"I don't build features. I identify market gaps, validate them with real pain signals, and design products that make enterprises smarter — faster than they thought possible."
After 18 years inside enterprise data programs, I've seen the same problems repeat across every industry:
- Data exists everywhere. Trust in that data exists nowhere.
- Pipelines break silently. Nobody knows until a dashboard is wrong.
- Products are loaded with SKUs from 500 suppliers. Half the data is unusable.
- Master data lives in 12 systems. None of them agree.
The market has point solutions for each of these. What it doesn't have is intelligent, AI-native products that understand the business context behind the data problem — not just the technical one.
That's the white space I'm building into.
THREE PRODUCT LINES — ONE THESIS
LINE 1: DATA TRUST PLATFORM
"Enterprises can't use data they don't trust.
I'm building the trust layer."
→ LineageIQ · HarmonyAI · QualityPulse
LINE 2: COMMERCE INTELLIGENCE
"eCommerce grows on product data quality.
Most catalogs are a mess."
→ CatalogIQ · EnrichAI · PriceGuard
LINE 3: AGENTIC DATA OPERATIONS
"The next frontier isn't dashboards.
It's AI that manages your data for you."
→ MasterMind MDM · StewardAI · PipelineGuardian
"You can't make decisions on data you don't trust."
"Know where every number came from — in one click"
Enterprise data teams spend 40% of their incident response time answering one question: "Where did this wrong number come from?"
Today's answer involves manually tracing through ETL jobs, Confluence pages written 2 years ago, and Slack messages to engineers who may have left the company. It takes hours. Sometimes days.
LineageIQ makes it seconds.
At Wabtec, I built a data lineage tool internally that transformed how our engineering team responded to data incidents. What took 4 hours of investigation became a 30-second graph traversal. Leadership immediately asked: "Why doesn't every company have this?"
Because nobody built it for the business user — only for engineers.
WHO IT'S FOR:
Primary: Data Engineering Leads, Analytics Managers
Secondary: CDOs, CIOs who need audit-ready lineage
Buyer: VP Data · CDO · CTO
WHAT IT DOES:
→ Auto-discovers data flow: source → transform → target
→ Visual interactive graph — click any table to see its full story
→ Impact analysis: "If supplier_b API changes, 34 reports are affected"
→ Proactive alerts: "Upstream source changed — 6 downstream owners notified"
→ Audit-ready export for compliance reviews
THE "AHA" MOMENT FOR THE BUYER:
"I can finally show the CFO exactly where the Q3 revenue number
came from — in a presentation, not a 40-page technical document."
- Wedge: Data incident response (immediate pain, clear ROI)
- Expand: Compliance reporting, change impact analysis, onboarding new engineers
- Competitor gap: dbt has lineage but only for dbt models. Most enterprises have 5+ tools. LineageIQ is tool-agnostic.
GDPR, CCPA, and emerging AI governance regulations all require data provenance. Lineage is moving from "nice to have" to "legally required." The market is forming — the product category leader isn't set yet.
"Turn 500 supplier data formats into one clean catalog — automatically"
Every eCommerce platform, retailer, and marketplace has the same nightmare: product data arrives from hundreds of suppliers in hundreds of different formats. Normalizing it is a full-time job for entire teams.
Supplier A sends: { "colour": "Wht/Blk", "sz": "10US" }
Supplier B sends: { "Color": "White Black", "size": "44EU" }
Supplier C sends: { "color_code": "#FFFFFF", "size_eu": 44 }
Same product. Three schemas. Zero usability.
Current solutions: expensive MDM platforms requiring 18-month implementations, or manual data teams that don't scale.
HarmonyAI does in minutes what takes teams weeks.
WHO IT'S FOR:
Primary: eCommerce Product Data Managers, Catalog Teams
Secondary: Marketplace operators, Retail buying teams
Buyer: VP Product · VP Operations · CPO
THE CORE PRODUCT LOOP:
1. Supplier uploads raw data (any format: CSV, JSON, API, XML)
2. HarmonyAI semantically maps fields using LLM understanding
("colour", "Color", "color_code" → all mean COLOR)
3. AI standardizes values against your schema
("44EU" → "10US" via size conversion · "Wht/Blk" → "White/Black")
4. Conflicts flagged for human review with confidence scores
("2 suppliers disagree on weight — which is correct?")
