Sales Intelligence Multi-Agent System

AI-Powered Prospect Research & Personalized Outreach — Fully Local, Privacy-First

13 autonomous AI agents working in a coordinated pipeline to transform raw company data
into fully personalized, evidence-backed sales outreach — in under 15 minutes.

Zero hallucination. Zero API costs for LLMs. Zero data leaving your machine.

Executive Summary · Business Impact · Architecture · Agents · Orchestrator · Tech Stack · Memory Layer · Intelligence Flow · Example Output · Installation · Running the System · Documentation · Roadmap

Executive Summary

Sales Intelligence Multi-Agent System is a production-grade, multi-agent AI pipeline that automates the entire prospect research and personalized outreach workflow — from raw company inputs to a fully crafted, evidence-backed communication strategy.

What It Does


Input	Company website URL, LinkedIn page, Instagram handle, founder profiles
Output	Personalized cold email, LinkedIn message, multi-touch follow-up sequence, objection handling, and full communication strategy
Agents	13 specialized AI agents, organized across 5 execution phases
LLMs	4 local models via Ollama — zero API costs, complete data privacy
Memory	Triple-layer persistence: SQLite (structured) + Qdrant (semantic) + Neo4j (graph relationships)

Core Value Proposition

Replace 4–6 hours of manual prospect research with a 15-minute automated pipeline that produces higher-quality, evidence-backed outreach — while keeping all data on your machine.

Privacy-First Design

Every component runs locally. LLM inference happens through Ollama on your hardware. No data is transmitted to external AI APIs. No prospect information leaves your network. Enterprise-grade privacy by architecture, not by policy.

Business Impact

The Problem

Sales teams spend 60–70% of their time on pre-outreach activities: researching prospects, analyzing competitors, identifying pain points, crafting personalized messages, and building follow-up sequences. This manual process is:

Slow — 4–6 hours per prospect for quality research
Inconsistent — output quality varies by rep skill and available time
Unscalable — more prospects requires more headcount, not more efficiency
Error-prone — generic outreach, missed signals, stale data

Manual vs. AI-Powered Workflow

┌─────────────────────────────────┐    ┌─────────────────────────────────┐
│       MANUAL WORKFLOW           │    │       AI-POWERED WORKFLOW       │
│                                 │    │                                 │
│  Website research    ~45 min    │    │  13 agents run in parallel      │
│  LinkedIn analysis   ~40 min    │    │  and sequential phases          │
│  Competitor mapping  ~60 min    │    │                                 │
│  Pain identification ~30 min    │    │  Total time: ~15 minutes        │
│  Solution mapping    ~30 min    │    │                                 │
│  Email drafting      ~45 min    │    │  Output:                        │
│  Follow-up sequence  ~30 min    │    │  ✓ Personalized email           │
│  Objection prep      ~20 min    │    │  ✓ LinkedIn message             │
│                                 │    │  ✓ Multi-touch sequence         │
│  Total: 4–6 hours               │    │  ✓ Objection handling           │
│  Quality: Variable              │    │  ✓ Full communication strategy  │
│  Scalability: Linear            │    │                                 │
│                                 │    │  Quality: Consistent            │
│                                 │    │  Scalability: Parallel          │
└─────────────────────────────────┘    └─────────────────────────────────┘

Operational Impact & ROI Metrics

Metric	Before	After	Improvement
Research time per prospect	4–6 hours	~15 minutes	95% reduction
Outreach personalization depth	Surface-level	Multi-signal, evidence-backed	10x deeper
Data sources analyzed	1–2 manually	6+ automatically	6x coverage
Consistency across reps	Variable	Standardized AI pipeline	100% consistent
Hallucination rate	N/A (human)	Zero — every claim is evidence-traced	Verified output
LLM API cost per prospect	$0.10–$0.50	$0.00 (local Ollama)	100% eliminated

