Board reloaded — remote changes applied.");
+ } catch (e) {
+ showToast(String(e.message || e), 6000);
+ }
+}
+
+async function saveBoard(commitMsg) {
+ const token = gh.getToken();
+ if (!token || !state.ownerRepo || !state.board || !state.sha) {
+ ensureLogin();
+ throw new Error("Not ready to save.");
+ }
+ try {
+ const newSha = await gh.saveBoard(
+ state.board,
+ commitMsg || "chore(board): update",
+ state.sha,
+ token,
+ state.ownerRepo,
+ );
+ state.sha = newSha;
+ state.collisionBanner = false;
+ renderAll();
+ } catch (e) {
+ if (
+ e.code === 409 ||
+ e.code === 422 ||
+ String(e.message || "") === "SHA_CONFLICT"
+ ) {
+ state.collisionBanner = true;
+ renderChrome();
+ throw e;
+ }
+ throw e;
+ }
+}
+
+function openPanel(which) {
+ state.panel = which;
+ renderChrome();
+}
+
+function closePanel() {
+ state.panel = null;
+ renderChrome();
+}
+
+/** Retro textarea template */
+function retroTemplate(ct) {
+ const tickId = ct.tickId || "T???";
+ const tickLabel = ct.tickLabel || "";
+ return `## Tick ${tickId} · ${tickLabel} Retro
+
+### What went well
+-
+
+### What didn't go well
+-
+
+### AAS process check
+- Validation Hours accuracy:
+- Review Tier distribution:
+- AI-rejection rate:
+
+### Action items for next Tick
+-
+`;
+}
+
+function renderCollisionBanner() {
+ const host = el("collisionBanner");
+ if (!host) return;
+ if (!state.collisionBanner) {
+ host.innerHTML = "";
+ return;
+ }
+ host.innerHTML = `
+
+ Someone else just updated the board. Refresh to see their changes, then re-apply yours.
+
+
+ Columns: backlog → demo. Shared state in
+ tick-board/data/board.json.
+ Shortcut help: ?-style —
+ n card
+ ·
+ / search · t tick · a archive · Esc close
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/tick-board/styles.css b/tick-board/styles.css
new file mode 100644
index 0000000..f0405bd
--- /dev/null
+++ b/tick-board/styles.css
@@ -0,0 +1,19 @@
+:root {
+ color-scheme: light;
+}
+
+body {
+ font-family:
+ ui-sans-serif,
+ system-ui,
+ -apple-system,
+ "Segoe UI",
+ Roboto,
+ "Helvetica Neue",
+ Arial,
+ sans-serif;
+}
+
+kbd {
+ font-size: 0.7rem;
+}
diff --git a/tools/lint_ses.py b/tools/lint_ses.py
new file mode 100644
index 0000000..c862d7d
--- /dev/null
+++ b/tools/lint_ses.py
@@ -0,0 +1,423 @@
+#!/usr/bin/env python3
+"""Custom linter for the eParts SES repo: enforces the **Agent Contract**.
+
+WHY THIS EXISTS
+---------------
+Every capability in this repo is a class under ``agents/`` that the orchestrator
+discovers by convention, not by compiler-checked interface. Nothing today stops
+an agent from being written in a way that *silently* never runs or never works:
+
+ * a module under ``agents/`` that forgets to subclass ``BaseAgent`` loses
+ metrics, the JSONL audit trail, retry/backoff and the ``requires_human_review``
+ gate — and ``orchestrator/registry.py`` cannot wrap it as a task handler;
+ * an agent class that is never wired into ``orchestrator/registry.py`` is dead
+ code: the queue has no handler for it, so it simply never fires and no error
+ is ever raised;
+ * a ``load_prompt("x.txt")`` call whose prompt file does not exist raises
+ ``FileNotFoundError`` only on the code path that uses it, i.e. in production,
+ long after the PR merged (``agents/base.py::load_prompt``);
+ * the trigger/outputs header in each agent's module docstring is the source
+ the architecture docs and artifact catalogue are written from — 28 agents
+ maintained by five people will drift the moment it is unenforced.
+
+All four of those are *deterministic* properties of the source tree. Checking
+them costs no tokens and no judgement, which is exactly the class of quality
+assurance that should be automated first (Cory Gwin coaching session,
+2026-07-24: "a great deal of quality assurance is deterministic and consumes no
+tokens ... if there is a rule the team wants enforced, work with AI to write a
+linter for it").
+
+THE RULE
+--------
+Blocking (exit 1):
+
+ SES001 Each module under ``agents/`` (except ``__init__.py`` and ``base.py``)
+ defines at least one class that inherits from ``BaseAgent``.
+ SES002 That module's docstring declares both ``Triggered by:`` and
+ ``Outputs:``, so the agent's contract is readable without running it.
+ SES003 Each ``BaseAgent`` subclass is imported *and* instantiated in
+ ``orchestrator/registry.py`` — i.e. it is actually wired to the queue.
+ SES004 Every prompt filename passed as a literal to ``load_prompt()`` or
+ ``call_claude()`` resolves to a real file under ``prompts/``.
+
+Advisory (reported, exit 0 unless ``--strict``):
+
+ SES005 ``commit_file(...)`` called from an agent without an explicit
+ ``branch`` argument. ``BitbucketMCP.commit_file`` /
+ ``GitHubMCP.commit_file`` default to ``branch="main"``, so such a call
+ writes straight to the protected branch with no PR and no human
+ approval gate. Several agents do this deliberately today (append-only
+ record keeping), so it warns rather than blocks — but any *new*
+ occurrence should be an explicit, reviewed decision.
+
+Stdlib only. Exit 0 = clean, exit 1 = blocking violations found.
+"""
+
+from __future__ import annotations
+
+import argparse
+import ast
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+
+BASE_AGENT = "BaseAgent"
+
+# Modules under agents/ that are infrastructure, not agents.
+AGENT_DIR_EXEMPT = {"__init__.py", "base.py"}
+
+RULES = {
+ "SES001": "agent module must define a BaseAgent subclass",
+ "SES002": "agent module docstring must declare 'Triggered by:' and 'Outputs:'",
+ "SES003": "agent class must be registered in orchestrator/registry.py",
+ "SES004": "referenced prompt file must exist in prompts/",
+ "SES005": "commit_file() without explicit branch= writes directly to main (advisory)",
+}
+
+ADVISORY_RULES = {"SES005"}
+
+PROMPT_LOADERS = {"load_prompt", "call_claude"}
+
+
+@dataclass(frozen=True)
+class Violation:
+ path: Path
+ line: int
+ code: str
+ message: str
+
+ def render(self, root: Path) -> str:
+ try:
+ rel = self.path.relative_to(root)
+ except ValueError:
+ rel = self.path
+ return f"{rel}:{self.line}: {self.code} {self.message}"
+
+ @property
+ def advisory(self) -> bool:
+ return self.code in ADVISORY_RULES
+
+
+# ---------------------------------------------------------------------------
+# AST helpers
+# ---------------------------------------------------------------------------
+
+
+def _base_name(node: ast.expr) -> str:
+ """Return the trailing identifier of a base-class expression.
+
+ ``BaseAgent`` -> "BaseAgent"; ``base.BaseAgent`` -> "BaseAgent";
+ anything else (subscripts, calls) -> "".
+ """
+ if isinstance(node, ast.Name):
+ return node.id
+ if isinstance(node, ast.Attribute):
+ return node.attr
+ return ""
+
+
+def agent_classes(tree: ast.Module) -> list[ast.ClassDef]:
+ """Classes in this module that transitively inherit from BaseAgent."""
+ classes = [n for n in tree.body if isinstance(n, ast.ClassDef)]
+ known = {BASE_AGENT}
+ found: list[ast.ClassDef] = []
+
+ # Fixpoint so `class A(BaseAgent)` / `class B(A)` are both recognised.
+ changed = True
+ while changed:
+ changed = False
+ for cls in classes:
+ if cls in found:
+ continue
+ if any(_base_name(b) in known for b in cls.bases):
+ found.append(cls)
+ known.add(cls.name)
+ changed = True
+ return found
+
+
+def _attr_calls(tree: ast.Module, attr_names: set[str]) -> list[ast.Call]:
+ """All ``.(...)`` calls whose attribute is in attr_names."""
+ out = []
+ for node in ast.walk(tree):
+ if (
+ isinstance(node, ast.Call)
+ and isinstance(node.func, ast.Attribute)
+ and node.func.attr in attr_names
+ ):
+ out.append(node)
+ return out
+
+
+def _first_str_arg(call: ast.Call) -> str | None:
+ if call.args and isinstance(call.args[0], ast.Constant) and isinstance(
+ call.args[0].value, str
+ ):
+ return call.args[0].value
+ return None
+
+
+# ---------------------------------------------------------------------------
+# Registry parsing (for SES003)
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class RegistryFacts:
+ """What orchestrator/registry.py imports from agents.* and what it builds."""
+
+ imported: set[tuple[str, str]] # (module dotted path, class name)
+ instantiated: set[str] # class names called as Foo(...)
+ parsed: bool
+
+
+def read_registry(registry_path: Path) -> RegistryFacts:
+ if not registry_path.is_file():
+ return RegistryFacts(set(), set(), parsed=False)
+
+ tree = ast.parse(registry_path.read_text(encoding="utf-8"), str(registry_path))
+ imported: set[tuple[str, str]] = set()
+ instantiated: set[str] = set()
+
+ for node in ast.walk(tree):
+ if isinstance(node, ast.ImportFrom) and node.module and node.level == 0:
+ if node.module.startswith("agents."):
+ for alias in node.names:
+ imported.add((node.module, alias.name))
+ elif isinstance(node, ast.Call):
+ name = _base_name(node.func)
+ if name:
+ instantiated.add(name)
+
+ return RegistryFacts(imported, instantiated, parsed=True)
+
+
+# ---------------------------------------------------------------------------
+# Checks
+# ---------------------------------------------------------------------------
+
+
+def module_dotted_path(path: Path, root: Path) -> str:
+ rel = path.relative_to(root)
+ return ".".join(rel.with_suffix("").parts)
+
+
+def check_agent_module(
+ path: Path,
+ tree: ast.Module,
+ root: Path,
+ registry: RegistryFacts,
+) -> list[Violation]:
+ violations: list[Violation] = []
+ classes = agent_classes(tree)
+
+ # --- SES001 -----------------------------------------------------------
+ if not classes:
+ violations.append(
+ Violation(
+ path,
+ 1,
+ "SES001",
+ f"module defines no {BASE_AGENT} subclass; agents must inherit "
+ f"{BASE_AGENT} (agents/base.py) to get logging, metrics, retry "
+ "and the human-review gate",
+ )
+ )
+
+ # --- SES002 -----------------------------------------------------------
+ doc = ast.get_docstring(tree) or ""
+ missing = [m for m in ("Triggered by:", "Outputs:") if m not in doc]
+ if missing:
+ detail = " and ".join(f"'{m}'" for m in missing)
+ violations.append(
+ Violation(
+ path,
+ 1,
+ "SES002",
+ f"module docstring is missing {detail}; every agent must state "
+ "what triggers it and what it produces",
+ )
+ )
+
+ # --- SES003 -----------------------------------------------------------
+ if classes and registry.parsed:
+ dotted = module_dotted_path(path, root)
+ wired = [
+ cls
+ for cls in classes
+ if (dotted, cls.name) in registry.imported
+ and cls.name in registry.instantiated
+ ]
+ if not wired:
+ names = ", ".join(c.name for c in classes)
+ violations.append(
+ Violation(
+ path,
+ classes[0].lineno,
+ "SES003",
+ f"{names} is not wired into orchestrator/registry.py "
+ f"(expected 'from {dotted} import ' plus an "
+ "instantiation in register_all_agents); an unregistered "
+ "agent never runs and never errors",
+ )
+ )
+
+ # --- SES005 (advisory) ------------------------------------------------
+ for call in _attr_calls(tree, {"commit_file"}):
+ kw_names = {kw.arg for kw in call.keywords}
+ if None in kw_names:
+ continue # **kwargs passthrough — cannot prove it is missing
+ # BitbucketMCP/GitHubMCP signature: (file_path, content, message, branch, agent_name)
+ branch_positional = len(call.args) >= 4
+ if "branch" not in kw_names and not branch_positional:
+ violations.append(
+ Violation(
+ path,
+ call.lineno,
+ "SES005",
+ "commit_file() has no explicit branch=; it defaults to "
+ "'main', so this writes to the protected branch with no PR "
+ "and no human approval gate",
+ )
+ )
+
+ return violations
+
+
+def check_prompt_references(path: Path, tree: ast.Module, prompts_dir: Path) -> list[Violation]:
+ """SES004 — literal prompt filenames must resolve under prompts/."""
+ violations: list[Violation] = []
+ for call in _attr_calls(tree, PROMPT_LOADERS):
+ literal = _first_str_arg(call)
+ if literal is None:
+ continue
+ loader = call.func.attr # type: ignore[union-attr]
+ # call_claude() takes an inline prompt *or* a bare prompt filename; only
+ # a .txt literal is a filename (see BaseAgent.call_claude resolution).
+ if loader == "call_claude" and not literal.endswith(".txt"):
+ continue
+ if "/" in literal or "\\" in literal:
+ continue # not a plain prompts/ filename; out of scope
+ if not (prompts_dir / literal).is_file():
+ violations.append(
+ Violation(
+ path,
+ call.lineno,
+ "SES004",
+ f"{loader}('{literal}') but prompts/{literal} does not "
+ "exist; this raises FileNotFoundError at runtime, not at "
+ "import time",
+ )
+ )
+ return violations
+
+
+# ---------------------------------------------------------------------------
+# Driver
+# ---------------------------------------------------------------------------
+
+
+def iter_python_files(base: Path) -> list[Path]:
+ return sorted(
+ p
+ for p in base.rglob("*.py")
+ if not any(part in {".git", ".venv", "venv", "__pycache__", "node_modules"} for part in p.parts)
+ )
+
+
+def lint(root: Path) -> tuple[list[Violation], int]:
+ """Returns (violations, number of files checked)."""
+ agents_dir = root / "agents"
+ prompts_dir = root / "prompts"
+ registry = read_registry(root / "orchestrator" / "registry.py")
+
+ violations: list[Violation] = []
+ checked = 0
+
+ # Prompt references: every Python file in the repo can load a prompt.
+ for path in iter_python_files(root):
+ try:
+ tree = ast.parse(path.read_text(encoding="utf-8"), str(path))
+ except (SyntaxError, UnicodeDecodeError) as exc:
+ violations.append(
+ Violation(path, getattr(exc, "lineno", 1) or 1, "SES000", f"cannot parse: {exc}")
+ )
+ continue
+ checked += 1
+ violations.extend(check_prompt_references(path, tree, prompts_dir))
+
+ in_agents_dir = agents_dir in path.parents
+ if in_agents_dir and path.name not in AGENT_DIR_EXEMPT:
+ violations.extend(check_agent_module(path, tree, root, registry))
+
+ violations.sort(key=lambda v: (str(v.path), v.line, v.code))
+ return violations, checked
+
+
+def build_parser() -> argparse.ArgumentParser:
+ parser = argparse.ArgumentParser(
+ prog="lint_ses.py",
+ description=(
+ "Enforce the eParts Agent Contract: every module under agents/ "
+ "subclasses BaseAgent, documents its trigger and outputs, is wired "
+ "into orchestrator/registry.py, and only references prompt files "
+ "that exist. Deterministic, stdlib-only, zero tokens."
+ ),
+ epilog=(
+ "Rules:\n"
+ + "\n".join(
+ f" {code} {desc}" for code, desc in RULES.items()
+ )
+ + "\n\nExit codes: 0 = clean, 1 = blocking violations."
+ ),
+ formatter_class=argparse.RawDescriptionHelpFormatter,
+ )
+ parser.add_argument(
+ "--root",
+ type=Path,
+ default=Path(__file__).resolve().parent.parent,
+ help="repository root to lint (default: the repo containing this script)",
+ )
+ parser.add_argument(
+ "--strict",
+ action="store_true",
+ help="treat advisory findings (%s) as blocking" % ", ".join(sorted(ADVISORY_RULES)),
+ )
+ parser.add_argument(
+ "--quiet",
+ action="store_true",
+ help="print violations only, no summary line",
+ )
+ return parser
+
+
+def main(argv: list[str] | None = None) -> int:
+ args = build_parser().parse_args(argv)
+ root = args.root.resolve()
+
+ if not (root / "agents").is_dir():
+ print(f"lint_ses: no agents/ directory under {root}", file=sys.stderr)
+ return 2
+
+ violations, checked = lint(root)
+
+ for v in violations:
+ prefix = "advisory: " if v.advisory and not args.strict else ""
+ print(prefix + v.render(root))
+
+ blocking = [v for v in violations if args.strict or not v.advisory]
+ advisory = [v for v in violations if not args.strict and v.advisory]
+
+ if not args.quiet:
+ if violations:
+ print()
+ print(
+ f"lint_ses: {checked} file(s) checked, "
+ f"{len(blocking)} blocking, {len(advisory)} advisory"
+ )
+
+ return 1 if blocking else 0
+
+
+if __name__ == "__main__":
+ sys.exit(main())
diff --git a/transcripts/GMT20260122-191430_Recording.cc.vtt b/transcripts/GMT20260122-191430_Recording.cc.vtt
new file mode 100644
index 0000000..54fc934
--- /dev/null
+++ b/transcripts/GMT20260122-191430_Recording.cc.vtt
@@ -0,0 +1,1865 @@
+WEBVTT
+
+00:00:02.000 --> 00:00:08.000
+Okay, so the next topic we have is how we all are going to be working together.
+
+00:00:08.000 --> 00:00:14.000
+And the major points we wanted to cover was your availability, modes, communication, onboarding, and…
+
+00:00:14.000 --> 00:00:18.000
+the documentation resources that we should have.
+
+00:00:18.000 --> 00:00:22.000
+So, availability, I think, this lot kind of works really empower us.
+
+00:00:22.000 --> 00:00:26.000
+Because that's when the RFP is looking really good, so…
+
+00:00:26.000 --> 00:00:30.000
+Okay. Those days around this time, if initial was sent.
+
+00:00:30.000 --> 00:00:34.000
+And very quickly, I think we all available on the instill as well.
+
+00:00:34.000 --> 00:00:38.000
+So if you ever have to ask something about organizers.
+
+00:00:38.000 --> 00:00:43.000
+And, um, for multiple community initiatives in theaters, depending on those of them.
+
+00:00:43.000 --> 00:00:46.000
+what individuals are in April.
+
+00:00:46.000 --> 00:00:48.000
+500 pounds for the eggs.
+
+00:00:48.000 --> 00:00:53.000
+You want to have, I don't recommend that extended for something done.
+
+00:00:53.000 --> 00:00:58.000
+I will not for the onboarding process, it's pretty open, which is, uh…
+
+00:00:58.000 --> 00:01:05.000
+We'd, um, give you access to whatever you need for our initial public, either the DIMS application or, um,
+
+00:01:05.000 --> 00:01:10.000
+In the 80s and resources, somehow the DRM was starting to miss parts of the perfect program.
+
+00:01:10.000 --> 00:01:14.000
+and maybe Tesla today will almost nothing.
+
+00:01:14.000 --> 00:01:19.000
+Anyhow, as we have.
+
+00:01:19.000 --> 00:01:24.000
+Yeah, so probably we'll ask you guys to, uh, in this annual conference partner up.
+
+00:01:24.000 --> 00:01:27.000
+I didn't give it to come back to me by itself.
+
+00:01:27.000 --> 00:01:34.000
+They don't do that, right? So we're just asking the items at the product, or they have those.
+
+00:01:34.000 --> 00:01:40.000
+And you can just upload your outdoor systems so that you have access to, like, use that to the other things.
+
+00:01:40.000 --> 00:01:44.000
+And as we grow… as we grow about the project, we give you more and more accessibility.
+
+00:01:44.000 --> 00:01:50.000
+Because it's not a long process at these regards, so as long as you bring the, um, GA code even,
+
+00:01:50.000 --> 00:01:53.000
+Um, you would need to have assistance with that.
+
+00:01:53.000 --> 00:01:56.000
+But also please include the mentors and access on Teams.
+
+00:01:56.000 --> 00:01:59.000
+Yeah, 100%.
+
+00:01:59.000 --> 00:02:04.000
+Well, have we now done that, actually. No, no, they're able to do that. Yeah, you should. Yeah.
+
+00:02:04.000 --> 00:02:09.000
+We'd like to know what you guys are talking about before.
+
+00:02:09.000 --> 00:02:15.000
+I want… I want the team to promise the moon.
+
+00:02:15.000 --> 00:02:20.000
+I also wanted to ask about the documentation, um, with that, uh, like, uh, which…
+
+00:02:20.000 --> 00:02:25.000
+opportunity ideas, um,
+
+00:02:25.000 --> 00:02:32.000
+You know, could have access to the previous DEANS documentation really depends. It might be useful for them to understand that.
+
+00:02:32.000 --> 00:02:34.000
+We can share that perspective.
+
+00:02:34.000 --> 00:02:37.000
+Okay.
+
+00:02:37.000 --> 00:02:43.000
+We didn't know where we've been, but…
+
+00:02:43.000 --> 00:02:49.000
+final onboarding route, so we can be set with those.
+
+00:02:49.000 --> 00:03:04.000
+So, just in case you want to ask. But you've changed things since then? Yeah. Yeah, we've not so much changed so much and added. We've added a lot of times, but yeah, we should make sure that fucked up the new play.
+
+00:03:04.000 --> 00:03:09.000
+At least, yeah. Let's make sure…
+
+00:03:09.000 --> 00:03:18.000
+That'd be up here for a bit, yeah.
+
+00:03:18.000 --> 00:03:21.000
+Um, next to the project purpose and background.
+
+00:03:21.000 --> 00:03:25.000
+Uh, from your guys' point of view, the motivation benefits, stakeholder,
+
+00:03:25.000 --> 00:03:28.000
+of the feature you try to build.
+
+00:03:28.000 --> 00:03:35.000
+Hey guys. Um, so, with the moderation of it, it's mainly to, I think,
+
+00:03:35.000 --> 00:03:39.000
+get the process of getting the initial routing data.
+
+00:03:39.000 --> 00:03:45.000
+in a good liable manner out of the system. That segment is an even better.
+
+00:03:45.000 --> 00:03:47.000
+Um, because I think right now,
+
+00:03:47.000 --> 00:03:52.000
+The only way we can probably do that better is by just getting more weaker and…
+
+00:03:52.000 --> 00:03:55.000
+They'll be having more, we can look at the data and understanding.
+
+00:03:55.000 --> 00:03:58.000
+Which is not the way to go about it, because that's not speaking the music.
+
+00:03:58.000 --> 00:04:01.000
+Just really throwing that forth.
+
+00:04:01.000 --> 00:04:03.000
+running both computers and bills.
+
+00:04:03.000 --> 00:04:09.000
+Anything I want to add that to you? Yeah, um, just… there's a lot… it's a very human process right now to, like,
+
+00:04:09.000 --> 00:04:14.000
+one vendor sends something one format, another vendor sends something another format.
+
+00:04:14.000 --> 00:04:18.000
+teams of people have to go through and interpret it, and then try and…
+
+00:04:18.000 --> 00:04:25.000
+gram, whatever that is, into the existing structure, or, like, attributes or ways of looking.
+
+00:04:25.000 --> 00:04:31.000
+And, uh, just that process. We'd like to make that a lot more hands-off, where they can just upload something,
+
+00:04:31.000 --> 00:00:00.000
+It'll get it in there, and then they can do future modification, maybe it's not right or wrong, but just getting it in there in the first place takes so much time right now.
+
+00:00:00.000 --> 00:00:30.000
+No? All good, all good, thank you.
+
+00:04:39.000 --> 00:04:43.000
+And then… and then once thin, making it as robust as possible, so…
+
+00:04:43.000 --> 00:04:53.000
+throwing out all of the attributes that a particular product might have, be that by a spray thing with EDFs, or scraping a vendor website, or whatever we need to do with the product.
+
+00:04:53.000 --> 00:04:56.000
+entrance.
+
+00:04:56.000 --> 00:05:03.000
+is kind of step two in our sense, and then step three is meetings.
+
+00:05:03.000 --> 00:05:14.000
+Um, I forget what he followed it, but it was, like, um, a man in the middle kind of concept for female balloon. Yeah, that's it.
+
+00:05:14.000 --> 00:05:21.000
+Uh, they simply went on the root concept where, yeah, we'll do as much as we can, and we'll probably need some approval on that and whatnot.
+
+00:05:21.000 --> 00:05:31.000
+But then also, like, what needs updated? What, um, can we already score products so that we see, like, how many product is that we can…
+
+00:05:31.000 --> 00:05:47.000
+pull up a download of products, ladies, like, all of these are, like, you know, 20% down and they only work.
+
+00:05:47.000 --> 00:05:52.000
+I'm curious, what is the frequency of new vendors coming on board? Do you have to just booked in this?
+
+00:05:52.000 --> 00:05:56.000
+I imagine it's very enormous for the history is.
+
+00:05:56.000 --> 00:06:06.000
+Yeah, we're open to increase this more and more public new product that we're launching, uh, is named at the small to medium-sized business market.
+
+00:06:06.000 --> 00:06:12.000
+Alright, so most of our lives right now are, like, enterprise clients, so…
+
+00:06:12.000 --> 00:06:17.000
+For them, we're always onboarding and more vendors, but it's not, like, 100% where they've got them yet.
+
+00:06:17.000 --> 00:06:19.000
+or hopefully in the future, and will be.
+
+00:06:19.000 --> 00:06:23.000
+Um, but a lot of that's gonna be overlap, so that…
+
+00:06:23.000 --> 00:06:25.000
+Vendor A sells, right,
+
+00:06:25.000 --> 00:06:29.000
+Apple, uh, and the vendor B also sells Apple.
+
+00:06:29.000 --> 00:06:38.000
+Well, we need to make a good catalog by Apple devices, and then be able to link them up via vendors, and add on the original vendor attributes in there.
+
+00:06:38.000 --> 00:06:44.000
+Part of that is just how things are remote when they were being built.
+
+00:06:44.000 --> 00:06:48.000
+We need to make sure that all the attributes that it means.
+
+00:06:48.000 --> 00:06:51.000
+We did a portal in the intervention as possible.
+
+00:06:51.000 --> 00:06:52.000
+And we don't…
+
+00:06:52.000 --> 00:06:55.000
+Do you have any, uh…
+
+00:06:55.000 --> 00:06:56.000
+Oh, go ahead.
+
+00:06:56.000 --> 00:06:58.000
+I was gonna say, do you have any…
+
+00:06:58.000 --> 00:07:00.000
+data around…
+
+00:07:00.000 --> 00:07:05.000
+I guess two things. One is how accurate humans are at this endeavor.
+
+00:07:05.000 --> 00:07:07.000
+And the second is…
+
+00:07:07.000 --> 00:07:13.000
+Um, if you think about the, you know, the total sort of workflow, I don't know what your workflow is, but I can imagine, you know,
+
+00:07:13.000 --> 00:07:17.000
+looking at the data, entering it, someone else verifying it, like,
+
+00:07:17.000 --> 00:07:19.000
+Where the, you know, analysis of
+
+00:07:19.000 --> 00:07:22.000
+how much time is spent on those different…
+
+00:07:22.000 --> 00:07:28.000
+activities within that work, uh, workflow.
+
+00:07:28.000 --> 00:07:33.000
+No, I was just gonna say, we are currently actually relaunching gyms to production now.
+
+00:07:33.000 --> 00:07:41.000
+So, all the workflows that we've done are on the legacy Power Mainbook systems. Number 7 slower.
+
+00:07:41.000 --> 00:07:46.000
+Um, so I don't have been efforts to start with a month on either date.
+
+00:07:46.000 --> 00:07:51.000
+Um, we, now that the PIMS is launching, we did start beforeting that as the current state.
+
+00:07:51.000 --> 00:07:56.000
+Uh, so that we can see how much better we are at the end of the year.
+
+00:07:56.000 --> 00:07:59.000
+Then, uh, what was the first part of that question, too? It was something about…
+
+00:07:59.000 --> 00:08:04.000
+Um, what… how accurate are humans at this activity?
+
+00:08:04.000 --> 00:08:06.000
+Um, I mean, I guess…
+
+00:08:06.000 --> 00:08:09.000
+we could use those metrics, um…
+
+00:08:09.000 --> 00:08:15.000
+Oh, no, I don't know if we have how accurate, like, we could easily show how to do better before. Yeah, like, what a good end…
+
+00:08:15.000 --> 00:08:21.000
+goal of it is based on, like, the PDF we took at the beginning, but for accuracy, that's a good question.
+
+00:08:21.000 --> 00:08:27.000
+I swore I was going to see was, uh, we could surely connect that information and send it, or send it across to you.
+
+00:08:27.000 --> 00:08:32.000
+Uh, we're just coming back to active in the middle of the night, I suppose.
+
+00:08:32.000 --> 00:08:36.000
+I don't know if the team's gonna need it, but just… these are just, like, things that, hey, I'm interested, I'm curious.
+
+00:08:36.000 --> 00:08:45.000
+Do you have, like, some metrics to see where people were on process and free. So I guess the one question would be, even just the data that you've got it there,
+
+00:08:45.000 --> 00:08:51.000
+Are there, uh, teacher reports to say the data's inaccurate? Do you have information like that, or…?
+
+00:08:51.000 --> 00:09:07.000
+you know, how do you discover that? It's usually word of mouth, or, uh, yeah, the people using our site, or the catalog team will notice something, and then they'll go in and change that, or our salespeople will, in talking to customers, realize something's wrong or not acting, or they're missing.
+
+00:09:07.000 --> 00:09:13.000
+Where we can work for it, what product there's…
+
+00:09:13.000 --> 00:09:16.000
+misinformation, I would like to.
+
+00:09:16.000 --> 00:09:25.000
+But you have no central line employees, including that. The bigger problem now, at least probably, as we had…
+
+00:09:25.000 --> 00:09:34.000
+new suppliers for our interest compliance. It's pretty easy to get a part and a price in there.
+
+00:09:34.000 --> 00:09:36.000
+Um, it actually meet all the attributes.
+
+00:09:36.000 --> 00:09:41.000
+Um, we're not doing it a lot of the time, um, because it's just too much effort.
+
+00:09:41.000 --> 00:09:45.000
+perhaps to spend on all of the new product lines that we have it.
+
+00:09:45.000 --> 00:09:50.000
+Um, but for the end user, of course, it would be very helpful to have the document piece.
+
+00:09:50.000 --> 00:09:55.000
+Um, so, for the sake of getting it up and running, and it works, and they can technically buy it,
+
+00:09:55.000 --> 00:09:57.000
+Not having them is fine, and that's what everybody will.
+
+00:09:57.000 --> 00:10:03.000
+But we're looking at how can we get that battery user experience with more data.
+
+00:10:03.000 --> 00:10:07.000
+We'll do some background context, too, into, like, the… just the attributes in general, um,
+
+00:10:07.000 --> 00:10:15.000
+We… we have a lot of attributes. The catalog team, really, the majority of their time is looking at more attributes and coming up with
+
+00:10:15.000 --> 00:10:18.000
+coming up with different ways to classify things, so…
+
+00:10:18.000 --> 00:10:20.000
+We're especially in the building controls industry.
+
+00:10:20.000 --> 00:10:23.000
+Um, like, um…
+
+00:10:23.000 --> 00:10:29.000
+There's not one in this group, but, like, a valve for a pipe, like, in the bathroom or something like that. Like, they want to know
+
+00:10:29.000 --> 00:10:36.000
+what material it is, what the pressure rating is, the heat resistance rating, like, we… it's really driven in that industry.
+
+00:10:36.000 --> 00:10:43.000
+is when they're going to look up parts, they don't really care about the brand, they just want to be able to filter by, I need this rating, I need at least to handle this pressure.
+
+00:10:43.000 --> 00:10:48.000
+He's been hit and handle this temperature range, automatic draw, like normal and closed, like, uh…
+
+00:10:48.000 --> 00:10:55.000
+It's really around the attributes, as opposed to where we've been making the iPhone, but that's…
+
+00:10:55.000 --> 00:11:05.000
+That's a little more, like, you know you want an iPhone when you go. This was more like, you don't care what brand it is. You know you need 16 gigs on your phone or 8GB, like, 5G. Yeah.
+
+00:11:05.000 --> 00:11:15.000
+So the thinking is, for each type of product category, a human is already doing this, and we probably could be making, like, this category of products needs this set of routes.
+
+00:11:15.000 --> 00:11:18.000
+So then when you're trying to ingest the product,
+
+00:11:18.000 --> 00:11:25.000
+First, we have to try to figure out how we were getting there. This fire had probably noticed something close on that, meaning they matched up ours.
+
+00:11:25.000 --> 00:11:28.000
+Uh, and then the second step is…
+
+00:11:28.000 --> 00:11:32.000
+Okay, now I know a category is, it needs all these attributes highlighted instead.
+
+00:11:32.000 --> 00:11:36.000
+to the venture, sometimes supply sheets, or… Yes, yes. And do you link?
+
+00:11:36.000 --> 00:11:38.000
+parts of the spec sheets or not?
+
+00:11:38.000 --> 00:11:46.000
+Yep, yeah, we have a lot of part-flowing inspections today, um, but again, the challenge with new parts, like,
+
+00:11:46.000 --> 00:11:51.000
+Ideally, the vendor is giving us a link to a spec sheet, but then giving us the product.
+
+00:11:51.000 --> 00:11:58.000
+And that can be helpful to you guys. We can kind of pull that spec sheet under our own control and work off of it.
+
+00:11:58.000 --> 00:12:01.000
+Alrighty. It's always going to be that easy.
+
+00:12:01.000 --> 00:12:12.000
+But in the best-case scenario, they do. And sometimes we don't, though, and the catalog team is literally goes out to their websites or lipsticks, they can go online and tries to infer and then put that in.
+
+00:12:12.000 --> 00:12:16.000
+I think you also mentioned in the presentation before that, uh, some vendors
+
+00:12:16.000 --> 00:12:24.000
+Um, give you guys some catalog, like a physical catalogs, or like, so the catalog team has to, like, manually enter all the data from that?
+
+00:12:24.000 --> 00:12:29.000
+And as a part of this, we'll want to automate that as well, right? Like, a way to…
+
+00:12:29.000 --> 00:12:41.000
+Yeah, so it's not so… it used to be that we'd get, like, a book, um, and they would try to, like, enter as much as we could. We're not dealing with that anymore, but they still might get if they can get it, right? Like…
+
+00:12:41.000 --> 00:12:49.000
+But more often than not, we'll then be able to give us something individually, some sort of Excel spreadsheet or whatever.
+
+00:12:49.000 --> 00:12:55.000
+Um, so that's the bulk of what we're trying to focus on. Good question. How structured and structured the data is? Yeah.
+
+00:12:55.000 --> 00:12:59.000
+Yeah. If there are…
+
+00:12:59.000 --> 00:13:01.000
+kind of, like, an ideal…
+
+00:13:01.000 --> 00:13:05.000
+layout of a farm that I have in mind for each part, like,
+
+00:13:05.000 --> 00:13:07.000
+Uh, actually, I'll start holding it.
+
+00:13:07.000 --> 00:13:10.000
+Sure, I really have one toothpaste both.
+
+00:13:10.000 --> 00:13:16.000
+Yeah, there's definitely, like, a basic schema if you lower, like, it needs a…
+
+00:13:16.000 --> 00:13:19.000
+product number, which is, like, um…
+
+00:13:19.000 --> 00:13:21.000
+what you refer to it as the name of the product, and then we have
+
+00:13:21.000 --> 00:13:29.000
+supplier product number, sometimes, like, suppliers have their own, uh, way that they, like, label a product versus what they sell to the public.
+
+00:13:29.000 --> 00:13:32.000
+Couple more fields, like description, list, cost, uh…
+
+00:13:32.000 --> 00:13:37.000
+But after that, the rest is all gets into attributes and just kind of more, like,
+
+00:13:37.000 --> 00:13:44.000
+things associated with it. Yeah, and that's what I'm saying, a human can say, like, what category is tablets, right? So, like,
+
+00:13:44.000 --> 00:13:53.000
+what's the screen size of the tablet? Is it, like, pen enabled? What, uh, what, like, um, standard of pen devices with this tablet use?
+
+00:13:53.000 --> 00:13:59.000
+Um, what colorism, of course, like, like, leaders, obviously brand this kind of separate thing, but
+
+00:13:59.000 --> 00:14:04.000
+When you keep going on and on, the human… we can have humans to find, like, tablets and mingles upon things.
+
+00:14:04.000 --> 00:14:09.000
+And then what's kind of, you gotta just figure out how they did those funny things from the, like,
+
+00:14:09.000 --> 00:14:14.000
+whatever we're given from the vendors, and work them into, like, what we want them to do with.
+
+00:14:14.000 --> 00:14:18.000
+I didn't mean I'm sitting there needing more data actually shows that.
+
+00:14:18.000 --> 00:14:23.000
+We do have, like, a cat meeting and an annual meeting, where we have
+
+00:14:23.000 --> 00:14:37.000
+Our attitudes map the latest products within Admin, and just take it with them and see it in the 48 minutes. So that's… so that way becomes the slowest, and… Yeah, so, like, with the tablets, Jared just listed some, like, you know, we would have the category of tablets
+
+00:14:37.000 --> 00:14:45.000
+And then we would have attributes assigned to that, so all tablets should have the attribute of screen size. All tablets should have the attribute of pen enabled.
+
+00:14:45.000 --> 00:14:55.000
+And then as we upload a new product into the tablet category, we automatically know, okay, it needs a value for string size, it needs a value for preventative, right? Like, so we have
+
+00:14:55.000 --> 00:15:03.000
+the value that the product has, um, at least downstream of us, like, before that, um, matching an attribute to a category, and then everything in the category.
+
+00:15:03.000 --> 00:15:06.000
+has to have values by no dashes. Very good.
+
+00:15:06.000 --> 00:15:13.000
+And that gets back to what I was saying earlier, like, the third part, uh, which is saying that this product is 20% ready.
+
+00:15:13.000 --> 00:15:19.000
+we would have a way to kind of do that based on, like, we've only entered two of the ten attributes associated with this
+
+00:15:19.000 --> 00:15:26.000
+products category, product, we need to know the screen size. We need to enter a screen size. That could be a way to determine what implementation mechanism is.
+
+00:15:26.000 --> 00:15:29.000
+We're not always doing this step. We could also bring 3 at least
+
+00:15:29.000 --> 00:15:35.000
+when you set up the spot for kicking each other out of absolutely needed to be able to make a sick kindness product.
+
+00:15:35.000 --> 00:15:40.000
+It would be nice to have them, but
+
+00:15:40.000 --> 00:15:44.000
+Again, we have these four things.
+
+00:15:44.000 --> 00:15:50.000
+Is there… I know before we talked about the most sensitive trend from the vendor with pricing, is there any proportion
+
+00:15:50.000 --> 00:15:52.000
+information about a product that makes it
+
+00:15:52.000 --> 00:15:55.000
+more sensitive or not, are we influencing the newest thing?
+
+00:15:55.000 --> 00:16:00.000
+And so there was the very same idea here. One or two brands that are a little…
+
+00:16:00.000 --> 00:16:04.000
+products, so, uh…
+
+00:16:04.000 --> 00:16:09.000
+Yeah, it's just a matter of, like, we can actually sell their products.
+
+00:16:09.000 --> 00:16:15.000
+You can still go on their website if they answer the most of our website.
+
+00:16:15.000 --> 00:16:22.000
+Um, so the only thing we really concerned about Goofy and Friday was…
+
+00:16:22.000 --> 00:16:30.000
+from the looks of it that I think we'll have to have discussions with the catalogue team just to understand how exactly they, like,
+
+00:16:30.000 --> 00:16:32.000
+social information, so we can do the same thing, just
+
+00:16:32.000 --> 00:16:35.000
+an automated fashion. So, like, we'll have…
+
+00:16:35.000 --> 00:16:38.000
+access to them, like you said, you can make some teams, or…
+
+00:16:38.000 --> 00:16:43.000
+love to… we can come over to EPAS as well for meeting them in person.
+
+00:16:43.000 --> 00:16:47.000
+And we could catalog team with the parts catalog team.
+
+00:16:47.000 --> 00:16:57.000
+We can probably, like… you would be able to message, like, the catalog work that we have at eParts, our parent company, Office Control Trolls, has a much larger, uh, catalog team.
+
+00:16:57.000 --> 00:17:13.000
+Which we… where we get most of them. We could organize information. No, with them, that would probably be, yeah, something in person would be ideal. But with the eParts catalog person, yeah, you could feel free to reach out to them on Teams.
+
+00:17:13.000 --> 00:17:19.000
+most of it. I don't think that was catalog work, so…
+
+00:17:19.000 --> 00:17:22.000
+We do some… like I said, we don't worry about the attributes as much.
+
+00:17:22.000 --> 00:17:28.000
+dispensary one or two people in our movement does catalog at all, and they also do other things.
+
+00:17:28.000 --> 00:17:33.000
+And they're gonna be… it was still only, like, 4 people, but they're all actually dedicating to catalogness.
+
+00:17:33.000 --> 00:17:40.000
+And they are Alps is a e-commerce, like, distributor that they're building control schools.
+
+00:17:40.000 --> 00:17:50.000
+So they are highly incentivized to make bread power dynamic, so that they're helping their customer enjoy the right thing because they bought the library instead of someone else.
+
+00:17:50.000 --> 00:17:54.000
+Most of our customers, it's the tools where
+
+00:17:54.000 --> 00:18:01.000
+Um, the engineer needs to, you know, they're, like, kind of locked in using our platform for corporate or whatever.
+
+00:18:01.000 --> 00:18:07.000
+Um, so, it's like, okay to not have the best catalog data, but we don't want
+
+00:18:07.000 --> 00:18:13.000
+Phelps needs to be able to have to make it better.
+
+00:18:13.000 --> 00:18:18.000
+Did you guys keep track from places, though, somebody orders something and turns out to prolonged things?
+
+00:18:18.000 --> 00:18:23.000
+Um, I think we'll have that now, figuring a CEC on…
+
+00:18:23.000 --> 00:18:36.000
+Right. Yeah, and we could do a lot of inserting, too, with RMAs. You could match RMAs against them. We don't have the structure for, you know, experimenting with me, so…
+
+00:18:36.000 --> 00:18:38.000
+Okay.
+
+00:18:38.000 --> 00:18:40.000
+Uh, I think, uh…
+
+00:18:40.000 --> 00:18:44.000
+Can we touch up on the… like, we had an ML component to the project? I was just a bit curious about…
+
+00:18:44.000 --> 00:18:50.000
+Uh, it said that we have to, like, you know, for future also, we plan to use
+
+00:18:50.000 --> 00:18:56.000
+enable further operations, how, like, which all aspects are you planning to use machine learning in?
+
+00:18:56.000 --> 00:18:58.000
+confidence scores and other things.
+
+00:18:58.000 --> 00:19:02.000
+So, financial scores would be available to a part where, uh…
+
+00:19:02.000 --> 00:19:08.000
+So, uh, pink remains, uh, you guys will be ready.
+
+00:19:08.000 --> 00:19:14.000
+Uh, in certain activities, and you should be able to grab these attributes. So, basically, right?
+
+00:19:14.000 --> 00:19:21.000
+We could be able to do things like face-to-face sampling and then figure out what would be the appropriate attribute for this testing.
+
+00:19:21.000 --> 00:19:28.000
+But it's just… it's just mattering, and it's also a little bit of, uh…
+
+00:19:28.000 --> 00:19:31.000
+This is just gone.
+
+00:19:31.000 --> 00:19:38.000
+what could be that event for this specific feeling. That's also in terms of figuring it out in what activities we can use.
+
+00:19:38.000 --> 00:19:45.000
+So, um, that's kind of the main thing. So, when we do make these predicates for these specific academies, we do assignments, but
+
+00:19:45.000 --> 00:19:48.000
+And that's where the confidence score comes in.
+
+00:19:48.000 --> 00:19:53.000
+Where you see the conference code is a little bit more important. Exactly.
+
+00:19:53.000 --> 00:19:55.000
+So, then the system learned that…
+
+00:19:55.000 --> 00:20:03.000
+this particular… this is the automatically corrected button. So that's… that's the next step, right? So…
+
+00:20:03.000 --> 00:20:06.000
+You actually have your booth, and then, you know, as you…
+
+00:20:06.000 --> 00:20:09.000
+As you keep getting more and more of similar errors.
+
+00:20:09.000 --> 00:20:16.000
+You can attach another part of the system, which kind of rectifies itself, or you can still try and do that if it's goodnight.
+
+00:20:16.000 --> 00:20:22.000
+So it all depends on how the project was, how it kind of turns out, again, how it develops, you know.
+
+00:20:22.000 --> 00:20:27.000
+Terral, they actually expect travel breeds are yield examples of photograph with a 3G rendering, and…
+
+00:20:27.000 --> 00:20:31.000
+from this series, those symptoms extend just beyond fetch Tools 30.
+
+00:20:31.000 --> 00:20:34.000
+attribute. We will have… then use it.
+
+00:20:34.000 --> 00:20:47.000
+product pictures, yeah, but I don't think anything that would be an actual big phone or an attribute of Stonehenge.
+
+00:20:47.000 --> 00:20:52.000
+Yeah, they wouldn't be, like, the Actives and just explain things.
+
+00:20:52.000 --> 00:21:01.000
+So, you guys just mentioned that you have the practical one who had, like, a schema of attributes, right? So if the attributes is the fixed part of it,
+
+00:21:01.000 --> 00:21:10.000
+Why are we applying the ML to the first part? Like, why do we need a confidence score to be assigned to BRIS? Like, why not to the category because
+
+00:21:10.000 --> 00:21:15.000
+That's what is a variable thing, uh, right?
+
+00:21:15.000 --> 00:21:18.000
+I mean, it could be for weeks, eh? So, those buttons…
+
+00:21:18.000 --> 00:21:23.000
+You've been trying that in the academy product, which could be contains both on the other machine learning.
+
+00:21:23.000 --> 00:21:27.000
+Next is, once you do figure that out and go to bed and we'll start things.
+
+00:21:27.000 --> 00:21:32.000
+Uh, you would know a specific, um, so for example, if I say,
+
+00:21:32.000 --> 00:21:36.000
+a specific attribute, for example,
+
+00:21:36.000 --> 00:21:43.000
+they take the pressure of the process immediately of the water or something like that. Like, for example, if it's a 30-kilo basket, then…
+
+00:21:43.000 --> 00:21:48.000
+Um, what we assign is actually, uh, something like 20 seconds in a row.
+
+00:21:48.000 --> 00:21:53.000
+Instead, so that would be strongly at the root and the document store.
+
+00:21:53.000 --> 00:21:56.000
+So, it might not know the exact kind of, uh…
+
+00:21:56.000 --> 00:22:02.000
+You need to put in there, value to put in there. It could be… it could just confuse that, uh…
+
+00:22:02.000 --> 00:22:06.000
+Um, I mean, I think as we have, like, this internally,
+
+00:22:06.000 --> 00:22:09.000
+module-based, which kind of purpose.
+
+00:22:09.000 --> 00:22:17.000
+lowest pathetic values, but you're painting in close to the distinction of a gain basket or leave architects of the individual towards January.
+
+00:22:17.000 --> 00:22:22.000
+then you would have to be already doing validations for that.
+
+00:22:22.000 --> 00:22:33.000
+So that, you know, okay, these certain values and the exact sort of values you're looking at, because they might not be enough data to actually implement… But then, your, uh, the values that we're talking about, uh, do I, like,
+
+00:22:33.000 --> 00:22:36.000
+You mentioned the sped dog, right? So…
+
+00:22:36.000 --> 00:22:39.000
+words can only tell you exactly? Um…
+
+00:22:39.000 --> 00:22:43.000
+Again, so it's not going to put you right…
+
+00:22:43.000 --> 00:22:53.000
+let's say it this way, uh, different suppliers will provide different sets of athletes. Apple might fall screen size, screen size.
+
+00:22:53.000 --> 00:22:57.000
+And someone else might holler at, um…
+
+00:22:57.000 --> 00:23:05.000
+And sometimes it's not that cut and dry, either. Sometimes it's not just string-sized
+
+00:23:05.000 --> 00:23:07.000
+stories are. Sometimes it's like,
+
+00:23:07.000 --> 00:23:13.000
+Um, the gas flow rate versus the water flow rate versus, like,
+
+00:23:13.000 --> 00:23:21.000
+There is some interpretation we've done between the buyers to be able to match them up to a similar standard.
+
+00:23:21.000 --> 00:23:27.000
+Two years to Barnes have ever stopped check their data? I mean, it's very feedback loop there and then.
+
+00:23:27.000 --> 00:23:32.000
+Uh, on the outside, they knew sometimes, um, that, um…
+
+00:23:32.000 --> 00:23:35.000
+On our side, we have an experience that some of our…
+
+00:23:35.000 --> 00:23:38.000
+Alright, take a little break, if…
+
+00:23:38.000 --> 00:23:45.000
+So, so the manual process right now, I mean thrives and we get the supplier to tell us our alpha meeting style.
+
+00:23:45.000 --> 00:23:51.000
+Um, so we deal with them, and it was a spreadsheet that describes the attributes that we want for these products.
+
+00:23:51.000 --> 00:23:56.000
+There's a lot of manual effort for them to actually build her out, and a lot of them just don't.
+
+00:23:56.000 --> 00:24:04.000
+Um, so they have the opportunity to try to make it right, but it's people with them for either gotten through.
+
+00:24:04.000 --> 00:24:12.000
+Thereabouts. Leading us to the question, but was it ever the other way down? Would they come up and tell you that, okay, this is how…
+
+00:24:12.000 --> 00:24:19.000
+We want to build product to be, you know, capitalized, so these are the attributes that, uh, and then…
+
+00:24:19.000 --> 00:24:24.000
+What I mean to ask is, uh, let's say the iPad. In your system, you just have 4.
+
+00:24:24.000 --> 00:24:31.000
+But then, since they know the internet working, maybe, like, I'm just saying, are they like, okay, I need a first one.
+
+00:24:31.000 --> 00:24:34.000
+So, were there a requirement?
+
+00:24:34.000 --> 00:24:38.000
+So, so first off, we can totally take up Bitcoin right now.
+
+00:24:38.000 --> 00:24:49.000
+Um, if they want to add in a particular attribute that isn't in that required step, that's fine. Is that even possible? Like, are they allowed to come up with a requirement that starts?
+
+00:24:49.000 --> 00:24:55.000
+They are really able to define the data set for a catalog.
+
+00:24:55.000 --> 00:25:03.000
+Uh, because… so they have their own definition, right? They have their own analog somewhere, and some days are they…
+
+00:25:03.000 --> 00:25:07.000
+Um, some of them can give us a lot of that pay that some of them can't, but…
+
+00:25:07.000 --> 00:25:13.000
+Um, uh, our job is to homogenize data between the manufacturers.
+
+00:25:13.000 --> 00:25:24.000
+So, we… we have to have the liberty to be able to change the modifier that begins with it. It's for the same ease of use for the actual application.
+
+00:25:24.000 --> 00:25:29.000
+But also, some of the vendors could want, um, more so in the case of, like, ELPS.
+
+00:25:29.000 --> 00:25:39.000
+Um, they could want their products to get more visibility to customers. They would want their products to show up in more searches based on more attributes, and especially when they're working out, like, the deal of
+
+00:25:39.000 --> 00:25:45.000
+the price they're going to be giving to buy it from them. There's, like, all these negotiations, and a lot of times they will…
+
+00:25:45.000 --> 00:25:53.000
+actually look at their catalog at ELPS and see, like, hey, this is the way we're… you're showing our products to, like,
+
+00:25:53.000 --> 00:25:56.000
+something right there.
+
+00:25:56.000 --> 00:26:00.000
+Okay, we go, next part.
+
+00:26:00.000 --> 00:26:05.000
+of your experience with the previous two-year project that you guys have sponsored.
+
+00:26:05.000 --> 00:26:10.000
+any, uh, like, just about the previous experience, any do's and don'ts that you have for us?
+
+00:26:10.000 --> 00:26:13.000
+usually the mind.
+
+00:26:13.000 --> 00:26:26.000
+Yeah, um, dues, um, reach out to us on Teams, like, as much as possible. Like, we do not want getting, like, a lot of communication, a lot of messages, a lot of posts, like, we prefer that rather than, um…
+
+00:26:26.000 --> 00:26:35.000
+Yeah, we're, like, saving it all for the next week's meeting. Like, if something comes up like that, just reach out. It's, you know, we're all very, uh…
+
+00:26:35.000 --> 00:26:37.000
+We have very fast communications.
+
+00:26:37.000 --> 00:26:39.000
+So I'd say that's a big one.
+
+00:26:39.000 --> 00:26:46.000
+We're like, very briefly in the presentation, mentioned it, but we… we were like, this idea of after our January debate.
+
+00:26:46.000 --> 00:26:55.000
+Um, so, like, that's because we're saying something needs to be submari, doesn't mean… like, you're already doing it, something that's great, but with this bad, you know.
+
+00:26:55.000 --> 00:27:02.000
+We'll be glad to think of it differently, or explain why we should think of it differently with, like, I'd rather have that discussion than later.
+
+00:27:02.000 --> 00:27:05.000
+We'll never take offense to you.
+
+00:27:05.000 --> 00:27:09.000
+Thinking about a different way, that would be it.
+
+00:27:09.000 --> 00:27:16.000
+is probably fine. Yeah, yeah, that's very helpful.
+
+00:27:16.000 --> 00:27:21.000
+I would say, like, asking the same question again and again is still fine. That will not ask me what you do.
+
+00:27:21.000 --> 00:27:27.000
+It kind of helps in clear the gap, and it keeps the homeless for the visual openness.
+
+00:27:27.000 --> 00:27:30.000
+Also, um, I'd say, too, um,
+
+00:27:30.000 --> 00:27:33.000
+follow up with us, like, if you…
+
+00:27:33.000 --> 00:27:38.000
+If we commit to something in a meeting, um, and you haven't heard much from us, like,
+
+00:27:38.000 --> 00:27:44.000
+quote-unquote, bother us. Like, reach out to me, like, hey, you know, like, I haven't heard back with this. Like, again, we want that.
+
+00:27:44.000 --> 00:27:53.000
+As opposed to then we get to the next week's meeting, and he, oh, did you get a chance to do that when you forgot about it all week, or something, you know?
+
+00:27:53.000 --> 00:27:56.000
+any particular don'ts that you guys think?
+
+00:27:56.000 --> 00:27:58.000
+You should be mindful of?
+
+00:27:58.000 --> 00:28:07.000
+Kind of covered it in between the side. Probably, uh, just don't go paperwork provider, and then just, uh, proper two weeks later and everything else.
+
+00:28:07.000 --> 00:28:11.000
+I mean, we completely privilege that, too, but…
+
+00:28:11.000 --> 00:28:16.000
+It'd just be a bit more easier for us to keep track of things that help you guys are programming.
+
+00:28:16.000 --> 00:28:23.000
+Yeah, I think that's the main one, and Matt, you've had pretty successful project right now. It's just that, I mean,
+
+00:28:23.000 --> 00:28:26.000
+don't miss bad, like, saying it takes me longer time to move onboard that.
+
+00:28:26.000 --> 00:28:31.000
+And you do a map like your studies and academics, that's what would be amazing.
+
+00:28:31.000 --> 00:28:34.000
+It might be difficult to stay that invited. I would say if, like,
+
+00:28:34.000 --> 00:28:38.000
+are random 2A, and we pay for something, and you send it. You have to say something?
+
+00:28:38.000 --> 00:28:42.000
+is send that to us, we'll take you look at it for a long time, but we'll still be taking time.
+
+00:28:42.000 --> 00:28:46.000
+if it won't be productiveness at that point, so…
+
+00:28:46.000 --> 00:28:54.000
+Yeah, you certainly know me, because this semester, they only have 4 barns a week to work on this, but they have zero intent for doing that, too, which one of those kind of thing.
+
+00:28:54.000 --> 00:29:00.000
+summer time around for a very good thing.
+
+00:29:00.000 --> 00:29:09.000
+How many of you have it done? Two or three, I don't remember how many people did 3. Okay, this is our fourth.
+
+00:29:09.000 --> 00:29:15.000
+That's good, yeah, it's good to be the experience. I mean, when you get a new client who's never had any experience with those things,
+
+00:29:15.000 --> 00:29:20.000
+They think, oh, the team is fully working for us 24-7.
+
+00:29:20.000 --> 00:29:24.000
+That other thing, not only important to note.
+
+00:29:24.000 --> 00:29:30.000
+to your point, we're getting… we're getting better and better every year with how we're interfacing.
+
+00:29:30.000 --> 00:29:40.000
+Well, a lot of it's about managing expectations, yes, for most of it.
+
+00:29:40.000 --> 00:29:45.000
+I'm gonna put words in the team's mouth, which is, you know, this is a lot of good experience and do's and don'ts.
+
+00:29:45.000 --> 00:29:50.000
+From the team's… I'll actually ask the team, from the team's perspective, is there anything that you want from
+
+00:29:50.000 --> 00:29:52.000
+from eParts, and how you work with them.
+
+00:29:52.000 --> 00:29:59.000
+Have you thought about that?
+
+00:29:59.000 --> 00:30:03.000
+I think from my point of view, it was just the communication part, like,
+
+00:30:03.000 --> 00:30:07.000
+We are bound to have a lot of questions, because it's going to be a kind of a…
+
+00:30:07.000 --> 00:30:10.000
+struggling with scratch point of view, so we'll have questions.
+
+00:30:10.000 --> 00:30:16.000
+And if, like, you guys can respond, like, that'll be… I think that's the main thing I have in mind.
+
+00:30:16.000 --> 00:30:19.000
+And that you already cared of, so that's really good.
+
+00:30:19.000 --> 00:30:24.000
+Elizabeth our third, uh, product data related.
+
+00:30:24.000 --> 00:30:26.000
+Uh, it's a new project.
+
+00:30:26.000 --> 00:30:32.000
+So, sometimes, like for me, sometimes it'll be hard to remember what I've shared with you versus, like, the other teams.
+
+00:30:32.000 --> 00:30:40.000
+So I'll always ask clarifying questions. Don't be afraid.
+
+00:30:40.000 --> 00:30:46.000
+Well, also, I guess, uh, I guess one thing, too, just to do in previous experience that we…
+
+00:30:46.000 --> 00:30:50.000
+We've had before, especially, like, with the initial PIMS, we had
+
+00:30:50.000 --> 00:30:59.000
+months of actually just kind of circling back to the initial kind of same question, with a lot of confusion, they weren't getting it. It's a very complex
+
+00:30:59.000 --> 00:31:05.000
+distribution model. We won't really get into the distribution between our tenants, but my main point is, like, uh…
+
+00:31:05.000 --> 00:31:15.000
+There's, like, something you're still unclear on overall, like, we can keep revisiting it. We found that if we get a really, really concrete understanding of that.
+
+00:31:15.000 --> 00:31:20.000
+Even if it takes a couple weeks, I'm touching on them every week, but it's gonna definitely be better in any of them.
+
+00:31:20.000 --> 00:31:28.000
+Yeah, it's important to have domain knowledge from the team's perspective for that.
+
+00:31:28.000 --> 00:31:31.000
+Moving on to the next part.
+
+00:31:31.000 --> 00:31:33.000
+So, now we have the product usage.
+
+00:31:33.000 --> 00:31:36.000
+I think we've already touched up a bit on this, but…
+
+00:31:36.000 --> 00:31:43.000
+Uh, the industry pipeline, I don't be a different, uh, like, client interface, or…
+
+00:31:43.000 --> 00:31:46.000
+How are we trying to, like, do we get to plan something that they can…
+
+00:31:46.000 --> 00:31:53.000
+share the data with, or just the PDF they already submit, and we just process it on our side.
+
+00:31:53.000 --> 00:31:55.000
+authorization pipeline.
+
+00:31:55.000 --> 00:31:58.000
+Yeah, from the Southern, yeah.
+
+00:31:58.000 --> 00:32:00.000
+Um, I mean…
+
+00:32:00.000 --> 00:32:03.000
+We either want to set, like, harder environments on this.
+
+00:32:03.000 --> 00:32:08.000
+Uh, we mean, right now, one of these files, we're getting, like,
+
+00:32:08.000 --> 00:32:11.000
+by emailing with the vendor,
+
+00:32:11.000 --> 00:32:18.000
+Uh, sometimes it would be big enough that I think Brian, who runs weekly or something that he set up to put them on his own.
+
+00:32:18.000 --> 00:32:22.000
+Um, you know, the best…
+
+00:32:22.000 --> 00:32:27.000
+The best long-term reading on this is to make it as often as possible, just like a February 9 days.
+
+00:32:27.000 --> 00:32:32.000
+One of the means that might be DBI, one of them means is going to be where they have some B.
+
+00:32:32.000 --> 00:32:37.000
+Uh, but it could… I don't want to limit it, but it could be, like,
+
+00:32:37.000 --> 00:32:43.000
+setting up a way for them to see this, and I think that's been corrected into our system.
+
+00:32:43.000 --> 00:32:49.000
+I don't want to say there's absolutely no vendor interface here. They took one of the best users to clean up.
+
+00:32:49.000 --> 00:32:54.000
+some of that, uh, given them loose type from state perspective is making.
+
+00:32:54.000 --> 00:32:57.000
+Um, so it just…
+
+00:32:57.000 --> 00:33:03.000
+Depends, uh, on kind of our mutual, uh, exploration of the problem.
+
+00:33:03.000 --> 00:33:10.000
+so many vendors who are giving us data, I don't…
+
+00:33:10.000 --> 00:33:17.000
+Obviously, I have some business requirements with the company, right? So why wouldn't we have a sanitized portal?
+
+00:33:17.000 --> 00:33:24.000
+Like, the… some people get written in CBA form, and then some send it over an email or via
+
+00:33:24.000 --> 00:33:30.000
+So, why… why not a single format like this, or where people just feed in where
+
+00:33:30.000 --> 00:33:39.000
+our data, and then we take it forward from you guys. That's the ultimate goal, and we originally had that with Tim at the very beginning,
+
+00:33:39.000 --> 00:33:42.000
+In addition to the plan… in addition to the catalog teams.
+
+00:33:42.000 --> 00:33:46.000
+vendors themselves could come in here, and they could do the work themselves.
+
+00:33:46.000 --> 00:34:01.000
+Uh, we'll… we'll be working on some of the efforts this year, too. So there's… there's, like, the historical reason as to why we haven't… I'm not in the past. Um, but kind of one maintenance team, uh, historically has been very, um,
+
+00:34:01.000 --> 00:34:09.000
+Um, like, they want to fully own the data that gets in the system, they don't want anyone else's fingerprints on it besides their own.
+
+00:34:09.000 --> 00:34:15.000
+within the vendors. Um, so it was a very intentional way to manual for a long time.
+
+00:34:15.000 --> 00:34:20.000
+Now, reverse cleared up for our own desire to begin pension board manual.
+
+00:34:20.000 --> 00:34:26.000
+Um, so yes, we are trying to answer questions like that. We'll stop break for waiting.
+
+00:34:26.000 --> 00:34:30.000
+Basically, one of the interesting experiments or research would be
+
+00:34:30.000 --> 00:34:33.000
+to take some data that… data sets you've had before,
+
+00:34:33.000 --> 00:34:39.000
+And, uh, sample data sets, we compare them to what's in PIMS now, or whatever.
+
+00:34:39.000 --> 00:34:41.000
+just to see how they kind of map out
+
+00:34:41.000 --> 00:34:45.000
+And see where the gaps are hopefully being in crypto thing dealing with that.
+
+00:34:45.000 --> 00:34:52.000
+But also give them an idea that familiar with.
+
+00:34:52.000 --> 00:34:57.000
+We're not answering the questions.
+
+00:34:57.000 --> 00:35:01.000
+Um, yeah, about the product vision, I think we also touched up on that.
+
+00:35:01.000 --> 00:35:03.000
+you know, particularly light about it.
+
+00:35:03.000 --> 00:35:08.000
+Uh, yeah, we can maybe… we can talk about the team responsibility and deliver those a bit.
+
+00:35:08.000 --> 00:35:11.000
+So, as, uh…
+
+00:35:11.000 --> 00:35:14.000
+as a deliverable, it's just going to be a system, right, where…
+
+00:35:14.000 --> 00:35:18.000
+Uh, we have the, like, engine pipeline with the…
+
+00:35:18.000 --> 00:35:21.000
+ML model to, uh, read the attributes.
+
+00:35:21.000 --> 00:35:25.000
+So, okay, is there anything else you want to add on these points?
+
+00:35:25.000 --> 00:35:34.000
+Um, not just, uh, we wanted also, like, from the application part of that, that actually may search some of, like, the staging was right, and it would do an additional repair and cylinder model.
+
+00:35:34.000 --> 00:35:39.000
+Um, in Yahoo and in India. It's a system of…
+
+00:35:39.000 --> 00:35:43.000
+So, they should leave it to be enough of these sources.
+
+00:35:43.000 --> 00:35:47.000
+probably churn out some sort of, uh,
+
+00:35:47.000 --> 00:35:49.000
+Prediction 30 is programmed at 8.
+
+00:35:49.000 --> 00:35:53.000
+a programmatic approach to just put these things into the right type of games.
+
+00:35:53.000 --> 00:35:58.000
+Uh, and they're probably paying attention to the developer. I think that's… that's the problem.
+
+00:35:58.000 --> 00:36:03.000
+Yeah, we want that, uh, that actually… the model, but then also…
+
+00:36:03.000 --> 00:36:11.000
+something that puts what the model determines at least in these tables. We do have staging environment domain, so we just have to believe.
+
+00:36:11.000 --> 00:36:13.000
+I still… I'm done for the day in the name.
+
+00:36:13.000 --> 00:36:19.000
+And then probably the community, and then we'll get the final table, which is probably to fill out.
+
+00:36:19.000 --> 00:36:22.000
+We already have a…
+
+00:36:22.000 --> 00:36:26.000
+the quick… our schema right now, basically, like, for the prob… we have, like, product table,
+
+00:36:26.000 --> 00:36:32.000
+But for every product options table, product attributes table, but for every table we have, we also have a…
+
+00:36:32.000 --> 00:36:47.000
+staging product table, the staging product options table, a staging product attribution table. And these tables, that combination is how we have interfaces where almost like a Git difference. They can see, like, this is the product before, and then these are the changes I'm making, and this is where they've been approved, or…
+
+00:36:47.000 --> 00:36:50.000
+decide, no, that's not right, and they go back and change something again.
+
+00:36:50.000 --> 00:36:58.000
+Ideally, we would just need the model, if you would like upload spec sheets to just pop it into the stating table, and then from there, Pint handles it in the opinion.
+
+00:36:58.000 --> 00:37:03.000
+see the differences, they can choose to go manually, edit more things, or whatnot, but um…
+
+00:37:03.000 --> 00:37:06.000
+Yeah, does that make sense?
+
+00:37:06.000 --> 00:37:15.000
+I'm curious, for the previous Studio Teams workflow, will they ever… did they ever do this exercise of doing the statement award?
+
+00:37:15.000 --> 00:37:27.000
+A requirements documents? We've gone through a requirements document.
+
+00:37:27.000 --> 00:37:31.000
+Um, any other possible constraints or risks?
+
+00:37:31.000 --> 00:37:34.000
+That would, like, we should take a keep mind while developing.
+
+00:37:34.000 --> 00:37:38.000
+Uh, without data sensitivity or something on that same.
+
+00:37:38.000 --> 00:37:42.000
+prices, so…
+
+00:37:42.000 --> 00:37:48.000
+just to be looking at NIV data, we should be able to edit.
+
+00:37:48.000 --> 00:37:50.000
+Where it was something I was expecting.
+
+00:37:50.000 --> 00:37:53.000
+If you can reactivate, and uh…
+
+00:37:53.000 --> 00:37:55.000
+I think…
+
+00:37:55.000 --> 00:37:58.000
+We need this test if we don't just put it all the time.
+
+00:37:58.000 --> 00:38:02.000
+Um, understanding the exact scope thing we need to show that.
+
+00:38:02.000 --> 00:38:05.000
+be kind of adjusted for the manual, you just…
+
+00:38:05.000 --> 00:38:10.000
+I think that's what it is.
+
+00:38:10.000 --> 00:38:14.000
+Okay, next is, um, about AI usage.
+
+00:38:14.000 --> 00:38:18.000
+Uh, I just want to ask, like, how do you guys…
+
+00:38:18.000 --> 00:38:21.000
+use AI today in, like, everyday development, or…
+
+00:38:21.000 --> 00:38:28.000
+Around the company, where we do. For development, especially for coding, we've been pretty…
+
+00:38:28.000 --> 00:38:36.000
+Pretty all-in versus February, since by doing… I mean, but 5.30 when I started getting people in the public eye, and we leave it a lot.
+
+00:38:36.000 --> 00:38:40.000
+Please pursue pretty extensively for all long through.
+
+00:38:40.000 --> 00:38:43.000
+Of course, all got me expanding well.
+
+00:38:43.000 --> 00:38:50.000
+I think the third use cases are not doing that in the news.
+
+00:38:50.000 --> 00:38:53.000
+We kind of…
+
+00:38:53.000 --> 00:39:03.000
+have the requirements and the state staff that our entities sort of start with the miscellane if they were, and then go building our meeting at the beginning of the week.
+
+00:39:03.000 --> 00:39:12.000
+I think it's testing heavyweight, so the way you do it is, uh, we generate through it, um, we made sure that we tested expenses and
+
+00:39:12.000 --> 00:39:15.000
+Um, and then, based on changes,
+
+00:39:15.000 --> 00:39:19.000
+Again, everything broke.
+
+00:39:19.000 --> 00:39:28.000
+Okay, and I'm also gonna be, uh, just to my, like, the end, these are, like, you know, we started with all the practice, and we're removed.
+
+00:39:28.000 --> 00:39:33.000
+With, like, documents inside the code so that it's reading in the future, like, the contacts are better at the text.
+
+00:39:33.000 --> 00:39:39.000
+We need a couple other things that are important. I mean, we do things with quite complex monitoring for, like, in the groundwork.
+
+00:39:39.000 --> 00:39:45.000
+For example, if you're working with Facebook features, so you wouldn't need to contact FDM5 application for that.
+
+00:39:45.000 --> 00:39:47.000
+So, we've had a better room.
+
+00:39:47.000 --> 00:39:50.000
+Teachers testing are markdown banned.
+
+00:39:50.000 --> 00:39:53.000
+Agent needed finding out. So, based off of that,
+
+00:39:53.000 --> 00:39:58.000
+or restrict name demand.
+
+00:39:58.000 --> 00:40:04.000
+And we're not, um… we use cursor, and we've all kind of… we evaluated Copilot early on, um…
+
+00:40:04.000 --> 00:40:18.000
+We obviously like Perstart, but we're not restricted to that. Like, if you all decide, do you want to lose cockpit or something, or buy, you know, other tools, like, person from what we already have, that's good for me.
+
+00:40:18.000 --> 00:40:23.000
+Um, yeah, I wanted to ask if he, for the, like, when we are using AI in our project, like,
+
+00:40:23.000 --> 00:40:26.000
+Any part you would expect us to use it, like,
+
+00:40:26.000 --> 00:40:29.000
+generated code and, like, other things, but any other…
+
+00:40:29.000 --> 00:40:33.000
+Uh, like, your expectation of where we should be using AI.
+
+00:40:33.000 --> 00:40:41.000
+I mean, I think that's the best experience we can go in.
+
+00:40:41.000 --> 00:40:46.000
+anticipated for me.
+
+00:40:46.000 --> 00:40:51.000
+probably just check the course, AMS.
+
+00:40:51.000 --> 00:40:56.000
+making sure… I mean, even, like, decisions of, uh…
+
+00:40:56.000 --> 00:40:59.000
+understanding workflow to be direct or something is to please.
+
+00:40:59.000 --> 00:41:04.000
+I would say it's just the representative available.
+
+00:41:04.000 --> 00:41:12.000
+That's quick, and for us using those LLMs, would be… should we use ours, or will we be provided one?
+
+00:41:12.000 --> 00:41:15.000
+from either agriculture subscription that you sell.
+
+00:41:15.000 --> 00:41:27.000
+Yeah, so within cursory, you can… you can choose for this prompt, I want to use Gemini, or you can switch it, I want to use my signals, like Sonic 4.5.
+
+00:41:27.000 --> 00:41:34.000
+But, uh, we do need… I think we should be on the phone.
+
+00:41:34.000 --> 00:41:38.000
+I've been doing it before it really is here.
+
+00:41:38.000 --> 00:41:46.000
+Okay, so, uh, you mentioned the North Winvestios, like, I've been using anti-mark video, and it seems to be dealing with digital.
+
+00:41:46.000 --> 00:41:53.000
+Do you not want us to use any other elements. You could, as long as, uh, it's…
+
+00:41:53.000 --> 00:41:56.000
+keeping the data analysis.
+
+00:41:56.000 --> 00:42:01.000
+I think… I think as long as the data is not being strife, depending on the run.
+
+00:42:01.000 --> 00:42:13.000
+Yeah, so yeah, that's the biggest concern. Well, we… other than that, what… Which is the platform look that we need? Yeah. Okay, we knew it was approved for that.
+
+00:42:13.000 --> 00:42:16.000
+The building of, um,
+
+00:42:16.000 --> 00:42:18.000
+In first, the product development.
+
+00:42:18.000 --> 00:42:24.000
+Were that the, uh, from our side, or would that be, uh, from there?
+
+00:42:24.000 --> 00:42:27.000
+We have open marketplace.
+
+00:42:27.000 --> 00:42:35.000
+I think it would be, like…
+
+00:42:35.000 --> 00:42:38.000
+So…
+
+00:42:38.000 --> 00:42:41.000
+I think a lot of the work that we're going to do with non-party.
+
+00:42:41.000 --> 00:42:44.000
+one side of the infrastructure team or anything like that.
+
+00:42:44.000 --> 00:42:54.000
+It was not yet, right? Um, and therefore, for all of that stuff, like, as long as they're deleting directly into the things free throws or anything like that.
+
+00:42:54.000 --> 00:43:05.000
+I don't mind it being used in that you're having Andy share the stuff for them, right? It kind of doesn't matter that you responded on ours yet. Again, as long as you're not, like,
+
+00:43:05.000 --> 00:43:10.000
+uploading vendor files to that. Like, I don't want to be… yeah, I don't…
+
+00:43:10.000 --> 00:43:16.000
+We're not… all the vendor data isn't super sensitive, but also don't want to be just, like, willy willy-nilly with it.
+
+00:43:16.000 --> 00:43:21.000
+Um, but you're welcome to move your own stuff.
+
+00:43:21.000 --> 00:43:39.000
+Like, if they decide group lottery looks good, like, I guess we paid for it. That would be kind of absolutely what I was going towards, uh, just from my experience, what our workplace.
+
+00:43:39.000 --> 00:43:47.000
+Yeah, I don't know what their… any pricing, or is there anything like that. I think a bi-Fi, the Black Friday is, like, the minimum 300 bucks.
+
+00:43:47.000 --> 00:43:53.000
+There's also not a minimum 200 month to March, so the person.
+
+00:43:53.000 --> 00:44:13.000
+Uh, so that's… that's part of where my mind is at, but again, if it's a deep flurse, then we have a good rationale for why I should tell you that. No, I… You might not understand that. I do understand that there's something that you're saying that this actually make a 30 times more productive, and there's no way that product appears if they do this to our city.
+
+00:44:13.000 --> 00:44:16.000
+let's have the discussion.
+
+00:44:16.000 --> 00:44:25.000
+Having to reach the heads of almost all the weeks to spend a time to stay in academic rational. There are all Asian-based first kind of…
+
+00:44:25.000 --> 00:44:33.000
+application kind. They all indexed by similarly, they're all numbers with similar model access, familiarity stuffing green.
+
+00:44:33.000 --> 00:44:43.000
+Yeah, I think it's just a problem. It's exactly, it's just done the level of problems, like, probably kind of have designed me.
+
+00:44:43.000 --> 00:44:52.000
+You can literally just change the body of the leaders of it.
+
+00:44:52.000 --> 00:45:01.000
+Yeah.
+
+00:45:01.000 --> 00:45:08.000
+Likewise decisions, too.
+
+00:45:08.000 --> 00:45:13.000
+We're a small company, we're not only an enterprise, so it would not make a deal comes to us.
+
+00:45:13.000 --> 00:45:19.000
+But it also does make the difference to our country grad schools and getting the private schools, like, whatever, so…
+
+00:45:19.000 --> 00:45:22.000
+Uh, so the money side works on burden.
+
+00:45:22.000 --> 00:45:27.000
+for us to spend money on this, but…
+
+00:45:27.000 --> 00:45:33.000
+Thank you, Smart. No problem.
+
+00:45:33.000 --> 00:45:35.000
+I think next, do you have any…
+
+00:45:35.000 --> 00:45:39.000
+questions you guys might have, or anyone from the team.
+
+00:45:39.000 --> 00:45:46.000
+That's having out-of-pind questions.
+
+00:45:46.000 --> 00:45:49.000
+Yeah, so, uh, I have one, uh…
+
+00:45:49.000 --> 00:45:55.000
+How do they handouts at the house to understand the testimonies?
+
+00:45:55.000 --> 00:46:01.000
+It depends on the phone, so, uh, so there are really, like, 3 parts of the pandemic as anybody who is, uh…
+
+00:46:01.000 --> 00:46:07.000
+the main, um, the data pipeline setting, just locking, uh, and the debaseable location.
+
+00:46:07.000 --> 00:46:11.000
+For that, we had, like, the proper knowledge transfer from some data after this.
+
+00:46:11.000 --> 00:46:17.000
+expecting me to try to set things up, and then I just type it on there on books as well.
+
+00:46:17.000 --> 00:46:22.000
+And then there will be change in brand.
+
+00:46:22.000 --> 00:46:26.000
+I mean, it was a super lightweight session, but you are spending that, I said.
+
+00:46:26.000 --> 00:46:32.000
+for other things. We had this kind of, like, documentation hangout.
+
+00:46:32.000 --> 00:46:37.000
+Where we had the 3-minute talk on important covers, and what's begin with.
+
+00:46:37.000 --> 00:46:40.000
+So, it depends on your use case.
+
+00:46:40.000 --> 00:46:48.000
+And what exactly knows. We are looking for more of, like, the stuff that would make lessons, but it stands for them, yes, the blueber hands on them.
+
+00:46:48.000 --> 00:46:54.000
+Uh, that has allowed the food stock might not be funded, so…
+
+00:46:54.000 --> 00:46:56.000
+How does your current, uh…
+
+00:46:56.000 --> 00:47:04.000
+doing the code checking process look like, you know, using our web buffer, and starting from the XFS doc?
+
+00:47:04.000 --> 00:47:11.000
+ACPRs, who are the stakeholders, then a member of… we should must get approved from?
+
+00:47:11.000 --> 00:47:17.000
+The bucket is what he knows, and again, I think…
+
+00:47:17.000 --> 00:47:20.000
+or whatever events that are important to restart the noise can be delivered.
+
+00:47:20.000 --> 00:47:31.000
+So they were literally in the private approvals and things like that. It should be intuitive. I would say you guys should probably look at the last year.
+
+00:47:31.000 --> 00:47:42.000
+And that was a few others. Yeah, the only… the only time I could see modifications to, like, something we already have existing is, uh, what we talked about earlier is, like, when it comes time to actually put the products in,
+
+00:47:42.000 --> 00:47:48.000
+We can talk down the road about how we want to do that, or maybe we just make some APIs that implement or something, I don't know.
+
+00:47:48.000 --> 00:47:57.000
+Or you could spin off of… spin off the committee if we branch together, and this update stage manual. So that you guys can still upload that report.
+
+00:47:57.000 --> 00:48:05.000
+And, um, we do also, though, we do use, um, a sonar. Uh, we use… we use sonar for limping.
+
+00:48:05.000 --> 00:48:12.000
+Todd, you got any third party for, like, security purposes? Like, something like a backup or something?
+
+00:48:12.000 --> 00:48:14.000
+To check vulnerabilities in the system.
+
+00:48:14.000 --> 00:48:17.000
+security moving these, uh, so I think someone does it.
+
+00:48:17.000 --> 00:48:23.000
+So I saw that as pretty, uh, extensive, very, uh, extensive in terms of, uh,
+
+00:48:23.000 --> 00:48:27.000
+what it covers.
+
+00:48:27.000 --> 00:48:34.000
+But yeah, it covers anything, so security into almost no checks, too.
+
+00:48:34.000 --> 00:48:40.000
+I have a trivial question. What were the names of the previous MSD tools? What would their team names be named?
+
+00:48:40.000 --> 00:48:46.000
+First one of Texas, and then there was, um…
+
+00:48:46.000 --> 00:48:51.000
+option logic, really unknown with that.
+
+00:48:51.000 --> 00:48:57.000
+That's alright. We've been in.
+
+00:48:57.000 --> 00:49:01.000
+Last year, like, did we even have anything? There was, I think it was…
+
+00:49:01.000 --> 00:49:05.000
+Maybe they never used, I mean, this used eBite's statements.
+
+00:49:05.000 --> 00:49:17.000
+I mean, you guys should have a new one. That's a rule. Be more fun, learn more. The team name, the scree de Corps. Yeah, the intensity was fun, especially, yeah, but…
+
+00:49:17.000 --> 00:49:23.000
+And a logo difference, email address.
+
+00:49:23.000 --> 00:49:26.000
+Uh, there's a decent chance we'll be switching from BitPuck.
+
+00:49:26.000 --> 00:49:32.000
+in my mind.
+
+00:49:32.000 --> 00:49:36.000
+We don't have any political conversation.
+
+00:49:36.000 --> 00:49:47.000
+And you would be our first team using Winning Man.
+
+00:49:47.000 --> 00:49:55.000
+That would be cycling me for asking the onboarding. So what were your… what was your rationale going from Jira to…
+
+00:49:55.000 --> 00:50:00.000
+I mean, one of the biggest benefits that I think is fits fast, like, we're gonna open an issue,
+
+00:50:00.000 --> 00:50:05.000
+is interceptions instead of multiple septants.
+
+00:50:05.000 --> 00:50:14.000
+And also the flexibility. You can very easily, like, earn something from an issue or test early to… like, it's much more flexible work.
+
+00:50:14.000 --> 00:50:17.000
+Jira was a lot more, like, locked in with DBA or the portal.
+
+00:50:17.000 --> 00:50:28.000
+And so it's a great opportunity in the philosophy of the problem. If you were following an aircraft methodologies, it accommodates everything completely with zero tolerance it.
+
+00:50:28.000 --> 00:50:34.000
+And Bobby just going to… it has… it has support for that. All of these already immunized with…
+
+00:50:34.000 --> 00:50:39.000
+So… and so it's a little milestone rhythm methodology in general.
+
+00:50:39.000 --> 00:50:41.000
+everyone having their phones.
+
+00:50:41.000 --> 00:50:52.000
+Um, a task for transport and class itself.
+
+00:50:52.000 --> 00:50:58.000
+Any found a date better, or later date with the chance of the energy, and the UI is more technical.
+
+00:50:58.000 --> 00:51:00.000
+I said, loaded with a commercial product.
+
+00:51:00.000 --> 00:51:03.000
+raising when it comes to more visibility, that's what we're thinking.
+
+00:51:03.000 --> 00:51:23.000
+So we know we can show our mentors.
+
+00:51:23.000 --> 00:51:29.000
+So we're gonna have a standing fund meeting on Thursday, 6 times every week to look at the funds.
+
+00:51:29.000 --> 00:51:40.000
+I mean, and if there's… if it says tough, uh, just shoot us some other times, uh, we do have a couple reoccurrings on our side that we'll have to… that are kind of like, uh,
+
+00:51:40.000 --> 00:51:48.000
+No, but other than that, if need be, we can get some more time or something. I mean, based on your schedules, too, I think that's the bigger factor, I think, so…
+
+00:51:48.000 --> 00:51:58.000
+Aren't this pretty much permitted in today, for sure, and based on you attributes, we can see what else possible in HP has itself.
+
+00:51:58.000 --> 00:52:02.000
+Obviously, it's domestic, and it's the reason.
+
+00:52:02.000 --> 00:52:11.000
+I'll let you know, I like to attend them with client meetings. I know Dennis will be able to give up. I always funded. Informative to understand
+
+00:52:11.000 --> 00:52:17.000
+what is the dialogues back and forth between the findings? Do we start? I'll be exploring the very simple.
+
+00:52:17.000 --> 00:52:25.000
+Well, during the day after the show.
+
+00:52:25.000 --> 00:52:28.000
+Well, I wouldn't ask you about an action item review.
+
+00:52:28.000 --> 00:52:38.000
+Any action item? So, for you guys, this already provide us the access using our annual email IDs and our mentors as well.
+
+00:52:38.000 --> 00:52:45.000
+And then, um, for any doubts, uh, just restating, uh, the point of contact between Harsha, Jake, and David.
+
+00:52:45.000 --> 00:52:53.000
+And then, uh, we'll also get access to the teams and the relevant workspaces, like document potions and Intel.
+
+00:52:53.000 --> 00:52:57.000
+subscription, uh, for the entire team.
+
+00:52:57.000 --> 00:53:06.000
+And, uh, I mean, not immediately, but then, uh, I guess you would also share details on the current, uh, data accuracy, like, uh, with respect to
+
+00:53:06.000 --> 00:53:13.000
+the existing quality metrics, like if there's any defined, so that once we develop our system, we can just baseline and compare
+
+00:53:13.000 --> 00:53:17.000
+How's the whole thing improved or something like this.
+
+00:53:17.000 --> 00:53:27.000
+Um, and on ours, um, yeah, we'll also, like, set up, uh, one recurring call with all of you guys who just check our calendars and then come up with a slot, and then we'll also
+
+00:53:27.000 --> 00:53:42.000
+I have to, like, come up with one day where we visit the operator and get to meet the standard of things to understand how the current… how they're working on certain businesses.
+
+00:53:42.000 --> 00:53:52.000
+I would have just said, I recommend sending the product. Yeah, sure, for sure, that'd be great.
+
+00:53:52.000 --> 00:53:59.000
+It's both a pleasure to have you on board here. I know it'll be excited, looking forward to working on this project, and uh…
+
+00:53:59.000 --> 00:54:02.000
+I think, uh, in the end, you know,
+
+00:54:02.000 --> 00:54:04.000
+You'll get an interesting project.
+
+00:54:04.000 --> 00:54:08.000
+And, uh, something that hopefully meets your requirements.
+
+00:54:08.000 --> 00:54:11.000
+And meet your expectation of these subset, so…
+
+00:54:11.000 --> 00:54:13.000
+We'll see how this works out.
+
+00:54:13.000 --> 00:54:16.000
+We're also really looking forward to, uh,
+
+00:54:16.000 --> 00:54:20.000
+kind of what the purpose of the project is more fancy. We're curious what…
+
+00:54:20.000 --> 00:54:26.000
+Not only this day, but all this seems fine with AI systems. We're also working with.
+
+00:54:26.000 --> 00:54:33.000
+It's not a silver bullet, but it's something that, uh, helpful.
+
+00:54:33.000 --> 00:54:37.000
+Well, very good. This is exciting, yes.
+
+00:54:37.000 --> 00:54:40.000
+I do have a slide back as well. Yeah, sure.
+
+00:54:40.000 --> 00:54:44.000
+alternative, and be safe over the weekend.
+
+00:54:44.000 --> 00:54:45.000
+Yeah.
+
+00:54:45.000 --> 00:54:50.000
+Me too, me too. Go snowboarding on Monday for whether that road is slow that much. Yeah, it's in a new base shooting.
+
+00:54:50.000 --> 00:54:55.000
+You missed out the December storm, yes.
+
+00:54:55.000 --> 00:54:58.000
+Were you in the struggle? I just slept before the storm.
+
+00:54:58.000 --> 00:55:02.000
+But he was in Canada, so… It was pretty bad.
+
+00:55:02.000 --> 00:55:05.000
+Well, years ago, when they had the super big snow shares,
+
+00:55:05.000 --> 00:55:12.000
+CMEs are the kind of… they want you to come to school, but the city of Pittsburgh beg them to go to school. They closed the school.
+
+00:55:12.000 --> 00:55:16.000
+We'll see what happens on Monday.
+
+00:55:16.000 --> 00:55:20.000
+Yeah, it'll be… it'll be interesting. I think there's some places, like,
+
+00:55:20.000 --> 00:55:23.000
+that aren't used to snow at all.
+
+00:55:23.000 --> 00:55:25.000
+Pittsburgh has gotten a little better about it, but like…
+
+00:55:25.000 --> 00:55:30.000
+you know, DC, if they get a, you know, half an inch of snow, the city closes down, and they're supposed to get…
+
+00:55:30.000 --> 00:55:33.000
+many inches, so it's gonna be…
+
+00:55:33.000 --> 00:55:35.000
+It's gonna be interesting.
+
+00:55:35.000 --> 00:55:39.000
+I was living and staying on the front step yesterday, and…
+
+00:55:39.000 --> 00:55:41.000
+There was nothing happening, but…
+
+00:55:41.000 --> 00:55:49.000
+The weather overnight, the weather was, it was very slick, very slippery. Yeah, fantastic.
+
+00:55:49.000 --> 00:55:56.000
+But we're good. Good to see y'all. Good to see you. Thank you so much.
+
+00:55:56.000 --> 00:55:58.000
+Yep.
+
+00:55:58.000 --> 00:56:00.000
+WaySec systems in the world.
+
+00:56:00.000 --> 00:56:03.000
+Are you still there, Dennis?
+
+00:56:03.000 --> 00:56:10.000
+That is political. Okay, alright.
+
+00:56:10.000 --> 00:56:16.000
+Okay, thank you. Thank you.
+
+00:56:16.000 --> 00:56:22.000
+I would certainly say that after a client meeting, it's always good to have a follow-on with the mentors there, just to say,
+
+00:56:22.000 --> 00:56:26.000
+what their perspective is on the meeting, if there's anything else that popped up, so…
+
+00:56:26.000 --> 00:56:32.000
+It's always good for you guys to also kind of have a discussion post-meeting about anything you've heard, or…
+
+00:56:32.000 --> 00:56:36.000
+Something you'll, uh, you need to follow up on, so…
+
+00:56:36.000 --> 00:56:37.000
+Uh, we can start with Friday.
+
+00:56:37.000 --> 00:56:41.000
+It was always good
+
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new file mode 100644
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@@ -0,0 +1,1454 @@
+WEBVTT
+
+1
+00:00:03.790 --> 00:00:08.980
+hrishikb@andrew.cmu.edu: Okay, so the next topic we have is how we are gonna be working together.
+
+2
+00:00:09.310 --> 00:00:18.079
+hrishikb@andrew.cmu.edu: And the major points we wanted to cover was your availability, modes of communication, onboarding, and the documentation resources that we should have.
+
+3
+00:00:19.350 --> 00:00:23.639
+hrishikb@andrew.cmu.edu: So, for availability, I think this lot kind of works in the enterprise.
+
+4
+00:00:23.750 --> 00:00:31.080
+hrishikb@andrew.cmu.edu: Because that's when the RFP is looking really bad. So, Thursdays around this time, it needs to respond.
+
+5
+00:00:31.370 --> 00:00:39.120
+hrishikb@andrew.cmu.edu: And very quickly, I think we're all available on GM still, so if you ever have to ask something about it.
+
+6
+00:00:39.420 --> 00:00:43.829
+hrishikb@andrew.cmu.edu: And, promotes a community initiative on the users, depending on those of them.
+
+7
+00:00:44.620 --> 00:00:53.649
+hrishikb@andrew.cmu.edu: What we do is we probably provision around, like, paragraphs for RP, so that you all have part of our teams, and we can extend it for any time.
+
+8
+00:00:54.480 --> 00:01:11.319
+hrishikb@andrew.cmu.edu: Other than that, for the onboarding process, it's pretty open, which is, we, give you access to whatever you need for the initial part of the application or, various resources, and how the data looks type for various parts of the project.
+
+9
+00:01:12.050 --> 00:01:15.389
+hrishikb@andrew.cmu.edu: And, you can test it to deliver on the windows.
+
+10
+00:01:15.570 --> 00:01:28.129
+hrishikb@andrew.cmu.edu: So probably we'll ask you guys to, invest in your annual times by graph, or we'll give you feedback, if that's,
+
+11
+00:01:28.460 --> 00:01:41.060
+hrishikb@andrew.cmu.edu: We don't do that, right? So we just ask you many items that we already have those, and then you just onboard you out to our system so that you have access to, like, the other things.
+
+12
+00:01:41.150 --> 00:01:53.639
+hrishikb@andrew.cmu.edu: And as we grow… as we go about the project, we give you more and more access. Because it's not a long process, at least with us, so as long as you ping me, Jake or David, you will get your access of that.
+
+13
+00:01:54.800 --> 00:01:59.960
+hrishikb@andrew.cmu.edu: And also, please include the mentor who's access on Teams. Yeah, 100%.
+
+14
+00:02:01.010 --> 00:02:15.870
+hrishikb@andrew.cmu.edu: Yeah, you should. Yeah. We'd like to know what you guys are talking about. I don't want the team to promise the moon.
+
+15
+00:02:16.740 --> 00:02:27.190
+hrishikb@andrew.cmu.edu: I also wanted to ask about the documentation, with that, like, which, Apprentice use,
+
+16
+00:02:27.340 --> 00:02:35.090
+hrishikb@andrew.cmu.edu: So to have access to the previous team's documentation really depends. It might be useful for them to understand that. You can share that across, yeah.
+
+17
+00:02:35.680 --> 00:02:36.400
+hrishikb@andrew.cmu.edu: Perfect.
+
+18
+00:02:37.520 --> 00:02:38.779
+hrishikb@andrew.cmu.edu: I'm going on.
+
+19
+00:02:39.110 --> 00:02:50.660
+hrishikb@andrew.cmu.edu: final onboarding docs so we can be set with those.
+
+20
+00:02:50.740 --> 00:03:05.319
+hrishikb@andrew.cmu.edu: But you've changed things since then? Yeah. Yeah, we've not so much changed so much and added. We've added a lot of things, but yeah, we should make sure that's…
+
+21
+00:03:05.750 --> 00:03:17.210
+hrishikb@andrew.cmu.edu: Yeah, that'd be okay for me, yeah.
+
+22
+00:03:19.230 --> 00:03:22.470
+hrishikb@andrew.cmu.edu: Next to the project purpose and background.
+
+23
+00:03:23.000 --> 00:03:29.760
+hrishikb@andrew.cmu.edu: From your guys' point of view, the motivation, benefits, stakeholder of the feature you're gonna try to build.
+
+24
+00:03:29.980 --> 00:03:30.980
+hrishikb@andrew.cmu.edu: Hey, guys.
+
+25
+00:03:31.570 --> 00:03:36.529
+hrishikb@andrew.cmu.edu: So, with the motivation of it, it's mainly to, I think.
+
+26
+00:03:37.080 --> 00:03:46.190
+hrishikb@andrew.cmu.edu: get the process of getting the initial product data in a reliable manner onto the system. That's, I think, is the only one we can play.
+
+27
+00:03:46.300 --> 00:04:04.189
+hrishikb@andrew.cmu.edu: Because I think right now, the only way we can probably do that better is by just getting more data and probably having more people look at the data and understand it, which is not the way to go about it, because that's not scalable anymore, like, just moving product.
+
+28
+00:04:04.510 --> 00:04:19.639
+hrishikb@andrew.cmu.edu: I want to add that too, yeah, just… there's a lot… it's a very human process right now to, like, one vendor sends something one format, another vendor sends something another format. Teams of people have to go through and interpret it, and then try and
+
+29
+00:04:19.640 --> 00:04:26.430
+hrishikb@andrew.cmu.edu: RAM, whatever that is, into the existing structure or, like, attributes or ways of looking.
+
+30
+00:04:26.450 --> 00:04:40.049
+hrishikb@andrew.cmu.edu: And, just that process. We'd like to make that a lot more hands-off, where they can just upload something, it'll get it in there, and then they can do future modification. Maybe it's not right or wrong, but just getting it in there in the first place takes so much time right now.
+
+31
+00:04:40.530 --> 00:04:57.640
+hrishikb@andrew.cmu.edu: And then… and then once thin, making it as robust as possible. So, filling out all of the attributes that a particular product might have, be that by scraping the PDFs, or scraping a vendor website, or whatever we need to do to try to get those back from people without them knowing an entrance.
+
+32
+00:04:57.910 --> 00:05:05.720
+hrishikb@andrew.cmu.edu: Is kind of step two, in my sense, and then step three is… is…
+
+33
+00:05:05.940 --> 00:05:15.679
+hrishikb@andrew.cmu.edu: I forget what you called it. It's like, there's, like, a man-in-the-middle kind of concept for female blue. Yeah, that's it.
+
+34
+00:05:15.790 --> 00:05:24.519
+hrishikb@andrew.cmu.edu: There's a freedom in the loop concept where, yeah, we'll do as much as we can, and we'll probably need some approvals on that and whatnot, but then also, like.
+
+35
+00:05:24.520 --> 00:05:39.040
+hrishikb@andrew.cmu.edu: what needs updated? What, can we, like, score products so that we see, like, how big each product is, that we can pull up a download of products that is, like, all of these are, like, you know, 20% down there. I mean, we're, like…
+
+36
+00:05:39.140 --> 00:05:47.380
+hrishikb@andrew.cmu.edu: How can we get to that next step of at least telling the human what they need to do more work on? They're not telling the AI what it needs to be able to work on.
+
+37
+00:05:48.470 --> 00:05:57.670
+hrishikb@andrew.cmu.edu: I'm curious, what is the frequency of new vendors coming on board and you have to ingest those data? I imagine it's very modest for you.
+
+38
+00:05:57.720 --> 00:06:13.340
+hrishikb@andrew.cmu.edu: Yeah, we're open to increase this more and more with the new product that we're launching. It is aimed at the small and medium-sized business market. So, most of our clients right now are, like, enterprise clients, so…
+
+39
+00:06:13.340 --> 00:06:20.760
+hrishikb@andrew.cmu.edu: For them, we're always onboarding more vendors, but it's not like 100 vendors haven't done yet. Hopefully, in the future, it will be moved.
+
+40
+00:06:20.760 --> 00:06:27.669
+hrishikb@andrew.cmu.edu: But a lot of that's gonna be overlap, so that vendor A sells, like, Apple.
+
+41
+00:06:27.720 --> 00:06:45.119
+hrishikb@andrew.cmu.edu: And vendor B also sells Apple, but we need to make a good catalog of, like, Apple devices, and then be able to link them up to each vendor, and add on the original vendor attributes from there. Part of that is just already built when they're being built, and are doing stuff.
+
+42
+00:06:45.440 --> 00:06:52.170
+hrishikb@andrew.cmu.edu: I mean, make sure that I have all the attributes that it needs with as little interconnection as possible.
+
+43
+00:06:52.620 --> 00:06:54.279
+Dennis Grinberg: And Bill, do you have any…
+
+44
+00:06:56.420 --> 00:06:57.249
+hrishikb@andrew.cmu.edu: Oh, go ahead.
+
+45
+00:06:57.700 --> 00:07:01.060
+Dennis Grinberg: I was gonna say, do you have any… data around…
+
+46
+00:07:01.870 --> 00:07:05.759
+Dennis Grinberg: I guess two things. One is how accurate humans are at this endeavor.
+
+47
+00:07:06.090 --> 00:07:07.919
+Dennis Grinberg: And the second is…
+
+48
+00:07:08.260 --> 00:07:23.030
+Dennis Grinberg: If you think about the, you know, the total sort of workflow, I don't know what your workflow is, but I can imagine, you know, looking at the data, entering it, someone else verifying it, like, where the, you know, analysis of how much time is spent on those different
+
+49
+00:07:23.160 --> 00:07:25.750
+Dennis Grinberg: Activities within that work, workflow.
+
+50
+00:07:29.420 --> 00:07:47.590
+hrishikb@andrew.cmu.edu: No, I was just gonna say, we are currently actually fully launching gyms to production now, so all the workflows that we've done are on the legacy L1 systems. So I don't have good methods to start with them on how long it takes.
+
+51
+00:07:47.690 --> 00:07:56.509
+hrishikb@andrew.cmu.edu: We… now that VIMS is launching, we can start recording that as the current state, so that we can see how much better we are at the end of the year.
+
+52
+00:07:57.460 --> 00:08:01.170
+hrishikb@andrew.cmu.edu: And then, what was the first part of that question, too?
+
+53
+00:08:01.170 --> 00:08:03.999
+Dennis Grinberg: What… how accurate are humans at this activity?
+
+54
+00:08:05.770 --> 00:08:10.400
+hrishikb@andrew.cmu.edu: I mean, I guess we could use those metrics,
+
+55
+00:08:10.700 --> 00:08:29.049
+hrishikb@andrew.cmu.edu: Oh, no, I don't know if we have how accurate, like, we could easily show… Yeah, like, what a good end, goal of it is based on, like, the PDF we took at the beginning, but for accuracy, that's a good question. So what I was going to say was, we could surely connect that information and send it to… send it across to you.
+
+56
+00:08:29.120 --> 00:08:33.650
+hrishikb@andrew.cmu.edu: We just haven't acted at the beginning of the night. I don't know if.
+
+57
+00:08:33.650 --> 00:08:38.069
+Dennis Grinberg: team's gonna need it, but just… these are just, like, things that, hey, I'm interested, I'm curious.
+
+58
+00:08:38.070 --> 00:08:52.469
+hrishikb@andrew.cmu.edu: You'd like some metrics to see what the overall process on which it's improved. So I guess the one question would be, you ingest the data, you've got it there. Are there, future reports to say the data's inaccurate? Do you have information like that, or…
+
+59
+00:08:52.470 --> 00:09:14.950
+hrishikb@andrew.cmu.edu: Yeah, how do you discover that? It's usually word of mouth, or yeah, the people using our site, or the catalog team will notice something, and then they'll go in and change that, or our salespeople will, in talking to customers, realize something's wrong, or not acting, or they're missing. In the new product, we'll probably have a crowdsourcing element doing that, where people
+
+60
+00:09:15.020 --> 00:09:17.900
+hrishikb@andrew.cmu.edu: That's for having this information on the record.
+
+61
+00:09:18.020 --> 00:09:26.980
+hrishikb@andrew.cmu.edu: But you have no centralized loading, according to that model. The bigger problem now is probably, as we add…
+
+62
+00:09:26.980 --> 00:09:38.079
+hrishikb@andrew.cmu.edu: new suppliers for our existing clients. It's pretty easy to get a part and a price in there, to actually know all the attributes.
+
+63
+00:09:38.080 --> 00:09:46.669
+hrishikb@andrew.cmu.edu: We're not doing it a lot of the time, because it's just too much effort, for us to spend on all of the new product lines that are being added.
+
+64
+00:09:46.700 --> 00:10:04.219
+hrishikb@andrew.cmu.edu: But for the end user, of course, it would be very helpful to have the gas release. So, for the sake of getting it up and running, and it works, and they can technically buy it, not having them is fine, and that's what we do, but we're looking at how can we get that better user experience with marketing.
+
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+00:10:04.590 --> 00:10:21.959
+hrishikb@andrew.cmu.edu: Just some background and context, too, into, like, the… just the attributes in general. We… we have a lot of attributes. The catalog team, really, the majority of their time is looking at more attributes and coming up with… coming up with different ways to classify things. So, we're especially in the building controls industry.
+
+66
+00:10:21.980 --> 00:10:37.329
+hrishikb@andrew.cmu.edu: Like, there's not one in this room, but, like, a valve for a pipe, like, in the bathroom or something like that. Like, they want to know what material it is, what the pressure rating is, the heat resistance rating, like, we… it's really driven, and that is we…
+
+67
+00:10:37.330 --> 00:10:56.100
+hrishikb@andrew.cmu.edu: Because when they're going to look up parts, they don't really care about the brand, they just want to be able to filter by, I need this rating, I need these to handle this pressure, needs to handle this temperature range, automatic off, like, normally closed, like, it's really around the attributes, as opposed to where we've been making the iPhone, but that's…
+
+68
+00:10:56.620 --> 00:11:06.360
+hrishikb@andrew.cmu.edu: that's a little more, like, you know you want an iPhone when you go. This was more like, you don't care what brand it is, you know you need 16 gigs on your phone, or you need, like, 5G. Yeah.