5. Clean unified record published to your catalog
OUTPUT:
{
"product_name": "Nike Air Max 90",
"color": "White/Black",
"size_us": "10",
"size_eu": "44",
"confidence": 0.94,
"source": { "color": "supplier_a", "size_eu": "supplier_b" },
"conflicts": []
}
- SaaS: per-record pricing (volume tiers)
- Enterprise: unlimited records + API access + dedicated support
- Marketplace partnerships: direct integration fee with Shopify, Flipkart, Amazon Seller Central
"Know your data is broken before your executives do"
Data quality failures are invisible until they're embarrassing. A pipeline silently produces 0 rows. Prices come in as $0. A column that should have 5 values suddenly has 5,000.
Nobody knows until a CFO questions a number in a board meeting.
QualityPulse monitors every table, every column, every run — and alerts you before the wrong number reaches a dashboard.
THE 5 QUALITY DIMENSIONS WE MONITOR:
1. Completeness → Is required data present?
2. Accuracy → Is the data correct vs expected range?
3. Consistency → Does it match across systems?
4. Timeliness → Was it updated on schedule?
5. Uniqueness → Are there unexpected duplicates?
THE ALERT THAT CHANGES EVERYTHING:
"⚠️ ALERT: prices table — 34% of values are $0
(baseline: 0.1% | detected at 14:32 UTC)
Likely cause: supplier_b API returned empty response
Affected downstream: 8 dashboards · 3 ML models · 2 reports
Suggested action: Re-trigger extraction from 13:00 snapshot"
INTEGRATIONS:
Snowflake · dbt · Airflow · AWS Glue · Looker · Power BI
Alert channels: Slack · PagerDuty · Email · Teams
- Great Expectations: developer tool, no AI, requires Python knowledge
- Monte Carlo: enterprise-only, expensive, complex setup
- QualityPulse: business-user friendly, AI-native, fast time-to-value
"eCommerce wins on product data. Most catalogs are a competitive liability."
"Your product catalog is your storefront. Most storefronts are broken."
- Amazon suppresses listings with incomplete data
- Search engines can't index products with inconsistent attributes
- Customers return products because descriptions were inaccurate
- Internal teams waste 60% of catalog time on data cleanup, not strategy
A raw supplier record looks like: { "sku": "NK-AM90-WHT-10", "price": 129.99 }
What a customer needs — and what drives conversion — is a rich, complete, consistent product record. The gap between those two states costs eCommerce companies 3-5% of revenue in suppressed listings, returns, and search failures.
THE TRANSFORMATION:
RAW INPUT → CATALOG IQ OUTPUT
────────── ────────────────
sku: NK-AM90 name: Nike Air Max 90 — White Running Shoe
price: 129.99 description: AI-generated, brand-voice matched
category: Men's Running / Casual Shoes
color: White/Black (standardized)
size: 10 US / 44 EU / 9 UK (converted)
tags: [running, nike, air-max, white, casual]
search_keywords: auto-generated
amazon_bullets: 5 compliant bullet points
sustainability_score: 72
similar_products: [NK-AM95, AD-UB22]
WORKFLOW:
Upload SKUs → AI enriches → Human reviews flagged items
→ Publish to Shopify / Amazon / Flipkart / internal catalog
"Every product record deserves a complete story"
A standalone enrichment API that connects to external data sources and AI generation to fill gaps in product data at scale.
ENRICHMENT SOURCES:
Brand Registry API → Official brand name, logo, story
GS1 / Open GTIN → Global product identifiers
Google Vision AI → Extract attributes from product images
OpenFoodFacts → Nutrition data for grocery SKUs
LLM Generation → Descriptions, features, search keywords
Internal taxonomy → Map to your category tree + attributes
USE CASE:
"I have 2 million SKUs from 200 suppliers.
30% are missing descriptions.
45% are missing category assignments.
EnrichAI fills them in — overnight."
"Your price is your promise. Make sure it's the right one."
Real-time price anomaly detection and competitive intelligence for eCommerce:
- Flags pricing errors before they go live (the $5 laptop problem)
- Monitors competitor pricing across channels
- Alerts when your price drifts outside healthy margin bands
- Suggests repricing actions with confidence scores
"The next decade of data management won't be dashboards. It will be AI agents that run your data operations."
"One version of the truth — finally"
Every enterprise knows they need Master Data Management. Almost none have successfully implemented it.
The reasons are always the same:
- Traditional MDM tools (Informatica, IBM MDM) cost $500K+ to implement
- 18-month implementation timelines
- Require dedicated MDM teams to operate
- Still require significant human stewardship
MasterMind MDM is AI-native MDM — built for the speed of modern data.
THE GOLDEN RECORD PROBLEM:
CRM: "Ashok Ankalla" · ashok@company.com
ERP: "A. Ankalla" · ANKALLA_A
Support: "ankalla_ashok" · ashok.ankalla@co.com
Marketing: "Ashok A." · ashok@company.com
Same person. 4 systems. 0 trust.