System Architecture

High-Level Architecture Diagram

flowchart TB
    subgraph INPUTS["📥 Company Inputs"]
        I1["🌐 Website URL"]
        I2["💼 LinkedIn URL"]
        I3["📸 Instagram Handle"]
        I4["👤 Founder Profiles"]
    end

    subgraph ORCHESTRATOR["🎛️ LangGraph Supervisor Orchestrator"]
        direction TB
        ORC["Phase Management · Packet Routing · Failure Handling"]
    end

    subgraph BUS["📡 Redis Pub/Sub Message Bus"]
        direction LR
        PUB["Publish"] --> SUB["Subscribe"]
    end

    subgraph PHASE1["⚡ Phase 1 — Collection (Parallel)"]
        direction LR
        A1["🌐 Agent 1\nWebsite Intel"]
        A2["💼 Agent 2\nLinkedIn Intel"]
        A3["📸 Agent 3\nInstagram Intel"]
        A4["👤 Agent 4\nFounder Intel"]
    end

    subgraph PHASE2["⚡ Phase 2 — Secondary Intelligence (Parallel)"]
        direction LR
        A5["📝 Agent 5\nBlog Intel"]
        A6["📊 Agent 6\nMarket Intel"]
    end

    subgraph PHASE3["⚡ Phase 3 — Reasoning (Parallel)"]
        direction LR
        A7["🎯 Agent 7\nOpportunity Signal"]
        A8["🔍 Agent 8\nPain Hypothesis"]
    end

    subgraph PHASE45["⚡ Phase 4–5 — Synthesis (Sequential)"]
        direction LR
        A9["👤 Agent 9\nTarget Person"]
        A10["📦 Agent 10\nInternal Product"]
        A11["🧩 Agent 11\nSolution Mapping"]
        A12["💰 Agent 12\nROI Intel"]
        A13["✉️ Agent 13\nCommunication"]
    end

    subgraph OUTPUT["📤 Output — CommunicationPacket"]
        O1["📧 Personalized Email"]
        O2["💼 LinkedIn Message"]
        O3["📅 Follow-up Sequence"]
        O4["🛡️ Objection Handling"]
        O5["📋 Communication Strategy"]
    end

    subgraph MEMORY["💾 Triple-Layer Memory"]
        direction LR
        M1["🗄️ SQLite\nStructured Profiles"]
        M2["🔮 Qdrant\nSemantic Vectors"]
        M3["🕸️ Neo4j\nRelationship Graph"]
    end

    INPUTS --> ORCHESTRATOR
    ORCHESTRATOR --> BUS
    BUS --> PHASE1
    PHASE1 -->|"Packet Merge"| PHASE2
    PHASE2 -->|"Packet Merge"| PHASE3
    PHASE3 --> PHASE45
    PHASE45 --> OUTPUT
    PHASE1 & PHASE2 & PHASE3 & PHASE45 <-->|"Read/Write"| MEMORY

End-to-End Intelligence Flow

Collection → Extraction → Deduplication → Reasoning → Scoring → Communication
     ↓            ↓              ↓             ↓           ↓           ↓
  Scrape &     Clean &       Hash +       Cross-source  Confidence   Strategy +
  gather raw   structure    semantic       inference &   gating &     email +
  data from    into typed   similarity     hypothesis    opportunity  LinkedIn +
  6+ sources   features     filtering      generation    scoring      sequence

Phase Execution Rules

Phase	Agents	Execution	Gate
Phase 1	Agents 1–4	⚡ Parallel	All 4 must complete (or timeout) before Phase 2
Phase 2	Agents 5–6	⚡ Parallel	Both must complete before Phase 3
Phase 3	Agents 7–8	⚡ Parallel	Both must complete before Phase 4
Phase 4–5	Agents 9–13	🔗 Sequential	Each feeds the next; always on, cannot be disabled

Multi-Agent Pipeline

All 13 Agents at a Glance

#	Agent	Phase	Mode	Input	Output Packet	Purpose
1	Website Intelligence	1	Parallel	Website URL	`WebsitePacket`	Company identity, products, customers, tech stack, growth & pain signals
2	LinkedIn Intelligence	1	Parallel	LinkedIn URL	`LinkedInPacket`	Hiring patterns, growth signals, content themes, audience, real-time activity
3	Instagram Intelligence	1	Parallel	Instagram handle	`InstagramPacket`	Brand tone, visual identity, audience type, engagement, emotional signals
4	Founder Intelligence	1	Parallel	Founder profiles	`FounderPacket`	Vision, technology preferences, decision style, beliefs (per-founder)
5	Blog Intelligence	2	Parallel	Blog URL + Phase 1	`BlogPacket`	Long-term thinking, case studies, roadmap direction, content themes
6	Market Intelligence	2	Parallel	Phase 1 packets	`MarketPacket`	Competitors (tiered), market position, technology shifts, future trends
7	Opportunity Signal	3	Parallel	Phase 1+2 packets	`OpportunityPacket`	Composite opportunity score across 7 signal dimensions
8	Pain Hypothesis	3	Parallel	Phase 1+2 packets	`PainPacket`	Evidence-backed hypotheses about operational friction (not facts)
9	Target Person	4	Sequential	All prior packets	`TargetPacket`	The real person who owns the pain — name, title, employment status
10	Internal Product	4	Sequential	Our company URLs	`InternalProductPacket`	Our own products, pricing, capabilities, integrations, limitations
11	Solution Mapping	5	Sequential	All packets	`SolutionPacket`	Pain → product/feature mapping, gap analysis, objection pre-generation
12	ROI Intelligence	5	Sequential	Pain+Solution+Market	`ROIPacket`	Quantified business impact: cost, time, productivity, revenue, risk
13	Communication	5	Sequential	ALL packets (1–12)	`CommunicationPacket`	Full communication strategy, personalized email, LinkedIn, sequence