+
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+00:11:06.360 --> 00:11:16.979
+hrishikb@andrew.cmu.edu: So the thinking is, for each kind of product category, a human is already doing this, and probably because she'll be making, like, this category of products needs this set of activities.
+
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+00:11:16.990 --> 00:11:26.809
+hrishikb@andrew.cmu.edu: So then, when you're trying to ingest the product, first we have to try to figure out what category is. This buyer can probably give us something first on that, meaning they match product for ours.
+
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+00:11:26.860 --> 00:11:33.080
+hrishikb@andrew.cmu.edu: And then the second step is, okay, now I know what category it is, it needs all these attributes, I like this.
+
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+00:11:33.460 --> 00:11:47.590
+hrishikb@andrew.cmu.edu: So does the vendor sometimes supply spec sheets? Yes, yes. Do you link parts to spec sheets or not? Yep, yeah, we have a lot of parts linked to spec sheets today. But again, the challenge with new parts, like.
+
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+00:11:47.590 --> 00:11:59.149
+hrishikb@andrew.cmu.edu: Ideally, the vendor is giving us a link to a spec sheet when they're giving us the product, and that can be helpful to you guys. We've been trying to pull that spec sheet into our own control and work off of it.
+
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+00:11:59.160 --> 00:12:13.000
+hrishikb@andrew.cmu.edu: It's not always going to be that easy, but in the best case scenario, they do. Sometimes we don't, though, and the catalog team literally goes out to their websites, their lipsticks, they can go online and tries to infer, and then put that in.
+
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+00:12:13.220 --> 00:12:25.440
+hrishikb@andrew.cmu.edu: I think you also mentioned in the presentation before that, some vendors, give you guys some catalogs, like, physical catalogs, or, like, so the catalog team has to, like, manually enter all the data from that?
+
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+00:12:25.790 --> 00:12:30.589
+hrishikb@andrew.cmu.edu: And as a part of this, we'll want to automate that as well, right? Like, a way to…
+
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+00:12:30.970 --> 00:12:50.340
+hrishikb@andrew.cmu.edu: Yeah, so it's not… it used to be that we'd get, like, they would try to, like, enter in as much as we could. We're not dealing with that anymore, but they still might get a big gap, right? Like, but more often than not, the vendor is able to give us something digitally, some sort of self-spread key or whatever.
+
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+00:12:50.710 --> 00:12:57.819
+hrishikb@andrew.cmu.edu: So that's the bulk of what we're trying to focus on. Good question, how structured or unstructured the data is? Yeah, yeah.
+
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+00:12:58.760 --> 00:13:00.000
+hrishikb@andrew.cmu.edu: If thereof.
+
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+00:13:00.260 --> 00:13:12.980
+hrishikb@andrew.cmu.edu: kind of, like, an ideal layout of form that you, kind of have in mind for, like, each part, like, accept those terms should ideally have, like, use both listed for each.
+
+81
+00:13:13.260 --> 00:13:20.050
+hrishikb@andrew.cmu.edu: Yeah, there's definitely, like, a basic schema below where, like, it needs a product number, which is, like,
+
+82
+00:13:20.220 --> 00:13:33.549
+hrishikb@andrew.cmu.edu: what you refer to it as the name of the product, and then we have supplier product number. Sometimes, like, suppliers have their own, way that they, like, label a product versus what they sell to the public. Couple more fields, like description, list, cost,
+
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+00:13:33.550 --> 00:13:54.520
+hrishikb@andrew.cmu.edu: But after that, the rest is all gets into attributes and just kind of more, like, things associated with it. Yeah, and that's what I'm saying, a human can say, like, what category is tablets, right? So, like, what's the screen size of the tablet? Is it, like, pen enabled? What, what, like, standard of pen devices does this tablet use?
+
+84
+00:13:54.520 --> 00:14:05.399
+hrishikb@andrew.cmu.edu: What color is it, of course, like, humans, obviously brand this kind of separate thing, but we keep going on and on. We can have humans to find, like, tablets and make those funny things.
+
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+00:14:05.480 --> 00:14:23.910
+hrishikb@andrew.cmu.edu: And then, it's kind of you guys' job to figure out how they get those funny things from the, like, whatever we're giving from the vendors, and work them into, like, what we want them to do. Yeah. We do have, like, a chat meeting and an annual meeting, where we have
+
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+00:14:24.470 --> 00:14:46.599
+hrishikb@andrew.cmu.edu: attributes map the latest products within the field. So that's… so that… Yeah, so, like, with the tablets, Joe just listed some, like, we would have the category of tablets, and then we would have attributes assigned to that. So all tablets should have the attribute of screen size. All tablets should have the attribute of pen enabled.
+
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+00:14:46.740 --> 00:14:55.969
+hrishikb@andrew.cmu.edu: And then as we upload a new product into the tablet category, we automatically know, okay, it needs a value for string size, it needs a value for Venity, like, so we have…
+
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+00:14:56.150 --> 00:15:07.830
+hrishikb@andrew.cmu.edu: the value that the product has, is downstream of us, like, before that, matching an attribute to a category, and then everything in the category has to have value to spend on the attribute.
+
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+00:15:08.040 --> 00:15:20.010
+hrishikb@andrew.cmu.edu: And that gets back to what Joe was saying earlier, like, the third part, which is saying this product is 20% ready. We would have a way to kind of do that based on, like, you've only entered 2 of the 10 attributes associated with this
+
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+00:15:20.180 --> 00:15:23.269
+hrishikb@andrew.cmu.edu: Products category, product, we need to know.
+
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+00:15:23.270 --> 00:15:42.089
+hrishikb@andrew.cmu.edu: the screen status. You can enter a screen status, that could be a way to determine what information is missing. We're not always doing this yet. We can also rank, right, these are the top four attributes that are absolutely needed to go into this product, and then the rest of them advanced away the extra… it would be nice to have them, but…
+
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+00:15:42.090 --> 00:15:44.260
+hrishikb@andrew.cmu.edu: Ideally, we have these four groups.
+
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+00:15:45.580 --> 00:15:56.649
+hrishikb@andrew.cmu.edu: Is there… I know before we talked about the most sensitive thing from the vendor when it's pricing. Is there any correction of information about a product that makes it more sensitive or not, or are you advancing in any state?
+
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+00:15:56.650 --> 00:16:16.179
+hrishikb@andrew.cmu.edu: It's really just the pricing. One or two brands that are a little picky with, like, who can see their products, so, we just… But, yeah, it's just a matter of, like, who can actually sell their products, but that's not something that we can still go on their website to buy and see most of their products publicly.
+
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+00:16:16.230 --> 00:16:20.789
+hrishikb@andrew.cmu.edu: So the only thing we're really concerned about doing Friday was probably
+
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+00:16:23.230 --> 00:16:36.449
+hrishikb@andrew.cmu.edu: from the looks of it, that I think we'll have to have discussions with the catalog team, just to understand how exactly they, like, search all the information, so we can do the same thing, just in an automated fashion. So, like, we'll have
+
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+00:16:36.450 --> 00:16:44.479
+hrishikb@andrew.cmu.edu: access to them, like you said, you can miss them on Teams, or love to… you can come over to ePass as well for meeting them in person.
+
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+00:16:44.530 --> 00:16:48.680
+hrishikb@andrew.cmu.edu: We could catalog team with the e-ports catalog team.
+
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+00:16:48.810 --> 00:16:58.839
+hrishikb@andrew.cmu.edu: we can probably, like… you would be able to message, so, like, the catalog work that we have at eBARTS. Our parent company, Ops Control Trolls, has a much larger, catalog team.
+
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+00:16:58.840 --> 00:17:20.619
+hrishikb@andrew.cmu.edu: No, with them, that would probably be, yeah, something in person would be ideal, but… Yeah, if you can come back, that'll be easier for them as well. But with the eParts catalog person, yeah, you could feel free to reach out to them on Teams. Apparently, it helps those most of the catalog work, so…
+
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+00:17:20.619 --> 00:17:24.119
+hrishikb@andrew.cmu.edu: We do some… like I said, we don't worry about the attributes as much.
+
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+00:17:24.140 --> 00:17:36.149
+hrishikb@andrew.cmu.edu: Just because there's, like, one or two people in our movement does catalog at all, and they also do other things. It's still only, like, four people, but they're all actually dedicated to cataloging. We'll get a ton of…
+
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+00:17:36.440 --> 00:17:51.149
+hrishikb@andrew.cmu.edu: And they are… Alps is an e-commerce, like, distributor in their building control space, so they are highly incentivized to make… break down our data wigs so that they're helping their customers buy the right thing because they bought them that instead of someone else.
+
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+00:17:51.550 --> 00:17:52.300
+hrishikb@andrew.cmu.edu: Alright.
+
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+00:17:52.530 --> 00:17:56.659
+hrishikb@andrew.cmu.edu: Most of our customers, it's the tools where,
+
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+00:17:56.690 --> 00:18:14.060
+hrishikb@andrew.cmu.edu: the engineer needs to, you know, they're, like, kind of locked in using our platform for the corporate or whatever. So it's, like, okay to not have the best catalog data. We don't want the better. Alps needs to be able to have to make it better, basically.
+
+107
+00:18:14.420 --> 00:18:19.120
+hrishikb@andrew.cmu.edu: Can you guys keep track of cases, though, somebody orders something and turns out it's the wrong things?
+
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+00:18:19.740 --> 00:18:35.919
+hrishikb@andrew.cmu.edu: I think we would have that now, figuring my CDC on, right? Yeah, and we could do a lot of inferring, too, with RMA, we could match RMAs against it. We don't have structured data on that, but…
+
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+00:18:37.330 --> 00:18:38.070
+hrishikb@andrew.cmu.edu: Okay.
+
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+00:18:39.580 --> 00:18:41.309
+hrishikb@andrew.cmu.edu: I think,
+
+111
+00:18:41.670 --> 00:18:59.849
+hrishikb@andrew.cmu.edu: Can we touch up on the, like, we had an ML component to the project? I was just a bit curious about, it said that we have to, like, you know, for future also, we plan to use ML for further operations. How, like, which all aspects are you planning to use machine learning and, like, confidence scores and other things?
+
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+00:18:59.870 --> 00:19:15.079
+hrishikb@andrew.cmu.edu: So, I mean, content scores would be relevant to the part where, we predict these attributes for that product. So, equivalence, you get a certain product, in a certain activity, and you should be able to map these attributes, so in case the basic course you have.
+
+113
+00:19:15.270 --> 00:19:22.240
+hrishikb@andrew.cmu.edu: We should be able to do some sort of step-by-step sampling, and then figure out what would be the appropriate attribute for this testing.
+
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+00:19:22.520 --> 00:19:24.070
+hrishikb@andrew.cmu.edu: context and emails.
+
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+00:19:24.170 --> 00:19:32.170
+hrishikb@andrew.cmu.edu: But it's just… it's just mapping, and it's also, a little bit of, This was just gone.
+
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+00:19:32.330 --> 00:19:39.049
+hrishikb@andrew.cmu.edu: what could be the attribute for this specific feeling. That's also in terms of figuring it out in your attributes we can use.
+
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+00:19:39.290 --> 00:19:40.800
+hrishikb@andrew.cmu.edu: So,
+
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+00:19:40.940 --> 00:19:49.659
+hrishikb@andrew.cmu.edu: And that's kind of the main thing. So, when we do make these predictions for these best academies, we do assign a conference for the year. And I think that's where confidence comes in.
+
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+00:19:49.800 --> 00:19:56.910
+hrishikb@andrew.cmu.edu: Where you see… so the confidence score is a little bit more important. Exactly. So then the system learned that
+
+120
+00:19:57.130 --> 00:20:10.480
+hrishikb@andrew.cmu.edu: this was low, and then beyond. This is the automatically corrected one. So that's the next step, right? So, you initially have given the rule, and then as you… as you keep getting more and more of similar errors.
+
+121
+00:20:10.590 --> 00:20:22.710
+hrishikb@andrew.cmu.edu: you can attach another part of the system which kind of rectifies itself, or you can still try and do that if it's goodnight. So it all depends on how the project goes, how it kind of turns out to me, and how it works, you know.
+
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+00:20:23.330 --> 00:20:33.939
+hrishikb@andrew.cmu.edu: So are all the attributes spectra greens, or is there anything that's a photograph of a 3D rendering, or ammsterious will sometimes extend just beyond touch to a certain attribute?
+
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+00:20:34.080 --> 00:20:47.510
+hrishikb@andrew.cmu.edu: We'll have images of, you know, docking, manuals, or spec sheets, or product pictures, yeah, but I don't think anything that would be an actual, like, fall under an attribute of skeleton.
+
+124
+00:20:48.880 --> 00:20:54.080
+hrishikb@andrew.cmu.edu: Yeah, there wouldn't be, like, the aggraves in the case.
+
+125
+00:20:54.080 --> 00:21:11.450
+hrishikb@andrew.cmu.edu: So, Chris just mentioned that you have the category as one who has, like, a schema of attributes, right? So, if the attributes is the fixed part here, why are we applying the ML to the fixed part? Like, why do we need a confidence score to be assigned to this? Like, why not to the category, because
+
+126
+00:21:11.450 --> 00:21:19.320
+hrishikb@andrew.cmu.edu: That's what is a variable thing, identifying with that process. I mean, it could be fluid, right? So, those patterns…
+
+127
+00:21:19.460 --> 00:21:28.440
+hrishikb@andrew.cmu.edu: you can try and turn the ketamine per product, which could be content. Next is, once you do figure that out and go into that, and we'll start things.
+
+128
+00:21:28.660 --> 00:21:36.709
+hrishikb@andrew.cmu.edu: You don't know a specific… so, for example, if I say, A specific attitude, for example.
+
+129
+00:21:37.890 --> 00:21:44.160
+hrishikb@andrew.cmu.edu: take, like, the pressure of the percentage of the water or something like that. Like, for example, if it's 20 kilopascals per day.
+
+130
+00:21:44.330 --> 00:21:57.719
+hrishikb@andrew.cmu.edu: What we assign is actually, something like 20 seconds in a row instead. So, that would be for the attribute and the purpose code. So, it might not know the exact kind of,
+
+131
+00:21:58.100 --> 00:22:03.130
+hrishikb@andrew.cmu.edu: you need to put in there, value to put in there. It could be… it could just confuse that,
+
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+00:22:03.410 --> 00:22:10.179
+hrishikb@andrew.cmu.edu: I mean, I think as we, have, like, this internally, model-based kind of products.
+
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+00:22:10.410 --> 00:22:23.210
+hrishikb@andrew.cmu.edu: learn specific values for the team, you can close the jurisdiction of a green basket during the vertex of being virtual, then you would have to… even when we do validations for that.
+
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+00:22:23.530 --> 00:22:43.329
+hrishikb@andrew.cmu.edu: So that, you know, okay, these certain values are the exact sort of values you're looking at, because there might not be enough data to actually implement… But then, you, the values that we're talking about, do I, like… you mentioned in the spec talk, right? So, what's going on with the value? Again, so it's not going to tell you, like.
+
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+00:22:44.440 --> 00:22:58.060
+hrishikb@andrew.cmu.edu: let's say it this way, different suppliers will provide different sets of athletes, like, like, Apple might ball spring size, spring size, and someone else might call it, like,
+
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+00:22:58.400 --> 00:23:08.689
+hrishikb@andrew.cmu.edu: Or whatever, like, display size, or whatever. And sometimes it's not that cut and dry, either. Sometimes it's not just string size, display size. Sometimes it's, like.
+
+137
+00:23:10.180 --> 00:23:21.870
+hrishikb@andrew.cmu.edu: the gas flow rate versus the water flow rate versus, like, there is some interpretation to be done between suppliers to be able to match them up to a similar standard. Yeah.
+
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+00:23:22.170 --> 00:23:27.869
+hrishikb@andrew.cmu.edu: Do your suppliers ever stop-check their data? I'm just curious if there was a feedback loop there now.
+
+139
+00:23:28.060 --> 00:23:36.300
+hrishikb@andrew.cmu.edu: On the outside, they do sometimes, that, on our side, we haven't experienced that so much, sorry.
+
+140
+00:23:36.520 --> 00:23:38.580
+hrishikb@andrew.cmu.edu: Alright, I think it would break his…
+
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+00:23:39.580 --> 00:23:46.290
+hrishikb@andrew.cmu.edu: So, the manual process right now, we thrive to get the supplier to tell us our algorithm style.
+
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+00:23:46.400 --> 00:23:58.210
+hrishikb@andrew.cmu.edu: So we give them a spreadsheet that describes the attributes that we want for these products. There's a lot of manual effort for them to actually build it out, and a lot of them just don't.
+
+143
+00:23:58.450 --> 00:24:04.620
+hrishikb@andrew.cmu.edu: So, they have the opportunity to try to make it right, but it's too much effort for either that.
+
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+00:24:05.240 --> 00:24:06.750
+hrishikb@andrew.cmu.edu: Thereabouts.
+
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+00:24:06.970 --> 00:24:20.129
+hrishikb@andrew.cmu.edu: maybe a good question, but was it ever the other way around? Did they come up and tell you that, okay, this is how we want the product to be, you know, categorized, or these are the attributes that, and then…
+
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+00:24:20.160 --> 00:24:35.449
+hrishikb@andrew.cmu.edu: what I mean to ask is, let's say the iPad. In your system, you just have 4, but then, since they know the internet working, maybe, like I'm just saying, are they like, okay, I need a first one. So, was there a requirement?
+
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+00:24:35.450 --> 00:24:51.959
+hrishikb@andrew.cmu.edu: So, so first off, we can totally take a fifth one right now, if they want to add in a particular attribute that isn't in that required set, that's fine. Is that even possible? Like, are they allowed to come up with a requirement that starts? They are.
+
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+00:24:51.960 --> 00:24:57.889
+hrishikb@andrew.cmu.edu: Really able to define the data set for our catalog, because
+
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+00:24:58.140 --> 00:25:09.279
+hrishikb@andrew.cmu.edu: So they… they have their own data, right? They have their own catalog somewhere, and some days they… some of them can give us a lot of that data, and some of them can't, but,
+
+150
+00:25:09.610 --> 00:25:25.049
+hrishikb@andrew.cmu.edu: Our job is to modernize data between different manufacturers, so we… we have to have the liberty to be able to change the modifier that begins with, for the sake of ease of use for the actual application.
+
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+00:25:25.200 --> 00:25:29.770
+hrishikb@andrew.cmu.edu: Yeah, but also, some of the vendors could want, more so in the case of, like, ELPS.
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+00:25:30.290 --> 00:25:40.820
+hrishikb@andrew.cmu.edu: They could want their products to get more visibility to customers. They would want their products to show up in more searches based on more attributes, and especially when they're working out, like, the deal of
+
+153
+00:25:40.940 --> 00:25:54.029
+hrishikb@andrew.cmu.edu: the price they're going to be giving Alps for Alps to buy it from them. There's, like, all these negotiations, and a lot of times they will actually look at their catalog at Alps and see, like, hey, this is the way we're… you're showing our products, we'd like.
+
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+00:25:54.230 --> 00:25:55.490
+hrishikb@andrew.cmu.edu: Something like this.
+
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+00:25:58.130 --> 00:25:58.980
+hrishikb@andrew.cmu.edu: Okay.
+
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+00:25:59.160 --> 00:26:00.639
+hrishikb@andrew.cmu.edu: You know, next part.
+
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+00:26:01.980 --> 00:26:06.390
+hrishikb@andrew.cmu.edu: Your experience with the previous two-year project that you guys have sponsored?
+
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+00:26:06.810 --> 00:26:11.339
+hrishikb@andrew.cmu.edu: Any, like, just about the previous experience, any do's and don'ts that you have for us?
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+00:26:11.460 --> 00:26:12.770
+hrishikb@andrew.cmu.edu: Individual mind.
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+00:26:14.370 --> 00:26:15.819
+hrishikb@andrew.cmu.edu: Yeah,
+
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+00:26:16.110 --> 00:26:35.520
+hrishikb@andrew.cmu.edu: do, reach out to us on Teams, like, as much as possible, like, we do not mind getting, like, a lot of communication, a lot of messages, a lot of posts, like, we prefer that rather than, yeah, like, saving it all for the next week's meeting. Like, if something comes up like that, just reach out. It's, you know, we're all very,
+
+162
+00:26:36.270 --> 00:26:40.070
+hrishikb@andrew.cmu.edu: we have very fast communication open, so I'd say that's a big one.
+
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+00:26:40.720 --> 00:26:47.649
+hrishikb@andrew.cmu.edu: We were like, very briefly in the presentation, mentioned it, but we… we were like, this idea of after our January deal and debate.
+
+164
+00:26:47.790 --> 00:26:56.900
+hrishikb@andrew.cmu.edu: So, like, just because we're saying something needs to be submarried doesn't mean… like, you're already doing something that's great, or something that's bad. Yes.
+
+165
+00:26:56.960 --> 00:27:08.649
+hrishikb@andrew.cmu.edu: we'll be glad to think of it differently, or explain why we shouldn't think of it differently, but, like, I'd rather have that discussion than things. We will never take offense to you thinking about it differently than we do.
+
+166
+00:27:10.450 --> 00:27:17.540
+hrishikb@andrew.cmu.edu: is probably the order as a document. They're no stupid virtual. Yeah, yeah, yeah.
+
+167
+00:27:17.910 --> 00:27:28.070
+hrishikb@andrew.cmu.edu: I would say, like, asking the same question again and again is still fine. It kind of helps to clear the gap and creates a lot of communication opportunities.
+
+168
+00:27:28.550 --> 00:27:30.840
+hrishikb@andrew.cmu.edu: Also, I'd say too,
+
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+00:27:31.390 --> 00:27:34.190
+hrishikb@andrew.cmu.edu: follow up with us, like, if you…
+
+170
+00:27:34.280 --> 00:27:53.389
+hrishikb@andrew.cmu.edu: if we commit to something in a meeting, and you haven't heard much from us, like, quote-unquote, bother us, like, reach out and be like, hey, you know, like, I haven't heard back with this, like, again, we want that, as opposed to then we get to the next week's meeting, and it's like, oh, did you get a chance to do that when you forgot about it all week or something?
+
+171
+00:27:54.760 --> 00:27:57.700
+hrishikb@andrew.cmu.edu: Any particular don'ts that you guys think?
+
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+00:27:57.950 --> 00:27:59.350
+hrishikb@andrew.cmu.edu: You should be mindful of.
+
+173
+00:28:00.040 --> 00:28:17.130
+hrishikb@andrew.cmu.edu: kind of covered it in between. Probably, just don't go AWOL provider and then just pop up two weeks later, whatever. I mean, we completely agree with that, too, but it'd just be way more easier for us to keep practicing that help you guys are doing.
+
+174
+00:28:17.460 --> 00:28:23.810
+hrishikb@andrew.cmu.edu: Yeah, I'd say that's the main one. Other than that, we've had a pretty successful project right there. It's just that, I mean.
+
+175
+00:28:24.280 --> 00:28:43.829
+hrishikb@andrew.cmu.edu: to be fair, like, seeing the auditor doesn't be nice to look into that, and you do have, like, your studies and academics, that's what you guys, and it might be difficult to say that, but I would say if it's, like, a random 2AM, you think of something, and you send it, you have to say something, just send that to us. We'll take you to look at it for our own time, but we're still taking time.
+
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+00:28:43.930 --> 00:28:47.139
+hrishikb@andrew.cmu.edu: It won't be brought in as NFL, so…
+
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+hrishikb@andrew.cmu.edu: Yeah, you certainly not make this semester, they only have 4 hours a week to work on this. They have a few other things that are done, so each one of those kind of thing. Summertime is probably one more time at the end of the full day, yeah.
+
+178
+00:29:01.280 --> 00:29:08.770
+hrishikb@andrew.cmu.edu: How many tunes you would have done? Two or three, I don't remember how many. We had three. Three tunes? Okay, this is our fourth.
+
+179
+00:29:10.310 --> 00:29:25.299
+hrishikb@andrew.cmu.edu: That's good, yeah, it's good to be an experiment. I mean, when you get a new client who's never had any experience with this team, they think, the team is fully working for us 24-7.
+
+180
+00:29:25.300 --> 00:29:31.270
+hrishikb@andrew.cmu.edu: To your point, we're getting… we're getting better and better every year with how we're interfacing.
+
+181
+00:29:31.810 --> 00:29:37.039
+hrishikb@andrew.cmu.edu: Well, a lot of it's about managing expectations, yes. I'm most of it.
+
+182
+00:29:40.340 --> 00:29:45.730
+Dennis Grinberg: And I'm gonna put words in the team's mouth, which is, you know, this is a lot of good experience and do's and don'ts.
+
+183
+00:29:46.020 --> 00:29:50.489
+Dennis Grinberg: from the team's… I'll actually ask the team, from the team's perspective, is there anything that you want from
+
+184
+00:29:50.800 --> 00:29:52.849
+Dennis Grinberg: from eParts, and how you work with them.
+
+185
+00:29:53.110 --> 00:29:54.430
+Dennis Grinberg: Have you thought about that?
+
+186
+00:29:56.630 --> 00:29:58.349
+hrishikb@andrew.cmu.edu: We need to be good enough for them does.
+
+187
+00:30:00.140 --> 00:30:08.140
+hrishikb@andrew.cmu.edu: I think from my point of view, it was just the communication part, like, we are bound to have a lot of questions, because it's going to be a kind of a…
+
+188
+00:30:08.250 --> 00:30:17.199
+hrishikb@andrew.cmu.edu: struggling from scratch point of view, so we'll have questions, and if, like, you guys can respond, like, that'll be… I think that's the main thing I had in mind.
+
+189
+00:30:17.970 --> 00:30:20.679
+hrishikb@andrew.cmu.edu: And that you already cleared up, so that's really good.
+
+190
+00:30:21.190 --> 00:30:25.069
+hrishikb@andrew.cmu.edu: This is our third, product data related.
+
+191
+00:30:25.300 --> 00:30:37.880
+hrishikb@andrew.cmu.edu: see a new project. So, sometimes, like, for me, sometimes it'll be hard to remember what I've shared with you versus, like, the other teams, so always ask clarifying questions.
+
+192
+00:30:41.440 --> 00:30:46.629
+hrishikb@andrew.cmu.edu: Well, also, I guess, I guess one thing, too, just to do in previous experience,
+
+193
+00:30:47.490 --> 00:31:00.209
+hrishikb@andrew.cmu.edu: we've had before, especially, like, with the initial PIMs, we had months of actually just kind of circling back to the initial kind of staying question. There's a lot of confusion, they weren't getting it, it's a very complex
+
+194
+00:31:00.390 --> 00:31:05.809
+hrishikb@andrew.cmu.edu: distribution model. We won't really get into the distribution between our tenants, but my main point is, like,
+
+195
+00:31:06.390 --> 00:31:26.040
+hrishikb@andrew.cmu.edu: If there's, like, something you're still unclear on overall, like, we can keep revisiting it. We found that if we get a really, really concrete understanding of that, even if it takes a couple weeks of touching on it every week, it's gonna definitely be better anyway. Yeah, it's important to have domain knowledge from the team's perspective.
+
+196
+00:31:29.960 --> 00:31:34.600
+hrishikb@andrew.cmu.edu: Go on to the next part. So, now we have the product usage.
+
+197
+00:31:34.750 --> 00:31:37.849
+hrishikb@andrew.cmu.edu: I think we've already touched up a bit on this, but…
+
+198
+00:31:38.100 --> 00:31:53.870
+hrishikb@andrew.cmu.edu: Does the ingest pipeline… I don't think there's going to be a different, like, client interface, or how are we trying to, like, do we give clients something that they can, share the data with, or just, the PDF they already submit, and we just process it on our side?
+
+199
+00:31:54.480 --> 00:31:56.030
+hrishikb@andrew.cmu.edu: For regulation pipeline.
+
+200
+00:31:56.520 --> 00:31:59.459
+hrishikb@andrew.cmu.edu: Yeah, from the southern, yeah.
+
+201
+00:31:59.860 --> 00:32:04.460
+hrishikb@andrew.cmu.edu: I mean, we don't want to set, like, harder requirements on this.
+
+202
+00:32:04.550 --> 00:32:22.529
+hrishikb@andrew.cmu.edu: We… right now, on these files, we're getting, like, by emailing with the vendor. Sometimes it'll be big enough that I think Brian, who runs weekly or something that he sets up with them on his own. We intend… the best…
+
+203
+00:32:23.150 --> 00:32:38.530
+hrishikb@andrew.cmu.edu: the best long-term reason on this is to make it as automated as possible, just, like, every time. One of them means to be that might be EDI, one of the means is going to be, like, FMP, but it could… I don't want to limit, but it could be, like.
+
+204
+00:32:38.650 --> 00:32:55.649
+hrishikb@andrew.cmu.edu: setting up a way for them to see this and index it directly into our system. Like, I don't want to say there's absolutely no vendor interface here. They could be one of the best users to clean up some of that, human-in-loop type mistakes that the system is making.
+
+205
+00:32:55.860 --> 00:33:04.170
+hrishikb@andrew.cmu.edu: So it just depends, on kind of our, our mutual, exploration of the problem.
+
+206
+00:33:04.840 --> 00:33:29.649
+hrishikb@andrew.cmu.edu: I have a fundamental question here. So, since we have, like, so many vendors who are giving us data, I don't… obviously, like, they have some business requirements with the company, right? So, why don't we have a centralized portal, like, where they just… we just mentioned, some people get it in PDF form, and then some send it over email or DFP? So, why… why not a single format like this? So, where people
+
+207
+00:33:29.650 --> 00:33:31.320
+hrishikb@andrew.cmu.edu: We'll just feed in there.
+
+208
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+hrishikb@andrew.cmu.edu: our data, and then we take it forward from you guys. That's the ultimate goal with him. We, originally had that with Tim from the very beginning, is
+
+209
+00:33:40.650 --> 00:33:47.830
+hrishikb@andrew.cmu.edu: In addition to the client… in addition to the catalog teams, vendors themselves could come in here and maybe do the work themselves.
+
+210
+00:33:47.830 --> 00:34:01.879
+hrishikb@andrew.cmu.edu: We'll… We'll be working on some of that this year, too. So there's… there's, like, historical reasons as to why we haven't done that in the past. The catalog maintenance team, historically has been very,
+
+211
+00:34:02.240 --> 00:34:10.129
+hrishikb@andrew.cmu.edu: Like, they want to fully own the data that gets in the system, they don't want anyone else's fingerprints on it besides their own.
+
+212
+00:34:10.159 --> 00:34:26.339
+hrishikb@andrew.cmu.edu: Even the vendors. So it was very intentionally manual for a long time. Now, we've drastically outgrown their desire to be intentionally manual. So yes, we are trying to answer questions like that, but it's not great for me.
+
+213
+00:34:27.790 --> 00:34:42.490
+hrishikb@andrew.cmu.edu: Basically, one of the interesting experiments or research would be to take some data that… data sets you've had before, and sample data sets, we compare them to what's in FENS now, or whatever, just to see how they kind of map out.
+
+214
+00:34:42.500 --> 00:34:50.309
+hrishikb@andrew.cmu.edu: And see where the gaps are in the crypto can deal with that. And also give them an idea of the data itself, right?
+
+215
+00:34:53.440 --> 00:34:55.540
+hrishikb@andrew.cmu.edu: We're not answering the questions.
+
+216
+00:34:58.430 --> 00:35:04.870
+hrishikb@andrew.cmu.edu: Yeah, about the product vision, I think we've also touched up on that, you know, pretty clear idea about it.
+
+217
+00:35:05.100 --> 00:35:09.070
+hrishikb@andrew.cmu.edu: Yeah, we can… maybe we can talk about the team responsibility and deliverables a bit.
+
+218
+00:35:09.210 --> 00:35:11.200
+hrishikb@andrew.cmu.edu: So, as,
+
+219
+00:35:12.200 --> 00:35:23.930
+hrishikb@andrew.cmu.edu: as a deliverable, it's just going to be a system, right, where we have the, like, engine pipeline with the ML model to array the attributes. So, okay.
+
+220
+00:35:23.970 --> 00:35:36.639
+hrishikb@andrew.cmu.edu: Is there anything else you want to add on these points? Not just that, we wanted also, like, an application part of that that actually inserts something like the stage and things, right? It would be an additional river cylinder model,
+
+221
+00:35:36.690 --> 00:35:48.299
+hrishikb@andrew.cmu.edu: India, too, and in India, it's… it's just too low with… it's not for companies, like, so usually when the resources, depending on process it, probably churn out some sort of,
+
+222
+00:35:48.580 --> 00:35:54.849
+hrishikb@andrew.cmu.edu: Predictions, or just programmatic, a programmatic approach to just put these things into the right attributes.
+
+223
+00:35:55.020 --> 00:36:14.809
+hrishikb@andrew.cmu.edu: Yeah, we want that, the actually… the model, but then also something that puts what the model determines inside these tables. We do have staging environment, so we just have to be a dump for the data name.
+
+224
+00:36:14.890 --> 00:36:20.760
+hrishikb@andrew.cmu.edu: And then probably the community will pop in, and then you get the final data, which is probably roughly allowed.
+
+225
+00:36:20.900 --> 00:36:21.730
+hrishikb@andrew.cmu.edu: Feeling.
+
+226
+00:36:21.820 --> 00:36:27.920
+hrishikb@andrew.cmu.edu: We already have, the quick… our schema right now, basically, like, for the, like, product table.
+
+227
+00:36:27.960 --> 00:36:51.450
+hrishikb@andrew.cmu.edu: But for every product options table, product attributes table, but for every table we have, we also have a staging product table, a staging product options table, a staging product attribution table. And these tables, that combination is how we have interfaced this. We're almost like a Git difference. They can see, like, this is the product before, and then these are the changes I'm making, and that's where they can approve or decide, no, that's not right, and they go back and change something again.
+
+228
+00:36:51.460 --> 00:37:04.650
+hrishikb@andrew.cmu.edu: Ideally, we would just need the model, if you would like upload spec sheets, to just pop it into the statement table, and then from there, PIMS handles it, they can see the differences, they can choose to go manually edit more things, or whatnot, but
+
+229
+00:37:04.800 --> 00:37:07.059
+hrishikb@andrew.cmu.edu: Yeah, does that make sense?
+
+230
+00:37:08.030 --> 00:37:14.580
+hrishikb@andrew.cmu.edu: I'm curious where the previous studio teams you worked with. Will they ever… did they ever do this an exercise of doing a statement award?
+
+231
+00:37:16.700 --> 00:37:25.020
+hrishikb@andrew.cmu.edu: Requirements documents? We've gone through requirements.
+
+232
+00:37:26.010 --> 00:37:27.120
+hrishikb@andrew.cmu.edu: That's gonna be close.
+
+233
+00:37:28.810 --> 00:37:32.090
+hrishikb@andrew.cmu.edu: Any other possible constraints or risks?
+
+234
+00:37:32.210 --> 00:37:35.830
+hrishikb@andrew.cmu.edu: That would, like, we should take a keep mind while developing.
+
+235
+00:37:36.020 --> 00:37:43.460
+hrishikb@andrew.cmu.edu: Without data sensitivity or something on that side? I mean, data sensitivity, just prices.
+
+236
+00:37:44.090 --> 00:37:54.290
+hrishikb@andrew.cmu.edu: I would say, just to be looking at any of the data, we should be able to provide anything with whatever samples are expected. And,
+
+237
+00:37:54.430 --> 00:37:55.290
+hrishikb@andrew.cmu.edu: I think…
+
+238
+00:37:56.480 --> 00:38:06.179
+hrishikb@andrew.cmu.edu: We need this test is really to just put it all down. Understanding the exact scope and making sure that, we have adjusted for the annual risk.
+
+239
+00:38:06.930 --> 00:38:08.330
+hrishikb@andrew.cmu.edu: I think that those puppies.
+
+240
+00:38:12.050 --> 00:38:15.700
+hrishikb@andrew.cmu.edu: Okay, next is, about AI usage.
+
+241
+00:38:15.810 --> 00:38:22.750
+hrishikb@andrew.cmu.edu: I just want to ask, like, how do you guys use AI today in, like, everyday development, or…
+
+242
+00:38:22.820 --> 00:38:41.010
+hrishikb@andrew.cmu.edu: Around the company. For development, especially for coding, we've been pretty… pretty all-in for since February, since Bobcoding… I mean, Bobcoding, like, I started getting people in the public eye, but we use it a lot. We use Persons pretty extensively for all coding.
+
+243
+00:38:42.190 --> 00:38:44.929
+hrishikb@andrew.cmu.edu: Of course, all got me inviting me to expanding off.
+
+244
+00:38:45.030 --> 00:38:51.010
+hrishikb@andrew.cmu.edu: I think the use cases of multiple data for planting, backend news.
+
+245
+00:38:51.290 --> 00:38:54.460
+hrishikb@andrew.cmu.edu: still paying, handling costs and stuff. We kind of…
+
+246
+00:38:54.800 --> 00:39:04.559
+hrishikb@andrew.cmu.edu: Have the requirements and all the steps that are… then we sort of start implementing framework, and then go beyond the beginning of the week.
+
+247
+00:39:04.990 --> 00:39:13.010
+hrishikb@andrew.cmu.edu: I guess, I think it's testing heavy at the event, so the way we do it is, we generate through it, we make sure that we test it extensively.
+
+248
+00:39:13.150 --> 00:39:21.599
+hrishikb@andrew.cmu.edu: And then, based on change in the attested step, the invoice, and it'll be blue.
+
+249
+00:39:22.090 --> 00:39:34.489
+hrishikb@andrew.cmu.edu: Also gonna be, just to my, like, the MDs are, like, you know, we started to develop a practice now with the, like, documents inside the code so that it's reading the feature, like, the context is better.
+
+250
+00:39:34.770 --> 00:39:40.269
+hrishikb@andrew.cmu.edu: Maybe a couple other things we're important. I mean, we do a few things with, like, compass management for, like, in terms of
+
+251
+00:39:40.530 --> 00:39:46.429
+hrishikb@andrew.cmu.edu: For example, if you're working with desktop features, so you wouldn't need the context of the entire application.
+
+252
+00:39:46.630 --> 00:39:54.749
+hrishikb@andrew.cmu.edu: So, we've had internal features testing, or markdown plans. Agent needed finding that. So, based off of that.
+
+253
+00:39:54.900 --> 00:39:57.150
+hrishikb@andrew.cmu.edu: Or at least keep maintaining the manual network.
+
+254
+00:39:57.400 --> 00:40:16.470
+hrishikb@andrew.cmu.edu: And we're not, we use Cursor, and we all kind of… we evaluated Copilot early on. We all decided we liked Cursor, but we're not restricted to that. Like, if you all decide you want to use TalkBuild or something, or buy other tools like Cursor for what we already have our subscription.
+
+255
+00:40:19.050 --> 00:40:34.469
+hrishikb@andrew.cmu.edu: Yeah, I wanted to ask if we… for the, like, when we are using AI in our project, like, any part you would expect us to use it, like genetic code and, like, other things, but any other, like, your expectation of where we should be using AI?
+
+256
+00:40:34.540 --> 00:40:45.319
+hrishikb@andrew.cmu.edu: So, in addition to that point, we will do… I mean, I think that's the best use case we can go, which is why that's always coming out for me.
+
+257
+00:40:47.170 --> 00:40:53.850
+hrishikb@andrew.cmu.edu: Yes. So, from the record, I guess, let me know.
+
+258
+00:40:53.980 --> 00:41:05.450
+hrishikb@andrew.cmu.edu: making sure… I mean, even, like, decisions of, understanding what Facebook could be directed opportunities. You can use that to shut down. I would say it's just website recommendation would be other newspost.
+
+259
+00:41:05.650 --> 00:41:06.550
+hrishikb@andrew.cmu.edu: Just a second.
+
+260
+00:41:06.830 --> 00:41:16.919
+hrishikb@andrew.cmu.edu: And, for us using those elements, would be… should we use ours, or will we be provided one from EPA versus the subscription that you sell?
+
+261
+00:41:17.090 --> 00:41:35.049
+hrishikb@andrew.cmu.edu: Yeah, so within Cursor, you can… you can choose for this prompt, I want to use Gemini, or you can switch it, I want to use the symbols, like Sonic, 4.5, or anything. Has anyone of you used Cursing at all? But, we do… I think we should be able to follow.
+
+262
+00:41:35.330 --> 00:41:53.650
+hrishikb@andrew.cmu.edu: Also, you guys do not want us to use… like, I've been using anything I would be looking, and it seems to be doing a really good job, so do you not want us to use any other algorithms? You could, as long as, it's…
+
+263
+00:41:54.400 --> 00:42:14.139
+hrishikb@andrew.cmu.edu: I think, I think as long as the data is not based, right? Yeah. Well, we, other than that, it's another… Which is the privacy of the data? Yeah. Okay, we lose approval for that.
+
+264
+00:42:14.260 --> 00:42:19.920
+hrishikb@andrew.cmu.edu: supporting Netherlands.
+
+265
+00:42:20.040 --> 00:42:28.780
+hrishikb@andrew.cmu.edu: Would that be, from our side, or would that be, from, like, some start on the campaign?
+
+266
+00:42:29.110 --> 00:42:31.239
+hrishikb@andrew.cmu.edu: I think it would be, like.
+
+267
+00:42:31.870 --> 00:42:36.719
+hrishikb@andrew.cmu.edu: We're in a close conclusion on our perspective time ago.
+
+268
+00:42:37.040 --> 00:42:42.709
+hrishikb@andrew.cmu.edu: So, I think a lot of the work you guys are gonna do is not of…
+
+269
+00:42:42.910 --> 00:42:47.369
+hrishikb@andrew.cmu.edu: were inside of it being hard to put 16 or anything like that. There was no bad, right?
+
+270
+00:42:47.450 --> 00:42:55.689
+hrishikb@andrew.cmu.edu: And therefore, for all that stuff, like, as long as you're not instability incorrectly, things free throws, anything like that.
+
+271
+00:42:55.760 --> 00:43:11.410
+hrishikb@andrew.cmu.edu: I don't mind if you use it that you're having Andy share the stuff with him, right? It doesn't matter that it's not even ours yet. Again, as long as you're not, like, uploading vendor files to that, like, I don't want to be… yeah, I don't…
+
+272
+00:43:11.460 --> 00:43:18.660
+hrishikb@andrew.cmu.edu: we're not… all the vendor data isn't super sensitive, but I also don't want to just, like, wonky-nilly with it.
+
+273
+00:43:18.940 --> 00:43:22.300
+hrishikb@andrew.cmu.edu: But, yeah, you're welcome to move your own stuff.
+
+274
+00:43:22.830 --> 00:43:39.610
+hrishikb@andrew.cmu.edu: Like, if they decide the plot is, like, good, like, I guess we paid for it. Is that what you kind of asked? Oh, that was what I was going towards, just from my experience when I worked at school.
+
+275
+00:43:40.700 --> 00:43:48.699
+hrishikb@andrew.cmu.edu: Yeah, I don't know what their education pricing is or anything like that. I think a bike ride that provides 5 code is, like, the minimum 300 bucks.
+
+276
+00:43:48.940 --> 00:43:54.320
+hrishikb@andrew.cmu.edu: For a month, per person. There's really not a minimum 200 bucks a month to a person.
+
+277
+00:43:54.320 --> 00:44:14.280
+hrishikb@andrew.cmu.edu: So that's… that's part of where my mind is at, but again, if it's a deep first, then we have a good rationale for why I should tell you that, or pitch it. No, I… And I am too, I'm just saying, if there's something that you're saying, that this is actually going to make it 30 times more productive, and there's no way of a product where it's like, here's a to-do list for our city.
+
+278
+00:44:14.430 --> 00:44:16.150
+hrishikb@andrew.cmu.edu: Let's have the discussion.
+
+279
+00:44:17.440 --> 00:44:25.759
+hrishikb@andrew.cmu.edu: I mean, to be fair, almost all of these schools kind of have the same. They're all Asian-based, or kind of…
+
+280
+00:44:26.180 --> 00:44:40.379
+hrishikb@andrew.cmu.edu: application time. They all index files similarly, they all have restrict similar model access to them, similar instability. I think it's just a fundamental… exactly, it's just the underlying problems, like, probably kind of designed
+
+281
+00:44:40.560 --> 00:44:43.940
+hrishikb@andrew.cmu.edu: EGU, right?
+
+282
+00:44:44.580 --> 00:45:02.100
+hrishikb@andrew.cmu.edu: But it's super expensive, so, like, I mean…
+
+283
+00:45:02.980 --> 00:45:14.220
+hrishikb@andrew.cmu.edu: And I would ask, but he spent our money, and yeah, it was your money. Make wise decisions. We're a small company, we're not legitimate enterprise, so it doesn't make a difference to us.
+
+284
+00:45:14.420 --> 00:45:23.469
+hrishikb@andrew.cmu.edu: But it also does make the difference to us that we passed, and getting the credit out faster, like, whatever. So, the… so the money side works on both.
+
+285
+00:45:23.890 --> 00:45:30.340
+hrishikb@andrew.cmu.edu: for us to spend money on this, but it'd be smart. That's all.
+
+286
+00:45:34.580 --> 00:45:39.620
+hrishikb@andrew.cmu.edu: I think next, do you have any questions you guys might have, or anyone from the team?
+
+287
+00:45:40.180 --> 00:45:42.450
+hrishikb@andrew.cmu.edu: That's having out-of-pank questions?
+
+288
+00:45:43.720 --> 00:45:50.849
+hrishikb@andrew.cmu.edu: Yeah. So, I have one,
+
+289
+00:45:51.020 --> 00:46:08.820
+hrishikb@andrew.cmu.edu: How do the handouts happen? Like, do we have proper understanding, right? It depends on the story. So, so there were, like, three parts of the cluster. In one universe, the main, the data pipeline setting, which is scoping, and the visibility.
+
+290
+00:46:09.030 --> 00:46:12.849
+hrishikb@andrew.cmu.edu: For that, we have, like, the proper knowledge times and daycare after this.
+
+291
+00:46:13.040 --> 00:46:18.029
+hrishikb@andrew.cmu.edu: We tried to set things up, and then I just tried to run their own books as well.
+
+292
+00:46:18.150 --> 00:46:27.610
+hrishikb@andrew.cmu.edu: and see if it was working, and if there were any change required. That was a… I mean, it was a super lightweight sessions with you all spent, and that's it.
+
+293
+00:46:27.810 --> 00:46:38.879
+hrishikb@andrew.cmu.edu: For other things, the document and stuff, we had this kind of, like, documentation handout, where we had to fill it out on what our covers and what we had it.
+
+294
+00:46:39.050 --> 00:46:54.150
+hrishikb@andrew.cmu.edu: So, it depends on the use case, and what exactly you use. It stands for probably SP Lumines, and, whereas a lot of code stuff.
+
+295
+00:46:55.700 --> 00:46:57.829
+hrishikb@andrew.cmu.edu: How does your current,
+
+296
+00:46:57.870 --> 00:47:21.499
+hrishikb@andrew.cmu.edu: the code checking process look like? And starting from, like, the SFS talk, raising theirs, who are the stakeholders, then we should must get approved from? How does that look like? The bucket is what we use, and again, I think whatever emails they look, they probably need to restart the nose at the beginning of
+
+297
+00:47:21.580 --> 00:47:43.580
+hrishikb@andrew.cmu.edu: So you weren't really, the approvals and things like that. It should be internal. I would say you guys should probably let in as well, and that's up to you guys. Yeah, the only… the only time I could see modifications to, like, something we already have in this thing is, what we talked about earlier is, like, when it comes time to actually put the products in and…
+
+298
+00:47:43.580 --> 00:47:49.819
+hrishikb@andrew.cmu.edu: We can talk down the road about how we want to do that, or maybe we just make some APIs that we've been playing a password or something down the road.
+
+299
+00:47:49.980 --> 00:48:07.290
+hrishikb@andrew.cmu.edu: Or you could spin off of… spin off a committee to put down the subway station, so that you guys can install. And, we do also, though, we do use, sonar. We use… we use sonar for limping, too.
+
+300
+00:48:08.140 --> 00:48:13.520
+hrishikb@andrew.cmu.edu: Do you guys use third-party for, like, security purposes? Like, something like a backdrop or something?
+
+301
+00:48:13.620 --> 00:48:19.149
+hrishikb@andrew.cmu.edu: To check vulnerabilities in the system? Security would be these, I think someone does it.
+
+302
+00:48:19.150 --> 00:48:34.199
+hrishikb@andrew.cmu.edu: So, that is pretty, extensive, very, extensive in terms of, what it covers. But yeah, it covers anything, so security to almost minor checks, too.
+
+303
+00:48:35.410 --> 00:48:47.360
+hrishikb@andrew.cmu.edu: I have a trivial question. What were the names of the previous MSD to follow the team names? First one was Pensate means… and then there was,
+
+304
+00:48:47.760 --> 00:48:52.300
+hrishikb@andrew.cmu.edu: Option logic will be, unknown event.
+
+305
+00:48:52.450 --> 00:49:02.019
+hrishikb@andrew.cmu.edu: That's alright. Last year, like, did we even have anything? There was, I think it was…
+
+306
+00:49:02.250 --> 00:49:06.580
+hrishikb@andrew.cmu.edu: They never used the name, they just used Epard's team.
+
+307
+00:49:06.940 --> 00:49:20.259
+hrishikb@andrew.cmu.edu: I mean, you guys should have a new one. That's a rule. Be more fun than what happens. I think it's always good to have a team name that is free to court. Yeah, no, it was fun, especially the very decor. And the logo is for the logo.
+
+308
+00:49:20.490 --> 00:49:21.950
+hrishikb@andrew.cmu.edu: Interesting.
+
+309
+00:49:24.520 --> 00:49:31.949
+hrishikb@andrew.cmu.edu: There's a decent chance we'll be switching from Bitfucket to GitHub in, like, March. So, we'll see.
+
+310
+00:49:33.470 --> 00:49:36.549
+hrishikb@andrew.cmu.edu: We don't have any political competition.
+
+311
+00:49:37.950 --> 00:49:47.980
+hrishikb@andrew.cmu.edu: And you would be our first team using Winninger. We've been in Jira and Bodith has to lose a little more than Fire himself.
+
+312
+00:49:48.300 --> 00:49:56.319
+hrishikb@andrew.cmu.edu: Obviously, it would need for us doing the onboarding tools. So what were your… what was your rationale going from Jira to…
+
+313
+00:49:56.340 --> 00:50:06.260
+hrishikb@andrew.cmu.edu: I mean, one of the biggest benefits is that I think just… it's fast. Like, when you open an issue is no sentence instead of multiple seconds.
+
+314
+00:50:06.260 --> 00:50:18.400
+hrishikb@andrew.cmu.edu: Also, the flexibility. You can very easily, like, earn something from initially to do a task, or, like, it's much more flexible, where Jira was a lot more, like, locked in with DB or the portal.
+
+315
+00:50:18.470 --> 00:50:29.140
+hrishikb@andrew.cmu.edu: Yeah, it's also bringing the flexibility and the velocity. It accommodates everything, like, 49 stone building the velocity if it accommodates it.
+
+316
+00:50:29.260 --> 00:50:35.099
+hrishikb@andrew.cmu.edu: And probably just a spring cube. It has… it has support for that one. Probably these are really nice, but…
+
+317
+00:50:35.260 --> 00:50:42.680
+hrishikb@andrew.cmu.edu: So… It's also a milestone-driven methodology in general, fully move, rather than having,
+
+318
+00:50:42.840 --> 00:50:47.590
+hrishikb@andrew.cmu.edu: A task for the Msport and class itself.
+
+319
+00:50:48.710 --> 00:50:54.480
+hrishikb@andrew.cmu.edu: It's best for me.
+
+320
+00:50:54.920 --> 00:51:01.350
+hrishikb@andrew.cmu.edu: And the UI is not better. Similar to the commercial product?
+
+321
+00:51:01.350 --> 00:51:15.570
+hrishikb@andrew.cmu.edu: So we know we can show up, and those… Oh, yeah.
+
+322
+00:51:18.660 --> 00:51:22.680
+hrishikb@andrew.cmu.edu: and… Oh, okay.
+
+323
+00:51:24.330 --> 00:51:48.719
+hrishikb@andrew.cmu.edu: So we're gonna have a, standing, fund meeting on Thursdays at this time every week to help the idea of the funds? I mean, and if there's… if it says tough, just shoot us some other times, yeah, we do have a couple reoccurrings on our side that we'll have to… that are kind of like, note, but other than that, if need be, we can get some other time or something. I mean, based on your schedules, too, I think that's the bigger factor, so…
+
+324
+00:51:48.720 --> 00:51:54.480
+hrishikb@andrew.cmu.edu: Yes, aren't… aren't days are pretty much pre-today and… today, for sure.
+
+325
+00:51:54.480 --> 00:51:58.710
+hrishikb@andrew.cmu.edu: And based on your attributes, we can see what else also they can actually ask as well.
+
+326
+00:51:59.090 --> 00:52:03.609
+hrishikb@andrew.cmu.edu: And obviously, change the message instead of being back in the year.
+
+327
+00:52:04.100 --> 00:52:20.420
+hrishikb@andrew.cmu.edu: And I'll let you know that I like to attend with client meetings. I know Dennis won't be able to do that, but I always find it informative to understand what is the dialogue back and forth from the client getting started. I'll be informing the law. Okay.
+
+328
+00:52:20.780 --> 00:52:21.740
+hrishikb@andrew.cmu.edu: Right there.
+
+329
+00:52:26.280 --> 00:52:29.299
+hrishikb@andrew.cmu.edu: And last we have an action item review.
+
+330
+00:52:29.480 --> 00:52:46.690
+hrishikb@andrew.cmu.edu: Any action items? So, for you guys, you, you segregate provide us the access using our annual email IDs, and to our mailroom as well. And then, for any doubts, just restating, the point of contact would be Harsha, Jake, and David.
+
+331
+00:52:46.690 --> 00:52:55.230
+hrishikb@andrew.cmu.edu: And then, we'll also get access to their teams and the relevant workspaces, like documentation and stuff.
+
+332
+00:52:55.230 --> 00:52:58.609
+hrishikb@andrew.cmu.edu: a bulk of subscription, for the entire team.
+
+333
+00:52:58.610 --> 00:53:08.189
+hrishikb@andrew.cmu.edu: And, I mean, not immediately, but then, I guess you would also share details on the current, data accuracy, like, with respect to
+
+334
+00:53:08.190 --> 00:53:18.670
+hrishikb@andrew.cmu.edu: the existing quality metrics, like, if there's any defined, so that once we develop our system, we can just baseline and compare, how is… how's the whole thing improved or something like this.
+
+335
+00:53:18.790 --> 00:53:38.769
+hrishikb@andrew.cmu.edu: And on ours, yeah, we'll also, like, set up one recurring call with all of you guys who just check our calendars and then come up with a slot, and then we'll also have to, like, come up with one day where we visit the office and get to meet the parts calendar to understand how the current… how they're working on their business.
+
+336
+00:53:43.570 --> 00:53:45.160
+hrishikb@andrew.cmu.edu: Where's the coming over?
+
+337
+00:53:46.230 --> 00:53:52.329
+hrishikb@andrew.cmu.edu: I would also send directly inside the portal. Yes, for sure. That'd be great, that's it.
+
+338
+00:53:53.490 --> 00:54:02.759
+hrishikb@andrew.cmu.edu: It's both a pleasure to have you on board here. I know the team is excited, looking forward to working on this project, and I think in the end, you know.
+
+339
+00:54:02.870 --> 00:54:13.900
+hrishikb@andrew.cmu.edu: You'll get an interesting project, and something that hopefully meets your requirements and meets your expectation-based success. So, we'll see how this works out.
+
+340
+00:54:14.050 --> 00:54:26.319
+hrishikb@andrew.cmu.edu: We're also really looking forward to, the perp… kind of what the purpose of the project is more fancy. We're curious what… what not only this team, but all the teams buying with AI, we're also looking forward to that.
+
+341
+00:54:28.100 --> 00:54:33.399
+hrishikb@andrew.cmu.edu: It's not a silver bullet, but it's something that's helpful, I hope.
+
+342
+00:54:34.300 --> 00:54:36.950
+hrishikb@andrew.cmu.edu: Well, very good! This is exciting, yes.
+
+343
+00:54:38.450 --> 00:54:44.029
+hrishikb@andrew.cmu.edu: I just have a slide deck as well. Yeah, sure. I'll turn them in. And be safe over the weekend.
+
+344
+00:54:44.180 --> 00:54:45.190
+hrishikb@andrew.cmu.edu: You too?
+
+345
+00:54:45.190 --> 00:54:45.520
+Dennis Grinberg: Yeah.
+
+346
+00:54:45.520 --> 00:54:56.090
+hrishikb@andrew.cmu.edu: Go snowboarding on Monday if the weather slowed that much down, so you guys should be a first big storm. Yeah, you must have the December storm. Yes.
+
+347
+00:54:56.380 --> 00:55:03.619
+hrishikb@andrew.cmu.edu: Were you in the store? No, no, I just left before the storm. But he was in Canada, so…
+
+348
+00:55:03.890 --> 00:55:17.249
+hrishikb@andrew.cmu.edu: Well, years ago, when they had the super big snow cherry, CMUs are the kind of… they want you to come to school, but the city of Pittsburgh did come to close school. They closed the school. We'll see what happens on Monday.
+
+349
+00:55:17.800 --> 00:55:31.269
+Dennis Grinberg: Yeah, it'll be… it'll be interesting. I think there's some places, like, that aren't used to snow at all. Pittsburgh has gotten a little better about it, but, like, you know, DC, if they get a, you know, half an inch of snow, the city closes down, and they're supposed to get
+
+350
+00:55:31.470 --> 00:55:35.560
+Dennis Grinberg: Many inches, so it's gonna be… Gonna be interesting.
+
+351
+00:55:37.250 --> 00:55:42.350
+hrishikb@andrew.cmu.edu: I was sleeping and staying on the front step yesterday, and there was nothing happening.
+
+352
+00:55:42.920 --> 00:55:48.970
+hrishikb@andrew.cmu.edu: The weather overnight, the sleet one where it was, is very slick, very slickering. Yeah, nasty.
+
+353
+00:55:50.200 --> 00:55:55.419
+hrishikb@andrew.cmu.edu: Very good. Good to see y'all. Thank you so much.
+
+354
+00:55:55.990 --> 00:55:56.690
+Dennis Grinberg: Hi, everyone.
+
+355
+00:55:57.360 --> 00:55:57.960
+hrishikb@andrew.cmu.edu: Yep.
+
+356
+00:55:59.300 --> 00:56:01.689
+hrishikb@andrew.cmu.edu: Let me wait a second thing I want.
+
+357
+00:56:02.000 --> 00:56:03.489
+hrishikb@andrew.cmu.edu: Are you still there, Dennis?
+
+358
+00:56:05.120 --> 00:56:08.640
+hrishikb@andrew.cmu.edu: Dennis, let the content. Alright.
+
+359
+00:56:11.440 --> 00:56:14.469
+hrishikb@andrew.cmu.edu: Okay, thank you. Thank you.
+
+360
+00:56:14.640 --> 00:56:15.810
+hrishikb@andrew.cmu.edu: Make movies.
+
+361
+00:56:17.490 --> 00:56:36.510
+hrishikb@andrew.cmu.edu: I would certainly say that after a client meeting, it's always good to have a follow-on with the mentors there, just to say what their perspective is on the meeting, if there's anything else that popped up. So, it's always good for you guys to also kind of have a discussion post-meeting about anything you heard, or something you need to follow up on, so…
+
+362
+00:56:37.350 --> 00:56:39.000
+hrishikb@andrew.cmu.edu: We can stop on Friday.
+
+363
+00:56:39.480 --> 00:56:41.490
+hrishikb@andrew.cmu.edu: It was always good to record everything.
+
diff --git a/transcripts/GMT20260212-190517_Recording.transcript.vtt b/transcripts/GMT20260212-190517_Recording.transcript.vtt
new file mode 100644
index 0000000..f84a95e
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@@ -0,0 +1,1690 @@
+WEBVTT
+
+1
+00:00:03.660 --> 00:00:06.930
+hrishikb@andrew.cmu.edu: A for, today's agenda.
+
+2
+00:00:09.130 --> 00:00:11.390
+hrishikb@andrew.cmu.edu: I think your mic is gone.
+
+3
+00:00:11.570 --> 00:00:26.610
+hrishikb@andrew.cmu.edu: model sound? Yeah, firstly, we'll discuss a few of the ML findings that we've had. All the points I've discussed in the last meeting, we have gone over those. We'll talk BERT and different models which you can use.
+
+4
+00:00:26.990 --> 00:00:29.849
+hrishikb@andrew.cmu.edu: And next, we have, terraformis bicep.
+
+5
+00:00:30.080 --> 00:00:42.739
+hrishikb@andrew.cmu.edu: This one is, still a bit open-ended, but we can discuss the benefits and ways we can use in our project. Then we have oral implementation and dockerization that Arjun will be handling.
+
+6
+00:00:43.130 --> 00:00:47.039
+hrishikb@andrew.cmu.edu: So, I guess we can start off with the ML findings.
+
+7
+00:00:50.910 --> 00:00:52.500
+hrishikb@andrew.cmu.edu: They're pony.
+
+8
+00:01:03.160 --> 00:01:04.710
+hrishikb@andrew.cmu.edu: If you can move Zoom.
+
+9
+00:01:07.320 --> 00:01:08.549
+hrishikb@andrew.cmu.edu: She's gonna show.
+
+10
+00:01:09.120 --> 00:01:11.060
+hrishikb@andrew.cmu.edu: Mine's only… The disabled.
+
+11
+00:01:34.140 --> 00:01:36.630
+hrishikb@andrew.cmu.edu: To put the Zoom theme there.
+
+12
+00:01:36.840 --> 00:01:42.489
+hrishikb@andrew.cmu.edu: I think we need to raise the accent. Yeah, we need to go over the HTML.
+
+13
+00:01:42.970 --> 00:01:43.770
+hrishikb@andrew.cmu.edu: Okay.
+
+14
+00:01:52.930 --> 00:01:54.559
+hrishikb@andrew.cmu.edu: You can stop for them.
+
+15
+00:01:55.660 --> 00:01:57.240
+hrishikb@andrew.cmu.edu: Do you have access to shit?
+
+16
+00:01:57.720 --> 00:02:00.420
+hrishikb@andrew.cmu.edu: They're stop sharing your name, other people's covenants.
+
+17
+00:02:05.790 --> 00:02:06.470
+hrishikb@andrew.cmu.edu: I'm doing.
+
+18
+00:02:13.580 --> 00:02:14.999
+hrishikb@andrew.cmu.edu: Yep, here it goes, Tom.
+
+19
+00:02:20.440 --> 00:02:23.569
+hrishikb@andrew.cmu.edu: David just got back to me, he'll be jumping in.
+
+20
+00:02:49.570 --> 00:02:51.140
+hrishikb@andrew.cmu.edu: Yep.
+
+21
+00:02:58.090 --> 00:02:58.840
+hrishikb@andrew.cmu.edu: Wonderful.
+
+22
+00:03:00.480 --> 00:03:03.100
+hrishikb@andrew.cmu.edu: That much data to increase the font size.
+
+23
+00:03:09.820 --> 00:03:11.650
+David Mine: No. So, Google.
+
+24
+00:03:11.810 --> 00:03:13.490
+David Mine: That's good. Real good, though.
+
+25
+00:03:13.490 --> 00:03:14.640
+hrishikb@andrew.cmu.edu: There is also him.
+
+26
+00:03:14.930 --> 00:03:15.560
+hrishikb@andrew.cmu.edu: Hi, David.
+
+27
+00:03:15.560 --> 00:03:22.020
+David Mine: I didn't talk to this guy, I think he's not blue.
+
+28
+00:03:22.120 --> 00:03:25.499
+hrishikb@andrew.cmu.edu: Yeah, yeah, I can see.
+
+29
+00:03:26.850 --> 00:03:41.559
+hrishikb@andrew.cmu.edu: Okay, so again, like, this is not a prescribed solution that we are proposing, it's just that we did a couple of experimentation, just, like, not a POC, but then theoretical real findings of
+
+30
+00:03:41.560 --> 00:03:53.990
+hrishikb@andrew.cmu.edu: where… how we want to, like, do the ML component, which is, like, assigning scores, and everything. So, one, all three of us will be, like, presenting three different approaches, and we just want to, like.
+
+31
+00:03:54.040 --> 00:04:10.489
+hrishikb@andrew.cmu.edu: present our findings till now, again. So, one of the approach that we came up with, like, it's called… it's called the semantic matcher, so I'll explain, in, like, the basics of it so that everyone understands, what it is and how is it supposed to work.
+
+32
+00:04:10.490 --> 00:04:15.569
+hrishikb@andrew.cmu.edu: So, it's like, let's say,
+
+33
+00:04:15.770 --> 00:04:37.019
+hrishikb@andrew.cmu.edu: like, let's say you want to paint this wall, right? You literally won't remember the color of the paint and go to the shop and get the paint color. So, you'll have a catalog, and you'll find… you'll have those swatches, and then you'll come and try matching it one by one. So, that's something like how the semantic matcher works.
+
+34
+00:04:37.020 --> 00:04:39.580
+hrishikb@andrew.cmu.edu: So, what'll happen here is,
+
+35
+00:04:39.580 --> 00:04:48.309
+hrishikb@andrew.cmu.edu: Like, to put it in the context of the project, when a supplier sends a description, like, let's say, a drill or a
+
+36
+00:04:48.310 --> 00:05:13.229
+hrishikb@andrew.cmu.edu: screwoo description. So, we use a… it's a very tiny model that we're trying to use here. It's called All MiniLM. Now, what it does is it will turn that text into a list of numbers. Now, this process in the ML world, it's called vectorization, where you turn… where you convert a text into numbers. That's called vectorization. Now, the next step is search. So, like you said, there's PIMS database, right?
+
+37
+00:05:13.230 --> 00:05:30.029
+hrishikb@andrew.cmu.edu: So, we'll use this vector, and we'll, vectorization search over the existing PIMS database. And now, since we already have, like, thousands of approved products in… sitting in the PIMS database, we'll leverage that knowledge base first.
+
+38
+00:05:30.030 --> 00:05:47.630
+hrishikb@andrew.cmu.edu: And then we'll, your… and this is what we're taking as the master, swatch book, like, the… where you get the paint catalog and you'll try matching it with the color of the paint. So your… the knowledge bank that is in the PIMS is your master swatch book.
+
+39
+00:05:47.630 --> 00:05:54.460
+hrishikb@andrew.cmu.edu: Now, the third one, there's
+
+40
+00:05:56.660 --> 00:06:06.390
+hrishikb@andrew.cmu.edu: Yeah. Now, the third… the third step here is you make a decision based on the confidence. So, like, let's say if the closest ma…
+
+41
+00:06:06.560 --> 00:06:25.800
+hrishikb@andrew.cmu.edu: again, like, the vectors, after you decide, the ML component, assigns, not assigns, like, it, concludes on a confidence code based on the distance. Like, how similar can, how similar is this particular, screw?
+
+42
+00:06:25.800 --> 00:06:47.280
+hrishikb@andrew.cmu.edu: To the one that is already present in the PIMS database. So, as soon as it comes with a distance factor over there, so the larger distance and the smaller distance, I'm not going to the technicalities, like, how the distance is measured. It'll, come up with the confidence code, like, obviously, the smaller distance, meaning it has more… it is more confident that, this product matches
+
+43
+00:06:47.280 --> 00:06:59.730
+hrishikb@andrew.cmu.edu: with this particular categorization that is already present in the PRIMS database. Otherwise, you get a very low confidence score. And we already, obviously, have a human checker, in the loop. Now.
+
+44
+00:06:59.730 --> 00:07:23.910
+hrishikb@andrew.cmu.edu: hear why, this approach is better than what these guys are gonna, like, propose Nexus. Like, he'll be talking about BERT, and, Leo will be talking about XGBoost, you will learn about that more in detail. With semantic search, what happens is you don't have to retrain as a developer, like, you don't need a developer's intervention for the model to be retrained every now and then, because
+
+45
+00:07:24.160 --> 00:07:36.159
+hrishikb@andrew.cmu.edu: our, whole architecture is prone to schema changes, right? Now, that's the biggest change point that I see over here. Everything else is constant, like.
+
+46
+00:07:36.160 --> 00:07:42.729
+hrishikb@andrew.cmu.edu: The attributes related to a particular product are constant, like, at least over a period of time.
+
+47
+00:07:42.730 --> 00:08:06.799
+hrishikb@andrew.cmu.edu: I mean, they are not, like, too subjective to change too quickly, right? But then the schema might change, schema as in the information that we're extracting from the raw data. Like, someone might just give you 1, 2, 3 items in the description, but the others can give you, like, four. So the schema that is entering into the ingestion pipeline will change, and the model has to, like, relearn
+
+48
+00:08:06.800 --> 00:08:18.449
+hrishikb@andrew.cmu.edu: okay, now this is the new data that I'm getting. So, we don't have to do that. The model will retain on its own, because we have connected with the central knowledge base, that is the PIMS database.
+
+49
+00:08:18.450 --> 00:08:30.030
+hrishikb@andrew.cmu.edu: And it's also, like, very… and one of the major constraints that Harsha mentioned the other day was, like, he wanted a small short model, like, which is not too compute-heavy, and also can access that
+
+50
+00:08:30.030 --> 00:08:43.149
+hrishikb@andrew.cmu.edu: I think… I forgot the number, how many records we maintain, like, the average number, but then this model actually is efficient enough, like, since it's an embedding model, we are not actually digging the text.
+
+51
+00:08:43.150 --> 00:08:51.280
+hrishikb@andrew.cmu.edu: But we are converting into a form of numbers, which is very condensed. That vectorization will help us,
+
+52
+00:08:51.280 --> 00:09:10.800
+hrishikb@andrew.cmu.edu: very much, like, lower the compute cost. And, this can be run on Azure as well, and this can be run on, like, independently if you want to, like, maintain. It can be done like that also. So, there's no platform dependency, like a blocker as such. So, that's the high level of
+
+53
+00:09:10.800 --> 00:09:12.859
+hrishikb@andrew.cmu.edu: The semantic matcher approach.
+
+54
+00:09:14.990 --> 00:09:15.670
+hrishikb@andrew.cmu.edu: Great.
+
+55
+00:09:17.520 --> 00:09:40.340
+hrishikb@andrew.cmu.edu: Yeah, that's a pitch, actually, like, it's the last sentence, like, it's maintenance-free, and it automatically adopts the new products, and that's the whole, pro that I saw in this approach when compared to BERT and the HCBoost one, wherein those models, even if they are maintained by the Azure platform.
+
+56
+00:09:40.340 --> 00:10:02.860
+hrishikb@andrew.cmu.edu: you have to, like, retrain your model and keep it updating, but it does not work like that, because it updates on its own. It is capable enough because it has some sentence transformers libraries ingested in it, and it is intelligent enough to, like, retrain on its own upon… like, it basically detects the schema change and understands by itself, okay, there's a change.
+
+57
+00:10:02.860 --> 00:10:09.729
+hrishikb@andrew.cmu.edu: So, I have to relearn. So, you know, there's no human trigger over here. That's the pro in this approach.
+
+58
+00:10:11.700 --> 00:10:17.439
+hrishikb@andrew.cmu.edu: But, do we expect, frequent schema changes? How currently the things are working?
+
+59
+00:10:17.580 --> 00:10:27.569
+hrishikb@andrew.cmu.edu: No. So wait, you said schema change can also include, like, adding more attributes? Yeah. Yeah, well, so in that case, like, over time, we will be…
+
+60
+00:10:27.680 --> 00:10:35.010
+hrishikb@andrew.cmu.edu: hopefully getting more data and adding on more and more attributes to a given product, or, yeah. So, that is the case.
+
+61
+00:10:35.220 --> 00:10:40.119
+hrishikb@andrew.cmu.edu: Yeah, that's a case, but I wouldn't say it's going to be all the time. Okay, okay.
+
+62
+00:10:41.500 --> 00:10:49.969
+hrishikb@andrew.cmu.edu: I don't know, like a product, they might go back and dig deeper on some products and enrich them and add some more attributes, but I…
+
+63
+00:10:50.540 --> 00:11:05.900
+hrishikb@andrew.cmu.edu: And yeah, like, after they go back and… from once we get something to the vendor, and they go out and do research and enrich it, I can't see it happening again and again and again. I don't think they're gonna be, like, over time, revisiting the same product over and over and over again, adding more and more. What are your thoughts on that, David?
+
+64
+00:11:09.040 --> 00:11:11.380
+David Mine: Yeah, I agree with all that. I think you got it.
+
+65
+00:11:12.460 --> 00:11:18.450
+hrishikb@andrew.cmu.edu: But then, don't you want your solution to be extensive? No, no, I mean, we do, I guess.
+
+66
+00:11:18.600 --> 00:11:38.390
+hrishikb@andrew.cmu.edu: I don't think it's going to be very often, though, that, like, there are suppliers coming out with more information about something that already exists. Like, yeah, they'll probably go through a period of discovery where they're searching the internet and looking for more resources on something, and… Okay. But yeah, if the product changes, then it's probably just going to be a brand new product. Yeah.
+
+67
+00:11:38.460 --> 00:11:47.429
+hrishikb@andrew.cmu.edu: So it means, there's definitely, more weightage given to the simpler solution against something that
+
+68
+00:11:47.430 --> 00:11:49.060
+hrishikb@andrew.cmu.edu: You know, that,
+
+69
+00:11:49.060 --> 00:12:07.910
+hrishikb@andrew.cmu.edu: brings up, like, for this one, an event… I don't know which is the simplest one, because we have not implemented it, and we have not made our hands dirty yet. So, let's say if the BERT is very simpler to, you know, work around. So, but then it does not do the retraining bit on its own.
+
+70
+00:12:07.910 --> 00:12:16.959
+hrishikb@andrew.cmu.edu: So, you are open to having that trade-off? Oh, no, no, I mean, we definitely like how it did all in China, so yeah, that's definitely a pro.
+
+71
+00:12:18.850 --> 00:12:23.969
+hrishikb@andrew.cmu.edu: Sure, would you… because my laptop requiring such thing.
+
+72
+00:12:34.900 --> 00:12:37.160
+hrishikb@andrew.cmu.edu: Gotcha. Is it this? Beautiful.
+
+73
+00:13:02.150 --> 00:13:07.669
+hrishikb@andrew.cmu.edu: So what is the source data for the semantic matter? Somebody gives you a PDF file of a product, or…
+
+74
+00:13:07.800 --> 00:13:23.960
+hrishikb@andrew.cmu.edu: Does that matter? Yeah, so, I mean, if it's coming… we're getting the information straight from the vendor, it's probably going to be more comprehensive. It'll be, like, PDFs, spec sheets, and whatnot. A lot of times, too, though, if we onboard, let's say, a distributor.
+
+75
+00:13:24.130 --> 00:13:41.809
+hrishikb@andrew.cmu.edu: they're giving us the catalog products they sell. They're not necessarily giving us all of their vendor spec sheets that they received, so in that case, the data might be a little more at least unfilled, and that's when we want to be going back and kind of doing our own discovery period with our catalog team of trying to search the internet for more sources.
+
+76
+00:13:44.470 --> 00:13:47.660
+hrishikb@andrew.cmu.edu: So… Okay.
+
+77
+00:13:51.980 --> 00:14:06.149
+hrishikb@andrew.cmu.edu: Is it… So, I basically went through and looked through some of the models, and Parsha did mention BERT when we were discussing it, and I went through and
+
+78
+00:14:06.340 --> 00:14:11.920
+hrishikb@andrew.cmu.edu: look through it. So, BERT is just a… it's basically an…
+
+79
+00:14:13.130 --> 00:14:30.789
+hrishikb@andrew.cmu.edu: It's based on the transformer model, and as for distilled BERT and tiny bird, they're the same thing, but they're just a… Distilled BERT is just a smaller model that's trained on the BERT itself as a mentor model, and it's designed to replicate it.
+
+80
+00:14:32.860 --> 00:14:40.200
+hrishikb@andrew.cmu.edu: It solves some of the issues that BERT has, that it's pretty large, but BERT is compared to the other options.
+
+81
+00:14:40.710 --> 00:14:50.719
+hrishikb@andrew.cmu.edu: It's… BERT is best used for text classification, and basically, it can also do semantic massing, but the best part of BERT is that
+
+82
+00:14:51.890 --> 00:15:10.599
+hrishikb@andrew.cmu.edu: it does bi-directional searching. So even if something is not clear, and there's a huge paragraph of text that we need to extract, BERT will be able to basically get out all of the required categories, as long as it's been trained on all the relevant ones for our product.
+
+83
+00:15:11.090 --> 00:15:19.509
+hrishikb@andrew.cmu.edu: Distilled Bird is just, I think a 40% smaller version, but it still has, like, around 97% of the accuracy.
+
+84
+00:15:19.900 --> 00:15:36.959
+hrishikb@andrew.cmu.edu: And best part is DistillBird has… it's available on Azure. There's no need to manually deploy it or anything like that. And training it, it does not cost that much time. It is longer than the semantic matching one, I'm pretty sure, but it's not that much longer.
+
+85
+00:15:40.270 --> 00:15:47.540
+hrishikb@andrew.cmu.edu: As for the other stuff, basically, as long as we have the OCR working properly and we've deleted all the data.
+
+86
+00:15:47.690 --> 00:16:00.400
+hrishikb@andrew.cmu.edu: It will go through, and it does not have any problems, like, for semantics, matching. If the text grows large enough, there might… future tweaks might be required.
+
+87
+00:16:00.400 --> 00:16:12.839
+hrishikb@andrew.cmu.edu: But Word can handle, like, very, very large text paragraphs, or, like, if… if it's, like, 5 pages long and has, like, multiple paragraph after paragraph, it can extract whatever you need and get into categories.
+
+88
+00:16:12.860 --> 00:16:22.639
+hrishikb@andrew.cmu.edu: The… I would… I will say, as a downside, that it does… it will need a retraining, that will be manually triggered. Like, if you add a bunch of products with new categories.
+
+89
+00:16:22.750 --> 00:16:25.909
+hrishikb@andrew.cmu.edu: Then it won't probably get them out.
+
+90
+00:16:26.040 --> 00:16:33.769
+hrishikb@andrew.cmu.edu: As… as well as before, and you will need to manually do it again, compared to a sugar solution.
+
+91
+00:16:35.240 --> 00:16:38.700
+hrishikb@andrew.cmu.edu: I think it's cool.
+
+92
+00:16:41.010 --> 00:16:41.860
+hrishikb@andrew.cmu.edu: Beautiful.
+
+93
+00:16:42.860 --> 00:16:58.499
+hrishikb@andrew.cmu.edu: both, are you gonna do some experiments to see which one works best for you? Yes, we were waiting on, like, Harsha told us that he would give us the sample schema of how it'll look like, so we thought we could just take that and, like.
+
+94
+00:16:58.500 --> 00:17:07.929
+hrishikb@andrew.cmu.edu: generate some synthetic data and, like, do some kind of a POC. So, for now, we just went ahead and, found out this. I think I can, I can…
+
+95
+00:17:07.930 --> 00:17:28.179
+hrishikb@andrew.cmu.edu: again, he's probably, in and out of the next month. I can get that to you. So you want to just sample of, like… A sample of the data, yeah. Even a small sample would do. Sure. Because we just want to do some test runs, and we, because right now, what we are, the accuracies we can, as best as we can tell us.
+
+96
+00:17:28.270 --> 00:17:38.829
+hrishikb@andrew.cmu.edu: The bigger the model, the better, but there might be a diminishing returns. So, for example, at a certain point, a bigger model might just be, like, 1 or 2% better, and if we could just
+
+97
+00:17:38.910 --> 00:17:45.719
+hrishikb@andrew.cmu.edu: do some test runs, we could just hit the optimal point much faster.
+
+98
+00:17:45.750 --> 00:18:10.459
+hrishikb@andrew.cmu.edu: information the supplier would give you, you know, PDF, or CSV, whatever, one of those formats that they usually give you, right? I guess so, so which one? Do you want… so you want the PDF, like, what the suppliers will give us? Do you also want what we have in our database? Yeah, so we want the PDF so we can start working on the… The transition side, and we also want the data part so they can start working on the end product. So that, at the end, we can combine the
+
+99
+00:18:10.830 --> 00:18:22.579
+hrishikb@andrew.cmu.edu: Got it, got it. Okay. The major hinging points in the different models is how the data is that we're going to be processing in the model, so data will help a lot in maintaining this. Okay.
+
+100
+00:18:23.930 --> 00:18:24.720
+hrishikb@andrew.cmu.edu: You?
+
+101
+00:18:25.530 --> 00:18:38.250
+hrishikb@andrew.cmu.edu: And I'll try and also give you ones that align. I'll make sure that the vendor data that I'm giving you, the spec sheets, the PDFs, are the exact same products of the actual things we have in our database. Okay, perfect.
+
+102
+00:18:39.970 --> 00:18:53.589
+hrishikb@andrew.cmu.edu: Well, that's the best case scenario. Yeah, best case scenario. Worst case scenario, or, you know, other edge cases that you have to deal with? I can… I can pick the catalog team's brain and see, yeah, see what other things…
+
+103
+00:18:56.400 --> 00:19:04.020
+hrishikb@andrew.cmu.edu: Okay, I… I have… just these days, I have investigated three can of common GP.
+
+104
+00:19:04.680 --> 00:19:06.820
+hrishikb@andrew.cmu.edu: DET terminal models.
+
+105
+00:19:07.430 --> 00:19:09.279
+hrishikb@andrew.cmu.edu: What's a market.
+
+106
+00:19:09.690 --> 00:19:16.659
+hrishikb@andrew.cmu.edu: So, this is for… Dealing with, downstream data from
+
+107
+00:19:16.880 --> 00:19:20.500
+hrishikb@andrew.cmu.edu: So… so this is the data that actually taught.
+
+108
+00:19:20.620 --> 00:19:23.970
+hrishikb@andrew.cmu.edu: Talking about, say, how… do some…
+
+109
+00:19:24.610 --> 00:19:28.829
+hrishikb@andrew.cmu.edu: Luminization, or to get some… get open tonight.
+
+110
+00:19:29.030 --> 00:19:36.449
+hrishikb@andrew.cmu.edu: Anyway, after we take those data, we can do some… towards, processing.
+
+111
+00:19:36.680 --> 00:19:53.520
+hrishikb@andrew.cmu.edu: So these three kinds of models, they are put at classification, regression, output scores. For probabilities, this is what we want to get. And, they are good at, you know, explain… explainabilities.
+
+112
+00:19:57.330 --> 00:20:01.010
+hrishikb@andrew.cmu.edu: And, yep.
+
+113
+00:20:02.690 --> 00:20:06.059
+hrishikb@andrew.cmu.edu: Yeah, because we… we wanted to have the…
+
+114
+00:20:07.620 --> 00:20:14.210
+hrishikb@andrew.cmu.edu: Processing data, so we don't need to be able to take care of all the… So, only that data.
+
+115
+00:20:23.180 --> 00:20:29.989
+hrishikb@andrew.cmu.edu: Yeah, just, some, make a table, and
+
+116
+00:20:30.530 --> 00:20:36.280
+hrishikb@andrew.cmu.edu: This… we could propel the… the characteristics.
+
+117
+00:20:36.890 --> 00:20:39.589
+hrishikb@andrew.cmu.edu: And, we can choose the best one, really.
+
+118
+00:20:39.760 --> 00:20:41.560
+hrishikb@andrew.cmu.edu: To predict our project.
+
+119
+00:20:41.660 --> 00:20:44.349
+hrishikb@andrew.cmu.edu: So is the acronym GBTP mean?
+
+120
+00:20:48.880 --> 00:20:51.800
+hrishikb@andrew.cmu.edu: GBT models, what is the acronym? GBT.
+
+121
+00:20:51.920 --> 00:20:55.739
+hrishikb@andrew.cmu.edu: It's called Gradient Boost Destiny Tree Modulence.
+
+122
+00:20:56.180 --> 00:20:57.080
+hrishikb@andrew.cmu.edu: Say it again.
+
+123
+00:20:57.240 --> 00:21:01.549
+hrishikb@andrew.cmu.edu: gradient boost, hosted in listening to you, I mean? Okay, questions.
+
+124
+00:21:03.750 --> 00:21:06.270
+hrishikb@andrew.cmu.edu: So… Nope.
+
+125
+00:21:06.500 --> 00:21:09.230
+hrishikb@andrew.cmu.edu: So, the first one is the campus.
+
+126
+00:21:11.870 --> 00:21:22.279
+hrishikb@andrew.cmu.edu: It can quickly deal with So, some heavy load work, like, generally some category, heavy tables.
+
+127
+00:21:26.050 --> 00:21:32.890
+hrishikb@andrew.cmu.edu: And, they only need some original information. They don't need to…
+
+128
+00:21:34.220 --> 00:21:37.770
+hrishikb@andrew.cmu.edu: Like, strong… need to strong mapping.
+
+129
+00:21:38.030 --> 00:21:41.180
+hrishikb@andrew.cmu.edu: Or need to change those.
+
+130
+00:21:41.880 --> 00:21:47.510
+hrishikb@andrew.cmu.edu: original information tool, so… token… tokenization…
+
+131
+00:21:47.790 --> 00:21:50.559
+hrishikb@andrew.cmu.edu: features, so it's useful to use.
+
+132
+00:21:51.760 --> 00:22:03.250
+hrishikb@andrew.cmu.edu: And, this does… they just have the same ability in the robust Julie, and…
+
+133
+00:22:03.690 --> 00:22:08.940
+hrishikb@andrew.cmu.edu: And, the catapult, the training speed is… Yes.
+
+134
+00:22:09.680 --> 00:22:14.230
+hrishikb@andrew.cmu.edu: Not very slow, but the light GPT So…
+
+135
+00:22:14.880 --> 00:22:18.999
+hrishikb@andrew.cmu.edu: Train speed and interface speed and scalability is…
+
+136
+00:22:20.200 --> 00:22:23.230
+hrishikb@andrew.cmu.edu: It's good then, catfold.
+
+137
+00:22:29.400 --> 00:22:42.810
+hrishikb@andrew.cmu.edu: Related to the teaching engineering, which is about how much the reprocessing digital work is required to tensor raw data to…
+
+138
+00:22:43.180 --> 00:22:45.450
+hrishikb@andrew.cmu.edu: moderated columns.
+
+139
+00:22:46.660 --> 00:22:57.210
+hrishikb@andrew.cmu.edu: So, can't book… like I said before, they don't… it doesn't need to… Some…
+
+140
+00:23:01.560 --> 00:23:07.110
+hrishikb@andrew.cmu.edu: You don't need to… There was, data.
+
+141
+00:23:11.760 --> 00:23:23.029
+hrishikb@andrew.cmu.edu: And, so, you don't need to pay much attention to the… Pre-processing all the data.
+
+142
+00:23:24.670 --> 00:23:29.610
+hrishikb@andrew.cmu.edu: So, if we… it can be, do a quick deployment.
+
+143
+00:23:31.040 --> 00:23:33.620
+hrishikb@andrew.cmu.edu: Well, the G export is very…
+
+144
+00:23:34.080 --> 00:23:37.809
+hrishikb@andrew.cmu.edu: Not good at this to deal with these things.
+
+145
+00:23:39.800 --> 00:23:44.059
+hrishikb@andrew.cmu.edu: Like, it needs final features to preparation.
+
+146
+00:23:46.790 --> 00:23:48.000
+hrishikb@andrew.cmu.edu: And,
+
+147
+00:23:51.910 --> 00:23:58.280
+hrishikb@andrew.cmu.edu: And also, so I'd like the GPT added XG boots.
+
+148
+00:23:58.620 --> 00:24:01.820
+hrishikb@andrew.cmu.edu: To have strong ecosystem activities.
+
+149
+00:24:02.320 --> 00:24:05.779
+hrishikb@andrew.cmu.edu: So, it can slow down the…
+
+150
+00:24:07.000 --> 00:24:11.860
+hrishikb@andrew.cmu.edu: They reduce the work for retaining, and we can find some…
+
+151
+00:24:12.060 --> 00:24:17.300
+hrishikb@andrew.cmu.edu: You know, they use cold coding from the community, or ask someone.
+
+152
+00:24:17.430 --> 00:24:22.569
+hrishikb@andrew.cmu.edu: That way, if we meet some common price change.
+
+153
+00:24:22.920 --> 00:24:24.989
+hrishikb@andrew.cmu.edu: Permanence will… we have.
+
+154
+00:24:25.340 --> 00:24:26.760
+hrishikb@andrew.cmu.edu: implementing to exist.
+
+155
+00:24:27.520 --> 00:24:28.370
+hrishikb@andrew.cmu.edu: project.
+
+156
+00:24:29.030 --> 00:24:34.250
+hrishikb@andrew.cmu.edu: And, and I think so.
+
+157
+00:24:34.660 --> 00:24:37.920
+hrishikb@andrew.cmu.edu: Typical limitation, or the… Excellent.
+
+158
+00:24:38.990 --> 00:24:40.439
+hrishikb@andrew.cmu.edu: So, the first one is.
+
+159
+00:24:43.860 --> 00:24:48.300
+hrishikb@andrew.cmu.edu: The scale support may be a little,
+
+160
+00:24:48.510 --> 00:24:50.959
+hrishikb@andrew.cmu.edu: Love… a little better to hand.
+
+161
+00:24:51.240 --> 00:24:53.109
+hrishikb@andrew.cmu.edu: The second one.
+
+162
+00:24:53.830 --> 00:24:56.169
+hrishikb@andrew.cmu.edu: But I think it is mostly very…
+
+163
+00:24:59.450 --> 00:25:01.240
+hrishikb@andrew.cmu.edu: We don't need to think about…
+
+164
+00:25:01.490 --> 00:25:07.970
+hrishikb@andrew.cmu.edu: much to carry weight, because I think we don't need to deal with such amount of data.
+
+165
+00:25:08.580 --> 00:25:15.160
+hrishikb@andrew.cmu.edu: So, this is my conclusion. We could take the catboard as a default scoring model.
+
+166
+00:25:15.560 --> 00:25:20.349
+hrishikb@andrew.cmu.edu: And, came… led GPT… GPM as the plan.
+
+167
+00:25:20.710 --> 00:25:28.409
+hrishikb@andrew.cmu.edu: scale-up opportunity if the throughput becomes very the domestic country.
+
+168
+00:25:30.670 --> 00:25:34.459
+hrishikb@andrew.cmu.edu: And which could also match our project architecture requirements.
+
+169
+00:25:36.380 --> 00:25:40.920
+hrishikb@andrew.cmu.edu: And, I read some… Amazing.
+
+170
+00:25:41.310 --> 00:25:51.950
+hrishikb@andrew.cmu.edu: So, from the architect phase, Well, which is based on… So, architecture responses and, So, Clint… Clyde?
+
+171
+00:25:52.090 --> 00:25:53.260
+hrishikb@andrew.cmu.edu: of fulfillments.
+
+172
+00:25:59.020 --> 00:26:03.680
+hrishikb@andrew.cmu.edu: So, it's just, Static effect and the functional phase.
+
+173
+00:26:04.240 --> 00:26:06.059
+hrishikb@andrew.cmu.edu: And a long position of faith.
+
+174
+00:26:12.660 --> 00:26:16.499
+hrishikb@andrew.cmu.edu: And I compelled why the other…
+
+175
+00:26:17.240 --> 00:26:20.410
+hrishikb@andrew.cmu.edu: Models that don't fit this project.
+
+176
+00:26:25.600 --> 00:26:33.480
+hrishikb@andrew.cmu.edu: And the trade-off. And this time is what we need to… supports, models with.
+
+177
+00:26:33.630 --> 00:26:36.660
+hrishikb@andrew.cmu.edu: to… tomorrow.
+
+178
+00:26:39.420 --> 00:26:44.750
+hrishikb@andrew.cmu.edu: So I guess, we can, probably import this document into your content as well.
+
+179
+00:26:44.890 --> 00:26:46.070
+hrishikb@andrew.cmu.edu: Or,
+
+180
+00:26:46.460 --> 00:26:56.699
+hrishikb@andrew.cmu.edu: when we keep doing our PEOCs, and if we have more desires, we can decide to do a document on confidence. Definitely. Yeah, Harsha's gonna wanna review this, too. Yeah. Great.
+
+181
+00:27:00.080 --> 00:27:09.189
+hrishikb@andrew.cmu.edu: Okay, so that's pretty much it. So, I think you guys, did you guys talk about constraints? Like, we wanted to,
+
+182
+00:27:09.410 --> 00:27:12.620
+hrishikb@andrew.cmu.edu: Think about the constraints as well when we were looking at these models.
+
+183
+00:27:12.790 --> 00:27:19.920
+hrishikb@andrew.cmu.edu: Let's say you have 100,000 suppliers versus 40,000 suppliers. That would change which model you're going to be using.
+
+184
+00:27:20.160 --> 00:27:28.070
+hrishikb@andrew.cmu.edu: So we wanted to get an estimate of those constraints, and how many different categories would have, like 5,000 categories versus 50,000 categories.
+
+185
+00:27:28.800 --> 00:27:38.469
+hrishikb@andrew.cmu.edu: Yeah. So, that kind of data as well, we would want from others.
+
+186
+00:27:38.960 --> 00:27:44.709
+hrishikb@andrew.cmu.edu: So I think, when you give us the sample data, sample schema, it wouldn't cover all of it.
+
+187
+00:27:44.960 --> 00:27:48.220
+hrishikb@andrew.cmu.edu: I can give you a…
+
+188
+00:27:49.100 --> 00:27:57.949
+hrishikb@andrew.cmu.edu: Yeah, I can do, kind of, just give you, you know, select count to, like, an addition of that, just from each of the things, yeah, categories, options, attributes, products.
+
+189
+00:27:59.250 --> 00:28:01.890
+hrishikb@andrew.cmu.edu: just the breadth of data would be enough, I think.
+
+190
+00:28:02.530 --> 00:28:07.239
+hrishikb@andrew.cmu.edu: Do you have any information that gives any trending stuff in terms of the science of things?
+
+191
+00:28:07.360 --> 00:28:19.090
+hrishikb@andrew.cmu.edu: I'm just saying, you know, your model right now made 100,000 items, but it last year over 80,000, whatever it is, I'm just trying to get some sort of scale type of thing. Yeah, definitely, that's good.
+
+192
+00:28:19.420 --> 00:28:33.010
+hrishikb@andrew.cmu.edu: We could definitely break that down, too. I could break that down and run a couple more little analysis on it on, you know, we store creative products and whatnot, all those things, so just scanning that out over time and seeing, yeah, what the transit look like.
+
+193
+00:28:41.260 --> 00:28:50.020
+hrishikb@andrew.cmu.edu: So do you, the people who do this work here doing this sort of stuff, do you have to track how much time it took for you to develop this? Doing research, or just starting?
+
+194
+00:28:50.230 --> 00:28:59.649
+hrishikb@andrew.cmu.edu: Well, you know, your results, your reports, you know. Yeah, I think we have a approximate time, yeah.
+
+195
+00:28:59.790 --> 00:29:06.940
+hrishikb@andrew.cmu.edu: at the granular level, to know for this work, how much time it took to do this particular effort. Okay. Okay.
+
+196
+00:29:07.910 --> 00:29:09.669
+hrishikb@andrew.cmu.edu: It's just useful to have that data.
+
+197
+00:29:09.850 --> 00:29:10.590
+hrishikb@andrew.cmu.edu: Right.
+
+198
+00:29:11.340 --> 00:29:12.020
+hrishikb@andrew.cmu.edu: Yeah.
+
+199
+00:29:12.960 --> 00:29:30.700
+hrishikb@andrew.cmu.edu: But again, it's just the initial research phase. I understand. Yes, we have not done any implementation. We spent 3 hours researching this aspect, you know, built this report, those types of stuff. That's all kind of useful historical data from before.
+
+200
+00:29:39.200 --> 00:29:41.240
+hrishikb@andrew.cmu.edu: Oh, thank you.
+
+201
+00:29:41.400 --> 00:29:42.100
+hrishikb@andrew.cmu.edu: Oh, boy.
+
+202
+00:29:43.610 --> 00:29:45.169
+hrishikb@andrew.cmu.edu: A lot of shades on that.
+
+203
+00:29:45.350 --> 00:29:46.740
+hrishikb@andrew.cmu.edu: Oh, I'm gonna assume.
+
+204
+00:29:49.670 --> 00:29:51.140
+hrishikb@andrew.cmu.edu: I'd rather show Zoom.
+
+205
+00:29:51.270 --> 00:29:54.100
+hrishikb@andrew.cmu.edu: I can't share this screen.
+
+206
+00:30:06.960 --> 00:30:07.810
+hrishikb@andrew.cmu.edu: Yes.
+
+207
+00:30:08.040 --> 00:30:12.220
+hrishikb@andrew.cmu.edu: Okay, so next we have is, Terraform versus Bicep.
+
+208
+00:30:13.340 --> 00:30:15.669
+hrishikb@andrew.cmu.edu: Oh, I suppose it.
+
+209
+00:30:17.540 --> 00:30:23.569
+hrishikb@andrew.cmu.edu: So I have created a small doc, but it is, a bit high level, so from…
+
+210
+00:30:23.970 --> 00:30:34.450
+hrishikb@andrew.cmu.edu: what I could, think of right now is the two major distinguished factors between Terraform and Bicep are that Bicep is very Azure-oriented, and
+
+211
+00:30:34.500 --> 00:30:47.279
+hrishikb@andrew.cmu.edu: It is, of course, better if we use it with Azure, but if we, around the time you try to use some other soft… some other services from other environments, other cloud providers, then Azure will become a bottleneck.
+
+212
+00:30:47.600 --> 00:30:53.870
+hrishikb@andrew.cmu.edu: But on the other hand, if you try to use Terraform, it is… it works with Azure, it has good support, but…
+
+213
+00:30:54.320 --> 00:31:03.139
+hrishikb@andrew.cmu.edu: we can say slightly less than Azure Bicep, but if you use Terraform, we still have those options available to us if we plan to switch, if you plan to use different
+
+214
+00:31:03.250 --> 00:31:05.560
+hrishikb@andrew.cmu.edu: Different services with it.
+
+215
+00:31:05.760 --> 00:31:10.300
+hrishikb@andrew.cmu.edu: And one of… another negative of Terraform would be that,
+
+216
+00:31:10.790 --> 00:31:18.360
+hrishikb@andrew.cmu.edu: it does give us drifts, so if you used Reform 2, deploy something, but then manually modify it, so there's a…
+
+217
+00:31:19.230 --> 00:31:25.200
+hrishikb@andrew.cmu.edu: discrepancy between what is actually deployed and what should we have in our telecom files. So that is something that we'll have to keep in mind.
+
+218
+00:31:25.610 --> 00:31:43.879
+hrishikb@andrew.cmu.edu: But there are, like, as of right now, I don't think we have a definitive answer of what we are going to choose, but as our ML research finalizer, and we know what models we're gonna use, and that's gonna drill down to which, cloud provider we'll use, then I think we can, have a better
+
+219
+00:31:44.730 --> 00:32:04.570
+hrishikb@andrew.cmu.edu: the, like, say in which one we need to use. But as of right now, I am leaning towards Terraform, because it might be a bit… a bit more tedious to work with, but it gives us more options to explore different things, and gives us flexibility to work on the project in different ways, and not just be stuck to Azure and all its…
+
+220
+00:32:04.700 --> 00:32:10.689
+hrishikb@andrew.cmu.edu: internal things. So, for my background, some of the terraform.
+
+221
+00:32:10.780 --> 00:32:21.140
+hrishikb@andrew.cmu.edu: So Terraform and Viceps, basically, they're used to deploy, services on the cloud.
+
+222
+00:32:21.160 --> 00:32:32.949
+hrishikb@andrew.cmu.edu: Its infrastructure as code, so you don't directly deploy on AWS or on Azure, so you use a git commit, and only then you verify it, and then it pushes to the cloud.
+
+223
+00:32:33.140 --> 00:32:37.090
+hrishikb@andrew.cmu.edu: Should automatically have reset how it's gonna…
+
+224
+00:32:37.530 --> 00:32:55.359
+hrishikb@andrew.cmu.edu: Microsoft product, a third-party product? What's that? It's an open source. Open source, yeah, okay. Azure Bicep is Microsoft. Okay, yeah, Bicep is something similar, or what? Yeah, it's Microsoft, but it is, it is similar, but it is very focused to Microsoft Azure.
+
+225
+00:32:55.590 --> 00:33:12.560
+hrishikb@andrew.cmu.edu: It's not, with Terraform, we can work with, Azure, GCP, AWS, everything, and different software services, but with Azure, it is very… sorry, with Bicep, it's very Azure-specific. You cannot, like, you can, but it is,
+
+226
+00:33:12.560 --> 00:33:15.610
+hrishikb@andrew.cmu.edu: Not really used, because the support is not there.
+
+227
+00:33:15.620 --> 00:33:21.879
+hrishikb@andrew.cmu.edu: to use different services. So it's kind of a… it'll force you down a very narrow path, and then you'll have to…
+
+228
+00:33:21.930 --> 00:33:30.869
+hrishikb@andrew.cmu.edu: make go with what you have, and you won't have the other options available. So we don't want to close that door right now. So, one more thing, I don't think Bicep does is,
+
+229
+00:33:31.420 --> 00:33:46.210
+hrishikb@andrew.cmu.edu: If we are going to integrate, the entire system with Datadog, I'll go there. Yeah. I don't think Microsoft natively gives it support for that. While Terraform does, we can create Terraform modules for routing, which feeds data directly to Teradog.
+
+230
+00:33:46.810 --> 00:33:53.069
+hrishikb@andrew.cmu.edu: So, David, do you… we… do we do any of our, Datadog setup in our, bicep?
+
+231
+00:33:53.920 --> 00:33:56.560
+hrishikb@andrew.cmu.edu: For, like, her number?
+
+232
+00:33:56.560 --> 00:34:04.630
+David Mine: I'm not sure if we do it with Bicep. What we do have our Azure-wide monitors, so we can tag a resource.
+
+233
+00:34:04.750 --> 00:34:14.449
+David Mine: With a tag that we've set up, and then Azure is going to take that data and ship it to Datadog in some way, shape, or form.
+
+234
+00:34:14.639 --> 00:34:24.960
+David Mine: So that's how we do… kind of native-level logging on our Azure environment.
+
+235
+00:34:24.960 --> 00:34:29.460
+hrishikb@andrew.cmu.edu: But, to your point, that takes, I guess, an extra step of setup that's inside of this.
+
+236
+00:34:30.199 --> 00:34:35.160
+hrishikb@andrew.cmu.edu: But Terraform, we can directly use it to set up Oh, Pipeline's digital dog.
+
+237
+00:34:35.770 --> 00:34:41.270
+David Mine: Yeah, I think my hesitation with Terraform is, firstly, that we don't use it right now.
+
+238
+00:34:41.489 --> 00:34:51.420
+David Mine: Which isn't a big deal if something's really good, but we're already using Bicep, so we're already locked into Azure for,
+
+239
+00:34:51.580 --> 00:34:53.620
+David Mine: For all of our new products.
+
+240
+00:34:53.730 --> 00:34:59.570
+David Mine: The ones that were… we would be the most likely to move if we needed to were already locked in.
+
+241
+00:34:59.780 --> 00:35:04.760
+David Mine: The other thing that worries me about Terraform
+
+242
+00:35:05.820 --> 00:35:13.839
+David Mine: I don't know if this is a valid worry or not, is that Terraform… my understanding is that it…
+
+243
+00:35:14.170 --> 00:35:17.859
+David Mine: Forms actions based on the state that it thinks
+
+244
+00:35:18.310 --> 00:35:25.710
+David Mine: your environment is in. Whereas Bicep describes the state that it wants
+
+245
+00:35:25.880 --> 00:35:42.370
+David Mine: like, you, in your code, describe the state you want your infrastructure, and it takes care of cobbling together the commands needed to get to that state. So it's more, declarative than imperative, so to speak.
+
+246
+00:35:42.570 --> 00:35:45.020
+David Mine: Save that slide up.
+
+247
+00:35:45.490 --> 00:36:01.449
+hrishikb@andrew.cmu.edu: I didn't get your point on Terraform being… I think if we deploy something about Terraform, but we modify the internals from, let's say, Azure itself, the Terraform will think it is in some other state, and the actual cloud will be in some other state. And if you put something else on Terraform.
