AI AgentsB2B Lead GenerationPrototype

Agentic LeadGen

~54 AI agents across 9 squads — orchestrator-worker topology

A production-grade agentic lead-gen platform powered by ~50 specialized AI agents operating in parallel under a central orchestrator. LangGraph + Temporal for durable orchestration, FastAPI backend, Next.js frontend, Postgres + Redis + Qdrant + Neo4j data plane, MCP servers as the unified tool interface.

~54 agents across 9 squadsLangGraph + Temporal durable orchestrationMCP as universal tool boundaryOrchestrator-worker topologyPostgres + Redis + Qdrant + Neo4jGDPR/CCPA compliance by construction

Investor view

agentic-leadgen.app.demo

Live demo

54/54

Interactive

8 views

Vision

"B2B lead-gen is a fragmented pipeline. Agentic LeadGen replaces it with a swarm of 54 specialized agents — sourcing, enriching, scoring, sequencing, and converting in parallel, governed by compliance agents."

Development progress35%

StagePrototype

Est. to MVP6 months to enterprise pilot

Suggested pricing$2K–$20K / month enterprise

The problem

Why this matters

Fragmented lead-gen

Sourcing, enrichment, outreach are separate tools.

No orchestration
Data silos
Manual handoffs

No durable execution

Long-running multi-touch sequences crash on failure.

Lost work
No retries
No human-in-loop waits

Compliance afterthought

GDPR/CAN-SPAM patched in, not designed in.

Legal risk
DNC lists manual
No audit trail

The solution

How it works

54-agent swarm

9 squads (sourcing, enrichment, scoring, copywriting, sequencing, sending, reply-classification, CRM-sync, compliance).

Durable orchestration

LangGraph (cognitive graph) + Temporal (durable workflows).

MCP tool boundary

All external tools (Apollo, Hunter, LinkedIn, CRM) as MCP servers — swappable, observable.

Compliance by construction

Suppression + Compliance Review agents enforce GDPR/CCPA/CAN-SPAM.

Capabilities

Key features

The core capabilities that make this product distinctive.

54 AI Agents

9 specialized squads

Orchestrator

Master DAG decomposition

Durable Workflows

LangGraph + Temporal

MCP Tools

Universal tool boundary

Compliance Agents

GDPR/CCPA/CAN-SPAM built-in

Multi-DB Plane

Postgres + Redis + Qdrant + Neo4j

Under the hood

Architecture

Orchestrator-worker, LangGraph + Temporal, MCP tools, multi-DB

FrontendNext.js 15 + tRPC

Type-safe control center

BackendFastAPI + Pydantic v2

Async-first API

OrchestrationLangGraph + Temporal

Cognitive graph + durable workflows

ToolsMCP servers

Apollo, Hunter, Clearbit, LinkedIn, CRM

Data PlanePostgres + Redis + Qdrant + Neo4j

Leads, hot state, vectors, graph

ObservabilityOpenTelemetry + LangSmith

Traces, evals, costs

Built with

Tech stack

Frontend

Next.js 15tRPCTailwindshadcn/ui

Backend

FastAPIPydantic v2asyncio

Orchestration

LangGraphTemporalMCP servers

Data

PostgreSQL 16Redis 7QdrantNeo4jKafka/Redpanda

LLM

Claude Opus 5Sonnet 5Haiku

Infra

Docker ComposeKubernetesTerraform

The path

Roadmap

Where each product stands and where it's going.

Phase 0Done

Principles & stack

8 non-negotiable principles
Locked tech stack
Phased plan

Phase 1In progress

Orchestration core

LangGraph setup
Temporal plugin
MCP scaffolding
Sourcing squad

Phase 2Planned

Agent squads

Enrichment, scoring, copywriting, sequencing squads
Compliance agents

Phase 3Planned

Production

Migration adapter
Kubernetes
Enterprise SSO

Phase 0Done

Principles & stack

8 non-negotiable principles
Locked tech stack
Phased plan

Phase 1In progress

Orchestration core

LangGraph setup
Temporal plugin
MCP scaffolding
Sourcing squad

Phase 2Planned

Agent squads

Enrichment, scoring, copywriting, sequencing squads
Compliance agents

Phase 3Planned

Production

Migration adapter
Kubernetes
Enterprise SSO

For partners

Investment & partnership

An honest look at what's done, what's missing, and what I'm looking for.