Phase 1 — Collection Agents (Agents 1–4)

All four agents run in parallel immediately after inputs are received. Each uses a tiered collector priority system:

P1 (ALWAYS, FREE):    requests + bs4 + sitemap + xml.etree
P2 (IF NEEDED, PAID): Firecrawl (max 8 pages) + Apify (max 20 posts)
P3 (LAST RESORT):     Playwright + EasyOCR + faster-whisper

Agent 1 — Website Intelligence Extracts company identity, products, customers, growth signals, pain signals, and tech stack from the company's website. Uses sitemap parsing, HTML extraction, and optional JavaScript rendering.

Agent 2 — LinkedIn Intelligence Analyzes what the company is doing right now — hiring patterns, recent posts, growth signals, audience engagement, and expansion activities.

Agent 3 — Instagram Intelligence Understands brand tone, audience type, content themes, engagement quality, and emotional pain signals. Includes Whisper-based speech extraction from reels.

Agent 4 — Founder Intelligence Maps founder vision, technology preferences, decision style, and beliefs. Runs per-founder — multiple founders are supported, each producing their own FounderPacket.

Phase 2 — Secondary Intelligence (Agents 5–6)

Runs in parallel after Phase 1 packets are merged.

Agent 5 — Blog Intelligence Extracts long-term company thinking, customer success patterns, roadmap direction, and messaging themes from the company blog.

Agent 6 — Market Intelligence Unique architecture — LLM-driven discovery before collection. First uses LLMs to infer competitors and market position from Phase 1 data, then sends collectors to validate against real competitor websites.

flowchart LR
    CF["Context Fusion\n(Phase 1 packets)"] --> MD["Market Discovery\n(LLM-inferred)"]
    MD --> MC["Market Collection\n(validate via scraping)"]
    MC --> RL["Reasoning Layer\n(tiering + analysis)"]
    RL --> MP["MarketPacket"]

Phase 3 — Reasoning Agents (Agents 7–8)

Both agents run in parallel and are required (cannot be disabled).

Agent 7 — Opportunity Signal Intelligence Scores the sales opportunity across 7 parallel signal layers:

Signal	Source
Growth	LinkedIn hiring + website careers
Activity	Post frequency + blog publishing
Technology	Founder tech preference + market trends
Competitive	Competitor launches vs. company state
Expansion	New products + sales hiring
Timing	Roadmap hints + market velocity
Pressure	AI adoption + competitor automation

Agent 8 — Pain Hypothesis Intelligence Generates evidence-backed hypotheses — not facts — about operational friction using a multi-LLM approach:

LLM	Role
`qwen2.5:7b`	Signal organization and structuring
`deepseek-r1:8b`	Cross-source inference and hypothesis generation
`llama3.1:8b`	Buyer simulation ("If I were this company's VP Sales, what friction would I feel?")

Validation Rule: A hypothesis is only valid if supported by ≥2 independent signals, OR (market + growth signal), OR (founder + operations signal).

Phase 4–5 — Synthesis Agents (Agents 9–13)

Run sequentially. All five are always on — cannot be disabled.

Agent 9 — Target Person Intelligence Identifies the real human who owns the pain: name, title, current employment status. Maps pain types to role owners (e.g., "lead overload" → VP Sales).

Agent 10 — Internal Product Intelligence Analyzes our own company's products, pricing, capabilities, integrations, and limitations — ensuring outreach is truthful and accurate.

Agent 11 — Solution Mapping Intelligence Maps prospect pain → our best product/feature/use case. Includes gap analysis and pre-generated objection handling.