+
+248
+00:36:01.450 --> 00:36:07.109
+hrishikb@andrew.cmu.edu: It can conflict and give you the RAM output. Yeah, it will never deploy it, because there's a conflict.
+
+249
+00:36:07.370 --> 00:36:15.510
+hrishikb@andrew.cmu.edu: I think that's… so, Terraform also has Terraform Plan, which is, specifically what you just said about Bicep as well. It gives you how the…
+
+250
+00:36:15.900 --> 00:36:20.340
+hrishikb@andrew.cmu.edu: Resources are going to look like when it's going to be created before you actually deploy it.
+
+251
+00:36:27.650 --> 00:36:29.880
+hrishikb@andrew.cmu.edu: I don't think Azure has the…
+
+252
+00:36:30.020 --> 00:36:36.040
+hrishikb@andrew.cmu.edu: Azure has, what we call drift, because I think Azure, when you…
+
+253
+00:36:36.160 --> 00:36:51.999
+hrishikb@andrew.cmu.edu: deploy something else using Bicep, it will take the latest state and work on that. Terraform does not… I don't think it pulls back from the actual… what's actually working on it. Yeah, but that is a security feature that is required, because let's say I push some code.
+
+254
+00:36:52.170 --> 00:36:55.010
+hrishikb@andrew.cmu.edu: And someone else logs in to the UI and just…
+
+255
+00:36:55.140 --> 00:37:11.419
+hrishikb@andrew.cmu.edu: changes something. That shouldn't happen, ideally. No, that will happen, right? No, that shouldn't happen, because people shouldn't just go and change code on the UI. That's the whole point of using something like that.
+
+256
+00:37:11.950 --> 00:37:31.000
+hrishikb@andrew.cmu.edu: Yes, yeah. I mean, yeah, it's just something to consider. That doesn't mean that right now, as we're, as we're growing, it shouldn't happen, but it doesn't get involved. Yeah. Okay, but, like, I think, so, from the discussion, I think David wants to go ahead with myself. Right, David?
+
+257
+00:37:31.700 --> 00:37:43.039
+David Mine: Yeah, I would pick Bicep, mostly because I think it's something that we know. I also think that the value-add of Terraform… one of the big value adds of Terraform that they claim is that you can
+
+258
+00:37:43.290 --> 00:37:50.859
+David Mine: kind of switch providers. But my understanding is upon further investigation.
+
+259
+00:37:51.210 --> 00:37:55.809
+David Mine: A lot of those commands are actually pretty cloud-specific.
+
+260
+00:37:55.930 --> 00:38:12.339
+David Mine: So there's a single language to describe everything, but you're still kind of… there's still kind of some vendor lock-in, which makes sense, because the cloud providers don't exactly offer the exact same offerings in the exact same way.
+
+261
+00:38:12.640 --> 00:38:14.579
+David Mine: So, to me.
+
+262
+00:38:14.710 --> 00:38:27.470
+David Mine: what's best for the product is one question, which is what you guys are considering, right? It's what you know the best, and what makes the most sense for this project. But one of the other things we have to make sure that we're not forgetting is
+
+263
+00:38:27.540 --> 00:38:44.750
+David Mine: when you guys hand over the project and give us the keys, so to speak, we're going to have a team that has to maintain that, and are we going to ask them to get good at Bicep and Terraform, or are we just going to ask them to get good at Bicep? So that's prob… that's another factor that's on my
+
+264
+00:38:44.850 --> 00:38:48.440
+David Mine: That's on my mind that might not be on your mind.
+
+265
+00:38:49.070 --> 00:38:55.299
+David Mine: So I would… I would prefer bicep mostly for those reasons. I've got to train a whole bunch of people who…
+
+266
+00:38:55.520 --> 00:39:01.179
+David Mine: I mean, they're developers, they don't care a whole lot about infrastructure, they don't care how it runs, as long as it runs on their local environment.
+
+267
+00:39:01.320 --> 00:39:08.419
+David Mine: And so I can train them on Bicep. That's a lot easier than getting them caught up on Bicep and Terraform.
+
+268
+00:39:10.080 --> 00:39:10.660
+hrishikb@andrew.cmu.edu: Okay.
+
+269
+00:39:13.030 --> 00:39:19.940
+hrishikb@andrew.cmu.edu: I think with that in mind, we can maybe go forward with Bicep itself. Yeah. Because anyways, if you are logged into Azure, then Bicep is the better way to go.
+
+270
+00:39:24.590 --> 00:39:41.809
+hrishikb@andrew.cmu.edu: But, I can just talk a little bit about hotel. I think, we had a discussion last week, David, we can have hotels, so we get, like, a waterfall view of what's happening in every request on a weekly basis.
+
+271
+00:39:42.000 --> 00:39:44.590
+hrishikb@andrew.cmu.edu: So OTL already handles this,
+
+272
+00:39:44.900 --> 00:39:54.960
+hrishikb@andrew.cmu.edu: I think it's called, so we don't need to have separate request IDs and RIDs for each service, or it already takes care of this.
+
+273
+00:39:55.200 --> 00:40:00.640
+hrishikb@andrew.cmu.edu: It's called W3C, standard header reporting index into every request.
+
+274
+00:40:00.780 --> 00:40:09.310
+hrishikb@andrew.cmu.edu: So, that header has, like, a global case only, then it has a pattern case only. And, like, for each request, it has a sample tag as well.
+
+275
+00:40:09.690 --> 00:40:24.969
+hrishikb@andrew.cmu.edu: So, let's say you were a data doc, you'd have to put the global tracer name so you get the entire waterfall of what's happening, which request is taking a lot longer than what's expected. And then if you want to see into a specific request, you can just put in the package.
+
+276
+00:40:24.980 --> 00:40:30.580
+hrishikb@andrew.cmu.edu: Okay, so this is… would be in addition to Datadog. I… I don't know about themselves, so it's not like a…
+
+277
+00:40:31.350 --> 00:40:49.010
+hrishikb@andrew.cmu.edu: It's not an alternative to Datadog, you're sending it with Datadog. Yeah, yeah, it's… yeah, it's a layer before Datadog where you send requests to, and then that feeds it to Datadog. Oh, it's open data metric. What's that? OpenChall metric? Yeah. It's like,
+
+278
+00:40:49.570 --> 00:41:04.479
+hrishikb@andrew.cmu.edu: like, the problem that you guys are dealing with, so you have vendors, different vendors, giving you different, giving you the data in different formats, right? So, the telemetry world came together, and they were like, let's build open telemetry.
+
+279
+00:41:04.480 --> 00:41:09.940
+hrishikb@andrew.cmu.edu: A standardized version of how your observability metrics should look like.
+
+280
+00:41:09.940 --> 00:41:19.230
+hrishikb@andrew.cmu.edu: And, where even if you end up using Datadog, or Telegraph, or, Premieres, or these, dashboards.
+
+281
+00:41:19.250 --> 00:41:37.280
+hrishikb@andrew.cmu.edu: You can have a centralized format, like OpenTelemetry format, and you can just, like, plug in these external sources. So that's, how… that's how, like, why Arjun is proposing, OpenTelemetry. But, since you, since David just mentioned,
+
+282
+00:41:37.570 --> 00:41:44.950
+hrishikb@andrew.cmu.edu: something about Azure, which I didn't actually catch, writing to Azure Louds and then passing that to Azure.
+
+283
+00:41:46.630 --> 00:41:54.969
+hrishikb@andrew.cmu.edu: David, are you able to talk more about that, expand more on how Azure writes to logs that have been tested?
+
+284
+00:41:54.970 --> 00:42:05.880
+David Mine: Oh, sorry, I'm… I caught half of that. It was… the audio's getting a little quiet. Let me bump this up. Can you say that one more time?
+
+285
+00:42:05.880 --> 00:42:11.349
+hrishikb@andrew.cmu.edu: Can you, explain a little bit on how, the data is written to Azure Logs, and then,
+
+286
+00:42:11.350 --> 00:42:12.630
+David Mine: Yeah, but yes.
+
+287
+00:42:12.630 --> 00:42:14.160
+hrishikb@andrew.cmu.edu: Azure Logs pushes the data onto it.
+
+288
+00:42:14.160 --> 00:42:28.160
+David Mine: Yeah, let me… maybe I can share my screen, and that would help clear up some confusion here. Of course, now that I want to do that, let's see if I can…
+
+289
+00:42:29.450 --> 00:42:40.240
+David Mine: We've got a Datadog… I requested access to share… there we go, let's share my…
+
+290
+00:42:40.240 --> 00:42:40.950
+hrishikb@andrew.cmu.edu: of…
+
+291
+00:42:41.370 --> 00:42:47.320
+David Mine: It was not so, just… There we go. So here on Azure.
+
+292
+00:42:48.090 --> 00:42:58.369
+David Mine: Oh, you guys are completely superior. Here on Azure, we've got this Datadog Azure Native ISV service. Not sure what that stands for, to be honest with you.
+
+293
+00:42:58.530 --> 00:43:05.819
+David Mine: But we have our prod, and we have our QA environment, which really should pare down, because we're not using that anymore. At least not there.
+
+294
+00:43:06.050 --> 00:43:10.169
+David Mine: And we have this connected to…
+
+295
+00:43:10.850 --> 00:43:28.339
+David Mine: organization. So, if we include Datadog logs true, anything that has Datadog logs true gets included there. So, for example, I could go to a… I hope I have an example up for you guys here, of a function app.
+
+296
+00:43:28.860 --> 00:43:42.500
+David Mine: Probably enough here… Data dialog's true, that means that instead of syncing this to, say, what would it be, like,
+
+297
+00:43:43.430 --> 00:43:45.590
+David Mine: We've got a logs here…
+
+298
+00:43:46.110 --> 00:44:01.570
+David Mine: Yeah, so instead of needing Application Insights, I could actually turn this off. I don't really need Application Insights inside of Azure, because these logs are supposed to be sending directly to Datadog through this native,
+
+299
+00:44:01.740 --> 00:44:06.500
+David Mine: Datadog service, which just kind of captures things
+
+300
+00:44:07.060 --> 00:44:15.709
+David Mine: Azure-wide makes it easy to capture logs and metrics Azure-wide and ship that off to our Datadog tenant.
+
+301
+00:44:16.230 --> 00:44:18.010
+David Mine: Does that make sense?
+
+302
+00:44:18.010 --> 00:44:19.110
+hrishikb@andrew.cmu.edu: Yeah.
+
+303
+00:44:19.490 --> 00:44:20.219
+David Mine: That's the only category.
+
+304
+00:44:20.220 --> 00:44:24.859
+hrishikb@andrew.cmu.edu: Can you, show me the Datadog, dashboard as well, how the.
+
+305
+00:44:24.860 --> 00:44:25.610
+David Mine: Right, okay.
+
+306
+00:44:25.610 --> 00:44:27.220
+hrishikb@andrew.cmu.edu: How it looks when…
+
+307
+00:44:27.220 --> 00:44:28.340
+David Mine: For your business.
+
+308
+00:44:28.340 --> 00:44:30.279
+hrishikb@andrew.cmu.edu: the requests come from Cisco.
+
+309
+00:44:30.280 --> 00:44:48.340
+David Mine: I didn't actually know about all this, but you can see all the different metrics and logs. Looks like our logs, we're doing a pretty terrible job on, but in terms of our metrics, a lot of that stuff's going over. So let's pick, let's pick a…
+
+310
+00:44:49.230 --> 00:44:59.119
+David Mine: Or maybe that's how they do it. They ship from Application Insights over to Datadogs. Maybe we do need an App Insights. Either way, let's find one that we know works.
+
+311
+00:44:59.390 --> 00:45:00.760
+David Mine: That's not helpful.
+
+312
+00:45:01.250 --> 00:45:07.690
+David Mine: We'll just go here and take a look at our…
+
+313
+00:45:09.160 --> 00:45:12.710
+David Mine: Oh, would that be service, maybe?
+
+314
+00:45:14.940 --> 00:45:25.530
+David Mine: So Azure itself has… Looks like calls into it. This would be our prod, and…
+
+315
+00:45:25.750 --> 00:45:31.899
+David Mine: caller IP address 10-1, that's probably our app gateway, on dev.
+
+316
+00:45:32.520 --> 00:45:35.540
+David Mine: What else can we see that might be really useful?
+
+317
+00:45:35.770 --> 00:45:40.359
+David Mine: These are our databases, whoops.
+
+318
+00:45:40.710 --> 00:45:41.810
+David Mine: That, pass.
+
+319
+00:45:42.230 --> 00:45:46.959
+David Mine: These are some microservices we have.
+
+320
+00:45:47.610 --> 00:45:52.919
+David Mine: And this is, again, this is just logs. Oh, here we go, yeah, let's…
+
+321
+00:45:53.110 --> 00:45:56.570
+David Mine: Get rid of all these. Check all of our hosts.
+
+322
+00:45:56.870 --> 00:46:01.259
+David Mine: So these are the… wish I could see a little more.
+
+323
+00:46:02.420 --> 00:46:03.829
+David Mine: There we go.
+
+324
+00:46:04.120 --> 00:46:06.169
+David Mine: These are all of our revisions.
+
+325
+00:46:06.440 --> 00:46:09.230
+David Mine: Of different, container apps.
+
+326
+00:46:09.940 --> 00:46:12.690
+David Mine: Thank you.
+
+327
+00:46:12.850 --> 00:46:15.299
+David Mine: And it looks like we also have…
+
+328
+00:46:18.130 --> 00:46:28.520
+David Mine: Saw some database stuff, Azure Functions? Yeah, okay, cool. So, Azure Functions, instead of going to, well, I guess they're called Function Apps now, but instead of going to…
+
+329
+00:46:28.570 --> 00:46:41.650
+David Mine: Application Insights directly, they also come here. I'm not sure exactly how that works, but you can see that I… I didn't configure that, I just put Datadog logs equals true in the tags, and then it shows up here.
+
+330
+00:46:42.000 --> 00:46:44.649
+David Mine: And then in terms of metrics or, or,
+
+331
+00:46:44.760 --> 00:46:47.159
+David Mine: That's not what I want. Do I have an APM?
+
+332
+00:46:48.340 --> 00:46:51.420
+David Mine: Services, maybe?
+
+333
+00:46:52.490 --> 00:46:54.039
+David Mine: Might just be AP.
+
+334
+00:46:54.040 --> 00:47:10.139
+hrishikb@andrew.cmu.edu: Right now, do we… do you guys have tracers enabled? Like, if there's… I'm sure there's multiple microservices, like, there's multiple services talking to each other via IPC calls. So, do you guys have, like, a chart on Datadog which shows how…
+
+335
+00:47:10.140 --> 00:47:14.870
+hrishikb@andrew.cmu.edu: Yeah, we do with our brand new product, Peregrine. It's still there.
+
+336
+00:47:14.870 --> 00:47:15.390
+David Mine: Yeah.
+
+337
+00:47:15.390 --> 00:47:24.310
+hrishikb@andrew.cmu.edu: product is just starting to get to prod right now, but yeah, the VIN tank has actually helped us, you know, we have the waterfall and everything you're talking about, traces connecting between different.
+
+338
+00:47:24.310 --> 00:47:27.720
+David Mine: Yeah, so between services.
+
+339
+00:47:27.940 --> 00:47:45.929
+David Mine: it's mostly in-app, right, by configuring in the application. Between apps, for our monoliths, it… this is basically all you get is a flame graph that… this isn't a flame graph, but it's… it's just this, right? It's not super helpful, in terms of tracing.
+
+340
+00:47:49.920 --> 00:47:58.529
+David Mine: So, that would… if you're looking to do that as a concern, to talk between multiple services and build traces, I'm not sure that…
+
+341
+00:47:59.980 --> 00:48:04.460
+David Mine: We've never made that happen with our current configuration. We've handled that.
+
+342
+00:48:04.670 --> 00:48:09.089
+David Mine: In our application, rather than in our infrastructure.
+
+343
+00:48:10.390 --> 00:48:11.190
+hrishikb@andrew.cmu.edu: Noted.
+
+344
+00:48:16.330 --> 00:48:19.080
+hrishikb@andrew.cmu.edu: I'm just… I'm very confused as to…
+
+345
+00:48:19.080 --> 00:48:19.550
+David Mine: Fair enough.
+
+346
+00:48:19.550 --> 00:48:22.420
+hrishikb@andrew.cmu.edu: If we should just use the current system, or if he should…
+
+347
+00:48:22.540 --> 00:48:24.889
+hrishikb@andrew.cmu.edu: I actually spent time on Instagram.
+
+348
+00:48:25.500 --> 00:48:26.320
+David Mine: Bye.
+
+349
+00:48:27.430 --> 00:48:38.650
+hrishikb@andrew.cmu.edu: I mean, because, like, right now, everything's working for you guys on Niger, and, like, just one system which we are building with our demos can afford us, because you have 100 other services with Nigeria.
+
+350
+00:48:38.840 --> 00:48:40.930
+hrishikb@andrew.cmu.edu: It's all in, yeah, it's all together.
+
+351
+00:48:41.790 --> 00:48:43.780
+David Mine: Yeah.
+
+352
+00:48:43.780 --> 00:48:49.539
+hrishikb@andrew.cmu.edu: I mean, I don't want to be the one to necessarily make a decision. This is more David than bending things around.
+
+353
+00:48:50.020 --> 00:48:50.740
+David Mine: I haven't.
+
+354
+00:48:51.180 --> 00:48:57.449
+David Mine: I, kind of got lost in the sauce a little bit. What's the… what decision are we trying to make again?
+
+355
+00:48:58.150 --> 00:49:10.649
+hrishikb@andrew.cmu.edu: Do we want to actually implement hotel, or do we just want to stick with whatever is working for you guys right now? Because, if we bring in hotel, it's just gonna be for the service we are creating, and…
+
+356
+00:49:11.020 --> 00:49:14.930
+hrishikb@andrew.cmu.edu: To actually move it system-wide would take a lot of effort, and…
+
+357
+00:49:14.930 --> 00:49:15.720
+David Mine: Oh, okay.
+
+358
+00:49:16.540 --> 00:49:20.019
+David Mine: I mean, I guess I would leave that up to you,
+
+359
+00:49:21.250 --> 00:49:29.330
+David Mine: That would be something that ultimately… Oh, sounds like added complexity.
+
+360
+00:49:30.370 --> 00:49:37.269
+David Mine: but could be a value add to eParts as a company. In terms of adding value to this particular product.
+
+361
+00:49:39.600 --> 00:49:40.760
+David Mine: I…
+
+362
+00:49:43.360 --> 00:49:53.080
+David Mine: If you have the time, sure, but I don't know that I would put that… rank that very high in terms of what I would… I would demand. I… I think,
+
+363
+00:49:54.340 --> 00:50:00.660
+David Mine: We're pretty locked in to a vendor right now. We're pretty locked into a way of doing things.
+
+364
+00:50:00.810 --> 00:50:04.070
+David Mine: And to switch from…
+
+365
+00:50:05.140 --> 00:50:12.700
+David Mine: data dog to someone else to change how we do logging or traces and metrics is going to be a project, if we were to do it.
+
+366
+00:50:13.480 --> 00:50:21.580
+David Mine: Our good old friend artificial intelligence would help us figure out how to do that, and doing that… for…
+
+367
+00:50:21.750 --> 00:50:23.500
+David Mine: 10 repositories.
+
+368
+00:50:24.080 --> 00:50:33.200
+David Mine: where 11 repositories doesn't make a big difference, right? If the difference is that we also have to convert your stuff over from OTEL to.
+
+369
+00:50:33.200 --> 00:50:33.700
+hrishikb@andrew.cmu.edu: Yeah.
+
+370
+00:50:34.140 --> 00:50:36.060
+David Mine: Or, from Datadog.
+
+371
+00:50:36.870 --> 00:50:39.070
+David Mine: To some other… sink.
+
+372
+00:50:39.520 --> 00:50:48.030
+David Mine: it's not really that much more work to me. Most of that stuff's just configured in the startup anyway, it's already easy to switch.
+
+373
+00:50:51.280 --> 00:50:56.349
+David Mine: I wouldn't make that a requirement. I also wouldn't say no if you want to do that as, like, a…
+
+374
+00:50:56.710 --> 00:50:59.200
+David Mine: a stretch goal.
+
+375
+00:50:59.310 --> 00:51:02.730
+David Mine: You can't push that Facebook, like, the…
+
+376
+00:51:06.470 --> 00:51:11.840
+hrishikb@andrew.cmu.edu: Okay, I guess the… the last part I wanted to talk about was dockerization.
+
+377
+00:51:12.280 --> 00:51:15.509
+hrishikb@andrew.cmu.edu: I think we spoke about this as well last week,
+
+378
+00:51:15.770 --> 00:51:34.509
+hrishikb@andrew.cmu.edu: So I did go over it, and I think dockerizing the entire system, what we're building, would not be the best way to move forward, because we have stateless compute components, so we could dockerize individual components. One would be the ingestion Gateway, and one would be the ML component. We can dockerize them separately, and they can talk to each other via network.
+
+379
+00:51:35.350 --> 00:51:40.590
+hrishikb@andrew.cmu.edu: yeah, that's pretty much it.
+
+380
+00:51:41.320 --> 00:51:43.500
+hrishikb@andrew.cmu.edu: But I had,
+
+381
+00:51:43.790 --> 00:51:50.200
+hrishikb@andrew.cmu.edu: I guess, once we get the data from you, maybe we can slowly start building the OCR functionality?
+
+382
+00:51:51.390 --> 00:51:54.870
+hrishikb@andrew.cmu.edu: And build on the ML model that we use.
+
+383
+00:51:55.000 --> 00:51:58.420
+hrishikb@andrew.cmu.edu: How did the doctorization topic come up? I'm curious.
+
+384
+00:51:59.790 --> 00:52:09.609
+hrishikb@andrew.cmu.edu: Last week, since we have multiple services talking to each other, so when we're building it on our local device versus when we're hosting it on the cloud.
+
+385
+00:52:09.610 --> 00:52:22.389
+hrishikb@andrew.cmu.edu: We don't want it to experience two separate behaviors. We want the behaviors to be exactly the same. So, if we have a Docker container, we know exactly how it's going to be going on both sides. We're just going to put this container up in the cloud, that's it.
+
+386
+00:52:24.760 --> 00:52:30.620
+hrishikb@andrew.cmu.edu: Then lastly, talk about it making it a bit agnostic, so that the software can work anywhere.
+
+387
+00:52:30.830 --> 00:52:33.839
+hrishikb@andrew.cmu.edu: So, that's where the localization point came into the virtual.
+
+388
+00:52:38.350 --> 00:52:42.450
+hrishikb@andrew.cmu.edu: I think that's all we had. Any questions from anyone?
+
+389
+00:52:43.210 --> 00:52:44.070
+hrishikb@andrew.cmu.edu: David?
+
+390
+00:52:45.880 --> 00:52:48.760
+David Mine: Nothing for me.
+
+391
+00:52:48.760 --> 00:52:59.269
+hrishikb@andrew.cmu.edu: Great, so I have a couple takeaways then. I can get you… I'll definitely be able to get you pretty quickly, a global set of our current, like, AMS product data.
+
+392
+00:52:59.450 --> 00:53:12.559
+hrishikb@andrew.cmu.edu: The vendor product data, like, from the suppliers, the spec sheets, the PDFs, so that might take a couple more days just off to actually go to the catalog team and kind of collect that, but I can, again, get you the actual, database data pretty quickly.
+
+393
+00:53:12.790 --> 00:53:28.749
+hrishikb@andrew.cmu.edu: And then also I'll try and get you some, metrics for constraints, such as, like, total number of suppliers, attributes, categories, and, also try and get some trends from that, over time, such as how many we've been adding per year, and for new tenants signed up, or something like that.
+
+394
+00:53:31.770 --> 00:53:42.690
+hrishikb@andrew.cmu.edu: We still haven't gotten the cursive. Yeah, well, Cursive, and yeah, David brought that up this morning. David said he was working on Cursive, he was running into an issue. I know he hasn't had much time today.
+
+395
+00:53:42.750 --> 00:53:43.850
+David Mine: Like, he was employed.
+
+396
+00:53:44.350 --> 00:53:49.060
+David Mine: Yeah, can we schedule a time where I could just sit with someone and try a bunch of things?
+
+397
+00:53:49.460 --> 00:53:52.750
+David Mine: Probably take half an hour to 45 minutes.
+
+398
+00:53:54.010 --> 00:54:01.919
+hrishikb@andrew.cmu.edu: Yeah, sure. Yes, like, one of you, like, in a call on this, he tries different things and it fails, he can try it.
+
+399
+00:54:02.950 --> 00:54:14.279
+David Mine: I would try on my own guest account, but the problem, I think, is that you guys have, rather than being, like, guest accounts with the username, password, your guest accounts to another ENTRE tenant.
+
+400
+00:54:14.420 --> 00:54:17.620
+David Mine: Which is… which adds some complexity to the equation.
+
+401
+00:54:19.000 --> 00:54:22.489
+hrishikb@andrew.cmu.edu: Okay. I think you just let us know what time you're available, and we can…
+
+402
+00:54:22.880 --> 00:54:26.170
+hrishikb@andrew.cmu.edu: So one of us should be free at that time, most likely.
+
+403
+00:54:26.480 --> 00:54:27.330
+hrishikb@andrew.cmu.edu: Okay.
+
+404
+00:54:27.340 --> 00:54:28.410
+David Mine: Okay.
+
+405
+00:54:29.190 --> 00:54:37.679
+hrishikb@andrew.cmu.edu: Did we get them in a team, David, on Microsoft Teams? Yeah. Well, I don't know, David, if you want to post the, potential meeting times there?
+
+406
+00:54:38.980 --> 00:54:41.369
+hrishikb@andrew.cmu.edu: Just make a post with, your availability.
+
+407
+00:54:51.130 --> 00:54:55.150
+hrishikb@andrew.cmu.edu: Both David and Cat Fence here. I'm checking out the picture behind it.
+
+408
+00:54:55.610 --> 00:54:57.770
+hrishikb@andrew.cmu.edu: Those are his cats. Those are your cats.
+
+409
+00:54:57.770 --> 00:54:59.869
+David Mine: Yeah, these are actually my cats.
+
+410
+00:55:01.960 --> 00:55:06.000
+hrishikb@andrew.cmu.edu: Oh, cool. Many of us have cats in the office.
+
+411
+00:55:08.120 --> 00:55:09.809
+hrishikb@andrew.cmu.edu: And what are the names of your cats?
+
+412
+00:55:10.050 --> 00:55:16.550
+David Mine: Let's see, finn, Bumi, and Simon.
+
+413
+00:55:18.540 --> 00:55:20.830
+hrishikb@andrew.cmu.edu: I love it.
+
+414
+00:55:22.630 --> 00:55:24.529
+David Mine: Best Christmas present I ever got.
+
+415
+00:55:29.460 --> 00:55:34.830
+David Mine: From private.
+
+416
+00:55:34.830 --> 00:55:41.930
+hrishikb@andrew.cmu.edu: Yeah, I'll also… Could we get a snapshot of, like, the production table that we've broken?
+
+417
+00:55:43.110 --> 00:55:54.220
+hrishikb@andrew.cmu.edu: What do you need a production table, right, before tables? Our… in our architecture… In the intermediate table, maybe? And that is something that we will create? No, the intermediate table is something we would create. Okay.
+
+418
+00:55:54.640 --> 00:55:58.130
+hrishikb@andrew.cmu.edu: So we would want to replicate whatever
+
+419
+00:55:58.170 --> 00:56:14.820
+hrishikb@andrew.cmu.edu: Oh, you just mean, like, the table? Yeah. Oh, yeah, I mean, that's kind of what I was going to give you, with the sample data from the database. I was just going to give you, you know, top 2,000 rows or something, whatever tables. Would that suffice? Yeah, yeah, that's fine.
+
+420
+00:56:15.030 --> 00:56:16.600
+hrishikb@andrew.cmu.edu: Oh, yeah, there was.
+
+421
+00:56:17.260 --> 00:56:17.920
+hrishikb@andrew.cmu.edu: Right.
+
+422
+00:56:21.280 --> 00:56:23.020
+hrishikb@andrew.cmu.edu: Okay, I guess I'm good.
+
diff --git a/transcripts/GMT20260226-190646_Recording.transcript.vtt b/transcripts/GMT20260226-190646_Recording.transcript.vtt
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@@ -0,0 +1,978 @@
+WEBVTT
+
+1
+00:00:00.000 --> 00:00:06.540
+hrishikb@andrew.cmu.edu: The two… the few talking points you have are around the ML components.
+
+2
+00:00:06.689 --> 00:00:07.089
+Harsha Tummala: Okay.
+
+3
+00:00:07.090 --> 00:00:13.499
+hrishikb@andrew.cmu.edu: prediction model, and we also had a question about if we can use LLMs instead of the traditional ML.
+
+4
+00:00:14.570 --> 00:00:19.899
+hrishikb@andrew.cmu.edu: If that will be viable, and some initial work we have done on the semantic matches.
+
+5
+00:00:22.690 --> 00:00:28.600
+hrishikb@andrew.cmu.edu: Okay, for the… okay, let's start. So the… I don't have anything present as such, but…
+
+6
+00:00:29.310 --> 00:00:31.710
+hrishikb@andrew.cmu.edu: for the ML component.
+
+7
+00:00:32.130 --> 00:00:38.960
+hrishikb@andrew.cmu.edu: We were… we had a few questions on how exactly the confidence scoring would work.
+
+8
+00:00:39.640 --> 00:00:40.180
+Harsha Tummala: Okay.
+
+9
+00:00:40.990 --> 00:00:44.200
+hrishikb@andrew.cmu.edu: So, Ashtar, you added anything to add?
+
+10
+00:00:44.460 --> 00:00:57.700
+hrishikb@andrew.cmu.edu: Okay. So, Harshal, we spoke to a couple of members here last, this week and, yeah, this week, and then, one constant suggestion that we were getting is, so if…
+
+11
+00:00:57.700 --> 00:01:07.810
+hrishikb@andrew.cmu.edu: if… I think in the last, or, like, in the… one of the previous meetings, Jake mentioned that, the updates are not going to be that frequent, right? So, it's…
+
+12
+00:01:07.810 --> 00:01:32.300
+hrishikb@andrew.cmu.edu: it's pretty much the same schema throughout, like, at least for a period of time. So, one of the suggestions that we got is instead of using a traditional ML model that, I mean, wherein you would be responsible for, like, any kind of retraining or, like, setting some confident threshold values and all of that, why don't we just use a LLM, that's available
+
+13
+00:01:32.300 --> 00:01:36.900
+hrishikb@andrew.cmu.edu: That's directly offered by Azure, and, plug it in.
+
+14
+00:01:36.900 --> 00:01:45.909
+hrishikb@andrew.cmu.edu: that's one of the approaches that they had suggested to taking. So we just wanted to know, like, what do you think about that, and then I can,
+
+15
+00:01:45.920 --> 00:01:51.219
+hrishikb@andrew.cmu.edu: So, but before that, we did one kind of a very… not…
+
+16
+00:01:51.220 --> 00:02:06.369
+hrishikb@andrew.cmu.edu: not a very solid POC, but a small POC using semantic matcher. I'll describe more about that, but I just wanted to, like, know your opinion on the LLM versus traditional ML approach.
+
+17
+00:02:07.620 --> 00:02:15.000
+Harsha Tummala: So, can you guys look at any of the other options available on Azure, other than LLMs, for this use case?
+
+18
+00:02:16.540 --> 00:02:17.550
+hrishikb@andrew.cmu.edu: For example.
+
+19
+00:02:18.150 --> 00:02:30.330
+Harsha Tummala: No, I mean, for the semantic matching. You suggested Azure for the LLMs, so I was just wondering if there's anything else that Azure offers for, the other ML stuff.
+
+20
+00:02:31.370 --> 00:02:40.339
+hrishikb@andrew.cmu.edu: Yeah, I think we were exploring BERT that is offered on Azure, and then, semantic matcher for, we were using all mini-LM model.
+
+21
+00:02:40.340 --> 00:02:56.449
+hrishikb@andrew.cmu.edu: That is also available on Azure. But we have not experimented yet, like, we don't have the accuracy scores and all of that, but for all mini-LM, like, the semantic measure, yeah, we did, some bit of exploration there.
+
+22
+00:02:56.740 --> 00:03:03.169
+Harsha Tummala: I mean, just like an off-topic question, Lewis, but coming back to the LLM question.
+
+23
+00:03:03.370 --> 00:03:18.829
+Harsha Tummala: So, Azure is pretty restrictive in terms of, like, just general fine-tuning of LLMs that they offer. So, I would highly suggest that you take a look at, what's being offered in terms of, just open models that you can train versus
+
+24
+00:03:18.890 --> 00:03:29.599
+Harsha Tummala: whatever other options are, because, one thing is Azure's AI subscription kind of is two parts, which is…
+
+25
+00:03:29.600 --> 00:03:40.290
+Harsha Tummala: they offer LLMs which are, basically open source and, self, like, just self-hosted by Azure, and the other is Azure OpenAI stuff.
+
+26
+00:03:40.510 --> 00:03:56.019
+Harsha Tummala: So, Azure OpenAI is a complete different thing in itself, and only, I think, GPD 4.0 is what's supporting fine-tuning, and that's pretty expensive in general, like, for a use case like this.
+
+27
+00:03:56.020 --> 00:03:56.370
+hrishikb@andrew.cmu.edu: Thank you.
+
+28
+00:03:56.370 --> 00:04:00.040
+Harsha Tummala: So that's… that's one point. And,
+
+29
+00:04:00.220 --> 00:04:08.659
+Harsha Tummala: So, I mean, the open… the other open source models that they kind of host and give us, I'm not sure how much offer fine-tuning support they offer in general.
+
+30
+00:04:09.110 --> 00:04:16.480
+Harsha Tummala: So, just check that out, I would say. That's probably one suggestion. But honestly, if that's a pretty viable approach, I would say go ahead with it.
+
+31
+00:04:18.060 --> 00:04:30.530
+hrishikb@andrew.cmu.edu: Yeah, like, I personally have experience with Amazon Bedrock, but not the Azure ecosystem, so we'll have to try that out. And, like, one follow-up question that I would want to ask is.
+
+32
+00:04:30.530 --> 00:04:44.070
+hrishikb@andrew.cmu.edu: So, we… initially, we just decided off two parts here, right? One is the OCR part, where would… where you would extract the text from the different sources that you have, and the… obviously, the prediction and the confidence score attributing part.
+
+33
+00:04:44.070 --> 00:04:52.320
+hrishikb@andrew.cmu.edu: So, if at all, like, obviously, like, we'll see how the LLM works in terms of cost and, resource feasibility-wise.
+
+34
+00:04:52.320 --> 00:05:06.720
+hrishikb@andrew.cmu.edu: But let's say it's possible to use that. Can we at least use the LLM part for, instead of the OCR? Because LLMs are quite good at extracting text, right?
+
+35
+00:05:07.090 --> 00:05:10.079
+hrishikb@andrew.cmu.edu: That would be, like, pretty straightforward, I agree.
+
+36
+00:05:10.080 --> 00:05:14.129
+Harsha Tummala: Yeah, yeah, they are. So again, for OCR purposes.
+
+37
+00:05:14.460 --> 00:05:25.519
+Harsha Tummala: using an LLM is not bad at all, I would say. It's pretty weird, because Azure has a separate OCR service.
+
+38
+00:05:26.010 --> 00:05:28.669
+Harsha Tummala: Oh, okay. So.
+
+39
+00:05:29.020 --> 00:05:34.830
+Harsha Tummala: So that OCR service is highly tunable in terms of how the document looks like and how it works.
+
+40
+00:05:35.040 --> 00:05:45.659
+Harsha Tummala: But it's not a great generalist, so, for example, if you ever have, like, a change in, change in schema, like, change in the type of document you're expecting, it kind of…
+
+41
+00:05:45.980 --> 00:05:49.189
+Harsha Tummala: It starts glitching, and, like, it doesn't really extract text that well.
+
+42
+00:05:49.480 --> 00:05:52.059
+Harsha Tummala: Because, there's no logical flow to it.
+
+43
+00:05:52.520 --> 00:05:59.019
+Harsha Tummala: There is, in fact, a recent paper that I was reading about, which was,
+
+44
+00:05:59.750 --> 00:06:10.820
+Harsha Tummala: It's basically trying to read a document the way a human reads it, and that's kind of how they semantically, like, extract the characters out of the document.
+
+45
+00:06:11.080 --> 00:06:27.310
+Harsha Tummala: It's… so what it does is, there's a small LLM which is corrected in the front, which feeds information to the bigger OCR extraction LLM. I'm not sure if Azure offers it at this point, but you could take a look at it, and…
+
+46
+00:06:27.380 --> 00:06:37.630
+Harsha Tummala: I mean, just… just probably experiment… I could… I could give you access to, like, the AI resources so that you can experiment with just general OCR extraction through the LLMs offered by Azure.
+
+47
+00:06:37.860 --> 00:06:45.260
+Harsha Tummala: And I think they're pretty restrictive on that end, because they do have a separate OCR service, so they kind of want to push people towards using that.
+
+48
+00:06:47.170 --> 00:06:47.930
+hrishikb@andrew.cmu.edu: Okay.
+
+49
+00:06:49.230 --> 00:06:52.590
+hrishikb@andrew.cmu.edu: I think for using the LLM to…
+
+50
+00:06:52.880 --> 00:07:10.450
+hrishikb@andrew.cmu.edu: instead of the ML model, we could even, initially try out with, maybe not on Azure, but, to seek its feasibility, we can try out with better models, the one we have access to, and see if it is able to do the work efficiently. Then we can maybe scale down the…
+
+51
+00:07:10.730 --> 00:07:25.360
+hrishikb@andrew.cmu.edu: like, go to, like, older versions or, like, less expensive versions, and then maybe, like, best case scenario would be we can host our own LAM model or something like that, which we can use to get a, like, good output.
+
+52
+00:07:26.040 --> 00:07:45.480
+Harsha Tummala: Yep, 100%. Again, I don't want to be restrictive in any way, so because this is just, like, the initial kind of finding slash research kind of phase, just, just do whatever at this point. I would say we should probably only think about if we want to go with LLMs or just stick with ML models at a later stage.
+
+53
+00:07:46.130 --> 00:07:46.800
+hrishikb@andrew.cmu.edu: Okay.
+
+54
+00:07:49.140 --> 00:08:02.160
+hrishikb@andrew.cmu.edu: So, at least for now, before the LEM topic even came up, we were just, playing around the semantic measure, approach, like, what… I mean, the data that we took, the,
+
+55
+00:08:02.320 --> 00:08:18.289
+hrishikb@andrew.cmu.edu: the amount was obviously, like, low, so, I'm not saying that the numbers are reliable or something. So I, so I think you gave us two spec docs, AM2 and RCT FlexCT, I think, yeah, I had, like, two, spec docs.
+
+56
+00:08:18.310 --> 00:08:37.190
+hrishikb@andrew.cmu.edu: So, basically, the model extracted all the attribute names from them, and, I think they totaled up to, like, around 42 attributes in total, to work with. So, we ran, through all of those, I mean, we ran the semantic matcher against the PIMS attribute index.
+
+57
+00:08:37.190 --> 00:08:43.610
+hrishikb@andrew.cmu.edu: Which has, I think, 480… I mean, something close to, like, 500 or 480, I don't remember the number.
+
+58
+00:08:43.610 --> 00:08:58.990
+hrishikb@andrew.cmu.edu: So, and then for each, supplier attribute, obviously, like, this is the internet working of the, model, like, it, it computed a cosine similarity against what it has and what the PIMS database has.
+
+59
+00:08:58.990 --> 00:09:17.580
+hrishikb@andrew.cmu.edu: So it, just gave an… I mean, it is supposed to, like, emit a number, similarity score, like, 0 or 1. For now, I don't have any rational behind setting it to, like, 0.25 as a threshold, but I just, like, intentionally gave it a very low score of 0.25. So…
+
+60
+00:09:17.610 --> 00:09:36.489
+hrishikb@andrew.cmu.edu: So, anything about that, got auto-accepted and, got written to PIMS, and anything below that got rejected. So, for now, the numbers are, like, pretty high, like, I think out of 42, 36 records got auto-accepted, so that's around, like, 86% to 87 percentage.
+
+61
+00:09:36.490 --> 00:09:42.559
+hrishikb@andrew.cmu.edu: And, 6… the other 5 or 6 got, routed to the human review.
+
+62
+00:09:42.630 --> 00:10:02.999
+hrishikb@andrew.cmu.edu: like, with this, we did not, like, want to, come to any conclusions, but we just want to, like, check, how the, internal working of the semantic matcher is done. So, this is just, like, our key findings, and moreover, the records are, like, too less to compare it against. Like, the ground truth, that we have is, like,
+
+63
+00:10:03.460 --> 00:10:19.560
+hrishikb@andrew.cmu.edu: too little, so we were thinking, like, should we, you know, produce some synthetic data, based on what we have, or how do we go about it? Because yesterday we were talking about this to, one of our coaches, and then he was like.
+
+64
+00:10:19.690 --> 00:10:30.109
+hrishikb@andrew.cmu.edu: At least for this use case, synthetic data might screw up the numbers in terms of accuracy, so how do we go about that?
+
+65
+00:10:30.400 --> 00:10:32.920
+Harsha Tummala: I can give you guys more data, honestly, like, it's not that difficult.
+
+66
+00:10:32.920 --> 00:10:33.315
+hrishikb@andrew.cmu.edu: So…
+
+67
+00:10:34.240 --> 00:10:40.180
+hrishikb@andrew.cmu.edu: That was huge. Last time you just dumped a lot of data, like the spec files and everything.
+
+68
+00:10:40.180 --> 00:10:46.390
+Harsha Tummala: Yeah, so that's the thing, right? Like, the spec files I sent you were, like, 2 or 3 out of.
+
+69
+00:10:46.390 --> 00:10:47.160
+hrishikb@andrew.cmu.edu: Yeah, yeah.
+
+70
+00:10:47.160 --> 00:10:48.390
+Harsha Tummala: 40,000 and a half.
+
+71
+00:10:48.900 --> 00:10:49.930
+hrishikb@andrew.cmu.edu: Oh, okay.
+
+72
+00:10:49.930 --> 00:10:54.609
+Harsha Tummala: Yeah, so I can always give you guys way more data if you need.
+
+73
+00:10:55.320 --> 00:10:55.930
+hrishikb@andrew.cmu.edu: Open.
+
+74
+00:10:56.350 --> 00:10:56.960
+Harsha Tummala: Tim.
+
+75
+00:10:57.510 --> 00:11:06.589
+Harsha Tummala: And I can… so I can also, like, grant access to, like, a blob storage, which we use. So, you can just pick your files from it and probably do whatever with it.
+
+76
+00:11:07.560 --> 00:11:10.819
+hrishikb@andrew.cmu.edu: Okay. Yeah, I think that would be really useful for us.
+
+77
+00:11:10.930 --> 00:11:29.069
+hrishikb@andrew.cmu.edu: Yeah. And if you could also, like, so we had the data of running final lines of, in the input into the schema, but if you have something, like, where those… where the data exactly came from, like, if you have a starting point, like, the PDF, and what that PDF extracted, so we could have a
+
+78
+00:11:29.160 --> 00:11:37.649
+hrishikb@andrew.cmu.edu: entire picture of how the existing… the, like, catalog team works, what they see and what they output from it.
+
+79
+00:11:37.650 --> 00:11:42.460
+Harsha Tummala: I would say for this, a good use case is just, like, interviewing the catalog team and understanding.
+
+80
+00:11:43.270 --> 00:11:44.380
+hrishikb@andrew.cmu.edu: Okay, yeah.
+
+81
+00:11:44.780 --> 00:11:45.320
+hrishikb@andrew.cmu.edu: Other than that.
+
+82
+00:11:45.320 --> 00:11:59.309
+Harsha Tummala: I can give you guys the data, but because there's a human there and, like, he's doing a lot of the thinking and putting the data into the database, you won't really see the steps taken there. You'll just see PDF and final data, and that's it.
+
+83
+00:12:01.040 --> 00:12:01.830
+hrishikb@andrew.cmu.edu: Night.
+
+84
+00:12:03.090 --> 00:12:09.889
+hrishikb@andrew.cmu.edu: Yeah, I think, I think we will probably should schedule a meeting with the catalog team sometime after spring break.
+
+85
+00:12:10.190 --> 00:12:10.830
+Harsha Tummala: Yeah.
+
+86
+00:12:11.520 --> 00:12:13.659
+Harsha Tummala: Wednesday for you guys, I forgot to tell you.
+
+87
+00:12:14.290 --> 00:12:15.340
+hrishikb@andrew.cmu.edu: Next week.
+
+88
+00:12:15.770 --> 00:12:16.350
+Harsha Tummala: Okay.
+
+89
+00:12:16.350 --> 00:12:17.730
+hrishikb@andrew.cmu.edu: Tomorrow's the last decision.
+
+90
+00:12:18.260 --> 00:12:21.269
+hrishikb@andrew.cmu.edu: Not if you're standing. A little bit.
+
+91
+00:12:21.270 --> 00:12:21.900
+Harsha Tummala: Uber.
+
+92
+00:12:22.080 --> 00:12:26.249
+Harsha Tummala: Yeah, so I'll just remove the meeting from next week, next week's calendar.
+
+93
+00:12:27.620 --> 00:12:30.580
+hrishikb@andrew.cmu.edu: Yeah, I'll send out a cancellation thing.
+
+94
+00:12:31.870 --> 00:12:33.040
+hrishikb@andrew.cmu.edu: Okay.
+
+95
+00:12:35.810 --> 00:12:36.939
+hrishikb@andrew.cmu.edu: Next we have…
+
+96
+00:12:39.560 --> 00:12:53.900
+hrishikb@andrew.cmu.edu: I think, yeah, for the initial ML models, you're thinking we might, like, if you're using specifically ML, we might have to use, two models for… one for the confidence scoring, and one for the initial mapping.
+
+97
+00:12:54.010 --> 00:13:03.139
+hrishikb@andrew.cmu.edu: So the data that we extract from the PDFs and other sources, like the example that Jake gave was the display size and screen size.
+
+98
+00:13:03.270 --> 00:13:05.029
+hrishikb@andrew.cmu.edu: To tackle things like that.
+
+99
+00:13:05.170 --> 00:13:11.159
+hrishikb@andrew.cmu.edu: We might have to have a smaller model which can map those key-value pairs more accurately.
+
+100
+00:13:11.370 --> 00:13:13.390
+hrishikb@andrew.cmu.edu: So that we don't have duplicate data.
+
+101
+00:13:13.710 --> 00:13:16.229
+hrishikb@andrew.cmu.edu: So, just wanted to get your opinion on that.
+
+102
+00:13:17.030 --> 00:13:23.620
+Harsha Tummala: Just, just wondering why, why, like, what's the rationale behind, like, using two models for that?
+
+103
+00:13:24.860 --> 00:13:36.100
+hrishikb@andrew.cmu.edu: I think the primary, goal for the model that we're currently thinking is to get the confidence score on each of the attributes that we extract, but in order to
+
+104
+00:13:36.460 --> 00:13:42.000
+hrishikb@andrew.cmu.edu: be able to put that into PEMS, we need to fit that data into the existing schema.
+
+105
+00:13:43.040 --> 00:13:52.959
+hrishikb@andrew.cmu.edu: So, I would say attribute matching would also require… I'm not sure if it requires a separate model, but that's the way we were initially thinking that it might.
+
+106
+00:13:52.960 --> 00:13:53.530
+Harsha Tummala: I'm in.
+
+107
+00:13:54.420 --> 00:14:05.010
+Harsha Tummala: I mean, nothing specific on that. I would say… so most, most attribute prediction kind of models, I would say, predict with a certain,
+
+108
+00:14:05.150 --> 00:14:07.799
+Harsha Tummala: Like, certain level of confidence themselves.
+
+109
+00:14:08.070 --> 00:14:15.300
+Harsha Tummala: So, when a value is generated, you could probably take that value itself. So, that… that can only be done if you're actually
+
+110
+00:14:15.530 --> 00:14:25.509
+Harsha Tummala: like, using a… using, like, an open source model where there's complete transparency on the process. But if you're using, like, an Azure kind of model, I'm not sure how… how visible it'll be.
+
+111
+00:14:29.730 --> 00:14:32.009
+Harsha Tummala: So yeah, in that case, two models would make sense.
+
+112
+00:14:39.960 --> 00:14:48.180
+hrishikb@andrew.cmu.edu: Yeah, I think that was the main, doubts we had regarding the… ML components.
+
+113
+00:14:49.020 --> 00:14:50.969
+hrishikb@andrew.cmu.edu: Yeah, but, I mean…
+
+114
+00:14:51.540 --> 00:15:11.119
+hrishikb@andrew.cmu.edu: at least, like, this week, whatever discussions we have had, everything were revolving around, the catalog team's inputs. At least, for example, for the ML thing also, like, like I said, the scores are pretty high, because it… I was just, like, trying to check how the semantic matcher works.
+
+115
+00:15:11.120 --> 00:15:21.909
+hrishikb@andrew.cmu.edu: And this is not something that you would set as a threshold, in the actual production, right? So I think the real, test would be, like,
+
+116
+00:15:21.920 --> 00:15:28.790
+hrishikb@andrew.cmu.edu: Is it Brian who's working, from the catalog? Yeah, so I think, I think when,
+
+117
+00:15:28.920 --> 00:15:46.690
+hrishikb@andrew.cmu.edu: you, my benchmark would be, like, getting the data from his team, like, where, like, with, where the, labels are manually, you know, which, I mean, mapped to which attribute,
+
+118
+00:15:46.820 --> 00:15:47.710
+hrishikb@andrew.cmu.edu: Sorry.
+
+119
+00:15:47.870 --> 00:16:00.899
+hrishikb@andrew.cmu.edu: when, so Brian does manual labeling of each attribute, right? So if we can get that data, and if I could compare… compare it against what my semantic matcher gave.
+
+120
+00:16:00.900 --> 00:16:12.159
+hrishikb@andrew.cmu.edu: That would be, like, a litmus test, but right now, I think, like you said, probably we can get all of this information only after talking to him or, like, someone from his team.
+
+121
+00:16:12.720 --> 00:16:22.139
+Harsha Tummala: Yeah, I mean, the primary interview with, like, Brian and team would help you guys, honestly, understand, like, more niche challenges as well, which they kind of encounter.
+
+122
+00:16:22.540 --> 00:16:28.740
+Harsha Tummala: And that might honestly, like, even end up making you guys deviate from whatever path you're on right now.
+
+123
+00:16:29.060 --> 00:16:42.210
+Harsha Tummala: So, they have, again, the catalog team works in their own way, and sometimes the way they kind of get files and data is straightforward.
+
+124
+00:16:42.430 --> 00:16:47.170
+Harsha Tummala: On good days, and just on very occasional days, it might not be that easy.
+
+125
+00:16:47.320 --> 00:16:52.210
+Harsha Tummala: So I sent you guys a template of… a template file, too, for, like, the data that is filled out.
+
+126
+00:16:52.760 --> 00:17:06.679
+Harsha Tummala: So, in that template file, what happens is, ePath generally sends that template file out to all the suppliers that we have, and that template file is just used by us to directly input data into our product catalog.
+
+127
+00:17:07.089 --> 00:17:11.310
+Harsha Tummala: And similar case is with Brian's team as well, in most days.
+
+128
+00:17:11.500 --> 00:17:18.190
+Harsha Tummala: And they also have a catalogue team, which kind of does this work of filling the Excel sheet out.
+
+129
+00:17:20.280 --> 00:17:31.199
+Harsha Tummala: themselves. So, there's certain suppliers that Alps basically deals with, who don't have, like, who don't have people to do that for them. And…
+
+130
+00:17:31.360 --> 00:17:36.309
+Harsha Tummala: For those kind of clients, we take on the work, and we kind of fill the catalog out for them.
+
+131
+00:17:36.790 --> 00:17:44.159
+Harsha Tummala: But that template is kind of, like, a good benchmark on how we expect the data to be after we,
+
+132
+00:17:44.840 --> 00:17:49.719
+Harsha Tummala: extract all the information from, like, the websites and the PDFs that we have.
+
+133
+00:17:51.700 --> 00:18:05.079
+hrishikb@andrew.cmu.edu: So, you just mentioned about some Excel where the data would be, present, right? So, how does that go to Prims? Like, do you have some APIs, or is it, like, some kind of a upload, release, or how does that work?
+
+134
+00:18:05.080 --> 00:18:15.390
+Harsha Tummala: This… the Excel part… so, the way it is right now is, I mean, this data goes into, like, master database through a few,
+
+135
+00:18:15.780 --> 00:18:19.220
+Harsha Tummala: Like, stored procedures we have, which are kind of lacy.
+
+136
+00:18:19.430 --> 00:18:24.100
+Harsha Tummala: And these load procedures ingest the data into the SQL database, SQL database.
+
+137
+00:18:24.410 --> 00:18:38.040
+Harsha Tummala: has this occasional sync with PIMS that goes on, and that's how it ends up in PIMS. But ideally, it should just go directly into PIMS, and that's… that's kind of the process. But I would say if you guys can generate this specific
+
+138
+00:18:38.140 --> 00:18:41.589
+Harsha Tummala: Like, generate the data in the…
+
+139
+00:18:41.810 --> 00:18:46.969
+Harsha Tummala: in the specific Excel template that we have, that is honestly, like, 70% of the work done.
+
+140
+00:18:47.170 --> 00:18:51.900
+Harsha Tummala: And ingesting it is kind of just the final programmatic step.
+
+141
+00:18:55.710 --> 00:18:58.980
+Harsha Tummala: I mean, Jake would be a better person to talk about the PIMS part of things.
+
+142
+00:18:59.370 --> 00:19:05.159
+Harsha Tummala: So, again, I think you should leave that question open for, like, next, next week, when you have a discussion.
+
+143
+00:19:06.130 --> 00:19:11.920
+hrishikb@andrew.cmu.edu: Yeah, so why did I ask? This is, again, like, one of our coaches,
+
+144
+00:19:11.960 --> 00:19:26.960
+hrishikb@andrew.cmu.edu: asked us about what is the UI part of it, like, we don't have an explicit UI here, but then, obviously, the API's interface or anything of that sort is also considered
+
+145
+00:19:26.960 --> 00:19:33.420
+hrishikb@andrew.cmu.edu: as… under that spectrum. So, we didn't have much details on that, so that's why I asked.
+
+146
+00:19:33.810 --> 00:19:38.739
+Harsha Tummala: Yeah, I mean, probably just designing the API would probably be the last cog in this project.
+
+147
+00:19:39.030 --> 00:19:44.220
+Harsha Tummala: And that would probably be as much UI UX would have to do, which is literally backing work.
+
+148
+00:19:45.210 --> 00:19:45.800
+hrishikb@andrew.cmu.edu: Okay.
+
+149
+00:19:46.410 --> 00:19:47.030
+Harsha Tummala: Yeah.
+
+150
+00:19:50.730 --> 00:19:51.350
+hrishikb@andrew.cmu.edu: Question?
+
+151
+00:19:58.940 --> 00:20:03.609
+hrishikb@andrew.cmu.edu: I think we covered all the main points and questions we had. Does anyone have anything else?
+
+152
+00:20:04.680 --> 00:20:16.339
+hrishikb@andrew.cmu.edu: I have a general question. Thank you for supplying all the sample data to the team. Of the sample data you provided, how representative is that of the spectrum of the data that you get?
+
+153
+00:20:18.000 --> 00:20:34.400
+Harsha Tummala: It pretty much represents, like, almost all of the data that we get. So, the way I've kind of created the sample data is actually by querying from our main production databases and up for scaling, like.
+
+154
+00:20:34.970 --> 00:20:36.450
+Harsha Tummala: key elements which…
+
+155
+00:20:36.640 --> 00:20:53.610
+Harsha Tummala: which we probably don't want to expose, and that's… that's pretty much it. And, the spectrum of data, it pretty much covers everything. So, the Word file kind of, details upon, like, how all the data interfaces and how the… how all the data flows.
+
+156
+00:20:53.880 --> 00:20:57.330
+Harsha Tummala: So, that has all the details on…
+
+157
+00:20:57.860 --> 00:21:04.649
+Harsha Tummala: How the data ties in, and the sample files are pretty much representative of the production data that we can deal with day-to-day.
+
+158
+00:21:09.590 --> 00:21:18.610
+hrishikb@andrew.cmu.edu: So I'm curious, in which files would you have to OCR anything? If you… I mean, you have text files, you have Word files, and you have PDF files.
+
+159
+00:21:19.080 --> 00:21:21.709
+hrishikb@andrew.cmu.edu: Where's the OCR need for that?
+
+160
+00:21:21.710 --> 00:21:37.169
+Harsha Tummala: Not really. So, again, I think OCR component left would only be for the PDFs, and that's pretty much it. So, there are two PDFs that I sent across to the team, and those are the kind of documents you would ever have to OCR in this process.
+
+161
+00:21:37.510 --> 00:21:39.329
+Harsha Tummala: And
+
+162
+00:21:39.430 --> 00:21:57.660
+Harsha Tummala: the Word files and CSV files, I mean, the CSV files are pretty much a SQL database extract that I've given them, and the rest of the CSV files, I mean, there's… if it's a CSV, I would say you can always write a program to kind of get data from it and never really do OCR.
+
+163
+00:22:02.960 --> 00:22:03.570
+hrishikb@andrew.cmu.edu: Okay.
+
+164
+00:22:03.570 --> 00:22:04.919
+Harsha Tummala: We're gonna be more standardized.
+
+165
+00:22:09.200 --> 00:22:11.189
+hrishikb@andrew.cmu.edu: I had another question.
+
+166
+00:22:11.750 --> 00:22:18.709
+hrishikb@andrew.cmu.edu: Could we also, like, I think in the files that you have sent, the prices part is removed?
+
+167
+00:22:19.050 --> 00:22:20.060
+hrishikb@andrew.cmu.edu: the…
+
+168
+00:22:20.440 --> 00:22:32.459
+hrishikb@andrew.cmu.edu: do… could we also get some, I guess, catalogs or PDFs which have prices, like, maybe old resident prices, but just to be able to see how that will look when we start doing our POC on
+
+169
+00:22:32.940 --> 00:22:35.290
+hrishikb@andrew.cmu.edu: The OCR part.
+
+170
+00:22:36.440 --> 00:22:44.410
+Harsha Tummala: So, in the PDFs, usually prices aren't present at all. So, prices are sent across as a separate CSV,
+
+171
+00:22:45.300 --> 00:22:57.340
+Harsha Tummala: which, detail, how the specific build… often build of a model, is priced, or is supposed to be priced by us. So, for example, if we take, like, a…
+
+172
+00:22:58.030 --> 00:22:59.780
+Harsha Tummala: Or something, and
+
+173
+00:23:00.060 --> 00:23:19.130
+Harsha Tummala: for example, there's, like, in the same type of tap, which is made of, like, brass, there's, like, 10 different flow rates that are available, and each different flow rate is spec'd with a different price. And each different material for that tap is spec'd with a different price. So all these combinations and how it can be built out.
+
+174
+00:23:19.250 --> 00:23:22.079
+Harsha Tummala: Those are basically sent as a CSV file to us.
+
+175
+00:23:23.010 --> 00:23:32.660
+Harsha Tummala: And that's how we build out the pricing. So, I would say, as long as you can create a product without the price component in there, it should be pretty much good.
+
+176
+00:23:33.280 --> 00:23:41.570
+Harsha Tummala: Because, ideally, I mean, we've been discussing that, I think since the beginning of the project, that prices are the only sensitive content here.
+
+177
+00:23:41.720 --> 00:23:48.259
+Harsha Tummala: And… I would say the price part is something we can work on it at a later phase.
+
+178
+00:23:48.460 --> 00:23:49.900
+Harsha Tummala: But,
+
+179
+00:23:50.040 --> 00:24:01.630
+Harsha Tummala: I mean, the project mainly is doing, like, a POC on how well can we actually streamline the whole process of getting the product data into our systems.
+
+180
+00:24:03.690 --> 00:24:04.670
+hrishikb@andrew.cmu.edu: Alright, okay.
+
+181
+00:24:04.800 --> 00:24:13.510
+Harsha Tummala: Yeah, because again, if you go down the price, price thing, it's… it's insane, like, just the way these products are built out is a full thing to understand itself.
+
+182
+00:24:15.340 --> 00:24:18.429
+Harsha Tummala: Because there's some parts which, honestly, I feel like…
+
+183
+00:24:18.920 --> 00:24:23.380
+Harsha Tummala: There are people in the company which have spent their entire lives to understand how they're priced out.
+
+184
+00:24:28.850 --> 00:24:33.599
+hrishikb@andrew.cmu.edu: Another thing, how much, do you have an approximation how much data
+
+185
+00:24:33.780 --> 00:24:37.230
+hrishikb@andrew.cmu.edu: does a catalog team ingest? Generally, like, how many…
+
+186
+00:24:37.400 --> 00:24:39.900
+hrishikb@andrew.cmu.edu: How much you're able to fill out?
+
+187
+00:24:40.910 --> 00:24:46.250
+Harsha Tummala: This varies, right? So right now, we have a certain set of suppliers, and it's…
+
+188
+00:24:46.550 --> 00:24:50.640
+Harsha Tummala: pretty much all stable at the given moment. But,
+
+189
+00:24:50.790 --> 00:24:58.560
+Harsha Tummala: There might be a case where, in the next 2 months, we have, like, 4 different new suppliers coming in, and that would mean there's a few
+
+190
+00:24:59.860 --> 00:25:03.100
+Harsha Tummala: From thousands to tens of thousands of products going in.
+
+191
+00:25:03.210 --> 00:25:04.349
+Harsha Tummala: Or even more.
+
+192
+00:25:04.610 --> 00:25:12.920
+Harsha Tummala: So, this, this all depends. And the thing is, sometimes one product might have, like, multiple variations to it, and…
+
+193
+00:25:13.070 --> 00:25:17.420
+Harsha Tummala: That might be a big task in itself.
+
+194
+00:25:18.530 --> 00:25:19.140
+hrishikb@andrew.cmu.edu: Okay.
+
+195
+00:25:21.830 --> 00:25:32.820
+Harsha Tummala: So just building a… so just, like, getting a product into the catalog in the right way, where, we mention the… mention all your variable specs in the correct manner is also a challenge in itself.
+
+196
+00:25:40.700 --> 00:25:42.059
+hrishikb@andrew.cmu.edu: That's what I have.
+
+197
+00:25:44.920 --> 00:25:49.160
+hrishikb@andrew.cmu.edu: Do you have any questions for us? I think we are over all our points.
+
+198
+00:25:51.000 --> 00:25:56.880
+Harsha Tummala: I mean, no specific question as such. I mean, it's just interesting to see what kind of approach you guys take and where you guys head every week.
+
+199
+00:25:57.650 --> 00:26:05.779
+Harsha Tummala: And I would say just keep at it, and let me know if there's any more, documents or data that I can send over that can help you guys out.
+
+200
+00:26:06.090 --> 00:26:11.999
+Harsha Tummala: And I'm actively working on it. Last week was just too much travel, so I just… it just took me forever to get you guys those documents.
+
+201
+00:26:13.390 --> 00:26:19.199
+hrishikb@andrew.cmu.edu: Yeah, we haven't gotten the Azure access, personal access as well.
+
+202
+00:26:19.400 --> 00:26:19.960
+Harsha Tummala: Oh shit, okay.
+
+203
+00:26:19.960 --> 00:26:20.819
+hrishikb@andrew.cmu.edu: Oh, beautiful.
+
+204
+00:26:21.370 --> 00:26:27.490
+hrishikb@andrew.cmu.edu: The blob storage, if you can give a small portion of it, or somehow give us access, so we can look at it.
+
+205
+00:26:27.490 --> 00:26:31.010
+Harsha Tummala: Let me do that. So, I can give you guys a small portion of the blob storage.
+
+206
+00:26:31.340 --> 00:26:39.810
+Harsha Tummala: And the Azure access is still not working USN. So what does that mean? You're, do you want to create, like, a separate,
+
+207
+00:26:40.140 --> 00:26:44.840
+Harsha Tummala: container instance and work on it, or do you want to support team on it? What's…
+
+208
+00:26:46.360 --> 00:26:56.859
+hrishikb@andrew.cmu.edu: No, I think last time… Just to experiment with the different Azure aspects. Yeah, I think David said he'd create one separate account, which is like a guest account, which doesn't have all the…
+
+209
+00:26:56.980 --> 00:27:00.269
+hrishikb@andrew.cmu.edu: Permissions, and he would give that to us, so we can look at it.
+
+210
+00:27:00.300 --> 00:27:01.180
+Harsha Tummala: Go ahead.
+
+211
+00:27:01.370 --> 00:27:07.809
+Harsha Tummala: So, I'll… I'll talk to David about it. So, he's been, out on some sort of,
+
+212
+00:27:08.380 --> 00:27:16.689
+Harsha Tummala: leadership retreat thingy, and he'll be back tomorrow from it. And, I'll add to him as soon as he's back from it.
+
+213
+00:27:19.850 --> 00:27:23.419
+hrishikb@andrew.cmu.edu: And I think, we still haven't gotten the cursor access.
+
+214
+00:27:24.360 --> 00:27:36.100
+Harsha Tummala: Yeah, true. Courser is still something we're working out, because we kind of, I think, asked the cursor support team on how we can figure it out, and they've been taking their own time getting back to us.
+
+215
+00:27:37.070 --> 00:27:37.830
+hrishikb@andrew.cmu.edu: Okay, okay.
+
+216
+00:27:37.830 --> 00:27:45.870
+Harsha Tummala: So, I would also say, like, in the meanwhile, do explore, like, if you guys can set anything up for yourselves in the meantime, mainly for ideation and other stuff.
+
+217
+00:27:46.560 --> 00:27:53.360
+Harsha Tummala: Especially when you guys have to work with Azure and other things, so… I'll get the Azure access done as soon as I can, but cursor, I'm not sure.
+
+218
+00:27:54.820 --> 00:27:55.720
+hrishikb@andrew.cmu.edu: Okay.
+
+219
+00:27:55.940 --> 00:28:00.750
+Harsha Tummala: Yeah, because it's so weird, like, the enterprise tier has these weird
+
+220
+00:28:01.120 --> 00:28:07.609
+Harsha Tummala: policies in place for Cursor, where you literally cannot do anything if a person's out of that enterprise and
+
+221
+00:28:07.950 --> 00:28:11.800
+Harsha Tummala: It just completely locks you in with that specific ending.
+
+222
+00:28:13.360 --> 00:28:16.070
+Harsha Tummala: And it needs to be connected to an outbox.
+
+223
+00:28:16.270 --> 00:28:30.760
+Harsha Tummala: Which is… which is just weird, because we kind of gave you an alias, right, initially? Like, Rishkej at, like, Outlook… at ePathservices.com, which was the alias, but it doesn't take an alias, because there's no outbox connected to it, and, like, it's…
+
+224
+00:28:32.840 --> 00:28:33.510
+hrishikb@andrew.cmu.edu: No.
+
+225
+00:28:34.540 --> 00:28:38.319
+Harsha Tummala: Yeah, it's… these are things that we've never really experienced.
+
+226
+00:28:43.870 --> 00:28:49.239
+hrishikb@andrew.cmu.edu: Okay. One last question. Did you leave Pittsburgh before the big snow, or after the big snow?
+
+227
+00:28:51.150 --> 00:28:53.639
+Harsha Tummala: I left Pittsburgh on, like,
+
+228
+00:28:53.770 --> 00:28:59.970
+Harsha Tummala: 8th of Feb, so I kind of, left exactly when it was getting slightly warm.
+
+229
+00:29:00.550 --> 00:29:01.090
+hrishikb@andrew.cmu.edu: Sorry.
+
+230
+00:29:02.440 --> 00:29:05.269
+Harsha Tummala: I'm glad, I'm glad to have missed all of this.
+
+231
+00:29:08.610 --> 00:29:09.350
+hrishikb@andrew.cmu.edu: Oh, very good.
+
+232
+00:29:09.350 --> 00:29:14.589
+Harsha Tummala: But India right now has been on the other end of the spectrum. It's been, like, high 80s in, like…
+
+233
+00:29:14.680 --> 00:29:16.040
+hrishikb@andrew.cmu.edu: Low 90s.
+
+234
+00:29:16.040 --> 00:29:17.220
+Harsha Tummala: Which is insane.
+
+235
+00:29:23.230 --> 00:29:24.739
+hrishikb@andrew.cmu.edu: Alright, thanks a lot, Hasha.
+
+236
+00:29:24.900 --> 00:29:26.600
+hrishikb@andrew.cmu.edu: What do you expect?
+
+237
+00:29:27.300 --> 00:29:33.329
+Harsha Tummala: Just let me know. Also, do send out a summary of this, so that Jake and everyone can go through it later, and…
+
+238
+00:29:33.550 --> 00:29:42.379
+Harsha Tummala: do reminders, keep bothering us with, like, the reminders for Azure Acts and cursor access, because that completely left our minds, I think, for the last one week.
+
+239
+00:29:43.560 --> 00:29:44.410
+hrishikb@andrew.cmu.edu: Okay.
+
+240
+00:29:44.410 --> 00:29:49.749
+Harsha Tummala: Yeah, just… just let us know, and I think… I think we should be on top of it. At least I'll be on top of it, no matter if…
+
+241
+00:29:50.220 --> 00:29:56.130
+Harsha Tummala: So, if you don't hear back until Monday, just ping me on Teams, and that should be good.
+
+242
+00:29:57.170 --> 00:29:58.679
+hrishikb@andrew.cmu.edu: Alright, I think we'll do that.
+
+243
+00:30:03.000 --> 00:30:03.670
+Harsha Tummala: Serious.
+
+244
+00:30:03.740 --> 00:30:06.680
+hrishikb@andrew.cmu.edu: Thank you. Have a good night.
+
diff --git a/transcripts/GMT20260402-180648_Recording.cc.vtt b/transcripts/GMT20260402-180648_Recording.cc.vtt
new file mode 100644
index 0000000..087787d
--- /dev/null
+++ b/transcripts/GMT20260402-180648_Recording.cc.vtt
@@ -0,0 +1,1247 @@
+WEBVTT
+
+00:00:00.000 --> 00:00:01.000
+Yes.
+
+00:00:01.000 --> 00:00:04.000
+Yeah, I'll just turn up my speaker.
+
+00:00:04.000 --> 00:00:09.000
+So… okay.
+
+00:00:09.000 --> 00:00:12.000
+Yep. So, the first item on the agenda is…
+
+00:00:12.000 --> 00:00:15.000
+For the timeline.
+
+00:00:15.000 --> 00:00:17.000
+Um…
+
+00:00:17.000 --> 00:00:00.000
+Yeah. Rishi, you're there, right?
+
+00:00:00.000 --> 00:00:22.000
+Yeah.
+
+00:00:22.000 --> 00:00:24.000
+Yes.
+
+00:00:24.000 --> 00:00:28.000
+Yeah. So…
+
+00:00:28.000 --> 00:00:34.000
+Yeah, sure, can you, uh, should I share my screen, or can you share your screen, uh, with the timeline?
+
+00:00:34.000 --> 00:00:37.000
+Um, I think you should, I'm having some issues with my system.
+
+00:00:37.000 --> 00:00:40.000
+Okay, never mind, I'll just do it then.
+
+00:00:40.000 --> 00:00:45.000
+So…
+
+00:00:45.000 --> 00:00:49.000
+Okay.
+
+00:00:49.000 --> 00:00:52.000
+So, is it visible, Harsha, David?
+
+00:00:52.000 --> 00:00:55.000
+Yeah, I can do.
+
+00:00:55.000 --> 00:00:58.000
+So, we just wanted to go over and, like, get your feedback.
+
+00:00:58.000 --> 00:01:01.000
+So, for now, what we have is, like,
+
+00:01:01.000 --> 00:01:07.000
+By April 15th, uh, for this, uh, this semester, we'll have, uh, the requirements
+
+00:01:07.000 --> 00:01:10.000
+Completed by then?
+
+00:01:10.000 --> 00:01:12.000
+And…
+
+00:01:12.000 --> 00:01:16.000
+I think we're making progress on this, so it should be doable by then, yeah.
+
+00:01:16.000 --> 00:01:19.000
+And for… by April 30th, uh…
+
+00:01:19.000 --> 00:01:21.000
+the SES system should be finalized, and…
+
+00:01:21.000 --> 00:01:25.000
+The risk document should also be completed.
+
+00:01:25.000 --> 00:01:32.000
+If the national requirements…
+
+00:01:32.000 --> 00:01:36.000
+Um, I think we can, uh, skip towards the end. This is, uh,
+
+00:01:36.000 --> 00:01:38.000
+bit more detail, I think you can add on.
+
+00:01:38.000 --> 00:01:41.000
+which parts you want to discuss.
+
+00:01:41.000 --> 00:01:45.000
+There's a high-level view on the end.
+
+00:01:45.000 --> 00:01:48.000
+Sure.
+
+00:01:48.000 --> 00:01:49.000
+Yeah, this one, right?
+
+00:01:49.000 --> 00:01:55.000
+Yeah, so, uh, we are targeting that towards the end of this month, we should have
+
+00:01:55.000 --> 00:01:58.000
+the basic requirements, risk process,
+
+00:01:58.000 --> 00:02:03.000
+like, not, uh, I'm not sure if you'll be able to have the complete architecture, because
+
+00:02:03.000 --> 00:02:06.000
+We need to still do some POCs.
+
+00:02:06.000 --> 00:02:12.000
+on the LLM versus ML front, so that we can finalize an approach and build the architecture around that.
+
+00:02:12.000 --> 00:02:15.000
+That is the plan for, um, early May.
+
+00:02:15.000 --> 00:02:18.000
+first couple of weeks of May, we should have that information with us.
+
+00:02:18.000 --> 00:02:23.000
+And, uh, towards the end of May, we plan to begin development.
+
+00:02:23.000 --> 00:02:26.000
+On all fronts, there'll be… I think we'll do it parallelly.
+
+00:02:26.000 --> 00:02:30.000
+Uh, we'll be working on the adjacent gateway along with…
+
+00:02:30.000 --> 00:02:34.000
+the ML or LLM components.
+
+00:02:34.000 --> 00:02:37.000
+Um, after that, uh, within the next couple of months,
+
+00:02:37.000 --> 00:02:42.000
+Till, uh, in June and July, we are expecting to be done with
+
+00:02:42.000 --> 00:02:44.000
+almost all of the development.
+
+00:02:44.000 --> 00:02:48.000
+We have a… we are targeting an aggressive approach.
+
+00:02:48.000 --> 00:02:51.000
+Uh, so that we have more time to cater to any…
+
+00:02:51.000 --> 00:02:53.000
+um, issue that we may have.
+
+00:02:53.000 --> 00:02:57.000
+These timelines might increase a little bit, because we have left around,
+
+00:02:57.000 --> 00:03:00.000
+two to three months of testing effort.
+
+00:03:00.000 --> 00:03:03.000
+Just in case we run into some issues.
+
+00:03:03.000 --> 00:03:07.000
+So, we are taking a optimistic approach here.
+
+00:03:07.000 --> 00:03:10.000
+And by the end of August,
+
+00:03:10.000 --> 00:03:15.000
+We are hoping that the individual modules are ready.
+
+00:03:15.000 --> 00:03:19.000
+for, um, to basically be integrated with each other.
+
+00:03:19.000 --> 00:03:22.000
+So that we have a complete system in place, and…
+
+00:03:22.000 --> 00:03:27.000
+In that time, you'll also be doing some sort of individual component testing.
+
+00:03:27.000 --> 00:03:30.000
+And we'll probably share the results with you.
+
+00:03:30.000 --> 00:03:34.000
+Um, on how we are able to process the files that we have.
+
+00:03:34.000 --> 00:03:39.000
+And, um, how the LLM components are doing the work.
+
+00:03:39.000 --> 00:03:43.000
+And after, uh, September in… I think in…
+
+00:03:43.000 --> 00:03:47.000
+October will probably be figuring out, uh,
+
+00:03:47.000 --> 00:03:55.000
+the exact test and the extent of the testing that we're following, and then the next month or two,
+
+00:03:55.000 --> 00:03:58.000
+will be, um, just…
+
+00:03:58.000 --> 00:04:00.000
+testing the entire system, and…
+
+00:04:00.000 --> 00:04:05.000
+looking at it, if you can incorporate some of the stretch goals that we have, the…
+
+00:04:05.000 --> 00:04:10.000
+Maybe the web scraping part, or some of the other things that…
+
+00:04:10.000 --> 00:04:16.000
+Would be good to have for you guys. We'll be trying to incorporate that, so we are leaving a couple of months to…
+
+00:04:16.000 --> 00:04:18.000
+be able to work on that as well.
+
+00:04:18.000 --> 00:04:24.000
+And in December, uh, we'll probably not be… we are hoping to be done with everything before that.
+
+00:04:24.000 --> 00:04:29.000
+And in December, it'll just be, um, any documentation or handover plans that we have.
+
+00:04:29.000 --> 00:04:32.000
+maybe demos, uh, whatever is required.
+
+00:04:32.000 --> 00:04:36.000
+That's the part we're leaving for end of November and…
+
+00:04:36.000 --> 00:04:39.000
+Uh, December.
+
+00:04:39.000 --> 00:04:47.000
+And if you want to go into details of any of these, um, it's mentioned above.
+
+00:04:47.000 --> 00:04:54.000
+Okay. bring the questions to my manager.
+
+00:04:54.000 --> 00:04:59.000
+Uh, we can share this doc with you, um, after the call.
+
+00:04:59.000 --> 00:05:02.000
+Yep, um…
+
+00:05:02.000 --> 00:05:05.000
+So, is it fine with both, uh…
+
+00:05:05.000 --> 00:05:10.000
+Uh, is it Vine Horscha and David, if you have anything, then we can make changes. If not,
+
+00:05:10.000 --> 00:05:14.000
+We can share this with you after the meeting.
+
+00:05:14.000 --> 00:05:29.000
+other than July is missing. But yeah, I think I think this makes sense. This is a decent timeline, or at least a decent breakdown of the units. I suspect some of those units might be larger than others, and.
+
+00:05:29.000 --> 00:05:46.000
+It's also possible that ePart's appetite would change, that things that might be stretch goals will work its way into core requirements. That seems to be just the way of the world. But we'll do our best to keep that from happening.
+
+00:05:46.000 --> 00:05:47.000
+Good.
+
+00:05:47.000 --> 00:05:48.000
+Yeah, I think this is a good 1st plan.
+
+00:05:48.000 --> 00:05:54.000
+Got it. I think July is not there just because, uh, summer semester ends by then, and then, you know, then that's just the gap.
+
+00:05:54.000 --> 00:05:57.000
+Um, yeah, July was, um…
+
+00:05:57.000 --> 00:06:05.000
+left out because of the entire development. There won't be any deliverable in that month, because we'll still be working on the…
+
+00:06:05.000 --> 00:06:11.000
+like, development of it, and we don't expect they'll be any deliverable in the month of July. It's like a two-month
+
+00:06:11.000 --> 00:06:14.000
+period in which we'll be developing everything.
+
+00:06:14.000 --> 00:06:20.000
+Okay, I must have misunderstood before. That's that's fine. That makes sense.
+
+00:06:20.000 --> 00:06:26.000
+So…
+
+00:06:26.000 --> 00:06:35.000
+you. And there's a few things I think I want to say from mine, but I think I think currently.
+
+00:06:35.000 --> 00:06:36.000
+Okay, um…
+
+00:06:36.000 --> 00:06:37.000
+Not regarding the bank.
+
+00:06:37.000 --> 00:06:42.000
+We also have the statement of work, I'll just share that as well.
+
+00:06:42.000 --> 00:07:00.000
+So…
+
+00:07:00.000 --> 00:07:06.000
+One second, uh… this is…
+
+00:07:06.000 --> 00:07:13.000
+So, we were, uh… is it visible, first of all?
+
+00:07:13.000 --> 00:07:14.000
+Okay. So, uh, we were, uh…
+
+00:07:14.000 --> 00:07:17.000
+Yeah, I'll help you.
+
+00:07:17.000 --> 00:07:20.000
+It was encouraged that we have a statement of work, just so that, uh…
+
+00:07:20.000 --> 00:07:23.000
+you know, we have a shared understanding with you.
+
+00:07:23.000 --> 00:07:30.000
+I'll be… this is the first version, and this is still, uh, I think it… a lot of changes might be required, but…
+
+00:07:30.000 --> 00:07:35.000
+I just wanted to share it with you guys, and uh…
+
+00:07:35.000 --> 00:07:38.000
+It just basically goes over what we understand of the project.
+
+00:07:38.000 --> 00:07:44.000
+And along with the scope that we think, and how we, you know,
+
+00:07:44.000 --> 00:07:48.000
+think about each of the activities that we're gonna have, so…
+
+00:07:48.000 --> 00:07:52.000
+It's a… it's a bit of a long document, but uh…
+
+00:07:52.000 --> 00:07:56.000
+And this is just basically all of our understanding and all of the things that we think
+
+00:07:56.000 --> 00:08:01.000
+are currently… we're gonna have to do. It also defines some of the…
+
+00:08:01.000 --> 00:08:10.000
+out-of-scope things, like, uh, right now, um, I just wanted to confirm again. So, generalized web scraping and all that, uh, stuff is not included, right?
+
+00:08:10.000 --> 00:08:15.000
+Because I have it here, but we can make changes if you want.
+
+00:08:15.000 --> 00:08:24.000
+Yeah, generally, that's great to know. Uh, if there's anything which is mentioned in the PDF document itself.
+
+00:08:24.000 --> 00:08:25.000
+Okay.
+
+00:08:25.000 --> 00:08:34.000
+Probably, but that's it. There's nothing. There's nothing out out of the bounds of it, which is you guys make a search and look for things and figure out what is correct. That kind of website is completely JavaScript.
+
+00:08:34.000 --> 00:08:38.000
+Yeah. So, we also have other things, uh…
+
+00:08:38.000 --> 00:08:39.000
+Such as, like, you know, that are…
+
+00:08:39.000 --> 00:08:42.000
+Okay.
+
+00:08:42.000 --> 00:08:46.000
+we will be working all the way up till PIMS, but the steps after that,
+
+00:08:46.000 --> 00:08:51.000
+is not something under our control, so I've also written these points here.
+
+00:08:51.000 --> 00:08:57.000
+And we, we, uh, the other things are just, like,
+
+00:08:57.000 --> 00:09:02.000
+Uh, what we think we are allowed to do, and the things that either are not under our control, or…
+
+00:09:02.000 --> 00:09:09.000
+things that, uh, we're not, uh, we do not expect that, uh, will come up or will have to do.
+
+00:09:09.000 --> 00:09:11.000
+So, I'll be set…
+
+00:09:11.000 --> 00:09:14.000
+Yep.
+
+00:09:14.000 --> 00:09:17.000
+Oh, I thought someone was asking a question.
+
+00:09:17.000 --> 00:09:19.000
+So…
+
+00:09:19.000 --> 00:09:24.000
+Yeah, so it, uh, there's also the project documentation deliverables, so…
+
+00:09:24.000 --> 00:09:26.000
+All of these things.
+
+00:09:26.000 --> 00:09:32.000
+And finally, I would just like to…
+
+00:09:32.000 --> 00:09:35.000
+go to…
+
+00:09:35.000 --> 00:09:38.000
+Yep. So…
+
+00:09:38.000 --> 00:09:41.000
+The last thing is just all the…
+
+00:09:41.000 --> 00:09:44.000
+general assumptions we have.
+
+00:09:44.000 --> 00:09:49.000
+And, you know, who we have met, uh, what tools we're expected to use.
+
+00:09:49.000 --> 00:09:52.000
+that, you know, we make sure that the…
+
+00:09:52.000 --> 00:09:54.000
+the constraints, like,
+
+00:09:54.000 --> 00:09:58.000
+not using any public LLM or, you know, not…
+
+00:09:58.000 --> 00:10:03.000
+using the company data on anything other than company resources.
+
+00:10:03.000 --> 00:10:06.000
+So that… that… all that stuff is here.
+
+00:10:06.000 --> 00:10:11.000
+And we, in general, I just wanted to show it to you guys so that, you know,
+
+00:10:11.000 --> 00:10:18.000
+that this, you know, statement is, uh, here is much more clear to everyone.
+
+00:10:18.000 --> 00:10:23.000
+And that, you know, if there are any gaps in our understanding that you might… you guys…
+
+00:10:23.000 --> 00:10:30.000
+might, uh, you know, say that, okay, this is actually different here, and we can go ahead and make those changes.
+
+00:10:30.000 --> 00:10:31.000
+So, I'll also…
+
+00:10:31.000 --> 00:10:34.000
+Yeah, sure. I mean hybrids. Sorry, my back. Go ahead.
+
+00:10:34.000 --> 00:10:41.000
+It's just, I'll also send this document over, uh, you guys can look at it, and if there's any
+
+00:10:41.000 --> 00:10:47.000
+Mistakes that you… or, like, misunderstandings there, that then, yeah, we'll go ahead and change it.
+
+00:10:47.000 --> 00:10:57.000
+Yeah, I mean, I can go through it in detail and then send any changes or something which doesn't align correctly to you guys. But that shouldn't be that.
+
+00:10:57.000 --> 00:11:06.000
+Yep. So, yeah, this is just an initial first, first version, so yeah, we'll, we'll definitely update it as we go along.
+
+00:11:06.000 --> 00:11:07.000
+Yeah.
+
+00:11:07.000 --> 00:11:12.000
+So, uh, yeah. I'll send this over.
+
+00:11:12.000 --> 00:11:18.000
+Along with the timeline.
+
+00:11:18.000 --> 00:11:21.000
+The other thing is…
+
+00:11:21.000 --> 00:11:22.000
+Yep, um…
+
+00:11:22.000 --> 00:11:25.000
+Uh, Liu is here with us, it's just…
+
+00:11:25.000 --> 00:11:29.000
+Uh, he had some specific questions. He has been looking through the ML portion.
+
+00:11:29.000 --> 00:11:32.000
+And, uh, he wanted to basically
+
+00:11:32.000 --> 00:11:34.000
+you know, uh…
+
+00:11:34.000 --> 00:11:37.000
+clarify what all he needed, and, you know,
+
+00:11:37.000 --> 00:11:41.000
+all the stuff, and why he needs it, and, you know, his future steps. So, leave a few…
+
+00:11:41.000 --> 00:11:45.000
+want to, like, share your document and, like, just…
+
+00:11:45.000 --> 00:11:48.000
+You can basically go ahead and, like…
+
+00:11:48.000 --> 00:11:51.000
+Uh…
+
+00:11:51.000 --> 00:11:53.000
+Send a row to third.
+
+00:11:53.000 --> 00:11:55.000
+Just… what is it in there.
+
+00:11:55.000 --> 00:11:58.000
+Because, uh, data needs.
+
+00:11:58.000 --> 00:12:01.000
+Okay, so…
+
+00:12:01.000 --> 00:12:05.000
+Yeah. Basically, uh, Liu was saying that, uh,
+
+00:12:05.000 --> 00:12:09.000
+Uh, he's looked into the, you know, different options, and…
+
+00:12:09.000 --> 00:12:13.000
+He's done some calculations on how many entries he would need.
+
+00:12:13.000 --> 00:12:15.000
+And so…
+
+00:12:15.000 --> 00:12:19.000
+After he's made initial document and, like, all the estimates,
+
+00:12:19.000 --> 00:12:25.000
+And he sent a… he's basically sent an email with all the requirements for the ML portion.
+
+00:12:25.000 --> 00:12:29.000
+And, uh, he needs that, like, uh…
+
+00:12:29.000 --> 00:12:35.000
+I can go further in the ML prototyping, so it would be really helpful if, uh, you know,
+
+00:12:35.000 --> 00:12:38.000
+Uh, to proceed, if you could get there.
+
+00:12:38.000 --> 00:12:56.000
+Yeah, 100%. I was. I was just going to say, uh, so… I've only taken a look at that email, and I've started collecting the data for that as well. So ideally, I was saying that either end of today or Monday, I should get in SME. Is there a timeline from your end that you expect me to give you the year back?
+
+00:12:56.000 --> 00:13:01.000
+Uh, no, it's… I think Monday's fine, right? Yep, so, yeah.
+
+00:13:01.000 --> 00:13:05.000
+Um, in the meantime, he's just, uh, gonna, like, you know…
+
+00:13:05.000 --> 00:13:09.000
+Work on the other stuff for the Albert types.
+
+00:13:09.000 --> 00:13:12.000
+So… I think…
+
+00:13:12.000 --> 00:13:16.000
+That's it, in the sense that…
+
+00:13:16.000 --> 00:13:22.000
+we… we, like, we were working on other stuff as well with…
+
+00:13:22.000 --> 00:13:25.000
+Uh, but that's more related to project management.
+
+00:13:25.000 --> 00:13:29.000
+I did have a question, Hutchin, so…
+
+00:13:29.000 --> 00:13:31.000
+Next week, we have the…
+
+00:13:31.000 --> 00:13:33.000
+Carnival, so…
+
+00:13:33.000 --> 00:13:38.000
+I think the building will, uh, will be shut down on Thursday?
+
+00:13:38.000 --> 00:13:43.000
+Okay, okay. I'm not sure.
+
+00:13:43.000 --> 00:14:04.000
+So, but, uh, is there… should we reschedule the time for it? I'm not certain about it, so I just wanted to bring it up.
+
+00:14:04.000 --> 00:14:05.000
+Yeah.
+
+00:14:05.000 --> 00:14:09.000
+I mean, uh, you guys will have a vacation for, like, the 2 or 3 day carbon period, right? So, ideally, I would say, if you want to have a meeting next week, you should do it to when you guys are actually in campus and actually have a working day. Otherwise, we can do it to be after today. That's fine.
+
+00:14:09.000 --> 00:14:11.000
+Um, Ashuta, Rishi, um…
+
+00:14:11.000 --> 00:14:16.000
+I would also, like, uh, what do you guys think?
+
+00:14:16.000 --> 00:14:22.000
+Uh, I probably might not be available for those two days, Thursday and Friday.
+
+00:14:22.000 --> 00:14:23.000
+Uh, Rishi?
+
+00:14:23.000 --> 00:14:26.000
+Yeah, I think I might probably be available on…
+
+00:14:26.000 --> 00:14:33.000
+Thursday… yeah, I think I can make Thursday, but I'll not be available for the weekend and Friday, right?
+
+00:14:33.000 --> 00:14:35.000
+Right. So, um…
+
+00:14:35.000 --> 00:14:38.000
+Yeah, we wanted to discuss PIMs and the like.
+
+00:14:38.000 --> 00:14:42.000
+go into detail regarding that, uh, Harsha?
+
+00:14:42.000 --> 00:14:45.000
+So, would it be alright if we came to your office?
+
+00:14:45.000 --> 00:14:52.000
+Actually, first of all, is it alright, Shruta, Rishi, Liu, what do you think?
+
+00:14:52.000 --> 00:14:57.000
+Yeah, uh, anytime, like, Monday, Tuesday, or Wednesday works for me.
+
+00:14:57.000 --> 00:14:58.000
+Yeah.
+
+00:14:58.000 --> 00:14:59.000
+Rasheen?
+
+00:14:59.000 --> 00:15:01.000
+Yes, uh, vote for me as well.
+
+00:15:01.000 --> 00:15:04.000
+Okay, okay. Yeah, you, uh…
+
+00:15:04.000 --> 00:15:09.000
+Your voice is a bit low. I'll just increase my speaker.
+
+00:15:09.000 --> 00:15:11.000
+Uh, okay, um…
+
+00:15:11.000 --> 00:15:21.000
+Which… which day would work for you, Hasha? David? We would really like to, I guess, come over and ask questions.
+
+00:15:21.000 --> 00:15:22.000
+Okay. Yeah.
+
+00:15:22.000 --> 00:15:25.000
+I would say G would be the poison for pairs for almost most of them. So I would say global message on teams.
+
+00:15:25.000 --> 00:15:30.000
+Yeah.
+
+00:15:30.000 --> 00:15:31.000
+Okay.
+
+00:15:31.000 --> 00:15:44.000
+And just, you give us your availability on when it's the most convenient for you guys. And Jake will just let you guys know on, like, what slot works for him. My guess is that it will be Wednesday. Yeah. He's probably gonna be in Erie Monday, Tuesday. Oh, yeah, so next Wednesday might be it.
+
+00:15:44.000 --> 00:15:45.000
+Yeah. Um, okay.
+
+00:15:45.000 --> 00:15:47.000
+But let's see.
+
+00:15:47.000 --> 00:15:50.000
+So, that sounds good to me.
+
+00:15:50.000 --> 00:15:56.000
+Just to confirm, Leo, Rishi, Ashutantha, is that fine with you guys?
+
+00:15:56.000 --> 00:15:57.000
+Okay.
+
+00:15:57.000 --> 00:15:58.000
+Yeah, let's, uh, check the calendar once and then confirm.
+
+00:15:58.000 --> 00:16:00.000
+Yep.
+
+00:16:00.000 --> 00:16:05.000
+Yeah, yeah, we'll definitely send over the availability times and, like, coordinate with you.
+
+00:16:05.000 --> 00:16:08.000
+With you guys, yeah.
+
+00:16:08.000 --> 00:16:09.000
+Uh…
+
+00:16:09.000 --> 00:16:15.000
+And… I mean, I had a few things to discuss, but you guys go on. We can do with that.
+
+00:16:15.000 --> 00:16:20.000
+No, no, please go ahead. I think that that was a lot of the high priority.
+
+00:16:20.000 --> 00:16:24.000
+Okay. So a few things as in one is.
+
+00:16:24.000 --> 00:16:38.000
+With respect to Liu's email, there is one thing where he mentions about having the documents attributed to the exact products that are mapped to it.
+
+00:16:38.000 --> 00:16:50.000
+So what I'll do is I'll be sending you guys the document links from BunnyCDN. So all you can do is, you can use that link to get the exact document that you want. That is attached there.
+
+00:16:50.000 --> 00:16:51.000
+Yeah.
+
+00:16:51.000 --> 00:16:57.000
+So does that sound okay? Or do you want me to send the actual document itself embedded?
+
+00:16:57.000 --> 00:17:03.000
+Luke?
+
+00:17:03.000 --> 00:17:06.000
+You can send me a link? So, he…
+
+00:17:06.000 --> 00:17:14.000
+camera this way so we can… Yeah, go ahead.
+
+00:17:14.000 --> 00:17:16.000
+So…
+
+00:17:16.000 --> 00:17:19.000
+works great.
+
+00:17:19.000 --> 00:17:22.000
+Okay, I gotta check the landing.
+
+00:17:22.000 --> 00:17:26.000
+Um, Harsha, uh, could you repeat the two options that you had? One was sending over the link, and the other one was the actual document?
+
+00:17:26.000 --> 00:17:42.000
+Yeah. So so what is mentioned in the email is that he wanted the raw supplier text extracted from the PDF or the spec sheets, or the CSV sent directly attributed to the products itself and sent over.
+
+00:17:42.000 --> 00:17:53.000
+Um, what I can actually do is, uh… Send a link instead of the extracted data from the PDFs.
+
+00:17:53.000 --> 00:18:06.000
+So that link will contain the exact document that connects to the project's website. So it'll just be the document link products that are later on this document and so on.
+
+00:18:06.000 --> 00:18:08.000
+Okay, um, I think…
+
+00:18:08.000 --> 00:18:11.000
+That should be fine.
+
+00:18:11.000 --> 00:18:12.000
+Ashatan, do you…
+
+00:18:12.000 --> 00:18:13.000
+Okay.
+
+00:18:13.000 --> 00:18:19.000
+Like, uh, like, we need it majorly for the input for our LLM model.
+
+00:18:19.000 --> 00:18:20.000
+So that we can actually see how it is working and what it can do.
+
+00:18:20.000 --> 00:18:23.000
+Sure.
+
+00:18:23.000 --> 00:18:32.000
+I think that should be fine, but I think, uh, we will require, um, a good chunk of data in that respect. Like, what we are giving it as input, and
+
+00:18:32.000 --> 00:18:37.000
+how the catalog team is refining it, and what the actual output is towards the end.
+
+00:18:37.000 --> 00:18:38.000
+So that we can…
+
+00:18:38.000 --> 00:18:39.000
+Mm-hmm.
+
+00:18:39.000 --> 00:18:40.000
+Yep.
+
+00:18:40.000 --> 00:18:48.000
+So, I'll send over as many as I can, so don't worry about that. It'll be at least more than a thousand.
+
+00:18:48.000 --> 00:19:03.000
+And another question, actually not a question. Another, I think a thing that I think we discussed in the earlier days of the project, but we kind of, I think we've forgotten about it, and I think we've forgotten to mention that to you guys about it too, which is.
+
+00:19:03.000 --> 00:19:20.000
+Initially, you were talking about the data standards, right? On how we could standardize the data. And I think in our previous meetings, we kind of, um… I established that we will be using the attributes from the product types which are in the tables that I provided you guys with.
+
+00:19:20.000 --> 00:19:32.000
+And that's how we'll be doing it. But the thing is, we kind of identified, like, 3 data standards that are already there in the industry, and a pretty much standardized, and no matter what.
+
+00:19:32.000 --> 00:19:37.000
+type of product you're looking at, you'll find the exact type of attributes that are needed for the product.
+
+00:19:37.000 --> 00:19:47.000
+So the 3 standards are like and UNSPSE. I'll ping this to you guys. But.
+
+00:19:47.000 --> 00:19:58.000
+These are basically open source standards where all the product types and attributes for these product types. It's all available on the website.
+
+00:19:58.000 --> 00:20:06.000
+Ideally, you can use this data itself. To create the initial set of like.
+
+00:20:06.000 --> 00:20:18.000
+filtering for, like, how the product should look like, or how the… Um, or how a specific GAN video growth data approach should look like?
+
+00:20:18.000 --> 00:20:27.000
+Um, I think that works out. What do you think?
+
+00:20:27.000 --> 00:20:33.000
+And…
+
+00:20:33.000 --> 00:20:36.000
+Yes.
+
+00:20:36.000 --> 00:20:38.000
+So, yeah, um…
+
+00:20:38.000 --> 00:20:43.000
+Yeah, Lee is also saying that he'll just go through it, and then, uh…
+
+00:20:43.000 --> 00:20:48.000
+Uh, you know, go for, uh, like, if there are anything else, and he can, I guess, inform you again.
+
+00:20:48.000 --> 00:20:56.000
+Yeah, and another great thing about this, these open standards is that all of these standards have documentation which.
+
+00:20:56.000 --> 00:21:12.000
+tells us that what these specific attributes, which are called in this standard map to in the other standard that is mentioned. So, like, how does Eton's attributes identified map to the E-class attributes? It's all clearly defined.
+
+00:21:12.000 --> 00:21:18.000
+And I mean, like a benefit of this would just be that.
+
+00:21:18.000 --> 00:21:24.000
+a document… sorry, a product attributed in a certain standard can always be mapped to another standard as well.
+
+00:21:24.000 --> 00:21:38.000
+And this would mean that that one product can be identified according to 3 different standards, because they're all interrelatable, and they they have like the language defined on like what are these different.
+
+00:21:38.000 --> 00:21:42.000
+What are the various possibilities for these attributes to be called in the industry?
+
+00:21:42.000 --> 00:21:44.000
+Yeah.
+
+00:21:44.000 --> 00:21:49.000
+So symptoms, everything I highlighted pretty clearly in this.
+
+00:21:49.000 --> 00:21:50.000
+Yeah, I…
+
+00:21:50.000 --> 00:21:54.000
+Or could you, uh, explain a bit about what these standards actually are? Um, I'm not very clear on the…
+
+00:21:54.000 --> 00:22:00.000
+Uh, you might… yeah.
+
+00:22:00.000 --> 00:22:01.000
+Right.
+
+00:22:01.000 --> 00:22:10.000
+So right now we have product types, categories and attributes and so these product types and attributes are pretty much what helps like our parent company has kind of come up with.
+
+00:22:10.000 --> 00:22:19.000
+And these are like just… just things they've identified over their years of working with these products.
+
+00:22:19.000 --> 00:22:21.000
+Mm-hmm.
+
+00:22:21.000 --> 00:22:33.000
+And what these standards actually do is instead of relying on algae controls for like how they call in certain things like how they map certain attributes or what they call certain attributes.
+
+00:22:33.000 --> 00:22:41.000
+Uh, it just goes with the investing standard of, like, what these attributes are called and what sort of terms are used for these avenues and product types and anything.
+
+00:22:41.000 --> 00:22:49.000
+Okay, um, so, um, is there a chance that there is a mismatch between what ALTS uses and what the actual
+
+00:22:49.000 --> 00:22:55.000
+Um, like, documentation of Shilvan users.
+
+00:22:55.000 --> 00:23:12.000
+That could be. So I would say we don't even have to go by standards right? Ideally, I would say, if we can map to these general industry standards, we should be more than happy, because in the end Alps is just one person who decides to.
+
+00:23:12.000 --> 00:23:14.000
+I'll call these things a certain way, and that's what it is.
+
+00:23:14.000 --> 00:23:16.000
+Right.
+
+00:23:16.000 --> 00:23:27.000
+This might also simplify, like, all the different lingo that app specifically uses, because a lot of these documents, the Pdfs for these specific products and.
+
+00:23:27.000 --> 00:23:34.000
+all of these all of these specification documents. They kind of go by the general industry standards themselves.
+
+00:23:34.000 --> 00:23:40.000
+And to actually map it to Alps is a more difficult task compared to mapping it to these open standards.
+
+00:23:40.000 --> 00:23:50.000
+And these open standards kind of give us more attributes that we can expect for a product type, and um… just… they just provide a more robust email identifying them.
+
+00:23:50.000 --> 00:23:56.000
+Okay, um, but in, uh, doing that, won't it also require a rework on the PEM side?
+
+00:23:56.000 --> 00:24:02.000
+To map those attributes, the values, and so it can be used downstream.
+
+00:24:02.000 --> 00:24:08.000
+I mean, so so since these are already like predefined tables.
+
+00:24:08.000 --> 00:24:14.000
+Technically, you won't have to… you won't be expected to create these.
+
+00:24:14.000 --> 00:24:21.000
+the specific attributes and prototypes or terms. As long as you can map the product to these attributes, and.
+
+00:24:21.000 --> 00:24:29.000
+product types, we should be good. For a little bit of things, that's something we look into on, like, how we should look like there.
+
+00:24:29.000 --> 00:24:37.000
+Okay. I think for now, we were relying on the schema you provided as source of truth, but I think we can compare it with the standards.
+
+00:24:37.000 --> 00:24:38.000
+Yeah.
+
+00:24:38.000 --> 00:24:42.000
+That you mentioned, and see if and where there's an overlap or a difference, and we can then work out.
+
+00:24:42.000 --> 00:24:49.000
+Yeah. So, because I was personally comparing it to the 3.
+
+00:24:49.000 --> 00:24:52.000
+to the 3-way ball, I have that we kind of have been looking at.
+
+00:24:52.000 --> 00:25:09.000
+And for that example, these standards are actually way more clearer. So, for example, if we call flow rate flow rate in one standard, it'll also give us like synonyms which are used for flow rate, which is some sort of.
+
+00:25:09.000 --> 00:25:20.000
+water flow rate or or liquid flow rate or some sort of weird term, which is like the actuator flow value or something like that.
+
+00:25:20.000 --> 00:25:23.000
+And all of these settings are defined in these standard threaders.
+
+00:25:23.000 --> 00:25:34.000
+So there's there was an initial question too, right? In one of our earlier meetings where how do we look at the synonyms are like, what if something is called something else in document? How do we understand that this match to a specific attribute?
+
+00:25:34.000 --> 00:25:36.000
+Yeah.
+
+00:25:36.000 --> 00:25:39.000
+These standards actually clear those 10 questions up as well.
+
+00:25:39.000 --> 00:25:45.000
+Okay. I think, uh, probably we can go through the standards and do a comparison, then
+
+00:25:45.000 --> 00:25:47.000
+We'll probably need, um…
+
+00:25:47.000 --> 00:25:51.000
+the verification from the catalog team, if you're doing it in the right way.
+
+00:25:51.000 --> 00:25:52.000
+Um, we can probably create a document
+
+00:25:52.000 --> 00:25:54.000
+Yeah.