MASTERMIND CREATES THE GOLDEN RECORD:
→ Deterministic matching (exact email, phone)
→ Probabilistic matching (fuzzy name, address similarity)
→ AI survivorship: "CRM wins for email, ERP wins for employee ID"
→ Confidence score per match decision
→ Human review queue for uncertain matches (<85% confidence)
→ Golden record synced back to all systems via event stream
DOMAINS SUPPORTED:
Customer · Product · Supplier · Location · Employee · Asset
Cloud-native companies are scaling faster than their data can keep up. The "we'll fix the data later" strategy is becoming a crisis. MDM is the solution — but the old tools are too slow and too expensive. The market needs MDM rebuilt for the cloud era.
"AI that manages your master data — so your team doesn't have to"
The next evolution beyond MasterMind MDM — an agentic system that handles data stewardship autonomously:
TRADITIONAL STEWARDSHIP:
Match engine flags uncertain match
→ Ticket created → Assigned to steward
→ Steward investigates → Makes decision
→ Updates record
Time: days to weeks per batch
STEWARDAI:
Match engine flags uncertain match (83% confidence)
→ StewardAI reviews additional signals
(phone match? address match? purchase history overlap?)
→ Applies learned rules from 10,000 previous decisions
→ Makes autonomous decision if confidence > 92%
→ Escalates to human ONLY if genuinely ambiguous
→ Decision logged with full reasoning chain
Time: seconds. Human only sees the hard ones.
This is MDM + Agentic AI = the product that replaces a team of 5 data stewards.
"Data pipelines that fix themselves"
CURRENT STATE (every enterprise):
Pipeline fails at 2am
→ Alert fires → Engineer wakes up
→ Investigates for 45 minutes
→ Applies fix → Documents in Confluence
→ Goes back to sleep
Cost: engineer time + data latency + downstream impact
PIPELINEGUARDIAN:
Pipeline fails at 2am
→ AI diagnoses failure pattern
→ Matches against known fix library
→ Applies fix autonomously (schema drift? retry? dedup? rollback?)
→ Logs decision with full audit trail
→ Only pages human if: fix is unknown OR risk is HIGH
Cost: near zero for known failures
Self-healing scenarios the product handles autonomously:
- Schema drift (source added/removed a column) → auto-adapt target
- NULL spike → re-trigger extraction from last clean snapshot
- Duplicate records → dedup and re-run merge step
- Rate limit hit → exponential backoff with jitter, retry
- Row count anomaly → quarantine batch, alert, await confirmation
| Quarter | Product | Stage | Goal |
|---|---|---|---|
| Q2 2026 | LineageIQ | POC + user interviews | 10 beta design partners |
| Q2 2026 | HarmonyAI | POC + supplier data testing | Validate core harmonization accuracy |
| Q3 2026 | QualityPulse | MVP | First paying customer |
| Q3 2026 | CatalogIQ | MVP | 1 eCommerce pilot |
| Q4 2026 | MasterMind MDM | Product design + POC | Design partner program |
| Q4 2026 | EnrichAI | API beta | 5 developer integrations |
| Q1 2027 | StewardAI | POC | Validate autonomous stewardship |
| Q2 2027 | PipelineGuardian | MVP | First enterprise pilot |
Before I build anything I answer these 6 questions:
1. WHO has this problem?
(Be specific — "data teams at mid-market eCommerce companies"
not "enterprises")
2. HOW PAINFUL is it today?
(Measured in time lost, revenue impact, or compliance risk)
3. HOW DO THEY SOLVE IT TODAY?
(If they have no current solution, question whether it's a real problem)
4. WHY IS NOW THE RIGHT TIME?
(What changed — regulation, technology, market — that makes this possible?)
5. WHAT DOES "DONE" LOOK LIKE FOR THE USER?
(The job-to-be-done, not the feature list)
6. HOW DO WE KNOW IT WORKED?
(The KPI that proves product-market fit, not vanity metrics)
Every product above is at a stage where the right collaborator changes everything.
I'm looking for:
- Design partners (companies with the problem who'll co-design the solution)
- Technical collaborators (engineers who want to build AI-native data products)
- Domain experts (data stewards, catalog managers, MDM practitioners)
If you're living any of these problems, I want to talk.
Ashok Ankalla — Senior Product Manager | Enterprise Data & AI Transformation 18 years of enterprise delivery · 3 Agentic AI products shipped · 9 products in vision
"The best products are built by people who've lived the problem. I've lived all of these."