Agent 12 — ROI Intelligence Converts pain + solution into quantified business impact language, personalized by target designation. VP Sales gets pipeline metrics; CTO gets automation metrics.

Agent 13 — Communication Intelligence The sales execution brain — not an email generator. A 5-stage communication strategy system:

flowchart LR
    S1["1. Strategy\nPersona · Channel\nTiming · Intent"] --> S2["2. Planning\nHook · Narrative\nValue · CTA"]
    S2 --> S3["3. Personalization\nFounder · Company\nPain · Target · Market"]
    S3 --> S4["4. Execution\nEmail · LinkedIn\nFollowups · Objections"]
    S4 --> S5["5. Validation\nHallucination ✓\nSpam ✓ · Human ✓"]

Orchestrator Architecture

LangGraph Supervisor

The orchestrator uses LangGraph in supervisor mode to manage the complete lifecycle of all 13 agents:

Capability	Implementation
Parallel Execution	Phase 1 (4 agents), Phase 2 (2 agents), and Phase 3 (2 agents) each run in parallel
Phase Synchronization	Strict gates: Phase N+1 only starts after all Phase N packets are received or timed out
Packet Routing	Redis Pub/Sub channels per agent (e.g., `phase1:agent1`, `phase2:agent5`)
Failure Handling	Agent failure → confidence reduction, NOT pipeline stop. Retry → Fallback → Degrade → Continue
Confidence Gating	Every packet carries a confidence score; agents escalate collection priority if below threshold
Timeout Management	10-minute agent timeout; timed-out agents produce partial packets with reduced confidence

Redis Communication Flow

sequenceDiagram
    participant O as Orchestrator
    participant R as Redis Pub/Sub
    participant A1 as Agent 1
    participant A2 as Agent 2
    participant A5 as Agent 5
    participant A6 as Agent 6

    O->>R: Spawn Phase 1
    R->>A1: Start (parallel)
    R->>A2: Start (parallel)
    A1->>R: Publish WebsitePacket → phase1:agent1
    A2->>R: Publish LinkedInPacket → phase1:agent2
    R->>O: Phase 1 gate check ✓
    O->>R: Spawn Phase 2
    R->>A5: Start with Phase1Bundle (parallel)
    R->>A6: Start with Phase1Bundle (parallel)
    A5->>R: Publish BlogPacket → phase2:agent5
    A6->>R: Publish MarketPacket → phase2:agent6
    R->>O: Phase 2 gate check ✓

Confidence System

Every packet carries a confidence score calculated as:

confidence = signal_count + evidence_count + graph_links - missing_signals

Condition	Action
confidence ≥ threshold	Proceed normally
confidence < threshold	Escalate to P2 collectors, then P3
Very low after P3	Reduce scope, produce partial packet, continue
Never	Block the pipeline entirely

Failure Handling Philosophy

Every agent follows the same failure chain — no exceptions:

Attempt → Retry (1x) → Fallback (cheaper collector) → Confidence Reduction → Continue

Failure Scenario	Fallback Action
Firecrawl quota exceeded	Switch to BS4
JS-heavy site fails	Use Playwright
Apify rate limit	Direct Profile + Post collectors
OCR extraction fails	Skip image, continue
Playwright timeout	Fall back to Apify
LinkedIn profile private	Metadata-only collection
Sitemap missing	Crawl all `<a>` links
Blog unavailable	Use LinkedIn articles
VP Sales not found	Try Head of Sales → Director → Manager
Target employee inactive	Move to next ranked person

Technology Stack

AI Layer

Component	Technology	Purpose
LLM Runtime	Ollama (local)	Free, private, zero API costs
Extraction Model	Qwen 2.5 (7B)	Fast structured JSON extraction, theme detection
Reasoning Model	DeepSeek-R1 (8B)	Chain-of-thought, cross-source inference
Simulation Model	Llama 3.1 (8B)	Buyer perspective simulation (Agent 8)
Embeddings	nomic-embed-text	Semantic search, deduplication, hallucination checking

Orchestration Layer

Component	Technology	Purpose
Agent Orchestrator	LangGraph (supervisor mode)	Agent lifecycle, routing, phase management
Message Bus	Redis Pub/Sub	Async packet routing between agents
API Server	FastAPI	REST endpoints, WebSocket/SSE for real-time updates