+
+00:25:54.000 --> 00:26:02.000
+Um, like, layering the differences that we have and what we're following, and we can get it verified by you guys once.
+
+00:26:02.000 --> 00:26:03.000
+Okay.
+
+00:26:03.000 --> 00:26:16.000
+Yeah. But that's it. Is there any problem with whatever what I just said? Any anything unclear, or anything that seems like it's out of scope, or anything that seems like this is not what they initially then do.
+
+00:26:16.000 --> 00:26:44.000
+Uh, just, I mean, uh, I understood what you're trying to say about the standardization part, so it's like, uh, okay, I have worked similar to this, like on the telemetry side of it, like, uh, open telemetry and then all of it. So it's basically, you want to be vendor agnostic and then standardize everything, right?
+
+00:26:44.000 --> 00:26:45.000
+Mm-hmm.
+
+00:26:45.000 --> 00:26:51.000
+So, ah. If you are sure that standardizing according to those rules won't cause any problem like in case you want to integrate with Alps or, you know, the EPATS, uh… So then I think we are good. Uh, should be actually a lot more easier, yeah.
+
+00:26:51.000 --> 00:26:56.000
+Yeah.
+
+00:26:56.000 --> 00:27:02.000
+Yeah, this this again, like another reason for like looking at these standards was also making your life easier, right? Because technically the app standards are.
+
+00:27:02.000 --> 00:27:06.000
+Okay.
+
+00:27:06.000 --> 00:27:10.000
+very… make you guys very dependent on what the ad guys are actually telling you guys to do.
+
+00:27:10.000 --> 00:27:12.000
+Correct. Yeah.
+
+00:27:12.000 --> 00:27:27.000
+And we also heard from Brian that these these attributes are basically decided by like a team who's in charge for those specific products, and they decide that, oh, these attributes are relevant, then we're going to show these attributes on the website, and that's how it works.
+
+00:27:27.000 --> 00:27:43.000
+Yeah. So this this is just making things simple, I think, but each of us. And I think as EPA said, like becomes its own thing, these standards will also help us like, just show that our data is more robust than.
+
+00:27:43.000 --> 00:27:52.000
+what did I injection? Because a certain person knows what item is, but a certain person won't know what actually is going by.
+
+00:27:52.000 --> 00:27:53.000
+Yeah.
+
+00:27:53.000 --> 00:27:57.000
+And as good as that, yeah.
+
+00:27:57.000 --> 00:28:03.000
+Um, yeah. I would have to, like, look through and, like, compare Harsha, like…
+
+00:28:03.000 --> 00:28:06.000
+To see the differences, but I think this would help us.
+
+00:28:06.000 --> 00:28:07.000
+Uh, but yeah.
+
+00:28:07.000 --> 00:28:17.000
+Yeah. Yeah, 100%. Just go through it. We can discuss this the week after, or the week after that, and you guys take your time and just understand what these standards are and how do you plan.
+
+00:28:17.000 --> 00:28:18.000
+I think the only major change would be that, uh,
+
+00:28:18.000 --> 00:28:21.000
+I if you need.
+
+00:28:21.000 --> 00:28:25.000
+the source of truth is kind of a change, but I think changing that would help us in the long run.
+
+00:28:25.000 --> 00:28:26.000
+Yeah. Yeah.
+
+00:28:26.000 --> 00:28:31.000
+If you have standardized it, so I think it's a good change.
+
+00:28:31.000 --> 00:28:32.000
+And this might, like, prevent…
+
+00:28:32.000 --> 00:28:35.000
+Yeah.
+
+00:28:35.000 --> 00:28:42.000
+This might actually prevent rework when you're actually expanding, so yeah, that… I think that makes sense.
+
+00:28:42.000 --> 00:28:43.000
+Uh… let me just go through.
+
+00:28:43.000 --> 00:28:45.000
+Yes. Yeah.
+
+00:28:45.000 --> 00:28:48.000
+Um, other than that, uh…
+
+00:28:48.000 --> 00:28:53.000
+I don't think, like, these, like, these were the things I wanted to discuss.
+
+00:28:53.000 --> 00:28:58.000
+As for the available timings and all these documents, I'll send them over.
+
+00:28:58.000 --> 00:29:05.000
+And, uh, whichever time works for you, we can, uh, you know, agree on something, and then…
+
+00:29:05.000 --> 00:29:12.000
+you know, we'll gather some questions and ask you regarding them. It's mostly BIMS, but there might be something else as well.
+
+00:29:12.000 --> 00:29:17.000
+I'll drop these standards on the team chat after the meeting.
+
+00:29:17.000 --> 00:29:18.000
+Yeah.
+
+00:29:18.000 --> 00:29:25.000
+Yeah, I'll just, uh, drop a message to Jake as well on Teams, and I'll just send him an email, both of them.
+
+00:29:25.000 --> 00:29:27.000
+Yeah.
+
+00:29:27.000 --> 00:29:32.000
+Uh, I think that's it, but, um, I'll just invite, like, Leo, Ashta,
+
+00:29:32.000 --> 00:29:37.000
+Is she this?
+
+00:29:37.000 --> 00:29:39.000
+I think…
+
+00:29:39.000 --> 00:29:40.000
+Okay.
+
+00:29:40.000 --> 00:29:41.000
+Same for me.
+
+00:29:41.000 --> 00:29:42.000
+No, I don't have anything to add.
+
+00:29:42.000 --> 00:29:48.000
+Blue.
+
+00:29:48.000 --> 00:29:55.000
+We can't hear you.
+
+00:29:55.000 --> 00:29:59.000
+Uh, he… Lee was talking about the Cloud code, uh…
+
+00:29:59.000 --> 00:30:04.000
+So, it's… he's saying that it lets… it hits the…
+
+00:30:04.000 --> 00:30:14.000
+limit… the token limit for that.
+
+00:30:14.000 --> 00:30:19.000
+It kind of becomes a big toe head right in general.
+
+00:30:19.000 --> 00:30:36.000
+I I think the main reason for that is.
+
+00:30:36.000 --> 00:30:37.000
+Yeah.
+
+00:30:37.000 --> 00:30:40.000
+I don't know. You can technically use an unlimited credits with cloud code at this point, and like we ourselves have personally been struggling with like keeping that in like a specific budget could stay. So hence the limited post. If that limit is too restrictive, we can.
+
+00:30:40.000 --> 00:30:42.000
+show you about it.
+
+00:30:42.000 --> 00:30:47.000
+Yep, uh, I think that's another topic that maybe we'll discuss more in depth, but…
+
+00:30:47.000 --> 00:30:50.000
+I've not personally, I think.
+
+00:30:50.000 --> 00:30:55.000
+gone through those same limitations, so I'm not so sure.
+
+00:30:55.000 --> 00:30:56.000
+But…
+
+00:30:56.000 --> 00:30:57.000
+I think, uh, bumping up the limits in a short term would be good, because
+
+00:30:57.000 --> 00:30:59.000
+Yeah.
+
+00:30:59.000 --> 00:31:06.000
+Currently, Claude is facing some, uh, token issues, like, even small quadies are using up much more tokens than it usually should.
+
+00:31:06.000 --> 00:31:08.000
+Um, hopefully I get it fixed soon enough, and we can maybe go back to the earlier tokens.
+
+00:31:08.000 --> 00:31:11.000
+And…
+
+00:31:11.000 --> 00:31:14.000
+But right now, even a small query just…
+
+00:31:14.000 --> 00:31:15.000
+like, uh, keeps on hitting different tokens on it, it's just…
+
+00:31:15.000 --> 00:31:17.000
+Yeah.
+
+00:31:17.000 --> 00:31:20.000
+I can download memory very quick.
+
+00:31:20.000 --> 00:31:36.000
+Yeah, there's a lot of I mean, in general, there's a lot of nuances to these tools too, right? Certain tools are very, very good with their limits. Certain tools, even though you end up being exhausted in the ones, they still run out of limits all the time.
+
+00:31:36.000 --> 00:31:47.000
+That's kind of the case with Claude Go, too. If you ever tend to use any opus model, it just runs out of limits in like 10 to 15 min.
+
+00:31:47.000 --> 00:31:49.000
+Yeah.
+
+00:31:49.000 --> 00:31:50.000
+Yeah, um…
+
+00:31:50.000 --> 00:31:51.000
+And you'll just be left wondering what happened.
+
+00:31:51.000 --> 00:31:54.000
+I've also kind of hit the limits with the Opus model, I guess.
+
+00:31:54.000 --> 00:31:58.000
+But that's a separate, uh, GitHub.
+
+00:31:58.000 --> 00:31:59.000
+student account, also, that's different.
+
+00:31:59.000 --> 00:32:00.000
+Yeah.
+
+00:32:00.000 --> 00:32:02.000
+Yeah.
+
+00:32:02.000 --> 00:32:09.000
+But yeah, I guess I'll gather some feedback, and we can just discuss this.
+
+00:32:09.000 --> 00:32:11.000
+Uh, maybe work out a solution.
+
+00:32:11.000 --> 00:32:12.000
+That's it, though. I think.
+
+00:32:12.000 --> 00:32:17.000
+Here.
+
+00:32:17.000 --> 00:32:22.000
+Yeah. So…
+
+00:32:22.000 --> 00:32:25.000
+Thanks, Hosha. Thanks, Jay. Thanks, David.
+
+00:32:25.000 --> 00:32:35.000
+Thanks a lot, guys. Very looking forward to a lot more lot more work with you guys, and hopefully.
+
+00:32:35.000 --> 00:32:36.000
+Yeah.
+
+00:32:36.000 --> 00:32:39.000
+It'll all kind of come there first. Yeah.
+
+00:32:39.000 --> 00:32:42.000
+Okay.
+
+00:32:42.000 --> 00:32:43.000
+Right?
+
+00:32:43.000 --> 00:32:44.000
+Bye, guys.
+
+00:32:44.000 --> 00:32:45.000
+I see you said.
+
+00:32:45.000 --> 00:32:50.000
+Bye-bye.
+
+00:32:50.000 --> 00:32:57.000
+Recording.
+
diff --git a/transcripts/GMT20260402-180648_Recording.transcript.vtt b/transcripts/GMT20260402-180648_Recording.transcript.vtt
new file mode 100644
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@@ -0,0 +1,1294 @@
+WEBVTT
+
+1
+00:00:01.150 --> 00:00:03.759
+jaivards@andrew.cmu.edu: Yeah, I'll just turn up my speaker.
+
+2
+00:00:04.900 --> 00:00:06.490
+jaivards@andrew.cmu.edu: So… okay.
+
+3
+00:00:09.250 --> 00:00:14.190
+jaivards@andrew.cmu.edu: Yep. So, the first item on the agenda is for the timeline.
+
+4
+00:00:15.060 --> 00:00:18.260
+jaivards@andrew.cmu.edu: Yeah.
+
+5
+00:00:19.300 --> 00:00:20.980
+jaivards@andrew.cmu.edu: Rishi, you're there, right?
+
+6
+00:00:22.950 --> 00:00:23.760
+hrishikb@andrew.cmu.edu: Yes.
+
+7
+00:00:25.080 --> 00:00:25.620
+jaivards@andrew.cmu.edu: Yeah.
+
+8
+00:00:26.530 --> 00:00:27.560
+jaivards@andrew.cmu.edu: So…
+
+9
+00:00:28.640 --> 00:00:34.540
+jaivards@andrew.cmu.edu: Yeah, sure, can you, should I share my screen, or can you share your screen, with the timeline?
+
+10
+00:00:35.280 --> 00:00:37.949
+hrishikb@andrew.cmu.edu: I think you shared I'm having some issues with my system.
+
+11
+00:00:38.240 --> 00:00:39.999
+jaivards@andrew.cmu.edu: Okay, nevermind, I'll just do it then.
+
+12
+00:00:41.090 --> 00:00:42.030
+jaivards@andrew.cmu.edu: So…
+
+13
+00:00:46.090 --> 00:00:46.970
+jaivards@andrew.cmu.edu: Okay.
+
+14
+00:00:49.480 --> 00:00:51.620
+jaivards@andrew.cmu.edu: So, is it visible, Harsha, dude?
+
+15
+00:00:51.620 --> 00:00:53.270
+Harsha Tummala: Yep, indeed.
+
+16
+00:00:55.150 --> 00:01:01.599
+jaivards@andrew.cmu.edu: So, we just wanted to go over and, like, get your feedback. So, for now, what we have is, like.
+
+17
+00:01:01.800 --> 00:01:03.399
+jaivards@andrew.cmu.edu: by April 15th.
+
+18
+00:01:03.550 --> 00:01:07.930
+jaivards@andrew.cmu.edu: For this, this semester, we'll have, the requirements.
+
+19
+00:01:08.080 --> 00:01:09.470
+jaivards@andrew.cmu.edu: Completed by then?
+
+20
+00:01:10.130 --> 00:01:11.130
+jaivards@andrew.cmu.edu: And…
+
+21
+00:01:12.340 --> 00:01:24.319
+jaivards@andrew.cmu.edu: I think we're making progress on this, so it should be doable by then, yeah. And for… by April 30th, the SES system should be finalized, and the risk document should also be completed.
+
+22
+00:01:25.510 --> 00:01:30.920
+jaivards@andrew.cmu.edu: If the notion of requirements… You might want to say that.
+
+23
+00:01:32.740 --> 00:01:33.990
+jaivards@andrew.cmu.edu: on his calendar.
+
+24
+00:01:34.270 --> 00:01:41.779
+hrishikb@andrew.cmu.edu: I think we can, skip towards the end. This is a bit more detail. I think you can add on which parts you want to discuss.
+
+25
+00:01:42.080 --> 00:01:44.200
+hrishikb@andrew.cmu.edu: There's a high-level view on the island.
+
+26
+00:01:45.500 --> 00:01:46.100
+jaivards@andrew.cmu.edu: Sure.
+
+27
+00:01:48.580 --> 00:01:50.680
+jaivards@andrew.cmu.edu: Yeah. This one, right?
+
+28
+00:01:50.680 --> 00:02:00.359
+hrishikb@andrew.cmu.edu: Yeah, so, we are targeting that towards the end of this month, we should have the basic, requirements, risk process, and
+
+29
+00:02:00.600 --> 00:02:06.909
+hrishikb@andrew.cmu.edu: like, not… I'm not sure if we'll be able to have the complete architecture, because we need to still do some POCs.
+
+30
+00:02:07.090 --> 00:02:19.109
+hrishikb@andrew.cmu.edu: on the LLM versus ML front, so that we can finalize an approach and build the architecture around that. That is the plan for early May, first couple of weeks of May. We should have that information with us.
+
+31
+00:02:19.350 --> 00:02:23.899
+hrishikb@andrew.cmu.edu: And, towards the end of May, we plan to begin development.
+
+32
+00:02:24.160 --> 00:02:30.749
+hrishikb@andrew.cmu.edu: on all fronts, there'll be… I think we'll do it parallelly. We'll be working on the Innesian Gateway, along with
+
+33
+00:02:31.290 --> 00:02:34.280
+hrishikb@andrew.cmu.edu: the ML or LLM components.
+
+34
+00:02:34.980 --> 00:02:42.539
+hrishikb@andrew.cmu.edu: After that, within the next couple of months, till, in June and July, we are expecting to be done with
+
+35
+00:02:42.770 --> 00:02:45.099
+hrishikb@andrew.cmu.edu: Almost all of the development.
+
+36
+00:02:45.380 --> 00:02:48.980
+hrishikb@andrew.cmu.edu: We have a… we are targeting an aggressive approach.
+
+37
+00:02:49.260 --> 00:03:01.080
+hrishikb@andrew.cmu.edu: So that we have more time to cater to any, issues that we may have. These timelines might increase a little bit, because we have left around 2 to 3 months of testing effort.
+
+38
+00:03:01.230 --> 00:03:03.840
+hrishikb@andrew.cmu.edu: Just in case we run into some issues.
+
+39
+00:03:03.950 --> 00:03:07.959
+hrishikb@andrew.cmu.edu: So, we are taking a optimistic approach here.
+
+40
+00:03:08.560 --> 00:03:11.460
+hrishikb@andrew.cmu.edu: And by the end of August.
+
+41
+00:03:11.590 --> 00:03:16.069
+hrishikb@andrew.cmu.edu: We are hoping that the individual modules are ready.
+
+42
+00:03:16.560 --> 00:03:30.269
+hrishikb@andrew.cmu.edu: For, to basically be integrated with each other, so that we have a complete system in place. And in that time, we'll also be doing some sort of individual component testing, and we'll probably share the results with you.
+
+43
+00:03:30.850 --> 00:03:35.240
+hrishikb@andrew.cmu.edu: On how we are able to process the files that we have.
+
+44
+00:03:35.400 --> 00:03:38.860
+hrishikb@andrew.cmu.edu: And, how the LLM components are doing the work.
+
+45
+00:03:39.950 --> 00:03:44.009
+hrishikb@andrew.cmu.edu: And after, September in, I think in…
+
+46
+00:03:44.360 --> 00:03:48.490
+hrishikb@andrew.cmu.edu: October, we'll probably be figuring out,
+
+47
+00:03:48.620 --> 00:03:58.630
+hrishikb@andrew.cmu.edu: the exact tests and the extent of the testing that we're following, and then the next month or two will be, just…
+
+48
+00:03:58.920 --> 00:04:01.480
+hrishikb@andrew.cmu.edu: Testing the entire system, and…
+
+49
+00:04:01.650 --> 00:04:12.000
+hrishikb@andrew.cmu.edu: Looking at it, if we can, incorporate some of the stretch codes that we have, the… maybe the web scraping part, or some of the other things that would be good to have.
+
+50
+00:04:12.290 --> 00:04:17.200
+hrishikb@andrew.cmu.edu: For you guys, we'll be trying to incorporate that, so we are leaving a couple of months to…
+
+51
+00:04:17.500 --> 00:04:29.950
+hrishikb@andrew.cmu.edu: be able to work on that as well. And in December, we'll probably not be… we are hoping to be done with everything before that, and in December, it'll just be, any documentation or handover plans that we have.
+
+52
+00:04:30.110 --> 00:04:37.700
+hrishikb@andrew.cmu.edu: maybe demos, whatever is required. That's the part we're leaving for end of November and December.
+
+53
+00:04:40.270 --> 00:04:44.469
+hrishikb@andrew.cmu.edu: And if you want to go into details of any of these, it's mentioned above.
+
+54
+00:04:47.110 --> 00:04:47.850
+Harsha Tummala: Okay
+
+55
+00:04:51.280 --> 00:04:52.949
+Harsha Tummala: Being the questions to my husband.
+
+56
+00:04:55.620 --> 00:04:58.269
+hrishikb@andrew.cmu.edu: We can share this doc with you, after the call.
+
+57
+00:04:59.790 --> 00:05:01.180
+jaivards@andrew.cmu.edu: Yep,
+
+58
+00:05:02.520 --> 00:05:13.719
+jaivards@andrew.cmu.edu: So, is it fine with both, is it fine, Hersha and David? If you have anything, then we can make changes. If not, we can share this with you after the meeting.
+
+59
+00:05:14.710 --> 00:05:21.490
+Harsha Tummala: Other than July's missing, but yeah, I think… I think this makes sense. This is a decent timeline.
+
+60
+00:05:21.610 --> 00:05:28.440
+Harsha Tummala: Or at least a decent breakdown of the units. I suspect some of those units might be larger than others, and…
+
+61
+00:05:28.780 --> 00:05:42.960
+Harsha Tummala: It's also possible that EPART's appetite would change, that things that might be stretch goals will work its way into core requirements. That seems to be just the way of the world, but we'll do our best to keep that from happening.
+
+62
+00:05:43.110 --> 00:05:48.340
+Harsha Tummala: Yeah, I think this is a good… a good first plan.
+
+63
+00:05:48.970 --> 00:05:56.259
+jaivards@andrew.cmu.edu: Got it. I think July is not there just because summer semester ends by then, and then, you know, that's just, again…
+
+64
+00:05:56.260 --> 00:06:11.790
+hrishikb@andrew.cmu.edu: Yeah, July was, left out because, the entire development… there won't be any deliverable in that month, because we'll still be working on the, like, development of it, and we don't expect there'll be any deliverable in the month of July. It's like a two-month
+
+65
+00:06:12.000 --> 00:06:14.030
+hrishikb@andrew.cmu.edu: Period in which we'll be developing everything.
+
+66
+00:06:14.530 --> 00:06:16.950
+Harsha Tummala: Okay, I must have misunderstood before, that's… that's fine.
+
+67
+00:06:17.060 --> 00:06:18.130
+Harsha Tummala: That makes sense.
+
+68
+00:06:19.750 --> 00:06:20.350
+jaivards@andrew.cmu.edu: Yep.
+
+69
+00:06:21.130 --> 00:06:22.230
+jaivards@andrew.cmu.edu: So…
+
+70
+00:06:26.320 --> 00:06:31.909
+Harsha Tummala: And, you know, there's a few things I think I want to say from mine, but I think that can cover the end of the key.
+
+71
+00:06:32.800 --> 00:06:34.389
+Harsha Tummala: Not regarding the deadline.
+
+72
+00:06:36.050 --> 00:06:42.000
+jaivards@andrew.cmu.edu: Okay, we also have the statement of work, I'll just share that as well.
+
+73
+00:06:42.800 --> 00:06:43.890
+jaivards@andrew.cmu.edu: So…
+
+74
+00:07:00.790 --> 00:07:03.099
+jaivards@andrew.cmu.edu: One second, this is…
+
+75
+00:07:06.530 --> 00:07:10.520
+jaivards@andrew.cmu.edu: So, we were, is it visible, first of all?
+
+76
+00:07:11.350 --> 00:07:12.689
+Harsha Tummala: Yeah, I'll…
+
+77
+00:07:13.500 --> 00:07:14.210
+jaivards@andrew.cmu.edu: Okay.
+
+78
+00:07:14.310 --> 00:07:20.700
+jaivards@andrew.cmu.edu: So, we were, it was encouraged that we have a statement of work, just so that,
+
+79
+00:07:20.870 --> 00:07:33.840
+jaivards@andrew.cmu.edu: you know, we have a shared understanding with you. I'll be… this is the first version, and this is still, I think it… a lot of changes might be required, but I just wanted to share it with you guys, and
+
+80
+00:07:35.140 --> 00:07:38.910
+jaivards@andrew.cmu.edu: It just basically goes over what we understand of the project.
+
+81
+00:07:39.030 --> 00:07:44.240
+jaivards@andrew.cmu.edu: And along with the scope that we think, and how we, you know.
+
+82
+00:07:44.380 --> 00:07:47.560
+jaivards@andrew.cmu.edu: think about each of the activities that we're gonna have, so…
+
+83
+00:07:48.040 --> 00:07:56.470
+jaivards@andrew.cmu.edu: It's a… it's a bit of a long document, but this is just basically all of our understanding and all of the things that we think
+
+84
+00:07:56.770 --> 00:08:11.719
+jaivards@andrew.cmu.edu: are currently… we're gonna have to do. It also defines some of the out-of-scope things, like, right now, I just wanted to confirm again, so generalized web scraping and all that, stuff is not included, right? Because…
+
+85
+00:08:11.960 --> 00:08:14.459
+jaivards@andrew.cmu.edu: I have it here, but we can make changes if you want.
+
+86
+00:08:15.200 --> 00:08:21.489
+Harsha Tummala: Yeah, generally, that's great, no. If there's anything which is mentioned in the PDF document itself.
+
+87
+00:08:21.740 --> 00:08:33.900
+Harsha Tummala: Probably, but that's… that's it. There's nothing… there's nothing out of the bounds of it, which is, you guys make a search, and look for things, and figure out what is correct. That kind of website is completely out of scope.
+
+88
+00:08:34.760 --> 00:08:41.670
+jaivards@andrew.cmu.edu: Yeah. So, we also have other things, such as, like, You know, that are…
+
+89
+00:08:42.510 --> 00:08:52.770
+jaivards@andrew.cmu.edu: we will be working all the way up till PIMS, but the steps after that is not something under our control, so I've also written these points here, and…
+
+90
+00:08:53.430 --> 00:08:57.009
+jaivards@andrew.cmu.edu: VAB, the other things are just, like.
+
+91
+00:08:57.130 --> 00:09:09.279
+jaivards@andrew.cmu.edu: What we think we are allowed to do, and the things that, either are not under our control, or things that, we're not, we do not expect that, will come up, or we'll have to do.
+
+92
+00:09:09.530 --> 00:09:12.260
+jaivards@andrew.cmu.edu: So, I'll be set… Yep.
+
+93
+00:09:12.630 --> 00:09:13.360
+jaivards@andrew.cmu.edu: Okay.
+
+94
+00:09:14.810 --> 00:09:17.179
+jaivards@andrew.cmu.edu: Oh, I thought someone was asking a question.
+
+95
+00:09:17.730 --> 00:09:26.259
+jaivards@andrew.cmu.edu: So… Yeah, so it, there's also the project documentation deliverables, so all of these things.
+
+96
+00:09:26.990 --> 00:09:29.610
+jaivards@andrew.cmu.edu: And finally, I would just like to…
+
+97
+00:09:32.650 --> 00:09:34.420
+jaivards@andrew.cmu.edu: Go to… one sec.
+
+98
+00:09:35.620 --> 00:09:36.330
+jaivards@andrew.cmu.edu: Yep.
+
+99
+00:09:37.350 --> 00:09:43.940
+jaivards@andrew.cmu.edu: So… The last thing is just all the… Yeah, general assumptions we have?
+
+100
+00:09:44.100 --> 00:09:49.220
+jaivards@andrew.cmu.edu: And, you know, who we have met, what tools we're expected to use.
+
+101
+00:09:49.340 --> 00:09:52.720
+jaivards@andrew.cmu.edu: That, you know, we make sure that the…
+
+102
+00:09:52.850 --> 00:09:58.830
+jaivards@andrew.cmu.edu: the constraints, like, not using any public LLM, or, you know, not…
+
+103
+00:09:59.010 --> 00:10:03.700
+jaivards@andrew.cmu.edu: Using the company data on anything other than company resources.
+
+104
+00:10:03.870 --> 00:10:06.189
+jaivards@andrew.cmu.edu: So that, that, all that stuff is here.
+
+105
+00:10:06.470 --> 00:10:12.370
+jaivards@andrew.cmu.edu: And we, in general, I just wanted to show it to you guys so that, you know,
+
+106
+00:10:12.610 --> 00:10:29.910
+jaivards@andrew.cmu.edu: That this, you know, statement is, here is much more clear to everyone. And that, you know, if there are any gaps in our understanding, that you might… you guys might, you know, say that, okay, this is actually different here, and we can go ahead and make those changes.
+
+107
+00:10:30.830 --> 00:10:31.970
+jaivards@andrew.cmu.edu: So, I'll also…
+
+108
+00:10:32.290 --> 00:10:34.430
+Harsha Tummala: Excuse me, sorry, bye-bye. Go ahead.
+
+109
+00:10:35.140 --> 00:10:46.910
+jaivards@andrew.cmu.edu: It's just, I'll also send this document over, you guys can look at it, and if there's any mistakes that you… or, like, misunderstandings there, then, yeah, we'll go ahead and change it.
+
+110
+00:10:47.660 --> 00:10:57.310
+Harsha Tummala: Yeah, I mean, I can go through it in detail and then send any changes or something which doesn't align correctly to you guys, but there shouldn't be any… yeah.
+
+111
+00:10:58.040 --> 00:10:58.590
+jaivards@andrew.cmu.edu: Yep.
+
+112
+00:10:59.310 --> 00:11:05.920
+jaivards@andrew.cmu.edu: So, yeah, this is just an initial first first version, so yeah, we'll definitely update it as we go along.
+
+113
+00:11:06.300 --> 00:11:06.960
+Harsha Tummala: Yeah.
+
+114
+00:11:08.100 --> 00:11:12.089
+jaivards@andrew.cmu.edu: So… Yeah, I'll send this over.
+
+115
+00:11:12.550 --> 00:11:14.839
+jaivards@andrew.cmu.edu: Along with the timeline.
+
+116
+00:11:15.970 --> 00:11:21.780
+jaivards@andrew.cmu.edu: The other thing is… yeah,
+
+117
+00:11:22.600 --> 00:11:41.729
+jaivards@andrew.cmu.edu: Liu is here with us. It's just… he had some specific questions. He has been looking through the ML portion, and he wanted to basically, you know, clarify what all he needed, and, you know, all the stuff, and why he needs it, and, you know, his future steps. So, Lou, if you…
+
+118
+00:11:41.840 --> 00:11:45.810
+jaivards@andrew.cmu.edu: Or want to, like, share your document and, like, just,
+
+119
+00:11:46.030 --> 00:11:53.020
+jaivards@andrew.cmu.edu: You can basically go ahead and, like, Send a mail to them.
+
+120
+00:11:53.270 --> 00:11:55.730
+jaivards@andrew.cmu.edu: So, just… closing down.
+
+121
+00:11:56.080 --> 00:11:57.430
+jaivards@andrew.cmu.edu: detailed data.
+
+122
+00:11:57.660 --> 00:11:58.320
+jaivards@andrew.cmu.edu: Neat.
+
+123
+00:11:58.570 --> 00:12:04.910
+jaivards@andrew.cmu.edu: Okay, so… Yeah. Basically, Liu's saying that,
+
+124
+00:12:05.460 --> 00:12:13.610
+jaivards@andrew.cmu.edu: He's looked into the, you know, different options, and he's done some calculations on how many entries he would need.
+
+125
+00:12:13.790 --> 00:12:19.490
+jaivards@andrew.cmu.edu: And, so… After he's made initial document and, like, all the estimates.
+
+126
+00:12:19.610 --> 00:12:34.660
+jaivards@andrew.cmu.edu: And he sent a… he's basically sent an email with all the requirements for the ML portion, and he needs that to, like, go further in the ML prototyping, so it would be really helpful if, you know.
+
+127
+00:12:35.170 --> 00:12:37.440
+jaivards@andrew.cmu.edu: To proceed, if you could get them.
+
+128
+00:12:38.110 --> 00:12:41.420
+Harsha Tummala: Yep, 100%. I was… I was just gonna say, also…
+
+129
+00:12:41.610 --> 00:12:56.270
+Harsha Tummala: I've already taken a look at that email, and I've started collecting the data for that as well. So ideally, I was saying that either end of today or Monday, I should, get you guys an email. Is there a timeline from your end that you expect me to give you the data by?
+
+130
+00:12:57.150 --> 00:13:05.420
+jaivards@andrew.cmu.edu: No, it's… I think Monday's fine, right? Yep, so, yeah, in the meantime, he's just, gonna, like, you know…
+
+131
+00:13:05.550 --> 00:13:08.099
+jaivards@andrew.cmu.edu: Work on the other stuff for the output types.
+
+132
+00:13:09.800 --> 00:13:18.070
+jaivards@andrew.cmu.edu: So… I think… that's it, in the sense that… we…
+
+133
+00:13:18.520 --> 00:13:21.909
+jaivards@andrew.cmu.edu: The, like, we were working on other stuff as well with…
+
+134
+00:13:22.170 --> 00:13:25.430
+jaivards@andrew.cmu.edu: But that's more related to project management.
+
+135
+00:13:25.770 --> 00:13:28.940
+jaivards@andrew.cmu.edu: I did have a question, Harcher, so…
+
+136
+00:13:29.290 --> 00:13:33.610
+jaivards@andrew.cmu.edu: Next week, we have the carnival, so…
+
+137
+00:13:33.790 --> 00:13:51.389
+jaivards@andrew.cmu.edu: I think the building will, will be shut down on Thursday? No? Okay, okay. I'm not sure. So, but, is there… should we reschedule the time for it? I'm not certain about it, so I just wanted to bring it up.
+
+138
+00:13:52.080 --> 00:14:08.160
+Harsha Tummala: I mean, you guys will have a vacation for, like, the 2 or 3 day cargo period, right? So, ideally, I would say if you want to have a meeting next week, reschedule it to when you guys are actually in campus and actually have a working day. Otherwise, you can do it the week after today, that's fine.
+
+139
+00:14:09.520 --> 00:14:14.860
+jaivards@andrew.cmu.edu: Ashuta, Rishi, I would also, like, what do you guys think?
+
+140
+00:14:16.210 --> 00:14:22.040
+Ashritha: I probably might not be available for those two days, Thursday and Friday.
+
+141
+00:14:22.900 --> 00:14:23.930
+jaivards@andrew.cmu.edu: Rishi?
+
+142
+00:14:23.930 --> 00:14:33.429
+hrishikb@andrew.cmu.edu: Yeah, I think I might not be available on Friday… Thursday… yeah, I think I can make Thursday, but I'll not be available for the weekend on Fridays.
+
+143
+00:14:33.430 --> 00:14:38.969
+jaivards@andrew.cmu.edu: But, so, yeah, we wanted to discuss PIMs and the like.
+
+144
+00:14:39.080 --> 00:14:42.010
+jaivards@andrew.cmu.edu: go into detail regarding that, Harsha.
+
+145
+00:14:42.200 --> 00:14:50.170
+jaivards@andrew.cmu.edu: So, would it be alright if we came to your office? Actually, first of all, is it alright, Shruta, Rishi, Liu? What do you think?
+
+146
+00:14:52.090 --> 00:14:56.899
+Ashritha: Yeah, anytime, like, Monday, Tuesday, or Wednesday works for me.
+
+147
+00:14:57.830 --> 00:14:58.450
+hrishikb@andrew.cmu.edu: Yeah.
+
+148
+00:14:59.620 --> 00:15:00.540
+jaivards@andrew.cmu.edu: Rishi?
+
+149
+00:15:00.540 --> 00:15:02.330
+hrishikb@andrew.cmu.edu: Yes, vote for me.
+
+150
+00:15:02.330 --> 00:15:07.320
+jaivards@andrew.cmu.edu: Okay. Yeah, your voice is a bit long. I'll just increase my speaker.
+
+151
+00:15:09.330 --> 00:15:17.679
+jaivards@andrew.cmu.edu: Okay, which, which day would, work for you, Harsha? David? We would really like to, I guess, come over and ask questions.
+
+152
+00:15:17.850 --> 00:15:25.809
+Harsha Tummala: So, I would say Jake would be the person for PIMS, for almost most of them. So, I would say drop a message on Teams.
+
+153
+00:15:26.000 --> 00:15:44.089
+Harsha Tummala: And just give us your availability on when it's the most convenient for you guys, and Jake will just let you guys know on, like, what slot works for him. My guess is that it will be Wednesday. He's probably gonna be in Erie Monday, Tuesday. So next Wednesday might be it, but let's see.
+
+154
+00:15:44.660 --> 00:15:47.359
+jaivards@andrew.cmu.edu: Yeah. Okay.
+
+155
+00:15:47.800 --> 00:15:54.319
+jaivards@andrew.cmu.edu: So, that sounds good to me. Just to confirm, Liu, Rishi, Ashitantha, is that fine with you guys?
+
+156
+00:15:54.630 --> 00:15:55.360
+Ashritha: Yup.
+
+157
+00:15:57.280 --> 00:16:00.500
+hrishikb@andrew.cmu.edu: Yeah, let's, check the calendar once and then confirm.
+
+158
+00:16:00.990 --> 00:16:07.420
+jaivards@andrew.cmu.edu: Yeah, we'll definitely send over the availability times and, like, coordinate with you, with you guys, yeah.
+
+159
+00:16:08.590 --> 00:16:09.970
+jaivards@andrew.cmu.edu: And… .
+
+160
+00:16:10.910 --> 00:16:15.479
+Harsha Tummala: I mean, I have a few things to discuss, but, you guys go on, begin with that.
+
+161
+00:16:15.480 --> 00:16:19.410
+jaivards@andrew.cmu.edu: No, no, please go ahead. I think that that was a lot of the high priority.
+
+162
+00:16:19.720 --> 00:16:23.520
+Harsha Tummala: Okay. So, a few things, as in, one is.
+
+163
+00:16:23.870 --> 00:16:28.109
+Harsha Tummala: with respect to, Liu's email.
+
+164
+00:16:28.310 --> 00:16:38.099
+Harsha Tummala: There is one thing where, he mentions, about having the documents attributed to the exact products that are mapped to it.
+
+165
+00:16:38.360 --> 00:16:49.569
+Harsha Tummala: So, what I'll do is, I'll be sending you guys the document links from BunnyCDN. So, all you can do is, you can use that link to get the exact document that you want, that is attached there.
+
+166
+00:16:49.830 --> 00:16:55.420
+Harsha Tummala: So, does that sound okay? Or do you want me to send the actual document itself, embedded?
+
+167
+00:16:57.840 --> 00:16:58.510
+jaivards@andrew.cmu.edu: loop.
+
+168
+00:17:03.320 --> 00:17:15.990
+jaivards@andrew.cmu.edu: So, he… Actually, just like, I'll just turn the camera this way so we can… So…
+
+169
+00:17:16.920 --> 00:17:19.070
+jaivards@andrew.cmu.edu: It works great. Yeah, yeah.
+
+170
+00:17:20.040 --> 00:17:21.799
+jaivards@andrew.cmu.edu: Okay, I got checks around me.
+
+171
+00:17:22.839 --> 00:17:26.209
+hrishikb@andrew.cmu.edu: Harsha, could you repeat the two options that you had? One was sending over
+
+172
+00:17:26.630 --> 00:17:28.310
+hrishikb@andrew.cmu.edu: Another one was the actual document.
+
+173
+00:17:28.600 --> 00:17:47.070
+Harsha Tummala: So, what is mentioned in the email is that, he wanted the raw supplier text extracted from the PDF, or the spec sheets, or the CSV sent directly attributed to the products itself, and, sent over. What I can actually do is,
+
+174
+00:17:47.260 --> 00:17:52.319
+Harsha Tummala: send a link instead of the, extracted data from the PDFs.
+
+175
+00:17:52.640 --> 00:17:58.579
+Harsha Tummala: So, that link will contain the exact document that, connects to the projects themselves.
+
+176
+00:17:58.690 --> 00:18:04.739
+Harsha Tummala: So, it'll just be the document link, products that are catered on this document, and so on.
+
+177
+00:18:07.150 --> 00:18:11.330
+hrishikb@andrew.cmu.edu: Okay, I think that should be fine.
+
+178
+00:18:11.800 --> 00:18:12.370
+hrishikb@andrew.cmu.edu: Go ahead.
+
+179
+00:18:12.970 --> 00:18:13.350
+jaivards@andrew.cmu.edu: Yeah.
+
+180
+00:18:13.350 --> 00:18:20.150
+hrishikb@andrew.cmu.edu: Ashata, do you… like, as, like, we need it majorly for the input for our LLM model.
+
+181
+00:18:20.150 --> 00:18:37.809
+hrishikb@andrew.cmu.edu: So that we can actually see how it is working and what it can do. I think that should be fine, but I think we will require a good chunk of data in that respect, like, what we are giving it as input, and how the catalog team is refining it, and what the actual output is towards the end.
+
+182
+00:18:37.870 --> 00:18:39.230
+hrishikb@andrew.cmu.edu: So that we can…
+
+183
+00:18:40.650 --> 00:18:47.260
+Harsha Tummala: So, I'll send over as many as I can, so don't worry about that. It'll be at least more than a thousand, so don't worry about it.
+
+184
+00:18:47.470 --> 00:19:05.559
+Harsha Tummala: And, another question… actually, not a question, another, I think, thing that I think we discussed in the earlier days of the project, but we kind of… I think we've forgotten about it, and I think we've forgotten to mention that to you guys have heard it too, which is, initially you were talking about the data standards, right?
+
+185
+00:19:05.620 --> 00:19:12.040
+Harsha Tummala: on, how we could standardize the data, and I think in our previous meetings, we kind of,
+
+186
+00:19:12.510 --> 00:19:19.210
+Harsha Tummala: establish that we will be using the attributes from the product types, which are in the tables that I provided you guys with.
+
+187
+00:19:19.650 --> 00:19:31.760
+Harsha Tummala: And that's how we'll be doing it. But the thing is, we kind of identified, like, 3 data standards that are already there in the industry, and are pretty much standardized. And no matter what
+
+188
+00:19:32.180 --> 00:19:37.160
+Harsha Tummala: Type of product you're looking at, you'll find the exact type of attributes that are needed for the product.
+
+189
+00:19:37.390 --> 00:19:46.259
+Harsha Tummala: So the three standards are, like, ETIM, E-Class, and UNSPSC. I'll ping this to you guys, but,
+
+190
+00:19:46.620 --> 00:19:56.039
+Harsha Tummala: These are basically open source standards, where all the product types and attributes for these product types, it's all available on the website.
+
+191
+00:19:56.170 --> 00:20:01.790
+Harsha Tummala: And, ideally, you can use this data itself
+
+192
+00:20:02.070 --> 00:20:05.590
+Harsha Tummala: To create the initial set of, like.
+
+193
+00:20:05.810 --> 00:20:09.440
+Harsha Tummala: Fittering for, like, how the product should look like, or how the…
+
+194
+00:20:09.710 --> 00:20:14.490
+Harsha Tummala: Or how a specific category or product type of product should look like.
+
+195
+00:20:18.090 --> 00:20:21.419
+jaivards@andrew.cmu.edu: I think that works, I'll tell you what you think.
+
+196
+00:20:24.480 --> 00:20:25.939
+jaivards@andrew.cmu.edu: Do you seem to agree.
+
+197
+00:20:26.580 --> 00:20:27.090
+jaivards@andrew.cmu.edu: This is true.
+
+198
+00:20:27.090 --> 00:20:28.009
+Harsha Tummala: I need to.
+
+199
+00:20:28.050 --> 00:20:28.740
+jaivards@andrew.cmu.edu: excuse me.
+
+200
+00:20:29.940 --> 00:20:32.820
+jaivards@andrew.cmu.edu: Just understandable.
+
+201
+00:20:33.470 --> 00:20:34.370
+jaivards@andrew.cmu.edu: Yes.
+
+202
+00:20:36.250 --> 00:20:43.120
+jaivards@andrew.cmu.edu: So, yeah, yeah, Lee is also saying that he'll just go through it, and then,
+
+203
+00:20:43.440 --> 00:20:47.989
+jaivards@andrew.cmu.edu: You know, go for, like, if there's anything else, then he can, I guess, inform you again.
+
+204
+00:20:48.760 --> 00:20:55.359
+Harsha Tummala: Yeah, and another great thing about this… these open standards is that all of these standards have documentation which
+
+205
+00:20:55.550 --> 00:21:11.500
+Harsha Tummala: tells us that what these specific attributes, which are called in this standard, map to in the other standard that is mentioned. So, like, how does ETIM, ETIM's attributes identified map to the E-class attributes? It's all clearly, defined.
+
+206
+00:21:11.950 --> 00:21:16.949
+Harsha Tummala: And, I mean, like, a benefit of this would just be that
+
+207
+00:21:17.540 --> 00:21:23.690
+Harsha Tummala: A document, sorry, a product attributed in a certain standard can always be mapped to another standard as well.
+
+208
+00:21:24.010 --> 00:21:37.559
+Harsha Tummala: And this would mean that that one product can be identified according to three different standards, because they're all interrelatable, and they have, like, the language defined on, like, what are these different
+
+209
+00:21:37.740 --> 00:21:42.040
+Harsha Tummala: What are the various possibilities for these attributes to be called in the industry?
+
+210
+00:21:43.020 --> 00:21:43.640
+jaivards@andrew.cmu.edu: Yeah.
+
+211
+00:21:44.200 --> 00:21:48.090
+Harsha Tummala: So, synonyms, everything are highlighted pretty clearly in this.
+
+212
+00:21:49.020 --> 00:21:49.380
+hrishikb@andrew.cmu.edu: Could you.
+
+213
+00:21:49.380 --> 00:21:49.720
+jaivards@andrew.cmu.edu: Yeah.
+
+214
+00:21:49.840 --> 00:21:54.959
+hrishikb@andrew.cmu.edu: explain a bit about what these standards actually are. I'm not very clear on the…
+
+215
+00:21:55.180 --> 00:21:55.600
+Harsha Tummala: What's.
+
+216
+00:21:55.600 --> 00:21:56.900
+hrishikb@andrew.cmu.edu: Right, yeah.
+
+217
+00:21:56.900 --> 00:22:13.700
+Harsha Tummala: So right now, we have product types, categories, and attributes, right, Vishkish? So, these product types and attributes are pretty much what Alps, like, our parent company has kind of come up with, and these are, these are, like, just…
+
+218
+00:22:14.300 --> 00:22:19.989
+Harsha Tummala: Just things they've identified over their years of working with these products.
+
+219
+00:22:20.800 --> 00:22:39.669
+Harsha Tummala: And what these standards actually do is, instead of relying on apps controls for, like, how they call certain things, like, how they map certain attitudes, or what they call certain attributes, it just goes with the industry standard of, like, what these attributes are called, and what sort of terms are used for these attributes.
+
+220
+00:22:39.800 --> 00:22:41.400
+Harsha Tummala: Road banks and anything.
+
+221
+00:22:42.800 --> 00:22:54.349
+hrishikb@andrew.cmu.edu: Okay, so, is there a chance that there is a mismatch between what Alps uses and what the actual, like, documentation the official one uses?
+
+222
+00:22:54.890 --> 00:23:11.930
+Harsha Tummala: There could be. So, I would say, we don't even have to go by ALPS standards, right? Ideally, I would say, if we can map to these general industry standards, we should be more than happy, because, in the end, ALPS is just one person who decides to
+
+223
+00:23:12.000 --> 00:23:15.009
+Harsha Tummala: Call these things a certain name, and that's what it is.
+
+224
+00:23:15.710 --> 00:23:26.919
+Harsha Tummala: This might also simplify, like, all the different lingo that I have specifically uses, because a lot of these documents, the PDFs for these specific products and,
+
+225
+00:23:27.240 --> 00:23:33.770
+Harsha Tummala: All of these, all of these specification documents, they kind of go by the general industry standards themselves.
+
+226
+00:23:33.960 --> 00:23:39.329
+Harsha Tummala: And to actually map it to ALPS is a more difficult task compared to mapping it to these open standards.
+
+227
+00:23:39.600 --> 00:23:46.260
+Harsha Tummala: And these open standards kind of give us more attributes that we can expect for a product type, and
+
+228
+00:23:46.510 --> 00:23:50.289
+Harsha Tummala: Just… they just provide a more robust way in identifying them.
+
+229
+00:23:51.240 --> 00:23:56.669
+hrishikb@andrew.cmu.edu: Okay, but in, doing that, won't it also require a rework on the PIM side?
+
+230
+00:23:56.930 --> 00:24:01.019
+hrishikb@andrew.cmu.edu: To map those attributes, the values, and so it can be used on stream?
+
+231
+00:24:01.930 --> 00:24:08.040
+Harsha Tummala: I mean, so, since these are already, like, predefined tailors,
+
+232
+00:24:08.380 --> 00:24:13.690
+Harsha Tummala: Technically, you won't have to… you won't be expected to create, these…
+
+233
+00:24:14.010 --> 00:24:20.490
+Harsha Tummala: these specific attributes and product types or terms, as long as you can map the product to these attributes and more.
+
+234
+00:24:20.720 --> 00:24:23.229
+Harsha Tummala: Product types, we should be good.
+
+235
+00:24:23.590 --> 00:24:30.000
+Harsha Tummala: But the whole page end of things, that's something we look into, on, like, how these things should look like there.
+
+236
+00:24:30.360 --> 00:24:37.700
+hrishikb@andrew.cmu.edu: Okay. I think for now, we were relying on the schema you provide at source of growth, but I think we can compare it with the standards that you mentioned.
+
+237
+00:24:37.700 --> 00:24:38.230
+Harsha Tummala: Sure.
+
+238
+00:24:38.230 --> 00:24:43.650
+hrishikb@andrew.cmu.edu: And see if and where there's an overlap or a difference, and we can…
+
+239
+00:24:44.440 --> 00:24:47.919
+Harsha Tummala: So, because I was personally comparing it to the three,
+
+240
+00:24:48.700 --> 00:24:52.030
+Harsha Tummala: To the 3-way ball valve that we kind of have been looking at.
+
+241
+00:24:52.390 --> 00:24:53.810
+Harsha Tummala: And,
+
+242
+00:24:53.860 --> 00:25:08.969
+Harsha Tummala: for that example, this… these standards are actually way more clearer. So, for example, if we call flow rate flow rate in one standard, it'll also give us, like, synonyms which are used for flow rate, which is some sort of,
+
+243
+00:25:08.980 --> 00:25:19.339
+Harsha Tummala: Water flow rate, or liquid flow rate, or some sort of weird term, which is, like, the actuator flow value, or something like that.
+
+244
+00:25:19.700 --> 00:25:22.999
+Harsha Tummala: And all of these surnames are defined in the standard thresholds.
+
+245
+00:25:23.200 --> 00:25:35.550
+Harsha Tummala: So, there's… there was an initial question, too, right, in one of our earlier meetings, where, how do we look at the synonyms, or like, what if something is called something else in a document? How do we understand that this matches to a specific category?
+
+246
+00:25:35.550 --> 00:25:36.090
+hrishikb@andrew.cmu.edu: Yeah.
+
+247
+00:25:36.440 --> 00:25:40.260
+Harsha Tummala: These, these standards actually clear those, those kind of questions up, I started.
+
+248
+00:25:40.930 --> 00:25:41.530
+hrishikb@andrew.cmu.edu: Okay.
+
+249
+00:25:41.640 --> 00:25:51.950
+hrishikb@andrew.cmu.edu: I think, probably we can go through the standards and do a comparison, and we'll probably need, the verification from the catalog team if we are doing it in the right way.
+
+250
+00:25:52.480 --> 00:25:53.020
+Harsha Tummala: noon.
+
+251
+00:25:53.020 --> 00:26:01.539
+hrishikb@andrew.cmu.edu: We can probably create a document, like, layering the differences that we have and what we're following, and we can get it verified by you guys once.
+
+252
+00:26:02.120 --> 00:26:02.870
+Harsha Tummala: Yeah.
+
+253
+00:26:03.230 --> 00:26:14.590
+Harsha Tummala: But that's it. Is there any problem with what I just said? Anything unclear, or anything that seems like it's out of scope, or anything that seems like this is not what we initially intended to do?
+
+254
+00:26:16.150 --> 00:26:28.120
+Ashritha: Just, I mean, I understood what you're trying to say about the standardization part, so it's like, okay, I've worked similar to this, like, on the telemetry side of it, like.
+
+255
+00:26:28.120 --> 00:26:36.840
+Ashritha: OpenTelemetry, and then all of it. So, it's basically, you want to be vendor agnostic, and then standardize everything, right? So,
+
+256
+00:26:37.220 --> 00:26:51.240
+Ashritha: if you are sure that standardizing according to those, rules, won't cause any problem, like, in case you want to integrate with ALPS or, you know, the EPADS,
+
+257
+00:26:51.240 --> 00:26:51.660
+Harsha Tummala: Yeah.
+
+258
+00:26:51.660 --> 00:26:56.379
+Ashritha: So then I think we're good. Should be actually a lot more easier, yeah.
+
+259
+00:26:56.380 --> 00:27:02.640
+Harsha Tummala: Yeah, this… this… again, like, another reason for, like, looking at these standards was also making your life easier, right?
+
+260
+00:27:02.640 --> 00:27:03.290
+Ashritha: Good.
+
+261
+00:27:03.550 --> 00:27:05.439
+Harsha Tummala: Technically, the ALP standards are…
+
+262
+00:27:05.740 --> 00:27:24.749
+Harsha Tummala: very… make you guys very dependent on what the guys are actually telling you guys to… Cool. And we also heard from Brian that these… these attributes are basically decided by, like, a team who's in charge for those specific products, and they decide that, oh, these attributes are relevant, and we're gonna show these attributes on the website, and that's how it works.
+
+263
+00:27:25.410 --> 00:27:26.110
+Harsha Tummala: Okay.
+
+264
+00:27:26.570 --> 00:27:43.310
+Harsha Tummala: Yeah, so this… this is just making things simpler, I think, for each of us. And I think as ePaths, like, separates and, like, becomes its own thing, these standards will also help us, like, like, just show that our data is more robust than
+
+265
+00:27:43.500 --> 00:27:51.699
+Harsha Tummala: more related, in general, because a certain person knows what ETIM is, but a certain person won't know what else is going by.
+
+266
+00:27:51.910 --> 00:27:52.540
+Ashritha: Yep.
+
+267
+00:27:53.050 --> 00:27:54.409
+Harsha Tummala: And simple as that, yeah.
+
+268
+00:27:57.820 --> 00:27:59.210
+jaivards@andrew.cmu.edu: Yeah.
+
+269
+00:27:59.480 --> 00:28:08.069
+jaivards@andrew.cmu.edu: I would have to, like, look through and, like, compare Harsha, like, to see the differences, but I think this would help us, yeah.
+
+270
+00:28:08.070 --> 00:28:17.899
+Harsha Tummala: just go through… go through it. We can discuss this the week after, or the week after that, and you basically could ever just understand what he sent, and how the input.
+
+271
+00:28:18.130 --> 00:28:21.650
+hrishikb@andrew.cmu.edu: I think the only major change would be that,
+
+272
+00:28:21.970 --> 00:28:30.679
+hrishikb@andrew.cmu.edu: the source of truth is kind of a change, but I think changing that would help us in the long run, if you have standardized it, so I think it's a good change.
+
+273
+00:28:31.070 --> 00:28:31.750
+Harsha Tummala: Yeah.
+
+274
+00:28:33.610 --> 00:28:40.900
+jaivards@andrew.cmu.edu: Yeah, this might, like, prevent… this might actually prevent rework when you're actually expanding, so yeah, that… I think that makes sense.
+
+275
+00:28:42.210 --> 00:28:45.230
+jaivards@andrew.cmu.edu: Let me just go through.
+
+276
+00:28:45.550 --> 00:28:52.759
+jaivards@andrew.cmu.edu: Other than that, I don't think, like, these, like, these were the things I wanted to discuss.
+
+277
+00:28:52.980 --> 00:28:58.219
+jaivards@andrew.cmu.edu: As for the available timings and all these documents, I'll send them over.
+
+278
+00:28:58.440 --> 00:28:59.400
+jaivards@andrew.cmu.edu: And…
+
+279
+00:28:59.560 --> 00:29:12.290
+jaivards@andrew.cmu.edu: Whichever time works for you, we can, you know, agree on something, and then, you know, we'll gather some questions and ask you regarding them. It's mostly BIMS, but there might be something else as well.
+
+280
+00:29:12.290 --> 00:29:16.550
+Harsha Tummala: I'll drop these standards on the team's chat after the meeting.
+
+281
+00:29:17.000 --> 00:29:17.830
+Harsha Tummala: Yeah.
+
+282
+00:29:17.830 --> 00:29:24.929
+jaivards@andrew.cmu.edu: Yeah, I'll just drop a message to Jake as well on Teams, and I'll just send him an email, both of them.
+
+283
+00:29:25.370 --> 00:29:26.050
+Harsha Tummala: Damn.
+
+284
+00:29:27.500 --> 00:29:34.389
+jaivards@andrew.cmu.edu: I think that's it, but, I'll just invite, like, you, Ashta, Is she this?
+
+285
+00:29:36.000 --> 00:29:38.339
+Ashritha: No, I don't have anything to add.
+
+286
+00:29:38.340 --> 00:29:39.060
+hrishikb@andrew.cmu.edu: Hmm.
+
+287
+00:29:39.280 --> 00:29:39.930
+hrishikb@andrew.cmu.edu: I think…
+
+288
+00:29:39.930 --> 00:29:40.500
+jaivards@andrew.cmu.edu: Okay.
+
+289
+00:29:40.690 --> 00:29:41.580
+hrishikb@andrew.cmu.edu: Same for me.
+
+290
+00:29:42.140 --> 00:29:42.830
+jaivards@andrew.cmu.edu: Blue?
+
+291
+00:29:47.210 --> 00:29:48.020
+jaivards@andrew.cmu.edu: Hello?
+
+292
+00:29:48.760 --> 00:29:49.750
+jaivards@andrew.cmu.edu: decoratively.
+
+293
+00:29:49.900 --> 00:29:50.959
+hrishikb@andrew.cmu.edu: We can't hear you.
+
+294
+00:29:51.350 --> 00:30:07.220
+jaivards@andrew.cmu.edu: He… Lou is talking about the Claude code, so… it's… he's saying that it lets… it hits the limit… the token limit for that.
+
+295
+00:30:09.150 --> 00:30:18.510
+Harsha Tummala: Yeah, I mean, we've kind of restricted total token limit, too, because it kind of becomes a big overhead, right, in general.
+
+296
+00:30:18.930 --> 00:30:22.400
+Harsha Tummala: I think the main reason for that is
+
+297
+00:30:23.910 --> 00:30:31.730
+Harsha Tummala: I don't know, you can technically use up unlimited credits with Cloud Core at this point, and, like, we ourselves have personally been struggling with, like.
+
+298
+00:30:31.910 --> 00:30:35.879
+Harsha Tummala: Keeping that in, like, the best in budget, good state.
+
+299
+00:30:36.260 --> 00:30:40.869
+Harsha Tummala: So, hence the limit imposed. If that limit is too restrictive, we can surely bump it up.
+
+300
+00:30:42.430 --> 00:30:50.169
+jaivards@andrew.cmu.edu: Yep, I think that's another topic that maybe we'll discuss more in depth, but I've not personally, I think.
+
+301
+00:30:50.610 --> 00:30:55.000
+jaivards@andrew.cmu.edu: gone through those same limitations, so I'm not so sure.
+
+302
+00:30:55.180 --> 00:30:55.720
+jaivards@andrew.cmu.edu: But…
+
+303
+00:30:55.800 --> 00:31:12.370
+hrishikb@andrew.cmu.edu: bumping up the limits in a short term would be good, because currently Claude is facing some token issues, like, even small KDs are using up much more tokens than it usually should. Hopefully that'll fix soon enough, and we can maybe go back to the earlier tokens.
+
+304
+00:31:12.460 --> 00:31:19.929
+hrishikb@andrew.cmu.edu: But right now, even a small query just, like, keeps on hitting different tokens, and it just runs out of memory very quick.
+
+305
+00:31:20.420 --> 00:31:35.740
+Harsha Tummala: Yeah, there's a lot of… I mean, in general, there's a lot of nuances to these tools too, right? Certain tools are very, very good with their limits. Certain tools, even though you end up paying exorbitant amounts, they still run out of limits all the time.
+
+306
+00:31:35.940 --> 00:31:44.059
+Harsha Tummala: That's kind of the case with CloudCo, too. If you ever tend to use any Opus model, it just runs out of limits in, like, 10 to 15 minutes.
+
+307
+00:31:44.190 --> 00:31:46.730
+Harsha Tummala: And you just were left wondering what happened.
+
+308
+00:31:48.350 --> 00:31:49.060
+hrishikb@andrew.cmu.edu: Yeah.
+
+309
+00:31:49.780 --> 00:31:51.000
+jaivards@andrew.cmu.edu: Yeah,
+
+310
+00:31:51.440 --> 00:32:01.079
+jaivards@andrew.cmu.edu: I've also kind of hit the limits with the Opus model, I guess, but that's a separate GitHub student account, so that's different.
+
+311
+00:32:02.540 --> 00:32:12.300
+jaivards@andrew.cmu.edu: But yeah, I guess we'll… I'll gather some feedback, and we can… we can just discuss this, maybe work out a solution.
+
+312
+00:32:12.300 --> 00:32:12.890
+Harsha Tummala: Here.
+
+313
+00:32:13.460 --> 00:32:18.730
+jaivards@andrew.cmu.edu: That's it, though. I think… Yeah.
+
+314
+00:32:19.230 --> 00:32:25.320
+jaivards@andrew.cmu.edu: So… Thanks, Hosha. Thanks, thanks, David.
+
+315
+00:32:26.430 --> 00:32:32.510
+Harsha Tummala: Thanks a lot, guys. Well, looking forward to a lot more work with you guys, and hopefully…
+
+316
+00:32:32.810 --> 00:32:35.990
+Harsha Tummala: It learned, kind of, on there first, yeah.
+
+317
+00:32:36.230 --> 00:32:36.810
+jaivards@andrew.cmu.edu: Yes.
+
+318
+00:32:40.060 --> 00:32:40.570
+jaivards@andrew.cmu.edu: Okay.
+
+319
+00:32:40.570 --> 00:32:42.660
+Harsha Tummala: I see you, Cliff.
+
+320
+00:32:44.370 --> 00:32:45.029
+hrishikb@andrew.cmu.edu: Bye, guys.
+
+321
+00:32:45.030 --> 00:32:46.000
+Ashritha: Bye-bye.
+
+322
+00:32:51.720 --> 00:32:52.870
+jaivards@andrew.cmu.edu: Recording.
+
+323
+00:32:55.910 --> 00:32:57.099
+Ashritha: I'll join.
+
diff --git a/transcripts/GMT20260402-180648_RecordingnewChat.txt b/transcripts/GMT20260402-180648_RecordingnewChat.txt
new file mode 100644
index 0000000..ecff31e
--- /dev/null
+++ b/transcripts/GMT20260402-180648_RecordingnewChat.txt
@@ -0,0 +1 @@
+00:07:58 Ashritha: brb
diff --git a/transcripts/GMT20260416-180324_Recording.cc.vtt b/transcripts/GMT20260416-180324_Recording.cc.vtt
new file mode 100644
index 0000000..ac917d2
--- /dev/null
+++ b/transcripts/GMT20260416-180324_Recording.cc.vtt
@@ -0,0 +1,590 @@
+WEBVTT
+
+00:00:07.000 --> 00:00:09.000
+Yeah, start it.
+
+00:00:09.000 --> 00:00:16.000
+So…
+
+00:00:16.000 --> 00:00:28.000
+I think…
+
+00:00:28.000 --> 00:00:39.000
+So, the…
+
+00:00:39.000 --> 00:00:41.000
+So the first thing…
+
+00:00:41.000 --> 00:00:45.000
+Uh, was it regarding the training data?
+
+00:00:45.000 --> 00:00:47.000
+So, uh, Leo, if you want to kind of…
+
+00:00:47.000 --> 00:00:52.000
+elaborate on that.
+
+00:00:52.000 --> 00:00:54.000
+So…
+
+00:00:54.000 --> 00:01:04.000
+It's basically, uh, he… there was a list of things that he had. He was saying that he could start with, uh, even without those, but…
+
+00:01:04.000 --> 00:01:06.000
+It would be great if we could have the…
+
+00:01:06.000 --> 00:01:13.000
+the label, like, what input goes to what output, and what output.
+
+00:01:13.000 --> 00:01:28.000
+Do you want to add anything there?
+
+00:01:28.000 --> 00:01:31.000
+So, when you look at you with some leadership, right?
+
+00:01:31.000 --> 00:01:36.000
+Yeah, yeah. I guess.
+
+00:01:36.000 --> 00:01:40.000
+Yeah, I think it was sent to everyone.
+
+00:01:40.000 --> 00:01:45.000
+So… yeah.
+
+00:01:45.000 --> 00:01:50.000
+Yeah, so I thought that…
+
+00:01:50.000 --> 00:02:01.000
+Yes, um…
+
+00:02:01.000 --> 00:02:06.000
+The 6th of April.
+
+00:02:06.000 --> 00:02:12.000
+And it includes a whole bunch of stuff.
+
+00:02:12.000 --> 00:02:14.000
+You have it?
+
+00:02:14.000 --> 00:02:16.000
+Do you have the… do you have the amount?
+
+00:02:16.000 --> 00:02:26.000
+It was sent to you. We could forward it back to you.
+
+00:02:26.000 --> 00:02:35.000
+Oh, favorite request on the 46 of FTU, and uh… You know, we can't find it, I'm gonna resend it.
+
+00:02:35.000 --> 00:02:40.000
+Yeah. And providing information you asked for.
+
+00:02:40.000 --> 00:02:43.000
+And I also, like, I think in the last meeting I posted it.
+
+00:02:43.000 --> 00:02:47.000
+I'll hear about you.
+
+00:02:47.000 --> 00:02:50.000
+It's this close, so…
+
+00:02:50.000 --> 00:02:57.000
+It's the… I think what happened was that he was looking for a fresh email instead of, like, a blank cell, so yeah, it did have it.
+
+00:02:57.000 --> 00:02:59.000
+Yeah.
+
+00:02:59.000 --> 00:03:05.000
+No. You remember, like, if you just see…
+
+00:03:05.000 --> 00:03:10.000
+I was responding to your email.
+
+00:03:10.000 --> 00:03:22.000
+I don't even know who you're asking that.
+
+00:03:22.000 --> 00:03:25.000
+Yeah, indeed.
+
+00:03:25.000 --> 00:03:34.000
+started using a bunch of words. Here's my inbox, and it pulls emails.
+
+00:03:34.000 --> 00:03:37.000
+Good idea.
+
+00:03:37.000 --> 00:03:42.000
+So…
+
+00:03:42.000 --> 00:03:46.000
+For these, I think you can just ask them, like…
+
+00:03:46.000 --> 00:03:48.000
+No, they're going through. Yeah.
+
+00:03:48.000 --> 00:03:51.000
+Yeah, correctly.
+
+00:03:51.000 --> 00:03:53.000
+Yeah, definitely.
+
+00:03:53.000 --> 00:04:06.000
+Yeah, that's true.
+
+00:04:06.000 --> 00:04:09.000
+Yeah, so you can just port…
+
+00:04:09.000 --> 00:04:14.000
+Yeah.
+
+00:04:14.000 --> 00:04:17.000
+Like, you just use the Android.
+
+00:04:17.000 --> 00:04:19.000
+So it should work.
+
+00:04:19.000 --> 00:04:22.000
+Yes, I am.
+
+00:04:22.000 --> 00:04:26.000
+Are you not able to contact me, let me just see…
+
+00:04:26.000 --> 00:04:28.000
+Yeah.
+
+00:04:28.000 --> 00:04:32.000
+So I guess the real question would be is how large is the data set?
+
+00:04:32.000 --> 00:04:35.000
+It's…
+
+00:04:35.000 --> 00:04:40.000
+I don't know if they wanted to focus on a specific product.
+
+00:04:40.000 --> 00:04:51.000
+I'm saying this is basically for everything.
+
+00:04:51.000 --> 00:04:54.000
+So, they can pick and choose what they want to look like.
+
+00:04:54.000 --> 00:04:58.000
+Okay. I mean, I wouldn't, I wouldn't aim to have that story.
+
+00:04:58.000 --> 00:05:04.000
+You want them drinking on a fertilizer. Come on.
+
+00:05:04.000 --> 00:05:19.000
+Yeah, I think he's not able to access, that's the thing. But, you know, we'll see.
+
+00:05:19.000 --> 00:05:23.000
+Yeah.
+
+00:05:23.000 --> 00:05:49.000
+What was this one?
+
+00:05:49.000 --> 00:05:51.000
+Yeah.
+
+00:05:51.000 --> 00:05:55.000
+I think that was the issue, though.
+
+00:05:55.000 --> 00:06:00.000
+include, like, the CS.
+
+00:06:00.000 --> 00:06:05.000
+Okay, yeah.
+
+00:06:05.000 --> 00:06:07.000
+Other than that…
+
+00:06:07.000 --> 00:06:22.000
+Yeah, there was one other thing. Uh, we're… Lou, he started work on it, and he's…
+
+00:06:22.000 --> 00:06:25.000
+Yeah, but uh… he also mentioned that
+
+00:06:25.000 --> 00:06:28.000
+the Claude, uh, Crokin limit as a…
+
+00:06:28.000 --> 00:06:36.000
+running out pretty fast. You'd think he ran out within 20 minutes or something, so…
+
+00:06:36.000 --> 00:06:45.000
+So, um, today is…
+
+00:06:45.000 --> 00:06:49.000
+Yeah. I'll probably implement, like,
+
+00:06:49.000 --> 00:06:56.000
+usage policy, but keep the limits the same. Yeah, yeah. So, I mean, if you guys…
+
+00:06:56.000 --> 00:06:58.000
+More than that?
+
+00:06:58.000 --> 00:07:01.000
+Yeah, yeah.
+
+00:07:01.000 --> 00:07:04.000
+Uh… yeah, I don't think because, uh…
+
+00:07:04.000 --> 00:07:10.000
+that portion is the one that's really heavy right now. Everyone else is more…
+
+00:07:10.000 --> 00:07:12.000
+Regarding making documents and stuff, so…
+
+00:07:12.000 --> 00:07:18.000
+You know, I don't think we're gonna hit that really soon.
+
+00:07:18.000 --> 00:07:21.000
+Yeah.
+
+00:07:21.000 --> 00:07:29.000
+Yeah, that good advice.
+
+00:07:29.000 --> 00:07:32.000
+I hope you came back and forth.
+
+00:07:32.000 --> 00:07:34.000
+And you can also, uh…
+
+00:07:34.000 --> 00:07:37.000
+as we go along.
+
+00:07:37.000 --> 00:07:43.000
+enterprise queue, where you have a team.
+
+00:07:43.000 --> 00:07:47.000
+Oh. So now we just have, like…
+
+00:07:47.000 --> 00:07:50.000
+Um, and you'll be rich for every single week.
+
+00:07:50.000 --> 00:07:53.000
+I did not know that.
+
+00:07:53.000 --> 00:07:56.000
+So, is the… still, like,
+
+00:07:56.000 --> 00:08:01.000
+the whole group policy possible, sort of, like, all the…
+
+00:08:01.000 --> 00:08:04.000
+So, let me see if I can…
+
+00:08:04.000 --> 00:08:10.000
+Okay, so it's a pay-as-you-go policy with skills.
+
+00:08:10.000 --> 00:08:17.000
+which they completely worked out.
+
+00:08:17.000 --> 00:08:19.000
+Yeah.
+
+00:08:19.000 --> 00:08:26.000
+I guess I've been facing a lot of outages with Clark, so I think the demand might be too much on there.
+
+00:08:26.000 --> 00:08:33.000
+use it every time, which is not a 9 to 5.
+
+00:08:33.000 --> 00:08:36.000
+Got it.
+
+00:08:36.000 --> 00:08:40.000
+Um, Google.
+
+00:08:40.000 --> 00:08:48.000
+I'm already recording it.
+
+00:08:48.000 --> 00:08:55.000
+Oh.
+
+00:08:55.000 --> 00:08:59.000
+Yeah, there's a trick people are finding where you start with the conversation before dying.
+
+00:08:59.000 --> 00:09:17.000
+started outside, even though you're using it during 95.
+
+00:09:17.000 --> 00:09:22.000
+Because now you can't even start a session on that, because…
+
+00:09:22.000 --> 00:09:24.000
+There's so many people using technology, right?
+
+00:09:24.000 --> 00:09:33.000
+And these values can be very good.
+
+00:09:33.000 --> 00:09:39.000
+Yeah, um… Credit, I just got the email for ODEX, yeah.
+
+00:09:39.000 --> 00:09:42.000
+I'm making the count right now.
+
+00:09:42.000 --> 00:09:48.000
+I already have way too many accounts for all this AI stuff, but I'm making a new one.
+
+00:09:48.000 --> 00:09:52.000
+They sent it both to my previous university at UMass, and…
+
+00:09:52.000 --> 00:09:56.000
+This one as well, so I'm making two accounts right now.
+
+00:09:56.000 --> 00:10:00.000
+Uh, they've not… they've not, like, closed down my previous whatsoever.
+
+00:10:00.000 --> 00:10:02.000
+Might as well.
+
+00:10:02.000 --> 00:10:05.000
+Uh, yeah, those were the two things that
+
+00:10:05.000 --> 00:10:10.000
+I think when I was talking with Liam, that, you know, when he was making it that he raised them.
+
+00:10:10.000 --> 00:10:13.000
+And, uh, I was also unsure about, like,
+
+00:10:13.000 --> 00:10:16.000
+how the group thing would work, isn't it?
+
+00:10:16.000 --> 00:10:23.000
+Other than that, uh, we're pretty much working on the ML portion right now. Leo, he's made some progress, and…
+
+00:10:23.000 --> 00:10:25.000
+But the training still has to be done.
+
+00:10:25.000 --> 00:10:29.000
+I don't think the training's done right. Yeah, it's done. So…
+
+00:10:29.000 --> 00:10:34.000
+We have the previous one and this one, so that's… that's about that.
+
+00:10:34.000 --> 00:10:37.000
+I would just suggest that you guys should take some time off.
+
+00:10:37.000 --> 00:10:41.000
+understanding how to just use AI tools again.
+
+00:10:41.000 --> 00:10:44.000
+Yeah. Because I think there's a lot of, uh…
+
+00:10:44.000 --> 00:10:48.000
+It's a lot to learn more than just a lot to understand.
+
+00:10:48.000 --> 00:10:59.000
+Because, uh, for example, just when it comes to, like, token usage or context management, there's just so much better.
+
+00:10:59.000 --> 00:11:05.000
+So the thing is, if you're using an only chat,
+
+00:11:05.000 --> 00:11:10.000
+And you're trying to…
+
+00:11:10.000 --> 00:11:14.000
+You just use regularly to explain everything that you work on.
+
+00:11:14.000 --> 00:11:30.000
+And every single time we technically open up a new section,
+
+00:11:30.000 --> 00:11:41.000
+I'm a bit enthused. I think there's an echo.
+
+00:11:41.000 --> 00:11:47.000
+Ashuta.
+
+00:11:47.000 --> 00:11:54.000
+Should be fine, yeah, this is fine. Sorry about that. So when you're talking about the context service, right? So.
+
+00:11:54.000 --> 00:12:06.000
+If you have already had a previous conversation, doesn't it just compact into working there? No, it has compacted, but the thing is, for example, if you're asking me about the unrelated question.
+
+00:12:06.000 --> 00:12:21.000
+Or a question which is very, like, slightly related to the current conversation. It looks still compact, all of the conversations that happened before, so instead of just using the 20 words that you send right now, if you use 20 plus 400, 500 words that you used before.
+
+00:12:21.000 --> 00:12:33.000
+Got it, so it still tries to relate, and I've seen it. So that would be like a bigger token that's been sent. I've also been facing a few issues, but I'm just not. I guess.
+
+00:12:33.000 --> 00:12:46.000
+how the… how it's like using the previous context. So it's not totally transparent, but you… there's, like, a few ways, or just practices which you can use to do, which is not that happens.
+
+00:12:46.000 --> 00:12:49.000
+And those practices might just help you guys a little bit.
+
+00:12:49.000 --> 00:13:10.000
+It is not in the inventory. Just to. I'd like to have a common set of best practices. You can just obviously you can just go on YouTube and or even if Claude has free courses which they give out. Those courses are like how much an hour long, or like an hour and a half.
+
+00:13:10.000 --> 00:13:18.000
+Yeah. And you can visit a little better. I think we did a couple of those participatory.
+
+00:13:18.000 --> 00:13:29.000
+Uh, the studio has two courses that, uh, we were basically part of the work that we do, and I think you get a kind of search experience. Yeah.
+
+00:13:29.000 --> 00:13:45.000
+So yeah, I've not gone through all of them, but probably should so much. It's changed me so there's too much change, there's too much to keep up, and that's that's kind of a problem, too. Yeah, it's Lord released us 4.7, right? Like.
+
+00:13:45.000 --> 00:13:57.000
+half an hour, 30 minutes ago, uh, ChatGPT, like, uh, oh god, actually, something.
+
+00:13:57.000 --> 00:14:01.000
+I think we backed up all my data as I heard about that.
+
+00:14:01.000 --> 00:14:20.000
+cybersecurity news about the finding those. Yeah, I didn't even think that open DSD was even possible to like do anything much less crash it from anywhere.
+
+00:14:20.000 --> 00:14:50.000
+It's also like to say. Yeah, sure. Yeah. Uh, we do have an updated SOW. So Cliff has just given some more feedback on that as well. So… I will like correct correct that and show like an updated version. So should we have digital signature on it, or should I print out and show it like for signatures? I'm not sure.
+
+00:14:50.000 --> 00:15:06.000
+Well, ideally, you know, you want to go through the process of doing the S1 to getting the client to provide their input on it. You know, you can do electronic signing, you can do physical signing. The whole idea is just to make sure that you have some sort of thing that people acknowledging that this current version is available.
+
+00:15:06.000 --> 00:15:08.000
+I don't know what you guys are sending it to.
+
+00:15:08.000 --> 00:15:15.000
+I'll still have to update it with his feedback, but I will be sending it to you anyway as well.
+
+00:15:15.000 --> 00:15:23.000
+Yeah, I'll go through it again. I went through the last one and I had a few comments, but I just said.
+
+00:15:23.000 --> 00:15:39.000
+I should have like there. There's the it was not as up to the there. There was a template that I could have used that that's much more in line with what CV uses. So it did forward like a lot of examples. So I I read using it.
+
+00:15:39.000 --> 00:15:56.000
+I think it's a lot better than previously. So just send us to the exam first. Yeah. Examples are good. I mean, the the whole point here is the exercise of doing it. It's it's not a legal document, but it's it's a good exercise for the team to do.
+
+00:15:56.000 --> 00:16:04.000
+It's good to contribute good taxes. Now, check what we're getting. Yeah.
+
+00:16:04.000 --> 00:16:17.000
+Other than that… So, we actually… So, we're still, I guess we're starting off into the South recording, I guess, and South Temple part.
+
+00:16:17.000 --> 00:16:45.000
+So we actually don't have a lot to show right now. It's still. And yeah, I was a bit hesitant. I considered… Uh… Maybe, like, uh, you know, that the meeting might be short, but we actually are kind of in the weeds right now, so it would take a bit before we can show, like, some of the things that we actually at least I wasn't expecting anything until like summer started.
+
+00:16:45.000 --> 00:17:10.000
+So that's… that's okay. I think right now, we've been working a bit on the architecture side of things, and we had a few… Any ideas on how to architecting. Uh, right now we had a… I think we've not finalized, but we have a basic structure in mind. I think we will be discussing the car architecture coach. We had a conversation before as well, and he had a few review points, and we can go back to him. I think.
+
+00:17:10.000 --> 00:17:16.000
+By the end of this month, we'll have a… I've got a little bit of more ground-level architecture.
+
+00:17:16.000 --> 00:17:23.000
+So currently, it's going to be pipe and filter kind of thing, because it is pretty sequential. The process that we have.
+
+00:17:23.000 --> 00:17:38.000
+There are a few open points, like the ML, how you can use it, but that's anybody going to come into one component. Yeah, exactly. So, uh, like, the architecture-wise, we should be good, uh, to start developing, uh, the next time, start late.
+
+00:17:38.000 --> 00:17:56.000
+Yeah. Probably the other thing to to be aware of. This is something that's new to us is that you know, normally at this point we are coming up on the semester presentations, and it looks like they're doing something different this year. They're doing crits, not in semester presentation. So I'll have to check and find out what what with the.
+
+00:17:56.000 --> 00:18:13.000
+The engagement with clients is at all for these crits or not, so that can be good. I mean, it was always good to have the instructor just to get a sense of what was going on, where they're going, what the plan is for the future, but we're having a meeting with all the mentors tomorrow, so I'll bring that question.
+
+00:18:13.000 --> 00:18:22.000
+Good. Yeah, I mean, Sasha sent across an email asking us about their availability, and she did mention that explicitly that.
+
+00:18:22.000 --> 00:18:27.000
+It's not… it's not necessary to, like, $10… we don't really, uh, think.
+
+00:18:27.000 --> 00:18:37.000
+There's no, like, kind of rules to the next story. And she also mentioned the word thing. Yeah. That was good.
+
+00:18:37.000 --> 00:18:51.000
+Yeah, um, yeah, semester's ending, I guess there's more things all at once.
+
+00:18:51.000 --> 00:19:12.000
+Uh, other than that, uh… No one else has something to add. Because I've mentioned all the points that I'm curious about your last trip to e parts. I had offered to give you a ride at my car died again, so how did you get there? Yeah, he's taken over. Okay.
+
+00:19:12.000 --> 00:19:22.000
+Yeah, that's the perfect thing going to fix, and we're gonna get cracked again. So we'll have another meeting so we can get you. Yeah.
+
+00:19:22.000 --> 00:19:38.000
+individual coils for each park plug and and those those that have tried those didn't work. You got to go back and get the expensive, unfortunately.
+
+00:19:38.000 --> 00:19:49.000
+So you want to reliable transportation. So I apologize, and I do look forward to coming out sometime.
+
+00:19:49.000 --> 00:20:19.000
+I think, um, from the last meeting, what can discuss for the PIMS architecture, like, now, even after, like, that meeting, I personally don't have any questions. Whatever I had was asking immediately, so… Yeah, I think the meeting related to the architecture part as well. So now we have a much more clarity about the options and how all those things are gonna work. Yeah. I think the next question we'll probably have is when we start writing something down, then that's when we'll be able to summer is probably when we'll be working together as well.
+
+00:20:20.000 --> 00:20:47.000
+Yeah. I don't know, did you clue him in with the injury to one of your teammates or not? I don't know what happened. He was, uh, he had a scooter accident, and then he was pretty bad injured. So that's why he's not with us right now. I mean, he is with us right now, but he's not like him. He's not physically in the room. Sorry.
+
+00:20:47.000 --> 00:21:05.000
+My choice, of course. I realized it after I spoke. What's the mistake?
+
+00:21:05.000 --> 00:21:06.000
+Hey! Hi, guys.
+
+00:21:06.000 --> 00:21:11.000
+He's on the call. He's on the call.
+
+00:21:11.000 --> 00:21:15.000
+So what is your, uh, plans for going back to India and getting your dental work? Yeah.
+
+00:21:15.000 --> 00:21:22.000
+Uh, I think I'll be traveling the end of this month, and then I'll be in India for, like, 2-3 weeks.
+
+00:21:22.000 --> 00:21:26.000
+But, uh, I don't think there'll be any disruption in the entire workflow, though.
+
+00:21:26.000 --> 00:21:30.000
+I will just be taking meetings from India itself.
+
+00:21:30.000 --> 00:21:32.000
+At the same time, so…
+
+00:21:32.000 --> 00:21:34.000
+Should be all good.
+
+00:21:34.000 --> 00:21:51.000
+I realize I spoke the wrong last time. It's apathy. Yeah. Physically. Yeah, that's that's what that was my opinion.
+
+00:21:51.000 --> 00:22:12.000
+Anyway, I thought it's important to tell me what's going on. We glad it's going to be okay. But sorry to be ahead.
+
+00:22:12.000 --> 00:22:13.000
+Thank you.
+
+00:22:13.000 --> 00:22:14.000
+Those darn scooters. Yeah. Was it like a. accident, which involves some other person hitting him, or they just… No, yeah, he got his bounds, and he…
+
+00:22:14.000 --> 00:22:27.000
+Well, he did tumble right? So yeah, yeah. Anyway, good to hear your voice now, Rude.
+
+00:22:27.000 --> 00:22:37.000
+Okay, see you all enjoy the nice weather. Yeah, it's it's warming. Yeah.
+
+00:22:37.000 --> 00:22:51.000
+Spring has always been too short. It transitions from winter to summer too late. Maybe you can just take the longest I've seen like the winter part luck at least.
+
+00:22:51.000 --> 00:23:05.000
+Usually micro. Oh, no, there's been many snow before. Not for a couple of years, though, I feel like it's not snowing Easter one time.
+
+00:23:05.000 --> 00:23:13.000
+I'm just sad that the weather hasn't been conducive to the world. It's just been windy every day. Yeah.
+
+00:23:13.000 --> 00:23:22.000
+I like the window. I know Wendy's great, but you just can't think about those ones, except for electronic.
+
+00:23:22.000 --> 00:23:28.000
+Yeah, nice to see you on a few years.
+
+00:23:28.000 --> 00:23:41.000
+Just let us know if you guys need any more data. The only thing that we haven't really sent across is it's covered in, like, the earlier data set, but it's just, like, the dim schema for, like, how the staging is there.
+
+00:23:41.000 --> 00:23:44.000
+So if you want me to send that across, do I?
+
+00:23:44.000 --> 00:24:03.000
+The audio schema has it marked, right? It does. It has all the attributes to the categories. So this has all the attributes mapped correctly. So BIM schema is only only useful for you guys if you're actually mapping the data into like a final staging schema. But right now you're just looking at the output kind of thing. So it's not only relevant.
+
+00:24:03.000 --> 00:24:20.000
+We're gonna have pretty significant rework of the pin schema, too. Yeah. But it should be fine for what you're doing, like we're just consolidating some tables, but it's still going to be the same idea of, like, you're just going to be outputting the safety table, which will allow for the difference. I mean, even in the meeting, you guys told that.
+
+00:24:20.000 --> 00:24:30.000
+Uh, industry standards were still similar enough to what is currently there. I mean, the industry standards are always something we'd recommend you guys to work with. Yes.
+
+00:24:30.000 --> 00:24:40.000
+Because those are standardized, they won't be affected by how we're feeling on a certain day, and it's always some of it.
+
+00:24:40.000 --> 00:24:49.000
+You too! How to stop the morning.
+
diff --git a/transcripts/GMT20260416-180324_Recording.transcript.vtt b/transcripts/GMT20260416-180324_Recording.transcript.vtt
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@@ -0,0 +1,838 @@
+WEBVTT
+
+1
+00:00:07.740 --> 00:00:09.060
+jaivards@andrew.cmu.edu: Yeah, starting.
+
+2
+00:00:09.940 --> 00:00:11.010
+jaivards@andrew.cmu.edu: So…
+
+3
+00:00:16.700 --> 00:00:19.173
+jaivards@andrew.cmu.edu: I think…
+
+4
+00:00:29.480 --> 00:00:30.780
+jaivards@andrew.cmu.edu: So the…
+
+5
+00:00:39.970 --> 00:00:45.139
+jaivards@andrew.cmu.edu: So, the first thing… Was it regarding the training data?
+
+6
+00:00:45.660 --> 00:00:49.889
+jaivards@andrew.cmu.edu: So, Liu, if you want to kind of elaborate on that?
+
+7
+00:00:51.120 --> 00:00:52.110
+jaivards@andrew.cmu.edu: Hmm.
+
+8
+00:00:53.440 --> 00:00:54.280
+jaivards@andrew.cmu.edu: So…
+
+9
+00:00:55.580 --> 00:01:11.900
+jaivards@andrew.cmu.edu: It's basically, he… there was a list of things that he had. He was saying that he could start with, even without those, but it would be great if he could have the label, like, what input goes to what output, and what we have to do.
+
+10
+00:01:14.330 --> 00:01:15.709
+jaivards@andrew.cmu.edu: Do you want to add anything there?
+
+11
+00:01:17.870 --> 00:01:18.759
+jaivards@andrew.cmu.edu: Mother .
+
+12
+00:01:24.700 --> 00:01:26.230
+jaivards@andrew.cmu.edu: health delivery, yeah.
+
+13
+00:01:30.070 --> 00:01:31.769
+jaivards@andrew.cmu.edu: Can you give somebody every day.
+
+14
+00:01:31.930 --> 00:01:34.510
+jaivards@andrew.cmu.edu: Yeah, yeah, yeah.
+
+15
+00:01:37.260 --> 00:01:43.130
+jaivards@andrew.cmu.edu: Yeah, I think it was sent to everyone, so… Yep.
+
+16
+00:01:45.890 --> 00:01:48.059
+jaivards@andrew.cmu.edu: Yeah, so I thought that…
+
+17
+00:01:51.550 --> 00:01:52.820
+jaivards@andrew.cmu.edu: Yes,
+
+18
+00:02:02.540 --> 00:02:04.780
+jaivards@andrew.cmu.edu: That's, the 6th of April.
+
+19
+00:02:06.730 --> 00:02:08.389
+jaivards@andrew.cmu.edu: You can see a whole bunch of stuff.
+
+20
+00:02:09.360 --> 00:02:09.965
+jaivards@andrew.cmu.edu: Expressive.
+
+21
+00:02:13.000 --> 00:02:14.519
+jaivards@andrew.cmu.edu: Do you have it?
+
+22
+00:02:15.170 --> 00:02:16.920
+jaivards@andrew.cmu.edu: Do you have… do you have the email?
+
+23
+00:02:17.450 --> 00:02:26.019
+jaivards@andrew.cmu.edu: If it was sent to you. We can forward it back to you.
+
+24
+00:02:26.920 --> 00:02:33.660
+jaivards@andrew.cmu.edu: If anyone can't find it, we'll resend it.
+
+25
+00:02:35.700 --> 00:02:36.540
+jaivards@andrew.cmu.edu: Yeah.
+
+26
+00:02:37.060 --> 00:02:44.380
+jaivards@andrew.cmu.edu: And providing the information you asked for. And I was looking… I think in the last meeting.
+
+27
+00:02:47.830 --> 00:02:48.890
+jaivards@andrew.cmu.edu: It's this cool.
+
+28
+00:02:49.360 --> 00:02:50.080
+jaivards@andrew.cmu.edu: So…
+
+29
+00:02:50.720 --> 00:02:57.950
+jaivards@andrew.cmu.edu: It's the… I think what happened was, he was looking for a fresh email instead of the reply itself, so yeah, it did have it.
+
+30
+00:02:58.090 --> 00:03:00.210
+jaivards@andrew.cmu.edu: Awesome. Yeah.
+
+31
+00:03:00.550 --> 00:03:01.440
+jaivards@andrew.cmu.edu: No.
+
+32
+00:03:02.180 --> 00:03:07.749
+jaivards@andrew.cmu.edu: You remember, like, if you're just seeing… It was responding to your email.
+
+33
+00:03:11.330 --> 00:03:13.210
+jaivards@andrew.cmu.edu: I don't even know who's asking that before.
+
+34
+00:03:23.250 --> 00:03:24.590
+jaivards@andrew.cmu.edu: Yeah, indeed.
+
+35
+00:03:26.470 --> 00:03:34.340
+jaivards@andrew.cmu.edu: Here's my inbox, and it pulls emails.
+
+36
+00:03:34.910 --> 00:03:36.609
+jaivards@andrew.cmu.edu: Good idea. Yeah.
+
+37
+00:03:37.930 --> 00:03:39.010
+jaivards@andrew.cmu.edu: So…
+
+38
+00:03:43.590 --> 00:03:59.680
+jaivards@andrew.cmu.edu: For these? Yeah, I think you can just ask them, like… Yeah. Yeah. Or actually… Yeah.
+
+39
+00:04:00.050 --> 00:04:06.800
+jaivards@andrew.cmu.edu: depending on whatever specific type of product they will target.
+
+40
+00:04:07.420 --> 00:04:15.230
+jaivards@andrew.cmu.edu: Yeah, so you can just sort… Yeah.
+
+41
+00:04:15.580 --> 00:04:19.490
+jaivards@andrew.cmu.edu: Like, you just use the Android. So it should work.
+
+42
+00:04:20.640 --> 00:04:26.499
+jaivards@andrew.cmu.edu: Yes, I am. Are you not able to see.
+
+43
+00:04:26.840 --> 00:04:28.950
+jaivards@andrew.cmu.edu: Yeah.
+
+44
+00:04:29.470 --> 00:04:32.529
+jaivards@andrew.cmu.edu: So I guess the question would be is, how large is the data set?
+
+45
+00:04:32.930 --> 00:04:40.960
+jaivards@andrew.cmu.edu: It's… They wanted to focus on a specific product, and then, like, understand how that goes down there.
+
+46
+00:04:41.500 --> 00:04:49.370
+jaivards@andrew.cmu.edu: But the data I send is basically for everything. So, I send them all sorts of cards, you know.
+
+47
+00:04:49.480 --> 00:04:50.490
+jaivards@andrew.cmu.edu: One time to know.
+
+48
+00:04:51.640 --> 00:04:56.850
+jaivards@andrew.cmu.edu: They can cooperate. Okay.
+
+49
+00:05:01.060 --> 00:05:06.409
+jaivards@andrew.cmu.edu: You want them drinking on it. Come on.
+
+50
+00:05:07.140 --> 00:05:14.659
+jaivards@andrew.cmu.edu: Yeah, I think he's not able to access, that's the thing, but yeah, let's get through that.
+
+51
+00:05:20.200 --> 00:05:21.530
+jaivards@andrew.cmu.edu: Yeah.
+
+52
+00:05:23.730 --> 00:05:24.720
+jaivards@andrew.cmu.edu: Moved here.
+
+53
+00:05:25.130 --> 00:05:26.330
+jaivards@andrew.cmu.edu: What was this one?
+
+54
+00:05:27.630 --> 00:05:29.359
+jaivards@andrew.cmu.edu: I'm standing there.
+
+55
+00:05:30.120 --> 00:05:37.439
+jaivards@andrew.cmu.edu: Can we Actually, just use a CS.
+
+56
+00:05:43.360 --> 00:05:44.190
+jaivards@andrew.cmu.edu: This…
+
+57
+00:05:49.740 --> 00:05:50.769
+jaivards@andrew.cmu.edu: Oh, this one.
+
+58
+00:05:51.140 --> 00:05:52.000
+jaivards@andrew.cmu.edu: Yeah.
+
+59
+00:05:52.150 --> 00:05:56.430
+jaivards@andrew.cmu.edu: I think that was the issue, though.
+
+60
+00:05:56.580 --> 00:05:58.190
+jaivards@andrew.cmu.edu: through, like, the CS.
+
+61
+00:06:01.580 --> 00:06:02.670
+jaivards@andrew.cmu.edu: Okay, yeah.
+
+62
+00:06:05.730 --> 00:06:06.880
+jaivards@andrew.cmu.edu: Other than that…
+
+63
+00:06:07.970 --> 00:06:15.310
+jaivards@andrew.cmu.edu: Yeah, there was one other thing. We're… Lou, he started work on it, and he's…
+
+64
+00:06:16.470 --> 00:06:17.480
+jaivards@andrew.cmu.edu: Let's tackle some more.
+
+65
+00:06:23.030 --> 00:06:26.140
+jaivards@andrew.cmu.edu: Yeah, but, he also mentioned that
+
+66
+00:06:26.320 --> 00:06:36.769
+jaivards@andrew.cmu.edu: The Claude, token limit is, running out pretty fast. I think he ran out within 20 minutes or something, so… Okay.
+
+67
+00:06:37.080 --> 00:06:45.280
+jaivards@andrew.cmu.edu: So, so there is… I believe one of you guys taking your token a little bit, and none of your…
+
+68
+00:06:46.400 --> 00:06:51.250
+jaivards@andrew.cmu.edu: Yeah. So, I'll probably implement, like, Usage policy?
+
+69
+00:06:51.610 --> 00:06:58.669
+jaivards@andrew.cmu.edu: But keep the limits the same. Yeah, yeah. So, I mean, if you guys do more than that, then I would say…
+
+70
+00:06:59.480 --> 00:07:02.909
+jaivards@andrew.cmu.edu: Yeah, yeah.
+
+71
+00:07:03.440 --> 00:07:17.729
+jaivards@andrew.cmu.edu: Yeah, I don't think, because that portion is the one that's really heavy right now. Everyone else is more regarding making documents and stuff, so you're not… I don't think we're gonna hit that really soon.
+
+72
+00:07:18.810 --> 00:07:20.950
+jaivards@andrew.cmu.edu: Yeah, don't even pop up.
+
+73
+00:07:22.410 --> 00:07:23.350
+jaivards@andrew.cmu.edu: Yeah.
+
+74
+00:07:23.710 --> 00:07:25.110
+jaivards@andrew.cmu.edu: Yeah, Dr. Douglas.
+
+75
+00:07:29.710 --> 00:07:31.200
+jaivards@andrew.cmu.edu: I hope you came by again.
+
+76
+00:07:33.070 --> 00:07:36.840
+jaivards@andrew.cmu.edu: And you can also, equipped.
+
+77
+00:07:37.040 --> 00:07:38.259
+jaivards@andrew.cmu.edu: I had people.
+
+78
+00:07:38.430 --> 00:07:39.220
+jaivards@andrew.cmu.edu: Cool.
+
+79
+00:07:39.750 --> 00:07:47.549
+jaivards@andrew.cmu.edu: they removed the whole promoting. Oh. So now you just have, like…
+
+80
+00:07:48.390 --> 00:07:50.439
+jaivards@andrew.cmu.edu: May you be billed for every single week.
+
+81
+00:07:51.190 --> 00:07:54.790
+jaivards@andrew.cmu.edu: I did not know that,
+
+82
+00:07:55.150 --> 00:08:01.499
+jaivards@andrew.cmu.edu: So, is the… still, like, the whole group policy possible for, like, all families?
+
+83
+00:08:01.820 --> 00:08:08.620
+jaivards@andrew.cmu.edu: member-level policy right now, so let me see if I can… Okay. So it's a pay-as-you-go policy with scrum.
+
+84
+00:08:11.010 --> 00:08:15.880
+jaivards@andrew.cmu.edu: We initially had this usage for anything, which they completely worked out.
+
+85
+00:08:18.340 --> 00:08:19.200
+jaivards@andrew.cmu.edu: Yeah.
+
+86
+00:08:20.620 --> 00:08:28.049
+jaivards@andrew.cmu.edu: I guess I've been facing a lot of outages with Florida, so I think the demand might be too much on there.
+
+87
+00:08:28.310 --> 00:08:33.210
+jaivards@andrew.cmu.edu: Like, if you're using that, I would just say use it anytime which is not at 95.
+
+88
+00:08:34.200 --> 00:08:37.010
+jaivards@andrew.cmu.edu: Alright, so…
+
+89
+00:08:40.720 --> 00:08:47.530
+jaivards@andrew.cmu.edu: I'm already recording it.
+
+90
+00:08:50.780 --> 00:08:51.580
+jaivards@andrew.cmu.edu: Oh.
+
+91
+00:08:55.760 --> 00:08:59.749
+jaivards@andrew.cmu.edu: Yeah, there's a trick people are finding where if you start the conversation before 9,
+
+92
+00:09:00.470 --> 00:09:09.079
+jaivards@andrew.cmu.edu: Then it, like, it won't take the people, like, it'll be grouped as, like, a message that's started outside, even though you're using it during 95.
+
+93
+00:09:09.230 --> 00:09:14.889
+jaivards@andrew.cmu.edu: Two weeks ago or something, one week ago.
+
+94
+00:09:16.660 --> 00:09:22.949
+jaivards@andrew.cmu.edu: But that… that just makes sure you get a session moving, though. Because now you can't even start a session for that, because…
+
+95
+00:09:23.060 --> 00:09:24.850
+jaivards@andrew.cmu.edu: There's so many people using technology.
+
+96
+00:09:25.450 --> 00:09:29.250
+jaivards@andrew.cmu.edu: And these are your handles everyone.
+
+97
+00:09:29.350 --> 00:09:30.189
+jaivards@andrew.cmu.edu: Music that one.
+
+98
+00:09:34.370 --> 00:09:47.369
+jaivards@andrew.cmu.edu: Yeah. Credit, I just got the email for codex, yeah. I'm making the account right now. I already have way too many accounts for all this AI stuff, but I'm making a new one.
+
+99
+00:09:49.340 --> 00:09:53.120
+jaivards@andrew.cmu.edu: they sent it both to my previous university at UMass, and
+
+100
+00:09:53.400 --> 00:09:56.080
+jaivards@andrew.cmu.edu: This one as well, so I'm making two accounts right now.
+
+101
+00:09:57.430 --> 00:10:01.930
+jaivards@andrew.cmu.edu: They've not… they've not, like, closed down my previous one, so I might as well.
+
+102
+00:10:02.970 --> 00:10:14.370
+jaivards@andrew.cmu.edu: Yeah, those were the two things that I think, when I was talking with Lou, that, you know, when he was making it, that he raised them. And, I was also unsure about, like.
+
+103
+00:10:14.570 --> 00:10:18.300
+jaivards@andrew.cmu.edu: how the group thing would work as well. Other than that.
+
+104
+00:10:18.480 --> 00:10:28.219
+jaivards@andrew.cmu.edu: We're pretty much working on the ML portion right now. Leo, he has made some progress, and… but the training still has to be done. I don't think the training's done right.
+
+105
+00:10:28.330 --> 00:10:34.390
+jaivards@andrew.cmu.edu: Yeah, excellent. So… We have the previous one and this one, so that's… that's about that.
+
+106
+00:10:35.080 --> 00:10:37.740
+jaivards@andrew.cmu.edu: I would also suggest that you guys should pick them up.
+
+107
+00:10:38.560 --> 00:10:43.510
+jaivards@andrew.cmu.edu: understand how to just use AI tools again? Yeah. Because…
+
+108
+00:10:43.810 --> 00:10:45.459
+jaivards@andrew.cmu.edu: I think there's a lot of,
+
+109
+00:10:45.640 --> 00:10:48.649
+jaivards@andrew.cmu.edu: It's a lot to learn, really, just a lot to understand.
+
+110
+00:10:48.870 --> 00:10:49.920
+jaivards@andrew.cmu.edu: It goes on.
+
+111
+00:10:50.100 --> 00:10:55.150
+jaivards@andrew.cmu.edu: For example, just when it comes to, like, token usage or context management, there's just so much for it.
+
+112
+00:10:56.260 --> 00:10:57.010
+jaivards@andrew.cmu.edu: Let's see.
+
+113
+00:10:57.160 --> 00:10:59.660
+jaivards@andrew.cmu.edu: Let me give you more details about our contact line.
+
+114
+00:10:59.990 --> 00:11:08.879
+jaivards@andrew.cmu.edu: So the thing is, if you're using an only chat, which only has too much in it, and you're trying to
+
+115
+00:11:09.050 --> 00:11:10.659
+jaivards@andrew.cmu.edu: I'll go forward in the chat.
+
+116
+00:11:12.250 --> 00:11:14.389
+jaivards@andrew.cmu.edu: We don't need to principles need to be worked on.
+
+117
+00:11:14.620 --> 00:11:15.970
+jaivards@andrew.cmu.edu: And that's just not the beat.
+
+118
+00:11:16.280 --> 00:11:18.760
+jaivards@andrew.cmu.edu: Every single time, we technically open up a new section.
+
+119
+00:11:19.860 --> 00:11:20.680
+jaivards@andrew.cmu.edu: It was great.
+
+120
+00:11:29.930 --> 00:11:31.540
+Ashritha: I'm a bit confused.
+
+121
+00:11:32.430 --> 00:11:33.300
+Ashritha: Take care.
+
+122
+00:11:33.670 --> 00:11:35.230
+Ashritha: I think there's an echo.
+
+123
+00:11:40.480 --> 00:11:41.759
+Ashritha: I should talk.
+
+124
+00:11:46.830 --> 00:11:50.490
+Ashritha: This should be fine. Yeah, this is… sorry about that.
+
+125
+00:11:50.640 --> 00:11:53.900
+Ashritha: So, when you're talking about the context serves, right, so…
+
+126
+00:11:54.070 --> 00:11:56.799
+Ashritha: If you have already had a previous conversation.
+
+127
+00:11:56.860 --> 00:12:05.779
+Ashritha: Doesn't it just compact it, though? It can still keep working there. No, it does compact it, but the thing is, for example, if you're asking me to completely understand the question.
+
+128
+00:12:05.830 --> 00:12:11.170
+Ashritha: Or a question which is very, like, slightly related to your current conversation.
+
+129
+00:12:11.170 --> 00:12:27.059
+Ashritha: it'll still compact all of the composition that happened before, so instead of just using the 20 words that you sent right now, it'll use the 20 plus 400, 500 words that you used before. Got it, so it still tries to relate to that email. So that would be, like, a bigger token that's been sent.
+
+130
+00:12:27.060 --> 00:12:32.959
+Ashritha: I've also been facing a few issues, but I'm just not, I guess, on the arena.
+
+131
+00:12:33.380 --> 00:12:48.000
+Ashritha: how the… how it's, like, using the previous context, so… It's not totally transparent, but you… there's, like, a few ways, or just practices which you can use to just not let that happen. And those practices might just help you guys a little bit.
+
+132
+00:12:48.440 --> 00:12:52.090
+Ashritha: to just not hit the victory. I'll definitely make that one.
+
+133
+00:12:52.560 --> 00:12:53.810
+Ashritha: Right now, just two.
+
+134
+00:12:54.260 --> 00:13:08.759
+Ashritha: I'd like to have a common set of best practices that people use. You can just… honestly, you can just go on YouTube, or even Claude has free courses, which they give out. Those courses are, like, how much? And how long?
+
+135
+00:13:08.760 --> 00:13:17.399
+Ashritha: I think we did a couple of those… We did do good, yeah.
+
+136
+00:13:17.560 --> 00:13:28.000
+Ashritha: The studio has two courses that, we were, basically part of the work that we do, and I think you get a cancer.
+
+137
+00:13:29.370 --> 00:13:47.950
+Ashritha: So, yeah, I've not gone through all that, but, probably should. There's so much that's changed, it's so kind of… there's too much change, there's too much to keep up, and that's kind of a problem, too. Yeah, it's… Lord released us 4.7, right? Like, half an hour, 30 minutes ago,
+
+138
+00:13:47.950 --> 00:13:54.059
+Ashritha: chargy, like, oh god, it's really something.
+
+139
+00:13:57.400 --> 00:14:00.999
+Ashritha: definitely backed up all my data analysis, and I heard about that.
+
+140
+00:14:01.310 --> 00:14:07.719
+Ashritha: cybersecurity news about the finding bugs and 37 rules. Oh, no, we've met those.
+
+141
+00:14:08.080 --> 00:14:15.370
+Ashritha: Yeah, I didn't even think that OpenVSD was even possible, to, like, do anything, much less crash it from internet.
+
+142
+00:14:19.850 --> 00:14:26.189
+Ashritha: It's also, like, automatic. Yeah, don't dig too much into that. Yeah.
+
+143
+00:14:27.710 --> 00:14:37.319
+Ashritha: We do have, updated SOW, so, Cliff has just given some more feedback on that as well, so…
+
+144
+00:14:38.590 --> 00:14:49.659
+Ashritha: We, I will, like, correct the… correct that and show, like, an updated version. So, should we have digital signature on it, or should I print it out and show it, like, for signatures? I'm not sure.
+
+145
+00:14:49.680 --> 00:15:07.750
+Ashritha: Well, ideally, yeah, you want to go through the process of doing the SOW and getting the client to provide their input on it. You know, you can do electronic signing, you can do physical signing. The whole idea is just to make sure that you have some sort of thing, people acknowledging that this current version is what you guys are sending it to.
+
+146
+00:15:07.970 --> 00:15:15.129
+Ashritha: I'll… I'll still have to update it with his, feedback, so I'll… but I will be sending it to you anyway as well.
+
+147
+00:15:15.320 --> 00:15:21.839
+Ashritha: Yeah, I'll go through it again. I went through the last one, I had a few moments, but I just noticed that.
+
+148
+00:15:22.500 --> 00:15:38.669
+Ashritha: No, no, it's, it's, I should have, like, there, there's, the… it was not as up to the… there, there was a template that I could have used that, that, that's much more in line with what CME uses, so it did forward, like, a lot of examples, so I re-remarked using that.
+
+149
+00:15:39.130 --> 00:15:41.299
+Ashritha: I think it's a lot better then.
+
+150
+00:15:41.300 --> 00:16:01.670
+Ashritha: Yeah. The examples are good. I mean, the whole point here is the exercise of doing it. It's not a legal document, but it's a good exercise for the team you do. Yeah, it's good to come to a consensus. Yeah.
+
+151
+00:16:03.470 --> 00:16:06.470
+Ashritha: Other than that… -Oh.
+
+152
+00:16:06.770 --> 00:16:08.139
+Ashritha: we actually…
+
+153
+00:16:08.890 --> 00:16:16.380
+Ashritha: we're still, I guess, starting off into the, some of the coding, I guess, and some TAML part.
+
+154
+00:16:16.520 --> 00:16:25.859
+Ashritha: So, we actually don't have a lot to show right now. It's still… And, yeah, I was a bit hesitant, I considered…
+
+155
+00:16:28.090 --> 00:16:46.770
+Ashritha: maybe, like, you know, that the meeting might be short, but we actually are kind of in the weeds right now, so it would take a bit before we can show, like, some of the things that we've actually finished. I mean, technically, at least I wasn't expecting anything until, like, summer started, so that's, that's okay.
+
+156
+00:16:46.990 --> 00:16:53.179
+Ashritha: I think right now, we've been working a bit on the architecture side of things, and, we had a few…
+
+157
+00:16:53.540 --> 00:17:11.710
+Ashritha: ideas on how we're going to architect the entire thing. Right now, we had a… I think we've not finalized, but we have a basic structure in mind. I think we will be discussing that with our architecture coach. We had one discussion before, I said, and he had a few review points, and we're gonna go back to him. I think by the end of this month, we'll have a…
+
+158
+00:17:12.250 --> 00:17:15.359
+Ashritha: I think what had a little bit of more ground-level architecture.
+
+159
+00:17:15.740 --> 00:17:22.530
+Ashritha: So, currently, it's going to be a pattern filter kind of thing, because it is pretty sequential, the process that we have.
+
+160
+00:17:23.000 --> 00:17:36.669
+Ashritha: There are a few open points, like the LMOS, the ML, how you can use it, but that's anyways gonna come into one component. Yeah, exactly. So, like, the architecture-wise, we should be good, to start developing, the next time, startling.
+
+161
+00:17:38.260 --> 00:17:39.030
+Ashritha: Yeah.
+
+162
+00:17:39.590 --> 00:18:03.610
+Ashritha: Probably the other thing, too, to be aware of, this is something that's new to us, is that, you know, normally at this point, we are coming up on end-of-semester presentations, and it looks like they're doing something a little different this year. They're doing crits, not end-of-semester presentations, so I'll have to check and find out what the engagement with clients is at all, for these crits or not, so that's kind of new us. I mean, it was always good to have the end of semester, just to get a sense of
+
+163
+00:18:03.610 --> 00:18:09.860
+Ashritha: What's going on, where they're going, what the plan is for the future, but we're having a meeting with the
+
+164
+00:18:09.860 --> 00:18:16.389
+Ashritha: all the mentors tomorrow, so Auburn. Yeah, I mean, Sasha sent across an email asking us.
+
+165
+00:18:16.400 --> 00:18:21.240
+Ashritha: About their availability, and she did mention it very explicitly that
+
+166
+00:18:21.500 --> 00:18:26.450
+Ashritha: It's not the… it's not necessary to, like, attend your dollars. We don't really, think…
+
+167
+00:18:26.700 --> 00:18:35.579
+Ashritha: There's no, like, hard rules to work against. And she also mentioned the little kid thing. Yeah, that was beautiful.
+
+168
+00:18:36.990 --> 00:18:50.820
+Ashritha: Yeah, yeah, as the semester is ending, I guess there's more things all at once, so… Yeah.
+
+169
+00:18:52.860 --> 00:18:57.940
+Ashritha: Other than that, no one else has something to add?
+
+170
+00:18:58.000 --> 00:19:12.200
+Ashritha: Because I've, I've mentioned all the points that I had. I'm curious about your last trip to eParts. I had offered to give you a ride, but my car died again, so how did you get there? Yeah, you took an Uber. You took an Uber? Okay, alright.
+
+171
+00:19:12.250 --> 00:19:21.319
+Ashritha: Yeah, that's a term if they got it fixed, and it'll be okay if they contract again, so… We'll have another meeting, so we can get…
+
+172
+00:19:22.410 --> 00:19:40.939
+Ashritha: My car has individual coils for each part, and those, those that, and I tried the, you know, third party, those didn't work! You gotta go back and get the expensive OEM. Yeah, unfortunately.
+
+173
+00:19:41.020 --> 00:19:46.750
+Ashritha: So, you want a reliable transportation, so I apologize, and I do look forward to coming out sometime.
+
+174
+00:19:48.520 --> 00:20:01.150
+Ashritha: I think, from the last meeting, what Prime discussed for the PIMS architecture, like, now, even after, like, that meeting, I personally don't have any questions. Whatever I had was answering any answers, so…
+
+175
+00:20:01.470 --> 00:20:20.209
+Ashritha: Yeah, I think the meeting really helped with the architecture part is it, so now we have much more clarity about the options and how all those things are gonna work. Yeah. I think the next question we'll probably have is when we start writing something down, then that's when we'll be happy. Sounds good. Yeah, I mean, summer is probably when we'll be working together as well. Yeah.
+
+176
+00:20:20.400 --> 00:20:21.360
+Ashritha: That makes sense.
+
+177
+00:20:21.780 --> 00:20:45.730
+Ashritha: I don't know, did you clue him in with the injury to one of your teammates or not? I don't know what happened? He was, he had a scooter accident, and then, he was pretty bad injured, so that's why he's not with us right now. I mean, he is with us right now, but he's not like… Bad choice. He's not physically in the room, sorry.
+
+178
+00:20:45.730 --> 00:20:57.779
+Ashritha: He's on Zoom, he's on Zoom. Sorry, engine.
+
+179
+00:20:57.780 --> 00:21:03.500
+Ashritha: bad choice, of course. I realized it after I spoke.
+
+180
+00:21:04.310 --> 00:21:06.539
+Ashritha: He's on the call. He's on the call.
+
+181
+00:21:07.980 --> 00:21:08.830
+arjunnai@andrew.cmu.edu: Bye, guys.
+
+182
+00:21:09.220 --> 00:21:10.130
+Ashritha: The character.
+
+183
+00:21:11.050 --> 00:21:15.990
+Ashritha: So what is your, plans for, going back to India and getting your dental work? Yeah.
+
+184
+00:21:16.440 --> 00:21:21.969
+arjunnai@andrew.cmu.edu: I think I'll be traveling the end of this month, and then I'll be in India for, like, 2-3 weeks.
+
+185
+00:21:22.740 --> 00:21:30.439
+arjunnai@andrew.cmu.edu: But, I don't think there'll be any disruption in the entire workflow, though. I'll just be taking meetings from India itself.
+
+186
+00:21:30.760 --> 00:21:33.830
+arjunnai@andrew.cmu.edu: At the same times, so… Should be all good.
+
+187
+00:21:34.020 --> 00:21:40.759
+Ashritha: I, I, I realize I spoke the wrong word.
+
+188
+00:21:41.180 --> 00:21:50.749
+Ashritha: Yeah, that's… that's what… that was my agenda.
+
+189
+00:21:51.070 --> 00:21:57.800
+Ashritha: Anyway, I thought it's important to kind of know what's going on with one of the teammates here. We're glad it's gonna be okay, but sorry to be ahead.
+
+190
+00:21:59.350 --> 00:22:05.680
+Ashritha: Yeah. Those darn scooters. Yeah. Was it, like,
+
+191
+00:22:05.900 --> 00:22:12.179
+Ashritha: accident which involved, like, some other person hitting him, or did he just… No, yeah, he got his balance and he…
+
+192
+00:22:12.690 --> 00:22:13.739
+Ashritha: That's… that's funny.
+
+193
+00:22:13.740 --> 00:22:14.410
+arjunnai@andrew.cmu.edu: Thank you.
+
+194
+00:22:14.950 --> 00:22:22.629
+Ashritha: But he did tumble, right? So… Yeah. That would have been so bad boys, yeah.
+
+195
+00:22:24.130 --> 00:22:26.090
+Ashritha: Anyway, good to hear your voice, everyone.
+
+196
+00:22:26.460 --> 00:22:36.700
+Ashritha: Yes. Okay. See y'all. Enjoy the nice weather. Yeah, it's warm. Yeah, it's supposed to be cold again, though.
+
+197
+00:22:36.840 --> 00:22:51.109
+Ashritha: Spring has always been too short. It transitions from winter to summer too quick. I wish we had longer. Definitely. I think this is, like, the longest I've seen, like, the winter part last, at least.
+
+198
+00:22:51.230 --> 00:23:03.960
+Ashritha: Oh, no, there's been May snow before. May snow? Yeah, there's definitely been snow before we had before. Not for a couple years, though, I feel like.
+
+199
+00:23:04.720 --> 00:23:11.770
+Ashritha: Just sad that the weather hasn't been conducive. It's just been windy every day. I hate it.
+
+200
+00:23:13.170 --> 00:23:20.709
+Ashritha: I like the windy weather. Windy's great, but you just can't take a lot of those months. Except for, like, running. Yeah.
+
+201
+00:23:22.310 --> 00:23:27.670
+Ashritha: Have a good day.
+
+202
+00:23:27.980 --> 00:23:40.930
+Ashritha: just let us know if you guys need any more data. The only thing that we haven't really sent across is… it's covered in, like, the earlier data set itself sent, but it's just, like, the PIM schema for, like, how the staging is there.
+
+203
+00:23:41.210 --> 00:23:43.460
+Ashritha: So if you want me to send that across, too, I can.
+
+204
+00:23:44.030 --> 00:24:03.829
+Ashritha: I think the earlier schema has it mapped, right? It does. This has all the attributes to the categories. So this has all the attributes mapped correctly. So, PIM schema is only useful for you guys if you're actually mapping the data into, like, a final staging schema. But right now, you're just looking at the output kind of thing, so it's not really relevant. We're gonna have…
+
+205
+00:24:03.890 --> 00:24:20.049
+Ashritha: pretty significant rework of the PIN schema, too. Yeah. But it should be fine for what you're doing, like, for just consolidating some tables, but it's still going to be the same idea of, like, you're just going to be outputting in the staking table, which will allow for the difference. I mean, even in the meeting, you guys told that
+
+206
+00:24:20.050 --> 00:24:29.310
+Ashritha: the industry standards were still similar enough to what is currently there. I mean, the industry standards are always something we'd recommend you guys to work with. Yes.
+
+207
+00:24:29.520 --> 00:24:39.649
+Ashritha: Because those are standardized, they won't be affected by how we're feeling on a certain day, and it's always easier to give us some of it.
+
+208
+00:24:40.290 --> 00:24:43.600
+Ashritha: You do?
+
+209
+00:24:44.040 --> 00:24:45.979
+Ashritha: How to stop the money?
+
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+WEBVTT
+
+1
+00:00:00.040 --> 00:00:01.320
+Ashritha: We'll put…
+
+2
+00:00:04.370 --> 00:00:07.079
+Ashritha: So, this is the failed way.
+
+3
+00:00:07.330 --> 00:00:10.969
+Ashritha: last month received from the EPAS.
+
+4
+00:00:11.600 --> 00:00:12.640
+Ashritha: dataset.
+
+5
+00:00:12.920 --> 00:00:27.720
+Ashritha: NFL helps, many, valuable information, and I do some, statistic analyze, and, I…
+
+6
+00:00:28.450 --> 00:00:34.649
+Ashritha: I will use 4 or the 5 bells to… To make our raw engine.
+
+7
+00:00:35.240 --> 00:00:41.599
+Ashritha: value repository and train our semantic machine learning model.
+
+8
+00:00:44.210 --> 00:00:54.520
+Ashritha: And as I said, before. So, this one is, what's the real… Lay of, cases.
+
+9
+00:00:56.770 --> 00:01:01.020
+Ashritha: This is a real, from… you'll…
+
+10
+00:01:02.810 --> 00:01:10.679
+Ashritha: collected from the layout situation, you, like, you correct the… Customers' email, like.
+
+11
+00:01:10.890 --> 00:01:13.830
+Ashritha: There are some… some… there are… some of their…
+
+12
+00:01:14.090 --> 00:01:18.149
+Ashritha: Paragraphs are fake, weak, or fizzy.
+
+13
+00:01:19.160 --> 00:01:22.820
+Ashritha: And, and, we, we needed, a real…
+
+14
+00:01:23.740 --> 00:01:26.859
+Ashritha: Data to train our semantic model.
+
+15
+00:01:27.180 --> 00:01:28.500
+Ashritha: Because we…
+
+16
+00:01:28.630 --> 00:01:39.529
+Ashritha: We just use the synthetic ones could make the model more… problem, more affordable than it real has.
+
+17
+00:01:41.430 --> 00:01:45.040
+Ashritha: And, this is my, 7 milestones.
+
+18
+00:01:45.420 --> 00:01:47.660
+Ashritha: The whole work…
+
+19
+00:01:48.020 --> 00:01:57.709
+Ashritha: could, be, completed in a month. And the first week, I have completed the first tool.
+
+20
+00:01:58.260 --> 00:01:59.440
+Ashritha: Mastomos.
+
+21
+00:02:00.210 --> 00:02:03.200
+Ashritha: Like, the layer 0, layer 1, and layer 2.
+
+22
+00:02:03.680 --> 00:02:07.130
+Ashritha: And, for now, what's,
+
+23
+00:02:07.610 --> 00:02:12.680
+Ashritha: 56 sanity tests are passed.
+
+24
+00:02:15.020 --> 00:02:17.379
+Ashritha: What kind of tests are those?
+
+25
+00:02:17.560 --> 00:02:23.010
+Ashritha: You said some tests are passed, right? Yeah, tests. What tests are those?
+
+26
+00:02:23.190 --> 00:02:29.450
+Ashritha: It's just, like, use some synthetic, information from the…
+
+27
+00:02:29.630 --> 00:02:34.689
+Ashritha: the fourth or five fails. Okay. Yeah.
+
+28
+00:02:38.150 --> 00:02:43.440
+Ashritha: And, I will… is this divide the…
+
+29
+00:02:43.790 --> 00:02:51.420
+Ashritha: third milestone into three parts, because this is the most complexity layer.
+
+30
+00:02:51.800 --> 00:02:55.940
+Ashritha: What was the whole, the whole model.
+
+31
+00:02:56.870 --> 00:02:59.270
+Ashritha: It's, thematic.
+
+32
+00:02:59.810 --> 00:03:01.200
+Ashritha: machine learning model.
+
+33
+00:03:02.150 --> 00:03:11.149
+Ashritha: But our, most stressful dealing with, cases is the…
+
+34
+00:03:11.700 --> 00:03:18.370
+Ashritha: low engine parts. Like, it will directly deal with 80% cases.
+
+35
+00:03:19.170 --> 00:03:30.700
+Ashritha: And, the machine remote part, it will deal with, the case is that… like, use…
+
+36
+00:03:31.990 --> 00:03:36.349
+Ashritha: use the vector matching, doesn't…
+
+37
+00:03:36.790 --> 00:03:42.610
+Ashritha: works, and it will pass through to the layer 3 and Layer 4 to get,
+
+38
+00:03:43.110 --> 00:03:49.250
+Ashritha: confidence score, and combined with the Layer 1 and Layer 2 score to get our final scores.
+
+39
+00:03:49.360 --> 00:03:51.190
+Ashritha: What these layers you are, you know?
+
+40
+00:03:51.640 --> 00:03:57.689
+Ashritha: Yes, I will show you the structure. And, this is the whole…
+
+41
+00:03:59.920 --> 00:04:05.040
+Ashritha: whole big picture, or the machine model piece of the case?
+
+42
+00:04:08.110 --> 00:04:09.660
+Ashritha: How to be seen, okay?
+
+43
+00:04:09.870 --> 00:04:11.030
+Ashritha: Crusade?
+
+44
+00:04:15.440 --> 00:04:16.320
+Ashritha: Okay.
+
+45
+00:04:18.910 --> 00:04:26.040
+Ashritha: So, at first, we received the raw materials, like, the same as we
+
+46
+00:04:26.220 --> 00:04:28.909
+Ashritha: and eval and PDF different files.
+
+47
+00:04:29.560 --> 00:04:35.229
+Ashritha: And, and… and the role fails.
+
+48
+00:04:40.230 --> 00:04:45.260
+Ashritha: Text extraction will… I think Jay will do this part, or work.
+
+49
+00:04:45.780 --> 00:04:48.830
+Ashritha: the text extraction? Yeah, yeah, yeah, because…
+
+50
+00:04:49.500 --> 00:04:52.560
+Ashritha: You have said you will use some…
+
+51
+00:04:52.900 --> 00:04:59.920
+Ashritha: Data extraction model to… to… Like, some scanning, or…
+
+52
+00:05:00.360 --> 00:05:09.760
+Ashritha: optimal, record… No, no, this is in the ML, yeah, yeah, so I leave some…
+
+53
+00:05:09.890 --> 00:05:17.630
+Ashritha: interfaces that opens to you. Yeah, okay. Okay. And from now, so… I…
+
+54
+00:05:18.770 --> 00:05:21.819
+Ashritha: The Lead Zero is designed for
+
+55
+00:05:22.190 --> 00:05:24.509
+Ashritha: Training our model, and
+
+56
+00:05:24.820 --> 00:05:28.339
+Ashritha: Directly use your, table fails.
+
+57
+00:05:28.610 --> 00:05:35.739
+Ashritha: table this dataset to create our model and to form our… Rom engine.
+
+58
+00:05:37.950 --> 00:05:46.069
+Ashritha: So, so layer one… layer zero is… that is how I use your… use your, distance to train our model.
+
+59
+00:05:46.540 --> 00:05:50.580
+Ashritha: It had… had… had been divided into three parts.
+
+60
+00:05:52.130 --> 00:05:56.780
+Ashritha: We have the standard data in the three bells.
+
+61
+00:05:57.970 --> 00:06:05.500
+Ashritha: It contains 3.5 million rows, and 1.6, Teacher bites.
+
+62
+00:06:06.240 --> 00:06:14.509
+Ashritha: It's… And, because the data is so huge, and it could not be directly
+
+63
+00:06:14.630 --> 00:06:17.469
+Ashritha: A load into our laptop.
+
+64
+00:06:17.600 --> 00:06:22.179
+Ashritha: So, I made it to, small chunks.
+
+65
+00:06:22.590 --> 00:06:26.810
+Ashritha: like, the peak memory is less than 1 gigabyte.
+
+66
+00:06:26.960 --> 00:06:32.519
+Ashritha: And, 200,000 laws as a chunk.
+
+67
+00:06:33.320 --> 00:06:37.470
+Ashritha: To fit in… into the… That type of sketch.
+
+68
+00:06:37.860 --> 00:06:47.319
+Ashritha: and wait… stratified the… the dataset into three parts. The first part.
+
+69
+00:06:48.290 --> 00:06:52.440
+Ashritha: The first 80% part as our trend stat.
+
+70
+00:06:52.550 --> 00:06:53.630
+Ashritha: And,
+
+71
+00:06:53.840 --> 00:07:03.290
+Ashritha: the 10% part at our validation set, and the last part is our testing set. And we use a fixed seed to
+
+72
+00:07:04.340 --> 00:07:12.370
+Ashritha: To, keep the… our… you want to keep our, results reproducible.
+
+73
+00:07:13.350 --> 00:07:19.339
+Ashritha: And, the Layer 1 is, I thought it's flu.
+
+74
+00:07:21.860 --> 00:07:23.720
+Ashritha: How you doing? Okay.
+
+75
+00:07:23.920 --> 00:07:29.820
+Ashritha: I can't believe it's spring, it's not spring out there. It's cold and zoomies.
+
+76
+00:07:32.630 --> 00:07:41.520
+Ashritha: Yeah, and the first layer is lived for… the text extraction, so… After this part.
+
+77
+00:07:41.810 --> 00:07:45.279
+Ashritha: Was completed by the extrusion team.
+
+78
+00:07:45.520 --> 00:07:49.990
+Ashritha: like, use the LLM or… Lambda.
+
+79
+00:07:50.900 --> 00:07:56.469
+Ashritha: The correlation machine learning models to… towards, information illustration.
+
+80
+00:07:57.610 --> 00:08:02.660
+Ashritha: I will… we will directly use those data to fit our models.
+
+81
+00:08:04.760 --> 00:08:19.200
+Ashritha: And, After weight gets, the… the… the data, we… use the raw engine to…
+
+82
+00:08:19.380 --> 00:08:21.400
+Ashritha: To our first walk.
+
+83
+00:08:22.120 --> 00:08:30.080
+Ashritha: And this… this path, Well, take 80%, pipeline suggests.
+
+84
+00:08:30.920 --> 00:08:36.490
+Ashritha: Or the work, because… Yes, I…
+
+85
+00:08:36.590 --> 00:08:42.529
+Ashritha: divided the low engine into three parts. The first part uses a parallel number match.
+
+86
+00:08:43.799 --> 00:08:49.320
+Ashritha: method to do a vector matching.
+
+87
+00:08:49.630 --> 00:08:56.500
+Ashritha: It contains 189 kilom… kilomon… Thousand?
+
+88
+00:08:56.690 --> 00:08:59.469
+Ashritha: PL number rejects it.
+
+89
+00:08:59.810 --> 00:09:03.640
+Ashritha: Most of all, it is, is, like, So…
+
+90
+00:09:04.260 --> 00:09:10.780
+Ashritha: product then, and the, venue, units, PELs.
+
+91
+00:09:11.310 --> 00:09:20.719
+Ashritha: it's like the… like this kind of format. And I made it into a regex union.
+
+92
+00:09:21.930 --> 00:09:25.799
+Ashritha: Because the… it's very… it's very long, the unit.
+
+93
+00:09:26.010 --> 00:09:29.539
+Ashritha: So, first loaded into our computer, it would
+
+94
+00:09:29.700 --> 00:09:40.050
+Ashritha: could take, 5 minutes… 5 seconds to load into our… the computer's cage. But after that,
+
+95
+00:09:40.370 --> 00:09:52.650
+Ashritha: it could… Dealing with, advocacy within And 0.1… Maybe 1 minute, millisecond.
+
+96
+00:09:52.820 --> 00:09:53.979
+Ashritha: So it's very fast.
+
+97
+00:09:54.560 --> 00:09:59.069
+Ashritha: And, compared to the, machine learning model.
+
+98
+00:09:59.830 --> 00:10:03.249
+Ashritha: It could consume, like, 50 millions.
+
+99
+00:10:03.680 --> 00:10:05.459
+Ashritha: To deal with every case.
+
+100
+00:10:06.200 --> 00:10:11.159
+Ashritha: What's… what exact, logic are we… are you talking about for partner matching?
+
+101
+00:10:11.490 --> 00:10:15.879
+Ashritha: So I get the regex part, but what's the part and what's the number, even?
+
+102
+00:10:16.230 --> 00:10:25.669
+Ashritha: You mean this layer 2? Yeah, a part number, or is it a partial part number? Yeah, exactly, like, what exactly are you referring to when you say part numbering?
+
+103
+00:10:27.290 --> 00:10:33.000
+Ashritha: Is it the product's part number, or is it the SKU, or is it, like, the…
+
+104
+00:10:33.340 --> 00:10:36.630
+Ashritha: Attributes, description, what are you talking about there?
+
+105
+00:10:41.770 --> 00:10:53.940
+Ashritha: Sometimes, you know, manufacturers have their own part number, then there's SKUs, and then you might have your own different part number. Yeah, exactly. I just want to know what this is, so that it keeps that going, that it's headed in the right direction.
+
+106
+00:10:54.600 --> 00:10:56.510
+Ashritha: Like, you input.
+
+107
+00:10:57.390 --> 00:11:10.589
+Ashritha: A segment, or… Information, and we will divided it into several, roads or information.
+
+108
+00:11:10.960 --> 00:11:22.140
+Ashritha: So, the… The cases is… every… Every information unit is a… Down anyways.
+
+109
+00:11:22.450 --> 00:11:29.440
+Ashritha: Is there a schema for the part numbers? I mean, you know, there can be a schema for a part number, right?
+
+110
+00:11:30.100 --> 00:11:30.800
+Ashritha: Whoa.
+
+111
+00:11:33.770 --> 00:11:49.960
+Ashritha: Yes, don't take this up, I mean, the point being is just trying to understand… Yeah, yeah, yeah. What are you thinking? I understand. Oh, but then, I think the simpler question would be, like, you said there are, like, one 89,000 part number rejected. Oh, so, so, you want to know… well, it's,
+
+112
+00:11:50.650 --> 00:12:01.250
+Ashritha: the number come from? Yeah, and how does it look like? What does the part number look like? Yeah, yeah. The one that you're referring to? What is the part number? Yeah, so if you just look at it, probably… Yeah, yeah, yeah.
+
+113
+00:12:02.020 --> 00:12:07.289
+Ashritha: So from this, in the… The product attribute pairs, so this is primarily just this one.
+
+114
+00:12:08.560 --> 00:12:14.010
+Ashritha: Like, like… So this, this is the most,
+
+115
+00:12:14.250 --> 00:12:23.700
+Ashritha: Detailed and plentiful information we could get, and nearly every… Useful information contains those
+
+116
+00:12:24.870 --> 00:12:28.149
+Ashritha: part numbers. Yeah, so those sway…
+
+117
+00:12:28.940 --> 00:12:36.079
+Ashritha: Can you give us a real example of a partner? Can you open that 1A file?
+
+118
+00:12:36.370 --> 00:12:38.979
+Ashritha: If you have… No, no, it's because it's very large.
+
+119
+00:12:39.850 --> 00:12:43.730
+Ashritha: We can't directly open it. Okay. It's one…
+
+120
+00:12:43.810 --> 00:13:01.290
+Ashritha: Gigabytes. Okay. Long. Okay. Yes. So if you want to say it, I will show you the next… Well, not even that. Can you just open Notepad and, like, can you show me what, like, a part number looks like, and how this would… Will work? …kind of work? Okay.
+
+121
+00:13:01.480 --> 00:13:14.100
+Ashritha: You don't have to do much, it's just, like, for me to visualize what this is. Yeah, I can ask the cloud to show you all some data. You can even, like, use this board if you want, but, like, just anything that just makes me understand.
+
+122
+00:13:20.740 --> 00:13:28.560
+Ashritha: Like, You wanna ride, or you wanna use your screen? Oh, I can.
+
+123
+00:13:28.990 --> 00:13:35.869
+Ashritha: I mean, you can just, you know… Oh, or I can… oh, yeah, check out something. Build the file. It's like…
+
+124
+00:13:36.620 --> 00:13:40.580
+Ashritha: We have so many different products. So we…
+
+125
+00:13:40.850 --> 00:13:46.280
+Ashritha: Just combine what the product use, some regulation method.
+
+126
+00:13:46.590 --> 00:13:50.460
+Ashritha: To make it a very big one, to do the matching.
+
+127
+00:13:51.640 --> 00:13:55.330
+Ashritha: So, we… Just combined with two
+
+128
+00:13:55.350 --> 00:14:14.629
+Ashritha: into three important information. So, the first one is the product attribute pairs. The second one is the manufacturer name. The third one… So you're appending everything into… Yeah, yeah, yeah, yeah, yeah. So I said it's… it could be a little long to load those
+
+129
+00:14:15.150 --> 00:14:19.519
+Ashritha: re… rejects unit into your cage. But after
+
+130
+00:14:19.700 --> 00:14:27.119
+Ashritha: loaded it, it could be… the matching is very, very quick. Less than… One millisecond.
+
+131
+00:14:27.270 --> 00:14:35.019
+Ashritha: I would recommend in a future meeting that you just do an example. Oh, okay, okay. So next time, I will…
+
+132
+00:14:35.020 --> 00:14:50.669
+Ashritha: every box, or the diagram, I will do a simple example. Just have a simple example. Yeah, yeah, yeah. Maybe I don't know how it was, you don't know. Yeah, because the thing is, you're the one who's been working on it, but I don't know what's going on. Oh, yeah, yeah, okay, okay. Yeah, yeah, yeah.
+
+133
+00:14:56.930 --> 00:15:06.189
+Ashritha: Yeah, so… So, the first one is, product and the attribute.
+
+134
+00:15:06.310 --> 00:15:13.850
+Ashritha: Apparels matching, and the second one is the manufacturer… manufacturer matching, and the third one is the value and the unit matching.
+
+135
+00:15:15.240 --> 00:15:20.030
+Ashritha: And the first one, if it could match, it will…
+
+136
+00:15:20.550 --> 00:15:26.139
+Ashritha: Directly gets the highest competence score, and will directly go
+
+137
+00:15:26.480 --> 00:15:31.600
+Ashritha: Go to, like, the dash 9 to the… Therefore.
+
+138
+00:15:31.900 --> 00:15:38.600
+Ashritha: And the other parts, if it got .85…
+
+139
+00:15:39.190 --> 00:15:45.680
+Ashritha: a competence score, or 0.65 competence score, it will towards,
+
+140
+00:15:45.970 --> 00:15:52.809
+Ashritha: semantic matching. This is what the machine learning model will get the work done.
+
+141
+00:15:53.160 --> 00:16:01.430
+Ashritha: And as a layer 4, we will get, conf… confused… confused, squaw.
+
+142
+00:16:03.250 --> 00:16:08.870
+Ashritha: The confidence score. The confidence score, yeah, yeah. Yeah, confidence score. In different ways.
+
+143
+00:16:09.130 --> 00:16:11.010
+Ashritha: And we will get the final one.
+
+144
+00:16:12.430 --> 00:16:13.980
+Ashritha: Yeah, that's the logic.
+
+145
+00:16:14.440 --> 00:16:21.419
+Ashritha: And the… Yeah, and after we get the, the,
+
+146
+00:16:21.740 --> 00:16:24.100
+Ashritha: The first goal we are to,
+
+147
+00:16:24.240 --> 00:16:32.409
+Ashritha: a safe… safety guide will, and then we will choose either census scores into
+
+148
+00:16:32.580 --> 00:16:37.919
+Ashritha: scores and the data information into the layer stray, and all the scores into the layer 4.
+
+149
+00:16:38.620 --> 00:16:43.090
+Ashritha: And, we will, send our…
+
+150
+00:16:44.380 --> 00:16:48.710
+Ashritha: Like, a flag to show how… How's this?
+
+151
+00:16:50.360 --> 00:16:53.489
+Ashritha: possible. If it's working well, or not.
+
+152
+00:16:53.850 --> 00:16:54.790
+Ashritha: Yes.
+
+153
+00:16:55.380 --> 00:16:58.400
+Ashritha: And this is my first week's work.
+
+154
+00:16:58.850 --> 00:17:06.429
+Ashritha: So this is the overview logic that you want to implement. Yeah, yeah, yeah, and this is, it is my milestone.
+
+155
+00:17:06.630 --> 00:17:08.750
+Ashritha: And how do you, how do you plan on testing?
+
+156
+00:17:09.310 --> 00:17:10.049
+Ashritha: Right now.
+
+157
+00:17:10.480 --> 00:17:23.749
+Ashritha: do some segment of this? You're gonna build all of this at once? Yeah, so I… I first… first get the big picture. Right. And I divide… I divide it to the 7 milestone
+
+158
+00:17:24.250 --> 00:17:33.970
+Ashritha: Depends on its… a correction on each part, and it's a… Complexity, or… each layer.
+
+159
+00:17:34.900 --> 00:17:35.620
+Ashritha: Yep.
+
+160
+00:17:36.320 --> 00:17:44.439
+Ashritha: So, the layer space, the most complex part, and it needs time to do pre-trained and fine-tuning.
+
+161
+00:17:45.250 --> 00:17:48.869
+Ashritha: Or may… we may need to change our…
+
+162
+00:17:49.000 --> 00:17:51.790
+Ashritha: Machine learning models to build our dataset.
+
+163
+00:17:51.900 --> 00:17:58.269
+Ashritha: So are you gonna test each layer, or what are you gonna… how are you gonna test this? Yeah, so… so this is…
+
+164
+00:17:58.770 --> 00:18:07.789
+Ashritha: How… this is… What the contribution is for their… the sales, they send it to us.
+
+165
+00:18:09.000 --> 00:18:16.330
+Ashritha: where I decided I used the 4 or 5 belts to…
+
+166
+00:18:16.660 --> 00:18:22.000
+Ashritha: Either, make our grow engine layer.
+
+167
+00:18:22.140 --> 00:18:24.339
+Ashritha: Or training our machine learning model.
+
+168
+00:18:24.740 --> 00:18:33.380
+Ashritha: But… My sanity testing is used also synonymic.
+
+169
+00:18:33.990 --> 00:18:34.870
+Ashritha: Data.
+
+170
+00:18:35.020 --> 00:18:54.130
+Ashritha: from the tables to test if it's work well. I think the main question is, for every layer, how do you implement some sort of testing so that, to make sure that the layer is correctly functioning, or if the layer is giving you what it needs? Yeah, so right now, I just do some unit tests.
+
+171
+00:18:54.600 --> 00:18:56.329
+Ashritha: But after… after…
+
+172
+00:18:57.340 --> 00:19:07.489
+Ashritha: After, contribute, every year, I will do, some basic unit testing and the whole testing.
+
+173
+00:19:07.600 --> 00:19:11.550
+Ashritha: to… What if I just work with well.
+
+174
+00:19:14.220 --> 00:19:27.899
+Ashritha: And obviously, it'd be nice to have a set of data that you know that is good data, and data that you know is going to cause a problem, and see how that reacts to your logic. That's a great point. I mean, like, you have to… I mean, for Layer 1 at least, I would highly recommend, like.
+
+175
+00:19:28.060 --> 00:19:29.230
+Ashritha: you should…
+
+176
+00:19:29.630 --> 00:19:37.440
+Ashritha: Like, someone should take a little bit of time. Yeah, yeah. Make sure that you understand if the data is… Yeah, yeah, to delete some…
+
+177
+00:19:37.640 --> 00:19:45.549
+Ashritha: Like, trash information to keep the vendor warm, and divide those information into like…
+
+178
+00:19:45.660 --> 00:19:51.040
+Ashritha: small elements. Okay. And vintage into… into…
+
+179
+00:19:51.520 --> 00:19:55.250
+Ashritha: To divide these segments into small elements.
+
+180
+00:19:55.600 --> 00:20:02.000
+Ashritha: Small and accompanied elements, and fit into our we want to… Yep.
+
+181
+00:20:02.810 --> 00:20:09.059
+Ashritha: Also, other than just the sheer counting of rows, is there any other sort of,
+
+182
+00:20:09.470 --> 00:20:12.300
+Ashritha: Like, data cleaning that was performed on the data.
+
+183
+00:20:14.080 --> 00:20:21.850
+Ashritha: Just DKing as in, like, just eliminating certain rows which are not, or certain rows which have always…
+
+184
+00:20:22.130 --> 00:20:24.450
+Ashritha: been incorrectly valued.
+
+185
+00:20:26.180 --> 00:20:33.059
+Ashritha: Yes, but… Yeah, yes, we can do this, but we… I think our…
+
+186
+00:20:33.310 --> 00:20:39.789
+Ashritha: Layer 2 and Layer 3 will help us judge if it's, like, a trash information or…
+
+187
+00:20:40.060 --> 00:20:56.570
+Ashritha: valuable information. So we don't need to do those actual work. Well, it'd be nice to have a table that shows, here's our original source data, it came from… Okay, okay. Maybe after I completely explained, I can, self-produce some
+
+188
+00:20:57.240 --> 00:20:59.930
+Ashritha: synthetic information.
+
+189
+00:21:00.280 --> 00:21:14.629
+Ashritha: They're just… they're very flirtatable. I use the data from these parts, you know, the intermediate store, whatever it is. Yeah, yeah, yeah, like, like… And I use data… Let's on those inputs on…
+
+190
+00:21:14.960 --> 00:21:20.580
+Ashritha: Wake information, or… Charging mobilization, or the wave motivation, or some…
+
+191
+00:21:21.080 --> 00:21:24.160
+Ashritha: No, it's not there. So I think what we're trying to say is.
+
+192
+00:21:24.310 --> 00:21:37.709
+Ashritha: we gave you a dataset, right? Yeah. A data set with, for example, 2 million rows. 2 million rows, yeah. And, you've taken that data, and you've made it data that's useful for the year 0 and year 1. Yeah.
+
+193
+00:21:37.900 --> 00:21:45.030
+Ashritha: So it would be good to see what's the difference on how to do your Layer 0, layer 1 data. Oh, okay, how to…
+
+194
+00:21:45.430 --> 00:21:57.380
+Ashritha: Oh, okay. How's that original data first, and manipulate it into doing what you want it to do. And how it works, and this is where you take… Okay, how to deal with different… You take a few examples.
+
+195
+00:21:57.380 --> 00:22:07.619
+Ashritha: And we walk through that, and that gives us a quicker picture. So next meeting, I believe. Yeah, yeah, just show us, how you, yeah, how exactly the samples. Yeah.
+
+196
+00:22:07.620 --> 00:22:09.290
+Ashritha: Pass loads, actually.
+
+197
+00:22:09.410 --> 00:22:32.909
+Ashritha: not pass through each layer. No, like, like, here you have, like, 1.6 GB of data, right? Like, with 3.4. And then you say you chunk them into these many number of rows. Oh, okay, so this one? Yeah, no, like, at every step, how is your data getting transformed? Oh, okay, okay. You want layer-wise? So, so you want… So, so you want the… you want to say some…
+
+198
+00:22:32.960 --> 00:22:34.069
+Ashritha: He told?
+
+199
+00:22:34.230 --> 00:22:44.739
+Ashritha: Design. Not even detailed design. So, what I'm trying to say is, so initially, we sent you data, which is technically data that you're not using in Year Zero directly. Yeah.
+
+200
+00:22:44.740 --> 00:22:55.699
+Ashritha: So, you took the data, you performed some sort of cleaning on it… Yeah, yeah, yeah. You made that data usable for your ML, and then put that in Layer 0, which is your… the standard data.
+
+201
+00:22:55.870 --> 00:23:14.940
+Ashritha: And then you took that standard data, chunked it. From there on, I get it, I get what's happening. Oh, okay. But how did you come to the standard? Oh, the initial one itself, okay. So, from here to here, you did some data transformation, right? Yeah. So that's what they want to see. Okay, just some examples.
+
+202
+00:23:15.300 --> 00:23:21.959
+Ashritha: Okay, okay, okay. Or probably the logic you used for coming to that. Yeah. Yeah, anything works on that end. Okay, okay.
+
+203
+00:23:22.180 --> 00:23:32.090
+Ashritha: Trying to make it more concrete. Yeah. Because there's so much happening underneath here, I would… we would want to see how things are going on underneath.
+
+204
+00:23:32.090 --> 00:23:42.759
+Ashritha: Because if there is, for example, an incorrect assumption on my end or your end, then we just get to know that you've done something wrong, and I could do something better, or you guys could just change something on the order of…
+
+205
+00:23:42.800 --> 00:23:46.369
+Ashritha: something that's happened behind these years. Okay, okay.
+
+206
+00:23:46.650 --> 00:23:52.030
+Ashritha: No, this sort of reasoning is excellent. We just want to have a good sense of…
+
+207
+00:23:52.030 --> 00:24:06.860
+Ashritha: Yeah, because there's no guarantee that even the data I send them is perfect, right? Yeah. So, that's it. I just want to know if there's anything that can be changed for you guys. Oh, okay, okay. Do whatever is better. Okay, yes.
+
+208
+00:24:06.980 --> 00:24:07.820
+Ashritha: Full costs.
+
+209
+00:24:09.210 --> 00:24:13.520
+Ashritha: So… I think that's the end of my journey. Okay.
+
+210
+00:24:21.540 --> 00:24:32.769
+Ashritha: What are you calling this table? What is this diagram table? What is it… what are you calling it? The big picture? What is this? Yeah, the big picture. I don't have a name for it. Okay.
+
+211
+00:24:33.130 --> 00:24:34.980
+Ashritha: But I think it could work.
+
+212
+00:24:35.120 --> 00:24:41.960
+Ashritha: It's… it's a well-designed structure. This is your version 1, or version 0.8? What is it?
+
+213
+00:24:42.430 --> 00:24:47.160
+Ashritha: What you do? Conversion system, okay.
+
+214
+00:24:50.880 --> 00:24:51.830
+Ashritha: Okay.
+
+215
+00:24:51.970 --> 00:25:05.700
+Ashritha: So, only, like, Leo worked on the ML part of it, so basically, we initiate… as per the time… I can stop sharing. On the screen, you can just pull up the plug, the… the cable.
+
+216
+00:25:06.930 --> 00:25:08.540
+Ashritha: Oh, there's…
+
+217
+00:25:15.490 --> 00:25:17.999
+Ashritha: No, it's fine, thank you.
+
+218
+00:25:23.030 --> 00:25:42.520
+Ashritha: We seem to be way behind the timelines we initially put up here. So, like, we initially thought of dividing the whole work into, like, three, the ingestion, OCR, and the ML. Liu will, like, majority be contributing at least the POC part of it on the ML.
+
+219
+00:25:42.520 --> 00:25:53.039
+Ashritha: Arjun and one of us would be doing the OCR, and then two of us would be doing the ingestion pipeline. So we haven't… as of today, we haven't,
+
+220
+00:25:53.100 --> 00:26:09.369
+Ashritha: divided the… this high-level task into subtasks, which we'll be doing by this week. But, by this week, we are planning to finalize the system architecture. So, our document was initially complete, in terms of the doc… The diagram and stuff.
+
+221
+00:26:09.370 --> 00:26:17.170
+Ashritha: But then in the studio session feedback, we had more comments with respect to how we were representing the…
+
+222
+00:26:17.170 --> 00:26:34.950
+Ashritha: the human review, etc. So, we are working on them, and then Friday, we have a meeting with our architecture coach. So, once they're done, at least from the architecture perspective, we'll be done and final. So, then, from Monday onwards, we'll adopt our, tick
+
+223
+00:26:34.980 --> 00:26:43.920
+Ashritha: Scrum, like, 3-day sprint. We have a separate sprint board deployed for it, so, and then we'll track all of this work accordingly.
+
+224
+00:26:43.920 --> 00:26:59.850
+Ashritha: So, you'll see, like, faster progress from next week onwards. No, like, as per what we proposed, the Scrum, the philosophy that we proposed, it actually facilitates faster work, so yeah, you'll see better progress from next week.
+
+225
+00:27:00.600 --> 00:27:06.330
+Ashritha: I would also say, like, isn't… isn't just you working on the initial big…
+
+226
+00:27:06.720 --> 00:27:10.019
+Ashritha: Chunk of work, just, like, gonna hold the entire team back.
+
+227
+00:27:10.360 --> 00:27:16.809
+Ashritha: In me, because, you're all depending on one person to, like, just come to a certain stage. Okay.
+
+228
+00:27:17.070 --> 00:27:20.119
+Ashritha: So I'm not sure if that's… that's gonna be a problem anytime.
+
+229
+00:27:20.370 --> 00:27:37.190
+Ashritha: I think, for the initial parts, we are planning to work parallelly, because the initial OCR part and the initial part, they can be worked inside of at least for the initial part, so I think, the first few weeks, at least, we can all work parallelly. When we try to integrate stuff, then we'll have to
+
+230
+00:27:37.190 --> 00:28:01.680
+Ashritha: Yeah, because by the architecture design that we proposed also, people… our selling point was we have our interface layer that separates the ML part of it with the rest of it, so I don't think they are interconnected. It's like, he can do his work in the background so that we can set up this whole data and the ingestion pipeline in place, everything, like, from scratch with respect to testing and stuff.
+
+231
+00:28:01.810 --> 00:28:09.849
+Ashritha: And then we can just plug and play whatever works. We need to create a standard interface that will track them in pipelines.
+
+232
+00:28:11.770 --> 00:28:16.330
+Ashritha: What about, like, different teams having to,
+
+233
+00:28:16.500 --> 00:28:21.110
+Ashritha: What's it? So for example, when you're connecting the ingestion part to,
+
+234
+00:28:21.820 --> 00:28:28.140
+Ashritha: whatever the above or below year is. How do you know it's gonna work?
+
+235
+00:28:29.830 --> 00:28:33.329
+Ashritha: Probably we'll test with the data that we have.
+
+236
+00:28:33.910 --> 00:28:53.760
+Ashritha: I didn't get your question. I feel like it's going to be a big bag, right? Okay. These different parts of the project are just going to be connected at one point. Yeah, yeah. Like, you're all working towards completing all those individual points, but how do you know those individual points and just, like, work together? So we can target it, what's the format, or the…
+
+237
+00:28:53.800 --> 00:29:00.560
+Ashritha: the data contacts or something. Yeah, yeah, yeah, yeah. So we can use the same format where we… identified.
+
+238
+00:29:00.930 --> 00:29:03.310
+Ashritha: To attribute some synthetic.
+
+239
+00:29:03.650 --> 00:29:20.540
+Ashritha: dataset to test that each part is working well. But, don't you think, yeah, the point that you mentioned, but don't you think the only thing all of us should be aligned with the schema? The schema. True, but I'm just trying to say from, like, a…
+
+240
+00:29:20.670 --> 00:29:35.630
+Ashritha: standpoint where, for example, if a certain level is 40… Okay. And it is probably not giving you the correct information downstream. Okay. Or it's getting the correct information downstream. Okay, okay. How do you know, how do you know
+
+241
+00:29:35.920 --> 00:29:38.860
+Ashritha: How you can identify the correct problem.
+
+242
+00:29:39.260 --> 00:29:44.000
+Ashritha: As in, so for example, someone working on another layer would never know what one.
+
+243
+00:29:44.000 --> 00:29:59.280
+Ashritha: what the issue is. Okay. But, I think, in that case, it'll be like, if you're working in separate layers, the person working in the downstream layer will know the expirator input that the person's layers needs, but if you're not getting it, then we can start tracing it back to
+
+244
+00:29:59.280 --> 00:30:00.480
+Ashritha: by unfair.
+
+245
+00:30:00.590 --> 00:30:09.809
+Ashritha: So I think that is the kind of thing we'll probably be doing. Yeah, makes sense. I mean, my only concern is… the thing is, you can always give inputs in the correct schema.
+
+246
+00:30:09.810 --> 00:30:21.179
+Ashritha: downstream, but the only problem comes in is the input actually correct? Correct. Yeah, yeah. Okay. So, the whole working flow is, like, continuous.
+
+247
+00:30:21.180 --> 00:30:34.030
+Ashritha: pipeline. So we can use, agreed upon date format for each part. So we can test each part is working well or not. And finally, we can compile all the data to,
+
+248
+00:30:34.250 --> 00:30:36.309
+Ashritha: The whole test.
+
+249
+00:30:36.730 --> 00:30:39.479
+Ashritha: What's more… what's a whole budget?
+
+250
+00:30:40.150 --> 00:30:50.510
+Ashritha: I only ask that because, like, you mentioned you're working in silos, and… I think this is the most efficient method, because this is just, like, a…
+
+251
+00:30:50.710 --> 00:30:53.710
+Ashritha: Continuous work booking flow.
+
+252
+00:30:54.650 --> 00:31:09.639
+Ashritha: We initially thought this might be an issue, so we have given a… I think, a few weeks gap for the integration part of it, because working silos will cause some issues while we're integrating everything parts together. So we just thought we'd just give it extra time so that any issues that arise, we can tackle it.
+
+253
+00:31:13.070 --> 00:31:15.769
+Ashritha: I'm trying to find out.
+
+254
+00:31:17.010 --> 00:31:17.840
+Ashritha: Interest.
+
+255
+00:31:22.400 --> 00:31:25.109
+Ashritha: They're good one month for interviews.
+
+256
+00:31:25.410 --> 00:31:28.430
+Ashritha: These things, right? Laws.
+
+257
+00:31:28.930 --> 00:31:35.950
+Ashritha: No, I think the… one of it, from July to August end, we'll be,
+
+258
+00:31:36.730 --> 00:31:39.250
+Ashritha: Okay. One month for our tradition, never been.
+
+259
+00:31:44.340 --> 00:31:48.000
+Ashritha: What is a fiscal name, or how do you find your core system?
+
+260
+00:31:51.050 --> 00:31:59.469
+Ashritha: Core systems are all the individual filters that we have specifically. All the individual components, ingestion, OCR, ML pipeline.
+
+261
+00:32:06.280 --> 00:32:13.630
+Ashritha: The other challenge I have right now is shifting from working cover this week to this Friday, because Thursday.
+
+262
+00:32:13.950 --> 00:32:17.619
+Ashritha: Branding up to that is an initial challenge, usually.
+
+263
+00:32:18.660 --> 00:32:23.800
+Ashritha: And you want to try and make sure that you leverage that time well. Yeah.
+
+264
+00:32:28.170 --> 00:32:31.189
+Ashritha: Well, this is just out of curiosity, is there any sort of,
+
+265
+00:32:31.410 --> 00:32:43.380
+Ashritha: meetings that you guys have internally, which will just, like, catch each and everyone up to speed on what's coming. Yeah, we're gonna have it, 3 alternate days, Monday, Wednesday, and Friday.
+
+266
+00:32:44.110 --> 00:32:52.239
+Ashritha: Stand-ups? Sorry? Stand-ups, where you come in? No, no, no, like, one and a half hours working session. Okay.
+
+267
+00:32:54.960 --> 00:33:07.380
+Ashritha: So that's really working out the details. Yeah, like, we'll work asynchronously, but then those one and a half hour slots are, like, for us to, like, come and sit together if we have any conflicts or blockers as such.
+
+268
+00:33:12.780 --> 00:33:19.340
+Ashritha: Is there a way we can also, like, move this meeting to, like, an hour squad leader, like, 3 to 4, something like that?
+
+269
+00:33:20.770 --> 00:33:28.869
+Ashritha: I think 3 to 4, we have our client meeting. We probably can swap… sorry, sorry, mentor meeting.
+
+270
+00:33:29.000 --> 00:33:31.999
+Ashritha: You can certainly move the fire, and we can document.
+
+271
+00:33:32.210 --> 00:33:51.009
+Ashritha: I mean, I would say I'm just proposing it because, it just… it just makes sense for, like, everyone, me, Jake, and David, for us to show up at that time. I see, okay. It's just easier for us, because we just wrap up working. Okay. Okay. Sure. They're flexible.
+
+272
+00:33:51.370 --> 00:33:52.310
+Ashritha: You've been doing that.
+
+273
+00:33:53.080 --> 00:33:58.040
+Ashritha: Even if it doesn't look at my head. No, yeah, we'll just discuss with Cliff and Dennis and get back.
+
+274
+00:33:59.440 --> 00:34:02.169
+Ashritha: Yeah, I think that's all we had for today.
+
+275
+00:34:05.160 --> 00:34:06.919
+Ashritha: It'll be great to see, like, just
+
+276
+00:34:06.980 --> 00:34:26.869
+Ashritha: other, like, underneath workings of each of these layers, and that's it. Just in terms of examples, that's, that's probably my only, point. Okay. Yeah, and tomorrow, sorry, not tomorrow. Next week, we'll also, like, come up with, the other core systems, like, for the ingestion.
+
+277
+00:34:26.889 --> 00:34:31.669
+Ashritha: sorry, for the ingestion, OCR, and also, like.
+
+278
+00:34:31.739 --> 00:34:55.599
+Ashritha: probably we should also come up with some sort of a QA plan. We obviously will keep on adding to it, but whatever we think of right now, since we'll be breaking up our ingestion and OCR pipeline, maybe we can also spend some time to come up with a QA plan, like, one of the use case… test cases that you just mentioned, like, each one, each of the core components should adhere by one…
+
+279
+00:34:55.600 --> 00:35:04.369
+Ashritha: expected input and output, right? So that could be one. So, a cure plan like that could… maybe we can just get validated by you guys also and see.
+
+280
+00:35:07.960 --> 00:35:08.640
+Ashritha: Something else.
+
+281
+00:35:10.380 --> 00:35:18.970
+Ashritha: So… column… We've got the next decline name in heaven, or… I just had some Okay.
+
+282
+00:35:20.860 --> 00:35:36.410
+Ashritha: Also, I had a quick question, so, if at all, like, not so soon, but maybe, like, 2 weeks from now, if we wanna, like, push some PRs or something, so do we just directly push it to the repository, the Bitbucket repository? Okay.
+
+283
+00:35:36.840 --> 00:35:54.690
+Ashritha: You can do anything there, as long as it's in your own SEO. Okay. Exactly. Okay. You can create your own pipeline, you can set everything up as… Okay. Because your admin's clear workspace. Got it. So you can technically do whatever in there, and you would have to update it on…
+
+284
+00:35:54.920 --> 00:35:56.770
+Ashritha: Okay, makes sense.
+
+285
+00:35:59.020 --> 00:36:06.839
+Ashritha: So, in the current Bitbucket, you… we are also allowed to set up workflows and stuff like that, right? Okay.
+
+286
+00:36:07.710 --> 00:36:19.890
+Ashritha: Can we also, like, publish those… any URLs? Like, a live URL or something? The live URL is for, the UI or whatever. Huh.
+
+287
+00:36:20.050 --> 00:36:26.849
+Ashritha: That'd be through Azure. Okay. So the publishing would be through Azure, but, you could technically connect
+
+288
+00:36:26.960 --> 00:36:30.420
+Ashritha: the workflow.
+
+289
+00:36:30.800 --> 00:36:40.710
+Ashritha: to an Azure deployment cycle, so that the… the end of your workflow would be something getting published to, like, URL. Okay, okay. For sure, make that work.
+
+290
+00:36:41.290 --> 00:36:56.009
+Ashritha: And, and it's also too early for that anyways. Sorry? It's also too early for that anyway. Okay. No, I was thinking we have a, since the tech… generally, whatever print boards we have, they are very, like.
+
+291
+00:36:56.010 --> 00:37:06.569
+Ashritha: traditional ones. So, now we're following a 3-day tick something. So, for that, we're gonna come up with our own custom spec, like, whatever components it requires.
+
+292
+00:37:06.570 --> 00:37:25.419
+Ashritha: So, right now, I was thinking to host it on my own repository, like, making it public, but if we have access to the Bitbucket, and if we could publish it, host it over there, I was just thinking if… can we do it then? I'll try it.
+
+293
+00:37:25.500 --> 00:37:42.559
+Ashritha: We can, we can. It can, right? No, no, like, let's say I move, like, let's assume it's a Kanban board, and then I move a particular one to done. So, we can see the history, right?
+
+294
+00:37:43.900 --> 00:37:48.379
+Ashritha: Linear… Linear supports this kind of a…
+
+295
+00:37:48.870 --> 00:37:54.759
+Ashritha: Scrum setting as well? It's a full Kanban setting. Okay. And you can customize it.
+
+296
+00:37:55.080 --> 00:37:56.309
+Ashritha: Oh, man.
+
+297
+00:37:57.050 --> 00:38:00.519
+Ashritha: I think… did we give you guys access to Leo?
+
+298
+00:38:00.800 --> 00:38:14.219
+Ashritha: I don't think so. Okay, then let me… let me see if I can just add you guys to the India account. Okay. Because it should be easier, because… so let me… I'll just show it to you, how it looks. You'll have a good idea.
+
+299
+00:38:14.530 --> 00:38:15.500
+Ashritha: Oh, God.
+
+300
+00:38:15.700 --> 00:38:16.689
+Ashritha: If I drilled it on.
+
+301
+00:38:25.160 --> 00:38:25.870
+Ashritha: Okay.
+
+302
+00:38:31.220 --> 00:38:32.209
+Ashritha: Is that a single thing.
+
+303
+00:38:38.030 --> 00:38:52.069
+Ashritha: You should just Google linear. Okay. But, linear basically makes you organize projects, so technically there's 3 different, sub-projects going on, so you can create a page for each other.
+
+304
+00:38:52.130 --> 00:39:01.690
+Ashritha: You could take tasks for each of them. Okay. You just move them across a different phase of the lifecycle. Okay. Just drag, drag and drop.
+
+305
+00:39:02.340 --> 00:39:11.390
+Ashritha: Okay, but, let's assume, like, the traditional sprint support that I have worked with on, like, they have, user story points and stuff like that.
+
+306
+00:39:12.360 --> 00:39:17.330
+Ashritha: you… there's nothing. It's a clean slate, and you can just customize it however you want.
+
+307
+00:39:17.860 --> 00:39:19.760
+Ashritha: So, it's…
+
+308
+00:39:20.100 --> 00:39:30.609
+Ashritha: like, underneath everything, it follows a very milestone-driven, epic kind of philosophy, but if you're not working with epics, if you're working with individual tasks itself… Okay.
+
+309
+00:39:30.610 --> 00:39:45.400
+Ashritha: Then, you could just do whatever with them. Okay. You could probably assign priorities if you want to, unless you want to assign priorities. You could, you could do t-shirt sizing. Okay. If you don't want to, just don't do it. You can do, just…
+
+310
+00:39:45.560 --> 00:39:54.510
+Ashritha: like, out of 10, the effort required for a specific task. If you don't want to do that, you can't do that. You don't want to do that. You can avoid that, too. Okay.
+
+311
+00:39:54.510 --> 00:40:06.050
+Ashritha: It could just be a simple, issue with just a heading, the issue description. Oh, but then, if you're allowed to use any… add any new fields, and then… then define them?
+
+312
+00:40:06.110 --> 00:40:07.730
+Ashritha: Then, yeah, then that makes sense.
+
+313
+00:40:10.070 --> 00:40:12.289
+Ashritha: It's, it's like Gina, but like…
+
+314
+00:40:12.910 --> 00:40:17.029
+Ashritha: on a very, very, customizable scale. I see.
+
+315
+00:40:18.470 --> 00:40:19.500
+Ashritha: Makes sense.
+
+316
+00:40:20.410 --> 00:40:22.900
+Ashritha: Linear also integrates really well with that.
+
+317
+00:40:23.250 --> 00:40:27.530
+Ashritha: Oh, okay. So, again, not even… You raised my…
+
+318
+00:40:27.920 --> 00:40:30.830
+Ashritha: Yeah, we also have to, like.
+
+319
+00:40:31.010 --> 00:40:38.859
+Ashritha: we have set up our whole SES one that you have heard the other day. We have to actually put it into practice this semester.
+
+320
+00:40:41.290 --> 00:40:44.650
+Ashritha: And the point I was thinking about was,
+
+321
+00:40:45.400 --> 00:40:47.999
+Ashritha: It was… it was just this stigma of…
+
+322
+00:40:48.780 --> 00:40:54.810
+Ashritha: You should probably invest a little time in thinking about the engineering aspect of it, rather than,
+
+323
+00:40:55.140 --> 00:40:59.270
+Ashritha: Developing too many features for what this specific project is.
+
+324
+00:40:59.380 --> 00:41:04.670
+Ashritha: I would say instead of, like, creating some, Advanced OCRM.
+
+325
+00:41:04.990 --> 00:41:17.629
+Ashritha: OCR way to, like, get all the text out cleanly, you should just probably, think about what's the easiest way to do OCR on a scale, and that's it. Okay. Rather than spend too much time
+
+326
+00:41:17.630 --> 00:41:34.870
+Ashritha: focusing on. Okay. That's it. Because that… that probably is, in the end, more value to you guys, too, right? Okay. Because you actually get to see how it's really implemented in the industry, versus just trying to make an experiment where there's no bounds or carries through the whole thing. Okay.
+
+327
+00:41:40.470 --> 00:41:41.400
+Ashritha: Thank you.
+
+328
+00:41:41.970 --> 00:41:42.919
+Ashritha: We would do.
+
+329
+00:41:43.220 --> 00:41:44.110
+Ashritha: Beautiful.
+
+330
+00:41:44.640 --> 00:41:54.150
+Ashritha: Good seeing you. Yeah, good seeing you. Sorry for delaying getting there right into a friend that I'll catch up with, so… Yeah, that makes sense.
+
+331
+00:41:58.960 --> 00:42:00.200
+Ashritha: Do you guys include…
+
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+WEBVTT
+
+1
+00:00:03.420 --> 00:00:04.150
+Ashritha: Yeah.
+
+2
+00:00:24.900 --> 00:00:26.729
+Ashritha: So can I directly use your mic?
+
+3
+00:00:27.060 --> 00:00:28.689
+Ashritha: Or use mine.
+
+4
+00:00:31.110 --> 00:00:33.060
+Ashritha: Yeah, you can use my mind.
+
+5
+00:00:33.410 --> 00:00:35.200
+Ashritha: You can't hear it.
+
+6
+00:00:35.590 --> 00:00:36.780
+Ashritha: Me commute?
+
+7
+00:00:37.410 --> 00:00:39.040
+Ashritha: J? Or… Yeah.
+
+8
+00:00:40.030 --> 00:00:40.849
+Ashritha: Okay, okay.
+
+9
+00:00:43.120 --> 00:00:47.800
+Ashritha: So today, I… I'd like to introduce you the…
+
+10
+00:00:48.360 --> 00:00:52.759
+Ashritha: Power layer, the layer 3 was, our machine.
+
+11
+00:00:53.470 --> 00:00:55.450
+Ashritha: lending systems.
+
+12
+00:00:55.620 --> 00:00:58.870
+Ashritha: And this layer is the most important and complex.
+
+13
+00:00:59.410 --> 00:01:01.790
+Ashritha: A layer, or the whole system.
+
+14
+00:01:01.910 --> 00:01:07.350
+Ashritha: And, this is the core, machine learning function layer.
+
+15
+00:01:07.820 --> 00:01:11.210
+Ashritha: And I divided this layer into 3.
+
+16
+00:01:11.550 --> 00:01:13.190
+Ashritha: important part.
+
+17
+00:01:13.330 --> 00:01:19.720
+Ashritha: Each… each path has its unique, functions.
+
+18
+00:01:21.250 --> 00:01:28.219
+Ashritha: And I will… First to introduce the… What layers?
+
+19
+00:01:29.080 --> 00:01:30.459
+Ashritha: Structure to you.
+
+20
+00:01:30.680 --> 00:01:33.459
+Ashritha: First, and then I will introduce you some.
+
+21
+00:01:33.620 --> 00:01:36.750
+Ashritha: night.
+
+22
+00:01:37.210 --> 00:01:47.410
+Ashritha: their customer input, and I'll show you how to… House assistant, We deal with this impulse.
+
+23
+00:01:48.970 --> 00:01:52.420
+Ashritha: So… First, Leah's Ray.
+
+24
+00:01:52.540 --> 00:01:57.439
+Ashritha: We permanently use these two fails to train our model.
+
+25
+00:02:01.720 --> 00:02:05.110
+Ashritha: This one… The first one has
+
+26
+00:02:05.370 --> 00:02:17.480
+Ashritha: nearly 200,000 rows, and the row is to… is, like, like a product rotor, and this one has…
+
+27
+00:02:17.690 --> 00:02:26.990
+Ashritha: even more roles, because this one dispels cartoon's attribability annual pals.
+
+28
+00:02:27.520 --> 00:02:32.899
+Ashritha: So… It could be 10 times what was the first bills.
+
+29
+00:02:35.460 --> 00:02:39.069
+Ashritha: And of course, Plus a fail.
+
+30
+00:02:39.480 --> 00:02:44.119
+Ashritha: We… primarily use this.
+
+31
+00:02:44.330 --> 00:02:47.089
+Ashritha: Three column information.
+
+32
+00:02:47.730 --> 00:02:51.220
+Ashritha: The first one is, product type ID.
+
+33
+00:02:51.920 --> 00:02:59.499
+Ashritha: The… the second one… the second one, the third one is the short or the extended description.
+
+34
+00:03:01.190 --> 00:03:11.360
+Ashritha: Which could be used to… formed a… The high-level dimensional data cloud.
+
+35
+00:03:15.820 --> 00:03:23.520
+Ashritha: And this one is used in the… the M3P.
+
+36
+00:03:23.880 --> 00:03:25.889
+Ashritha: Consensus and statistics.
+
+37
+00:03:26.260 --> 00:03:27.060
+Ashritha: pumps.
+
+38
+00:03:27.470 --> 00:03:33.529
+Ashritha: And we will use product ID and attribute name and value to train the second layers.
+
+39
+00:03:34.290 --> 00:03:35.770
+Ashritha: attributes.
+
+40
+00:03:38.750 --> 00:03:43.240
+Ashritha: Yeah, this is a WOTC, D… this response.
+
+41
+00:03:52.540 --> 00:03:54.220
+Ashritha: I will skip this.
+
+42
+00:03:59.290 --> 00:04:05.639
+Ashritha: So now I will, introduce you the technical overview on each part.
+
+43
+00:04:09.020 --> 00:04:14.050
+Ashritha: Layer 3, the primary… Raw it is received.
+
+44
+00:04:14.300 --> 00:04:20.160
+Ashritha: Input from layer 1 layer 2 cannot fully resolve the customer's request.
+
+45
+00:04:30.620 --> 00:04:42.240
+Ashritha: So, the first part, Or… or this layer… this… this top layer is to train The… the model.
+
+46
+00:04:42.650 --> 00:04:45.209
+Ashritha: And get, adapt.
+
+47
+00:04:45.590 --> 00:04:46.740
+Ashritha: So…
+
+48
+00:04:51.650 --> 00:04:53.340
+Ashritha: We use this.
+
+49
+00:04:54.620 --> 00:04:56.959
+Ashritha: Major trainer model.
+
+50
+00:04:57.180 --> 00:05:03.920
+Ashritha: to produce I would.
+
+51
+00:05:04.480 --> 00:05:12.420
+Ashritha: data… datasets, layer. They said… Image.
+
+52
+00:05:18.360 --> 00:05:27.700
+Ashritha: And, after training, we get, the metrics result, or… or the… information.
+
+53
+00:05:36.040 --> 00:05:38.370
+Ashritha: And we get two artifacts.
+
+54
+00:05:43.760 --> 00:05:49.050
+Ashritha: So, this is how… So, sub-layer works.
+
+55
+00:05:54.560 --> 00:06:00.589
+Ashritha: So, after we get a customer request, It's like a long pace.
+
+56
+00:06:01.160 --> 00:06:06.549
+Ashritha: The encoder produced a 384-dimensional query vector.
+
+57
+00:06:11.380 --> 00:06:19.170
+Ashritha: And we… Use our trained Artifact to do a compare… comparison.
+
+58
+00:06:20.230 --> 00:06:30.420
+Ashritha: And find the, most likely 50… 50 product type candidates.
+
+59
+00:06:32.000 --> 00:06:37.740
+Ashritha: And, get, primarily scores.
+
+60
+00:06:40.140 --> 00:06:43.490
+Ashritha: And handed it to the second sublayer.
+
+61
+00:06:46.670 --> 00:06:49.429
+Ashritha: And, this is its performance.
+
+62
+00:06:49.710 --> 00:06:57.880
+Ashritha: it… It could only consume 0.2 to 1.4 milliseconds.
+
+63
+00:06:58.620 --> 00:07:08.199
+Ashritha: Well, you… the traditional method could… could… consume… 30 minutes, many minutes, milliseconds.
+
+64
+00:07:11.600 --> 00:07:15.720
+Ashritha: And now we went to the, second sublayer.
+
+65
+00:07:19.980 --> 00:07:21.210
+Ashritha: So…
+
+66
+00:07:25.070 --> 00:07:35.690
+Ashritha: Yeah, so from the first sub-year, we get the top 5… top 50 candidly, likely neighborhoods from the first sub-year.
+
+67
+00:07:37.180 --> 00:07:49.630
+Ashritha: And, and this layer… So, so this layer, we first train,
+
+68
+00:07:49.950 --> 00:07:55.890
+Ashritha: Train the model and, get, are defense.
+
+69
+00:08:04.140 --> 00:08:09.720
+Ashritha: Those artifacts is, trample, curtains, this, just…
+
+70
+00:08:10.640 --> 00:08:14.720
+Ashritha: This way, so they can't, or… information.
+
+71
+00:08:33.470 --> 00:08:48.280
+Ashritha: Yes, and… We… the first sublayer, we get the… Likely, product type.
+
+72
+00:08:48.640 --> 00:08:57.040
+Ashritha: And for this layer, we choose, most likely prototype, and, School always… is…
+
+73
+00:08:57.320 --> 00:09:03.349
+Ashritha: attribute value pairs and get, suburbs, grades.
+
+74
+00:09:14.170 --> 00:09:16.280
+Ashritha: Yeah, this is the latest performance.
+
+75
+00:09:16.530 --> 00:09:23.329
+Ashritha: So, product-type voting only… Consume, less than 0.1 milliseconds.
+
+76
+00:09:28.130 --> 00:09:35.030
+Ashritha: And for the last sub-layer, we… Do a parent attribute screening.
+
+77
+00:09:38.040 --> 00:09:39.150
+Ashritha: You'll cease.
+
+78
+00:09:42.150 --> 00:09:43.330
+Ashritha: Eglutin.
+
+79
+00:09:49.340 --> 00:09:56.030
+Ashritha: And in this layer, we will also consign… take… take the…
+
+80
+00:09:59.030 --> 00:10:06.789
+Ashritha: The usage put… the usage… Like, frequency… the usage, or the frequency, or the…
+
+81
+00:10:06.890 --> 00:10:09.180
+Ashritha: A product to take into account.
+
+82
+00:10:12.220 --> 00:10:15.729
+Ashritha: And we get the final… compete in the school.
+
+83
+00:10:20.700 --> 00:10:25.769
+Ashritha: And in this sublayer, we'll consume 2 to 10 milliseconds.
+
+84
+00:10:31.640 --> 00:10:38.269
+Ashritha: So the total layer 3, the link latency, Could be 21 milliseconds.
+
+85
+00:10:39.260 --> 00:10:44.550
+Ashritha: So, it's less than our estimated 50 milliseconds.
+
+86
+00:10:46.310 --> 00:11:02.200
+Ashritha: And, we yield… 180… 100 and, 20, test to…
+
+87
+00:11:03.010 --> 00:11:06.420
+Ashritha: Test all the layers function well.
+
+88
+00:11:12.750 --> 00:11:18.899
+Ashritha: And I will show you some, most likely, customer request inputs.
+
+89
+00:11:19.150 --> 00:11:22.880
+Ashritha: And how these layers could deal with those inputs.
+
+90
+00:11:25.740 --> 00:11:28.509
+Ashritha: We primarily have 4 scenarios.
+
+91
+00:11:31.690 --> 00:11:33.509
+Ashritha: And, this is the background.
+
+92
+00:11:33.800 --> 00:11:37.560
+Ashritha: You can see, we have… Dividend pens or imports.
+
+93
+00:11:38.020 --> 00:11:42.679
+Ashritha: And, after layer 1's extractions inputs.
+
+94
+00:11:43.880 --> 00:11:52.020
+Ashritha: were redacted to neutral language, like texts that are accompanied by optional structure belt.
+
+95
+00:11:53.160 --> 00:11:56.420
+Ashritha: The Layer 2 is a real engine layer.
+
+96
+00:11:56.680 --> 00:12:03.400
+Ashritha: Which… It is low… it's like to do, row-based matching.
+
+97
+00:12:04.020 --> 00:12:06.250
+Ashritha: From this rate level.
+
+98
+00:12:06.800 --> 00:12:07.890
+Ashritha: a matching.
+
+99
+00:12:09.050 --> 00:12:11.919
+Ashritha: The Tier 1 is to do, example.
+
+100
+00:12:12.090 --> 00:12:13.700
+Ashritha: parts Langarduca.
+
+101
+00:12:14.100 --> 00:12:18.330
+Ashritha: If it… if it could be… Matched, matched.
+
+102
+00:12:19.220 --> 00:12:24.060
+Ashritha: We could directly get, confidence Wang.
+
+103
+00:12:24.450 --> 00:12:26.849
+Ashritha: And, pass it to the Layer 4.
+
+104
+00:12:29.250 --> 00:12:34.549
+Ashritha: If there's a first tier, could not match. We tried the…
+
+105
+00:12:35.110 --> 00:12:42.280
+Ashritha: Tell 2, the manufacturer, the expressive matching, and if it could be matched, we get,
+
+106
+00:12:42.860 --> 00:12:46.099
+Ashritha: Start competence score, 0.85.
+
+107
+00:12:46.200 --> 00:12:55.440
+Ashritha: And then translate, so… contest into the S3 to do, domestic match.
+
+108
+00:12:57.010 --> 00:13:03.640
+Ashritha: And if the tier 2 could not match, we tried to compare…
+
+109
+00:13:03.930 --> 00:13:08.430
+Ashritha: To match the numeric value and the unit look up against the
+
+110
+00:13:08.570 --> 00:13:15.329
+Ashritha: to a table, and we get a relatively low confidence score, and pass it to the state.
+
+111
+00:13:18.300 --> 00:13:22.729
+Ashritha: So, this rate process all requests that Layer 2 cannot fully resolve.
+
+112
+00:13:28.250 --> 00:13:31.529
+Ashritha: And, this is, Senator Guang.
+
+113
+00:13:32.270 --> 00:13:37.680
+Ashritha: The first scenario awarded by information density from the highest to lowest.
+
+114
+00:13:40.660 --> 00:13:44.950
+Ashritha: So, in the scenario one, way, the…
+
+115
+00:13:46.030 --> 00:13:53.269
+Ashritha: We have, most likely, most detailed natural language description was identified for that type.
+
+116
+00:13:54.450 --> 00:13:57.930
+Ashritha: The likely input could be, looking for,
+
+117
+00:13:58.430 --> 00:14:09.300
+Ashritha: 24, with damped motorway with spring returned, and need to control, 0 to 10, way… stigma.
+
+118
+00:14:09.430 --> 00:14:11.540
+Ashritha: Next seconds, which entirely will work.
+
+119
+00:14:11.840 --> 00:14:18.250
+Ashritha: So we could know That first low part number is provided.
+
+120
+00:14:19.660 --> 00:14:22.750
+Ashritha: That's… we have the description.
+
+121
+00:14:23.530 --> 00:14:26.020
+Ashritha: Like, that's a, attribute.
+
+122
+00:14:26.490 --> 00:14:29.470
+Ashritha: When you pairs, this is sufficient detail.
+
+123
+00:14:31.600 --> 00:14:41.380
+Ashritha: So, how's the layer to… deal with it, and this is the possible outcomes for layer 2.
+
+124
+00:14:41.940 --> 00:14:43.040
+Ashritha: So…
+
+125
+00:14:43.370 --> 00:14:55.290
+Ashritha: We don't have pair number, part number, the product number, and we don't have the manufacturer information. We only have the numeric and unit.
+
+126
+00:14:55.400 --> 00:14:58.269
+Ashritha: We could extract this information.
+
+127
+00:15:00.770 --> 00:15:03.970
+Ashritha: And so, their true producer is no usable support.
+
+128
+00:15:04.360 --> 00:15:10.429
+Ashritha: And, then… So this case will pass to layer screen to process.
+
+129
+00:15:18.140 --> 00:15:25.459
+Ashritha: So, first, we will… So there's very well, encoder. So…
+
+130
+00:15:26.130 --> 00:15:38.840
+Ashritha: initial language information into a high-level 384-dimensional vector into the
+
+131
+00:15:39.800 --> 00:15:43.699
+Ashritha: to the system. And we already trained,
+
+132
+00:15:44.960 --> 00:15:49.059
+Ashritha: This is, Facebook AI search.
+
+133
+00:15:50.510 --> 00:15:53.050
+Ashritha: image.
+
+134
+00:15:53.600 --> 00:15:55.370
+Ashritha: To do a match.
+
+135
+00:15:56.070 --> 00:16:01.939
+Ashritha: in, High-level… high-dimensional space.
+
+136
+00:16:02.190 --> 00:16:06.930
+Ashritha: to… To, compare in the data cloud.
+
+137
+00:16:08.770 --> 00:16:11.579
+Ashritha: We actually compared the…
+
+138
+00:16:11.900 --> 00:16:22.280
+Ashritha: Distance with the… with each cluster, and, the angle, or the geometry direction with the…
+
+139
+00:16:22.960 --> 00:16:29.689
+Ashritha: The data cloud to get the first top likely product at a time.
+
+140
+00:16:31.560 --> 00:16:40.989
+Ashritha: And the M3… the layer… the sublayer 2, the second sublayer dual voting, And the way that…
+
+141
+00:16:41.240 --> 00:16:50.959
+Ashritha: The, most likely product type, like, like, it could be the damper act… actual… actuator.
+
+142
+00:16:52.010 --> 00:16:57.430
+Ashritha: or the, or what other product type.
+
+143
+00:17:00.070 --> 00:17:05.470
+Ashritha: Because… This could… could be the… it…
+
+144
+00:17:06.750 --> 00:17:13.529
+Ashritha: it matches 43 per dots, so… We get,
+
+145
+00:17:14.990 --> 00:17:23.569
+Ashritha: A permanent score, a competence product type competence score, like, 35.62.
+
+146
+00:17:25.599 --> 00:17:31.729
+Ashritha: And, so… and this score could… is… is higher… it's safe.
+
+147
+00:17:32.100 --> 00:17:38.019
+Ashritha: than 0.80. So this could be considered as high constant suspension.
+
+148
+00:17:42.880 --> 00:17:46.939
+Ashritha: And then, the last several year scores.
+
+149
+00:17:47.450 --> 00:17:51.909
+Ashritha: Across four, attributes under this product type.
+
+150
+00:17:56.740 --> 00:18:01.839
+Ashritha: This information is extracted from the… Context.
+
+151
+00:18:03.320 --> 00:18:04.450
+Ashritha: Like this.
+
+152
+00:18:14.350 --> 00:18:16.800
+Ashritha: And for the attribute score.
+
+153
+00:18:16.900 --> 00:18:20.640
+Ashritha: So, first 3 is higher than 80…
+
+154
+00:18:21.010 --> 00:18:28.960
+Ashritha: 0.85, so it's waterprocessed, and the last one, is less than… 8 port…
+
+155
+00:18:29.110 --> 00:18:34.340
+Ashritha: 0.85, so it could hand out to human… Review.
+
+156
+00:18:38.630 --> 00:18:41.950
+Ashritha: And, this is the second scenario.
+
+157
+00:18:43.560 --> 00:18:46.020
+Ashritha: We have complete information.
+
+158
+00:18:47.080 --> 00:18:49.839
+Ashritha: But no standard terminology.
+
+159
+00:18:51.920 --> 00:19:07.090
+Ashritha: And, this is… And this scenario could, Show how our… Symmetric…
+
+160
+00:19:08.800 --> 00:19:12.269
+Ashritha: Mechanisms to deal with this condition.
+
+161
+00:19:15.610 --> 00:19:23.850
+Ashritha: And this is what the machine learning… Good, good tool, to deal.
+
+162
+00:19:25.720 --> 00:19:33.399
+Ashritha: Like, the customer input is, like, wait, he needs, needs, some mister that…
+
+163
+00:19:33.940 --> 00:19:41.530
+Ashritha: clumps onto a pipe, then the 10,000 can for an outdoor air handle.
+
+164
+00:19:41.850 --> 00:19:50.300
+Ashritha: So… It could be translated to, the customer provides
+
+165
+00:19:51.680 --> 00:19:56.770
+Ashritha: This song, the information is, like, We know the product type.
+
+166
+00:19:57.320 --> 00:20:02.420
+Ashritha: The resistance value, the mounting type, and the intensity element.
+
+167
+00:20:03.410 --> 00:20:10.850
+Ashritha: But, the input is not… is not… standard technology.
+
+168
+00:20:12.390 --> 00:20:20.040
+Ashritha: But we… after we're mapping those information into the… high-level… Dimension of space.
+
+169
+00:20:20.480 --> 00:20:28.780
+Ashritha: We could… could… It could be, gotcha.
+
+170
+00:20:29.910 --> 00:20:30.780
+Ashritha: Who?
+
+171
+00:20:30.780 --> 00:20:36.559
+Harsha Tummala: I had a small question. So you mentioned customer input, right?
+
+172
+00:20:36.840 --> 00:20:41.869
+Harsha Tummala: And aren't we trying to, create something which is, like,
+
+173
+00:20:42.380 --> 00:20:46.339
+Harsha Tummala: Like a catalog, creator.
+
+174
+00:20:46.950 --> 00:20:51.049
+Harsha Tummala: It looks like the customer is doing the catalog search instead.
+
+175
+00:20:57.730 --> 00:21:05.080
+Ashritha: Yes, it's like to… Where we collect the customers.
+
+176
+00:21:05.460 --> 00:21:08.660
+Ashritha: This is the primary information from the customer's input.
+
+177
+00:21:09.170 --> 00:21:12.750
+Ashritha: And to do, matching.
+
+178
+00:21:17.240 --> 00:21:18.700
+Ashritha: Yeah, you can go.
+
+179
+00:21:19.220 --> 00:21:30.119
+Ashritha: To do a matching in our trained artifacts, like, the chin… Clusters in our… Through the cloud.
+
+180
+00:21:35.790 --> 00:21:36.660
+Ashritha: Amazing.
+
+181
+00:21:37.370 --> 00:21:39.050
+Harsha Tummala: I mean, just…
+
+182
+00:21:39.570 --> 00:21:46.750
+Harsha Tummala: I think I was just lost on, why, we were… we were, doing queries, or, like.
+
+183
+00:21:46.920 --> 00:21:50.379
+Harsha Tummala: What sort of parts the customers were looking for.
+
+184
+00:21:51.480 --> 00:21:52.539
+Harsha Tummala: And that's it.
+
+185
+00:21:52.960 --> 00:21:58.360
+Harsha Tummala: Because technically, what we're trying to do is take in the…
+
+186
+00:21:58.530 --> 00:22:02.710
+Harsha Tummala: Catalog data given by customers, and trying to
+
+187
+00:22:02.960 --> 00:22:07.009
+Harsha Tummala: Refine it into usable catalog data.
+
+188
+00:22:16.770 --> 00:22:18.219
+Ashritha: You…
+
+189
+00:22:22.140 --> 00:22:35.260
+Ashritha: So… it's like… In my mind, I think we are trying to do some… Product matching job.
+
+190
+00:22:35.450 --> 00:22:38.669
+Ashritha: To help the customers to identify
+
+191
+00:22:39.430 --> 00:22:43.230
+Ashritha: Which product is the most possible Wang.
+
+192
+00:22:46.440 --> 00:22:53.399
+Harsha Tummala: That sounds a little different, right, isn't it?
+
+193
+00:22:54.240 --> 00:23:01.909
+Harsha Tummala: I don't know, I think Ashita, it helped, just like… Am I… am I saying something that's not in line, Ashita?
+
+194
+00:23:02.920 --> 00:23:04.340
+Harsha Tummala: Oil.
+
+195
+00:23:06.370 --> 00:23:09.980
+Ashritha: Like, what… you understood the question, right?
+
+196
+00:23:10.550 --> 00:23:15.340
+Ashritha: Yeah. Okay, Aharsha, do you mind repeating the question again?
+
+197
+00:23:15.870 --> 00:23:18.099
+Harsha Tummala: Yeah, the question is,
+
+198
+00:23:18.270 --> 00:23:24.279
+Harsha Tummala: I see you discussing cases where the customer's trying to find a certain type of product.
+
+199
+00:23:24.850 --> 00:23:28.919
+Harsha Tummala: But technically, what we're trying to do is…
+
+200
+00:23:29.270 --> 00:23:35.060
+Harsha Tummala: take the data from, like, CSV, PDF, etc, etc, whatever it is.
+
+201
+00:23:35.250 --> 00:23:38.309
+Harsha Tummala: And turn it into usable catalog data.
+
+202
+00:23:39.220 --> 00:23:43.890
+Harsha Tummala: But, like, looking at this, the use case seems to be,
+
+203
+00:23:44.020 --> 00:23:47.669
+Harsha Tummala: How do I help the customer find the most accurate product?
+
+204
+00:23:48.880 --> 00:23:51.359
+Harsha Tummala: In our database, which is already there, yeah.
+
+205
+00:23:51.360 --> 00:23:55.570
+David Mine: Yeah, this seems to me to be something where it takes the,
+
+206
+00:23:55.920 --> 00:23:58.929
+David Mine: The customer is searching for something.
+
+207
+00:23:59.830 --> 00:24:00.440
+Harsha Tummala: Yeah.
+
+208
+00:24:00.660 --> 00:24:04.319
+David Mine: Well, I think what we were looking for is that the customer is providing
+
+209
+00:24:04.510 --> 00:24:07.990
+David Mine: A catalog, and they just got it from…
+
+210
+00:24:08.230 --> 00:24:17.019
+David Mine: you know, 7 or 8 different suppliers. It's all different formats, and there are varying levels of effort put into
+
+211
+00:24:17.210 --> 00:24:25.959
+David Mine: how robust those catalogs are. So in order to be helpful to them ordering from all these catalogs.
+
+212
+00:24:26.430 --> 00:24:29.010
+David Mine: What we would want to do is standardize
+
+213
+00:24:29.230 --> 00:24:40.079
+David Mine: The names for things, standardize, the product types, the attributes, so that way it all kind of feels the same to the catalog.
+
+214
+00:24:40.240 --> 00:24:42.960
+David Mine: Or it feels the same to the customer.
+
+215
+00:24:43.480 --> 00:24:54.979
+David Mine: And then they could… we could, you know, search on that. There are other ways to improve the customer catalog experience as well in searching, but right now, it's that ingestion piece, right? If I have
+
+216
+00:24:55.400 --> 00:25:02.530
+David Mine: 20 CSV files from 20 different suppliers, how can it appear to be the same catalog?
+
+217
+00:25:02.780 --> 00:25:05.660
+David Mine: Where everything's in the right place, with the same name.
+
+218
+00:25:08.490 --> 00:25:09.960
+Ashritha: Oh, yes.
+
+219
+00:25:09.960 --> 00:25:12.460
+Harsha Tummala: how we get the data into PIMS, in a way.
+
+220
+00:25:12.740 --> 00:25:14.460
+Harsha Tummala: Rather than, how.
+
+221
+00:25:14.820 --> 00:25:16.559
+Harsha Tummala: search for the dynamics panel.
+
+222
+00:25:17.640 --> 00:25:26.050
+Ashritha: Ha, so I think the… I mean, if I understand it correctly… Yeah, yeah, so I… Just to,
+
+223
+00:25:27.890 --> 00:25:30.480
+Ashritha: To prove the system is.
+
+224
+00:25:31.440 --> 00:25:35.679
+Ashritha: Blue Bust, and so I just chose the most.
+
+225
+00:25:36.520 --> 00:25:47.439
+Ashritha: Like, vague input, further input to test the… how our… Like, the second layers.
+
+226
+00:25:47.490 --> 00:26:06.359
+Ashritha: No, I think, Harsha, like, is the confusion about usable output, like, I think by usable output, we are trying to say that, he is… he would, in the ML layer itself, he's gonna, like, normalize the data and validate it against the,
+
+227
+00:26:06.380 --> 00:26:11.999
+Ashritha: industry stand… the conventions that you were… nomenclature that you were talking about, right? So that…
+
+228
+00:26:12.400 --> 00:26:19.289
+Ashritha: whatever just David just mentioned, we are ingesting it into PIMS, not to make it…
+
+229
+00:26:19.530 --> 00:26:25.359
+Ashritha: how do I say, search efficient for the customer, but make it
+
+230
+00:26:25.770 --> 00:26:30.630
+Ashritha: Aligned with the industry, prescribed nomenclature.
+
+231
+00:26:31.350 --> 00:26:35.590
+Harsha Tummala: Yeah, exactly. I mean, I think I got thrown off by the example that Leo just gave.
+
+232
+00:26:35.790 --> 00:26:47.239
+Harsha Tummala: Okay, okay. Which was… which was just, like, a customer specifying a certain type of product. Like, if you go back to, like, Layer 2, the example that was there in Layer 2,
+
+233
+00:26:48.060 --> 00:26:54.769
+Harsha Tummala: Like, the examples I gave, just up a little more. If you can scroll up a little.
+
+234
+00:26:57.410 --> 00:27:03.000
+Harsha Tummala: Yeah, like, the input… oh, sorry, can you go to the layer 2 input? Go down, go down, sorry.
+
+235
+00:27:06.110 --> 00:27:11.449
+Harsha Tummala: Yeah, this one. So, need a thermistor that clamps onto a pipe, the 10K kind?
+
+236
+00:27:11.740 --> 00:27:21.180
+Harsha Tummala: This looked like a search criteria for the customer, and that's what threw me off. I think, I think what Leo wanted to convey was.
+
+237
+00:27:21.500 --> 00:27:27.509
+Harsha Tummala: How these, how these… How the words… how the words are, like, connected to each.
+
+238
+00:27:27.510 --> 00:27:27.910
+Ashritha: Yeah.
+
+239
+00:27:27.910 --> 00:27:31.159
+Harsha Tummala: In terms of similarity and how they're ranked according to similarities.
+
+240
+00:27:31.160 --> 00:27:36.690
+Ashritha: But, usually imports could be standardized and not so vague.
+
+241
+00:27:37.590 --> 00:27:38.340
+Ashritha: And,
+
+242
+00:27:38.340 --> 00:27:39.180
+Harsha Tummala: Yay.
+
+243
+00:27:39.440 --> 00:27:47.860
+Ashritha: And I think we can perfectly deal with that situation. So, I mean, in the most badly…
+
+244
+00:27:48.000 --> 00:27:54.769
+Ashritha: situation, I hope… we could deal with those cases, that's what I want to show you.
+
+245
+00:27:54.890 --> 00:28:00.170
+Harsha Tummala: Yeah, this makes sense. I was thrown off completely by the way the question was phrased, and that's it.
+
+246
+00:28:01.710 --> 00:28:02.960
+Ashritha: Yes.
+
+247
+00:28:03.530 --> 00:28:11.029
+Harsha Tummala: Yeah, because even the previous question seemed to be phrased in a similar manner, where a person was looking for something, rather than giving information.
+
+248
+00:28:11.030 --> 00:28:11.610
+Ashritha: Yes, yes.
+
+249
+00:28:11.610 --> 00:28:13.090
+Harsha Tummala: So there's no…
+
+250
+00:28:13.090 --> 00:28:13.460
+Ashritha: I…
+
+251
+00:28:13.460 --> 00:28:15.630
+Harsha Tummala: It was just a communication thing, and that's it. Bye-bye.
+
+252
+00:28:15.630 --> 00:28:16.270
+Ashritha: Yes.
+
+253
+00:28:17.410 --> 00:28:28.960
+Ashritha: Yeah, I know. So… I just want to show you the, most bad knee condition the… how the…
+
+254
+00:28:29.220 --> 00:28:30.430
+Ashritha: mechanisms.
+
+255
+00:28:31.060 --> 00:28:31.929
+Harsha Tummala: Input can be, yeah.
+
+256
+00:28:31.930 --> 00:28:32.970
+Ashritha: delicate.
+
+257
+00:28:33.090 --> 00:28:34.170
+Ashritha: Yeah, yes, yes.
+
+258
+00:28:35.120 --> 00:28:37.390
+Harsha Tummala: Yeah, I got it, I got what you meant now, yeah.
+
+259
+00:28:38.260 --> 00:28:38.790
+Ashritha: Yes.
+
+260
+00:28:47.000 --> 00:28:51.300
+Ashritha: It's, like, in this scenario,
+
+261
+00:28:56.360 --> 00:29:06.290
+Ashritha: So we could… we could let the customer terminology diverges from, Can you… Canonical database vocabulary.
+
+262
+00:29:11.450 --> 00:29:16.330
+Ashritha: So it could be… could understand all the synonymi relationships.
+
+263
+00:29:21.250 --> 00:29:25.520
+Ashritha: And it gets, Write complaint scores.
+
+264
+00:29:28.540 --> 00:29:30.859
+Ashritha: And the instantaneous way.
+
+265
+00:29:31.250 --> 00:29:35.899
+Ashritha: If the input contains ambiguous product type.
+
+266
+00:29:37.430 --> 00:29:40.940
+Ashritha: Like, the customer's input could be, like.
+
+267
+00:29:41.690 --> 00:29:48.180
+Ashritha: Need pricing on this manufacturer, like, this product.
+
+268
+00:29:48.360 --> 00:29:51.439
+Ashritha: And with some attribute value pairs.
+
+269
+00:29:52.640 --> 00:30:01.369
+Ashritha: So, because this product… that type could be vague, it could turn… could match in several,
+
+270
+00:30:01.470 --> 00:30:03.590
+Ashritha: Different product types.
+
+271
+00:30:05.460 --> 00:30:08.449
+Ashritha: Like, these two different kinds of products.
+
+272
+00:30:09.570 --> 00:30:17.190
+Ashritha: And with, similar… Competence scores, or working competency scores.
+
+273
+00:30:21.470 --> 00:30:28.859
+Ashritha: So, lastly must identify such ambiguity explicit, rather than commit it… commit to a single prototype.
+
+274
+00:30:30.230 --> 00:30:32.870
+Ashritha: This is what they choose output.
+
+275
+00:30:33.390 --> 00:30:41.650
+Ashritha: The layer one, the part number, could not exactly match.
+
+276
+00:30:42.510 --> 00:30:46.629
+Ashritha: And, for the manufacturer, way.
+
+277
+00:30:48.070 --> 00:30:53.990
+Ashritha: We could, we could, definitely, accurately match this.
+
+278
+00:30:54.720 --> 00:30:56.000
+Ashritha: manufactured.
+
+279
+00:30:56.180 --> 00:31:01.780
+Ashritha: We… so we could get a… First, the competence score, 0.85.
+
+280
+00:31:03.130 --> 00:31:09.189
+Ashritha: And, yes, we… we ejects, ejects and value pairs.
+
+281
+00:31:12.000 --> 00:31:17.659
+Ashritha: So, for this condition, it… It also needs this data to process.
+
+282
+00:31:19.750 --> 00:31:24.720
+Ashritha: So we… first, we embed the… Useful information.
+
+283
+00:31:26.400 --> 00:31:27.950
+Ashritha: into a vector.
+
+284
+00:31:30.400 --> 00:31:35.310
+Ashritha: So, the top 5, candidates products.
+
+285
+00:31:35.810 --> 00:31:37.820
+Ashritha: Could be, these two.
+
+286
+00:31:40.410 --> 00:31:51.330
+Ashritha: And, their prototype confidence scores could… Could be, similar, but… Notably very large.
+
+287
+00:31:51.680 --> 00:32:00.409
+Ashritha: So, because the product type confidence score is less than 0.60, So, the ambiguity tape check.
+
+288
+00:32:03.130 --> 00:32:15.740
+Ashritha: This means… No matter how… how high the… Suburb… attribution… Attributes, value, pair, scores.
+
+289
+00:32:17.070 --> 00:32:20.800
+Ashritha: is… it could… it's… what's a product?
+
+290
+00:32:21.650 --> 00:32:26.880
+Ashritha: type with its attribute pair scores need to be sent to human.
+
+291
+00:32:27.490 --> 00:32:29.329
+Ashritha: To do a human review.
+
+292
+00:32:36.100 --> 00:32:42.850
+Ashritha: Yes, and we'll… and the system will inform It's a… it's a human.
+
+293
+00:32:44.210 --> 00:32:46.779
+Ashritha: The product type is ambiguous.
+
+294
+00:32:50.370 --> 00:32:58.180
+Ashritha: And, the… and provide the human some basic information, and the most likely candidates.
+
+295
+00:33:04.470 --> 00:33:16.140
+Ashritha: And in this scenario, It's, so, frequency, or the product use.
+
+296
+00:33:16.540 --> 00:33:21.300
+Ashritha: Could take into a higher level, or can say… can say it.
+
+297
+00:33:22.000 --> 00:33:24.649
+Ashritha: High level or weights, because
+
+298
+00:33:25.000 --> 00:33:29.440
+Ashritha: The voting, score is very…
+
+299
+00:33:30.040 --> 00:33:36.720
+Ashritha: It's almost the same between the… The two product types.
+
+300
+00:33:37.260 --> 00:33:51.360
+Ashritha: So… The consensus results could… More likely to use, product frequency.
+
+301
+00:33:51.990 --> 00:33:59.219
+Ashritha: And usage information to choose which one could be the most suitable.
+
+302
+00:34:07.440 --> 00:34:12.849
+Ashritha: And, the last… for the last scenario, We could all…
+
+303
+00:34:13.170 --> 00:34:17.959
+Ashritha: So, for the input, we could only have some first information.
+
+304
+00:34:19.850 --> 00:34:30.770
+Ashritha: like… We could only extract the information, the useful information, like HAAC, pipe, temporary digital monitoring, like this.
+
+305
+00:34:32.300 --> 00:34:42.110
+Ashritha: so, this request does not distinguish between sensor and some more state, and most specific manuals are given.
+
+306
+00:34:43.630 --> 00:34:47.219
+Ashritha: So, for the Layer 2,
+
+307
+00:34:49.280 --> 00:34:55.639
+Ashritha: It has no actionable extraction, no numeric manual, no manufacturer mention.
+
+308
+00:34:56.420 --> 00:34:57.979
+Ashritha: For the latest wave.
+
+309
+00:35:00.320 --> 00:35:06.410
+Ashritha: So the last day processing will permanently use the usage count prior.
+
+310
+00:35:06.550 --> 00:35:13.149
+Ashritha: become… due to… Do a judgment between different products.
+
+311
+00:35:19.790 --> 00:35:26.320
+Ashritha: Like, we… in the first… in the second layer, or the… This way.
+
+312
+00:35:27.010 --> 00:35:31.689
+Ashritha: We get, most unlikely for that type.
+
+313
+00:35:31.910 --> 00:35:34.360
+Ashritha: It's a temperature sensor.
+
+314
+00:35:39.240 --> 00:35:41.980
+Ashritha: And we get a product type competence score.
+
+315
+00:35:43.360 --> 00:35:54.639
+Ashritha: And the attributes under, this product type are scored, because the customer provides no specific, specific values.
+
+316
+00:35:55.350 --> 00:36:01.690
+Ashritha: This is our algorithm to… To get the scores.
+
+317
+00:36:02.070 --> 00:36:12.419
+Ashritha: Yes, so the distance is comparable across candidates' value, and the usage count prior… prior dominates the ranking.
+
+318
+00:36:14.190 --> 00:36:22.769
+Ashritha: You can say, what the candidates get the… Similar… working squad.
+
+319
+00:36:23.310 --> 00:36:30.889
+Ashritha: So we used the second, Judgment Like, the usage prior to…
+
+320
+00:36:31.330 --> 00:36:33.570
+Ashritha: Identify which one could be the best.
+
+321
+00:36:36.270 --> 00:36:40.830
+Ashritha: So, this one could be… could be the best common yields.
+
+322
+00:36:41.490 --> 00:36:46.860
+Ashritha: Because the frequency of this one could be higher than other ones.
+
+323
+00:36:50.990 --> 00:36:51.800
+Ashritha: Yes.
+
+324
+00:36:53.800 --> 00:36:57.290
+Ashritha: And for its… attributes.
+
+325
+00:36:57.550 --> 00:36:59.480
+Ashritha: Like, the mounting candidates.
+
+326
+00:36:59.980 --> 00:37:03.409
+Ashritha: this one could be the best.
+
+327
+00:37:03.650 --> 00:37:08.959
+Ashritha: And, for the… Resistance, all candidates, this one would be the best.
+
+328
+00:37:12.930 --> 00:37:22.709
+Ashritha: Because the information is dispersed, so all the confidence final values fall in the range 0.2 to 0.45.
+
+329
+00:37:23.540 --> 00:37:28.350
+Ashritha: So… Because the, the score is so low.
+
+330
+00:37:28.680 --> 00:37:35.879
+Ashritha: This could be less than 0.50, so it will be flagged as flag unclear.
+
+331
+00:37:46.060 --> 00:37:48.319
+Ashritha: And we will keep this juice.
+
+332
+00:37:48.810 --> 00:37:53.220
+Ashritha: Information into our… Looking into our datasets.
+
+333
+00:38:00.390 --> 00:38:05.070
+Ashritha: Yes, and it's… This is… this is how we deal with
+
+334
+00:38:05.960 --> 00:38:09.180
+Ashritha: Those vague and thirsty information.
+
+335
+00:38:09.590 --> 00:38:11.159
+Ashritha: In the last way.
+
+336
+00:38:11.530 --> 00:38:15.099
+Ashritha: So, so, this, these four scenarios is the most…
+
+337
+00:38:15.530 --> 00:38:19.959
+Ashritha: Likely, you know, better conditions, less they could meet.
+
+338
+00:38:20.900 --> 00:38:22.300
+Ashritha: in graduates.
+
+339
+00:38:29.040 --> 00:38:32.030
+Ashritha: Yeah, this is a table for the four scenarios.
+
+340
+00:38:36.110 --> 00:38:41.470
+Ashritha: And I also made a, curating coverage matrix.
+
+341
+00:38:41.920 --> 00:38:53.930
+Ashritha: Spore… tend to, to make testing Like, in the most significant a checkpoint.
+
+342
+00:38:58.010 --> 00:38:59.000
+Ashritha: Yes.
+
+343
+00:38:59.190 --> 00:39:00.960
+Ashritha: I think that's all.
+
+344
+00:39:07.440 --> 00:39:08.030
+Ashritha: Cheer.
+
+345
+00:39:08.030 --> 00:39:13.559
+Harsha Tummala: I had, two questions, mainly. One was,
+
+346
+00:39:13.910 --> 00:39:23.229
+Harsha Tummala: where do these layers… I mean, it's… it's a question for later, but, where do you think these layers will kind of reside, from, like, a…
+
+347
+00:39:24.070 --> 00:39:29.970
+Harsha Tummala: to make, hosting slash infrastructure point of view, as in…
+
+348
+00:39:30.660 --> 00:39:42.029
+Harsha Tummala: all this… most of this, like, all of this is really impressive and really nice, but, I… I'm not sure how we can host this, and how we can,
+
+349
+00:39:42.170 --> 00:39:46.130
+Harsha Tummala: Build this pipeline out, but…
+
+350
+00:39:46.290 --> 00:39:50.930
+Harsha Tummala: Not on a local machine, but something which is more up to scale.