Memory Layer

Component	Technology	Purpose
Structured DB	SQLite	13 profile tables, fast reads, agent outputs
Vector DB	Qdrant (local)	Semantic retrieval, cross-agent search, deduplication
Graph DB	Neo4j (local)	Entity relationships, evidence tracing

Collection Layer

Tool	Purpose	Cost Tier
`requests` + `BeautifulSoup`	HTML scraping	FREE (P1)
`xml.etree`	Sitemap parsing	FREE (P1)
`Wappalyzer` / `builtwith`	Tech stack detection	FREE (P1)
`Firecrawl`	Structured page extraction	PAID API (P2)
`Apify`	LinkedIn/Instagram scraping	PAID API (P2)
`Playwright`	JS-rendered pages	LOCAL COMPUTE (P3)
`EasyOCR`	Image text extraction	LOCAL COMPUTE (P3)
`faster-whisper`	Reel/video speech-to-text	LOCAL COMPUTE (P3)

Frontend Layer

Component	Technology
Framework	React / Next.js
Styling	Tailwind CSS
State Management	Zustand / React Context
Real-time Updates	WebSocket / SSE

Memory Layer

Triple-Layer Persistence Architecture

Every agent writes to all three memory systems, creating a comprehensive, queryable intelligence store:

flowchart TB
    subgraph SQLITE["🗄️ SQLite — Structured Profiles"]
        S1["website_profile"]
        S2["linkedin_profile"]
        S3["instagram_profile"]
        S4["founder_profile"]
        S5["blog_profile"]
        S6["market_profile"]
        S7["opportunity_profile"]
        S8["operational_hypothesis_profile"]
        S9["target_profile"]
        S10["internal_product_profile"]
        S11["solution_profile"]
        S12["roi_profile"]
        S13["communication_profile"]
    end

    subgraph QDRANT["🔮 Qdrant — Semantic Vectors"]
        Q1["Pages · Posts · Docs"]
        Q2["Case Studies · Signals"]
        Q3["Competitor Data · Trends"]
        Q4["Communication Messages"]
    end

    subgraph NEO4J["🕸️ Neo4j — Relationship Graph"]
        N1["Company → SELLS → Product"]
        N2["Company → SERVES → Customer"]
        N3["Company → HIRING → Role"]
        N4["Founder → PREFERS → Technology"]
        N5["Pain → SOLVED_BY → Product"]
        N6["Product → INTEGRATES → CRM"]
        N7["Market → MOVING_TO → AI Agents"]
    end

Why Three Layers?

Layer	Solves	Example Query
SQLite	"What did we learn about this company?"	Retrieve structured profile for any agent's output
Qdrant	"Find similar pain signals across companies"	Semantic search across all collected content
Neo4j	"What relationships exist between entities?"	Trace: Pain → solved_by → Product → integrates → CRM

Hallucination Prevention

Agent 13 (Communication) validates every claim in the final output against all three memory layers. If a statement cannot be traced to evidence in the graph, vector store, or structured database — it is removed.

Intelligence Flow

The system transforms raw data into actionable sales intelligence through six distinct stages:

 ┌──────────┐   ┌────────────┐   ┌──────────┐   ┌───────────┐   ┌─────────┐   ┌───────────────┐
 │Collection│──▶│ Extraction │──▶│  Dedup   │──▶│ Reasoning │──▶│ Scoring │──▶│Communication  │
 │          │   │            │   │          │   │           │   │         │   │               │
 │ Scrape   │   │ Clean &    │   │ Hash +   │   │ Cross-    │   │ Confi-  │   │ Strategy +    │
 │ 6+ data  │   │ structure  │   │ semantic │   │ source    │   │ dence   │   │ email +       │
 │ sources  │   │ into typed │   │ similar- │   │ inference │   │ gating  │   │ LinkedIn +    │
 │          │   │ features   │   │ ity      │   │ & hypo-   │   │ & opp.  │   │ sequence +    │
 │          │   │            │   │ filter   │   │ thesis    │   │ scoring │   │ objections    │
 └──────────┘   └────────────┘   └──────────┘   └───────────┘   └─────────┘   └───────────────┘
   Agents 1–4      All agents      All agents     Agents 7–8      Agent 7        Agent 13

Packet Architecture

Every agent produces a typed packet with this base structure:

class BasePacket:
    structured: dict        # Core extracted JSON output
    vector_refs: list[str]  # Qdrant collection references
    graph_refs: list[str]   # Neo4j node references
    evidence: list          # Raw evidence items used
    screenshots: list       # Screenshot file paths
    confidence: float       # 0.0 – 1.0

Packets flow downstream and accumulate. Agent 13 has access to ALL packets from Agents 1–12 simultaneously.