+
+351
+00:40:02.660 --> 00:40:06.470
+Ashritha: So, this positive deployment. Yeah.
+
+352
+00:40:07.810 --> 00:40:13.029
+Ashritha: I think it could… directly, mounting…
+
+353
+00:40:13.300 --> 00:40:19.300
+Ashritha: laptop. We cannot do it because it cannot scale, right?
+
+354
+00:40:21.740 --> 00:40:29.490
+Ashritha: So, Hershey, I think for now, we can probably do it locally, but, if…
+
+355
+00:40:29.700 --> 00:40:41.509
+Ashritha: If we, like, decide on the one… any particular models or lightweight LLMs, we can see if those are available on Azure and are deployed over there? Does that answer the question?
+
+356
+00:40:41.510 --> 00:40:46.330
+Harsha Tummala: But it's… it's just a train of thought that you guys… I just want you guys to be thinking, and that's it.
+
+357
+00:40:46.330 --> 00:40:46.720
+Ashritha: Okay.
+
+358
+00:40:46.720 --> 00:40:49.289
+Harsha Tummala: It's nothing that's, pressing, yeah.
+
+359
+00:40:49.810 --> 00:41:01.629
+Harsha Tummala: Because this all seems really good, but I forget that this looks more like an experimental project than, like, something which is, you know, more usable than yet.
+
+360
+00:41:02.710 --> 00:41:15.990
+Ashritha: yes, so… If you want to maybe change the model, or… You want to…
+
+361
+00:41:16.690 --> 00:41:27.480
+Ashritha: Mount it to, like, other plantable, or… that you… to… to… You'll use more… you'll use…
+
+362
+00:41:27.650 --> 00:41:35.139
+Ashritha: Much amount of the… the data fresh… update data to train the model, I think it's…
+
+363
+00:41:36.300 --> 00:41:38.350
+Ashritha: It's relatively easy to do it.
+
+364
+00:42:00.750 --> 00:42:02.270
+Ashritha: Are you searching for something?
+
+365
+00:42:13.260 --> 00:42:17.369
+Ashritha: Yeah, that's about it. You had one more question?
+
+366
+00:42:17.870 --> 00:42:24.619
+Harsha Tummala: This is different, I just wanted to check in and see, if you guys are able to use linear and,
+
+367
+00:42:24.770 --> 00:42:27.790
+Harsha Tummala: If… if the free tier was enough for you guys.
+
+368
+00:42:28.420 --> 00:42:39.559
+Ashritha: Okay, so the… yeah, we… I signed up for Linear, and I got all the access, I mean, with all the… the… I… I mean, I just… I thought that…
+
+369
+00:42:39.560 --> 00:42:55.009
+Ashritha: this is good enough for us to create the custom fields and stuff like that, but, since we… I mean, I don't know if you remember, we were like, we'll do a 3-day, sprint, right? So, probably, like, our one cycle is 3 days.
+
+370
+00:42:55.010 --> 00:43:04.479
+Ashritha: So, but then linear has, the minimum, cycle is one week, so, I don't know how to go about that over there.
+
+371
+00:43:06.060 --> 00:43:12.429
+Harsha Tummala: It should… technically, you don't have to set a cycle in linear, right? You could…
+
+372
+00:43:12.830 --> 00:43:16.680
+Harsha Tummala: Have a sprint board for almost, like, every single
+
+373
+00:43:17.230 --> 00:43:21.480
+Harsha Tummala: Every single 3-day cycle that you're planning to do.
+
+374
+00:43:24.040 --> 00:43:28.030
+Ashritha: Okay, but our… sorry, you were saying something?
+
+375
+00:43:28.550 --> 00:43:31.450
+Harsha Tummala: No, sir, Tom. I mean, or,
+
+376
+00:43:31.920 --> 00:43:39.950
+Harsha Tummala: the way you kind of, I think… I think the way you, create tasks would be for, like, a 3-day cycle, and…
+
+377
+00:43:40.280 --> 00:43:50.830
+Harsha Tummala: I mean, I'm just thinking out loud. So, like, if you have a backlog, and if you have, like, the in-cycle tasks, then I think you can just move things around.
+
+378
+00:43:51.090 --> 00:43:56.279
+Harsha Tummala: For your 3-day period, and then, do it for the rest.
+
+379
+00:43:58.400 --> 00:44:00.000
+Harsha Tummala: Like, in a similar fashion.
+
+380
+00:44:02.310 --> 00:44:04.200
+Ashritha: Okay, but,
+
+381
+00:44:04.720 --> 00:44:14.920
+Ashritha: I thought, like, I… I mean, I kind of web-coded a custom board, so I just used, Node.js, and React, and, kind of…
+
+382
+00:44:15.570 --> 00:44:40.500
+Ashritha: spawned up a small, server with, like, I… it's not fancy, just vibe-coded it, so it's also interactable, we… I… we could just, like, drag-drop, and, I can also, like, archive whatever progress that we have done, so the team gets the editor access, and I get… sorry, the team gets the view access, and I get the editor access, because, we do Scrum calls
+
+383
+00:44:40.500 --> 00:44:49.620
+Ashritha: Monday and Wednesday. So, for now, I mean, as of today in the afternoon, I kind of thought that probably this is the most easiest one.
+
+384
+00:44:49.620 --> 00:44:51.130
+Ashritha: linear…
+
+385
+00:44:51.150 --> 00:45:10.179
+Ashritha: Yeah, you're right, I thought about it. We could just create a backlog and, play around those definitions, or maybe, like, one cycle, in linear terms is basically 3 days in, in the agentic scrum that we defined. But…
+
+386
+00:45:10.180 --> 00:45:17.560
+Ashritha: again, there were, like, few other settings that I had to change, like workflow settings, and I had to, like, add a few more states.
+
+387
+00:45:17.560 --> 00:45:31.910
+Ashritha: unopened, which are not defined. I had to, like, create new labels and stuff like that. So that was too much of UI work, so I thought, webcoding is much easier than me learning how to go about linear now.
+
+388
+00:45:33.580 --> 00:45:41.739
+Harsha Tummala: Makes sense, I mean, if you guys can figure out just hosting and, like, doing everything about it, then more than… you guys should do whatever you can about it.
+
+389
+00:45:41.740 --> 00:45:42.840
+Ashritha: Yeah, yeah.
+
+390
+00:45:42.840 --> 00:45:43.930
+Harsha Tummala: And eating, that's it.
+
+391
+00:45:44.380 --> 00:46:03.010
+Ashritha: Yeah, yeah. So, I mean, it's quite… it's actually good, like, it does not restrict you with any templates or fields, so that's why I thought I would get started with it, but then, I mean, I didn't think about the cycle thingy and other, stuff, but then as I went ahead, I felt like it was, like.
+
+392
+00:46:03.060 --> 00:46:26.200
+Ashritha: not restricting, but maybe if I will… if I get to spend more time on it, probably I'll figure it out, but then we had to, like, keep pro… make, keep a progress, like, a visible progress of what we have done this week, so I had to bring up some UI for this, so I thought, let me just get done with this, and maybe over the weekend I can sit and play around.
+
+393
+00:46:27.550 --> 00:46:31.299
+Harsha Tummala: Makes sense. Also, I had a question, very random one.
+
+394
+00:46:31.690 --> 00:46:35.510
+Harsha Tummala: Why the 3-day cycle, though, compared to a week, for example?
+
+395
+00:46:36.870 --> 00:46:44.710
+Ashritha: Okay, so that's what the Agent Tech Scrum prescribes, so by definition, and
+
+396
+00:46:44.930 --> 00:46:56.349
+Ashritha: It's like the first… since, we don't have… we don't have to spend the entire week, like, the conventional week, or, like, two weeks of time in coding, it's more like…
+
+397
+00:46:56.350 --> 00:47:06.880
+Ashritha: deciding and breaking down the problem statement, and it's more of prompt engineering work later on. So, like, we thought that this would best suit the
+
+398
+00:47:07.180 --> 00:47:27.559
+Ashritha: current dynamics of the project, as in, the first day, we would just define the spec cards, in very detail, like, okay, what this task is about, what would the test cases be, who would write the test cases, is it the human, like, is it one among us, or probably the…
+
+399
+00:47:27.760 --> 00:47:45.880
+Ashritha: agent itself, and stuff like that. And, like, one day in between, we would just spend the entire day to, review the work it, it has done, it produced. And third day, we would just give a demo slash, review of what
+
+400
+00:47:46.020 --> 00:48:03.450
+Ashritha: we did over, Monday and Tuesday, suppose. So we thought, like, 3 days is a good amount of time, to get the entire cycle complete, wherein that happens in a 2-week time in a traditional scrum
+
+401
+00:48:03.490 --> 00:48:12.800
+Ashritha: scenario. So we're just, like, experimenting this, and by definition, this is what, the… the Scrum org, prescribes
+
+402
+00:48:12.980 --> 00:48:16.969
+Ashritha: A tick to be, like, a 3-rate, sprint.
+
+403
+00:48:17.800 --> 00:48:21.060
+Harsha Tummala: Okay. Yeah, just a question for my knowledge, that's all.
+
+404
+00:48:21.450 --> 00:48:22.760
+Ashritha: Yeah, yeah.
+
+405
+00:48:25.200 --> 00:48:38.890
+Harsha Tummala: Got it. Okay, so whatever you guys are comfortable with in the end. And also, like, the thing was, as Lou was presenting, I was… I was just getting so confused, because all of his examples were in, like, this,
+
+406
+00:48:39.170 --> 00:48:47.189
+Harsha Tummala: Like, like a user manner where, where someone is searching for something, or when someone is, like.
+
+407
+00:48:47.370 --> 00:49:00.340
+Harsha Tummala: looking for a product, and our whole use case was centered around, like, curating the product itself, so, like, it just constantly threw me out in this, state of confusion. And, that's… that was my, I think.
+
+408
+00:49:00.540 --> 00:49:02.600
+Harsha Tummala: That was my question in the middle of the meeting.
+
+409
+00:49:04.310 --> 00:49:12.859
+Ashritha: Yeah, I understood. Yeah, I think his example was more, like, to just make it explainable. Yeah, it's the most dangerous conditions Leia could…
+
+410
+00:49:13.040 --> 00:49:17.330
+Ashritha: Yeah. Yeah, it just… just shows this, because I think it's a…
+
+411
+00:49:17.470 --> 00:49:30.120
+Ashritha: like, in our, primarily testing, the accuracy is almost, like, 90… 95% on 200… 2,000 training samples, so I think the,
+
+412
+00:49:30.690 --> 00:49:34.829
+Ashritha: In the common… in the normal imports, so…
+
+413
+00:49:35.330 --> 00:49:41.670
+Ashritha: For the machine learning system to deal with, if this information is… it could not be,
+
+414
+00:49:42.430 --> 00:49:45.450
+Ashritha: looking be up.
+
+415
+00:49:45.640 --> 00:49:50.660
+Ashritha: be bad, I think. It could deal with it, those information smoothly.
+
+416
+00:49:52.870 --> 00:50:05.790
+Harsha Tummala: Yeah, I mean, that was it. It was just, like, something on clarity that kind of, threw me off, and that's it. But other than that, that's it. I think one thing to think about is just, like, how… where these layers can live.
+
+417
+00:50:06.070 --> 00:50:09.940
+Harsha Tummala: When we're trying to build an application at scale.
+
+418
+00:50:10.280 --> 00:50:19.010
+Harsha Tummala: It can just be in theory, doesn't have to be worked out perfectly, but, like, I feel like designing something with that in mind.
+
+419
+00:50:19.520 --> 00:50:22.840
+Harsha Tummala: Helps actually achieve that goal.
+
+420
+00:50:23.190 --> 00:50:35.450
+Harsha Tummala: Whereas, when you design something in a purely experimental basis, where, you try to just create a layer which does everything in the best way possible on your local.
+
+421
+00:50:35.520 --> 00:50:49.699
+Harsha Tummala: might not exactly translate to, like, a at-scale application as we move further. It could… I mean, in the end, it could be something which is like a… like a virtual… like a… like, just a virtual machine-hosted, kind of system, too.
+
+422
+00:50:49.970 --> 00:51:00.660
+Harsha Tummala: Which is… which is fine, but it's also very inefficient in a way, so, like, that's why I'm trying to get you guys to think about it in… think about it from, like, a… from, like, a…
+
+423
+00:51:00.800 --> 00:51:04.439
+Harsha Tummala: MLOps slash AIOps kind of perspective.
+
+424
+00:51:05.560 --> 00:51:07.120
+Ashritha: Yeah, makes sense.
+
+425
+00:51:09.010 --> 00:51:12.779
+Harsha Tummala: Yeah, that's it. That's pretty much all my questions from this.
+
+426
+00:51:12.890 --> 00:51:25.950
+Harsha Tummala: And… it would also help, like, if you could share this document with us, mainly so that I can just spend, like, an hour or 30 minutes just, like, slowly going through it and understanding, different parts of it.
+
+427
+00:51:26.170 --> 00:51:31.860
+Ashritha: So, because the time is limited, so I just skipped many technical details.
+
+428
+00:51:31.860 --> 00:51:40.140
+Harsha Tummala: Yeah, exactly. I kind of got that, too. I mean, it's tough to… it's tough to present something in an hour which is this technically heavy, so…
+
+429
+00:51:40.140 --> 00:51:40.780
+Ashritha: It…
+
+430
+00:51:41.110 --> 00:51:43.370
+Harsha Tummala: I mean, I would tell you today, but .
+
+431
+00:51:44.260 --> 00:51:50.939
+Ashritha: Yes, if you want to say it, I will send you the four detailed documents.
+
+432
+00:51:51.070 --> 00:51:52.120
+Ashritha: You can see.
+
+433
+00:51:52.120 --> 00:51:53.859
+Harsha Tummala: 100%, that'd be really helpful.
+
+434
+00:51:54.020 --> 00:51:55.720
+Harsha Tummala: I'd want to go through that.
+
+435
+00:51:56.000 --> 00:51:59.989
+Harsha Tummala: Just for my reference, because, like, it's just very interesting to look at.
+
+436
+00:52:03.930 --> 00:52:09.449
+Ashritha: Yeah, I'll put this along the action items that I'll be sending in the evening today.
+
+437
+00:52:09.960 --> 00:52:11.000
+Harsha Tummala: Jakes up, yeah.
+
+438
+00:52:11.410 --> 00:52:24.889
+Ashritha: Yeah. We also wanted to go over the architecture part, but then I think we're just on time. Maybe next week, or maybe we can just send over those documents as well, you can just, like, take a look.
+
+439
+00:52:24.940 --> 00:52:34.329
+Ashritha: And if you have any… I mean, it's pretty simple and self-explanatory. If we have any questions, we can discuss it in our next meeting.
+
+440
+00:52:35.180 --> 00:52:41.949
+Harsha Tummala: Yeah, makes sense. I mean, also, just like, just in case you guys want to meet
+
+441
+00:52:42.080 --> 00:52:54.919
+Harsha Tummala: twice in a week, we could also, like, schedule that ad hoc, as in, if you have more to present, we could just decide on a… decide on another slot, and we could get that done as well. That's an option to you guys.
+
+442
+00:52:55.780 --> 00:52:59.789
+Ashritha: Oh, okay. I think we'll just think about this as a team, Ellie.
+
+443
+00:52:59.790 --> 00:53:03.819
+Harsha Tummala: Again, like, I'm just putting all options on the table for you guys, and that's it.
+
+444
+00:53:03.820 --> 00:53:04.740
+Ashritha: Yeah, yeah.
+
+445
+00:53:05.160 --> 00:53:09.140
+Harsha Tummala: Yeah. So, sending the document completely works. I'll go through it and send it.
+
+446
+00:53:09.600 --> 00:53:10.610
+Ashritha: Okay, yeah.
+
+447
+00:53:13.060 --> 00:53:13.860
+Ashritha: Okay.
+
+448
+00:53:14.690 --> 00:53:17.059
+Ashritha: Cool, and see you guys next week. Bye-bye.
+
+449
+00:53:22.340 --> 00:53:23.389
+David Mine: Yeah, thanks.
+
+450
+00:53:23.520 --> 00:53:24.070
+David Mine: Dude.
+
+451
+00:53:24.070 --> 00:53:24.770
+Harsha Tummala: Thank you so much.
+
+452
+00:53:27.140 --> 00:53:27.570
+hrishikb@andrew.cmu.edu: But…
+
diff --git a/transcripts/GMT20260528-190703_Recording.transcript.vtt b/transcripts/GMT20260528-190703_Recording.transcript.vtt
new file mode 100644
index 0000000..3c10e9a
--- /dev/null
+++ b/transcripts/GMT20260528-190703_Recording.transcript.vtt
@@ -0,0 +1,1054 @@
+WEBVTT
+
+1
+00:15:04.030 --> 00:15:05.000
+Ashritha: Night.
+
+2
+00:15:05.270 --> 00:15:08.959
+Ashritha: It will, collect as a low samples cluster.
+
+3
+00:15:10.820 --> 00:15:16.860
+Ashritha: And if the score is… the score is less than .70, What's up?
+
+4
+00:15:19.460 --> 00:15:20.579
+Ashritha: A bunch of bills.
+
+5
+00:15:20.800 --> 00:15:24.160
+Ashritha: In the case will pass to a human's review.
+
+6
+00:15:27.240 --> 00:15:31.729
+Ashritha: And flag unclear, it says return to sender. Who's the sender?
+
+7
+00:15:35.350 --> 00:15:36.630
+Ashritha: Cinder.
+
+8
+00:15:36.810 --> 00:15:40.930
+Ashritha: In the red… lower right, that box, keep that down.
+
+9
+00:15:41.050 --> 00:15:44.089
+Ashritha: Confidence final list.
+
+10
+00:15:44.380 --> 00:15:47.670
+Ashritha: Oh, this one. Yeah, it was returned to sender.
+
+11
+00:15:47.810 --> 00:15:48.990
+Ashritha: What is that named?
+
+12
+00:15:54.010 --> 00:15:56.559
+Ashritha: Because this score is less
+
+13
+00:15:56.660 --> 00:16:01.489
+Ashritha: even less than a human-to-use range. So, maybe…
+
+14
+00:16:01.880 --> 00:16:07.410
+Ashritha: This, this, this area is not… And…
+
+15
+00:16:08.480 --> 00:16:12.639
+Ashritha: it's a big problem for the machine learning systems. Like.
+
+16
+00:16:12.930 --> 00:16:21.160
+Ashritha: We don't use this kind of cluster to train the model, so the model… like…
+
+17
+00:16:24.060 --> 00:16:41.409
+Ashritha: Well, I understand the confidence is super low. Yeah, super low, yeah. So, so, we need to talk to the EPA team to procure this land, or…
+
+18
+00:16:41.540 --> 00:16:44.289
+Ashritha: Snack, so we need to move…
+
+19
+00:16:44.700 --> 00:16:48.509
+Ashritha: Like, to build some pressure with that.
+
+20
+00:16:48.630 --> 00:16:50.520
+Ashritha: Within this… this…
+
+21
+00:16:51.990 --> 00:17:06.130
+Ashritha: So you really mean talk to somebody in the catalog team? Yeah, yeah. So you might want to say something like that, as opposed to regarding the sender. Yeah, yeah, because… Because it's a fail.
+
+22
+00:17:06.130 --> 00:17:20.010
+Ashritha: We have nearly 400 product types. Only 200, 250 of them have floor… Data cluster, like…
+
+23
+00:17:20.839 --> 00:17:24.839
+Ashritha: But many of us only had one or two
+
+24
+00:17:27.220 --> 00:17:30.359
+Ashritha: But other types, so it may be noteworthy.
+
+25
+00:17:30.550 --> 00:17:42.920
+Ashritha: clearly to train the model. Yeah, I understand, so I just… So, yeah. Yeah, make a correction there, like, you know, review with the catalog team. Yeah, because maybe their products are… Yes. Yeah, yeah, so…
+
+26
+00:17:43.490 --> 00:18:01.159
+Ashritha: Just a quick question. I like all this so far, just, are we gonna be able to, over time, like, tweak the actual values, like, are these going to be easily modifiable, like, in the future? Like, oh… Oh, yes, yes. Yeah. Like, those values, like, maybe for the local sample plus… Yeah, we designed to…
+
+27
+00:18:01.600 --> 00:18:13.870
+Ashritha: for a frequent retrain. Okay. Were there configuration problems? Yeah, that's what I was gonna ask. Yeah, every, every retrain, the whole retrain, the whole period is about…
+
+28
+00:18:14.140 --> 00:18:28.810
+Ashritha: 5 to 7 hours, you can renew all the artifacts. But I mean, like, is there just an easy config file where I can change, like, the low sample cluster cap from 0.6 to 0.6? Yeah.
+
+29
+00:18:29.680 --> 00:18:35.699
+Ashritha: I guess that would be harsh if I would say that. Yeah, well, one away, it would be judged by film.
+
+30
+00:18:37.500 --> 00:18:44.510
+Ashritha: And another, task for… So they have voiced the…
+
+31
+00:18:45.110 --> 00:18:51.069
+Ashritha: State market duration, or overnight training, because…
+
+32
+00:18:53.810 --> 00:19:05.189
+Ashritha: Since, when we don't have the radio input or the, products, So, I add, sigma.
+
+33
+00:19:05.390 --> 00:19:13.110
+Ashritha: for what the regulation, for the scores. Like, We want the… competence.
+
+34
+00:19:13.470 --> 00:19:21.620
+Ashritha: Produced by the model. It's… It's what, what is real… Is…
+
+35
+00:19:22.570 --> 00:19:29.350
+Ashritha: is dispatched for its real, scores, like…
+
+36
+00:19:31.320 --> 00:19:35.750
+Ashritha: The probability of raining for today is 70%.
+
+37
+00:19:36.020 --> 00:19:45.710
+Ashritha: We just… we want to, like, make sure it's the… it is… it could be the real, producing.
+
+38
+00:19:46.280 --> 00:19:46.980
+Ashritha: Yep.
+
+39
+00:19:47.350 --> 00:19:56.089
+Ashritha: So at, and, to this… And to this…
+
+40
+00:19:56.890 --> 00:20:04.100
+Ashritha: metrics is due to the amount of how long this distance.
+
+41
+00:20:05.300 --> 00:20:06.440
+Ashritha: like.
+
+42
+00:20:09.880 --> 00:20:21.810
+Ashritha: way… No, no. That was… I know you want to make your mark here, but… That's what I'm assuming.
+
+43
+00:20:22.030 --> 00:20:25.220
+Ashritha: Just before going regularly, my dude.
+
+44
+00:20:25.970 --> 00:20:36.540
+Ashritha: That way… Yes. We map the input into a higher dimensional data space.
+
+45
+00:20:37.150 --> 00:20:41.680
+Ashritha: So the trained model done that.
+
+46
+00:20:41.900 --> 00:20:46.090
+Ashritha: We kept, like, 500.
+
+47
+00:20:47.420 --> 00:20:52.330
+Ashritha: different, so, so every different…
+
+48
+00:20:53.020 --> 00:21:00.460
+Ashritha: But that type has its own, own… on… That's third.
+
+49
+00:21:01.270 --> 00:21:12.400
+Ashritha: So, maybe some… Some product types have a big cluster, so… The distribution on the…
+
+50
+00:21:12.660 --> 00:21:14.969
+Ashritha: Data space could be like this.
+
+51
+00:21:15.820 --> 00:21:20.240
+Ashritha: This is… it's a century, century center.
+
+52
+00:21:21.560 --> 00:21:29.490
+Ashritha: So, so this would be guilt of… Big Sigma.
+
+53
+00:21:29.820 --> 00:21:34.339
+Ashritha: Because the distribution of the cluster is very large.
+
+54
+00:21:35.520 --> 00:21:48.100
+Ashritha: Fixigma means if… It can, let, You're a… So, the predictor case is Far from the center.
+
+55
+00:21:48.360 --> 00:21:50.690
+Ashritha: We can still get a high confidence.
+
+56
+00:21:51.180 --> 00:22:03.030
+Ashritha: Because the cluster or is this very large. But if the cluster's distribution is very, very narrow, So, even…
+
+57
+00:22:03.440 --> 00:22:05.500
+Ashritha: a small distance.
+
+58
+00:22:05.680 --> 00:22:08.149
+Ashritha: Or angle from the center.
+
+59
+00:22:09.250 --> 00:22:13.440
+Ashritha: Like, on, like, the display, it could be,
+
+60
+00:22:13.610 --> 00:22:18.990
+Ashritha: have a big influence. So, the Sigma could be small to…
+
+61
+00:22:19.300 --> 00:22:26.450
+Ashritha: Judges, confidence score to make it even more… But… That's brief.
+
+62
+00:22:28.920 --> 00:22:29.720
+Ashritha: Oh, sweet.
+
+63
+00:22:31.680 --> 00:22:50.659
+Ashritha: So what does PTs mean in this context? Product type. What's that? For product type. Product type? Yeah, yes. So, the four, layers, like, four layers, the first layer is categories, the second layer is product type, the third is attributes, and attributes…
+
+64
+00:22:50.710 --> 00:22:51.710
+Ashritha: your needs.
+
+65
+00:22:52.200 --> 00:22:59.409
+Ashritha: You should have a glossary somewhere in your radiations, so they're not this… not ambiguous.
+
+66
+00:22:59.910 --> 00:23:00.900
+Ashritha: Yeah, yeah.
+
+67
+00:23:01.150 --> 00:23:04.520
+Ashritha: As I said, only…
+
+68
+00:23:09.590 --> 00:23:18.229
+Ashritha: as I said, only, 242 of the whole product type.
+
+69
+00:23:18.500 --> 00:23:19.949
+Ashritha: how,
+
+70
+00:23:20.640 --> 00:23:31.580
+Ashritha: large enough cluster to use, sigma. So we… so the… and the last 135 fallback to the fourth sigma.
+
+71
+00:23:34.480 --> 00:23:44.789
+Ashritha: And we've also designed, the last layer, But this, this is not… Company team.
+
+72
+00:23:46.750 --> 00:23:48.789
+Ashritha: Well, we just to make sure.
+
+73
+00:23:50.300 --> 00:23:55.320
+Ashritha: We, when we get some, feedback from the human.
+
+74
+00:23:57.770 --> 00:24:05.959
+Ashritha: We don't need a fully train, but we can slightly change some important metrics for the…
+
+75
+00:24:08.740 --> 00:24:14.040
+Ashritha: So we made… with, I designed a…
+
+76
+00:24:14.300 --> 00:24:17.120
+Ashritha: M6 to use the online update.
+
+77
+00:24:18.380 --> 00:24:27.439
+Ashritha: So this is the next door. So, for the current clustering of data, what's the source of that data that you're using to cluster this? Does that make sense?
+
+78
+00:24:29.020 --> 00:24:31.909
+Ashritha: You're talking about results now that you've…
+
+79
+00:24:32.110 --> 00:24:35.730
+Ashritha: You've run data, and it's clustered in some way.
+
+80
+00:24:35.950 --> 00:24:48.009
+Ashritha: Where… what's the source of that data that you're close to? I'm just asking, is this sample data from eParts, or what was… Yeah, it is just from these… these two files. Okay, alright.
+
+81
+00:24:55.850 --> 00:24:57.460
+Ashritha: So I have a… have a jump.
+
+82
+00:24:58.020 --> 00:25:00.640
+Ashritha: I was just so it's technical.
+
+83
+00:25:09.750 --> 00:25:14.550
+Ashritha: Yes, and after I communicated towards a machine learning model.
+
+84
+00:25:15.090 --> 00:25:20.280
+Ashritha: I used the rare data from the participant.
+
+85
+00:25:20.540 --> 00:25:21.759
+Ashritha: Do you spend any time?
+
+86
+00:25:23.300 --> 00:25:30.100
+Ashritha: to test the… accuracy of the Layer 3 and Layer 4.
+
+87
+00:25:31.070 --> 00:25:40.219
+Ashritha: I used, this document that's included tags, because it contains product description and extended description pairs.
+
+88
+00:25:40.550 --> 00:25:45.889
+Ashritha: So I use it as an input, and I use this file.
+
+89
+00:25:46.530 --> 00:25:57.940
+Ashritha: Because it provides the ground truth labels to verify the accuracy of the output, or the last two layers, the layers 3 and layer 4.
+
+90
+00:25:59.140 --> 00:26:02.899
+Ashritha: Like, we… just a yoga.
+
+91
+00:26:03.430 --> 00:26:14.090
+Ashritha: to split the… this file into these three parts. So we use the… that's the 10%
+
+92
+00:26:15.390 --> 00:26:18.179
+Ashritha: Take a step to test our system.
+
+93
+00:26:21.740 --> 00:26:27.140
+Ashritha: which have… like, 30,000 products.
+
+94
+00:26:30.590 --> 00:26:32.870
+Ashritha: And this is a testing result.
+
+95
+00:26:35.420 --> 00:26:41.279
+Ashritha: The project type accuracy, the result is almost a negative 6%.
+
+96
+00:26:43.110 --> 00:26:47.219
+Ashritha: Which means… So, machine learning can…
+
+97
+00:26:47.770 --> 00:26:52.230
+Ashritha: System could collectively identify what kind of products the customer is asking about.
+
+98
+00:26:52.420 --> 00:26:55.290
+Ashritha: or 19th or early 30 studies cases.
+
+99
+00:26:55.960 --> 00:26:59.650
+Ashritha: And, for the attribute, unit, Todd.
+
+100
+00:26:59.900 --> 00:27:04.950
+Ashritha: The top three, accuracy, reached… 85%.
+
+101
+00:27:11.050 --> 00:27:16.550
+Ashritha: And the whole process, it consumes 20 minutes.
+
+102
+00:27:27.590 --> 00:27:30.869
+Ashritha: So is this document a Google Doc, or what is this document?
+
+103
+00:27:33.550 --> 00:27:37.800
+Ashritha: Just a word. It's a Word document. Okay.
+
+104
+00:27:38.800 --> 00:27:41.309
+Ashritha: stored in your Google Drive, or is it in?
+
+105
+00:27:42.300 --> 00:27:43.520
+Ashritha: No.
+
+106
+00:27:43.880 --> 00:27:48.450
+Ashritha: But just a little… our teams.
+
+107
+00:27:50.360 --> 00:28:00.279
+Ashritha: No, it's not yet on the Google Drive. It's… it was just for today's meeting. Like, he wanted to use this to explain. Right now, it's not on the Google Drive yet.
+
+108
+00:28:02.320 --> 00:28:09.430
+Ashritha: Okay, so is this going to be a document that's stored somewhere? Yeah, after the meeting, all of these will be uploaded, yeah.
+
+109
+00:28:11.390 --> 00:28:14.109
+Ashritha: And finally, I do a statistic work.
+
+110
+00:28:14.880 --> 00:28:27.629
+Ashritha: The test set depends 244 statistics prototype spanning, 140,000 samples.
+
+111
+00:28:30.430 --> 00:28:33.859
+Ashritha: And I just chose the top 5 product type.
+
+112
+00:28:41.650 --> 00:28:48.070
+Ashritha: And for the pressure, independent values and actuators.
+
+113
+00:28:48.690 --> 00:28:50.330
+Ashritha: It's, overwhelmed.
+
+114
+00:28:50.430 --> 00:28:53.210
+Ashritha: accuracy is very low, so I…
+
+115
+00:28:55.840 --> 00:29:00.090
+Ashritha: ask why the spread of love, because…
+
+116
+00:29:05.800 --> 00:29:15.740
+Ashritha: Because it's not from… it's not… it's very… it's wrong, because we… I use… Man, what is…
+
+117
+00:29:16.680 --> 00:29:19.579
+Ashritha: The employment is like this, so it…
+
+118
+00:29:22.470 --> 00:29:25.809
+Ashritha: So, this is the system.
+
+119
+00:29:26.140 --> 00:29:30.429
+Ashritha: justify Islam, but this is incorrectly.
+
+120
+00:29:30.890 --> 00:29:36.150
+Ashritha: If… just to… The message is this one.
+
+121
+00:29:37.930 --> 00:29:44.859
+Ashritha: So the, the result is… Gulza, and it's, is, is, it's estimated.
+
+122
+00:29:50.530 --> 00:29:52.669
+Ashritha: We might be storing some of that.
+
+123
+00:29:53.190 --> 00:30:02.530
+Ashritha: Have you… do you know how, like, the attribute values means, like, the triple tildes, that old hacky legacy thing where, like, you can store multiple values against, like…
+
+124
+00:30:03.010 --> 00:30:07.660
+Ashritha: a single… Product tapes, as you need.
+
+125
+00:30:08.840 --> 00:30:14.329
+Ashritha: a product's value for it. I know it doesn't exist, but I'm not sure that the exact way it's stored.
+
+126
+00:30:14.810 --> 00:30:25.110
+Ashritha: Yeah, but I know what you mean, yeah. Yeah, this is a case we know of, though, like the one you showed, like, we have different… sometimes I think you have different values because of exactly what you showed, like…
+
+127
+00:30:25.390 --> 00:30:33.009
+Ashritha: 24VAC is the exact same thing as 24VAC, so yeah. And the… So, next reason is…
+
+128
+00:30:33.120 --> 00:30:39.629
+Ashritha: The PR wave product distinction is every selectable value, not the accurate shaped value.
+
+129
+00:30:39.880 --> 00:30:45.029
+Ashritha: like, a typical shared way of product description, at least like this.
+
+130
+00:30:46.420 --> 00:30:50.169
+Ashritha: So… It's Mr. Always.
+
+131
+00:30:50.980 --> 00:30:57.150
+Ashritha: So it is maybe hard for the machine learning system to judge which one would be the best.
+
+132
+00:30:58.480 --> 00:31:05.459
+Ashritha: Yeah, so the output could be a little vague, because it has a lot of, true…
+
+133
+00:31:05.820 --> 00:31:09.030
+Ashritha: description. Yeah. Yes, yes, so just,
+
+134
+00:31:09.570 --> 00:31:13.460
+Ashritha: That's why it has a low condensed wall. Makes sense.
+
+135
+00:31:19.670 --> 00:31:22.480
+Ashritha: That is all I want to say about today.
+
+136
+00:31:22.830 --> 00:31:38.579
+Ashritha: Yeah, and I think some of that range stuff, especially, is, it's fine that it's gonna end up having a low confidence and failing, because that's probably going to need more humans to interpret that. Yeah, but the top three attribute, you know, is they all have a high confidence, so…
+
+137
+00:31:39.520 --> 00:31:45.449
+Ashritha: After it passed to… even if it passes the wheel, it will be very quick. Yeah.
+
+138
+00:31:47.660 --> 00:31:57.409
+Ashritha: I mean, this is in respect to the documents you send across, too. I had a few questions on that, but, like, one of the questions was,
+
+139
+00:31:57.670 --> 00:32:01.259
+Ashritha: How does the system deal with a completely new product, which…
+
+140
+00:32:01.570 --> 00:32:04.990
+Ashritha: Doesn't really relate to any of the currently existing products.
+
+141
+00:32:05.380 --> 00:32:09.920
+Ashritha: It could be defined as a low score. Yeah. Just a natural student.
+
+142
+00:32:10.280 --> 00:32:14.089
+Ashritha: I see, okay. And… So, so,
+
+143
+00:32:14.190 --> 00:32:16.410
+Ashritha: I think if you will help out.
+
+144
+00:32:16.580 --> 00:32:25.949
+Ashritha: So you get a change from the data set, like, or you'll need to delete some oldest cluster. You'll need to retrain the model.
+
+145
+00:32:26.640 --> 00:32:31.170
+Ashritha: Because, in some of the layers, the way I saw them made is…
+
+146
+00:32:31.660 --> 00:32:41.020
+Ashritha: they try to understand what is the most similar product to whatever product is the… Yes, yes, yes. So… It is… it's a continuous.
+
+147
+00:32:41.340 --> 00:32:44.859
+Ashritha: a vector in the space. It's like a data cloud. Yeah.
+
+148
+00:32:46.030 --> 00:32:59.840
+Ashritha: Yeah, I mean, just a question. So, for example, so right now, the system is engineered in a way where it tries to map to the most similar existing product, and then, like, figure out all the attributes and the values for it.
+
+149
+00:33:00.150 --> 00:33:10.740
+Ashritha: Based off of, what the most similar product is. But, for example, all we know is a product number and product type. Yeah.
+
+150
+00:33:11.450 --> 00:33:17.859
+Ashritha: how we plan… how are we planning on, like, filling the values? Are the values picked up from the…
+
+151
+00:33:18.020 --> 00:33:19.620
+Ashritha: PDF that we supply.
+
+152
+00:33:19.750 --> 00:33:27.110
+Ashritha: Or are these values just… kind of guessed based on the most successful.
+
+153
+00:33:28.510 --> 00:33:36.020
+Ashritha: Like I said, like I said, for Eric's project type, it's trained,
+
+154
+00:33:36.450 --> 00:33:44.190
+Ashritha: product cluster. So, if the attributes, the value is not very far from the
+
+155
+00:33:44.530 --> 00:33:48.200
+Ashritha: And a distribution range that is…
+
+156
+00:33:48.820 --> 00:33:57.790
+Ashritha: its shape and the distance from it, it could be even to have a relatively high score. But if the, attribute
+
+157
+00:33:58.070 --> 00:34:00.250
+Ashritha: Those unique queries.
+
+158
+00:34:00.380 --> 00:34:08.880
+Ashritha: far from… If each brunk, or from the geometry distance, or the direction of the chain.
+
+159
+00:34:09.909 --> 00:34:15.759
+Ashritha: Even if the instance is very bad, it could be… have a low scores.
+
+160
+00:34:15.760 --> 00:34:40.529
+Ashritha: Is there a different process that needs to go through for a targeted product category? Yeah, I mean, what I'm trying to get to is, you mean a new product and a new product type? Not just the new product? Yes, yes. No, no, no, new product title. New product, but existing product. Okay, because technically, I don't think we can begin together until the standards.
+
+161
+00:34:40.530 --> 00:34:45.640
+Ashritha: That'll have some combined values. So, if you want to add some, you'll put up time.
+
+162
+00:34:46.150 --> 00:34:47.760
+Ashritha: As in the retreats.
+
+163
+00:34:48.020 --> 00:34:58.549
+Ashritha: is needed. But if you just add some new attributes to the pair, since, the new…
+
+164
+00:34:59.500 --> 00:35:00.630
+Ashritha: actually builds.
+
+165
+00:35:00.770 --> 00:35:06.439
+Ashritha: We, we… and they're, familiar with, its existing product.
+
+166
+00:35:06.790 --> 00:35:21.959
+Ashritha: So it can also get a higher permanent score. Got it. So, so basically, a case where there is an attribute value which is… which has never been seen by the system, we'll always have a low permanent score, is what it is.
+
+167
+00:35:25.700 --> 00:35:41.409
+Ashritha: Like, let's say, go in that one, like, 24 volts for, like, the power. Yeah. Let's say that's a common one, but then let's say we get a brand new vendor supplier that sells products that are, like, crazy high voltages that we've never seen, and that'll be outside the system.
+
+168
+00:35:41.470 --> 00:35:48.480
+Ashritha: That's, like, a new value that we don't previously… we didn't previously sell any products. Yeah, yeah, it's a range… if it's a value range is…
+
+169
+00:35:48.720 --> 00:35:58.580
+Ashritha: within this… just politics. Yeah. From… 0 to, 200.
+
+170
+00:35:59.270 --> 00:36:00.399
+Ashritha: And what teacher?
+
+171
+00:36:00.890 --> 00:36:03.619
+Ashritha: But if your new input is
+
+172
+00:36:03.790 --> 00:36:15.339
+Ashritha: 400, it's far from this 200, it can be a low score. But if, if you, like, in the, in this range, you have
+
+173
+00:36:15.470 --> 00:36:32.590
+Ashritha: 0, 10, 100, 200, but your S, like, 550 is within this range, so it could be why I have a high turn that score. This is the… this… this is what I was getting to. So, problem is…
+
+174
+00:36:33.170 --> 00:36:39.519
+Ashritha: The system that's being built is only good at mapping products that are extremely similar to the current products that are there.
+
+175
+00:36:39.760 --> 00:36:50.720
+Ashritha: And that is kind of a problem, too, by the current future, because technically a new person comes in, even though it's a similar prototype, but the values are just completely off of what we have in the system.
+
+176
+00:36:50.720 --> 00:37:01.970
+Ashritha: it'll look pretty quickly. Could… but could we… could we kind of use this same idea with also the standards? Like, so standards have, like.
+
+177
+00:37:02.050 --> 00:37:10.929
+Ashritha: Some standards even go so far as to say, like, this attribute will always have these values. Some standards actually really define, like, values. Could we also use those values?
+
+178
+00:37:10.930 --> 00:37:23.950
+Ashritha: Because then I think that that would allow what you're saying. Yeah, right. Yeah. It's not a value that we have in our existing products, but it is a value that, like, the standard, like, ETIM defines as, like, a potential range for volts or something.
+
+179
+00:37:24.480 --> 00:37:40.760
+Ashritha: It seems like there should be some documentation somewhere that talks about these types. Yeah, the standard, yeah, I'll define it, but yeah, like, the standards, some of the standards for certain things do actually define what the value should be. So, ETEM does. I mean, ETEM's something I think you might mention.
+
+180
+00:37:41.070 --> 00:37:49.060
+Ashritha: ETM is mainly there, I think, because technically, we're just a small subset of whatever exists out in the market, and…
+
+181
+00:37:49.160 --> 00:38:06.599
+Ashritha: If we build whatever this is for the specific subset that we offer, then it'll already stay in the subset, and technically anything that lies outside of this, which is currently what we're trying to do, to go to subsets which are outside of our current subset of products, it'll always kind of not work.
+
+182
+00:38:06.790 --> 00:38:23.140
+Ashritha: So if we… I think rather than sticking to whatever… so the values, if they're more defined by the ETM classification, which provides a range of values that, for a specific attribute, these are the range… these are the different ranges, and this is what you get in the industry.
+
+183
+00:38:23.220 --> 00:38:30.889
+Ashritha: That might be a better… that might be a better plot compared to, the value ranges that we have within our system right now.
+
+184
+00:38:32.960 --> 00:38:41.580
+Ashritha: Or use both, yeah. Or use both. Oh, but the thing is, with our system, it'll always be this… there'll always be this problem that it'll be within the bounds of what you already know.
+
+185
+00:38:41.690 --> 00:38:52.979
+Ashritha: And it might be off for, like, a lot of your specifications. For example, if a product type has a… has an attribute called length or a dimension.
+
+186
+00:38:53.100 --> 00:38:59.519
+Ashritha: It could be vastly different for different… for different products within the same product type, and…
+
+187
+00:38:59.660 --> 00:39:03.530
+Ashritha: I feel like dimension will always be off, given this current system.
+
+188
+00:39:03.760 --> 00:39:08.440
+Ashritha: So, rather than that, if you… if either we have a system which
+
+189
+00:39:09.170 --> 00:39:13.939
+Ashritha: Just gives us what's the most closest value to this attribute.
+
+190
+00:39:14.220 --> 00:39:21.079
+Ashritha: Or, if it's a system which bases it off of a standardized range, which is like an Ethernet classification range.
+
+191
+00:39:21.450 --> 00:39:29.629
+Ashritha: And based off of those product value ranges, like, attribute value ranges, it predicts it, that might be a better system overall.
+
+192
+00:39:29.820 --> 00:39:44.129
+Ashritha: In general. Because this seems to be too narrow for a use case. This might be good for, like, a good POC. Yeah, yes. But as you're trying to, generalize it, or… Yes, yes. Just make it, make it more robust, is what I mean. Yeah.
+
+193
+00:39:45.240 --> 00:39:56.939
+Ashritha: Yes, I… so the first question was assisting just to… gets, high accuracy score, so… So, generalizations…
+
+194
+00:39:57.680 --> 00:40:00.460
+Ashritha: Could not be very good.
+
+195
+00:40:00.970 --> 00:40:17.749
+Ashritha: But if you want, I can make it more challenging. I think it'd be good to lean into, like, some of the standards, too. Yeah, yeah. Yeah, so, so yes, or you have… or you have other things I need to consider you can, but, let's one of them down, so I can…
+
+196
+00:40:18.270 --> 00:40:28.130
+Ashritha: So, what I'll do is I'll actually extract all of the ETM data that's there, and send it across in a similar fashion as I sent the initial data to you guys.
+
+197
+00:40:28.360 --> 00:40:45.929
+Ashritha: mainly so that you can only… the only reason for ETEM is to get the attribute values and different type of product types that are there in the industry, rather than picking those up from the dataset I sent you, which is more or less supposed to be, like, a dataset for the type of data we deal with.
+
+198
+00:40:46.090 --> 00:40:53.299
+Ashritha: A standardized mapping our data into a standardized set of attributes might be the better way to look at this.
+
+199
+00:40:53.900 --> 00:40:58.349
+Ashritha: Because this way… so this way, you'll always be running into these weird hash cases that…
+
+200
+00:40:58.610 --> 00:41:06.850
+Ashritha: we have… we have too small of a data… like, our DSL is too small, and the ranges are too low, and we can never map accurate accurately.
+
+201
+00:41:07.560 --> 00:41:22.809
+Ashritha: So it's EDOM an industry standard? It is, yeah, it's going… and they've come out for a lot of categories and been, like, table… like, tables in general in the world could be made from metal or wood or glass, whereas our eParts catalog, we might only have examples of wood, yeah.
+
+202
+00:41:23.200 --> 00:41:24.479
+Ashritha: That's… that's how it is.
+
+203
+00:41:25.080 --> 00:41:31.369
+Ashritha: I mean, Ethan, do we have any specific ones we showed you? Because, they are multiple
+
+204
+00:41:31.720 --> 00:41:43.809
+Ashritha: files with a lot of value in it. Is there something, like, which is more EFAS-specific, or the entire thing? So, ETM is exactly the industry we're in, technically. So, ETEM captures everything that
+
+205
+00:41:44.430 --> 00:41:49.650
+Ashritha: our industry kind of captures. So, it's, it's, it's like a parent subset.
+
+206
+00:41:53.160 --> 00:42:12.709
+Ashritha: Yeah, we've been wanting… we're looking… like, the standards thing is pretty new to us, too. I mean, like, we're looking more to leverage that for this, and then for other things on our platform, too, but we don't really… we haven't used it too much so far. Yeah, I mean, doing this project in general for a stand… with a standard in mind is a better use case for you guys as well, because…
+
+207
+00:42:12.710 --> 00:42:20.970
+Ashritha: If you go by our product types and, like, our specific data, it'll… it'll just be, like, too narrow for use case for you guys, and that might…
+
+208
+00:42:21.960 --> 00:42:30.760
+Ashritha: that might either lead to over-engineering, or that might either lead to, like, fewer use cases that might never be required. There could be some user.
+
+209
+00:42:30.960 --> 00:42:44.919
+Ashritha: It's like, we've talked about, like, helping… there is a case where… so our new platform, Harrigan, let's say we sign up, like, a plumber down the street, and they could sign up for the purchasing platform.
+
+210
+00:42:45.030 --> 00:42:55.459
+Ashritha: They could just upload all their products that they have and say, like, hey, we have these as our products we normally buy. We want to see these in the platform for, like, us to search and buy these.
+
+211
+00:42:55.520 --> 00:43:13.600
+Ashritha: And in that case, we might already have a lot of those products, so in that case, it would be probably good to say, like, hey, we already know this thing they're uploading, we already have that. And your thing is perfect for that user. Perfect. Yeah, for brand new products, like, the standards might be… Yeah. Like, if it's one we don't have. Yes, yes.
+
+212
+00:43:13.800 --> 00:43:32.239
+Ashritha: So how does the EDAM standard exist? Does it exist as a huge file someplace? It's so… You can find it online? Yeah, so if you search for EDAM, on their website, they have this broad list of files, where they define different type of product types, different types of attributes these product types have.
+
+213
+00:43:32.250 --> 00:43:43.929
+Ashritha: And then they also specify what sort of values these attributes will have. And there's also, like, this synonym thing that they have, which is, this specific product type or this specific attribute can be called these five things.
+
+214
+00:43:44.030 --> 00:43:49.720
+Ashritha: And it's very… it tries to be as generic and as useful as it can be.
+
+215
+00:43:50.010 --> 00:43:59.209
+Ashritha: Which, technically, our dataset is not. So, it's just a better way to, like… you can even find synonyms to what we use in our database in either.
+
+216
+00:43:59.380 --> 00:44:22.740
+Ashritha: Because that's… that's how generic they try to be, and it's just a bunch of files which all map to each other. Someone, somewhere, some organization decided, yeah, to compile all this. It is a non-profit organization. I don't know who makes it. There's a couple, like, one of them's a more European one, is it E-Class, or… That's E-Class, yeah. Yeah, E-Class is more standard in Europe, they use that. That one's pain.
+
+217
+00:44:22.740 --> 00:44:25.529
+Ashritha: Whereas ETEM's actually open to everyone.
+
+218
+00:44:25.700 --> 00:44:33.820
+Ashritha: So, it's… I think it's just, like, a project by some big company who tried to, like, standardize most of these non-gatures, and that's what it is.
+
+219
+00:44:34.900 --> 00:44:40.690
+Ashritha: Do you guys have quarters to it? Obviously, you said… sounded like you did. Yeah, I loaded the item.
+
+220
+00:44:41.410 --> 00:44:47.969
+Ashritha: I'm just trying to figure out how we can use these, or how, like, to visualize it properly, because it's a huge file.
+
+221
+00:44:48.280 --> 00:45:00.980
+Ashritha: All their files are massive. They have so much data, but I would say you can narrow the use case to only the product types we're dealing with right now. The rest of the product types might be our problem for later.
+
+222
+00:45:00.980 --> 00:45:09.080
+Ashritha: Yeah, because we, we, whatever, 400-some product types, like, we kind of specialized so far, with
+
+223
+00:45:09.080 --> 00:45:20.160
+Ashritha: building automation and electric and controls, but we… plumbing stuff, mechanical stuff, like, that's all an ETEM, and those aren't really… we don't have any customers yet, so we just don't have any…
+
+224
+00:45:20.220 --> 00:45:34.890
+Ashritha: customers in those, segments, so we don't really have any data from that. But again, it might be useful to have a documentation of some use case saying, yeah, we want to now incorporate this new product set that we're not familiar with. Yeah. Here's how you ingest
+
+225
+00:45:35.030 --> 00:45:44.630
+Ashritha: the Enum standard for that stuff. Yeah, that's gonna be some rough guidelines. Great for us going into other markets and stuff. Yeah, because I think one of iMag's, like.
+
+226
+00:45:44.800 --> 00:45:47.680
+Ashritha: individually was to… to get.
+
+227
+00:45:47.820 --> 00:46:01.819
+Ashritha: to get almost all of our data mapped into ETM standard as well. Yes. Just so that… I think we've discussed this as well at some point, but, it's… this is mainly to make sure that whatever data we have is stored in a standardized DSL.
+
+228
+00:46:01.960 --> 00:46:04.350
+Ashritha: Just in case we do want to move to…
+
+229
+00:46:04.480 --> 00:46:21.569
+Ashritha: complete ETIM standard, or a completely industry-specific standard. Yeah, as we, like, all of our data right now has been in the Alps control standard, and as we kind of separate from Alps and look to go, we want to take what they have and actually, yeah, turn it into ETEMS, and we want to start using more of the ETIMS standard.
+
+230
+00:46:22.900 --> 00:46:34.769
+Ashritha: I think this was once one of the reasons why, in the last meeting, I had a lot of questions on what was this… what we tried to do? Because technically, that is what I was imagining in my mind, and what you're presenting was not…
+
+231
+00:46:34.770 --> 00:46:46.300
+Ashritha: very clean line with that, and that's why I was confused. But again, that is really good, though, so far for just someone going in and uploading a product, we do that, and we'll instantly know that, hey, they're uploading this product.
+
+232
+00:46:47.780 --> 00:46:48.909
+Ashritha: I don't do it.
+
+233
+00:46:49.610 --> 00:47:08.669
+Ashritha: So you don't like to have a mapping from your current, help stuff to even… we can detect the subset right now, and deal with that. Because only the product types that… so technically, we can only deal with the product types that I've currently sent across to a team.
+
+234
+00:47:08.810 --> 00:47:12.710
+Ashritha: So, only those prototypes from Eden can be utilized, and that's what we can…
+
+235
+00:47:13.070 --> 00:47:16.410
+Ashritha: That's what we can work with right now. So that helps us, like…
+
+236
+00:47:16.650 --> 00:47:35.549
+Ashritha: reduce the whole ETM set to a smaller subset, and also help certify my use cases. So it'd be a big win for eParts to map all your stuff when you get them. Yeah, for sure. 100%. That'll be a great value addition overall, like, if you're able to do this, because this pipeline actually names a lot of these things.
+
+237
+00:47:35.600 --> 00:47:44.330
+Ashritha: But it just needs to be for a slightly broader subset, or a slightly broader set of products and values. That's pretty much it.
+
+238
+00:47:46.430 --> 00:47:49.029
+Ashritha: I think that was a mismatched in the discussion.
+
+239
+00:47:54.460 --> 00:47:57.389
+Ashritha: Okay, well, that's valuable input.
+
+240
+00:47:57.910 --> 00:48:08.340
+Ashritha: I mean, let us know. That's why I keep asking you guys questions on, like, if you guys need any other sort of data which helps you guys work with this current data, or any sort of,
+
+241
+00:48:08.630 --> 00:48:12.130
+Ashritha: any sort of work that I can do for you guys, which can help
+
+242
+00:48:12.440 --> 00:48:15.990
+Ashritha: help with you… help with you dealing with internal, right?
+
+243
+00:48:16.640 --> 00:48:17.769
+Ashritha: Any other way?
+
+244
+00:48:18.350 --> 00:48:23.169
+Ashritha: Yeah, I think there are… I have a few questions about ETM, but I think I'll look into it a bit more than I'll…
+
+245
+00:48:24.430 --> 00:48:37.699
+Ashritha: But then if it's easier for you to, like, extract the data, because you know ePaths better, just align with what you guys work with, so if it is possible, then probably it can help,
+
+246
+00:48:38.100 --> 00:48:43.379
+Ashritha: I can, I can show you that. Okay. Yeah, like, spend, like, a few days on just trying to not beat them.
+
+247
+00:48:43.550 --> 00:48:44.590
+Ashritha: I've got names.
+
+248
+00:48:44.960 --> 00:48:46.730
+Ashritha: If it's…
+
+249
+00:48:47.410 --> 00:48:53.210
+Ashritha: If it's too small of a task, then it's good. If it's too big of a task, I'll just let you guys.
+
+250
+00:48:53.920 --> 00:48:54.610
+Ashritha: Beautiful.
+
+251
+00:48:54.930 --> 00:49:05.430
+Ashritha: We're gonna have to… because we're gonna have to, like, CNS is really wanting us to… so this has been our direction since, like, the last 3 or 4 months, you know, since we built, like, this new…
+
+252
+00:49:05.560 --> 00:49:17.790
+Ashritha: platform very exist. Like, that was… that was… that was the reason why a lot of us weren't available in, like, this whole Feb, initial Feb, end of Jan kind of period, and this is… this is the kind of shift that's overall happening now.
+
+253
+00:49:18.870 --> 00:49:22.459
+Ashritha: And I think since then, we've been trying to focus on it a little bit, like.
+
+254
+00:49:22.590 --> 00:49:25.270
+Ashritha: It's, I think it's got missed somewhere.
+
+255
+00:49:25.490 --> 00:49:27.280
+Ashritha: It's not the discussions we had, okay.
+
+256
+00:49:29.130 --> 00:49:40.850
+Ashritha: Is Eton the only standard, or… There's others, too. Like, UNSPC is another big one, too. But UNSPC doesn't have really good data. It's paid, first of all, and it's also,
+
+257
+00:49:40.850 --> 00:49:49.779
+Ashritha: something was specific to a very small industry, I think. So UNSBC wasn't as good. From what I've seen, ETL was the only thing which kind of…
+
+258
+00:49:50.070 --> 00:49:53.589
+Ashritha: Gives us open source information, along with
+
+259
+00:49:53.850 --> 00:49:57.379
+Ashritha: a lot of level mapping, because ETEM actually maps to eClass.
+
+260
+00:49:57.670 --> 00:50:09.030
+Ashritha: Yeah, they have a code, I think, that's, like, you know, that, like, this corresponds to this and the other standard. So that makes things easier for us, so as long as you guys only work with ETH, that should be enough for, like, the school's project token.
+
+261
+00:50:09.800 --> 00:50:10.630
+Ashritha: Yep.
+
+262
+00:50:16.830 --> 00:50:18.479
+Ashritha: I think, you know,
+
+263
+00:50:18.620 --> 00:50:37.869
+Ashritha: Yeah, we can send it over mail, since you guys have a good idea about the architecture, the components, and the ML, so you can just, like, quickly go over. If you have, like, any outstanding comments, you can just drop in, and we can do a review next week with the updated version.
+
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+WEBVTT
+
+1
+00:00:06.300 --> 00:00:07.220
+hrishikb@andrew.cmu.edu: Okay.
+
+2
+00:00:09.530 --> 00:00:14.110
+hrishikb@andrew.cmu.edu: So, first we have… On the agenda, the initial OCR endings.
+
+3
+00:00:36.260 --> 00:00:41.969
+hrishikb@andrew.cmu.edu: So, I did a few POCs with whatever was available out there for the OCR part of
+
+4
+00:00:42.120 --> 00:00:49.279
+hrishikb@andrew.cmu.edu: things, which is what I'm doing right away. So, I texted about 8 different, source tools.
+
+5
+00:00:49.460 --> 00:00:55.850
+hrishikb@andrew.cmu.edu: These are, like, there are 3 different, categories of tools that I…
+
+6
+00:00:57.280 --> 00:01:15.059
+hrishikb@andrew.cmu.edu: There's three different strategies that I went through. There's one with just the plain old select, which is, like, capital, which just basically goes through the document and then picks up towards it. It has no idea what a table looks like. That's not fully.
+
+7
+00:01:15.680 --> 00:01:28.090
+hrishikb@andrew.cmu.edu: The second one's a document password, like, DocLink, Mayanuru, Surya, and these are, they build a little more, deeper, finding labels and structure.
+
+8
+00:01:28.090 --> 00:01:36.050
+hrishikb@andrew.cmu.edu: So once it finds, like, a word, and it makes it into a table, then, like, it does a little more recognition than the playoffs there.
+
+9
+00:01:36.050 --> 00:01:40.520
+hrishikb@andrew.cmu.edu: And then there's the ability models. So that's the Chandra tools.
+
+10
+00:01:40.630 --> 00:01:51.249
+hrishikb@andrew.cmu.edu: So, Chamberto is a very, powerful one, but the problem is you have to post it via, Data Lab, which is a service they offer.
+
+11
+00:01:51.690 --> 00:01:58.560
+hrishikb@andrew.cmu.edu: You can self-host it, but the problem is you don't get the performance, you don't get the same amount of performance
+
+12
+00:01:58.770 --> 00:02:01.640
+hrishikb@andrew.cmu.edu: As you would get if you hosted by a data lab.
+
+13
+00:02:02.090 --> 00:02:07.449
+hrishikb@andrew.cmu.edu: And I have a couple of, benchmark scores as well that I got
+
+14
+00:02:07.570 --> 00:02:15.209
+hrishikb@andrew.cmu.edu: So, I have only 8 PDF documents right now, which are based from you guys. Like, if you can give me a bit more, we can…
+
+15
+00:02:15.220 --> 00:02:33.970
+hrishikb@andrew.cmu.edu: do another benchmark on all these platforms. You can use the links itself in the links. The CDN and the… yeah, the CDN link. Oh, those, yeah, the software, you can just use the CD links and, like, how many may want. Okay, I'll do that. So, for now, I just use the 8 PDFs that I had downloaded.
+
+16
+00:02:34.050 --> 00:02:48.860
+hrishikb@andrew.cmu.edu: And, Chandra 2 with the data lab was the most accurate one with 97%, right? At a 3.4% elevate, but this is a paid service, so you wouldn't be paying one cent per page.
+
+17
+00:02:49.440 --> 00:02:51.970
+hrishikb@andrew.cmu.edu: Minor New is another one which…
+
+18
+00:02:52.160 --> 00:03:03.119
+hrishikb@andrew.cmu.edu: I ran locally. You need your own server. Basically you need GPU, that is for this, so I had a model account, and I needed over there on an 800, GPU.
+
+19
+00:03:03.350 --> 00:03:09.970
+hrishikb@andrew.cmu.edu: But, like, I expect if we can run it on Edward as well, and you can run it under that, too.
+
+20
+00:03:10.300 --> 00:03:18.920
+hrishikb@andrew.cmu.edu: That was pretty much similar to Chandra, too, except it had a few things that it must actually work. And,
+
+21
+00:03:18.920 --> 00:03:32.370
+hrishikb@andrew.cmu.edu: I didn't… I did the POC for the rest of it, but I don't want to go anywhere below the 5% error rate, because I don't think it makes sense, because otherwise I would have to, again, come and collect stuff out.
+
+22
+00:03:34.300 --> 00:03:38.209
+hrishikb@andrew.cmu.edu: So how do you actually determine the error rate? Do you…
+
+23
+00:03:38.430 --> 00:03:54.099
+hrishikb@andrew.cmu.edu: Do you manually go back and do that? I mean, how do you know? So, I had, Claude actually go through the PDF document once, Claude is really about Claude and ChatGP perspective, and then I went through the document as well, and we had, like, a comparison benchmark that we ran against.
+
+24
+00:03:54.320 --> 00:03:56.060
+hrishikb@andrew.cmu.edu: So that's probably not the other thing.
+
+25
+00:03:57.340 --> 00:03:58.230
+hrishikb@andrew.cmu.edu: Okay.
+
+26
+00:03:59.270 --> 00:04:09.840
+hrishikb@andrew.cmu.edu: So, if we… I don't think we would be going via data lab, because we are entering tied to it. So,
+
+27
+00:04:10.070 --> 00:04:14.050
+hrishikb@andrew.cmu.edu: I'm guessing if we were going to self-post it, we would be self-posting, right?
+
+28
+00:04:14.290 --> 00:04:18.290
+hrishikb@andrew.cmu.edu: Either on some kind of a GPU service.
+
+29
+00:04:18.890 --> 00:04:38.340
+hrishikb@andrew.cmu.edu: And there's also dockling, which has some amount of error, but it means only a CPU doesn't deploy US dollars. So, you don't… you can get it cheaper as well, but the error rate is still a little bit high. I can see if I can fine-tune the parameters to… I mean, technically, even for general articles, it's…
+
+30
+00:04:38.660 --> 00:04:47.699
+hrishikb@andrew.cmu.edu: If it's a service, as long as we get an output, and we put that output wherever, it doesn't matter. Like, so, if it's easy to host it there, we might as reduce that service.
+
+31
+00:04:48.830 --> 00:04:57.489
+hrishikb@andrew.cmu.edu: Right, but again, we would have to see how many pages, yeah, how expensive it gets reported.
+
+32
+00:04:58.330 --> 00:05:03.490
+hrishikb@andrew.cmu.edu: So I did some research on if there's some alternative options.
+
+33
+00:05:03.610 --> 00:05:08.060
+hrishikb@andrew.cmu.edu: And it did say that there was an EA documented citizens.
+
+34
+00:05:08.210 --> 00:05:27.520
+hrishikb@andrew.cmu.edu: But I have had… I did not have a chance to try it out, because I don't think I have access to the Azure, authentication. Okay. Yeah, so if you give me that, I can just try it out with the current PDF that I've got, create PDFs, and come up with some final benchmarkings.
+
+35
+00:05:28.000 --> 00:05:31.489
+hrishikb@andrew.cmu.edu: And how often… I mean, this is,
+
+36
+00:05:33.270 --> 00:05:35.579
+hrishikb@andrew.cmu.edu: This is not going to need to be run
+
+37
+00:05:37.560 --> 00:05:52.109
+hrishikb@andrew.cmu.edu: It only needs to run when the document changes, right? Correct. Or when there's new documents coming in, and we would ideally be batching those as well. We don't want to run it one by one, because, like, the COPPA, computers on top of that.
+
+38
+00:05:55.000 --> 00:05:58.649
+hrishikb@andrew.cmu.edu: Yeah, I guess I don't know how often that changes,
+
+39
+00:05:59.290 --> 00:06:05.430
+hrishikb@andrew.cmu.edu: You know, doing our entire catalog, call it, 800 bucks.
+
+40
+00:06:05.620 --> 00:06:08.569
+hrishikb@andrew.cmu.edu: Probably about, you know, 10 bucks a year.
+
+41
+00:06:08.750 --> 00:06:10.040
+hrishikb@andrew.cmu.edu: That's pretty good.
+
+42
+00:06:15.630 --> 00:06:20.840
+hrishikb@andrew.cmu.edu: It kind of depends on how many new vendors you onboard quickly.
+
+43
+00:06:20.940 --> 00:06:24.969
+hrishikb@andrew.cmu.edu: If you have, like, how many clinches they make. Yeah, yeah, okay.
+
+44
+00:06:25.540 --> 00:06:30.380
+hrishikb@andrew.cmu.edu: What is an output? Just…
+
+45
+00:06:31.280 --> 00:06:43.939
+hrishikb@andrew.cmu.edu: Just the text? Yeah, so it outputs… my output goes directly to the injection service, what would be agreed. The injection service then passes around to the ML modules for itself. Suppose,
+
+46
+00:06:44.040 --> 00:06:49.810
+hrishikb@andrew.cmu.edu: Supposedly had… I don't know, 40 clients, and they all have the same vendor.
+
+47
+00:06:50.520 --> 00:06:59.350
+hrishikb@andrew.cmu.edu: We're… we might… we might want to segment this data. They might have Contractual requirements that say
+
+48
+00:06:59.490 --> 00:07:11.609
+hrishikb@andrew.cmu.edu: We don't want any data to touch, even public data, right? So, like, if we're paying for such and such a service to help with our catalog ingestion, we want that to be hours and hours alone.
+
+49
+00:07:12.580 --> 00:07:13.610
+hrishikb@andrew.cmu.edu: Oh, cool.
+
+50
+00:07:14.590 --> 00:07:16.060
+hrishikb@andrew.cmu.edu: Sorry, I'm trying to crinkle it.
+
+51
+00:07:18.570 --> 00:07:20.830
+hrishikb@andrew.cmu.edu: ordinance for us, okay?
+
+52
+00:07:23.400 --> 00:07:39.640
+hrishikb@andrew.cmu.edu: Would it be possible just to take the output of that file and store it somewhere? Yeah, we can have it. I think we are planning on doing something on the lines of, the client, with the vendor and having that in the key, so that each client has a distinct,
+
+53
+00:07:39.710 --> 00:07:45.269
+hrishikb@andrew.cmu.edu: client-vendor relationship. So, you know, the same client as multi… like, same vendor, each with multiple clients, they'll all have a different
+
+54
+00:07:45.410 --> 00:07:47.389
+hrishikb@andrew.cmu.edu: basically catalog. Okay.
+
+55
+00:07:48.210 --> 00:07:52.719
+hrishikb@andrew.cmu.edu: Cool. So the data, the Sierra data is structured data that you send to the…
+
+56
+00:07:52.900 --> 00:08:03.219
+hrishikb@andrew.cmu.edu: Yeah, it's structured data. That's the whole, that's the whole reason I did the next part. We want structured data. If it's, like, raw data, the plain OCR model works.
+
+57
+00:08:06.170 --> 00:08:12.260
+hrishikb@andrew.cmu.edu: So, a couple of things that it still did not pick up, even the best model was,
+
+58
+00:08:16.860 --> 00:08:25.350
+hrishikb@andrew.cmu.edu: So, always here… So, over here, there's dimensions, and over here.
+
+59
+00:08:25.600 --> 00:08:28.339
+hrishikb@andrew.cmu.edu: Like, coil diameter and wide length.
+
+60
+00:08:28.830 --> 00:08:33.769
+hrishikb@andrew.cmu.edu: I wasn't able to pick that up, even the, Chandler tool with the data platform.
+
+61
+00:08:35.110 --> 00:08:38.590
+hrishikb@andrew.cmu.edu: So, I don't know if,
+
+62
+00:08:39.429 --> 00:08:44.649
+hrishikb@andrew.cmu.edu: A bit more fine-tuning on the model itself could maybe help it out already.
+
+63
+00:08:45.420 --> 00:08:53.719
+hrishikb@andrew.cmu.edu: What we could probably do is, like, offload some of those which don't really work out to…
+
+64
+00:08:54.100 --> 00:08:58.499
+hrishikb@andrew.cmu.edu: a better than, like, GPT host run already.
+
+65
+00:08:58.610 --> 00:09:06.399
+hrishikb@andrew.cmu.edu: That would be as almost 100%. Yeah, even Azure OCDR might, Mike. Yeah, yeah, I could compare this, yeah.
+
+66
+00:09:07.210 --> 00:09:15.010
+hrishikb@andrew.cmu.edu: There was one which was the Siemens Power Catalog, this one.
+
+67
+00:09:15.480 --> 00:09:24.360
+hrishikb@andrew.cmu.edu: this gave a 0% success rate. Like, all the models paid down this one, just because it has no tables, and it's just paraphors.
+
+68
+00:09:24.700 --> 00:09:28.270
+hrishikb@andrew.cmu.edu: So, this, pretty much requires,
+
+69
+00:09:28.750 --> 00:09:35.280
+hrishikb@andrew.cmu.edu: LLM itself to, like, go through a technical, outcome technicians.
+
+70
+00:09:36.690 --> 00:09:41.750
+hrishikb@andrew.cmu.edu: So there are preferred structures In these documents that would make…
+
+71
+00:09:42.470 --> 00:09:55.409
+hrishikb@andrew.cmu.edu: the analysis better, that's what I'm hearing. Yeah, okay. Is that… is that a very clear path forward? Because what's going to happen immediately is one of the suppliers is going to say, why does your…
+
+72
+00:09:56.330 --> 00:10:00.240
+hrishikb@andrew.cmu.edu: Catalog data looks so much better for my competitor.
+
+73
+00:10:00.380 --> 00:10:06.519
+hrishikb@andrew.cmu.edu: Not for me. We'll say, well, you give us crappy PDFs, and say, what do I have to do to change the PDFs? And we'll go.
+
+74
+00:10:07.070 --> 00:10:08.710
+hrishikb@andrew.cmu.edu: Alright, alright.
+
+75
+00:10:08.820 --> 00:10:23.360
+hrishikb@andrew.cmu.edu: Yeah, I can… it's mostly tabular data. If it's end tables, results here, engines usually make it, much better to scale. Good. But I can dig a little more deeper into how,
+
+76
+00:10:23.430 --> 00:10:31.500
+hrishikb@andrew.cmu.edu: something like Chanda, which is not just an OCR engine, but it has some amount of VA elements underneath the UR as well.
+
+77
+00:10:32.650 --> 00:10:34.970
+hrishikb@andrew.cmu.edu: So, I guess,
+
+78
+00:10:35.190 --> 00:10:41.300
+hrishikb@andrew.cmu.edu: One thing I need is the Azure credentials so that I can test out the Azure OCL.
+
+79
+00:10:42.690 --> 00:10:51.110
+hrishikb@andrew.cmu.edu: I don't think it would be a lot better than Fundra 2, because right now it's state-of-the-art, and every enterprise project…
+
+80
+00:10:51.690 --> 00:10:53.330
+hrishikb@andrew.cmu.edu: But we can try it out.
+
+81
+00:10:53.680 --> 00:11:01.149
+hrishikb@andrew.cmu.edu: And, Datalab does offer zero retention and SOC2, just in case there's something for that.
+
+82
+00:11:02.310 --> 00:11:07.860
+hrishikb@andrew.cmu.edu: If it's self-hosted, then we can just run it on, yeah, machines are everywhere.
+
+83
+00:11:08.010 --> 00:11:13.539
+hrishikb@andrew.cmu.edu: So, all the top contenders, are they commercial, or are they open source? What's the breakdown of that?
+
+84
+00:11:14.190 --> 00:11:18.149
+hrishikb@andrew.cmu.edu: the… the… I haven't, tested out any,
+
+85
+00:11:18.380 --> 00:11:23.830
+hrishikb@andrew.cmu.edu: Oh, I've only tested our open source ones right now. Yeah, these are our open source ones, which…
+
+86
+00:11:24.110 --> 00:11:30.790
+hrishikb@andrew.cmu.edu: Minus Chandra 2, which is, service. It's software as a service, we can upgrade.
+
+87
+00:11:32.430 --> 00:11:39.410
+hrishikb@andrew.cmu.edu: And we can also, if none of this works out, we can always follow back to the GPT on network as well.
+
+88
+00:11:41.080 --> 00:11:43.890
+hrishikb@andrew.cmu.edu: So, yeah, let's movements.
+
+89
+00:11:46.460 --> 00:11:50.290
+hrishikb@andrew.cmu.edu: But, yeah, these are the added dates, basically.
+
+90
+00:11:54.960 --> 00:11:58.579
+hrishikb@andrew.cmu.edu: And the output from this is JSON or KV pairs.
+
+91
+00:11:59.110 --> 00:12:04.720
+hrishikb@andrew.cmu.edu: We can… Signature can configure that too.
+
+92
+00:12:05.950 --> 00:12:20.319
+hrishikb@andrew.cmu.edu: So, like, I was working on the ingestion part, actually, which is the next step. So I also, like, I made a mock this thing, using the, like, test rack and stuff, just to get some information out. So right now, in the ingestion part, we are…
+
+93
+00:12:20.920 --> 00:12:35.470
+hrishikb@andrew.cmu.edu: like, I've not tested it that much, but we are able to, identify which data is, like, corrupted and which data is not, so we are able to see the data in its, like, kind of more defined format.
+
+94
+00:12:35.650 --> 00:12:50.200
+hrishikb@andrew.cmu.edu: we get, basically, JSON data, which has the product name, the values, which document it came from, and basic things like that. So, like, testing is left, but yeah, after we are done to OCR part, we can connect these two, and then we should have a…
+
+95
+00:12:50.440 --> 00:12:55.800
+hrishikb@andrew.cmu.edu: basic running flow. So, I think, some amount of what was the person
+
+96
+00:12:56.220 --> 00:13:05.369
+hrishikb@andrew.cmu.edu: overlap. My module is just, just, like, a using throw kind of thing, and that's not meant to go in the code anyways. It's just so that we can work in parallel.
+
+97
+00:13:05.580 --> 00:13:15.179
+hrishikb@andrew.cmu.edu: Right now, in the addition part, I'm able to get the data in a decent shape. The next part would be maybe to…
+
+98
+00:13:16.950 --> 00:13:25.599
+hrishikb@andrew.cmu.edu: get all the data and put it into a specific schema that you guys already have for PIMS, so that part is not done yet. So, next steps are that we…
+
+99
+00:13:25.770 --> 00:13:33.230
+hrishikb@andrew.cmu.edu: like, first we continue the OCR part, and in the addition part, we go ahead and… Just…
+
+100
+00:13:33.450 --> 00:13:41.979
+hrishikb@andrew.cmu.edu: put the data in canonical tables so that we can use it on the time? So, I think OCR does the passing and extraction, whereas Engine does an operator. Okay.
+
+101
+00:13:42.300 --> 00:13:43.150
+hrishikb@andrew.cmu.edu: Jesus.
+
+102
+00:13:43.360 --> 00:13:54.490
+hrishikb@andrew.cmu.edu: And then expect that from the… by the end one, so… Okay, so this is, like, the first two steps in there. Yeah, then we have the LM models, which will give us a content scores, and…
+
+103
+00:13:55.420 --> 00:13:58.659
+hrishikb@andrew.cmu.edu: So the third step will come back into the second step.
+
+104
+00:13:58.960 --> 00:14:02.450
+hrishikb@andrew.cmu.edu: Where the staging table is available, and then go back in.
+
+105
+00:14:02.600 --> 00:14:04.660
+hrishikb@andrew.cmu.edu: bookings.
+
+106
+00:14:05.270 --> 00:14:21.760
+hrishikb@andrew.cmu.edu: So, for training, we actually are planning to have a separate, database. So, whenever, like, after, let's say, our ML models have given us some output, and we deem that's not good enough, and the human reviewer modifies it, changes it.
+
+107
+00:14:21.760 --> 00:14:28.870
+hrishikb@andrew.cmu.edu: That modification would be fed into our database, and then after a certain amount of time, we'll use that data to train the model again.
+
+108
+00:14:29.030 --> 00:14:36.799
+hrishikb@andrew.cmu.edu: It's basically BIMS data going in one of the database. Okay, yeah. Now, you don't want to touch BIMS with any of the interpreted data?
+
+109
+00:14:38.700 --> 00:14:57.540
+hrishikb@andrew.cmu.edu: I want to go back to the Siemens example for a minute. Isn't there some structure to the Siemens data, or regularity to it? It would seem to me that, you know, they're talking as specs, but if someone were to parse that out, you could go back and digest it that way, just curious. So, the problem here is,
+
+110
+00:14:57.630 --> 00:15:04.910
+hrishikb@andrew.cmu.edu: Every single PDF is very different, and these OCR engines work on some amount of rules.
+
+111
+00:15:05.190 --> 00:15:09.849
+hrishikb@andrew.cmu.edu: written for genetic OCR, by them, so…
+
+112
+00:15:10.080 --> 00:15:20.049
+hrishikb@andrew.cmu.edu: For example, in the semen data, it just looks like a paragraph. As a human, we can just read through and understand that these are the part… the part numbers are the ones we need to
+
+113
+00:15:20.140 --> 00:15:23.949
+hrishikb@andrew.cmu.edu: Take into consideration, pass it downstream, but then…
+
+114
+00:15:23.970 --> 00:15:33.519
+hrishikb@andrew.cmu.edu: For an OCR engine, this is just text, and it doesn't know which part of it to expect and get put into work. I understand, but I'm just saying the… the paragraph of stuff.
+
+115
+00:15:33.520 --> 00:15:46.070
+hrishikb@andrew.cmu.edu: Is that regular or not, or is it… Yeah, it is regular, but it's still not a table. So, I'm sure we can improve the model to, like, fit in things which we would also want, because it's open source.
+
+116
+00:15:46.120 --> 00:15:49.689
+hrishikb@andrew.cmu.edu: But, like, just the base model right now would not fall through that.
+
+117
+00:15:54.300 --> 00:15:57.659
+hrishikb@andrew.cmu.edu: I mean, in general, at least those are Apple, because,
+
+118
+00:15:57.850 --> 00:16:06.200
+hrishikb@andrew.cmu.edu: The way written to work is you exactly define what part of the wage is what, and then it tends to perform the best.
+
+119
+00:16:06.350 --> 00:16:25.270
+hrishikb@andrew.cmu.edu: But if you see, like, invoicing is probably the best case producer, and invoicing is pretty standardized, you point it to what corresponds to what aspect of invoicing, and it performs a variance, but when it's unstructured… Yeah, generally, like, if the data is in tabular format, it's very easy to extract in KV pairs.
+
+120
+00:16:25.380 --> 00:16:28.140
+hrishikb@andrew.cmu.edu: But, like, in this, we have paragraphs and…
+
+121
+00:16:28.240 --> 00:16:37.139
+hrishikb@andrew.cmu.edu: So, that a normal OCR can… at best, it can probably just give us the entire paragraph, but not the exact numbers and values for it. But honestly, like.
+
+122
+00:16:37.480 --> 00:16:40.280
+hrishikb@andrew.cmu.edu: If… I can do a rather benchmark of
+
+123
+00:16:41.290 --> 00:17:04.590
+hrishikb@andrew.cmu.edu: Just using LLM. You'll take the text, chuck it into the… in a way, it's trying to optimize cost, right? So, you can either drop the entire PDF into an LLM and split the data out. Yeah. That would cost more. Technically, it's cheaper to do OCR on it and then chuck it into the LLM, because LLMs work better with JSON, Markdown, and all of these colors, yeah.
+
+124
+00:17:04.589 --> 00:17:10.319
+hrishikb@andrew.cmu.edu: So… And PDF is, like, a bit expensive as well. Yeah, just raw TXT files in a little bit cheaper.
+
+125
+00:17:10.490 --> 00:17:24.069
+hrishikb@andrew.cmu.edu: Rather than, like, a PDF in which it has to be manually. Yeah, I can then probably just use the basic OCR models. Yeah, it could be a two-step thing as well, if it's… if it's just that much easier. Yeah.
+
+126
+00:17:25.079 --> 00:17:34.359
+hrishikb@andrew.cmu.edu: Because GBD5 Mini and, just models of that sort are extremely cheap. So they cost, I think, around,
+
+127
+00:17:35.180 --> 00:17:48.779
+hrishikb@andrew.cmu.edu: 0.3… 0.037 per 10,000 tokens, I think? 1,000 tokens, which is… which would be extremely…
+
+128
+00:17:49.120 --> 00:17:58.110
+hrishikb@andrew.cmu.edu: Because technically, we don't have that many years in the end, the number, 10,000 to 30,000 kind of range there, and the expense will never, like, do our…
+
+129
+00:18:00.510 --> 00:18:11.309
+hrishikb@andrew.cmu.edu: Yeah, I mean, maybe try that. That would, I think, simplify it by a lot. There's also Microsoft Open Source Repository in GitHub. I forgot its name, but you might have mentioned it.
+
+130
+00:18:12.090 --> 00:18:17.810
+hrishikb@andrew.cmu.edu: But that's… that's… that's currently, I think, a standardized one, too. I think it was the iteration one last week.
+
+131
+00:18:18.040 --> 00:18:23.139
+hrishikb@andrew.cmu.edu: It's, it's used to convert all PDFs into markdowns.
+
+132
+00:18:23.420 --> 00:18:32.340
+hrishikb@andrew.cmu.edu: Oh, okay, yeah. Yeah, I think there's a bunch of open source tool, PDF stuff. Yeah, so that… you could probably check with that as well.
+
+133
+00:18:35.000 --> 00:18:42.469
+hrishikb@andrew.cmu.edu: Yeah, also, if it could give some, information to how many of these catalogs are changing every year, that's…
+
+134
+00:18:45.240 --> 00:18:51.069
+hrishikb@andrew.cmu.edu: Then we can… and, like, what the… what kind of budget we work for. So, yeah, that I know.
+
+135
+00:18:51.730 --> 00:19:00.689
+hrishikb@andrew.cmu.edu: what we want to be using, yeah. I mean, technically, you guys would be doing it per day task without… it's more or less a POC of…
+
+136
+00:19:00.850 --> 00:19:02.600
+hrishikb@andrew.cmu.edu: Great, yeah.
+
+137
+00:19:03.380 --> 00:19:06.170
+hrishikb@andrew.cmu.edu: So the budget planning is the biggest it will be.
+
+138
+00:19:06.390 --> 00:19:13.290
+hrishikb@andrew.cmu.edu: So the main thing is, the catalogs which are already present right now in PIM doesn't need to be re-indexed, right?
+
+139
+00:19:13.660 --> 00:19:15.390
+hrishikb@andrew.cmu.edu: That only needs to go…
+
+140
+00:19:16.370 --> 00:19:30.119
+hrishikb@andrew.cmu.edu: What's the ML? What do you mean? Global OCR, and the organization. I mean, we could try to enrich the current catalog as well. That could be a part of it as well. Okay.
+
+141
+00:19:30.330 --> 00:19:37.410
+hrishikb@andrew.cmu.edu: It could be multi… it could be different aspects of things, like trying to edit the current one, trying to…
+
+142
+00:19:37.410 --> 00:19:51.899
+hrishikb@andrew.cmu.edu: experiment with a few new catalogs, and see how it goes, and how it works. So those could be, like, use cases which you can probably gauge your metrics on how it works. Yeah. What about the mapping? Was it the Eagle standard? Is that the standard? Yeah,
+
+143
+00:19:52.300 --> 00:20:08.589
+hrishikb@andrew.cmu.edu: Yeah, that's, like, after we are done with these parts, that's what we'll look at, because we're thinking about using that, standard in the initiation part, but that doesn't seem very feasible to do right now. So we'll probably, try to merge that in with the LLM, sorry, the ML part.
+
+144
+00:20:08.590 --> 00:20:17.070
+hrishikb@andrew.cmu.edu: And then maybe have a small component which matches it up with the standards. Yeah, I mean, ETAM should mainly dictate the attribute values for you guys, and…
+
+145
+00:20:17.090 --> 00:20:23.949
+hrishikb@andrew.cmu.edu: I'm just setting, like, a baseline for what would be the acceptable range of outputs.
+
+146
+00:20:26.260 --> 00:20:41.589
+hrishikb@andrew.cmu.edu: I think we've not really made progress on the ETEM front as of right now. Like, once we go a bit deeper into it, we'll probably have much more questions regarding, like, the last time I checked, I was not able to figure out which data is relevant to us, which is not.
+
+147
+00:20:42.540 --> 00:21:00.159
+hrishikb@andrew.cmu.edu: I think, yeah, after we have the required data, we should be able to use it in a pretty decent way. I would say a good way to probably just experiment with imaging data would be take a small sample of product types and attributes for that product types, and see if you can map it.
+
+148
+00:21:00.200 --> 00:21:08.519
+hrishikb@andrew.cmu.edu: If that mapping is easier, then you could just put it in Cloud and ask it to do your own mapping for all the rock types and avenues, and that might just…
+
+149
+00:21:09.130 --> 00:21:12.319
+hrishikb@andrew.cmu.edu: That you guys pass the whole magic step of mapping anything.
+
+150
+00:21:12.810 --> 00:21:15.870
+hrishikb@andrew.cmu.edu: Right? As long as you can do a small subset, you say anything.
+
+151
+00:21:16.030 --> 00:21:16.930
+hrishikb@andrew.cmu.edu: Right, right.
+
+152
+00:21:17.500 --> 00:21:18.410
+hrishikb@andrew.cmu.edu: Right.
+
+153
+00:21:20.260 --> 00:21:35.790
+hrishikb@andrew.cmu.edu: Yeah, we… yeah, we can do that. Yeah, I think after we… the initial mapping part, we'll use Claude to map all of it with the product, because it is going to be huge. The files are, like, really big. So, a little bit you can do, and then just eat the rest of it.
+
+154
+00:21:36.700 --> 00:21:37.880
+hrishikb@andrew.cmu.edu: Okay.
+
+155
+00:21:39.420 --> 00:21:41.949
+hrishikb@andrew.cmu.edu: Next, February.
+
+156
+00:21:42.290 --> 00:21:42.990
+hrishikb@andrew.cmu.edu: Yeah.
+
+157
+00:21:46.860 --> 00:21:50.380
+hrishikb@andrew.cmu.edu: Next, I think we can discuss the LMP OC update.
+
+158
+00:21:52.390 --> 00:21:54.680
+hrishikb@andrew.cmu.edu: On the phones.
+
+159
+00:21:55.790 --> 00:21:56.640
+hrishikb@andrew.cmu.edu: How about?
+
+160
+00:21:56.950 --> 00:21:57.960
+hrishikb@andrew.cmu.edu: Let me stop sharing.
+
+161
+00:22:02.250 --> 00:22:06.040
+hrishikb@andrew.cmu.edu: Newskeeper,
+
+162
+00:22:07.510 --> 00:22:08.230
+hrishikb@andrew.cmu.edu: Wait.
+
+163
+00:22:09.880 --> 00:22:10.580
+hrishikb@andrew.cmu.edu: Hold on.
+
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+hrishikb@andrew.cmu.edu: Yeah.
+
+165
+00:22:20.960 --> 00:22:24.839
+hrishikb@andrew.cmu.edu: So… I just tried to see…
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+166
+00:22:25.660 --> 00:22:32.559
+hrishikb@andrew.cmu.edu: if we could, lose doing his approach, and everyone wanted to see whether there are different LLMs.
+
+167
+00:22:32.840 --> 00:22:38.800
+hrishikb@andrew.cmu.edu: approach would work. So, these were the five models I used. These are all open source, and…
+
+168
+00:22:38.950 --> 00:22:42.700
+hrishikb@andrew.cmu.edu: I just used Olama to, run everything on them.
+
+169
+00:22:42.920 --> 00:22:51.510
+hrishikb@andrew.cmu.edu: So, currently, Llama 3.18 billion priority model, gives pretty, the highest result, pretty much.
+
+170
+00:22:51.690 --> 00:22:59.160
+hrishikb@andrew.cmu.edu: Fee, by 4, 14 billion one gives, like, slightly better results, but there's a bit of a caveat to that.
+
+171
+00:22:59.300 --> 00:23:09.549
+hrishikb@andrew.cmu.edu: I could not see any, regularity. It kind of goes up and down, and there's also the problem of…
+
+172
+00:23:12.980 --> 00:23:15.780
+hrishikb@andrew.cmu.edu: Yeah, so the problem is that
+
+173
+00:23:16.050 --> 00:23:29.759
+hrishikb@andrew.cmu.edu: All these, Lew's model is pretty good with calibration as well. Here, when I try to, structure it in such a way that it gives a confidence, like, how confident is it when it's making the prediction?
+
+174
+00:23:29.770 --> 00:23:37.220
+hrishikb@andrew.cmu.edu: It… it does not improve at all. Quen, 2.5, 14 billion, it's actually…
+
+175
+00:23:37.340 --> 00:23:43.469
+hrishikb@andrew.cmu.edu: Performs worse at conference, accuracy than, everything else.
+
+176
+00:23:43.580 --> 00:23:46.329
+hrishikb@andrew.cmu.edu: There are some exceptions, but,
+
+177
+00:23:46.650 --> 00:23:51.279
+hrishikb@andrew.cmu.edu: there's no trend I can see that, as the models get bigger.
+
+178
+00:23:51.600 --> 00:23:56.789
+hrishikb@andrew.cmu.edu: it gets better. I have not tested with the biggest ones yet.
+
+179
+00:23:56.910 --> 00:23:59.999
+hrishikb@andrew.cmu.edu: Because, when I played around with this,
+
+180
+00:24:00.510 --> 00:24:03.989
+hrishikb@andrew.cmu.edu: there were a lot of errors that I, kind of…
+
+181
+00:24:04.150 --> 00:24:09.710
+hrishikb@andrew.cmu.edu: Had to get through, and doing that with paid models would have been quite expensive.
+
+182
+00:24:10.040 --> 00:24:16.329
+hrishikb@andrew.cmu.edu: So, unless I go and check the really large ones, I don't think any of the ones that you could…
+
+183
+00:24:16.630 --> 00:24:25.519
+hrishikb@andrew.cmu.edu: I guess self-host would work that… I mean, these can all be run on your laptop, but, unless you guys have,
+
+184
+00:24:25.680 --> 00:24:31.780
+hrishikb@andrew.cmu.edu: a full, I guess, server rack, the really big ones, like Kimi 2.5, you could not run that.
+
+185
+00:24:32.000 --> 00:24:44.959
+hrishikb@andrew.cmu.edu: on the 80, like, you cannot even run that with a 5090, or two 1590s and, like, 128GB of RAM. I think that's about the maximum consumer… So, for the confidence part, have you tried experimenting with the temperature of
+
+186
+00:24:47.330 --> 00:25:02.670
+hrishikb@andrew.cmu.edu: Actually, I have not tested that. I have changed other things, how… what kind of format it's been fed in, and that kind of gave… gave some results, but I'm not testing with the temperature. Because it's a simple slider in a grammar. Yeah.
+
+187
+00:25:02.710 --> 00:25:17.730
+hrishikb@andrew.cmu.edu: a temperature of 1 means it will only give you results on things it's completely certain on. So, it'll give you way less output, but it'll… so you can just play around on how different temperatures kind of give you results. Yes sir.
+
+188
+00:25:17.780 --> 00:25:35.379
+hrishikb@andrew.cmu.edu: I actually didn't think about it. I can do that, and if you guys want, I can… now that I've kind of figured it out, it wouldn't take that much for me to test it with, I guess, 3, 4, 3 or 4 of the leading models, and depending on how you want it, I can also test it
+
+189
+00:25:37.920 --> 00:25:54.789
+hrishikb@andrew.cmu.edu: I don't think there should be any problems with data retention or anything like that, because every cloud provider has something which just protects that one. I would also say you… I can give you access to Azure Foundry, and you can experiment with, like, different models. Okay.
+
+190
+00:25:54.790 --> 00:26:12.149
+hrishikb@andrew.cmu.edu: It gives you a simple UI in which you can just test each model out, and that… that UI usage is completely free to live. Okay, so all of the credits you spend on the UI end of Azure Boundary is… is pretty much… since it's testing, it's considered, like.
+
+191
+00:26:13.570 --> 00:26:19.210
+hrishikb@andrew.cmu.edu: The other thing is… basically…
+
+192
+00:26:20.540 --> 00:26:28.219
+hrishikb@andrew.cmu.edu: The amount of, it's not shown here, actually, but… The amount of,
+
+193
+00:26:28.330 --> 00:26:34.300
+hrishikb@andrew.cmu.edu: tokens that are being used do not rise. I thought that as the model size became bigger, it would kind of
+
+194
+00:26:34.710 --> 00:26:47.209
+hrishikb@andrew.cmu.edu: I guess think about it more, or use more tokens, but it stays within, like, 5%, even when… you can go from a 3 billion model to a 14 billion parameters model, and it doesn't increase that much.
+
+195
+00:26:47.490 --> 00:27:03.210
+hrishikb@andrew.cmu.edu: I would like to increase it. I'm not… I'm not certain that even with the highest one, it will actually, like… you can see at the top, it's, like, 94.7% that we can theoretically get. But I'm not even sure that even with the…
+
+196
+00:27:03.450 --> 00:27:16.289
+hrishikb@andrew.cmu.edu: biggest model that you can get that. And there's also the problem that you can kind of need to have some sort of structure around it, because just feeding it in one by one… there's a specific size over which it kind of,
+
+197
+00:27:16.950 --> 00:27:30.479
+hrishikb@andrew.cmu.edu: For these, the specific size is very low, how much you can take in advance, but I would assume that even with Claude or GPT-5 World5, if you try to feed in, like, I don't know, a 10MB PDF, it would just break down.
+
+198
+00:27:31.050 --> 00:27:43.680
+hrishikb@andrew.cmu.edu: So, what is the input for all this? Was it directly the PDF itself, and then you were trying to output? No, no. I took, there was a clean version of that, probably was,
+
+199
+00:27:43.680 --> 00:27:51.240
+hrishikb@andrew.cmu.edu: work, and it basically took it, like, proper clean data, and, it was orbiting it in case.
+
+200
+00:27:51.780 --> 00:27:54.329
+hrishikb@andrew.cmu.edu: Okay, so, you gave in…
+
+201
+00:27:54.530 --> 00:28:00.760
+hrishikb@andrew.cmu.edu: clean inputs. How did it know what sort of, attribute… attributes to map to?
+
+202
+00:28:00.900 --> 00:28:13.000
+hrishikb@andrew.cmu.edu: That, that I also… basically, Lou had a list that, yeah, so I could just take it from this branch, and, that I also just fed it in with instructions, so that…
+
+203
+00:28:13.150 --> 00:28:24.840
+hrishikb@andrew.cmu.edu: I could play around with some, I guess, how… what kind of data, kind of the structure of it, and I… more importantly, how large the chunks that are given all at once are.
+
+204
+00:28:25.070 --> 00:28:29.250
+hrishikb@andrew.cmu.edu: That has helped the most, I would say. But…
+
+205
+00:28:29.980 --> 00:28:43.790
+hrishikb@andrew.cmu.edu: depending on what we… what works best, that does change, so we cannot use… the LAMA coin are very different, and I would like to, like, check with KV2.5.
+
+206
+00:28:44.040 --> 00:28:50.290
+hrishikb@andrew.cmu.edu: And, plaid, and GPT-5.5 and all these things again.
+
+207
+00:28:50.580 --> 00:28:54.190
+hrishikb@andrew.cmu.edu: But, I did not use them here because…
+
+208
+00:28:54.710 --> 00:29:08.000
+hrishikb@andrew.cmu.edu: These are free, and I can just make many mistakes when I'm figuring it out, but doing that with the ones that I talked about, even Kiwi 2.5 can kind of quickly racket the cost up, so…
+
+209
+00:29:08.260 --> 00:29:11.770
+hrishikb@andrew.cmu.edu: So, how do you get the theoretical maximum?
+
+210
+00:29:11.980 --> 00:29:25.889
+hrishikb@andrew.cmu.edu: Oh, that's… that's what Lou figured out when, when he was doing it. He just said that, if you followed certain rules and, you see the regularities in the data, that that would be kind of the maximum that you could get with the pro set.
+
+211
+00:29:26.040 --> 00:29:34.220
+hrishikb@andrew.cmu.edu: So that's what I'm comparing. As for the accuracy itself, I did not have anything, I just…
+
+212
+00:29:34.380 --> 00:29:43.189
+hrishikb@andrew.cmu.edu: I was just, that was just compared against 100% accuracy for how, when it's giving its confidence rating, so…
+
+213
+00:29:43.320 --> 00:29:46.179
+hrishikb@andrew.cmu.edu: That does change, but I don't think…
+
+214
+00:29:46.580 --> 00:29:54.289
+hrishikb@andrew.cmu.edu: The point here is that 67.6% or something accuracy is probably not gonna be good enough in almost
+
+215
+00:29:54.680 --> 00:29:55.840
+hrishikb@andrew.cmu.edu: Any case?
+
+216
+00:29:56.150 --> 00:30:04.059
+hrishikb@andrew.cmu.edu: And… I could basically… this proves that anything that you could self-host, meaning, like.
+
+217
+00:30:04.080 --> 00:30:18.259
+hrishikb@andrew.cmu.edu: Anything you could run on 25090s and 128GB of RAM, that's the… that's the maximum configuration I could see for a consumer PC. That's something you could have in your office. It would not improve it that much, because
+
+218
+00:30:18.580 --> 00:30:33.629
+hrishikb@andrew.cmu.edu: the max you could go to, it would probably top out at 75%, which is way too low to be of any use. So I'll try to work with what you said, and try out the highest models, and kind of give a cost estimate, like, I'll do.
+
+219
+00:30:33.720 --> 00:30:42.110
+hrishikb@andrew.cmu.edu: And, see how that works, and see what kind of silos chunks I should do for those, but yeah, these are…
+
+220
+00:30:42.340 --> 00:30:52.590
+hrishikb@andrew.cmu.edu: I think one more thing is, Azure gives you access to 800 GPUs. Yeah. You can try running the bigger models on that, so I have a couple of hundred models running.
+
+221
+00:30:52.760 --> 00:30:59.249
+hrishikb@andrew.cmu.edu: So, it pretty much easily runs, like, 30 to 40 billion.
+
+222
+00:30:59.790 --> 00:31:01.180
+hrishikb@andrew.cmu.edu: I think we'll get ready.
+
+223
+00:31:01.650 --> 00:31:05.090
+hrishikb@andrew.cmu.edu: Quite opinion model, right? That's still not good.
+
+224
+00:31:06.380 --> 00:31:10.770
+hrishikb@andrew.cmu.edu: It would have to be, like… Poor market.
+
+225
+00:31:11.760 --> 00:31:19.049
+hrishikb@andrew.cmu.edu: Because the KMI 2.5 is not… No, I meant the highest…
+
+226
+00:31:20.740 --> 00:31:30.370
+hrishikb@andrew.cmu.edu: These… I mean, the highest one is a futility. That will never end there. No, that's a mixture of experts, so…
+
+227
+00:31:30.610 --> 00:31:32.899
+hrishikb@andrew.cmu.edu: So it only, kind of…
+
+228
+00:31:33.780 --> 00:31:39.269
+hrishikb@andrew.cmu.edu: has so many of them, that's, working at once. So, for the basic…
+
+229
+00:31:39.270 --> 00:31:54.420
+hrishikb@andrew.cmu.edu: question here is, is a large language model a good substitute for the amount? So I can say that anything, yeah, anything working on a commercial PC is not good enough, basically. Anything less than around, I would say, one…
+
+230
+00:31:54.420 --> 00:32:13.069
+hrishikb@andrew.cmu.edu: I can be pretty sure that anything less than 120 billion parameters would be useless. I have not tested… is that, just experimenting with temperature, experimenting with fine-tuning the models as well. Because the thing is, there's these hyper-specific modules which are exactly tuned to one single use case.
+
+231
+00:32:13.070 --> 00:32:17.639
+hrishikb@andrew.cmu.edu: they actually tend to perform really well. It was, I think it was…
+
+232
+00:32:17.820 --> 00:32:25.110
+hrishikb@andrew.cmu.edu: what was this? What was the model that you wanted? It was… it was basically a Python numpy expert kind of model, which was only good at NumPy package.
+
+233
+00:32:25.260 --> 00:32:36.469
+hrishikb@andrew.cmu.edu: these singular package level 2 point level models were there, right? So, they find… they basically fine-tuned every single model to be just an expert in the single package.
+
+234
+00:32:36.490 --> 00:32:47.629
+hrishikb@andrew.cmu.edu: And they kind of tried to lay around with it, and it got to a really good level. So similarly, like, if you can probably fine-tune it with structured data of what you use or involving with.
+
+235
+00:32:47.890 --> 00:32:51.059
+hrishikb@andrew.cmu.edu: And it could get really good at, like, understanding what's there.
+
+236
+00:32:51.300 --> 00:32:59.560
+hrishikb@andrew.cmu.edu: after a few runs of fine tuning, then you can also try evaluating and see what the performance is. I checked again, the…
+
+237
+00:32:59.860 --> 00:33:02.490
+hrishikb@andrew.cmu.edu: How it was doing myself as well, then.
+
+238
+00:33:02.640 --> 00:33:11.849
+hrishikb@andrew.cmu.edu: Yeah, some changes do help a lot, but I've not played around with the temperature, so I don't do that.
+
+239
+00:33:12.100 --> 00:33:13.100
+hrishikb@andrew.cmu.edu: So…
+
+240
+00:33:13.270 --> 00:33:24.770
+hrishikb@andrew.cmu.edu: You did mention that, I could use the bigger models with, what was the specific thing? With Azure Project. Yeah, Azure Project, yeah. I mean, that gives you access to almost all of OpenAI, and,
+
+241
+00:33:25.180 --> 00:33:26.860
+hrishikb@andrew.cmu.edu: Bigger than what you suspect.
+
+242
+00:33:27.040 --> 00:33:28.029
+hrishikb@andrew.cmu.edu: Good question.
+
+243
+00:33:29.840 --> 00:33:31.530
+hrishikb@andrew.cmu.edu: I like to just move along.
+
+244
+00:33:32.400 --> 00:33:37.460
+hrishikb@andrew.cmu.edu: Yeah, just… just drop me a text, and I'll add you to the actual function.
+
+245
+00:33:39.490 --> 00:33:49.440
+hrishikb@andrew.cmu.edu: I think you also need the Azure access for the… You're all in Azure. The thing is, I don't think you have access for, like, a resource, and I think… I think you can…
+
+246
+00:33:49.720 --> 00:33:56.099
+hrishikb@andrew.cmu.edu: We can just dedicate, like, lands on the resource groups.
+
+247
+00:33:56.640 --> 00:33:57.670
+hrishikb@andrew.cmu.edu: Not sure.
+
+248
+00:33:58.430 --> 00:34:01.419
+hrishikb@andrew.cmu.edu: But just check, and just let us know.
+
+249
+00:34:03.940 --> 00:34:06.300
+hrishikb@andrew.cmu.edu: Let me check the resource group as well.
+
+250
+00:34:09.889 --> 00:34:22.650
+hrishikb@andrew.cmu.edu: It'll be interesting to see what I can do with the biggest ones, and if they kind of, like, go to, like, I don't know, 97, 98%, that would be, depending on the cost, the best approach may be, but I'll have to check that.
+
+251
+00:34:23.989 --> 00:34:27.650
+hrishikb@andrew.cmu.edu: But also, any, any, any approach on, like, how you can…
+
+252
+00:34:27.909 --> 00:34:30.330
+hrishikb@andrew.cmu.edu: How we can work on, like, farm building as well.
+
+253
+00:34:32.690 --> 00:34:54.979
+hrishikb@andrew.cmu.edu: for those, I don't think, you can change the temperature, I guess, but I don't know how you can fine-tune those. So, fine-tuning is basically inference level changes, so what you do is… Oh, okay, okay, yeah, so… Fun in multiple, types of data, and that's the… that's the ground truth for the model, then. Okay, okay. So, because basically a model is nothing but,
+
+254
+00:34:55.330 --> 00:35:01.480
+hrishikb@andrew.cmu.edu: its main ground truth, if you don't fine-tune it, is basically all of redirected. Yeah, you mean,
+
+255
+00:35:02.130 --> 00:35:20.390
+hrishikb@andrew.cmu.edu: I thought, okay, I thought I was getting the wrong thing, because in open source models, you can kind of go in… Inference level fine-tuning is what I was talking about. If you mean inference level, yeah, I did do some of that. It was more about the kind of examples that I gave it, that…
+
+256
+00:35:20.390 --> 00:35:28.630
+hrishikb@andrew.cmu.edu: if you have this kind of input, this is the kind of output that you should have. And there were… I guess there was an entire file that you could use it.
+
+257
+00:35:28.640 --> 00:35:29.510
+hrishikb@andrew.cmu.edu: But…
+
+258
+00:35:30.100 --> 00:35:40.990
+hrishikb@andrew.cmu.edu: at least for these models, the result was, like, less than 2% gain, so… I don't know, like, maybe for the bigger ones, there would be huge accuracy gains, but for these, there were less than 2%.
+
+259
+00:35:41.150 --> 00:35:44.330
+hrishikb@andrew.cmu.edu: Yeah, because Lava 3.1…
+
+260
+00:35:44.450 --> 00:35:54.539
+hrishikb@andrew.cmu.edu: for the bigger one. It actually just… it was pretty good when it worked, but for some of them, it just outright failed with no amount of, like, giving it examples.
+
+261
+00:35:57.130 --> 00:36:12.539
+hrishikb@andrew.cmu.edu: Because I was pretty surprised with the LAMA 3.1. It's really good when it works. Even the 8 building parameter model is… that's the one that's pretty sharpened, but it just fails on some things, and I tried many things, not technical, but other things I need to.
+
+262
+00:36:14.830 --> 00:36:23.440
+hrishikb@andrew.cmu.edu: Also good work, you know, continue to do some more investigation, yeah, very good. It would be, interesting to see what I can do with Teshop.
+
+263
+00:36:23.570 --> 00:36:26.690
+hrishikb@andrew.cmu.edu: I mean, it's all new things.
+
+264
+00:36:28.640 --> 00:36:33.730
+hrishikb@andrew.cmu.edu: That's it.
+
+265
+00:36:34.980 --> 00:36:36.129
+hrishikb@andrew.cmu.edu: Alright, I'm sure.
+
+266
+00:36:37.330 --> 00:36:43.479
+hrishikb@andrew.cmu.edu: I just have, like, a standing group that has access to… Like the resource group.
+
+267
+00:36:43.610 --> 00:36:47.309
+hrishikb@andrew.cmu.edu: Just, like, swap out whoever's… Team the time.
+
+268
+00:36:47.810 --> 00:36:50.270
+hrishikb@andrew.cmu.edu: So, keep doing this, and it hasn't…
+
+269
+00:36:51.390 --> 00:36:55.600
+hrishikb@andrew.cmu.edu: work once the way I want it. It's Microsoft.
+
+270
+00:36:59.070 --> 00:37:03.569
+hrishikb@andrew.cmu.edu: I think after you had a person, it was something other things, like.
+
+271
+00:37:03.830 --> 00:37:15.960
+hrishikb@andrew.cmu.edu: an oddity amount of days ago, I can generate the email for the person. I think it was still their statement. By the way, it was 24 hours to be able to see resources whenever I made myself a contributor on the subscription.
+
+272
+00:37:17.970 --> 00:37:19.000
+hrishikb@andrew.cmu.edu: Thank you.
+
+273
+00:37:20.140 --> 00:37:26.530
+hrishikb@andrew.cmu.edu: I mean, they can't even… they're even… like, the operating system itself is kind of going down a little faster.
+
+274
+00:37:27.110 --> 00:37:29.790
+hrishikb@andrew.cmu.edu: So did I try to move down.
+
+275
+00:37:29.940 --> 00:37:30.930
+hrishikb@andrew.cmu.edu: Excellent.
+
+276
+00:37:31.670 --> 00:37:33.190
+hrishikb@andrew.cmu.edu: It was being tested.
+
+277
+00:37:33.580 --> 00:37:51.739
+hrishikb@andrew.cmu.edu: I mean, even specific things, not vague things, that the performance is going down. There was an outcry recently that they just downgrade your drivers, because they have automated processes in place, which can't figure out that you already have the latest ones, and then they just
+
+278
+00:37:51.840 --> 00:37:55.710
+hrishikb@andrew.cmu.edu: Kind of repeatable ones, and… Yeah.
+
+279
+00:37:55.920 --> 00:37:58.969
+hrishikb@andrew.cmu.edu: And that's a very specific thing that you can hear.
+
+280
+00:38:03.630 --> 00:38:07.539
+hrishikb@andrew.cmu.edu: I think that's all the progress updates we had to share.
+
+281
+00:38:08.020 --> 00:38:10.289
+hrishikb@andrew.cmu.edu: Do you guys have anything you'd like to add?
+
+282
+00:38:10.980 --> 00:38:12.050
+hrishikb@andrew.cmu.edu: impressions?
+
+283
+00:38:12.450 --> 00:38:23.459
+hrishikb@andrew.cmu.edu: Something that will be here. Just probably, I think, when you guys start working with LinkedIn, then I'll probably have a look at what I would say. But otherwise,
+
+284
+00:38:23.780 --> 00:38:28.470
+hrishikb@andrew.cmu.edu: Right now, things are looking good, like, this is late, and the film is…
+
+285
+00:38:29.240 --> 00:38:36.189
+hrishikb@andrew.cmu.edu: I'm interested to, like, hear about Leo's work as well, because it's… his work is insanely complicated.
+
+286
+00:38:36.670 --> 00:38:38.129
+hrishikb@andrew.cmu.edu: I guess it's pharmacy.
+
+287
+00:38:38.520 --> 00:38:41.690
+hrishikb@andrew.cmu.edu: But other than that, I think we just think we'll be able to do.
+
+288
+00:38:42.400 --> 00:38:46.560
+hrishikb@andrew.cmu.edu: I'm curious to see an update to be back again.
+
+289
+00:38:46.920 --> 00:38:48.500
+hrishikb@andrew.cmu.edu: Looks like a lot of progress.
+
+290
+00:38:49.260 --> 00:38:50.160
+hrishikb@andrew.cmu.edu: Oopsin.
+
+291
+00:38:50.900 --> 00:39:10.719
+hrishikb@andrew.cmu.edu: Just, just, just make sure you guys drop me a message. We can work ad hoc as well. I feel like it's come to this point where only in meetings the actual points where we get access, or things like this, they should be very, like, async, in my opinion. You should just let us know, and then I'll get to work on it whenever I get a little time in the room.
+
+292
+00:39:11.110 --> 00:39:13.190
+hrishikb@andrew.cmu.edu: He just… when I pitched on the island.
+
+293
+00:39:15.150 --> 00:39:28.590
+hrishikb@andrew.cmu.edu: Great, great to see y'all. Wonderful day. Yeah, for sure. Yeah, just wait till the weekend beforehand. Well, it's getting hotter next week. Oh, is it? Yeah, okay.
+
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+WEBVTT
+
+1
+00:00:02.500 --> 00:00:05.519
+hrishikb@andrew.cmu.edu: Great. Whatever you have that began.
+
+2
+00:00:32.759 --> 00:00:34.209
+hrishikb@andrew.cmu.edu: Did I put it on the channels?
+
+3
+00:00:37.040 --> 00:00:40.319
+Harsha Tummala: Can you see… can you hear us, guys?
+
+4
+00:00:40.320 --> 00:00:41.620
+hrishikb@andrew.cmu.edu: Yeah, no, we can hear you.
+
+5
+00:00:42.120 --> 00:00:47.329
+Harsha Tummala: Yeah, I think there was some network issue, we lost power, and everything restarted.
+
+6
+00:00:48.940 --> 00:00:59.089
+Harsha Tummala: Yeah, that was it. We're all back up. But, I think just to continue what Jake was saying, for when we have a lot of features, but we can only map to, for example, like.
+
+7
+00:00:59.440 --> 00:01:08.579
+Harsha Tummala: a few of the features that Eton provides. I would say you should just map to the few features and just show the additional features and empty fields. That's good enough, I would say.
+
+8
+00:01:09.080 --> 00:01:17.809
+hrishikb@andrew.cmu.edu: Okay, so there's not gonna be any mandatory features on… from ETEM side, like, and even from, I think, PIM's side, there's not mandatory features, right? For a particular product?
+
+9
+00:01:20.560 --> 00:01:39.019
+Harsha Tummala: Yeah, kind of. I mean, for a product type, we would want a certain set of attributes, but if we can't map to everything, it is what it is, right? So, it's just that the mindset has to be, like, we want to be curious about more data, as in, we want to push our suppliers towards giving us more data.
+
+10
+00:01:39.320 --> 00:01:45.779
+Harsha Tummala: So, for example, if we have, like, a hundred, eaten features for a set, a specific class.
+
+11
+00:01:45.950 --> 00:01:55.599
+Harsha Tummala: And we can only map to 30 of them. We want to map to the 30, and then show that all these 70 are, like, empty, and these are fields you can give us further information on.
+
+12
+00:01:55.730 --> 00:01:57.130
+Harsha Tummala: And that's what it can be.
+
+13
+00:01:58.240 --> 00:02:01.959
+hrishikb@andrew.cmu.edu: okay, so in that case,
+
+14
+00:02:02.130 --> 00:02:18.839
+hrishikb@andrew.cmu.edu: I think the mandatory fields part is in PIMS, so we'll have to first map it to those fields so that we know which fields are actually mandatory and not present. Then, after we have those values, we can then go ahead and map them with the ETIN categories and ETIN class and the things.
+
+15
+00:02:19.800 --> 00:02:26.390
+Harsha Tummala: Yeah, I mean, you don't have to be so restrictive also. So, if, for example, mapping all of these
+
+16
+00:02:26.930 --> 00:02:34.140
+Harsha Tummala: all of the attributes that we have on our system to the ETEM, ETM attributes.
+
+17
+00:02:34.310 --> 00:02:39.120
+Harsha Tummala: If it's… if it's not that cut and dry, then you should probably just…
+
+18
+00:02:41.370 --> 00:02:43.630
+Harsha Tummala: We should probably just, like.
+
+19
+00:02:44.190 --> 00:02:49.699
+Harsha Tummala: Work with what you can do, and that's… just keep the scope within how much can actually be achieved.
+
+20
+00:02:50.100 --> 00:02:57.380
+Harsha Tummala: And leave the rest to, like, oh, this is all… this is all things that we cannot really, like, see data for or map, or…
+
+21
+00:02:58.090 --> 00:03:01.439
+Harsha Tummala: Okay. It's a clear kind of situation here.
+
+22
+00:03:04.420 --> 00:03:07.670
+hrishikb@andrew.cmu.edu: Okay, I think that clarifies a few things.
+
+23
+00:03:08.920 --> 00:03:12.190
+hrishikb@andrew.cmu.edu: Let me see if I have more questions…
+
+24
+00:03:30.840 --> 00:03:41.489
+hrishikb@andrew.cmu.edu: Yep, I think that is pretty much it. So, I was able to find a couple of files that I'm using for the ETEM, like, to analyze what ETEM does right now.
+
+25
+00:03:41.760 --> 00:03:47.220
+hrishikb@andrew.cmu.edu: the ones that are relevant. I guess those are… Very big names.
+
+26
+00:03:47.990 --> 00:03:54.440
+hrishikb@andrew.cmu.edu: ETEM 10.0 all sectors, and ETEM 10.0 CSV metrics.
+
+27
+00:03:55.060 --> 00:03:55.410
+Harsha Tummala: Yeah.
+
+28
+00:03:55.410 --> 00:03:58.090
+hrishikb@andrew.cmu.edu: What is the latest ones, but… Yeah.
+
+29
+00:03:58.750 --> 00:04:08.169
+Harsha Tummala: Okay. Yeah, I mean, I would say just stick to a version, and that's pretty much it. I wouldn't stress too much about, like, keeping up to date with the latest one, the latest supplies, yeah.
+
+30
+00:04:08.170 --> 00:04:09.480
+hrishikb@andrew.cmu.edu: So, 10.01.
+
+31
+00:04:09.820 --> 00:04:10.889
+Harsha Tummala: Yeah, that's it.
+
+32
+00:04:10.890 --> 00:04:11.550
+hrishikb@andrew.cmu.edu: forward.
+
+33
+00:04:15.320 --> 00:04:21.600
+hrishikb@andrew.cmu.edu: Yeah, and last week I told you I'm gonna be working on the schema part of it, but right now there are a few hiccups.
+
+34
+00:04:21.820 --> 00:04:26.880
+hrishikb@andrew.cmu.edu: So, I'm not very sure if you can put it in the exact PIM schema.
+
+35
+00:04:27.080 --> 00:04:34.870
+hrishikb@andrew.cmu.edu: Right now, so we'll be probably, doing some sort of a key pair thingy, before we reach them in the pipeline, and.
+
+36
+00:04:34.870 --> 00:04:35.260
+Harsha Tummala: Okay.
+
+37
+00:04:35.260 --> 00:04:40.169
+hrishikb@andrew.cmu.edu: So you might have to do some extra work after the pipeline to put it into proper,
+
+38
+00:04:40.800 --> 00:04:43.619
+hrishikb@andrew.cmu.edu: let's say, proper format before we push anything to pens.
+
+39
+00:04:44.380 --> 00:04:53.129
+Harsha Tummala: Yeah, I would say take your time just understanding how to map out all of these classes and features to the attributes and the product types.
+
+40
+00:04:53.660 --> 00:04:54.060
+Harsha Tummala: Right.
+
+41
+00:04:54.650 --> 00:05:07.769
+Harsha Tummala: Yeah, that could… that could probably be a lot of help as we go down the line as well. But, I think another thing I wanted to bring up was, I… I would… I just… I was just talking to Jake about this, but, he's…
+
+42
+00:05:07.900 --> 00:05:13.729
+Harsha Tummala: I asked him if he could do, like, a PIMS demo, because a lot of changes have been happening in PIMS for us.
+
+43
+00:05:14.220 --> 00:05:32.359
+Harsha Tummala: Not right now, but, like, probably in two weeks to a month. We want to do a PIMS demo to you guys, so that you guys can know the state of things, of PIMS as well, so that when it comes to the point of, oh, how well can things integrate with PIMS, or, like, how well can things even, like, work with PIMS, then you'll have a better idea standing.
+
+44
+00:05:32.700 --> 00:05:33.340
+Harsha Tummala: Good morning.
+
+45
+00:05:34.410 --> 00:05:41.980
+hrishikb@andrew.cmu.edu: Yeah, I think that would be pretty helpful. Whenever you guys think we should, yeah, maybe, like, have a demo, like, we'd probably be ready for it.
+
+46
+00:05:42.680 --> 00:05:46.579
+Harsha Tummala: It could be online, it could be in person, whatever you guys are comfortable.
+
+47
+00:05:47.430 --> 00:05:48.080
+hrishikb@andrew.cmu.edu: Sure.
+
+48
+00:05:55.450 --> 00:05:57.480
+hrishikb@andrew.cmu.edu: Okay, I think that's all from our side.
+
+49
+00:05:58.320 --> 00:06:02.369
+hrishikb@andrew.cmu.edu: That's updates for today.
+
+50
+00:06:03.580 --> 00:06:05.639
+hrishikb@andrew.cmu.edu: We just need the access as well.
+
+51
+00:06:05.640 --> 00:06:09.740
+Harsha Tummala: We'll get to it, ideally, next week.
+
+52
+00:06:10.220 --> 00:06:15.339
+Harsha Tummala: Today was kind of hectic. This week has been kind of crazy.
+
+53
+00:06:15.340 --> 00:06:16.720
+hrishikb@andrew.cmu.edu: But.
+
+54
+00:06:16.800 --> 00:06:29.089
+Harsha Tummala: tomorrow or next week is what I would say would be good. We already have it on a priority. Basically, every day we kind of keep talking about it, but we just don't have the time to get to it. That's it.
+
+55
+00:06:29.380 --> 00:06:33.780
+Harsha Tummala: Even the foundry access for, Java, and we'll get to it as well.
+
+56
+00:06:35.460 --> 00:06:36.450
+hrishikb@andrew.cmu.edu: Thanks, thanks.
+
+57
+00:06:37.420 --> 00:06:39.760
+hrishikb@andrew.cmu.edu: Alright, thank you. Thank you.
+
+58
+00:06:40.380 --> 00:06:42.459
+Harsha Tummala: That's it. Thanks, guys. Have a great week.
+
+59
+00:06:43.160 --> 00:06:44.290
+hrishikb@andrew.cmu.edu: See you next week.
+
+60
+00:06:44.290 --> 00:06:46.220
+Harsha Tummala: Hope summer's hitting you as well now.
+
+61
+00:06:46.460 --> 00:06:47.840
+hrishikb@andrew.cmu.edu: Yeah, just for the New York.
+
+62
+00:06:48.230 --> 00:06:50.470
+Harsha Tummala: Okay. See you. Bye-bye.
+
+63
+00:06:50.940 --> 00:06:51.320
+hrishikb@andrew.cmu.edu: Yeah, bye.
+
diff --git a/transcripts/GMT20260618-190618_Recording.transcript.vtt b/transcripts/GMT20260618-190618_Recording.transcript.vtt
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@@ -0,0 +1,602 @@
+WEBVTT
+
+1
+00:00:02.000 --> 00:00:09.050
+hrishikb@andrew.cmu.edu: Okay, so primarily, what we wanted to discuss was things around Azure access.
+
+2
+00:00:09.870 --> 00:00:12.489
+hrishikb@andrew.cmu.edu: Arjun Jay, you had a question about Arjun?
+
+3
+00:00:14.470 --> 00:00:16.350
+hrishikb@andrew.cmu.edu: As of Michael. Okay.
+
+4
+00:00:17.020 --> 00:00:24.490
+hrishikb@andrew.cmu.edu: So The only thing for me was, I can get started with the work, but…
+
+5
+00:00:27.930 --> 00:00:36.000
+hrishikb@andrew.cmu.edu: There are no limits, I think, to the account itself in Azure. So, if you could,
+
+6
+00:00:36.180 --> 00:00:47.209
+hrishikb@andrew.cmu.edu: if I could know around how much I could spend, or you could just put a limit in for the amount of training, or… and, like, on the group, or, like, for me specifically, that would be great.
+
+7
+00:00:50.930 --> 00:01:03.060
+Harsha Tummala: David, I think… so they were asking about, like, any limits around how much they can spend on the, Azure group that we created for them, or how they want to use it.
+
+8
+00:01:03.390 --> 00:01:06.540
+Harsha Tummala: What's the ballpark right now?
+
+9
+00:01:07.690 --> 00:01:09.009
+hrishikb@andrew.cmu.edu: We don't know yet.
+
+10
+00:01:09.380 --> 00:01:17.039
+hrishikb@andrew.cmu.edu: So, we didn't want to start working on it, unless we had some kind of, cost monitoring or, like.
+
+11
+00:01:17.200 --> 00:01:35.770
+hrishikb@andrew.cmu.edu: some kind of cost cap, so that we don't overspend. Like, you can set a cap, whatever kind of cap you like, and I can start, and then I can give further feedback. I just didn't want to start, and there is a risk that it may be more than expected, so I didn't start on that.
+
+12
+00:01:39.000 --> 00:01:45.589
+Harsha Tummala: Well, I mean, my gut's saying right now, you know, a thousand bucks a month for… is… is fine.
+
+13
+00:01:46.000 --> 00:01:55.169
+Harsha Tummala: If the ongoing cost is that, that might not be fine.
+
+14
+00:01:55.740 --> 00:02:01.370
+Harsha Tummala: So, like, right… I'm imagining that this process is going to be very training-heavy to start.
+
+15
+00:02:01.680 --> 00:02:07.699
+Harsha Tummala: And then there's gonna be some sort of… ongoing… Costs or spend.
+
+16
+00:02:07.880 --> 00:02:15.820
+Harsha Tummala: I'm… I'm pretty open to… A higher amount.
+
+17
+00:02:16.100 --> 00:02:25.909
+Harsha Tummala: During this development time, any sort of ongoing cost would be much more difficult to justify.
+
+18
+00:02:26.620 --> 00:02:30.350
+Harsha Tummala: I will make that a to-do…
+
+19
+00:02:30.730 --> 00:02:35.629
+Harsha Tummala: And… get back to you. But I'm saying, like, is a…
+
+20
+00:02:36.560 --> 00:02:41.189
+Harsha Tummala: Is 1,000 ridiculously low? Is it way too high? Like, what are…
+
+21
+00:02:41.210 --> 00:02:51.119
+hrishikb@andrew.cmu.edu: That's way too much. Actually, I was, yeah, yeah, I don't think it… this is preliminary, I cannot give again, but I don't think it should go anywhere near that.
+
+22
+00:02:51.540 --> 00:02:53.010
+hrishikb@andrew.cmu.edu: So, yeah.
+
+23
+00:02:53.510 --> 00:02:57.279
+Harsha Tummala: Sorry, can you… I'm playing on your speakers, I'm having trouble hearing you.
+
+24
+00:02:57.280 --> 00:03:03.820
+hrishikb@andrew.cmu.edu: Yeah, yeah, I don't think, right now, this is preliminary, but I don't think it should go anywhere near that.
+
+25
+00:03:04.260 --> 00:03:05.590
+Harsha Tummala: Okay, yeah.
+
+26
+00:03:07.200 --> 00:03:10.570
+Clifford Huff: Are you thinking in the hundreds of dollars, is what you're thinking?
+
+27
+00:03:10.570 --> 00:03:19.270
+hrishikb@andrew.cmu.edu: Yeah, it would be great if you could just set a cap on the group, directly, just because these things are…
+
+28
+00:03:19.390 --> 00:03:27.559
+hrishikb@andrew.cmu.edu: it would be good as a precautionary measure, just so that there's no overflow, it, like, overruns.
+
+29
+00:03:27.840 --> 00:03:32.400
+hrishikb@andrew.cmu.edu: So if you could set it on the group policy, or for me individually, any of that would be fine.
+
+30
+00:03:34.260 --> 00:03:35.050
+Harsha Tummala: Okay.
+
+31
+00:03:35.230 --> 00:03:51.440
+Harsha Tummala: I would also recommend that any sort of resource that you guys plan on using, or anything that you do, you should probably start off with, like, the minimum computes for it, and then keep scaling up, because that is how
+
+32
+00:03:51.920 --> 00:03:55.670
+Harsha Tummala: It is sure we kind of try to keep the costs low for the range.
+
+33
+00:03:55.800 --> 00:04:01.570
+Harsha Tummala: And I would, I would not recommend, like, just setting an estimated,
+
+34
+00:04:01.680 --> 00:04:11.590
+Harsha Tummala: an estimated, compute that you think would be… would give you the best performance for things. Rather than that, you just put the lowest one and then keep scaling up from there.
+
+35
+00:04:12.390 --> 00:04:25.570
+hrishikb@andrew.cmu.edu: Yeah, no, I was thinking of something along those lines, but the thing is, especially with the newer models, there's a tendency for it to, kind of, the cost to go
+
+36
+00:04:25.740 --> 00:04:29.980
+hrishikb@andrew.cmu.edu: hired pretty fast, so that's why I was asking for the limits, just as a…
+
+37
+00:04:29.980 --> 00:04:41.150
+Harsha Tummala: Are you planning on deploying the models on, like, the… like, the resource group, or are you planning on spinning up, like, Container App Center and doing it, or…
+
+38
+00:04:41.340 --> 00:04:42.990
+Harsha Tummala: using GPUs for it.
+
+39
+00:04:44.110 --> 00:04:48.019
+hrishikb@andrew.cmu.edu: No, no, no, not right now, I'm not planning to do that.
+
+40
+00:04:48.520 --> 00:04:49.110
+Harsha Tummala: Whoa.
+
+41
+00:04:49.510 --> 00:05:00.019
+Harsha Tummala: Just, just making sure, because if you want to do any models, I would say AI Founty is the way. I was looking into adding you to, like, a separate AI Founty thing, should be done probably today.
+
+42
+00:05:00.370 --> 00:05:10.210
+Harsha Tummala: But that is an account which is… if… if basically you're doing anything through the UI for your nesting, it should be completely free from what I know.
+
+43
+00:05:11.950 --> 00:05:15.529
+hrishikb@andrew.cmu.edu: Sorry, Harsha, could you repeat? I can barely hear you.
+
+44
+00:05:15.930 --> 00:05:17.920
+Harsha Tummala: Sorry, can you hear me now?
+
+45
+00:05:19.510 --> 00:05:20.380
+Harsha Tummala: Hello?
+
+46
+00:05:20.380 --> 00:05:23.490
+hrishikb@andrew.cmu.edu: Yeah, yeah, it's better now.
+
+47
+00:05:25.050 --> 00:05:27.450
+Harsha Tummala: I'm not sure what the problem is.
+
+48
+00:05:28.030 --> 00:05:32.719
+Harsha Tummala: It looks good from my audio, but I don't know why. Okay.
+
+49
+00:05:33.570 --> 00:05:35.509
+hrishikb@andrew.cmu.edu: Yeah, yeah, go ahead, go ahead.
+
+50
+00:05:36.900 --> 00:05:39.970
+Harsha Tummala: Yeah, so, what I was saying was…
+
+51
+00:05:40.590 --> 00:05:47.340
+Harsha Tummala: If you're planning on deploying GPUs and just running a model randomly, I would highly advise against that.
+
+52
+00:05:47.460 --> 00:06:01.190
+Harsha Tummala: Mainly because that is probably not the way to do it. I was looking into adding you to the Azure Foundry, like a Foundry membership, so that you could just use the UI part of things.
+
+53
+00:06:01.310 --> 00:06:07.999
+Harsha Tummala: Where you can navigate within boundary, go to a model, see, test by sending a few messages to it.
+
+54
+00:06:08.290 --> 00:06:14.019
+Harsha Tummala: Using the UI component of Azure Foundry is negligible cost, or free for the most part.
+
+55
+00:06:14.690 --> 00:06:16.339
+hrishikb@andrew.cmu.edu: Okay, I did not know that.
+
+56
+00:06:16.340 --> 00:06:22.059
+Harsha Tummala: So, chatbot sort of, chatbot sort of interface to actually try and test things through it.
+
+57
+00:06:22.180 --> 00:06:27.490
+Harsha Tummala: Because it's mainly meant for testing and just trying, it's mainly free for that reason.
+
+58
+00:06:27.610 --> 00:06:31.999
+Harsha Tummala: It also gives you an API. When you use the API, it starts charging you.
+
+59
+00:06:33.120 --> 00:06:34.949
+hrishikb@andrew.cmu.edu: Got it, got it.
+
+60
+00:06:35.090 --> 00:06:49.470
+hrishikb@andrew.cmu.edu: Yeah, for testing, a few I could do manually, just to start off and see for myself. But my… like, I have had some experience with using, AWS for this kind of stuff.
+
+61
+00:06:49.550 --> 00:06:59.379
+hrishikb@andrew.cmu.edu: And, setting a cap really helped me there, just because, there are, there is a bit of a risk of it going kind of a…
+
+62
+00:06:59.770 --> 00:07:05.289
+hrishikb@andrew.cmu.edu: a bit too high, so that's why I'm kind of asking for that. That's it.
+
+63
+00:07:05.290 --> 00:07:08.770
+Harsha Tummala: Yeah, I totally agree with that, though. It's something we should do.
+
+64
+00:07:08.770 --> 00:07:12.400
+hrishikb@andrew.cmu.edu: Yeah, because if I set a, like, a few hour training run.
+
+65
+00:07:12.400 --> 00:07:13.210
+Harsha Tummala: Okay.
+
+66
+00:07:13.210 --> 00:07:22.880
+hrishikb@andrew.cmu.edu: if I, kind of, I don't know, maybe I'm, for one hour, I'm not looking at it carefully enough, then it can kind of spike right then.
+
+67
+00:07:24.350 --> 00:07:34.799
+Harsha Tummala: Yeah, that's the thing. I mean, with ML in general, I would say only do it when I think you have, like, your attention towards that task.
+
+68
+00:07:34.930 --> 00:07:49.720
+Harsha Tummala: Because the problem is, especially when you're running training or inference, which just tends to run forever, unless you actually stop it and you're constantly looking at the metrics to monitor how it's performing and then manually stop it.
+
+69
+00:07:49.890 --> 00:07:54.159
+Harsha Tummala: I would recommend not doing it at all. Yeah.
+
+70
+00:07:54.360 --> 00:07:54.890
+Harsha Tummala: But in the.
+
+71
+00:07:54.890 --> 00:07:55.260
+hrishikb@andrew.cmu.edu: No other.
+
+72
+00:07:56.400 --> 00:07:57.960
+Harsha Tummala: Sorry, no, I'll just go on, yeah.
+
+73
+00:07:58.800 --> 00:08:07.250
+hrishikb@andrew.cmu.edu: I, yeah, I'm just, this is just for, precautionary, but, I would, yeah,
+
+74
+00:08:07.500 --> 00:08:10.429
+hrishikb@andrew.cmu.edu: I would prefer to, I guess, go about it this way.
+
+75
+00:08:11.430 --> 00:08:12.060
+Harsha Tummala: Yeah.
+
+76
+00:08:14.630 --> 00:08:15.330
+hrishikb@andrew.cmu.edu: Oh.
+
+77
+00:08:16.190 --> 00:08:23.579
+Harsha Tummala: David already set a limit, by the way, I thought he's not. So, to 1,000, you said, yeah. Your forecast right now is 115.
+
+78
+00:08:24.370 --> 00:08:25.220
+Harsha Tummala: So…
+
+79
+00:08:26.790 --> 00:08:27.920
+hrishikb@andrew.cmu.edu: Okay, okay.
+
+80
+00:08:30.580 --> 00:08:33.029
+hrishikb@andrew.cmu.edu: That's it from my side.
+
+81
+00:08:34.169 --> 00:08:39.459
+hrishikb@andrew.cmu.edu: And that's all you had for the Azure questions.
+
+82
+00:08:39.900 --> 00:08:52.169
+hrishikb@andrew.cmu.edu: In the last week, we've been working on the task we told about. There has… there were some project management kind of work, which we had to do, so that took a bit of the time, but yeah, we are…
+
+83
+00:08:52.710 --> 00:08:57.720
+hrishikb@andrew.cmu.edu: Steady progressing towards the… Next, I'll say milestone.
+
+84
+00:08:59.670 --> 00:09:03.780
+hrishikb@andrew.cmu.edu: Yeah, I think, probably by next week, we might have…
+
+85
+00:09:04.150 --> 00:09:07.429
+hrishikb@andrew.cmu.edu: something to show from the OCR and engine part.
+
+86
+00:09:09.360 --> 00:09:11.550
+hrishikb@andrew.cmu.edu: Yeah, that's something to look forward to.
+
+87
+00:09:15.590 --> 00:09:19.580
+hrishikb@andrew.cmu.edu: I think, yeah, that is pretty much all the updates for this week.
+
+88
+00:09:19.770 --> 00:09:21.810
+hrishikb@andrew.cmu.edu: Many questions from you guys?
+
+89
+00:09:27.690 --> 00:09:35.360
+Harsha Tummala: kind of nothing for now. We were mainly expecting just, to catch up on the updates for things, and that's leadership.
+
+90
+00:09:36.290 --> 00:09:43.239
+hrishikb@andrew.cmu.edu: So, right now, we were working in parallel for our different modules, so now what we're doing is we're putting all the code on…
+
+91
+00:09:43.460 --> 00:09:53.710
+hrishikb@andrew.cmu.edu: Bitbucket, and we're gonna start compiling things which are ready to be compiled. Like, a few parts of OCRN ingestion can be compiled, so we'll be doing that next.
+
+92
+00:09:55.640 --> 00:10:04.240
+hrishikb@andrew.cmu.edu: along with that, we're expecting the LLM POC to finish soon. Then, with those results, we can move forward and see
+
+93
+00:10:04.400 --> 00:10:09.840
+hrishikb@andrew.cmu.edu: Where to go next, and then we'll probably have more manpower towards the other tasks.
+
+94
+00:10:10.280 --> 00:10:12.350
+hrishikb@andrew.cmu.edu: Well, let's just progression further, quickly.
+
+95
+00:10:17.090 --> 00:10:20.740
+Harsha Tummala: Nothing, nothing much from my end, other than that,
+
+96
+00:10:20.890 --> 00:10:32.909
+Harsha Tummala: It's, I'll… I'll probably… if you have some time, sometime early next week, like Monday, Tuesday, I'll probably just sit with you for, like, a few minutes, show you around Azure Foundry, and just,
+
+97
+00:10:33.030 --> 00:10:38.800
+Harsha Tummala: Just, just, like, get you up to speed on how to do things from that.
+
+98
+00:10:39.640 --> 00:10:50.480
+hrishikb@andrew.cmu.edu: Yeah, that would be great. So, you could send me your time, or… I'm free at, on Monday, after, basically, 9.30, the entire day.
+
+99
+00:10:52.310 --> 00:10:53.970
+Harsha Tummala: to Monday, any time in the day is good.
+
+100
+00:10:55.450 --> 00:10:56.839
+hrishikb@andrew.cmu.edu: Yeah.
+
+101
+00:10:59.000 --> 00:11:00.750
+Harsha Tummala: Okay, I'll send you an invite then for it.
+
+102
+00:11:00.750 --> 00:11:01.460
+hrishikb@andrew.cmu.edu: Yeah.
+
+103
+00:11:04.170 --> 00:11:06.350
+Harsha Tummala: It should be short 15 variables, I think.
+
+104
+00:11:06.350 --> 00:11:12.909
+hrishikb@andrew.cmu.edu: Yeah, I just… I've not worked with Azure before. I mean, I know a little bit, but not…
+
+105
+00:11:13.370 --> 00:11:13.950
+Harsha Tummala: Huh.
+
+106
+00:11:15.290 --> 00:11:27.090
+Harsha Tummala: Okay, that makes sense. We'll do that, and yeah, that's pretty much all your updates to me as well. let, like, are there any, sort of roadblocks with ETEM using it?
+
+107
+00:11:27.390 --> 00:11:33.100
+Harsha Tummala: I know we discussed last week on, like, the issues you guys had with Trying to,
+
+108
+00:11:33.680 --> 00:11:38.539
+Harsha Tummala: trying to kind of create the connection between, our databases and intermitt.
+
+109
+00:11:41.230 --> 00:11:54.560
+hrishikb@andrew.cmu.edu: Right now, I think we need to do a little more research. Like, research, and like, I would say it's a roadblock, we're just trying to figure out how best to integrate ETM, at which part of the pipeline, so that it's most useful to you guys.
+
+110
+00:11:56.310 --> 00:12:02.409
+hrishikb@andrew.cmu.edu: Yeah, I think… and plus, yeah, we'll have to, do some sort of mapping with the existing PIMs.
+
+111
+00:12:02.860 --> 00:12:06.950
+hrishikb@andrew.cmu.edu: I think you mentioned you guys are already doing that, or something on the similar lines?
+
+112
+00:12:07.320 --> 00:12:11.050
+hrishikb@andrew.cmu.edu: You can map into the Azure products in your catalogs.
+
+113
+00:12:11.600 --> 00:12:17.639
+Harsha Tummala: No, no, no, we're not doing that yet. What you're talking about in… about that last week was…
+
+114
+00:12:17.850 --> 00:12:29.080
+Harsha Tummala: This is something that would be a value add when we give it to our future clients, which is, mapping our internal product types and attributes to the TIM class.
+
+115
+00:12:29.690 --> 00:12:30.280
+hrishikb@andrew.cmu.edu: Okay.
+
+116
+00:12:30.630 --> 00:12:31.620
+hrishikb@andrew.cmu.edu: Yeah.
+
+117
+00:12:32.980 --> 00:12:34.490
+Harsha Tummala: Yeah, bad.
+
+118
+00:12:34.990 --> 00:12:40.510
+Harsha Tummala: So Jake, I think, is currently putting in placeholders for pimps, which will help us do it.
+
+119
+00:12:40.610 --> 00:12:50.269
+Harsha Tummala: But other than that, there's no reward being done. Placeholders hasn't just had provisions on the UI side of things to see different versions work, right?
+
+120
+00:12:52.560 --> 00:12:59.730
+hrishikb@andrew.cmu.edu: Okay, so yeah, from our side, we are still figuring out how to make the ETEM integration most useful.
+
+121
+00:12:59.920 --> 00:13:05.609
+hrishikb@andrew.cmu.edu: Like, we have a few things we can do, but yeah, we're still figuring out how best to integrate that part.
+
+122
+00:13:07.700 --> 00:13:08.490
+Harsha Tummala: Makes sense.
+
+123
+00:13:10.060 --> 00:13:12.680
+hrishikb@andrew.cmu.edu: But yeah, we do have all the data that's required for eating.
+
+124
+00:13:12.840 --> 00:13:19.719
+hrishikb@andrew.cmu.edu: the particular files we have. Like, we've gone through data, it looks, it's pretty well formatted, so it's a lot, but…
+
+125
+00:13:19.880 --> 00:13:22.820
+hrishikb@andrew.cmu.edu: It's, easy to comprehend what it is.
+
+126
+00:13:23.710 --> 00:13:24.440
+Harsha Tummala: Okay.
+
+127
+00:13:25.250 --> 00:13:33.040
+Harsha Tummala: Then that's pretty much it then. Any other updates, you know, from, like, Rosia or any other… any other part of things?
+
+128
+00:13:33.630 --> 00:13:41.339
+hrishikb@andrew.cmu.edu: From the OCR and ingestion, the, like, there's no particular updates, but we're just trying to,
+
+129
+00:13:41.480 --> 00:13:44.129
+hrishikb@andrew.cmu.edu: I'll combine both of them, integrate them together.
+
+130
+00:13:44.640 --> 00:13:52.950
+hrishikb@andrew.cmu.edu: So, we haven't gotten to that yet, because I still have to finish the OCR on Azure, and I was looking at Azure Document Intelligence.
+
+131
+00:13:53.320 --> 00:14:00.549
+hrishikb@andrew.cmu.edu: And, I think it also has one more thing called Azure AI Document Intelligence, or something like that as well.
+
+132
+00:14:00.740 --> 00:14:09.609
+hrishikb@andrew.cmu.edu: So I was just looking at the pricing, and we just wanted to get more clarity on the, spending cap, and I think we can, finish that by tomorrow, max.
+
+133
+00:14:09.850 --> 00:14:21.780
+hrishikb@andrew.cmu.edu: Once that's done, Rishi has given me access to the ingestion repo as well, then I can, integrate it with the ingestion repo, and then I think by next week, we should have the OCR plus ingestion pipeline complete.
+
+134
+00:14:25.100 --> 00:14:36.379
+hrishikb@andrew.cmu.edu: like, after we have a few, like, we'll run it a few times with the, what do you call it, the PDFs, and, like, we have to still have to figure out… we actually know, we have to kind of implement the…
+
+135
+00:14:36.830 --> 00:14:44.100
+hrishikb@andrew.cmu.edu: way we're gonna pass that data to the ML module. So that indication will be next. First, those CR indications, then…
+
+136
+00:14:44.240 --> 00:14:46.649
+hrishikb@andrew.cmu.edu: ingestion to, I mean.
+
+137
+00:14:50.230 --> 00:14:51.520
+Harsha Tummala: Okay, excellent.
+
+138
+00:14:57.440 --> 00:15:16.449
+Harsha Tummala: No, I'm coming off of, like, 6 hours of meetings, so I'm… I'm terribly hanging on here, to be honest. Yeah. Please let me know what else you need. You guys, you guys were able… right, yep, you were able to get the resource group and add to it. Let me know what else I can do to unblock you.
+
+139
+00:15:16.930 --> 00:15:18.920
+Harsha Tummala: Or whatever questions I can answer.
+
+140
+00:15:20.620 --> 00:15:24.909
+hrishikb@andrew.cmu.edu: Yeah, we're good for now. We'll probably message you on Teams if we have anything.
+
+141
+00:15:25.910 --> 00:15:31.030
+Harsha Tummala: Yeah, I would… I was just gonna say that. I was gonna recommend that if you have any sort of questions, just…
+
+142
+00:15:31.100 --> 00:15:46.479
+Harsha Tummala: Because ad hoc, because I think, I think it's getting to this point where we only catch up on the weekends, and just having one of our meetings, and if there's anything I have blocking you, just let us know. I think, I think, the only other blocker in the last one or two weeks was the Azure Access, and that's it.
+
+143
+00:15:46.510 --> 00:15:52.010
+Harsha Tummala: Yeah. Since that is… since that's resolved, if you have questions on, like, deploying things, understanding.
+
+144
+00:15:52.060 --> 00:15:59.320
+Harsha Tummala: any nuances in things, I would say ping me or David, or, we should be good. We should be able to help you out.
+
+145
+00:16:00.070 --> 00:16:01.429
+hrishikb@andrew.cmu.edu: Yeah, definitely we'll do that.
+
+146
+00:16:06.750 --> 00:16:09.259
+hrishikb@andrew.cmu.edu: I think that's all for the meeting.
+
+147
+00:16:11.690 --> 00:16:16.569
+Harsha Tummala: That's it. I see you guys, then. Nice seeing you. Nice seeing you, Cliff.
+
+148
+00:16:17.760 --> 00:16:18.380
+hrishikb@andrew.cmu.edu: Yeah.
+
+149
+00:16:19.590 --> 00:16:20.960
+hrishikb@andrew.cmu.edu: You guys… Yeah.
+
+150
+00:16:21.180 --> 00:16:21.830
+Harsha Tummala: You guys…
+
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+WEBVTT
+
+1
+00:00:00.000 --> 00:00:01.190
+hrishikb@andrew.cmu.edu: Labor Premier.
+
+2
+00:00:02.970 --> 00:00:06.390
+hrishikb@andrew.cmu.edu: And they don't have air conditioning on sports.
+
+3
+00:00:10.060 --> 00:00:17.129
+hrishikb@andrew.cmu.edu: Okay, I guess, we can get started. I did not… I wasn't going to send an agenda, because there were a few things changing last moment.
+
+4
+00:00:17.230 --> 00:00:19.300
+hrishikb@andrew.cmu.edu: As to what Planet discussed.
+
+5
+00:00:19.410 --> 00:00:28.890
+hrishikb@andrew.cmu.edu: So primarily, I'll give you an update of how things are going on. We'll all go around, I'll handle the engine part, and then ML and LLM will go from there.
+
+6
+00:00:29.150 --> 00:00:33.010
+hrishikb@andrew.cmu.edu: So, for the ingestion part,
+
+7
+00:00:33.070 --> 00:00:36.169
+hrishikb@andrew.cmu.edu: Like I said, the next task was gonna be to…
+
+8
+00:00:36.180 --> 00:00:54.349
+hrishikb@andrew.cmu.edu: map the schema, but when I was analyzing the ETEM work that you have to do, so before I can finalize the schema, I had to kind of integrate ETEM into the engine part, so that, like, load all the data from ETEM, like, separate out all the, like, classes, product types.
+
+9
+00:00:54.430 --> 00:01:10.290
+hrishikb@andrew.cmu.edu: And things like that. So, currently, I have done that part, and before we can have a proper schema that we can possibly push to the other module, we need to finalize more details on the ETM side. Like, it is,
+
+10
+00:01:10.890 --> 00:01:26.919
+hrishikb@andrew.cmu.edu: like, it's not something we need help in, it's just something that'll require a bit of work. So, I'm not sharing anything. Like, I don't have much to share. I… I did get Todd to make me an update of how many, like, what sort of data we have.
+
+11
+00:01:27.160 --> 00:01:31.500
+hrishikb@andrew.cmu.edu: kind of, ingested, sir.
+
+12
+00:01:34.860 --> 00:01:45.370
+hrishikb@andrew.cmu.edu: Where did you get the, ETM data from? Oh, from the site. Did you guys get a subscription, or, no, that's completely e-class. Oh, I'm thinking of E-Class, okay.
+
+13
+00:01:45.970 --> 00:01:48.650
+hrishikb@andrew.cmu.edu: We're using ETEM 10.0.
+
+14
+00:01:54.040 --> 00:01:55.620
+hrishikb@andrew.cmu.edu: Got it, one sec.
+
+15
+00:01:59.680 --> 00:02:00.720
+hrishikb@andrew.cmu.edu: Oh…
+
+16
+00:02:13.600 --> 00:02:15.469
+hrishikb@andrew.cmu.edu: So this is the high level of it.
+
+17
+00:02:15.760 --> 00:02:29.200
+hrishikb@andrew.cmu.edu: the, like, varsity product drops around 600 product classes and features, so now we have all that mapped out to a, like, local, database, so whenever we need to… because we cannot really…
+
+18
+00:02:29.200 --> 00:02:36.969
+hrishikb@andrew.cmu.edu: map these values to the, like, stuff we'll get from the OCR, because you don't want to modify the data before we hit the ML pipeline.
+
+19
+00:02:36.970 --> 00:02:48.689
+hrishikb@andrew.cmu.edu: So, we have data ready, which, we want the ML pipeline to use later on, so that we can map the accurate ETEM values for the product, class, all those things. So, we plan to do…
+
+20
+00:02:48.690 --> 00:03:00.100
+hrishikb@andrew.cmu.edu: that after we have done, coordinate score, we'll also match it to a ETEM standard. So, the output would also have, let's say, a couple of ETM columns with the ETEM ID, or whatever flags are necessary.
+
+21
+00:03:00.230 --> 00:03:02.829
+hrishikb@andrew.cmu.edu: So, this is the, kind of, the groundwork for that.
+
+22
+00:03:03.440 --> 00:03:11.769
+hrishikb@andrew.cmu.edu: after this, probably, what's gonna happen in the initial part is, Arjun is currently working on the integration with OCR,
+
+23
+00:03:11.840 --> 00:03:23.840
+hrishikb@andrew.cmu.edu: There's some, like, headway there also. After the ETEM standard is ready to use, that's when, looking into… including that, I'll create a schema of what we're gonna push.
+
+24
+00:03:24.090 --> 00:03:27.830
+hrishikb@andrew.cmu.edu: And like that, that's gonna be the next thing after the 18 work is complete.
+
+25
+00:03:28.050 --> 00:03:33.609
+hrishikb@andrew.cmu.edu: This was not, like… I thought it was gonna be pretty small, but the…
+
+26
+00:03:33.860 --> 00:03:43.540
+hrishikb@andrew.cmu.edu: trying to integrate ETEM is a bit… not tricky, but a bit lengthy. So, yeah, that is primarily what I had been working on.
+
+27
+00:03:43.840 --> 00:03:48.255
+hrishikb@andrew.cmu.edu: And, like, I don't have any questions right now, it is pretty straightforward, but…
+
+28
+00:03:49.980 --> 00:04:02.199
+hrishikb@andrew.cmu.edu: I think later on, we might have to, like, after we have the initial OCL initial part combined, we might have to, like, if it's working, we might have to have someone, like, review it once to see what's missing, what's not.
+
+29
+00:04:03.760 --> 00:04:14.180
+hrishikb@andrew.cmu.edu: Yeah, so did you have to derive the schema, or did they provide you a schema to look at their data? Schema… like, ETEM? Yeah. ETEM is basically, it's a…
+
+30
+00:04:14.330 --> 00:04:20.630
+hrishikb@andrew.cmu.edu: It is kind of like mapping, like, we have actuators and valves, so it has things like this.
+
+31
+00:04:20.649 --> 00:04:40.109
+hrishikb@andrew.cmu.edu: electrically controlled two-way control valves, so the ETEM code is EC104408. So, these are, like, kind of the global standards which are following. So, all of these, they have different standardized values, and when we… we are planning, when we see one of these values, we'll map it to our ETEM code that we have.
+
+32
+00:04:40.640 --> 00:04:45.979
+hrishikb@andrew.cmu.edu: There is no standardized schema, but it's gonna be, like, we'll be mapping
+
+33
+00:04:46.130 --> 00:04:48.509
+hrishikb@andrew.cmu.edu: what we get to the item values.
+
+34
+00:04:49.290 --> 00:04:57.630
+hrishikb@andrew.cmu.edu: We just need to store all this data in a database so we can query it as and when we need it. You're storing all the EDAM data in a database so you can query data, right?
+
+35
+00:04:59.210 --> 00:05:01.970
+hrishikb@andrew.cmu.edu: This is just some brief overviews of your examples.
+
+36
+00:05:02.840 --> 00:05:04.140
+hrishikb@andrew.cmu.edu: Nothing much higher.
+
+37
+00:05:06.280 --> 00:05:19.179
+hrishikb@andrew.cmu.edu: Correct me if I'm wrong, is there… the code, does that also translate to other standards, like eClass? Like, I know that there's a way that they translate to each other with a shared value. Is it that same, like, EC…
+
+38
+00:05:20.150 --> 00:05:23.510
+hrishikb@andrew.cmu.edu: Do you have an available phone?
+
+39
+00:05:23.510 --> 00:05:43.250
+hrishikb@andrew.cmu.edu: I've not, looked into that, but you're saying that if you have EDUM data, and if you have e-class data, there's a way you can convert… Yeah, someone's already gone ahead and, like… There's a one-to-one mapping. Yeah, there is a one-to-one mapping, and we'll… like, whatever field is used to those, we'll probably also want to use that field to map, like, as the key, if you will, to the ePARS data. I imagine we'd want to start storing that.
+
+40
+00:05:43.520 --> 00:05:47.439
+hrishikb@andrew.cmu.edu: Okay. Universal code against our product types and categories.
+
+41
+00:05:48.690 --> 00:06:06.550
+hrishikb@andrew.cmu.edu: eTime is what we want to use overall, but ideally, like, we'll also have a database of eClass at some point, so if we have customers who use eClass, they can… almost like they're translating a page to another language. E-Class is ENClass, right? Yeah. I'll take a look at that.
+
+42
+00:06:06.750 --> 00:06:10.780
+hrishikb@andrew.cmu.edu: I mean, the, like, end goal is, as Jake mentioned, and it's still, like.
+
+43
+00:06:11.040 --> 00:06:23.339
+hrishikb@andrew.cmu.edu: have support for that. Yeah, but we as eParks want to kind of make our standard moving forward mostly based on ETIM. We might have an additional thing or change something, but it's based on the ETIM.
+
+44
+00:06:23.440 --> 00:06:31.159
+hrishikb@andrew.cmu.edu: Okay. And what's the scores of the class in these periods? Two classes, the European one? Okay.
+
+45
+00:06:31.540 --> 00:06:43.119
+hrishikb@andrew.cmu.edu: Then there's another one, too. UNSPC? UNSBC system. I know what's happening. It's a point of this, but…
+
+46
+00:06:45.640 --> 00:06:50.720
+hrishikb@andrew.cmu.edu: Yeah, I think that is about the update I have for the… In Spark.
+
+47
+00:06:51.410 --> 00:06:54.119
+hrishikb@andrew.cmu.edu: Okay, I can go next.
+
+48
+00:06:54.760 --> 00:07:06.200
+hrishikb@andrew.cmu.edu: See, there is a direct marketing, but it's not a one-to-one. It's a one-to-one translation by somebody. Yeah, they've been trying it since 2001.
+
+49
+00:07:06.580 --> 00:07:09.240
+hrishikb@andrew.cmu.edu: Wow, that's…
+
+50
+00:07:10.610 --> 00:07:21.049
+hrishikb@andrew.cmu.edu: to getting different standards organizations on the same page. So, this is the main goal, like, you guys come up with the final standard of the U.S.
+
+51
+00:07:21.530 --> 00:07:29.399
+hrishikb@andrew.cmu.edu: I think ET was probably mostly video sites.
+
+52
+00:07:30.990 --> 00:07:33.809
+hrishikb@andrew.cmu.edu: Okay, I can… Hiking around.
+
+53
+00:07:34.350 --> 00:07:38.040
+hrishikb@andrew.cmu.edu: So… I, I looked through what
+
+54
+00:07:38.470 --> 00:07:41.399
+hrishikb@andrew.cmu.edu: Harsha had, shown me, and…
+
+55
+00:07:41.890 --> 00:07:45.440
+hrishikb@andrew.cmu.edu: This is… oh, yeah, sorry.
+
+56
+00:07:45.890 --> 00:07:47.040
+hrishikb@andrew.cmu.edu: So…
+
+57
+00:07:47.780 --> 00:07:59.439
+hrishikb@andrew.cmu.edu: This is not… sorry, this is GPT 5.4. I could, like Hashan said, I could not use 5.5 because… yeah, so… I just, gave it 2,000 exam… of the examples.
+
+58
+00:07:59.570 --> 00:08:04.750
+hrishikb@andrew.cmu.edu: And it came back with around, it came back with 81.45% accuracy.
+
+59
+00:08:04.910 --> 00:08:15.920
+hrishikb@andrew.cmu.edu: Its confidence is pretty good, it's 94%, so it's… when it says that it's 80% sure in an answer, it's,
+
+60
+00:08:16.320 --> 00:08:25.639
+hrishikb@andrew.cmu.edu: the chances of it being wrong is what they say it is. There's also an ECE calibration error, but that's, I think, on another page.
+
+61
+00:08:25.890 --> 00:08:31.110
+hrishikb@andrew.cmu.edu: The token usage for 2,000 examples was, 320,000 tokens.
+
+62
+00:08:31.490 --> 00:08:37.930
+hrishikb@andrew.cmu.edu: And… So, this is how its confidence was basically distributed.
+
+63
+00:08:38.190 --> 00:08:40.400
+hrishikb@andrew.cmu.edu: She probably got a kid to go convincing it.
+
+64
+00:08:41.120 --> 00:08:47.950
+hrishikb@andrew.cmu.edu: So… It looks at, it looks at… the…
+
+65
+00:08:48.410 --> 00:08:51.290
+hrishikb@andrew.cmu.edu: It gives the confidence scores for each of its predictions.
+
+66
+00:08:51.420 --> 00:08:53.440
+hrishikb@andrew.cmu.edu: That is a bit of text. Yes.
+
+67
+00:08:53.600 --> 00:08:56.660
+hrishikb@andrew.cmu.edu: And then… then we can just check against them. So…
+
+68
+00:08:57.390 --> 00:09:15.400
+hrishikb@andrew.cmu.edu: The thing is, it still, likes to almost, always give a very high con… between 90% to 100%. Yeah, and it's always super confident, but this is much better than what the smaller example I just showed you. It will very rarely, like, you can see that when it gives a…
+
+69
+00:09:15.540 --> 00:09:23.489
+hrishikb@andrew.cmu.edu: smaller score, it is half of the time it is wrong, so there are still issues, but I can actually show the rest and…
+
+70
+00:09:23.650 --> 00:09:27.530
+hrishikb@andrew.cmu.edu: And this is the reliability versus, I would say.
+
+71
+00:09:28.050 --> 00:09:31.640
+hrishikb@andrew.cmu.edu: How good it gets as we increase the number of examples.
+
+72
+00:09:31.930 --> 00:09:42.009
+hrishikb@andrew.cmu.edu: It does take, quite a huge number just to… the perfect calibration would be the dashed line, so it does take, around…
+
+73
+00:09:42.400 --> 00:09:50.319
+hrishikb@andrew.cmu.edu: It's in the thousands of examples. I did not test with on thousands. This is just, how I picked it. What's the price of GBD 5.4 again?
+
+74
+00:09:50.610 --> 00:09:51.810
+hrishikb@andrew.cmu.edu: Did you take it a bit?
+
+75
+00:09:52.390 --> 00:09:55.910
+hrishikb@andrew.cmu.edu: I… I did try to,
+
+76
+00:09:56.100 --> 00:10:10.289
+hrishikb@andrew.cmu.edu: see how much money it was, showing, but it only showed the number of requests. The money amount was always zero, no matter how many requests. No, that's because it's the playground. But, no, no, as in,
+
+77
+00:10:10.290 --> 00:10:28.829
+hrishikb@andrew.cmu.edu: you can just look at the price they charge on the Discover page. I see. You can… they literally list down all the different pricing models, but I can't want any problem. I… I can… it's, I… it's, the… I do have the token total, so I can just divide and, like, give it to you. So it's…
+
+78
+00:10:29.380 --> 00:10:33.390
+hrishikb@andrew.cmu.edu: 160-ish tokens per… and then…
+
+79
+00:10:33.710 --> 00:10:44.419
+hrishikb@andrew.cmu.edu: I think it's better if I gave it in terms of thousand, otherwise the not value would be transparent. Yeah, I mean, total number of tokens mapped to dollar value would actually be a better estimate of, like, how much…
+
+80
+00:10:44.590 --> 00:10:51.770
+hrishikb@andrew.cmu.edu: how feasible that solution actually is. It's $2.5 per 1 million input total, and $15 per 1 million input.
+
+81
+00:10:52.930 --> 00:10:58.429
+hrishikb@andrew.cmu.edu: So, input and output, yeah, okay. I can convert that.
+
+82
+00:10:59.380 --> 00:11:04.530
+hrishikb@andrew.cmu.edu: Deep… this is DeepSeekv4. Before this, I did try to use Cloud.
+
+83
+00:11:04.860 --> 00:11:10.049
+hrishikb@andrew.cmu.edu: But Cloud 4.8, had… it's just not available, I cannot deploy it.
+
+84
+00:11:10.200 --> 00:11:25.519
+hrishikb@andrew.cmu.edu: 4.7… these are all of those. 4.7, it said insufficient quota. 4.6, it also said insufficient quota. Finally, I was… I tried to use Sonic 4.6, but that just is not available in the region, so I just kind of gave us at that point.
+
+85
+00:11:25.790 --> 00:11:41.270
+hrishikb@andrew.cmu.edu: So, this is for deep-seq V4. Ignore the V2, the first one had some errors. So, this is also for the 2,000 examples. It's pretty similar, that one was, there's just a 1% difference, it's also 82%.
+
+86
+00:11:41.570 --> 00:11:48.289
+hrishikb@andrew.cmu.edu: The token amount is also similar. It's also 350, that was 320.
+
+87
+00:11:48.400 --> 00:11:53.119
+hrishikb@andrew.cmu.edu: And this is the calibration, so it gets 13% of the…
+
+88
+00:11:53.590 --> 00:12:01.460
+hrishikb@andrew.cmu.edu: Basically, its confidence scores are wrong, so… And finally, this is Kimi.
+
+89
+00:12:02.050 --> 00:12:12.569
+hrishikb@andrew.cmu.edu: This has, I would say, the highest accuracy, and its confidence is also… its confidence scoring is also good, its calibration error is the lowest.
+
+90
+00:12:12.910 --> 00:12:21.309
+hrishikb@andrew.cmu.edu: But it uses, I don't know why it uses, like, more than twice the amount of tokens, it's 840s.
+
+91
+00:12:23.100 --> 00:12:24.239
+hrishikb@andrew.cmu.edu: Can I check the price.
+
+92
+00:12:25.980 --> 00:12:35.270
+hrishikb@andrew.cmu.edu: This is all on Azure. Yes, these are all on the… So… Yeah.
+
+93
+00:12:35.730 --> 00:12:43.520
+hrishikb@andrew.cmu.edu: basically, it's the best, I think, of all of them, especially for the calibration error, and…
+
+94
+00:12:43.920 --> 00:12:47.639
+hrishikb@andrew.cmu.edu: I can add another column with the prices and stuff.
+
+95
+00:12:47.960 --> 00:12:54.509
+hrishikb@andrew.cmu.edu: But the thing is, 83, 82, and 81%, these are much below what
+
+96
+00:12:54.770 --> 00:13:01.500
+hrishikb@andrew.cmu.edu: Leo has. His is 95.1 or something like that. So… and…
+
+97
+00:13:02.070 --> 00:13:10.899
+hrishikb@andrew.cmu.edu: Even though the calibration errors are 11%, or at the… at the least, the thing is.
+
+98
+00:13:11.650 --> 00:13:15.649
+hrishikb@andrew.cmu.edu: That would still mean a lot of rework, and…
+
+99
+00:13:16.480 --> 00:13:34.000
+hrishikb@andrew.cmu.edu: So I don't think, overall, that this is, suitable. Yeah. Okay, then that's actually lower than the rest, even, even if it's, like, two and a half times the total usage.
+
+100
+00:13:34.250 --> 00:13:44.599
+hrishikb@andrew.cmu.edu: So, these are, I think, the… these are the most latest models that I had. There's also Rock, but that just did not give good results, so I did not even bother including it.
+
+101
+00:13:44.740 --> 00:14:01.900
+hrishikb@andrew.cmu.edu: And plot, as I told you, nothing worked, and I didn't want to go to, like, 4.5 or something weird on here. So these were the latest three that I could use. If they have higher models, they… like, the more late, or, I guess, current models, those don't work.
+
+102
+00:14:02.020 --> 00:14:06.569
+hrishikb@andrew.cmu.edu: Yeah, I think one of the reasons why we keep catching the insufficient thing was
+
+103
+00:14:06.940 --> 00:14:11.899
+hrishikb@andrew.cmu.edu: Azure actually provisions models based on how much the water usage is within the company.
+
+104
+00:14:12.110 --> 00:14:29.429
+hrishikb@andrew.cmu.edu: for their APIs. So if you actually have a very, very high API usage for them, they kind of provision, like, better models, because they know that these guys do. Okay. Because we don't really use that much… that much of, like, self-provisioned AI within the company, or any sort of features we offer.
+
+105
+00:14:29.520 --> 00:14:37.610
+hrishikb@andrew.cmu.edu: It's just one single appointment, let's say. So that's… that's probably the reason why we were… So…
+
+106
+00:14:37.810 --> 00:14:38.700
+hrishikb@andrew.cmu.edu: Yeah.
+
+107
+00:14:38.960 --> 00:14:43.240
+hrishikb@andrew.cmu.edu: it turned out to be pretty cheap. I was thinking that it would take more attempts, but…
+
+108
+00:14:43.390 --> 00:15:03.199
+hrishikb@andrew.cmu.edu: when you told me about the playground stuff, that helped me, like, run through a lot of, errors, because if I had done all the 2,000 examples without those, like, initially, I just fed in 20 examples with structure, so I could see if it was giving kind of the right output, and that helped a lot. So, in total, like,
+
+109
+00:15:03.680 --> 00:15:05.909
+hrishikb@andrew.cmu.edu: You can check them on, but it's Gale.
+
+110
+00:15:06.160 --> 00:15:11.979
+hrishikb@andrew.cmu.edu: And… I can kind of confidently say that with 2,000 examples, the
+
+111
+00:15:14.140 --> 00:15:26.710
+hrishikb@andrew.cmu.edu: It's a very high confidence. The statistical power of the test is enough that I can say that it's much more noise. It's in that no amount of tweaking will get 83 to 95. Yeah, and there's no point. I mean, if you can't…
+
+112
+00:15:26.990 --> 00:15:34.179
+hrishikb@andrew.cmu.edu: like, for 2000 examples, if we were to cross a certain threshold, then it's something else, I think it's just best invested value, so…
+
+113
+00:15:34.820 --> 00:15:42.400
+hrishikb@andrew.cmu.edu: You've done your due diligence to investigate this aspect. I've tried every sample available on that service.
+
+114
+00:15:46.280 --> 00:15:49.369
+hrishikb@andrew.cmu.edu: That is it? I don't know, Ryan.
+
+115
+00:15:50.130 --> 00:16:01.630
+hrishikb@andrew.cmu.edu: I think we didn't move forward from the LLM part. Yeah, no, move forward, I mean, like, we can drop the POC.
+
+116
+00:16:05.090 --> 00:16:07.090
+hrishikb@andrew.cmu.edu: Yeah, we didn't know.
+
+117
+00:16:07.580 --> 00:16:13.410
+hrishikb@andrew.cmu.edu: We'll show up, Pete. Alright, I don't have anything to show, but, like, I finished my POC on my first
+
+118
+00:16:13.550 --> 00:16:19.509
+hrishikb@andrew.cmu.edu: So use document intelligence, which is, yeah, Azure's, like, inbuilt in.
+
+119
+00:16:19.820 --> 00:16:26.409
+hrishikb@andrew.cmu.edu: Along with that, I use GPT4 only, from Foundation, and
+
+120
+00:16:27.040 --> 00:16:34.739
+hrishikb@andrew.cmu.edu: And it was not able to be, Chandra, which was on Data Lab. I think I mentioned this last week, before the 9 export for CS service.
+
+121
+00:16:34.910 --> 00:16:40.509
+hrishikb@andrew.cmu.edu: But then I kept… so one… one issue was that, the prompt.
+
+122
+00:16:40.870 --> 00:16:50.179
+hrishikb@andrew.cmu.edu: So, document information was able to extract everything, but the LLM wasn't able to, like, properly classify them, so that's where the problem started.
+
+123
+00:16:50.370 --> 00:17:03.790
+hrishikb@andrew.cmu.edu: Yeah, I tried with 4.0 after, 4-0 minutes, but it was worse, so I don't know what went up. Then I made the prompt a little better, a little more work, and then, the error rate back down.
+
+124
+00:17:03.890 --> 00:17:18.920
+hrishikb@andrew.cmu.edu: Then what I did is I, combined both Azure Kodomary and Photos, and then I took a union set of both of them, and that gave the least amount of evidence. We almost came down to 2%, and, Chandra updated that was 4%.
+
+125
+00:17:19.140 --> 00:17:27.490
+hrishikb@andrew.cmu.edu: So, two personality was okay, but I wanted to go lower. So, I had Dockering running Looply, that's an open source stuff, mostly running loop.
+
+126
+00:17:27.630 --> 00:17:41.379
+hrishikb@andrew.cmu.edu: So it, ran document intelligence first, and then passed it on to, Poro Mini, Doppling, and, Poro, and got the union set up there, and that got it down to 1.3%. So that's the lowest I've gotten now.
+
+127
+00:17:41.610 --> 00:17:53.060
+hrishikb@andrew.cmu.edu: And I can still keep going till, like, I don't know, like, 0.5% is where I think I feel comfortable, so that we don't need any human intervention there, because we use photo, though.
+
+128
+00:17:53.060 --> 00:18:07.209
+hrishikb@andrew.cmu.edu: Just probably use 5 Mini, because that's the cheapest model that GPD offers. Oh, yeah. Oh, I thought because… I just started with 40, because… I think GPD5 Mini is one of the most widely used, like.
+
+129
+00:18:07.310 --> 00:18:21.720
+hrishikb@andrew.cmu.edu: APIs injected for them. Okay, so they actually give the best price for primary. I would just say, like, look at the Discover page, see all the prices, and I would say, just based on that, just, like, okay, yeah. Because you can even go with something higher as well, but,
+
+130
+00:18:21.720 --> 00:18:39.670
+hrishikb@andrew.cmu.edu: it all depends on how easy it gets, right? For how many tokens… how many tokens are actually consumed versus… Right, so much it has to use. So, looking at the full catalog, which is, like, 5,000 documents, and it comes to 25,000 pages, yeah. So, we're going with this two-reader thing, which was 4AM mini plus 4AM.
+
+131
+00:18:40.140 --> 00:18:51.310
+hrishikb@andrew.cmu.edu: The entire thing would be done in, like, 500 to $750. It's a one-time thing, and then we don't have to worry about it. And, the third reader also, docking is packaging, you can run it…
+
+132
+00:18:51.430 --> 00:19:04.559
+hrishikb@andrew.cmu.edu: On a VBS or something. Yeah, so, but I'll try out with, 5… 5 minutes, 5 minutes. Okay, yeah, I'll try it out with 5 minutes, and then see what it is. So what was the cost again?
+
+133
+00:19:04.990 --> 00:19:13.239
+hrishikb@andrew.cmu.edu: 5 Mini? No, per his result for the photo. Oh, for photo mini plus 40, it comes to $500.
+
+134
+00:19:13.320 --> 00:19:21.319
+hrishikb@andrew.cmu.edu: To find it to 750, that's the problem. That's if I do both of them together and get the unions, so that I can reduce the error percentage.
+
+135
+00:19:21.340 --> 00:19:36.389
+hrishikb@andrew.cmu.edu: But that's for all the documents we have. That's all the documents, right? So that's a… would be an infrequent cost, right? It's… yeah, it's a one-time cost. It's a one-time cost, and then, like, whenever the documents come in, it would probably be, like, $1,000
+
+136
+00:19:38.180 --> 00:19:46.380
+hrishikb@andrew.cmu.edu: Yeah, that's pretty much what I had, along with that, I got Rishi's, report English.
+
+137
+00:19:46.550 --> 00:20:02.560
+hrishikb@andrew.cmu.edu: And, like, we had done some work on TestRack, which was another OCR engine, but, like, I'd also done the same, one. That's just for, like, mock purpose, so I can continue my work. Yeah, so I need to remove TestRack, and then I need to plug in this one. And then, once that's complete, the integrations
+
+138
+00:20:02.690 --> 00:20:18.740
+hrishikb@andrew.cmu.edu: fully done, and then we can continue working on the… Yeah, ETAM and the schema thing is that we push to email. Yeah, ETAM and eta. So, one more thing we wanted to know is, downstream of Aglestion, we are storing… I think right now, locally, we are testing out with Postgres.
+
+139
+00:20:18.870 --> 00:20:38.269
+hrishikb@andrew.cmu.edu: So, how are we planning to, store things? Like, I think we mentioned about some kind of a staging table that you can get. Yeah, it's the exact same, did we not get the staging schema? Maybe not. We… that is schema, we don't have a place to store it. Yeah, the staging tables will be where we want to store the output of all this.
+
+140
+00:20:38.350 --> 00:20:46.630
+hrishikb@andrew.cmu.edu: Yeah, but, I guess, is that your question? Like, what tables? Yeah, yeah, if all the… anything that ends in, underscore staging is…
+
+141
+00:20:46.630 --> 00:21:11.120
+hrishikb@andrew.cmu.edu: Okay, and we get to view that on our job at the stage? I… we can give you access for that. Okay, can you make, like, a duplicate of this table? Duplicate, exactly. Also, the current… the current PIMS is a Microsoft SQL Server. Okay, no, but we will be moving to Postgres, like… Okay, like, I'm doing a big rework for PIMS right now, and I have a Postgres version of it. The tables are a little different, so… Okay. Okay. We'll plan for it to be Postgres.
+
+142
+00:21:11.120 --> 00:21:24.579
+hrishikb@andrew.cmu.edu: field of staging that we can give them that. I think we… yeah, we should do that same… because my schema's a little different. It's mostly the same idea, there's a staging table for every, like, normal product table, and yeah, there's a couple fields that we want to be simplified.
+
+143
+00:21:24.580 --> 00:21:32.030
+hrishikb@andrew.cmu.edu: Okay. So, we'll continue working on post itself, since you have… So, one more thing is, like,
+
+144
+00:21:32.030 --> 00:21:56.730
+hrishikb@andrew.cmu.edu: We need to post this code somewhere, like, I don't know what… where do you… Right now, for, like, the ingestion needs to run on some kind of a server, okay? We're seeing the code exposed so that they can talk to each other. So, we need some place to run this code on, so how do we… the Azure resourcing you have access to? Yeah, you could probably split up a container app. Oh, okay.
+
+145
+00:21:56.730 --> 00:22:08.949
+hrishikb@andrew.cmu.edu: Yeah, I was just asking, there's some container that whether or not you would… the resource actually gives FPG access to create more data. Okay. Okay, we'll just try spinning up a very basic container for working on that.
+
+146
+00:22:09.620 --> 00:22:15.390
+hrishikb@andrew.cmu.edu: And… So right now, you just want us to continue working locally on a Postgres basis.
+
+147
+00:22:15.750 --> 00:22:25.940
+hrishikb@andrew.cmu.edu: Yeah, that's fine. I mean, we need to get you the updated schema, but yeah, that's totally fine. That's what the end goal will be, is outputting all this… Yeah, I think, I mean,
+
+148
+00:22:26.720 --> 00:22:49.779
+hrishikb@andrew.cmu.edu: we could probably just wait to get them into post-list, and we can report them, and once it's finalized from the end, we can actually give them the… Yeah, I don't think… So I think the schema's finalized on my end. I'm still… I'm still doing stuff with the actual, like, rework of the applications and work with the schema, but I think… I think that schema will be useful for the staging part. Like, right now, we have not reached that yet. We are, like, that will be after the ML part, before the staging.
+
+149
+00:22:49.780 --> 00:22:53.829
+hrishikb@andrew.cmu.edu: In a month or a month and a half, we did both provision, like, a…
+
+150
+00:22:53.880 --> 00:23:04.380
+hrishikb@andrew.cmu.edu: Yeah, yeah. I think for now, like, we are using poster, because it's just for our internal use, so that we can, have the data stored and communicate in the components.
+
+151
+00:23:04.410 --> 00:23:22.600
+hrishikb@andrew.cmu.edu: So, I'm still confused as to where this EDIM thing lands in. So, does that also get stored in some kind of a table? Niosity. Oh, so that's a separate table? It would be another table, and it maps to the tablet. I'd be curious, actually, we should… I'm curious…
+
+152
+00:23:22.680 --> 00:23:32.999
+hrishikb@andrew.cmu.edu: I've kind of made something intermediate for kind of mapping and some tables, and users can go in and define that, like, this ouch controls category equals this ETINS category, and…
+
+153
+00:23:33.000 --> 00:23:43.389
+hrishikb@andrew.cmu.edu: But, I'd be curious how you're doing it, too, and maybe come up with what's best for, like, a final thing. I'm still flexible on what we're implementing right now with
+
+154
+00:23:43.440 --> 00:23:47.210
+hrishikb@andrew.cmu.edu: standards and the PIMS rework, so…
+
+155
+00:23:47.950 --> 00:23:57.239
+hrishikb@andrew.cmu.edu: Maybe you guys are doing a better way of storing those mappings as you start to play with that right now? So what we're doing right now is basically,
+
+156
+00:23:57.270 --> 00:24:04.290
+hrishikb@andrew.cmu.edu: like, ETAM already has a very distinguished list of, like, classes and prototypes.
+
+157
+00:24:04.290 --> 00:24:22.799
+hrishikb@andrew.cmu.edu: So, we're just storing that particular data in our, like, in our database, and then we are gonna, like, we haven't figured that part out yet, how we're gonna map it, but we don't have any manual input as of right now. So, the mapping part will be handled by the ML module. So, but if we need to have a, like, some sort of a…
+
+158
+00:24:24.230 --> 00:24:41.440
+hrishikb@andrew.cmu.edu: Well, we want to store the results of that ML model that that map is using for the feature. So that, that, like, we're not mad about the schema yet, but I was thinking about something, like, along with the, like, we have the product, prototype, the attributes, and each of those will map, have their own,
+
+159
+00:24:41.760 --> 00:24:57.679
+hrishikb@andrew.cmu.edu: let's say ETEM IDs, that'll be a separate table. Yeah, so the way I'm doing it right now is kind of what you're saying, like, the categories table, the attributes table, there's just an additional column on there for standard ID, and it just maps to a separate table where we, you know… Yeah, something like that, yeah.
+
+160
+00:24:57.860 --> 00:25:09.240
+hrishikb@andrew.cmu.edu: And that way, you can have all of them in one single table, but within that table is the ops, or the eParts Unified Standard, or the e-standard, or E-Class, and more standards. And we can sort the mapping into the network table, yeah.
+
+161
+00:25:09.830 --> 00:25:27.200
+hrishikb@andrew.cmu.edu: And then, as long as you map ETM product type to the bar's product type, that's enough for the guys. So that could be something people said that that should open those. Yeah. I think that, like, after Devon model's trained on the Ethernet as well, like, we should have a good nodes.
+
+162
+00:25:27.200 --> 00:25:30.769
+hrishikb@andrew.cmu.edu: So, is that the same ML model that we split?
+
+163
+00:25:30.770 --> 00:25:37.820
+hrishikb@andrew.cmu.edu: That, like, that we have to still figure it out. If it is best we do it in the same one, or we have another component which does the EPA mapping.
+
+164
+00:25:37.960 --> 00:25:49.510
+hrishikb@andrew.cmu.edu: Right now, we'll have the content scoring, we'll have the, like, all the ETEM data, then we have to figure out how we map that. We really need an ML model, because it's a one-time thing again, right, for all the current catalog.
+
+165
+00:25:49.950 --> 00:25:51.610
+hrishikb@andrew.cmu.edu: No, but,
+
+166
+00:25:52.480 --> 00:25:57.699
+hrishikb@andrew.cmu.edu: Because I'm thinking the, like, the names might be a bit different, so we need some sort of an accuracy score there also.
+
+167
+00:25:57.810 --> 00:26:05.300
+hrishikb@andrew.cmu.edu: Because ETEM has a very specific standard, like, the one I showed, like, it has two-way valves or something like that, and if…
+
+168
+00:26:05.300 --> 00:26:18.259
+hrishikb@andrew.cmu.edu: a company called something else, we need to be able to map it to that particular thing. Exactly. Like, even after we get the Alps mapping, yeah, we want anyone to come in and maybe they have their own categories that aren't any standard. You want to be able to map those together.
+
+169
+00:26:18.350 --> 00:26:20.680
+hrishikb@andrew.cmu.edu: Anyways.
+
+170
+00:26:22.070 --> 00:26:23.679
+hrishikb@andrew.cmu.edu: Jeez, excuse me.
+
+171
+00:26:24.240 --> 00:26:26.060
+hrishikb@andrew.cmu.edu: Okay Okay.
+
+172
+00:26:26.220 --> 00:26:37.130
+hrishikb@andrew.cmu.edu: Luke, you're… Or just, like, adjust the permits, so, two, two tests.
+
+173
+00:26:37.560 --> 00:26:43.379
+hrishikb@andrew.cmu.edu: That's the integration test for the whole, model and the component test.
+
+174
+00:26:43.630 --> 00:26:54.379
+hrishikb@andrew.cmu.edu: But I haven't completed the report, so I will actually show you all the results next week. But all the tests are passed.
+
+175
+00:26:54.520 --> 00:26:58.919
+hrishikb@andrew.cmu.edu: Right? We only have 150… One has facilities.
+
+176
+00:27:00.810 --> 00:27:05.949
+hrishikb@andrew.cmu.edu: And, I want to show you something about doing this builder.
+
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+00:27:11.710 --> 00:27:15.380
+hrishikb@andrew.cmu.edu: Aye. You too.
+
+178
+00:27:35.930 --> 00:27:37.120
+hrishikb@andrew.cmu.edu: Yes.
+
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+00:27:37.310 --> 00:27:44.830
+hrishikb@andrew.cmu.edu: So… As the whole model have been completed, the next…
+
+180
+00:27:44.950 --> 00:27:49.240
+hrishikb@andrew.cmu.edu: Since I think I can do… is to do some…
+
+181
+00:27:49.450 --> 00:27:54.129
+hrishikb@andrew.cmu.edu: concurrent both tests, and, try to…
+
+182
+00:27:56.430 --> 00:28:02.699
+hrishikb@andrew.cmu.edu: Lower the… reduce the latency for each component of the model.
+
+183
+00:28:03.040 --> 00:28:06.590
+hrishikb@andrew.cmu.edu: And to test the… Hmm.
+
+184
+00:28:09.300 --> 00:28:10.250
+hrishikb@andrew.cmu.edu: Night.
+
+185
+00:28:11.240 --> 00:28:16.230
+hrishikb@andrew.cmu.edu: I have strong… standards I may preserve.
+
+186
+00:28:16.770 --> 00:28:18.180
+hrishikb@andrew.cmu.edu: for each component.
+
+187
+00:28:22.410 --> 00:28:26.630
+hrishikb@andrew.cmu.edu: And the second is I identified the…
+
+188
+00:28:27.890 --> 00:28:32.820
+hrishikb@andrew.cmu.edu: The heaviest part for the model is the encoded part.
+
+189
+00:28:34.120 --> 00:28:39.440
+hrishikb@andrew.cmu.edu: So I want to… But some ways to…
+
+190
+00:28:40.230 --> 00:28:42.810
+hrishikb@andrew.cmu.edu: reduce the latency are also important.
+
+191
+00:28:43.060 --> 00:28:44.059
+hrishikb@andrew.cmu.edu: For the model.
+
+192
+00:28:44.660 --> 00:28:54.770
+hrishikb@andrew.cmu.edu: And to sit alone, since there are… the model has many, like, super… Hyper… superbitters.
+
+193
+00:28:55.630 --> 00:29:01.969
+hrishikb@andrew.cmu.edu: So, I want to… make, detailed documents for you.
+
+194
+00:29:03.170 --> 00:29:06.819
+hrishikb@andrew.cmu.edu: And, the people I handle.
+
+195
+00:29:07.010 --> 00:29:10.799
+hrishikb@andrew.cmu.edu: just… this model to, to, things.
+
+196
+00:29:11.940 --> 00:29:25.250
+hrishikb@andrew.cmu.edu: And if possible, I want to implement an automatic update overflow based on valuation results after testing, focus on very… focus on the metrics that have to
+
+197
+00:29:25.520 --> 00:29:27.929
+hrishikb@andrew.cmu.edu: Be outstanding, and truly.
+
+198
+00:29:28.840 --> 00:29:30.520
+hrishikb@andrew.cmu.edu: To, to change that movement.
+
+199
+00:29:32.060 --> 00:29:34.810
+hrishikb@andrew.cmu.edu: And, the, the first thing is.
+
+200
+00:29:36.980 --> 00:29:46.629
+hrishikb@andrew.cmu.edu: as I said, I want some, like, 50 or 100 corrections, like, some real data from you.
+
+201
+00:29:47.700 --> 00:29:57.090
+hrishikb@andrew.cmu.edu: But it's still okay if I don't get the gist data, because it's just too… Refines the model.
+
+202
+00:29:58.340 --> 00:30:00.430
+hrishikb@andrew.cmu.edu: And finally, I've got to,
+
+203
+00:30:01.460 --> 00:30:04.189
+hrishikb@andrew.cmu.edu: for test or the HTTP interface.
+
+204
+00:30:04.310 --> 00:30:05.310
+hrishikb@andrew.cmu.edu: And training.
+
+205
+00:30:06.110 --> 00:30:08.710
+hrishikb@andrew.cmu.edu: It's suggest what I want to do in the next few weeks.
+
+206
+00:30:10.560 --> 00:30:19.240
+hrishikb@andrew.cmu.edu: So, repeat collection examples that you mentioned. Yeah. So how do you… what sort of data do you do with that?
+
+207
+00:30:19.460 --> 00:30:23.909
+hrishikb@andrew.cmu.edu: Is it supposed to be, what the original…
+
+208
+00:30:24.020 --> 00:30:29.230
+hrishikb@andrew.cmu.edu: Oh, it's just that… How is it veribly prepared into the system?
+
+209
+00:30:29.430 --> 00:30:32.510
+hrishikb@andrew.cmu.edu: Or, do you want… So,
+
+210
+00:30:35.700 --> 00:30:42.739
+hrishikb@andrew.cmu.edu: even… I can make a jet for you today, and that'll send it to you.
+
+211
+00:30:43.100 --> 00:30:55.520
+hrishikb@andrew.cmu.edu: I will write a logic, but I don't send you yet. Yeah, that makes sense. If you just send us, like, the exact expectation on how we want these connections to look like, I can see how I can get the data. Okay, of course.
+
+212
+00:30:57.780 --> 00:30:58.800
+hrishikb@andrew.cmu.edu: And it's a fool.
+
+213
+00:31:07.650 --> 00:31:11.600
+hrishikb@andrew.cmu.edu: Yeah, I think that was our updates.
+
+214
+00:31:12.480 --> 00:31:15.490
+hrishikb@andrew.cmu.edu: I don't think, we have any…
+
+215
+00:31:15.720 --> 00:31:21.709
+hrishikb@andrew.cmu.edu: Yeah, I think, what we need to focus on in the coming weeks, at least.
+
+216
+00:31:22.500 --> 00:31:36.429
+hrishikb@andrew.cmu.edu: like, we've done a lot of POCs, we want to drop what doesn't work, and move on with… I think we'll be dropping the LM part, we want to work on product, and what's going to move to production, or, like, boost people. So, probably take some time to, like, see…
+
+217
+00:31:36.870 --> 00:31:44.330
+hrishikb@andrew.cmu.edu: how, I mean, how the timeline looks like with things drop and things stand over time?
+
+218
+00:31:44.630 --> 00:32:02.260
+hrishikb@andrew.cmu.edu: Yeah, again, yeah, we should reassess. I think, this ETEM thing should take us some time to fill it out. Initially, I thought it's not really that big a deal, but, like, it's gonna take a bit of work, not, I think.
+
+219
+00:32:02.260 --> 00:32:04.950
+hrishikb@andrew.cmu.edu: not significantly impact anything.
+
+220
+00:32:05.230 --> 00:32:15.310
+hrishikb@andrew.cmu.edu: And you plan to make that mapping between our categories in, like, each end category? Or, like, I guess, what about that part?
+
+221
+00:32:15.510 --> 00:32:33.540
+hrishikb@andrew.cmu.edu: I think we can do both, because mapping to the different categories, that should be a one-time thing. And, like, moving on, normal that in just the new documents that come in, they'll just automatically map to those categories. So I think we can do that part as well. But that'll,
+
+222
+00:32:33.670 --> 00:32:44.769
+hrishikb@andrew.cmu.edu: we might not have to, include that as part of the entire system, because that's going to be a one-time thing. Yeah, that could be essentially… Could be a separate… Yeah, I'm just curious, because I'm…
+
+223
+00:32:44.770 --> 00:33:04.129
+hrishikb@andrew.cmu.edu: I myself am doing a lot of similar things where I'm already kind of… for my test data, as I test this little rework our pins, I've already maxed some, just, like, 10 categories and attributes, but yeah, I'm gonna eventually need the same thing. I'm just wondering, like, should we be parts to it? We could have our catalog team review it, or were you already planning to, like,
+
+224
+00:33:04.200 --> 00:33:16.310
+hrishikb@andrew.cmu.edu: So, if you have something that you're already working on, can you send us, like, what kind of thing? Yeah, again, mine's just a light schema, but I've definitely, like, already started to map some of the categories together. And you're doing this…
+
+225
+00:33:16.710 --> 00:33:33.609
+hrishikb@andrew.cmu.edu: manually? I mean, I'm using Claude, but, like, that's… I mean, but I'm taking just, like, a small mock data set we use that I'm using for my local testing, and based out of those categories I have there and those attributes, I've already found their ETEM equivalents, and as I have these interfaces for switching back and forth.
+
+226
+00:33:33.610 --> 00:33:37.790
+hrishikb@andrew.cmu.edu: Yeah, honestly, we can use Plur, like, if it's a one-time thing… Yeah, exactly.
+
+227
+00:33:38.260 --> 00:33:42.339
+hrishikb@andrew.cmu.edu: Spalling, like, I mean, Sabradi here is, I think.
+
+228
+00:33:42.870 --> 00:33:52.309
+hrishikb@andrew.cmu.edu: Yeah, I think, we can do that, but, that's a bit, laid down the line for us. Okay. To map, like, after we have the initial, like.
+
+229
+00:33:52.540 --> 00:34:04.810
+hrishikb@andrew.cmu.edu: all of it indicated, then we'll probably map it even to the category. Yeah, whenever you get to that, like, I gotta be curious… I would be interested in jumping in there, if I could have a lot of that more Guardian done at that point. Okay, definitely, yeah.
+
+230
+00:34:04.940 --> 00:34:23.209
+hrishikb@andrew.cmu.edu: We'll let you know. And, like, if you have anything done, you could also share that with us, so that… Yeah, yeah, we just don't want to do it again. Like, if it's the same thing, we're implementing what you're doing. Before we move on to that part, we'll let you know, so that we can sync up on that. Yeah, either way, I want to get you those new versions and the staging tables, and at that same time, I can give you the schema I have right now. Yeah, that'll be good.
+
+231
+00:34:24.550 --> 00:34:40.500
+hrishikb@andrew.cmu.edu: Okay, and then, I think one more thing is, like, the whole pipeline is just broken into pieces. Yeah, that's the next part. I'll set up a server, and then we can start with, like, doctors and components and networking and stuff like that.
+
+232
+00:34:41.580 --> 00:34:54.359
+hrishikb@andrew.cmu.edu: We have it, but the components are segregated still. Correct. So we have to integrate all of those, then have a common repo, which we have.
+
+233
+00:34:56.150 --> 00:34:58.590
+hrishikb@andrew.cmu.edu: And I think documentary needs there.
+
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+00:34:58.880 --> 00:35:00.700
+hrishikb@andrew.cmu.edu: That's… yeah.
+
+235
+00:35:00.920 --> 00:35:09.840
+hrishikb@andrew.cmu.edu: I would say it's just keep doing a little bit of it as you go. In the end, if you do all of the together, then it gets cloudy. Yeah.
+
+236
+00:35:10.040 --> 00:35:14.880
+hrishikb@andrew.cmu.edu: There's a bit of documentary there, but nothing…
+
+237
+00:35:15.140 --> 00:35:18.760
+hrishikb@andrew.cmu.edu: Yeah, I think with Cloud, it's a lot easier as well.
+
+238
+00:35:20.170 --> 00:35:20.899
+hrishikb@andrew.cmu.edu: Love it.
+
+239
+00:35:22.390 --> 00:35:28.099
+hrishikb@andrew.cmu.edu: Yeah, I think, before we do the Azure part, deploying it, you have to first make it work internally.
+
+240
+00:35:28.470 --> 00:35:35.009
+hrishikb@andrew.cmu.edu: like, connect the OCR and ingestion for them, and that'll take a bit of… some time, at least.
+
+241
+00:35:35.470 --> 00:35:39.950
+hrishikb@andrew.cmu.edu: with that, and then we'll probably move on to Azure, see how it works.
+
+242
+00:35:43.190 --> 00:35:48.849
+hrishikb@andrew.cmu.edu: That's… that's all we have today. Do you have any questions for them?
+
+243
+00:35:50.900 --> 00:35:53.619
+hrishikb@andrew.cmu.edu: Maybe there's an awesome subject.
+
+244
+00:35:54.730 --> 00:36:14.050
+hrishikb@andrew.cmu.edu: So you guys meeting next Thursday? Theoretically, next Thursday is a community day, and theoretically, there's no CMU5, but that's theoretical, it's up to you guys. And Friday's a holiday.
+
+245
+00:36:14.290 --> 00:36:31.350
+hrishikb@andrew.cmu.edu: Yeah. Okay, yeah. I guess you don't have to observe the holiday, because it's a scene… Yeah. To get the day off, you get Friday off? Yeah. Nice, nice.
+
+246
+00:36:34.120 --> 00:36:38.250
+hrishikb@andrew.cmu.edu: You brought to 5,000?
+
+247
+00:36:38.760 --> 00:36:39.470
+hrishikb@andrew.cmu.edu: Weekend.
+
+248
+00:36:41.380 --> 00:36:48.009
+hrishikb@andrew.cmu.edu: Seniors, I mean, Pittsburgh's got great fireworks. They have a great fireworks. There's a lot of things, there's, the,
+
+249
+00:36:48.460 --> 00:36:52.139
+hrishikb@andrew.cmu.edu: What's… what's the bang? Besides that,
+
+250
+00:36:52.460 --> 00:37:05.279
+hrishikb@andrew.cmu.edu: Yeah, I know what you're talking about. They're performing in Point State Park and stuff. Yeah. And there's a bunch of band performing there for Pete.
+
+251
+00:37:05.390 --> 00:37:14.440
+hrishikb@andrew.cmu.edu: Oh, it's gonna be a mess, but, like, the problem is your info profile.
+
+252
+00:37:15.780 --> 00:37:16.580
+hrishikb@andrew.cmu.edu: Where?
+
+253
+00:37:17.180 --> 00:37:33.449
+hrishikb@andrew.cmu.edu: I think this is one of the… It's gonna be crazy coming year. This year, it's gonna be really… Yeah, yeah, yeah.
+
+254
+00:37:33.980 --> 00:37:48.079
+hrishikb@andrew.cmu.edu: The fight already happened. Oh, it hadn't happened. But in general. They had a great flyby for that. They had a combination of Blue Angels and the Thunderbirds.
+
+255
+00:37:52.310 --> 00:38:09.710
+hrishikb@andrew.cmu.edu: Hopefully we come back. Because, like, I know that BC is actually expecting, like, additional traffic. They have a blocking cases up, so that they just…
+
+256
+00:38:10.180 --> 00:38:11.580
+hrishikb@andrew.cmu.edu: Are you playing with Agnes?
+
+257
+00:38:11.580 --> 00:38:34.599
+hrishikb@andrew.cmu.edu: Yeah. It's an easy drive. Well, up until the last hour, and then it can be crazy traffic, depending on the time you get there. Well, if you drive down to the area and then take the transportation. Yeah, the best part, the exchange of road is, like, the farthest one out.
+
+258
+00:38:34.600 --> 00:38:42.840
+hrishikb@andrew.cmu.edu: The metro is actually… it takes too much time to get to the speed. Oh, so, like, the… the tram, the training service, that kind of…
+
+259
+00:38:42.850 --> 00:38:44.170
+hrishikb@andrew.cmu.edu: Oh, it's okay.
+
+260
+00:38:44.610 --> 00:38:45.440
+hrishikb@andrew.cmu.edu: It's…
+
+261
+00:38:45.650 --> 00:38:51.099
+hrishikb@andrew.cmu.edu: as few as done as possible. But it's what's impossible to find a place to park to be driving your car.
+
+262
+00:38:54.670 --> 00:38:58.500
+hrishikb@andrew.cmu.edu: You may not know I'm gonna be the one. But yeah, that's true.
+
+263
+00:39:00.170 --> 00:39:04.950
+hrishikb@andrew.cmu.edu: Well, great seeing y'all. Yeah. Have a good weekend. See you soon.
+
+264
+00:39:05.880 --> 00:39:07.250
+hrishikb@andrew.cmu.edu: It's…
+
+265
+00:39:07.690 --> 00:39:16.019
+hrishikb@andrew.cmu.edu: There you go, guys. Let us know if you need any help yet. We kind of got to things somehow.
+
+266
+00:39:16.020 --> 00:39:28.669
+hrishikb@andrew.cmu.edu: like, the boundary setup and, like, the Azure setup. I actually set up a new project on boundary for my time. Okay. It's very straightforward. So the resource is there, so as long as it's connected to your resource.
+
+267
+00:39:28.670 --> 00:39:41.330
+hrishikb@andrew.cmu.edu: You're good. So the thing is, I didn't do it on Foundry Playground, I did it the other way, because I wanted an API easily. Okay. Does that increase? No, no, so you're deploying the… you're deploying the model, you're not selling a project.
+
+268
+00:39:41.500 --> 00:39:42.220
+hrishikb@andrew.cmu.edu: Okay.
+
+269
+00:39:42.570 --> 00:39:49.260
+hrishikb@andrew.cmu.edu: Yeah, depending on… you can do how many of models you want, as I'll use it. Oh, okay.
+
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@@ -0,0 +1,1142 @@
+WEBVTT
+
+1
+00:00:02.760 --> 00:00:10.560
+hrishikb@andrew.cmu.edu: So, in the agenda for today, we just have, our updates on what we all have been working on.
+
+2
+00:00:10.700 --> 00:00:16.550
+hrishikb@andrew.cmu.edu: I'll start, so… I'm still working on the schema part.
+
+3
+00:00:16.710 --> 00:00:19.690
+hrishikb@andrew.cmu.edu: For the ingestion gateway.
+
+4
+00:00:20.060 --> 00:00:27.610
+hrishikb@andrew.cmu.edu: there… I'm making a lot of, ETM tables. I'm trying to separate out, each ETM value in a different table.
+
+5
+00:00:27.710 --> 00:00:30.660
+hrishikb@andrew.cmu.edu: For example, ETM feature, unit, values.
+
+6
+00:00:30.780 --> 00:00:34.939
+hrishikb@andrew.cmu.edu: ETM class groups, because there are so many different,
+
+7
+00:00:35.790 --> 00:00:40.010
+hrishikb@andrew.cmu.edu: like, different categories, I'm planning to having each of those as a separate table.
+
+8
+00:00:40.760 --> 00:00:41.600
+hrishikb@andrew.cmu.edu: And then…
+
+9
+00:00:41.600 --> 00:00:56.919
+Harsha Tummala: Actually, I wanted to give some input on this, too. Just in the past 3 days, I've actually, managed to import the entire eTIM standard into our schema, that we're going to be using for PIMS to include all the different values, units.
+
+10
+00:00:57.160 --> 00:01:00.290
+Harsha Tummala: Features, classes, everything. So,
+
+11
+00:01:00.590 --> 00:01:04.440
+Harsha Tummala: I can just give you a drop of that, if that would be helpful.
+
+12
+00:01:04.650 --> 00:01:08.330
+hrishikb@andrew.cmu.edu: Yeah, that would be helpful, because we can use the same thing in our system as well, then.
+
+13
+00:01:09.000 --> 00:01:09.530
+Harsha Tummala: Yep.
+
+14
+00:01:09.530 --> 00:01:10.909
+hrishikb@andrew.cmu.edu: Probably even more in sync.
+
+15
+00:01:11.330 --> 00:01:29.670
+Harsha Tummala: I don't have the mappings, though, in terms of, like, our existing… this category corresponds to this existing ETIMS class, but other words, that I did get classes in his categories, features in his attributes, and then all that… all the other tables that are associated with it. So, yeah, I can send that to you.
+
+16
+00:01:29.940 --> 00:01:31.510
+hrishikb@andrew.cmu.edu: Okay, yeah, that'll be really good.
+
+17
+00:01:32.000 --> 00:01:49.209
+hrishikb@andrew.cmu.edu: Then I think I'll probably… for now, what I have is I just imported all the ETEM data. It doesn't correspond to anything in PIMS. It's just that all the ETEM data has its own, rows and tables and columns. But yeah, if you have already… that already mapped with some of the
+
+18
+00:01:49.770 --> 00:01:51.840
+hrishikb@andrew.cmu.edu: Attributes, then that'll be helpful.
+
+19
+00:01:52.870 --> 00:01:56.790
+Harsha Tummala: Yeah, I think I'll just give that to you as a back file for Postgres.
+
+20
+00:01:56.990 --> 00:01:58.069
+hrishikb@andrew.cmu.edu: Hmm, yeah, that'll work.
+
+21
+00:01:58.850 --> 00:01:59.630
+Harsha Tummala: Boom.
+
+22
+00:02:01.420 --> 00:02:05.170
+hrishikb@andrew.cmu.edu: Yep, that was pretty much for the ingestion site.
+
+23
+00:02:05.690 --> 00:02:11.000
+hrishikb@andrew.cmu.edu: I guess, Arjun, you can tell us a bit about OCR and integration.
+
+24
+00:02:11.000 --> 00:02:12.809
+arjunnai@andrew.cmu.edu: Yeah, so,
+
+25
+00:02:13.510 --> 00:02:22.369
+arjunnai@andrew.cmu.edu: I did what, karsha, you asked me to check out the GPT 5.4 last week, so I checked that out, but, like, the…
+
+26
+00:02:22.730 --> 00:02:36.240
+arjunnai@andrew.cmu.edu: Results were not much better. We still need, like, two models, like, two loops. Once we run it with GPD 5.4, and then with, like, a vision model, which helps us get down the error rates to, like, less than 2%.
+
+27
+00:02:36.450 --> 00:02:41.469
+arjunnai@andrew.cmu.edu: Apart from that, Rishi's ingestion repo.
+
+28
+00:02:41.600 --> 00:02:51.770
+arjunnai@andrew.cmu.edu: Was given to me, so I've integrated, like, my OCR work, along with Rishi's ingestion work, and I've created a new Bitbucket repo.
+
+29
+00:02:51.950 --> 00:02:54.800
+arjunnai@andrew.cmu.edu: So I've put all of that.
+
+30
+00:02:55.260 --> 00:02:59.960
+arjunnai@andrew.cmu.edu: So, I think right now, my main priority would be to still optimize the…
+
+31
+00:03:00.430 --> 00:03:12.059
+arjunnai@andrew.cmu.edu: OCR part a little more, so we can get the error rates done. I'm still figuring out how to do that. But, like, the whole, integration part is pretty much done. The next step would be, like.
+
+32
+00:03:12.900 --> 00:03:20.389
+arjunnai@andrew.cmu.edu: I think, assign it to, like, a schema, and then put it in, like, staging tables, and then we should take it from there.
+
+33
+00:03:23.130 --> 00:03:23.800
+Harsha Tummala: et cetera.
+
+34
+00:03:24.980 --> 00:03:32.029
+Harsha Tummala: Also, I think I mentioned 5 mini, not partners for… Yeah, I…
+
+35
+00:03:32.030 --> 00:03:43.950
+arjunnai@andrew.cmu.edu: I tried from… I think I tried everything from 5 onwards, like, 5, 5 Mini, and then 5.1… there was 5.5 also, but it said quota exceeded, so I couldn't try that out.
+
+36
+00:03:45.230 --> 00:03:46.199
+arjunnai@andrew.cmu.edu: Look at him.
+
+37
+00:03:49.100 --> 00:03:59.679
+arjunnai@andrew.cmu.edu: I think, I'll have to do something. Since it's a base model, I don't know if I can create, like, a fine-tuned model on Foundry itself and try something out with that.
+
+38
+00:04:00.500 --> 00:04:03.139
+arjunnai@andrew.cmu.edu: So I can try that out as well.
+
+39
+00:04:05.940 --> 00:04:07.280
+Harsha Tummala: Makes sense.
+
+40
+00:04:09.220 --> 00:04:10.180
+arjunnai@andrew.cmu.edu: Yeah.
+
+41
+00:04:11.290 --> 00:04:13.059
+Harsha Tummala: Got a lot of questions from you, man.
+
+42
+00:04:16.890 --> 00:04:20.830
+hrishikb@andrew.cmu.edu: Okay, then, Jay can give us a B for the QA plans.
+
+43
+00:04:24.820 --> 00:04:27.999
+jaivards@andrew.cmu.edu: Can I scare my screen regarding that, or should I just stop?
+
+44
+00:04:28.000 --> 00:04:29.290
+hrishikb@andrew.cmu.edu: Yeah, you can share your screen.
+
+45
+00:04:51.100 --> 00:04:52.390
+jaivards@andrew.cmu.edu: Yeah, is it visible?
+
+46
+00:04:53.060 --> 00:04:53.690
+hrishikb@andrew.cmu.edu: Yeah.
+
+47
+00:04:57.670 --> 00:05:03.880
+jaivards@andrew.cmu.edu: So, this is what we currently have for, as we go along, to the coding site.
+
+48
+00:05:04.360 --> 00:05:05.360
+jaivards@andrew.cmu.edu: So…
+
+49
+00:05:05.550 --> 00:05:12.619
+jaivards@andrew.cmu.edu: we've made sure that our QA plan focuses on the most important characteristics first, so that those are accuracy.
+
+50
+00:05:12.790 --> 00:05:16.390
+jaivards@andrew.cmu.edu: And reliability, in our confidence scores.
+
+51
+00:05:16.960 --> 00:05:20.569
+jaivards@andrew.cmu.edu: And then… Accountability that has…
+
+52
+00:05:21.240 --> 00:05:25.939
+jaivards@andrew.cmu.edu: Whenever we make a prediction, and whenever a record comes in, is it being recorded or not?
+
+53
+00:05:26.720 --> 00:05:32.900
+jaivards@andrew.cmu.edu: And from that, we are, designing tests for each of them. So, for the ingestion gateway.
+
+54
+00:05:33.270 --> 00:05:35.329
+jaivards@andrew.cmu.edu: For the normalization.
+
+55
+00:05:36.020 --> 00:05:37.809
+jaivards@andrew.cmu.edu: The prediction service.
+
+56
+00:05:38.840 --> 00:05:40.200
+jaivards@andrew.cmu.edu: And the routing engine?
+
+57
+00:05:42.040 --> 00:05:48.699
+jaivards@andrew.cmu.edu: So… Not all of them, have been implemented as,
+
+58
+00:05:49.000 --> 00:05:51.429
+jaivards@andrew.cmu.edu: Most, most of the, things for,
+
+59
+00:05:51.890 --> 00:05:56.450
+jaivards@andrew.cmu.edu: the ML part, have tests, as you has written a lot of them.
+
+60
+00:05:56.660 --> 00:06:02.260
+jaivards@andrew.cmu.edu: But we're still, going through them, and, we'll be, updating them as they go along.
+
+61
+00:06:02.430 --> 00:06:04.789
+jaivards@andrew.cmu.edu: But this is our current lab, and…
+
+62
+00:06:05.280 --> 00:06:08.240
+jaivards@andrew.cmu.edu: I can go into depth if you want, on any of these.