Example Output

OpportunityPacket (Agent 7)

{
  "score": 0.89,
  "growth": "high",
  "technology": "high",
  "timing": "good",
  "pressure": "high",
  "confidence": 0.92
}

PainPacket (Agent 8)

{
  "hypotheses": [
    {
      "name": "qualification bottleneck risk",
      "confidence": 0.67,
      "evidence": ["SDR hiring signal", "AI market pressure", "manual workflow detected"]
    },
    {
      "name": "technology modernization need",
      "confidence": 0.72,
      "evidence": ["competitor AI adoption", "manual CRM detected", "founder automation vision"]
    }
  ]
}

CommunicationPacket (Agent 13)

{
  "strategy": {
    "persona": "sales leader",
    "channel": "email+linkedin",
    "intent": "relationship",
    "timing": "morning"
  },
  "email": [{
    "subject": "Quick thought on your SDR scaling",
    "body": "Noticed you've been hiring SDRs aggressively this quarter — three new roles opened in the last 30 days. Teams scaling that fast usually hit a qualification bottleneck before the new reps are ramped. Worth a quick conversation?",
    "CTA": "Open to a 15-minute discussion on how similar teams have handled this?"
  }],
  "linkedin": [{
    "message": "Saw your recent post on AI in sales — aligns closely with something we've been building for teams in exactly your position."
  }],
  "followups": ["day3_pain_insight", "day7_roi_followup", "day14_check_in"],
  "sequence": ["email", "linkedin", "email", "ROI followup"],
  "objections": {
    "budget": "We've seen teams offset the cost within 60 days through SDR productivity gains.",
    "timing": "Most teams start with a pilot on one segment — no full rollout needed.",
    "integration": "We integrate natively with HubSpot, which you're already using."
  },
  "confidence": 0.95
}

Impact Metrics

Metric	Value
Autonomous Agents	13
Data Sources Analyzed	6+ per prospect
Time Reduction	95% (4–6 hrs → ~15 min)
Intelligence Layers	5 phases, 7 signal dimensions
Memory Systems	3 (SQLite + Qdrant + Neo4j)
Data Privacy	100% local — zero external LLM API calls
LLMs Used	4 specialized local models
Confidence Scoring	Every packet, every agent
Hallucination Rate	Zero — evidence-traced output
LLM Cost Per Prospect	$0.00

Installation

Prerequisites

Python 3.11+
Node.js 18+
Docker & Docker Compose
16GB+ RAM (for local LLM inference)

1. Clone the Repository

git clone https://github.com/your-username/sales-intelligence-multi-agent-system.git
cd sales-intelligence-multi-agent-system

2. Start Infrastructure Services (Docker)

cd infra
docker compose up -d

This starts:

Redis on port 6379 — message bus
Qdrant on port 6333 — vector database (dashboard)
Neo4j on ports 7474 (browser) / 7687 (bolt) — graph database (browser)

3. Install & Pull Ollama Models

# Install Ollama (macOS)
brew install ollama

# Pull all required models
ollama pull qwen2.5:7b
ollama pull deepseek-r1:8b
ollama pull llama3.1:8b
ollama pull nomic-embed-text

# Or use the convenience script
./scripts/pull_models.sh

4. Set Up the Backend

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env — add Firecrawl and Apify keys (optional, for P2 collectors)

# Start the API server
uvicorn backend.api.main:app --reload --port 8000

5. Set Up the Frontend

cd frontend
npm install
npm run dev

6. Verify Installation

redis-cli ping                          # → PONG
curl http://localhost:6333/healthz      # → OK
open http://localhost:7474              # → Neo4j Browser
ollama list                             # → Shows all 4 models
curl http://localhost:8000/docs         # → FastAPI Swagger UI

Running the System

Quick Start — One Command

The fastest way to launch the entire stack:

./scripts/start_all.sh

This single script handles everything: Docker infrastructure, Ollama model checks, Python dependencies, FastAPI backend, and Next.js frontend. Once complete, you'll see:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  System running:
    Frontend   → http://localhost:3000
    Backend    → http://localhost:8000
    API docs   → http://localhost:8000/docs
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Press Ctrl+C to gracefully shut down all services.

Step-by-Step Manual Startup

A. Infrastructure Startup

Start all Docker services (Redis, Qdrant, Neo4j):

cd infra
docker compose up -d

Verify all containers are running:

docker compose ps

Expected output:

NAME                  STATUS
sales_intel_redis     Up      → Port 6379
sales_intel_qdrant    Up      → Port 6333, 6334
sales_intel_neo4j     Up      → Port 7474, 7687

B. Ollama Model Startup

Ensure Ollama is running, then verify all four models are available:

# Start Ollama (if not already running)
ollama serve &

# Verify models
ollama list

If any model is missing, pull it:

ollama pull qwen2.5:7b        # Extraction model
ollama pull deepseek-r1:8b    # Reasoning model
ollama pull llama3.1:8b       # Buyer simulation model
ollama pull nomic-embed-text  # Embedding model

C. Backend Startup

source venv/bin/activate
uvicorn backend.api.main:app --host 0.0.0.0 --port 8000 --reload

Available at:

API Base → http://localhost:8000
Interactive Docs → http://localhost:8000/docs
OpenAPI Schema → http://localhost:8000/openapi.json

D. Frontend Startup

In a separate terminal:

cd frontend
npm run dev

Available at → http://localhost:3000

Service Health Checks

Service	Check Command	Expected
Redis	`redis-cli ping`	`PONG`
Qdrant	`curl -s http://localhost:6333/healthz`	`OK`
Neo4j	`open http://localhost:7474`	Neo4j Browser loads
Ollama	`ollama list`	Shows all 4 models
Backend	`curl -s http://localhost:8000/docs`	FastAPI Swagger UI
Frontend	`open http://localhost:3000`	React app loads

Running the Pipeline

Via the Frontend UI

Open http://localhost:3000
Panel A — Enter company inputs (Website URL + LinkedIn URL required)
Panel B — Add founder profiles (optional, up to 5 founders)
Panel C — Toggle optional agents on/off per phase
Click "Run intelligence pipeline"
Watch real-time agent status updates as each phase executes
View the final CommunicationPacket output

Via the REST API

curl -X POST http://localhost:8000/api/run-pipeline \
  -H "Content-Type: application/json" \
  -d '{
    "company": {
      "website_url": "https://example-company.com",
      "linkedin_url": "https://linkedin.com/company/example",
      "instagram_handle": "@example",
      "blog_url": "https://example-company.com/blog"
    },
    "founders": [
      {
        "name": "Jane Doe",
        "linkedin_url": "https://linkedin.com/in/janedoe",
        "twitter_url": "https://x.com/janedoe"
      }
    ],
    "agent_toggles": {
      "agent2_linkedin": true,
      "agent3_instagram": true,
      "agent4_founder": true,
      "agent5_blog": true
    }
  }'

Check pipeline status:

curl http://localhost:8000/api/status/{run_id}

Stopping the System

If using `start_all.sh`

Press Ctrl+C — the cleanup handler stops the backend, frontend, and Docker services automatically.

Manual Shutdown

cd infra
docker compose stop

# To remove containers and volumes entirely
docker compose down -v

Documentation

Full technical documentation lives in the docs/ directory:

docs/
├── README_1_SYSTEM_ARCHITECTURE.md    # Complete agent reference, packets, collectors, memory writes
├── README_2_BUILD_TASK_STRUCTURE.md   # 12-session implementation blueprint, 100+ tasks
├── architecture.png                   # System architecture diagram
├── orchestrator.png                   # Orchestrator flow diagram
└── pipeline.png                       # Agent pipeline diagram

Document	Description	Audience
System Architecture	Full technical reference — every agent, every collector, every packet, every memory write	Engineers, Architects
Build Task Structure	Implementation blueprint — 12 sessions, 100+ tasks, verification checklists	Developers, Project Managers

System Principles

Four principles govern every agent in the system. They must never be violated:

1. LLMs never scrape. Collectors scrape. LLMs only understand. Raw HTML is never passed to an LLM. Collectors extract text. LLMs receive only structured, cleaned feature data.

2. Every agent produces a typed Packet. No agent passes raw strings downstream. Every output is a structured Packet with defined fields, confidence scores, and evidence references.

3. Confidence scores gate progression. Every packet carries a confidence score (0.0–1.0). Low confidence triggers escalation to more expensive collectors — never a full stop.

4. Fallbacks prevent full failures. Every collector has a fallback. Every agent has a reduced-scope path. The pipeline always produces output, even with partial data.