+
+63
+00:06:08.490 --> 00:06:13.679
+jaivards@andrew.cmu.edu: But, right now, it's mostly the prediction service that has the tests.
+
+64
+00:06:14.210 --> 00:06:15.150
+jaivards@andrew.cmu.edu: And…
+
+65
+00:06:16.100 --> 00:06:23.120
+jaivards@andrew.cmu.edu: this is more of a, I guess, whenever a change is made, or you guys want to make a change, so just to see nothing is breaking.
+
+66
+00:06:23.220 --> 00:06:25.039
+jaivards@andrew.cmu.edu: Or, failing silent.
+
+67
+00:06:25.520 --> 00:06:31.739
+jaivards@andrew.cmu.edu: But, yeah, I'm… Right now, after this document, I'm more focused on
+
+68
+00:06:32.090 --> 00:06:34.550
+jaivards@andrew.cmu.edu: How we will be going about,
+
+69
+00:06:35.790 --> 00:06:38.780
+jaivards@andrew.cmu.edu: Which tests to write first, and how we'll be going about it.
+
+70
+00:06:38.900 --> 00:06:41.490
+jaivards@andrew.cmu.edu: And also collecting metrics for our…
+
+71
+00:06:41.600 --> 00:06:45.630
+jaivards@andrew.cmu.edu: usage of AI during codings, just to see how that's done.
+
+72
+00:06:46.030 --> 00:06:49.690
+jaivards@andrew.cmu.edu: So… Those are my updates.
+
+73
+00:06:50.740 --> 00:06:54.810
+jaivards@andrew.cmu.edu: Does anyone want to get into, like… The details are anymore.
+
+74
+00:06:54.940 --> 00:06:56.229
+jaivards@andrew.cmu.edu: I can go after that.
+
+75
+00:07:05.610 --> 00:07:10.580
+Clifford Huff: Are you going to share this document with the clients and the mentors, or just give us a link to it?
+
+76
+00:07:11.170 --> 00:07:15.980
+jaivards@andrew.cmu.edu: Yes, I will be sharing that. There are a few changes first, but I'll be sharing this.
+
+77
+00:07:26.130 --> 00:07:30.620
+Clifford Huff: And was this suggested based on your meeting with Jeff for the QA pack?
+
+78
+00:07:31.560 --> 00:07:35.610
+jaivards@andrew.cmu.edu: Yeah, Jeff actually… focus more on, I would say.
+
+79
+00:07:36.150 --> 00:07:43.910
+jaivards@andrew.cmu.edu: The… how we are collecting the metrics, and how we know that this is being implemented, rather than changing the document itself.
+
+80
+00:07:44.340 --> 00:07:48.909
+jaivards@andrew.cmu.edu: I think he's pretty okay with the rough shape of the document, but…
+
+81
+00:07:49.030 --> 00:07:52.650
+jaivards@andrew.cmu.edu: He's much more focused on the metrics collection and
+
+82
+00:07:52.920 --> 00:07:56.989
+jaivards@andrew.cmu.edu: That what we have written, like, we have links regarding
+
+83
+00:07:57.840 --> 00:08:02.589
+jaivards@andrew.cmu.edu: These, all these quantity attributes, and exactly how,
+
+84
+00:08:02.730 --> 00:08:05.159
+jaivards@andrew.cmu.edu: You know, equivalence, partition, and stuff like that.
+
+85
+00:08:05.300 --> 00:08:09.629
+jaivards@andrew.cmu.edu: But he's more focused on whether those steps and those metrics are being implemented.
+
+86
+00:08:09.630 --> 00:08:10.689
+Harsha Tummala: It's right.
+
+87
+00:08:12.680 --> 00:08:13.850
+Clifford Huff: Okay, thanks.
+
+88
+00:08:18.990 --> 00:08:25.349
+jaivards@andrew.cmu.edu: I'll try to share, like, concrete metrics next time, as we go and as we're recording them.
+
+89
+00:08:25.800 --> 00:08:31.110
+jaivards@andrew.cmu.edu: And, that will kind of also give a more fair idea, I would say, about
+
+90
+00:08:32.289 --> 00:08:41.600
+jaivards@andrew.cmu.edu: The progress of the project itself, and how ready it is for, you know, any changes, and if you guys want to make them as…
+
+91
+00:08:47.450 --> 00:08:48.370
+jaivards@andrew.cmu.edu: That's it.
+
+92
+00:08:50.180 --> 00:08:50.950
+hrishikb@andrew.cmu.edu: Okay.
+
+93
+00:08:52.100 --> 00:08:57.020
+hrishikb@andrew.cmu.edu: Leo, can you have an update on the ML side?
+
+94
+00:08:58.220 --> 00:08:59.619
+hrishikb@andrew.cmu.edu: The testing you've been doing.
+
+95
+00:09:00.520 --> 00:09:03.230
+zhelianl@andrew.cmu.edu: Yes, could I share my document?
+
+96
+00:09:04.100 --> 00:09:04.680
+hrishikb@andrew.cmu.edu: Yeah.
+
+97
+00:09:14.910 --> 00:09:20.699
+zhelianl@andrew.cmu.edu: Yeah, so, last week, we… Have completed all the…
+
+98
+00:09:21.480 --> 00:09:24.930
+zhelianl@andrew.cmu.edu: Component test and, integration test.
+
+99
+00:09:25.400 --> 00:09:27.749
+zhelianl@andrew.cmu.edu: for the ML model.
+
+100
+00:09:28.320 --> 00:09:35.410
+zhelianl@andrew.cmu.edu: So, this time, I will share you with the results for these two kinds of testing.
+
+101
+00:09:36.430 --> 00:09:42.189
+zhelianl@andrew.cmu.edu: So, for the overview, we mainly have four layers of the…
+
+102
+00:09:42.780 --> 00:09:50.040
+zhelianl@andrew.cmu.edu: machine learning system. The Layer 1 is extraction. This work had depended on TrueJ.
+
+103
+00:09:50.170 --> 00:09:52.919
+zhelianl@andrew.cmu.edu: And, the Layer 2 is the raw engine.
+
+104
+00:09:53.460 --> 00:10:00.269
+zhelianl@andrew.cmu.edu: Because this is just some mapping work, so we don't have many scenes to test.
+
+105
+00:10:00.600 --> 00:10:03.559
+zhelianl@andrew.cmu.edu: And, we put out the most.
+
+106
+00:10:03.700 --> 00:10:12.229
+zhelianl@andrew.cmu.edu: Effort and time on the Layer 3 and Layer 4, and these two layers are the most complicated part of the…
+
+107
+00:10:12.430 --> 00:10:13.589
+zhelianl@andrew.cmu.edu: machine learning model.
+
+108
+00:10:14.150 --> 00:10:26.359
+zhelianl@andrew.cmu.edu: So, for the… this way, the searching is encode the description to a vector and retrieve the most familiar category products. The category prediction part.
+
+109
+00:10:26.540 --> 00:10:31.369
+zhelianl@andrew.cmu.edu: Have to… The retrieved product vote on the product type.
+
+110
+00:10:31.760 --> 00:10:42.170
+zhelianl@andrew.cmu.edu: The attribute scoring scores the negative values for each attribute of the product type, and for the layer 4, the decision part layer.
+
+111
+00:10:42.870 --> 00:10:51.999
+zhelianl@andrew.cmu.edu: They do a decision and the calculation work, combine the low and the semester signals, applying safety capes, and do the result.
+
+112
+00:10:52.980 --> 00:10:59.600
+zhelianl@andrew.cmu.edu: So, from this pathway, The shoot contains, well, 106 component tests.
+
+113
+00:10:59.820 --> 00:11:02.860
+zhelianl@andrew.cmu.edu: Across the four modules, or parts.
+
+114
+00:11:04.330 --> 00:11:11.859
+zhelianl@andrew.cmu.edu: And, those are the… Detailed testing area, or each part of the model.
+
+115
+00:11:12.470 --> 00:11:17.350
+zhelianl@andrew.cmu.edu: So, for the search part, we test the connectedness
+
+116
+00:11:18.180 --> 00:11:21.949
+zhelianl@andrew.cmu.edu: And, index integration and,
+
+117
+00:11:22.250 --> 00:11:27.969
+zhelianl@andrew.cmu.edu: Configuration and the input handling. And, with their validated behavior.
+
+118
+00:11:28.430 --> 00:11:37.890
+zhelianl@andrew.cmu.edu: Like, for the collectors, a product's nearest label is itself at similarity equal to Whoa.
+
+119
+00:11:39.020 --> 00:11:41.180
+zhelianl@andrew.cmu.edu: For the index integration.
+
+120
+00:11:41.500 --> 00:11:48.799
+zhelianl@andrew.cmu.edu: Because the report size and the demonstration are correct, and the save load returns identical results.
+
+121
+00:11:49.220 --> 00:11:55.350
+zhelianl@andrew.cmu.edu: For the configuration, unsupported index types and rejected with a clear arrow.
+
+122
+00:11:56.220 --> 00:12:02.129
+zhelianl@andrew.cmu.edu: And, the input handlings, single queries and batches are handled equivalently.
+
+123
+00:12:03.430 --> 00:12:07.439
+zhelianl@andrew.cmu.edu: For the category prediction, We test voting.
+
+124
+00:12:07.910 --> 00:12:14.250
+zhelianl@andrew.cmu.edu: The validated behavior could be confidence reflects the similarity which the votes show.
+
+125
+00:12:15.050 --> 00:12:17.380
+zhelianl@andrew.cmu.edu: So, and nope, nope.
+
+126
+00:12:17.770 --> 00:12:23.039
+zhelianl@andrew.cmu.edu: Normally those case labels, agrees yield confidence one.
+
+127
+00:12:23.410 --> 00:12:30.090
+zhelianl@andrew.cmu.edu: So, confidence bands, ambiguous less than 0.6 normal and high consensus.
+
+128
+00:12:30.410 --> 00:12:35.270
+zhelianl@andrew.cmu.edu: Great, then… 0.8 bands behavior distinctly.
+
+129
+00:12:36.380 --> 00:12:47.709
+zhelianl@andrew.cmu.edu: And, so, input handle… handling, unlaw products, negative similarities, and empty results are handled very gracefully.
+
+130
+00:12:50.520 --> 00:12:55.760
+zhelianl@andrew.cmu.edu: For the scoring part, we tested the upper bound and the lower bound.
+
+131
+00:12:55.940 --> 00:12:58.860
+zhelianl@andrew.cmu.edu: Or the scores, and the range of the scores.
+
+132
+00:12:59.360 --> 00:13:01.920
+zhelianl@andrew.cmu.edu: And, the popularity.
+
+133
+00:13:02.330 --> 00:13:04.759
+zhelianl@andrew.cmu.edu: a popularity player.
+
+134
+00:13:05.600 --> 00:13:07.480
+zhelianl@andrew.cmu.edu: like.
+
+135
+00:13:07.780 --> 00:13:17.949
+zhelianl@andrew.cmu.edu: The values used more often in the catalog get a small confidence boost, and this adjustment is relevant to stay within safe limits.
+
+136
+00:13:18.510 --> 00:13:27.609
+zhelianl@andrew.cmu.edu: And for the birth date, So values with insufficient training data will fall back to a safe calculation.
+
+137
+00:13:29.040 --> 00:13:32.050
+zhelianl@andrew.cmu.edu: And for the layer 4, we test the field gene.
+
+138
+00:13:32.280 --> 00:13:35.500
+zhelianl@andrew.cmu.edu: The final confidence followed the committee debate.
+
+139
+00:13:35.980 --> 00:13:39.599
+zhelianl@andrew.cmu.edu: 30% do, and, 35% machine learning parts.
+
+140
+00:13:39.950 --> 00:13:41.390
+zhelianl@andrew.cmu.edu: So, safety tapes.
+
+141
+00:13:42.110 --> 00:13:48.110
+zhelianl@andrew.cmu.edu: On certain category Cape and spurs data Cape fail correctly.
+
+142
+00:13:48.240 --> 00:13:52.510
+zhelianl@andrew.cmu.edu: The stricture VIN… the visible supply.
+
+143
+00:13:54.290 --> 00:14:02.510
+zhelianl@andrew.cmu.edu: like, either way, Kent… If the semester Parts cannot give a.
+
+144
+00:14:02.690 --> 00:14:06.200
+zhelianl@andrew.cmu.edu: higher enough category CAPE score.
+
+145
+00:14:06.660 --> 00:14:11.620
+zhelianl@andrew.cmu.edu: Wait, the highest score, or the final result?
+
+146
+00:14:11.860 --> 00:14:14.539
+zhelianl@andrew.cmu.edu: Well, less than 0.75.
+
+147
+00:14:14.890 --> 00:14:25.650
+zhelianl@andrew.cmu.edu: And, if that… If the samples is less than, like, They could fight. We…
+
+148
+00:14:26.760 --> 00:14:29.600
+zhelianl@andrew.cmu.edu: Treat this case as a sparse data.
+
+149
+00:14:29.820 --> 00:14:39.710
+zhelianl@andrew.cmu.edu: So, the final score could narrow higher than 0.6, 0.7, For the routing part.
+
+150
+00:14:40.130 --> 00:14:46.529
+zhelianl@andrew.cmu.edu: Results look to auto-process human review flagged unclear by competence threshold.
+
+151
+00:14:47.930 --> 00:14:51.629
+zhelianl@andrew.cmu.edu: And we've also tested some… the boundary tests.
+
+152
+00:14:51.930 --> 00:14:53.570
+zhelianl@andrew.cmu.edu: And edit these ones.
+
+153
+00:14:54.640 --> 00:14:58.999
+zhelianl@andrew.cmu.edu: We chose turn-test target edge and fatal cases.
+
+154
+00:14:59.470 --> 00:15:07.510
+zhelianl@andrew.cmu.edu: Like, missing or corrupt data, empty import, exact ties, and the precise points where a decision flips.
+
+155
+00:15:07.720 --> 00:15:14.759
+zhelianl@andrew.cmu.edu: The situation is most likely to behave unexpectedly and least likely to be caught by outdated use.
+
+156
+00:15:15.670 --> 00:15:21.390
+zhelianl@andrew.cmu.edu: So, we have 5… Such… Cases.
+
+157
+00:15:21.910 --> 00:15:22.900
+zhelianl@andrew.cmu.edu: Everywhere.
+
+158
+00:15:23.290 --> 00:15:26.339
+zhelianl@andrew.cmu.edu: And, two, one category.
+
+159
+00:15:26.740 --> 00:15:29.219
+zhelianl@andrew.cmu.edu: And test the tool scoring area.
+
+160
+00:15:29.420 --> 00:15:32.020
+zhelianl@andrew.cmu.edu: And the two, decision area.
+
+161
+00:15:33.560 --> 00:15:38.720
+zhelianl@andrew.cmu.edu: The result is all the, 106 component tests pass.
+
+162
+00:15:41.230 --> 00:15:43.469
+zhelianl@andrew.cmu.edu: For the coverage scope.
+
+163
+00:15:44.630 --> 00:15:53.719
+zhelianl@andrew.cmu.edu: The component testing confirms each module is logically correct on controlling inputs, including the boundary cases described above.
+
+164
+00:15:54.310 --> 00:15:59.420
+zhelianl@andrew.cmu.edu: But it doesn't cover real-world accuracy.
+
+165
+00:15:59.610 --> 00:16:05.359
+zhelianl@andrew.cmu.edu: Since the tests use synthetic import phase, With long answers.
+
+166
+00:16:05.800 --> 00:16:09.669
+zhelianl@andrew.cmu.edu: But I think this could be… not be a very big problem.
+
+167
+00:16:10.100 --> 00:16:16.309
+zhelianl@andrew.cmu.edu: Because this could not block our next processing for the model.
+
+168
+00:16:16.720 --> 00:16:20.720
+zhelianl@andrew.cmu.edu: We just need more data, rare data, to refine the model.
+
+169
+00:16:21.070 --> 00:16:23.590
+zhelianl@andrew.cmu.edu: And, for the second one, the…
+
+170
+00:16:24.100 --> 00:16:27.400
+zhelianl@andrew.cmu.edu: Each model is tested in isolation by design.
+
+171
+00:16:27.550 --> 00:16:34.379
+zhelianl@andrew.cmu.edu: So, we don't know how it really works in the LUs or the model.
+
+172
+00:16:36.100 --> 00:16:37.469
+zhelianl@andrew.cmu.edu: The other days.
+
+173
+00:16:37.630 --> 00:16:57.470
+zhelianl@andrew.cmu.edu: A few risk input cases were deliberately left for later on, since, like, unusual loan descriptions, uncommon technical codes, or duplicate category entries, those are skipped, but not because they are uncommon in normal operation and low risk there.
+
+174
+00:16:57.680 --> 00:17:00.090
+zhelianl@andrew.cmu.edu: So, a bunch of cases were prioritized.
+
+175
+00:17:00.470 --> 00:17:05.649
+zhelianl@andrew.cmu.edu: We focus first on the cases most likely to affect everyday results.
+
+176
+00:17:06.480 --> 00:17:10.719
+zhelianl@andrew.cmu.edu: And I also list 3, remaining waste.
+
+177
+00:17:11.640 --> 00:17:18.700
+zhelianl@andrew.cmu.edu: First, it does not establish how accurately the system predicts on real customers' language.
+
+178
+00:17:19.060 --> 00:17:23.849
+zhelianl@andrew.cmu.edu: And the rare import cases noted above are unvalified.
+
+179
+00:17:24.349 --> 00:17:26.819
+zhelianl@andrew.cmu.edu: The tests validate.
+
+180
+00:17:27.260 --> 00:17:36.179
+zhelianl@andrew.cmu.edu: Against our design specifications, like, Integration testing partially covers this by existing in the module together.
+
+181
+00:17:39.510 --> 00:17:44.870
+zhelianl@andrew.cmu.edu: And, for the integration tests, Oh,
+
+182
+00:17:45.020 --> 00:17:53.809
+zhelianl@andrew.cmu.edu: The integration testing confirms they worked correctly when connected into a chain, and when wrong behind the rail web service.
+
+183
+00:17:54.610 --> 00:18:02.780
+zhelianl@andrew.cmu.edu: So the whole change, like, customer description flawed through the road engine, then the domestic matcher.
+
+184
+00:18:03.330 --> 00:18:05.000
+zhelianl@andrew.cmu.edu: The answer decision layer.
+
+185
+00:18:05.500 --> 00:18:09.079
+zhelianl@andrew.cmu.edu: Which combines the signal and the rules… the result.
+
+186
+00:18:11.700 --> 00:18:19.120
+zhelianl@andrew.cmu.edu: And the reverse confirmation or correction fades back into the model to update the model.
+
+187
+00:18:19.830 --> 00:18:29.130
+zhelianl@andrew.cmu.edu: And, the result is… is, like, the… the source contains Sati… 7 integration tests will pass.
+
+188
+00:18:30.230 --> 00:18:39.459
+zhelianl@andrew.cmu.edu: the coverage we test… Like, 9 aerials was a whole chain was modeled.
+
+189
+00:18:39.660 --> 00:18:43.670
+zhelianl@andrew.cmu.edu: And the width is… But they keep behave it.
+
+190
+00:18:44.580 --> 00:18:53.640
+zhelianl@andrew.cmu.edu: We test end-to-end prediction, routine propagation, low-cross category corresponding, Empty category pass.
+
+191
+00:18:53.800 --> 00:18:55.770
+zhelianl@andrew.cmu.edu: Think of fusion end-to-end.
+
+192
+00:18:55.920 --> 00:18:57.159
+zhelianl@andrew.cmu.edu: feedback loop.
+
+193
+00:18:57.530 --> 00:19:01.460
+zhelianl@andrew.cmu.edu: Service contract. Consistency, traceability.
+
+194
+00:19:02.570 --> 00:19:06.110
+zhelianl@andrew.cmu.edu: So, the chemistry scope could…
+
+195
+00:19:06.470 --> 00:19:10.029
+zhelianl@andrew.cmu.edu: It doesn't cover the real-world accuracy.
+
+196
+00:19:10.380 --> 00:19:19.300
+zhelianl@andrew.cmu.edu: And, the test exists correctness, those performance under many simultaneous requests.
+
+197
+00:19:19.950 --> 00:19:26.370
+zhelianl@andrew.cmu.edu: Behaved under sustained… Concurate load is validated separately, and it's still outstanding.
+
+198
+00:19:26.690 --> 00:19:30.110
+zhelianl@andrew.cmu.edu: So, the next step, we need to…
+
+199
+00:19:30.260 --> 00:19:39.130
+zhelianl@andrew.cmu.edu: Do some, performance testing, like, to… to input a lot of…
+
+200
+00:19:39.290 --> 00:19:49.339
+zhelianl@andrew.cmu.edu: cases simultaneously to the system and, to see the latency and other, performers, behavior could…
+
+201
+00:19:49.840 --> 00:19:53.170
+zhelianl@andrew.cmu.edu: Be expected as a… as a way to expect it.
+
+202
+00:19:55.590 --> 00:19:59.280
+zhelianl@andrew.cmu.edu: And the ramenic music list.
+
+203
+00:19:59.650 --> 00:20:05.780
+zhelianl@andrew.cmu.edu: The first is we require real data to test the whole system, and the second
+
+204
+00:20:06.180 --> 00:20:09.010
+zhelianl@andrew.cmu.edu: We need to test the load behavior.
+
+205
+00:20:10.070 --> 00:20:13.650
+zhelianl@andrew.cmu.edu: And, during this test, we…
+
+206
+00:20:14.030 --> 00:20:17.710
+zhelianl@andrew.cmu.edu: Actually found a defect, and we fixed it.
+
+207
+00:20:18.030 --> 00:20:19.230
+zhelianl@andrew.cmu.edu: This is…
+
+208
+00:20:19.500 --> 00:20:30.129
+zhelianl@andrew.cmu.edu: When a reviewer's collection was being recorded, but it could never reach the live prediction, so the online landing loop doesn't actually close.
+
+209
+00:20:30.260 --> 00:20:33.580
+zhelianl@andrew.cmu.edu: It's, it, it's like… the system…
+
+210
+00:20:34.150 --> 00:20:43.239
+zhelianl@andrew.cmu.edu: To create a brand new repository to… To store the…
+
+211
+00:20:44.210 --> 00:20:48.500
+zhelianl@andrew.cmu.edu: The new center was a cluster, but… the system…
+
+212
+00:20:48.800 --> 00:20:55.529
+zhelianl@andrew.cmu.edu: Still use the old one to do the… matching book, so…
+
+213
+00:20:55.850 --> 00:20:58.490
+zhelianl@andrew.cmu.edu: We fixed it. And just water.
+
+214
+00:20:58.810 --> 00:21:00.790
+zhelianl@andrew.cmu.edu: That's what I want to show this.
+
+215
+00:21:14.050 --> 00:21:19.550
+Clifford Huff: So, are these results on your… on production code, or is this proof of concept code?
+
+216
+00:21:19.700 --> 00:21:23.139
+Clifford Huff: I just want to be… I want to be clear about understanding that.
+
+217
+00:21:24.800 --> 00:21:30.530
+zhelianl@andrew.cmu.edu: All the tests are… Hardly tested on the rail code.
+
+218
+00:21:33.610 --> 00:21:36.940
+Clifford Huff: So, your real code is intended to be production code?
+
+219
+00:21:37.730 --> 00:21:38.990
+zhelianl@andrew.cmu.edu: Yes.
+
+220
+00:21:39.930 --> 00:21:40.690
+Clifford Huff: Okay.
+
+221
+00:21:41.030 --> 00:21:45.870
+Clifford Huff: And has other members of the team reviewed that production code yet?
+
+222
+00:21:46.750 --> 00:21:49.429
+zhelianl@andrew.cmu.edu: Yeah, I think Brish and, Jay…
+
+223
+00:21:49.630 --> 00:21:51.769
+zhelianl@andrew.cmu.edu: have reviewed it, and I have…
+
+224
+00:21:52.030 --> 00:21:59.670
+zhelianl@andrew.cmu.edu: Upload all the results and the testing code into the GitHub and, Bitbucket.
+
+225
+00:22:02.820 --> 00:22:03.790
+Clifford Huff: Okay.
+
+226
+00:22:05.060 --> 00:22:06.530
+zhelianl@andrew.cmu.edu: Oh, thank… thank you.
+
+227
+00:22:07.960 --> 00:22:08.990
+zhelianl@andrew.cmu.edu: So…
+
+228
+00:22:11.680 --> 00:22:12.720
+Harsha Tummala: In theory.
+
+229
+00:22:12.830 --> 00:22:19.669
+Harsha Tummala: Actually, coming, to your email as well, you, where you asked for, like, correction… Oh, yes.
+
+230
+00:22:20.070 --> 00:22:36.989
+Harsha Tummala: We… I mean, I was looking into it, mainly the processes to concoct that data, talking to a few people here, and then, see… see… just… just creating it for a few cases, and that's pretty much it.
+
+231
+00:22:37.140 --> 00:22:46.430
+Harsha Tummala: And hopefully I can send you this data by next week, and it won't be too much, it'll just be, like, I think 50 to 100 records at max.
+
+232
+00:22:46.690 --> 00:22:47.830
+Harsha Tummala: Yes.
+
+233
+00:22:47.830 --> 00:22:48.870
+zhelianl@andrew.cmu.edu: I think that's enough.
+
+234
+00:22:49.490 --> 00:22:50.440
+zhelianl@andrew.cmu.edu: Thank you.
+
+235
+00:22:50.930 --> 00:22:53.100
+Harsha Tummala: Yeah.
+
+236
+00:22:53.330 --> 00:22:53.880
+Harsha Tummala: That's it.
+
+237
+00:22:53.880 --> 00:23:00.710
+zhelianl@andrew.cmu.edu: Yeah, yeah, and during the reuse of the system, we could collect more data from the reuse, yeah.
+
+238
+00:23:01.240 --> 00:23:02.249
+Harsha Tummala: Yeah, I agree.
+
+239
+00:23:19.030 --> 00:23:26.399
+hrishikb@andrew.cmu.edu: Yeah, I guess… That is mostly all for update. Ashita, Yatsun, go ahead.
+
+240
+00:23:28.220 --> 00:23:29.580
+Ashritha: So…
+
+241
+00:23:29.650 --> 00:23:46.689
+Ashritha: I, I mean, I hope all of you are doing okay. So, the first… I think I worked the previous week, I was just busy with the code reviews, I didn't do any much of the development task, but then this week, I've picked up the ETIN schema task.
+
+242
+00:23:46.750 --> 00:23:56.830
+Ashritha: So, me and Jay are gonna work on it, and, since Jake just mentioned about, some of his findings, so we'll pick it up from there, yeah.
+
+243
+00:24:02.960 --> 00:24:09.979
+Harsha Tummala: Also, I think we should also talk about, like, how, how we can combine Jake's findings with your findings, and…
+
+244
+00:24:10.450 --> 00:24:22.409
+Harsha Tummala: We could have a meeting just for that, if you guys want, sometime next week. I think Jake is still in the process of, like, finalizing all the things. No,
+
+245
+00:24:22.600 --> 00:24:30.379
+Harsha Tummala: I mean, I'm finalizing, like, interfaces and pimps to utilize it and assign it to products, but in terms of the data in our schema, no, that's…
+
+246
+00:24:30.590 --> 00:24:34.659
+Harsha Tummala: Pretty much finalized on my side. Then…
+
+247
+00:24:35.300 --> 00:24:48.080
+Harsha Tummala: Did we, talk about, how we wanted to, see how the mappings go? I think Rishkesh was working on it. Ending the mappings to, like, the existing categories, the old categories? Yeah, yeah.
+
+248
+00:24:48.080 --> 00:25:02.259
+Harsha Tummala: So, yeah, I think, what we can do is, over next week or the week after, Jake could just show how all of it looks in PIMS, and we could do, like, a PIMS demo with the updated version of how things stand on it.
+
+249
+00:25:02.390 --> 00:25:06.750
+Harsha Tummala: So that we could just get you guys up to speed and empowered.
+
+250
+00:25:07.130 --> 00:25:14.949
+Harsha Tummala: How… how… how progress is looking on our end, and how that translates to, the things that you guys are doing.
+
+251
+00:25:16.680 --> 00:25:19.370
+hrishikb@andrew.cmu.edu: Yeah, I think, that works, that works well.
+
+252
+00:25:19.370 --> 00:25:20.380
+Harsha Tummala: Yeah.
+
+253
+00:25:20.650 --> 00:25:28.870
+hrishikb@andrew.cmu.edu: I think before that, it would be good if, Jake, you could share the, schema that you have in a PEMS, dump, or something similar.
+
+254
+00:25:29.200 --> 00:25:32.059
+hrishikb@andrew.cmu.edu: So, meanwhile, before the meeting, I can also take a look.
+
+255
+00:25:32.160 --> 00:25:36.230
+hrishikb@andrew.cmu.edu: And so that I can get in line with, what are Genesis exactly.
+
+256
+00:25:36.490 --> 00:25:38.549
+hrishikb@andrew.cmu.edu: Then we can have a meeting next week.
+
+257
+00:25:39.750 --> 00:25:41.449
+Harsha Tummala: Yep, makes sense.
+
+258
+00:25:42.300 --> 00:25:44.359
+Harsha Tummala: And I'll remind Jake to send it to us.
+
+259
+00:25:44.660 --> 00:25:51.680
+Harsha Tummala: But that should be good. We should have that data by next week, or next week, hopefully.
+
+260
+00:25:53.440 --> 00:25:54.460
+hrishikb@andrew.cmu.edu: That, that sounds great.
+
+261
+00:25:54.460 --> 00:26:04.239
+Harsha Tummala: I'm in the background right now trying to extort it, but yeah. I think there's some data I want to leave out, some test data that's, like, mixed in with it right now, so yeah, I'll add that to you as soon as I can.
+
+262
+00:26:05.690 --> 00:26:16.229
+Harsha Tummala: And… any other updates from us or you guys? I can't think of any other questions. To be fair, it's a lot of work going on for you providing.
+
+263
+00:26:17.350 --> 00:26:19.120
+hrishikb@andrew.cmu.edu: I think, no.
+
+264
+00:26:20.890 --> 00:26:27.919
+Harsha Tummala: Yeah, and I think… Liu, can you also send across the QA document that you were just talking through?
+
+265
+00:26:28.090 --> 00:26:34.979
+Harsha Tummala: So that… not QA, the testing document that you will put on it through, so that we could also take a look, and I could spend a little more.
+
+266
+00:26:34.980 --> 00:26:35.660
+zhelianl@andrew.cmu.edu: Okay, okay.
+
+267
+00:26:36.340 --> 00:26:38.750
+zhelianl@andrew.cmu.edu: And I can share you a more detailed one.
+
+268
+00:26:39.290 --> 00:26:39.630
+Harsha Tummala: Yeah.
+
+269
+00:26:39.630 --> 00:26:44.150
+zhelianl@andrew.cmu.edu: Yeah, with just some code explan… planning.
+
+270
+00:26:45.360 --> 00:26:47.070
+Harsha Tummala: Yeah, that sounds good, yeah.
+
+271
+00:26:47.360 --> 00:26:53.430
+Harsha Tummala: And yeah, that's pretty much it. Other than that,
+
+272
+00:26:53.840 --> 00:26:57.730
+Harsha Tummala: I can't think of anything else from my own. Any other questions for you guys?
+
+273
+00:27:01.390 --> 00:27:03.180
+hrishikb@andrew.cmu.edu: Nothing relevant right now for me.
+
+274
+00:27:05.870 --> 00:27:10.200
+Harsha Tummala: If anyone's good, then… I'm good. Long meeting, guys.
+
+275
+00:27:10.780 --> 00:27:13.890
+Harsha Tummala: It's pretty much everything that's said.
+
+276
+00:27:14.620 --> 00:27:18.189
+Harsha Tummala: good extra long weekend for the event. Right, yeah. Yeah.
+
+277
+00:27:18.530 --> 00:27:24.750
+Harsha Tummala: Good boy. That's it. See you at another, and have a long… good long weekend, Cliff.
+
+278
+00:27:25.370 --> 00:27:25.970
+Clifford Huff: Same to you.
+
+279
+00:27:25.970 --> 00:27:28.400
+Harsha Tummala: We'll enjoy your holiday.
+
+280
+00:27:28.750 --> 00:27:31.769
+Harsha Tummala: I like the shirt clip, I didn't even see that until…
+
+281
+00:27:31.770 --> 00:27:32.450
+Clifford Huff: Yeah.
+
+282
+00:27:32.750 --> 00:27:38.419
+Clifford Huff: This is a shirt I got from Fort McHenra. I even got to raise the flag at the fort, that was pretty special.
+
+283
+00:27:38.890 --> 00:27:40.450
+Harsha Tummala: Whoa, that's awesome.
+
+284
+00:27:44.070 --> 00:27:47.930
+Harsha Tummala: See you guys for that. See you. Have a good weekend.
+
+285
+00:27:47.930 --> 00:27:49.230
+hrishikb@andrew.cmu.edu: You guys, bye-bye.
+
diff --git a/transcripts/GMT20260709-190533_Recording.transcript.vtt b/transcripts/GMT20260709-190533_Recording.transcript.vtt
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@@ -0,0 +1,918 @@
+WEBVTT
+
+1
+00:00:03.530 --> 00:00:10.539
+hrishikb@andrew.cmu.edu: So, primarily, last… Thank you, thank you, thank you.
+
+2
+00:00:13.330 --> 00:00:15.080
+hrishikb@andrew.cmu.edu: Okay, so…
+
+3
+00:00:15.720 --> 00:00:22.869
+hrishikb@andrew.cmu.edu: This week, primarily, from the OCR, in addition part of things, we were starting on integration. We have…
+
+4
+00:00:25.590 --> 00:00:27.779
+hrishikb@andrew.cmu.edu: Okay. We have…
+
+5
+00:00:28.700 --> 00:00:40.220
+hrishikb@andrew.cmu.edu: we have started the integration part, and it is coming on along pretty good. Right now, we are able to ingest long PDFs, and we are getting a good, around 95%
+
+6
+00:00:40.550 --> 00:00:48.989
+hrishikb@andrew.cmu.edu: accuracy with, like, on getting all the attributes out, but I recently, I just, I saw a bug because
+
+7
+00:00:49.430 --> 00:00:53.229
+hrishikb@andrew.cmu.edu: When we're trying to OCR some of the PDFs,
+
+8
+00:00:53.750 --> 00:01:06.309
+hrishikb@andrew.cmu.edu: like, there's a difference between doing an OCR and looking at the underlying embedded text. So, in some cases, the OCR has given us a better output, and in some cases, the internal embedded
+
+9
+00:01:06.640 --> 00:01:07.850
+hrishikb@andrew.cmu.edu: And…
+
+10
+00:01:08.480 --> 00:01:19.570
+hrishikb@andrew.cmu.edu: like alphabets or letters are giving some better output so that part is something that we have to fix currently in the OCR part and after that we are planning to
+
+11
+00:01:20.060 --> 00:01:26.170
+hrishikb@andrew.cmu.edu: align the schema that Liu has for the ML pipeline. We have the inputs ready for it.
+
+12
+00:01:26.380 --> 00:01:29.549
+hrishikb@andrew.cmu.edu: And we should be proceeding with that in the coming week.
+
+13
+00:01:30.340 --> 00:01:37.700
+hrishikb@andrew.cmu.edu: I can maybe, like, there's not a demo as such, but I can quickly show you the last test run which I did.
+
+14
+00:01:38.120 --> 00:01:41.780
+hrishikb@andrew.cmu.edu: Maybe it'll give you a bit more time on things.
+
+15
+00:01:41.930 --> 00:01:43.540
+hrishikb@andrew.cmu.edu: Just give me a second.
+
+16
+00:01:49.770 --> 00:01:50.890
+hrishikb@andrew.cmu.edu: As long as it's okay
+
+17
+00:01:56.320 --> 00:02:01.020
+hrishikb@andrew.cmu.edu: It's right. So let me share my screen.
+
+18
+00:02:20.350 --> 00:02:24.750
+hrishikb@andrew.cmu.edu: So this is one of the initial PDFs that we have had.
+
+19
+00:02:25.550 --> 00:02:29.989
+hrishikb@andrew.cmu.edu: I was using this one, like, I just did a test on this one.
+
+20
+00:02:30.110 --> 00:02:39.570
+hrishikb@andrew.cmu.edu: So it has a lot of stuff which is not really relevant, and the primary relevancy comes from the, like, spec doc, which is towards the bottom of it.
+
+21
+00:02:40.230 --> 00:02:40.820
+Harsha Tummala: Mmhm.
+
+22
+00:02:40.820 --> 00:02:45.460
+hrishikb@andrew.cmu.edu: So we have two products, AccuHMOA, and there's one more, I think, OAW.
+
+23
+00:02:45.850 --> 00:02:51.320
+hrishikb@andrew.cmu.edu: They have similar, like, similar values.
+
+24
+00:02:51.830 --> 00:02:53.619
+hrishikb@andrew.cmu.edu: Attributes and values.
+
+25
+00:02:53.810 --> 00:02:56.899
+hrishikb@andrew.cmu.edu: So, when we run into the pipeline,
+
+26
+00:02:57.720 --> 00:03:04.469
+hrishikb@andrew.cmu.edu: Let me see if this is… oh, yeah. So this is a pipeline that would actually parse out what's relevant or what's not relevant, or do you have to manually
+
+27
+00:03:04.600 --> 00:03:06.889
+hrishikb@andrew.cmu.edu: No, it automatically doesn't.
+
+28
+00:03:08.190 --> 00:03:10.560
+JakeMonroe: I was about to ask that too.
+
+29
+00:03:10.720 --> 00:03:15.280
+JakeMonroe: So it'll ignore those first couple pages, then, of just, like, more of the instructions and stuff.
+
+30
+00:03:17.940 --> 00:03:26.029
+hrishikb@andrew.cmu.edu: I mean, ideally, the OCR part just puts an order value, and the MN model is supposed to finally pass it out, because that's the main…
+
+31
+00:03:26.450 --> 00:03:28.920
+hrishikb@andrew.cmu.edu: Part of this problem.
+
+32
+00:03:31.060 --> 00:03:34.229
+hrishikb@andrew.cmu.edu: And so where's the hell?
+
+33
+00:03:34.260 --> 00:03:39.739
+hrishikb@andrew.cmu.edu: a disconnect here. So, you're saying your current stuff right now does not… does or does not
+
+34
+00:03:39.740 --> 00:03:42.000
+hrishikb@andrew.cmu.edu: parts of that we'd have to wait to the Ml.
+
+35
+00:03:42.000 --> 00:04:06.530
+hrishikb@andrew.cmu.edu: It does parse everything in the document that we have. Right. And we're still using an Llm. 5.4. So it does have some amount of context. But the final passing is going to be done by the end. Okay, the the exact relevancy will be done with Ml. Part. We parse out all the entire document, and, like the like, most relevant stuff is there? Like there, there's no data which is missing. But the final refinement will be done by the Ml. Part of things.
+
+36
+00:04:07.420 --> 00:04:09.479
+hrishikb@andrew.cmu.edu: Okay, thanks for clarification.
+
+37
+00:04:09.600 --> 00:04:14.020
+hrishikb@andrew.cmu.edu: And this is the, basically, the JSON dump that we currently
+
+38
+00:04:14.160 --> 00:04:25.060
+hrishikb@andrew.cmu.edu: have… we need a few refinements, but, let's just see a few… This is the output from the parsing? Yeah, so we have the AQHMOA mounting configuration, pole mount.
+
+39
+00:04:25.240 --> 00:04:27.729
+hrishikb@andrew.cmu.edu: We have the values along with them.
+
+40
+00:04:28.110 --> 00:04:40.100
+hrishikb@andrew.cmu.edu: These are, I did a check, these are around 95% accuracy as of right now. There are a few minor issues that is, related to a few of the… I'll be showing in the document itself.
+
+41
+00:04:40.110 --> 00:04:59.050
+hrishikb@andrew.cmu.edu: So how do you measure? So provided was, I gave these documents to plot first. So then we make a goal set with plot like a table format, and then we run it through our past, and then we compare it, and then we get the editor.
+
+42
+00:04:59.110 --> 00:05:14.180
+hrishikb@andrew.cmu.edu: Yeah, I did the same thing. So, the problem I saw was in this part of things, because these have the OM symbol, and the OCR GPT-5 was reading it as QA, or Q2, in a few cases.
+
+43
+00:05:14.180 --> 00:05:30.689
+hrishikb@andrew.cmu.edu: But if we pass it through a, something like a PDF plumber, it is supposed to give us a better output in this specific scenario. So I think that is something we'll take a look at next in order to reduce that error percentage even lower. So if we go back to the extracted stuffs.
+
+44
+00:05:31.010 --> 00:05:33.259
+hrishikb@andrew.cmu.edu: What was it called?
+
+45
+00:05:34.080 --> 00:05:36.810
+hrishikb@andrew.cmu.edu: Thermostat accuracy.
+
+46
+00:05:39.400 --> 00:05:41.430
+hrishikb@andrew.cmu.edu: Where's books?
+
+47
+00:05:42.680 --> 00:05:43.970
+hrishikb@andrew.cmu.edu: What else?
+
+48
+00:05:45.340 --> 00:05:50.130
+hrishikb@andrew.cmu.edu: Yeah, so here we have a 10K Q2. It is supposed to be 10K ohm.
+
+49
+00:05:50.360 --> 00:05:55.149
+hrishikb@andrew.cmu.edu: And, like, again, 10KQ to solo 10K home, so this is a slight mismatch.
+
+50
+00:05:55.420 --> 00:06:01.200
+hrishikb@andrew.cmu.edu: But it is pretty minor, and we should be able to fix that and move ahead with it.
+
+51
+00:06:02.050 --> 00:06:15.530
+hrishikb@andrew.cmu.edu: because here we are losing information that demo part will never get. So I think, right now we have like a 2 personality, and most of it comes from like.
+
+52
+00:06:15.780 --> 00:06:16.800
+hrishikb@andrew.cmu.edu: Oh.
+
+53
+00:06:17.670 --> 00:06:28.290
+hrishikb@andrew.cmu.edu: like, symbols like ohms, and then even if there are images, I think sometimes GPT is kind of struggling, unless we use a union model, which is
+
+54
+00:06:28.470 --> 00:06:45.610
+hrishikb@andrew.cmu.edu: Gpt. 5.4 union Gpt. 5.4 vision. So when we take the union of both, we get a redo high school, which is less than one personality, and probably can fix this as well. But doing that would increase the costs overall, because
+
+55
+00:06:45.960 --> 00:06:49.269
+hrishikb@andrew.cmu.edu: We are sending it to the other employees.
+
+56
+00:06:49.690 --> 00:06:55.319
+hrishikb@andrew.cmu.edu: And then we take those, you know, the unions that are over with that. Well, the question is, what is the marginal cost?
+
+57
+00:06:55.890 --> 00:07:01.909
+hrishikb@andrew.cmu.edu: I mean, it's… it's gonna be a one-time cost of, like, $500 to $700. $500 if it's just
+
+58
+00:07:02.070 --> 00:07:11.969
+hrishikb@andrew.cmu.edu: doing it once for all the datasets, and, like, telling others if they're doing it with both the… But that's everything in the e-parts. Yeah, everything on the e-parts catalog, yeah.
+
+59
+00:07:12.230 --> 00:07:13.110
+hrishikb@andrew.cmu.edu: Okay.
+
+60
+00:07:16.220 --> 00:07:21.560
+hrishikb@andrew.cmu.edu: Yeah, so this is, currently what we're doing in the oceanation part.
+
+61
+00:07:21.800 --> 00:07:30.059
+hrishikb@andrew.cmu.edu: And yeah, I got a mail, Jake, with the upgrade PEM schema. I was going through it. I had a couple of questions around it.
+
+62
+00:07:30.290 --> 00:07:31.420
+hrishikb@andrew.cmu.edu: Okay.
+
+63
+00:07:32.090 --> 00:07:33.520
+hrishikb@andrew.cmu.edu: Let's see…
+
+64
+00:07:34.380 --> 00:07:47.080
+hrishikb@andrew.cmu.edu: So, in most of these, tables I'm seeing, you have… I think I'm assuming the code part is the ETEM code, and the standard is the standard it maps to. I think 2 maps to ETEM, and 3 is for E-Class.
+
+65
+00:07:47.190 --> 00:07:55.390
+hrishikb@andrew.cmu.edu: So, is there only, like, a particular row is supposed to have only one code? Like, not ETEM and ECLASS both?
+
+66
+00:07:55.740 --> 00:07:56.990
+hrishikb@andrew.cmu.edu: Is that the case?
+
+67
+00:07:56.990 --> 00:08:02.710
+JakeMonroe: Yeah, we'll have a different row with ID Standard 3 for when we eventually move to, E-Class.
+
+68
+00:08:04.420 --> 00:08:08.590
+JakeMonroe: And then same, like, ID standard,
+
+69
+00:08:08.590 --> 00:08:15.449
+hrishikb@andrew.cmu.edu: duplicated this thing, like, two circuit breakers and fuses, if it has, IHC2 and 1, we'll have 3, the same.
+
+70
+00:08:15.450 --> 00:08:36.369
+JakeMonroe: Yeah, we'll need some kind of way to link them together, probably another table for us to map alias or map different standards together. But right now, in terms of just storing these, is this the category? Yeah, just for storing the categories, we're just going to have one. There could be multiple circuit breaker and fuse rows in here, depending on the different…
+
+71
+00:08:36.549 --> 00:08:39.479
+JakeMonroe: Standards, and we'll have to map that together with another table.
+
+72
+00:08:39.940 --> 00:08:41.619
+hrishikb@andrew.cmu.edu: Okay, okay, okay.
+
+73
+00:08:43.100 --> 00:08:50.259
+hrishikb@andrew.cmu.edu: So initially, we were thinking about doing a bit of ETM part in the, before the ML portion, but, after…
+
+74
+00:08:50.630 --> 00:09:06.159
+hrishikb@andrew.cmu.edu: like, getting a bit further, that seems a bit difficult, so we need more information that we'll get after them part. So I think we'll, after we have a certain confidence course, we'll be using the schema you provided us, and kind of mapping all of it to that.
+
+75
+00:09:06.740 --> 00:09:11.970
+JakeMonroe: Great, thank you Yeah, and the reason for that, we just want to keep it flexible, too, for,
+
+76
+00:09:12.250 --> 00:09:29.090
+JakeMonroe: we could… I think standard 2… standard ID 2 just says ETIM, but really it should say, ETIM 10.0, and then when we get ETIM 12.0 a couple years from now, that can be a whole separate set of rows, or we can keep expanding. Otherwise, we'd have to have, you know, like, a column for each new standard we add in.
+
+77
+00:09:29.320 --> 00:09:30.100
+hrishikb@andrew.cmu.edu: Right, right.
+
+78
+00:09:30.100 --> 00:09:30.940
+JakeMonroe: Yeah, yeah.
+
+79
+00:09:32.200 --> 00:09:32.860
+hrishikb@andrew.cmu.edu: Okay.
+
+80
+00:09:35.340 --> 00:09:40.860
+hrishikb@andrew.cmu.edu: Okay, yeah, that is, pretty much from the OCR part of things.
+
+81
+00:09:41.960 --> 00:09:45.999
+hrishikb@andrew.cmu.edu: I can show my workplace. Yeah.
+
+82
+00:09:48.060 --> 00:09:50.410
+hrishikb@andrew.cmu.edu: You're, I'm, I'm already moved.
+
+83
+00:09:54.270 --> 00:09:54.960
+hrishikb@andrew.cmu.edu: Okay.
+
+84
+00:09:58.030 --> 00:09:59.530
+hrishikb@andrew.cmu.edu: Oh, that's excellent.
+
+85
+00:10:01.990 --> 00:10:05.060
+hrishikb@andrew.cmu.edu: You can speak up. Yeah, am I audible, Jake?
+
+86
+00:10:06.620 --> 00:10:07.450
+hrishikb@andrew.cmu.edu: You… yeah.
+
+87
+00:10:07.450 --> 00:10:08.240
+Harsha Tummala: Thank you so much.
+
+88
+00:10:09.310 --> 00:10:13.459
+hrishikb@andrew.cmu.edu: Okay, so let me just share my screen.
+
+89
+00:10:14.010 --> 00:10:17.010
+hrishikb@andrew.cmu.edu: Thomas, let's see.
+
+90
+00:10:18.230 --> 00:10:20.150
+hrishikb@andrew.cmu.edu: That is the one second.
+
+91
+00:10:31.120 --> 00:10:37.719
+hrishikb@andrew.cmu.edu: So who's in Puerto Rico? Is this a fun thing in Puerto Rico? Someone with knee parts is in Puerto Ric.
+
+92
+00:10:38.230 --> 00:10:50.880
+JakeMonroe: Yeah, I… My wife's Puerto Rican, so I'm down here probably twice a year. This is… this is our usual summer… we do… we take a trip in the summer, usually, and around the holidays, too.
+
+93
+00:10:51.520 --> 00:10:52.920
+hrishikb@andrew.cmu.edu: So what city are you in?
+
+94
+00:10:53.460 --> 00:11:00.419
+JakeMonroe: She's in Guaynabo, which is a, it's a suburb of San Juan, but yeah, San Juan, the,
+
+95
+00:11:00.560 --> 00:11:01.490
+JakeMonroe: Capital.
+
+96
+00:11:02.460 --> 00:11:04.659
+hrishikb@andrew.cmu.edu: Very beautiful. I've been there once. Very nice.
+
+97
+00:11:05.410 --> 00:11:07.109
+JakeMonroe: Yeah, I love it down here.
+
+98
+00:11:07.260 --> 00:11:09.719
+JakeMonroe: Well, it's a little a little humid.
+
+99
+00:11:10.120 --> 00:11:11.070
+hrishikb@andrew.cmu.edu: Thank you.
+
+100
+00:11:13.060 --> 00:11:14.070
+hrishikb@andrew.cmu.edu: Oh.
+
+101
+00:11:14.380 --> 00:11:15.090
+hrishikb@andrew.cmu.edu: Hello?
+
+102
+00:11:15.210 --> 00:11:20.009
+hrishikb@andrew.cmu.edu: Oh, okay. So… I've been working on,
+
+103
+00:11:20.360 --> 00:11:23.589
+hrishikb@andrew.cmu.edu: the ETMS class matching, so currently.
+
+104
+00:11:23.820 --> 00:11:27.099
+hrishikb@andrew.cmu.edu: It is done. I still have to…
+
+105
+00:11:27.520 --> 00:11:30.710
+hrishikb@andrew.cmu.edu: Get, do some revisions on this.
+
+106
+00:11:30.860 --> 00:11:35.880
+hrishikb@andrew.cmu.edu: I have tested it, and it's currently at around 92%.
+
+107
+00:11:36.200 --> 00:11:38.109
+hrishikb@andrew.cmu.edu: The…
+
+108
+00:11:38.990 --> 00:11:49.130
+hrishikb@andrew.cmu.edu: There are some errors in this one. And for example, like for this product type name and ETM class names, most of them match up well, but
+
+109
+00:11:50.040 --> 00:11:51.079
+hrishikb@andrew.cmu.edu: Oh, okay.
+
+110
+00:11:51.480 --> 00:12:01.190
+hrishikb@andrew.cmu.edu: For some reason, when using the current rules, there are two components. First, it tries to see if the words match up.
+
+111
+00:12:01.190 --> 00:12:12.349
+hrishikb@andrew.cmu.edu: Word by word, and if… if there… there is, something for that, then it tries to use that. And if not, it can call up, 5.4, using the Azure, token that I have.
+
+112
+00:12:12.930 --> 00:12:17.469
+hrishikb@andrew.cmu.edu: But it's, it's still currently, I think, not,
+
+113
+00:12:18.030 --> 00:12:33.480
+hrishikb@andrew.cmu.edu: Good enough, especially if you need to rerun it. If it, it was only very infrequent, then I think, the current approach should be enough, as the mistakes can be corrected, that they're pretty easy to spot. But if this is done again and again, then that's a little bit different.
+
+114
+00:12:33.750 --> 00:12:40.979
+hrishikb@andrew.cmu.edu: So I'm still working on this and also the features that each of them have.
+
+115
+00:12:41.250 --> 00:12:46.069
+hrishikb@andrew.cmu.edu: And I think I'll have a much more comprehensive,
+
+116
+00:12:46.710 --> 00:12:54.150
+hrishikb@andrew.cmu.edu: I think Jake has sent an integrated schema, like, with PIMS, which has all these already mapped. Oh.
+
+117
+00:12:54.150 --> 00:13:07.020
+JakeMonroe: No, no. So I don't have it mapped to the existing category. So this is great. But I do just have like a category table which has the ETIM category. But in terms of mapping it to the existing ALP ones, I haven't done any of that yet.
+
+118
+00:13:07.540 --> 00:13:13.090
+hrishikb@andrew.cmu.edu: Okay, okay, that's that's very good, then, because I would have just used that.
+
+119
+00:13:13.320 --> 00:13:14.010
+JakeMonroe: Okay.
+
+120
+00:13:14.430 --> 00:13:16.010
+hrishikb@andrew.cmu.edu: Yeah.
+
+121
+00:13:16.470 --> 00:13:23.289
+hrishikb@andrew.cmu.edu: I'll have much more to show on the, features, I think in a few days, but yeah, currently,
+
+122
+00:13:23.740 --> 00:13:34.100
+hrishikb@andrew.cmu.edu: the, the classes and the… it is getting the… 92% of the time, it is getting the correct, class name for the product, names that are right now, and…
+
+123
+00:13:34.430 --> 00:13:40.350
+hrishikb@andrew.cmu.edu: It does have, I don't think it's showing, but there are alternatives.
+
+124
+00:13:40.600 --> 00:13:46.850
+hrishikb@andrew.cmu.edu: for each of them. Some of them have no alternatives. But there is a little bit of
+
+125
+00:13:47.630 --> 00:13:54.040
+hrishikb@andrew.cmu.edu: I would say it's a hundred, it's not a hundred percent sure of what… which one…
+
+126
+00:13:54.190 --> 00:13:58.819
+hrishikb@andrew.cmu.edu: It's definitely the same one, so I'll work on that.
+
+127
+00:13:59.360 --> 00:14:07.930
+hrishikb@andrew.cmu.edu: Yeah, this is pretty much complete. How many roles in this take roles?
+
+128
+00:14:09.120 --> 00:14:13.620
+hrishikb@andrew.cmu.edu: Currently, it's 38 382 rooms, that's all.
+
+129
+00:14:14.260 --> 00:14:20.930
+hrishikb@andrew.cmu.edu: For the ETM standard. That's it. No, the ETM standard is much larger than that. Yes. But what.
+
+130
+00:14:21.100 --> 00:14:25.839
+hrishikb@andrew.cmu.edu: What we have, what, what the number that was available.
+
+131
+00:14:25.860 --> 00:14:43.619
+hrishikb@andrew.cmu.edu: is, is, that it's being matched, yeah, the ETM standard is actually being… There's multiple, there's ETM groups, classes, or attributes, so there are multiple, like, there is ETM values for all of these, but there are different, like, different types of, like, it could be a, it could be a value, it could be a class name, so these are different number of…
+
+132
+00:14:43.620 --> 00:14:45.880
+hrishikb@andrew.cmu.edu: And not all of them are specialists.
+
+133
+00:14:47.800 --> 00:15:00.370
+JakeMonroe: And I think, which you'll see in, in, in what I, in what I sent over earlier, the groupings, I just made, I made groups a category in addition to,
+
+134
+00:15:00.990 --> 00:15:02.870
+JakeMonroe: Not feature.
+
+135
+00:15:03.250 --> 00:15:18.140
+JakeMonroe: what's class, a category as well. And I just made, essentially, class a, a child category that had, a parent category of what I translated the groups to. So I did cram groups and classes both into the same category, schema.
+
+136
+00:15:18.600 --> 00:15:19.380
+hrishikb@andrew.cmu.edu: Okay.
+
+137
+00:15:21.170 --> 00:15:21.950
+hrishikb@andrew.cmu.edu: Okay.
+
+138
+00:15:24.370 --> 00:15:28.999
+hrishikb@andrew.cmu.edu: I think we can take a look at that and come back to you with questions.
+
+139
+00:15:29.920 --> 00:15:36.549
+hrishikb@andrew.cmu.edu: It's a little different on my side, but yeah, that guy, I think I can also account for that.
+
+140
+00:15:42.450 --> 00:15:47.399
+hrishikb@andrew.cmu.edu: Okay, so this one in co-pilot or something in azure. How did how did you do this?
+
+141
+00:15:47.800 --> 00:15:53.010
+hrishikb@andrew.cmu.edu: First, there's a rule space word by word matching. Okay. And then…
+
+142
+00:15:53.200 --> 00:15:56.990
+hrishikb@andrew.cmu.edu: When that was not, that was not working. Yeah.
+
+143
+00:15:57.150 --> 00:16:07.760
+hrishikb@andrew.cmu.edu: I just used the Azure Foundry token for ChatGPT 5.4 to do the matches for the rest. And the verification was done just using
+
+144
+00:16:08.530 --> 00:16:15.400
+hrishikb@andrew.cmu.edu: There's there's a worldly set of things that was already available that the
+
+145
+00:16:15.730 --> 00:16:22.900
+hrishikb@andrew.cmu.edu: It already had… it did not have for all of them, but it did have a large enough number that I could compare against, so it was around 92%.
+
+146
+00:16:24.000 --> 00:16:29.360
+hrishikb@andrew.cmu.edu: That that's, I've not checked each of them. Yeah, we'll figure those points there. Okay.
+
+147
+00:16:35.060 --> 00:16:57.560
+hrishikb@andrew.cmu.edu: So on the ML side, Leo has been working primarily on, like, just setting up the fundamental, like, and I don't want to go over the whole milestone plan that we had laid out. So, I started building on top of it, since Monday, so I picked up three tasks. So one is, I set up the load testing for our, prediction service.
+
+148
+00:16:57.560 --> 00:17:18.479
+hrishikb@andrew.cmu.edu: so up until now we would only like we only measured it like one request at a time so we actually had no idea if it holds up under the real traffic right so I kind of build a harness to you know to just check if it keeps up with the concurrent request and also like check whether it keeps up with the
+
+149
+00:17:18.480 --> 00:17:20.560
+hrishikb@andrew.cmu.edu: like, a 50,
+
+150
+00:17:20.569 --> 00:17:27.270
+hrishikb@andrew.cmu.edu: Queries, like, per second, while staying under the 200 millisecond target latency that we had initially planned on.
+
+151
+00:17:27.270 --> 00:17:50.219
+hrishikb@andrew.cmu.edu: so that is kind of validated in the load testing part my PR are up we have not yet merged it because simultaneously I've also worked on like setting up a CI on the repo so as per our QA plan and the testing plan which is all like in the air and theoretical we kind of have like more than two
+
+152
+00:17:50.220 --> 00:17:52.069
+hrishikb@andrew.cmu.edu: I think 250 test cases.
+
+153
+00:17:52.070 --> 00:17:57.010
+hrishikb@andrew.cmu.edu: But, nothing till date was actually running on any of the pull requests, so…
+
+154
+00:17:57.010 --> 00:18:14.399
+hrishikb@andrew.cmu.edu: the build column, if you see, once the PRs get merged, were, like, mostly empty. So, like, from now onwards, like, every PR that runs, that gets merged would run the whole test suite, plus the linting and the type checks automatically. So, from now onwards, probably we'll have a better,
+
+155
+00:18:14.400 --> 00:18:23.989
+hrishikb@andrew.cmu.edu: like, defect catching strategy before the actual stuff gets merged. So that was about the load testing and the CI that I set up on the repo.
+
+156
+00:18:23.990 --> 00:18:36.439
+hrishikb@andrew.cmu.edu: And also, like, one important thing that I also took up was I… we ran our evaluation with the rule engine, turned on. So, like, I… I mean, I looked up,
+
+157
+00:18:36.440 --> 00:18:59.520
+hrishikb@andrew.cmu.edu: I looked at the numbers actually the old eval numbers were like bad basically by bad what I mean is basically like there was like zero autopressing but that's because it I I believe that's because it was only running the semantic match or half of the pipeline like which was kind of capping the
+
+158
+00:18:59.520 --> 00:19:17.519
+hrishikb@andrew.cmu.edu: confidence, confidence numbers artificially. So, the numbers were kind of misleading, so I kind of wired the, rules back in and re-ran the whole, evaluation pipeline. So, now we kind of have, like, realistic accuracy. I mean, I've…
+
+159
+00:19:17.530 --> 00:19:40.390
+hrishikb@andrew.cmu.edu: I mean, the numbers are low because I'm not yet run on the proper data. But I think by end of tomorrow, we'll have realistic numbers and we'll have more accurate numbers and also the auto process numbers for the entire pipeline end to end. And also since the OCR and the ingestion is also taking some shape, so we were planning by this.
+
+160
+00:19:40.390 --> 00:19:45.240
+hrishikb@andrew.cmu.edu: end of the week, maybe, like, if things go smoothly.
+
+161
+00:19:45.240 --> 00:20:00.920
+hrishikb@andrew.cmu.edu: whatever we have on the ML side as of today, given, plus the OCR and the ingestion work, we might, do a, like, a run with the actual data and see how,
+
+162
+00:20:00.920 --> 00:20:12.780
+hrishikb@andrew.cmu.edu: how that works, because right now, Arjun and Rishi, they also got a good idea about the… how… what is the input expectation of the ML pipeline, so I think they're gonna… they are working on it.
+
+163
+00:20:12.780 --> 00:20:26.780
+hrishikb@andrew.cmu.edu: So I think by end of this week, we'll have, I mean, we are not expecting a smooth pipeline setup, but at least we'll have like pretty good number of defects and probably we can work on the entire three modules as of date, yeah.
+
+164
+00:20:27.310 --> 00:20:29.150
+hrishikb@andrew.cmu.edu: So we talked about.
+
+165
+00:20:29.570 --> 00:20:33.900
+hrishikb@andrew.cmu.edu: Subset of real name, or all of them? A subset of the real name.
+
+166
+00:20:36.330 --> 00:20:37.880
+hrishikb@andrew.cmu.edu: albeit subset.
+
+167
+00:20:38.080 --> 00:20:38.880
+hrishikb@andrew.cmu.edu: Don't worry.
+
+168
+00:20:40.840 --> 00:20:58.379
+hrishikb@andrew.cmu.edu: No, I think Liu will have better… like, I think since he had, like, good understanding of the data, like, maybe he can tell us, like, how much amount of data we can use. Because, like he said, we have a problem with our validation data, right? So…
+
+169
+00:20:58.550 --> 00:21:03.199
+hrishikb@andrew.cmu.edu: I don't know how we will work through that.
+
+170
+00:21:03.840 --> 00:21:11.530
+hrishikb@andrew.cmu.edu: Yes, so… I think it's a good idea to use real data to test an ML model, because
+
+171
+00:21:11.890 --> 00:21:19.869
+hrishikb@andrew.cmu.edu: and… So, the model is trained by the
+
+172
+00:21:20.110 --> 00:21:29.379
+hrishikb@andrew.cmu.edu: description or the data they give us. So I use the fabricated data to test the model.
+
+173
+00:21:29.510 --> 00:21:33.399
+hrishikb@andrew.cmu.edu: the the result could be plausible. And also.
+
+174
+00:21:33.510 --> 00:21:39.719
+hrishikb@andrew.cmu.edu: in their fails, many products, their attributes are
+
+175
+00:21:40.020 --> 00:21:45.420
+hrishikb@andrew.cmu.edu: only have one or 2 samples. So the data is Oh.
+
+176
+00:21:45.840 --> 00:21:53.300
+hrishikb@andrew.cmu.edu: very, very extremely like sparsely distribution data. So we have.
+
+177
+00:21:53.550 --> 00:21:57.620
+hrishikb@andrew.cmu.edu: so maybe that's one reason.
+
+178
+00:21:57.970 --> 00:22:06.170
+hrishikb@andrew.cmu.edu: Why, the user data to test the model, the result would be happy and not so good as it.
+
+179
+00:22:06.300 --> 00:22:14.979
+hrishikb@andrew.cmu.edu: should be shown before. Yeah, no doubt that. Yes, the the real question is, how much of the real data are you planning on using?
+
+180
+00:22:15.450 --> 00:22:20.400
+hrishikb@andrew.cmu.edu: like, I… use, like, 30 million rolls.
+
+181
+00:22:21.290 --> 00:22:22.510
+hrishikb@andrew.cmu.edu: 30 million.
+
+182
+00:22:23.350 --> 00:22:25.810
+hrishikb@andrew.cmu.edu: Yeah, in my, in my bank, right?
+
+183
+00:22:30.340 --> 00:22:37.310
+hrishikb@andrew.cmu.edu: I think that's what we have used already. Like, we're asking about the new data that we will need. Yeah.
+
+184
+00:22:37.710 --> 00:22:39.749
+hrishikb@andrew.cmu.edu: For testing, how much will we need?
+
+185
+00:22:40.830 --> 00:22:43.100
+hrishikb@andrew.cmu.edu: Real data.
+
+186
+00:22:43.680 --> 00:22:47.399
+hrishikb@andrew.cmu.edu: You mean the data to validate the model.
+
+187
+00:22:48.150 --> 00:22:51.600
+hrishikb@andrew.cmu.edu: like, only… Perfect.
+
+188
+00:22:54.730 --> 00:22:56.879
+hrishikb@andrew.cmu.edu: Tens… tens of thousands.
+
+189
+00:22:57.120 --> 00:22:58.840
+hrishikb@andrew.cmu.edu: 10,000. Okay. Yeah, yeah.
+
+190
+00:23:00.210 --> 00:23:02.319
+hrishikb@andrew.cmu.edu: Like, maybe you subtitle.
+
+191
+00:23:02.580 --> 00:23:03.570
+hrishikb@andrew.cmu.edu: Subject.
+
+192
+00:23:03.880 --> 00:23:05.790
+hrishikb@andrew.cmu.edu: Those. 30,000.
+
+193
+00:23:07.290 --> 00:23:09.239
+hrishikb@andrew.cmu.edu: Close, okay.
+
+194
+00:23:09.440 --> 00:23:11.069
+hrishikb@andrew.cmu.edu: Yeah, but that's not that much.
+
+195
+00:23:17.330 --> 00:23:23.749
+hrishikb@andrew.cmu.edu: Oh… Okay, I think, from the update's point of view, that was…
+
+196
+00:23:24.150 --> 00:23:31.860
+hrishikb@andrew.cmu.edu: pretty much all we have right now. We will try to, finalize the post-generation and link it with
+
+197
+00:23:32.130 --> 00:23:49.280
+hrishikb@andrew.cmu.edu: the ML part by the end of next week, but that is slightly optimistic, but yeah, we'll try to do the best, and we'll try to have… try to deliver some sort of an MVP to you guys as a proper demo by the end of the month. So, like, after fixing all the minor bugs that we have currently.
+
+198
+00:23:49.760 --> 00:23:54.909
+hrishikb@andrew.cmu.edu: So, at that time, there should be a… Like, decently working.
+
+199
+00:23:55.030 --> 00:23:59.120
+hrishikb@andrew.cmu.edu: at least those generation and some sort of Ml values will be getting from the data.
+
+200
+00:24:02.950 --> 00:24:03.950
+Harsha Tummala: Yeah, intense.
+
+201
+00:24:06.920 --> 00:24:07.620
+JakeMonroe: Great.
+
+202
+00:24:09.200 --> 00:24:11.610
+hrishikb@andrew.cmu.edu: Yeah, it's all from our side. Anything for you guys.
+
+203
+00:24:13.140 --> 00:24:24.930
+Harsha Tummala: I mean, no, no more questions for me. That's pretty much it. I think I was waiting for like a document from Liu about the technical implementation of the Msn.
+
+204
+00:24:25.100 --> 00:24:27.550
+Harsha Tummala: I don't know who to send it to us, just checking.
+
+205
+00:24:27.830 --> 00:24:31.459
+hrishikb@andrew.cmu.edu: Okay, I forgot it. I have to send it, after the meeting.
+
+206
+00:24:34.230 --> 00:24:39.499
+hrishikb@andrew.cmu.edu: Yeah, Liu forgot to send the document. He's saying he's… he'll send after the meeting. Yeah, yeah, I.
+
+207
+00:24:41.010 --> 00:24:57.839
+Harsha Tummala: Yeah, that's, that's pretty much all the questions I had. And with respect to like just getting the pipeline ready, I would also say like it would help, if you guys, like just check with the timeline of like events in the next two, three weeks, because I know end of semester are coming up.
+
+208
+00:24:58.230 --> 00:25:02.899
+Harsha Tummala: And yeah, just, just, just, just making sure that
+
+209
+00:25:03.560 --> 00:25:07.880
+Harsha Tummala: We're all, like, aware, and, like, we're, like, accommodating to all that.
+
+210
+00:25:08.940 --> 00:25:09.830
+hrishikb@andrew.cmu.edu: Yep.
+
+211
+00:25:10.130 --> 00:25:15.110
+hrishikb@andrew.cmu.edu: we should be able to accommodate this along with the NSAMs.
+
+212
+00:25:15.570 --> 00:25:16.220
+Harsha Tummala: Makes sense.
+
+213
+00:25:17.810 --> 00:25:20.760
+hrishikb@andrew.cmu.edu: So, Jake, when do you come back to Pittsburgh?
+
+214
+00:25:21.460 --> 00:25:28.920
+JakeMonroe: I'll be back Wednesday, I guess Tuesday night, but like Wednesday, technically at like whatever, 12:30 AM, something like that, late flight in.
+
+215
+00:25:30.680 --> 00:25:33.899
+hrishikb@andrew.cmu.edu: Is that a direct flight, or do you have to go somewhere else?
+
+216
+00:25:33.900 --> 00:25:34.969
+JakeMonroe: I wish…
+
+217
+00:25:35.150 --> 00:25:35.750
+hrishikb@andrew.cmu.edu: That's always.
+
+218
+00:25:35.750 --> 00:25:36.840
+JakeMonroe: It's always a connection.
+
+219
+00:25:36.840 --> 00:25:37.340
+Harsha Tummala: So…
+
+220
+00:25:37.610 --> 00:25:46.429
+JakeMonroe: Always a connection. Past couple times, it's been easier to just drive to DC and fly out of DC rather than deal with a tight connection and all that. It's, yeah.
+
+221
+00:25:47.290 --> 00:25:51.509
+Harsha Tummala: Apparently, Pittsburgh Airport is getting more international flights next year around the world.
+
+222
+00:25:52.340 --> 00:25:54.269
+Harsha Tummala: Based on words, I think.
+
+223
+00:25:54.830 --> 00:25:55.420
+JakeMonroe: Think so.
+
+224
+00:25:55.420 --> 00:25:56.550
+Harsha Tummala: I have to go to the bas.
+
+225
+00:25:57.500 --> 00:26:00.050
+hrishikb@andrew.cmu.edu: Well, enjoy the rest of your time in Puerto Rico.
+
+226
+00:26:00.050 --> 00:26:01.499
+JakeMonroe: Thank you. Thank you.
+
+227
+00:26:01.990 --> 00:26:02.640
+Harsha Tummala: And.
+
+228
+00:26:03.040 --> 00:26:04.899
+Harsha Tummala: See you guys, bye bye, have a.
+
+229
+00:26:04.900 --> 00:26:05.910
+JakeMonroe: Do you… do you have.
+
diff --git a/transcripts/GMT20260716-190119_Recording.transcript.vtt b/transcripts/GMT20260716-190119_Recording.transcript.vtt
new file mode 100644
index 0000000..cebab29
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@@ -0,0 +1,870 @@
+WEBVTT
+
+1
+00:00:02.240 --> 00:00:09.369
+Ashritha: So, like Rishi mentioned, we are just working on the integration part of it, like,
+
+2
+00:00:09.440 --> 00:00:31.459
+Ashritha: So basically, I am simultaneously… like, we found a few bugs on the ML side, so I am resolving those, and we have, like, few other tasks that are open. We'll give you updates on that pretty soon. And then, so the team is quite busy with the integration part, like Rishi and Arjun are busy wrapping up the…
+
+3
+00:00:31.610 --> 00:00:40.409
+Ashritha: OCR and ingestion pipeline, and Jay is involved with the ETM, side of it. So…
+
+4
+00:00:40.410 --> 00:00:59.509
+Ashritha: I mean, yeah, so individually, we can go ahead and, like, give our own updates, I guess. So, I mean, starting, maybe, like, Jay can start with, what he did, around ETM, and then if he needs any help from Jake or someone, he can ask you guys. So, Jay, you wanna go first?
+
+5
+00:01:00.300 --> 00:01:03.449
+jaivards@andrew.cmu.edu: Sure, sure. You guys can hear me, right?
+
+6
+00:01:04.220 --> 00:01:06.489
+Harsha Tummala: We can hear you, but it's still quiet.
+
+7
+00:01:08.230 --> 00:01:11.130
+jaivards@andrew.cmu.edu: Okay. Sorry, my laptop's,
+
+8
+00:01:11.390 --> 00:01:14.460
+jaivards@andrew.cmu.edu: a bit finicky about its microphone.
+
+9
+00:01:14.910 --> 00:01:18.180
+jaivards@andrew.cmu.edu: Hopefully it's all right right now.
+
+10
+00:01:20.300 --> 00:01:23.130
+jaivards@andrew.cmu.edu: You know.
+
+11
+00:01:23.630 --> 00:01:28.809
+jaivards@andrew.cmu.edu: So… The class matchings were done, and as for the attributes one,
+
+12
+00:01:28.930 --> 00:01:36.479
+jaivards@andrew.cmu.edu: That's also, mostly, it is also done, so it is matching properly,
+
+13
+00:01:36.640 --> 00:01:45.180
+jaivards@andrew.cmu.edu: each of the products, types has their own attribute names. So, for example, access doors have height, material, type, width.
+
+14
+00:01:45.380 --> 00:01:47.340
+jaivards@andrew.cmu.edu: And it does that for all of them.
+
+15
+00:01:47.930 --> 00:01:55.610
+jaivards@andrew.cmu.edu: So… Basically, for… For, product types?
+
+16
+00:01:56.010 --> 00:02:03.629
+jaivards@andrew.cmu.edu: And for features, that part is done. What I'm a bit more unsure about is the
+
+17
+00:02:05.080 --> 00:02:09.540
+jaivards@andrew.cmu.edu: ETM, basically value normalization, so…
+
+18
+00:02:10.690 --> 00:02:15.469
+jaivards@andrew.cmu.edu: There was an example such like this, so 4.7 kilo ohms and yeah.
+
+19
+00:02:15.630 --> 00:02:22.110
+jaivards@andrew.cmu.edu: It's written in different ways. So I'm working through it right now, and I'm able to get around.
+
+20
+00:02:22.340 --> 00:02:30.619
+jaivards@andrew.cmu.edu: This is, a bit, under than what I have gotten currently. It's more like 42%, 43%.
+
+21
+00:02:30.980 --> 00:02:39.950
+jaivards@andrew.cmu.edu: And out of, all those exam out of around 50,000 examples, 43% are work automatically.
+
+22
+00:02:40.070 --> 00:02:48.589
+jaivards@andrew.cmu.edu: And… They're pretty easy to normalize the values for, but the others I'm still having problems on.
+
+23
+00:02:48.870 --> 00:02:56.279
+jaivards@andrew.cmu.edu: And… It's it's not finished yet, so I can. I can. I can. I think I have.
+
+24
+00:02:56.970 --> 00:03:02.100
+jaivards@andrew.cmu.edu: that… Yeah, only around 18,000 out of,
+
+25
+00:03:02.480 --> 00:03:06.229
+jaivards@andrew.cmu.edu: 50,000 have been done, I would say.
+
+26
+00:03:06.920 --> 00:03:11.219
+jaivards@andrew.cmu.edu: Scenely, and then there's some more that have been done, but I'm not sure about.
+
+27
+00:03:11.710 --> 00:03:12.710
+jaivards@andrew.cmu.edu: So…
+
+28
+00:03:12.710 --> 00:03:29.790
+Harsha Tummala: Just from my understanding, you're talking about mapping like the existing ALPS values to ETIMS, like 43 of them were easy to, were able to map right away. And the other ones due to, yeah, different, like one saying KO as opposed to kilo or something like that is all spelled out. Okay.
+
+29
+00:03:29.790 --> 00:03:30.360
+jaivards@andrew.cmu.edu: Yes.
+
+30
+00:03:31.210 --> 00:03:35.749
+jaivards@andrew.cmu.edu: So the the class matching and feature. These 2 are done.
+
+31
+00:03:35.860 --> 00:03:41.130
+jaivards@andrew.cmu.edu: But the value and unit normalization I'm having a some trouble with.
+
+32
+00:03:41.350 --> 00:03:44.349
+jaivards@andrew.cmu.edu: But I'll keep working on it, and
+
+33
+00:03:44.650 --> 00:03:49.350
+jaivards@andrew.cmu.edu: I think I can get maybe not all of it, but
+
+34
+00:03:49.540 --> 00:03:54.040
+jaivards@andrew.cmu.edu: a much higher percentage than the 43% that I have right now.
+
+35
+00:03:54.960 --> 00:03:55.990
+jaivards@andrew.cmu.edu: And.
+
+36
+00:03:56.350 --> 00:04:05.980
+jaivards@andrew.cmu.edu: I think I'll, just message you, Jake, after I'm kind of, like, exhaust everything, because I'm not sure if I can, how high of a percentage I can get this one on.
+
+37
+00:04:06.400 --> 00:04:09.179
+Harsha Tummala: Yeah, no, for sure. I, I think,
+
+38
+00:04:09.570 --> 00:04:18.040
+Harsha Tummala: I'm already anticipating that there's definitely gonna be some things that just are gonna have to be mapped by, like, a human on the catalog team, yeah.
+
+39
+00:04:18.790 --> 00:04:25.960
+jaivards@andrew.cmu.edu: Yeah, because I was initially quite optimistic, because the class and feature matching had gone well.
+
+40
+00:04:26.460 --> 00:04:26.810
+Harsha Tummala: Yeah, hu.
+
+41
+00:04:26.810 --> 00:04:29.239
+jaivards@andrew.cmu.edu: One is much more finicky and.
+
+42
+00:04:30.300 --> 00:04:36.779
+jaivards@andrew.cmu.edu: if my initial efforts are only getting 43%, I don't know how I'm supposed to, like, push that to…
+
+43
+00:04:37.070 --> 00:04:43.999
+jaivards@andrew.cmu.edu: close to, you know, 100. So I'll just do everything possible and then kind of discuss with your teams, I guess.
+
+44
+00:04:44.440 --> 00:04:46.290
+Harsha Tummala: Awesome. Good stuff.
+
+45
+00:04:47.090 --> 00:04:54.190
+jaivards@andrew.cmu.edu: And yeah, I'm still working through it. There are a few more things I can do. I did not do them because I…
+
+46
+00:04:54.300 --> 00:04:59.240
+jaivards@andrew.cmu.edu: They're, they're, quite, I would say… Oh.
+
+47
+00:04:59.890 --> 00:05:03.820
+jaivards@andrew.cmu.edu: They're quite experiment intensive, so I'd have to, like.
+
+48
+00:05:04.400 --> 00:05:12.279
+jaivards@andrew.cmu.edu: We use a few different things and to see what works. But yeah, I've exhausted the easy methods and that, that's only yielding like 42%. Okay.
+
+49
+00:05:15.520 --> 00:05:16.190
+Harsha Tummala: 3.
+
+50
+00:05:17.650 --> 00:05:18.250
+Ashritha: Okay.
+
+51
+00:05:19.340 --> 00:05:20.010
+Ashritha: Yes.
+
+52
+00:05:20.010 --> 00:05:21.410
+jaivards@andrew.cmu.edu: That's it from my end, I.
+
+53
+00:05:21.610 --> 00:05:22.180
+Ashritha: Okay.
+
+54
+00:05:22.400 --> 00:05:34.150
+Ashritha: So, from my end, so this week I've been… I've been spending some time to bump up the test coverage for the repo, so that's, like, up to 85% right now.
+
+55
+00:05:34.150 --> 00:05:43.769
+Ashritha: And… but then, there's one thing, that, like… I mean, like, a significant issue, that I found out is
+
+56
+00:05:43.770 --> 00:05:46.720
+Ashritha: So we have our configuration settings.
+
+57
+00:05:46.720 --> 00:06:02.099
+Ashritha: set, right? So basically, the ones that we decided would control how the model updates itself from the reviewer feedback, and it would get, recalibrated. So, there's one question that I had, like, which of these could
+
+58
+00:06:02.430 --> 00:06:16.739
+Ashritha: like, you guys, like, the client safely, adjust them… adjust yourself. So, and then, which of them were, like, too risky? By adjusting, I mean the configuration settings. So, I…
+
+59
+00:06:16.740 --> 00:06:29.890
+Ashritha: kind of, like, figured out that, most of it should stay logged, and then, the one that should genuinely be adjusted is the calibration setting.
+
+60
+00:06:29.950 --> 00:06:34.159
+Ashritha: And, but it is also, like, very,
+
+61
+00:06:34.160 --> 00:06:58.319
+Ashritha: I mean not dangerous per se but then you should be like aware of the settings that you would edit and so probably I think that we should automate it instead of like hand editing so because even if like suppose a human comes and like tweaks that config file maybe there might be some issues so we could just like automate it
+
+62
+00:06:58.950 --> 00:07:04.629
+Ashritha: the calibration setting also. So, I… like, this is a very… I mean…
+
+63
+00:07:04.700 --> 00:07:13.040
+Ashritha: this is a small feature of the entire thing, but I had a lot of questions, like, this is one of… a question that I just… I'm just putting across.
+
+64
+00:07:13.040 --> 00:07:30.040
+Ashritha: So, I wrote… I wrote up a short doc with some open questions, so I might, send it to you, like, after this meeting, so that, Harsha and, like, you and, sorry, Jake and Harsha, like, you guys can give some feedback on that, and we can adjust our, config file, or…
+
+65
+00:07:30.040 --> 00:07:31.130
+Ashritha: Whatever, yeah.
+
+66
+00:07:33.870 --> 00:07:34.909
+Harsha Tummala: the basics.
+
+67
+00:07:35.050 --> 00:07:50.619
+Ashritha: But otherwise, I think, like I said, to just, like, from the MLOps side of it, just for the whole, pipeline, nothing related to the project, actually, but then, we have set up our CI pipelines.
+
+68
+00:07:50.620 --> 00:08:01.949
+Ashritha: They have been failing for some reason, so, that's something that we have to handle. That's something… that's what I'm working… that's what I've picked up for this week again.
+
+69
+00:08:01.950 --> 00:08:06.569
+Ashritha: Yeah, that's it from my side. Like, there were a few,
+
+70
+00:08:06.570 --> 00:08:30.110
+Ashritha: issues in the evaluation reports that I think, Liu… I'm not sure if Liu had shared with… shared those reports with you guys earlier, like, last month when I was not there, but all… like, all of our reports, like, the milestone reports are published on the repository itself. Like, if you guys are interested, I can point out to those reports, when I send out the meeting minutes, so…
+
+71
+00:08:30.120 --> 00:08:33.519
+Ashritha: So, there were, like, few issues over there, so if you guys can just…
+
+72
+00:08:33.539 --> 00:08:53.510
+Ashritha: spend some time over there and, like, point out some very obvious issues, or, like, maybe, like, we are contradicting ourselves. Like, I found out that, there was one issue with, like, the scoring part of it and the profiling part of it, so I corrected it, and now the report is up to date,
+
+73
+00:08:53.580 --> 00:08:58.420
+Ashritha: So, it was something to do with the encoder piece that we just committed. So…
+
+74
+00:08:58.420 --> 00:09:14.750
+Ashritha: like, I mean, we felt like, like, since you guys know the product better than us and the ML side of it, so I can give… I can point out that link also, you guys can review it and maybe send us some feedback over the next week, like, before we meet or something.
+
+75
+00:09:16.870 --> 00:09:22.160
+Harsha Tummala: make fun. Also send us the links. We look at the report card as well, and
+
+76
+00:09:22.440 --> 00:09:26.539
+Harsha Tummala: See what we can… what we can get.
+
+77
+00:09:26.540 --> 00:09:27.120
+Ashritha: Okay.
+
+78
+00:09:27.340 --> 00:09:34.079
+Harsha Tummala: And, yeah, that's… that sounds pretty good overall. Let us know all the… all the…
+
+79
+00:09:34.230 --> 00:09:39.839
+Harsha Tummala: all the configurations you're talking about as well, and if you can make a list of that in the document, that'll be helpful as well.
+
+80
+00:09:39.840 --> 00:09:40.460
+Ashritha: Okay.
+
+81
+00:09:40.700 --> 00:09:46.249
+Harsha Tummala: So that when they're answering the questions they're not getting lost on, how will this impact the system?
+
+82
+00:09:47.110 --> 00:09:48.640
+Ashritha: Okay, yeah, I'll send that.
+
+83
+00:09:50.210 --> 00:09:53.509
+Ashritha: Okay. Lee, do you wanna, like, update on your work?
+
+84
+00:09:54.080 --> 00:09:54.600
+Harsha Tummala: Yep.
+
+85
+00:09:56.770 --> 00:09:57.770
+zhelianl@andrew.cmu.edu: Oh.
+
+86
+00:09:58.120 --> 00:10:07.010
+zhelianl@andrew.cmu.edu: From my side, I… I have not many things to add, but I… For this week, I just…
+
+87
+00:10:07.190 --> 00:10:09.480
+zhelianl@andrew.cmu.edu: adjust some algorithms.
+
+88
+00:10:09.910 --> 00:10:12.580
+zhelianl@andrew.cmu.edu: like… to adjust the song.
+
+89
+00:10:12.780 --> 00:10:17.430
+zhelianl@andrew.cmu.edu: Combined value clusters outrank specific value clusters.
+
+90
+00:10:17.900 --> 00:10:22.910
+zhelianl@andrew.cmu.edu: like… for some products.
+
+91
+00:10:23.360 --> 00:10:27.260
+zhelianl@andrew.cmu.edu: They have some combined values, and okay.
+
+92
+00:10:28.180 --> 00:10:33.690
+zhelianl@andrew.cmu.edu: our… Can I share?
+
+93
+00:10:39.740 --> 00:10:41.169
+zhelianl@andrew.cmu.edu: Cassius, okay.
+
+94
+00:10:43.400 --> 00:10:46.439
+Ashritha: Oh, yeah, can you try now? I've given you the access.
+
+95
+00:10:47.090 --> 00:10:47.650
+zhelianl@andrew.cmu.edu: Okay.
+
+96
+00:10:51.620 --> 00:11:04.530
+zhelianl@andrew.cmu.edu: like, the… The ML system could choose, could choose the top three, Possible…
+
+97
+00:11:04.680 --> 00:11:14.110
+zhelianl@andrew.cmu.edu: match values towards the one product, but it can't choose which one could be the best.
+
+98
+00:11:14.550 --> 00:11:24.660
+zhelianl@andrew.cmu.edu: So I… try to find a method to solve these problems.
+
+99
+00:11:26.050 --> 00:11:35.679
+zhelianl@andrew.cmu.edu: be… So I chose one method named multiple negatives ranking loss.
+
+100
+00:11:36.260 --> 00:11:41.209
+zhelianl@andrew.cmu.edu: Since we… It's this phenomenon.
+
+101
+00:11:41.850 --> 00:11:51.760
+zhelianl@andrew.cmu.edu: emerge, because or the… the… Not… not… Like, less…
+
+102
+00:11:53.680 --> 00:11:57.039
+zhelianl@andrew.cmu.edu: It's kind of on the training.
+
+103
+00:11:57.740 --> 00:12:05.510
+zhelianl@andrew.cmu.edu: problem, so… I… used this method to
+
+104
+00:12:08.770 --> 00:12:10.579
+zhelianl@andrew.cmu.edu: To to train the model.
+
+105
+00:12:10.970 --> 00:12:21.850
+zhelianl@andrew.cmu.edu: So… This method is still testing, so I can't tell you whether it would without these problems, but
+
+106
+00:12:21.970 --> 00:12:24.100
+zhelianl@andrew.cmu.edu: I will try to resolve this.
+
+107
+00:12:26.280 --> 00:12:33.390
+Harsha Tummala: What was the three values again? I know that the two were very similar, the VAC and then the VACVDC.
+
+108
+00:12:33.390 --> 00:12:34.530
+zhelianl@andrew.cmu.edu: Oh, yes, yes.
+
+109
+00:12:34.910 --> 00:12:37.520
+Harsha Tummala: The 110 over 230.
+
+110
+00:12:39.330 --> 00:12:40.930
+zhelianl@andrew.cmu.edu: On.
+
+111
+00:12:42.940 --> 00:12:44.720
+zhelianl@andrew.cmu.edu: This one, to me.
+
+112
+00:12:45.230 --> 00:12:45.930
+Harsha Tummala: Yeah.
+
+113
+00:12:46.650 --> 00:12:54.439
+zhelianl@andrew.cmu.edu: Yes, maybe, in this example, this one could be the ground truth.
+
+114
+00:12:55.240 --> 00:13:01.290
+zhelianl@andrew.cmu.edu: maybe these tools are similar.
+
+115
+00:13:01.410 --> 00:13:10.959
+zhelianl@andrew.cmu.edu: But our system will choose the most possible three attributes, so they add this, but this could not be
+
+116
+00:13:11.590 --> 00:13:12.970
+zhelianl@andrew.cmu.edu: It's a match.
+
+117
+00:13:13.390 --> 00:13:14.070
+zhelianl@andrew.cmu.edu: So…
+
+118
+00:13:14.070 --> 00:13:16.950
+Harsha Tummala: What do you think? What do you think, Harsha, about
+
+119
+00:13:17.330 --> 00:13:23.670
+Harsha Tummala: It is a case where we would want there to be multiple values against a attribute.
+
+120
+00:13:24.510 --> 00:13:26.240
+Harsha Tummala: Yeah, it's amazing.
+
+121
+00:13:26.240 --> 00:13:29.530
+zhelianl@andrew.cmu.edu: Possible values towards the product.
+
+122
+00:13:30.340 --> 00:13:33.639
+Harsha Tummala: It might not be bad, yeah, if we put them all in.
+
+123
+00:13:33.820 --> 00:13:35.059
+Harsha Tummala: Yeah, we can just send.
+
+124
+00:13:35.060 --> 00:13:36.130
+zhelianl@andrew.cmu.edu: Oh, yes, yes.
+
+125
+00:13:36.550 --> 00:13:54.869
+Harsha Tummala: Yeah, so that could be an alternative to Leo. Instead of like trying to figure out which one out of these three is the correct one by elimination, what we can do is we can just put all three in and make it like a user approved thing where the user chooses which attributes they want.
+
+126
+00:13:55.180 --> 00:13:56.759
+Harsha Tummala: Which values they want.
+
+127
+00:13:57.070 --> 00:13:58.120
+Harsha Tummala: So, like, they might choose.
+
+128
+00:13:58.120 --> 00:14:11.260
+zhelianl@andrew.cmu.edu: Yes, yes. So that's a kind of result method. Like when we first meet this condition, the human.
+
+129
+00:14:11.900 --> 00:14:19.510
+zhelianl@andrew.cmu.edu: If the machine could not choose the best one, human could help it, and the result will…
+
+130
+00:14:19.810 --> 00:14:26.579
+zhelianl@andrew.cmu.edu: Then feed into the systems and next time it will know which one could be the best.
+
+131
+00:14:27.490 --> 00:14:32.690
+zhelianl@andrew.cmu.edu: So that's what I mean, because we have not so many
+
+132
+00:14:32.890 --> 00:14:37.900
+zhelianl@andrew.cmu.edu: data to train the model. It's kind of like on the on the train.
+
+133
+00:14:38.480 --> 00:14:45.800
+zhelianl@andrew.cmu.edu: phenomena, so… Yes, this could definitely be helpful.
+
+134
+00:14:48.320 --> 00:14:48.910
+Harsha Tummala: Good.
+
+135
+00:14:49.530 --> 00:14:52.259
+zhelianl@andrew.cmu.edu: Yeah, that's what I want to share with you.
+
+136
+00:14:58.850 --> 00:15:00.040
+Harsha Tummala: Reasonably.
+
+137
+00:15:00.920 --> 00:15:07.909
+Harsha Tummala: And, also, let us know if you guys have any more issues with, like, the CICD stuff, and…
+
+138
+00:15:08.010 --> 00:15:16.580
+Harsha Tummala: any of the DevOps-y stuff, because they do tend to take a lot of time, because those are things to figure out as you go.
+
+139
+00:15:16.730 --> 00:15:19.719
+Harsha Tummala: And they also do our.
+
+140
+00:15:20.640 --> 00:15:27.890
+Harsha Tummala: you might not be able to, like, find an accurate solution for it just by yourself. In those cases she's creating us.
+
+141
+00:15:28.050 --> 00:15:32.690
+Harsha Tummala: Me, David, we can just hop onto it and see what's going on.
+
+142
+00:15:33.740 --> 00:15:50.839
+Ashritha: Yeah, okay. So, but, the right now, the CI pipeline that I was talking about was something specific to the PR's build process, so, like, that has nothing to do with the product, so we haven't gotten yet there. So, once we have that plan in place, probably we'll, like, take some suggestions, yeah.
+
+143
+00:15:51.350 --> 00:15:58.669
+Harsha Tummala: Yeah, no, I mean, just set up in general, like just the big package pipeline, say, Amplify, that is like a whole beast by itself.
+
+144
+00:15:58.670 --> 00:15:59.600
+Ashritha: Yes.
+
+145
+00:15:59.600 --> 00:16:00.260
+Harsha Tummala: But, yeah.
+
+146
+00:16:00.420 --> 00:16:00.970
+Ashritha: Okay.
+
+147
+00:16:03.460 --> 00:16:14.680
+Ashritha: Rishi is here, like, Rishi, do you wanna, like, update on the, like, urgent OCR work and yours, or… and, like, how the ingestion… Sorry, integration thing is going on?
+
+148
+00:16:15.710 --> 00:16:19.049
+hrishikb@andrew.cmu.edu: I think this data is pretty similar to the last…
+
+149
+00:16:19.330 --> 00:16:24.369
+hrishikb@andrew.cmu.edu: Last update, but right now we are working on getting the output…
+
+150
+00:16:24.570 --> 00:16:29.680
+hrishikb@andrew.cmu.edu: In a place where we can send it forward to the ML pipeline.
+
+151
+00:16:30.200 --> 00:16:32.269
+hrishikb@andrew.cmu.edu: We are…
+
+152
+00:16:33.060 --> 00:16:44.359
+hrishikb@andrew.cmu.edu: I would say we have a few, like, we have planned it out. We have around 5 to 6 tickets that we need to complete. I think we're almost halfway through. Like, we can say we are halfway through, and…
+
+153
+00:16:44.680 --> 00:16:47.560
+hrishikb@andrew.cmu.edu: we should be able to, I guess…
+
+154
+00:16:48.450 --> 00:17:00.569
+hrishikb@andrew.cmu.edu: Complete from our sides by early next week, if there are no issues. We're also writing a few of the test cases, and, like, I did find a couple of bugs, so that's going on parallel.
+
+155
+00:17:01.390 --> 00:17:04.579
+hrishikb@andrew.cmu.edu: Yeah, so that's the thing we're working on right now.
+
+156
+00:17:08.389 --> 00:17:18.469
+Harsha Tummala: We would also love to see, like, things a little more visually, like, not the Android flows, but just, just understanding,
+
+157
+00:17:18.849 --> 00:17:23.919
+Harsha Tummala: The different pipelines that you guys are designing, and how just the architecture will add up to those.
+
+158
+00:17:24.049 --> 00:17:29.329
+Harsha Tummala: Just so that we have a better idea on, like, what party we're taking in.
+
+159
+00:17:30.169 --> 00:17:32.739
+Harsha Tummala: Because it just feels like a black box at this point.
+
+160
+00:17:34.020 --> 00:17:34.900
+hrishikb@andrew.cmu.edu: Okay.
+
+161
+00:17:35.340 --> 00:17:35.960
+Harsha Tummala: Okay.
+
+162
+00:17:37.850 --> 00:17:40.730
+Harsha Tummala: And that's it. That's that's all I have to say.
+
+163
+00:17:45.180 --> 00:18:01.509
+Ashritha: Yeah, I think, honestly, like, by next week, we should have something, like, to demo… not, like, the entire pipeline at least, but, like, some of all the work that we have done so far in, like, a demo-able form, because,
+
+164
+00:18:01.660 --> 00:18:18.900
+Ashritha: I mean, even though, like, you asked us, we have this, end semester presentation coming, and then as part of it, we have to demonstrate our solution, so we already started working towards it. But yeah, you rightly mentioned it, it'd be more visible, for you and for us to, like, if…
+
+165
+00:18:18.900 --> 00:18:26.160
+Ashritha: it's… everything, like, right now, probably, like, everything just feels in the air, and, like, in the documents, or, like, in the Bitbucket code.
+
+166
+00:18:26.160 --> 00:18:36.659
+Ashritha: So, yeah, honestly, it would boost some confidence to us also. So, yeah, I think, we spent some good amount of time this week on the integration part of it, because
+
+167
+00:18:36.760 --> 00:18:53.220
+Ashritha: like, the modules are done, but I think the major lacking is in how, so basically we are now, like, we, know that what the ML pipeline expects the input to be, so Rishi is working on to align with that schema.
+
+168
+00:18:53.220 --> 00:18:57.490
+Ashritha: So once that's there, we can just test out with few batches of examples.
+
+169
+00:18:57.620 --> 00:19:02.849
+Ashritha: At least we would know how much, like, how much of it is white or black or whatever, so…
+
+170
+00:19:03.140 --> 00:19:06.410
+Ashritha: Yeah, I think next week we'll be able to, like.
+
+171
+00:19:06.670 --> 00:19:11.870
+Ashritha: Give a good, like, a short demo or something with, like, a bunch of examples.
+
+172
+00:19:12.230 --> 00:19:21.419
+Harsha Tummala: Yeah, no stress at all. I mean, don't, don't worry yourself with a demo that is working, or, like, a short demo that's working. It could be, like, a flowchart, even though it works.
+
+173
+00:19:21.420 --> 00:19:21.970
+Ashritha: Okay.
+
+174
+00:19:21.970 --> 00:19:23.879
+Harsha Tummala: That's it, just something we should have said.
+
+175
+00:19:23.880 --> 00:19:24.470
+Ashritha: Oh yeah, sure.
+
+176
+00:19:24.470 --> 00:19:25.810
+Harsha Tummala: Yeah, that's it.
+
+177
+00:19:26.010 --> 00:19:26.540
+Ashritha: Okay.
+
+178
+00:19:30.800 --> 00:19:34.000
+Harsha Tummala: That's a good question. No.
+
+179
+00:19:35.370 --> 00:19:36.239
+Harsha Tummala: Nope, I think it.
+
+180
+00:19:36.240 --> 00:19:36.690
+Ashritha: Oh.
+
+181
+00:19:36.690 --> 00:19:37.160
+Harsha Tummala: What do you.
+
+182
+00:19:37.160 --> 00:19:37.720
+Ashritha: I think…
+
+183
+00:19:37.750 --> 00:19:39.330
+Harsha Tummala: But I see a lease.
+
+184
+00:19:40.910 --> 00:19:43.569
+Harsha Tummala: Sorry. Anyone else any updates?
+
+185
+00:19:43.570 --> 00:19:48.800
+Ashritha: No, I think, we are good from our side. Cliff, do you want to add something to it?
+
+186
+00:19:50.320 --> 00:19:53.980
+Clifford Huff: I just want to talk to you for a little bit after the client's done. That's all.
+
+187
+00:19:53.980 --> 00:19:54.630
+Ashritha: Okay.
+
+188
+00:19:56.670 --> 00:20:00.189
+Harsha Tummala: It seems that it's like you have to come back to him.
+
+189
+00:20:00.190 --> 00:20:00.810
+Clifford Huff: Okay.
+
+190
+00:20:01.370 --> 00:20:04.440
+Harsha Tummala: Have a good one. See you guys. Bye-bye. Bye. See.
+
+191
+00:20:05.040 --> 00:20:05.480
+hrishikb@andrew.cmu.edu: Bye by.
+
+192
+00:20:05.480 --> 00:20:07.029
+Harsha Tummala: I'll be here for that.
+
+193
+00:20:11.370 --> 00:20:23.009
+Clifford Huff: Hi, team. So I just had a question about your current CI/CD pipeline. Is this stuff internal or are you trying to work with an eParts flow at this point?
+
+194
+00:20:24.000 --> 00:20:43.050
+Ashritha: When I meant internal, it's for the CI-CD pipeline, as for the code that we check in, so for the Bitbucket pull requests and stuff like that. So, our MLOps for the product is not yet… we have not yet committed any code for that, so I,
+
+195
+00:20:43.090 --> 00:20:56.479
+Ashritha: like, once we thought that once the integration part is done, wherein we test the OCR ingestion and the machine learning part of it, we can just figure out the ML loss, which should be easy, it should not take a lot of time, because
+
+196
+00:20:56.710 --> 00:21:10.629
+Ashritha: we know the plan, it's just that we have to get the coding part of it done, but we have the CI part of it for the build process, like, the code build process, but not for the actual
+
+197
+00:21:10.880 --> 00:21:13.990
+Ashritha: ML part of it. So, MLOps is not there.
+
+198
+00:21:14.170 --> 00:21:17.800
+Ashritha: you could say, like, the CICD for the code is present here.
+
+199
+00:21:18.720 --> 00:21:19.410
+Clifford Huff: Okay.
+
+200
+00:21:19.680 --> 00:21:30.659
+Clifford Huff: So did you guys have a priority interface spec between your pieces of code that you're building? Or is that something you're figuring out as you integrate? That wasn't clear.
+
+201
+00:21:31.430 --> 00:21:43.519
+Clifford Huff: as you guys integrate different components that you've been working on, did you have… originally have a interface spec figured out, or is that something that you're just refining at this point? Does that make sense?
+
+202
+00:21:43.830 --> 00:22:02.120
+Ashritha: Oh, we have that figured out already. Like, when we initially made the specification document for the entire project, Liu, since he was a primary person working on the ML part, he told us initially itself, like, this is the expected input format. So we just kind of…
+
+203
+00:22:02.120 --> 00:22:07.439
+Ashritha: Aligning with it right now, but we are not modifying any of our initial, spec…
+
+204
+00:22:07.440 --> 00:22:19.939
+Ashritha: commitment, so, yeah. We're not refining it. We're just, like, aligning the output that is coming from the intuition with the input that he wants it to be, that's it. So nothing extra that we added recently.
+
+205
+00:22:20.490 --> 00:22:33.689
+Clifford Huff: Okay, sounds good. All right. Are you having the mentor meeting in person, or is it on Zoom? So, I… I noticed that the… the mentor… the client meeting was possibly going to be on Zoom. I just want to confirm about the mentor meeting.
+
+206
+00:22:34.070 --> 00:22:48.439
+Ashritha: Yeah, it's gonna be on Zoom, like, I just dropped a message, like, I mean, the entire team was on remote, so I thought I just… I would ask you and Dennis as well. So Dennis said he would be on Zoom, so yeah, I just stayed on phone.
+
+207
+00:22:48.740 --> 00:22:55.699
+Clifford Huff: Okay. All right. Well, I missed that. I've been troubleshooting things at my church, so I was, I was doing that. Okay.
+
+208
+00:22:55.860 --> 00:22:56.440
+Clifford Huff: Alright.
+
+209
+00:22:56.440 --> 00:22:59.429
+zhelianl@andrew.cmu.edu: I think I'm in the room next to you now.
+
+210
+00:22:59.510 --> 00:23:00.310
+Clifford Huff: Hahaha.
+
+211
+00:23:00.310 --> 00:23:01.469
+zhelianl@andrew.cmu.edu: And also in the cave.
+
+212
+00:23:02.090 --> 00:23:02.920
+Clifford Huff: Okay.
+
+213
+00:23:03.050 --> 00:23:07.809
+Clifford Huff: Well, yeah, I'll stay here until then, and then leave, that's good. Alright.
+
+214
+00:23:07.990 --> 00:23:10.320
+Clifford Huff: See you all at 4 o'clock.
+
+215
+00:23:11.110 --> 00:23:11.640
+zhelianl@andrew.cmu.edu: Okay.
+
+216
+00:23:12.190 --> 00:23:12.720
+Ashritha: Bye.
+
+217
+00:23:12.920 --> 00:23:13.520
+hrishikb@andrew.cmu.edu: Bye.
+
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