LLM Usage by Agent

Agent	Qwen 2.5 (Extract)	DeepSeek-R1 (Reason)	Llama 3.1 (Simulate)
1–5 (Collection + Blog)	Theme extraction	Relationship inference	—
6 (Market)	Competitor extraction	Market reasoning & tiering	—
7 (Opportunity)	Signal extraction	Cross-source reasoning	—
8 (Pain)	Signal organization	Hypothesis generation	Buyer simulation
9 (Target)	Role extraction	Ownership reasoning	—
10 (Product)	Feature extraction	Use case reasoning	—
11 (Solution)	Feature mapping	Gap analysis	—
12 (ROI)	Impact extraction	ROI inference	—
13 (Communication)	Planning & structure	Writing & reasoning	—

Roadmap

Near-Term

Batch Processing — Run pipeline against multiple prospects in parallel
CRM Integration — Direct export to HubSpot, Salesforce, Pipedrive
Learning Loop — Feed successful outreach responses back to improve Agent 13
A/B Testing Engine — Generate multiple email variants with different hooks

Mid-Term

Real-Time Monitoring — Continuous tracking of prospect signals (new hires, posts, funding)
Team Dashboard — Multi-user support with shared prospect intelligence
Custom Agent Plugins — SDK for adding domain-specific collection agents
Model Fine-Tuning — Fine-tune local models on successful outreach data

Long-Term

Autonomous Outreach — Agent 13 sends emails and LinkedIn messages directly (with approval gate)
Multi-Language Support — Generate outreach in the prospect's native language
Voice Intelligence — Analyze podcast appearances and webinar recordings
Enterprise Deployment — Kubernetes orchestration, role-based access, audit logging

Built by Vagish Kumar G

13 agents. 5 phases. 3 memory layers. Zero hallucination. Zero API costs.

Transforming sales intelligence from manual research to autonomous AI pipelines.

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Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
backend		backend
docs		docs
frontend		frontend
infra		infra
scripts		scripts
.env.example		.env.example
.gitignore		.gitignore
README.md		README.md
README_1_SYSTEM_ARCHITECTURE.md		README_1_SYSTEM_ARCHITECTURE.md
README_2_BUILD_TASK_STRUCTURE.md		README_2_BUILD_TASK_STRUCTURE.md
pytest.ini		pytest.ini
requirements.txt		requirements.txt
sales_multi_agent_architecture.svg		sales_multi_agent_architecture.svg

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Sales Intelligence Multi-Agent System

AI-Powered Prospect Research & Personalized Outreach — Fully Local, Privacy-First

Executive Summary

What It Does

Core Value Proposition

Privacy-First Design

Business Impact

The Problem

Manual vs. AI-Powered Workflow

Operational Impact & ROI Metrics

System Architecture

High-Level Architecture Diagram

End-to-End Intelligence Flow

Phase Execution Rules

Multi-Agent Pipeline

All 13 Agents at a Glance

Phase 1 — Collection Agents (Agents 1–4)

Phase 2 — Secondary Intelligence (Agents 5–6)

Phase 3 — Reasoning Agents (Agents 7–8)

Phase 4–5 — Synthesis Agents (Agents 9–13)

Orchestrator Architecture

LangGraph Supervisor

Redis Communication Flow

Confidence System

Failure Handling Philosophy

Technology Stack

AI Layer

Orchestration Layer

Memory Layer

Collection Layer

Frontend Layer

Memory Layer

Triple-Layer Persistence Architecture

Why Three Layers?

Hallucination Prevention

Intelligence Flow

Packet Architecture

Example Output

OpportunityPacket (Agent 7)

PainPacket (Agent 8)

CommunicationPacket (Agent 13)

Impact Metrics

Installation

Prerequisites

1. Clone the Repository

2. Start Infrastructure Services (Docker)

3. Install & Pull Ollama Models

4. Set Up the Backend

5. Set Up the Frontend

6. Verify Installation

Running the System

Quick Start — One Command

Step-by-Step Manual Startup

A. Infrastructure Startup

B. Ollama Model Startup

C. Backend Startup

D. Frontend Startup

Service Health Checks

Running the Pipeline

Via the Frontend UI

Via the REST API

Stopping the System

If using start_all.sh

Manual Shutdown

Documentation

System Principles

LLM Usage by Agent

Roadmap

Near-Term

Mid-Term

Long-Term

Built by Vagish Kumar G

About

Topics

Resources

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If using `start_all.sh`

Packages