What's done

8 engineering principles
Locked tech stack
Phased execution plan
MASTER_PROMPT spec
LangGraph + Temporal design
MCP tool boundary spec
Compliance-by-construction design
Observability design (OTel + LangSmith)

What's missing

Runnable agent implementations
MCP server builds
Migration from legacy
Kubernetes deployment
Enterprise auth
Production monitoring

Looking for

Strategic InvestorCo-FounderTechnical Partner

Needs

Funding for LLM compute
Infrastructure engineer
Enterprise pilot
Compliance legal review

Market potential

B2B lead-gen ~$12B, AI-native growing 50% YoY

Competitive advantages

54-agent swarm
Durable Temporal workflows
MCP universal tools
Compliance by construction

Target audience

B2B SaaS sales teams
Revenue ops
Enterprise marketing
Outbound agencies

Business model

Enterprise SaaS — Per-seat with volume tiers
Agent compute — Metered LLM + tool usage
MCP marketplace — Third-party tool integrations

Investor Intelligence

Valuation & Documentation

Estimated Valuation

AI-powered

$4.5M$3.5M - $5.5M

Berkus + Scorecard + Risk Factor SummationUpdated 8/1/2026Confidence: 55%

Valuation Factors

Market Size (TAM)

820%

Team & Execution

515%

Product Completion

415%

Competitive Moat

715%

Revenue Potential

715%

Scalability

610%

Timing & Market Readiness

810%

⚠️ This is an AI-generated estimate for reference only. Actual valuation depends on due diligence, market conditions, and negotiation.

READMEproject documentation

# Agentic LeadGen

~54 AI agents across 9 squads — orchestrator-worker topology

## Overview
A production-grade agentic lead-gen platform powered by ~50 specialized AI agents operating in parallel under a central orchestrator. LangGraph + Temporal for durable orchestration, FastAPI backend, Next.js frontend, Postgres + Redis + Qdrant + Neo4j data plane, MCP servers as the unified tool interface.

## Vision
B2B lead-gen is a fragmented pipeline. Agentic LeadGen replaces it with a swarm of 54 specialized agents — sourcing, enriching, scoring, sequencing, and converting in parallel, governed by compliance agents.

## Problem
### Fragmented lead-gen
Sourcing, enrichment, outreach are separate tools.
- No orchestration
- Data silos
- Manual handoffs
### No durable execution
Long-running multi-touch sequences crash on failure.
- Lost work
- No retries
- No human-in-loop waits
### Compliance afterthought
GDPR/CAN-SPAM patched in, not designed in.
- Legal risk
- DNC lists manual
- No audit trail

## Solution
### 54-agent swarm
9 squads (sourcing, enrichment, scoring, copywriting, sequencing, sending, reply-classification, CRM-sync, compliance).
### Durable orchestration
LangGraph (cognitive graph) + Temporal (durable workflows).
### MCP tool boundary
All external tools (Apollo, Hunter, LinkedIn, CRM) as MCP servers — swappable, observable.
### Compliance by construction
Suppression + Compliance Review agents enforce GDPR/CCPA/CAN-SPAM.

## Key Features
- **54 AI Agents**: 9 specialized squads
- **Orchestrator**: Master DAG decomposition
- **Durable Workflows**: LangGraph + Temporal
- **MCP Tools**: Universal tool boundary
- **Compliance Agents**: GDPR/CCPA/CAN-SPAM built-in
- **Multi-DB Plane**: Postgres + Redis + Qdrant + Neo4j

## Architecture
**Pattern:** Orchestrator-worker, LangGraph + Temporal, MCP tools, multi-DB

- **Frontend** (Next.js 15 + tRPC): Type-safe control center
- **Backend** (FastAPI + Pydantic v2): Async-first API
- **Orchestration** (LangGraph + Temporal): Cognitive graph + durable workflows
- **Tools** (MCP servers): Apollo, Hunter, Clearbit, LinkedIn, CRM
- **Data Plane** (Postgres + Redis + Qdrant + Neo4j): Leads, hot state, vectors, graph
- **Observability** (OpenTelemetry + LangSmith): Traces, evals, costs

## Tech Stack
### Frontend
- Next.js 15
- tRPC
- Tailwind
- shadcn/ui
### Backend
- FastAPI
- Pydantic v2
- asyncio
### Orchestration
- LangGraph
- Temporal
- MCP servers
### Data
- PostgreSQL 16
- Redis 7
- Qdrant
- Neo4j
- Kafka/Redpanda
### LLM
- Claude Opus 5
- Sonnet 5
- Haiku
### Infra
- Docker Compose
- Kubernetes
- Terraform

## Business Model
- **Enterprise SaaS**: Per-seat with volume tiers
- **Agent compute**: Metered LLM + tool usage
- **MCP marketplace**: Third-party tool integrations

## Target Audience
- B2B SaaS sales teams
- Revenue ops
- Enterprise marketing
- Outbound agencies

## Competitive Advantages
- 54-agent swarm
- Durable Temporal workflows
- MCP universal tools
- Compliance by construction

## Market Potential
B2B lead-gen ~$12B, AI-native growing 50% YoY

## Pricing
$2K–$20K / month enterprise

### Pricing Tiers
- **Team** ($2,000/mo) — B2B SaaS sales teams and RevOps
  - 5 seats
  - 10 agents
  - Temporal workflows
  - MCP basics
  - Standard support
- **Business** ($8,000/mo) — Enterprise marketing and outbound agencies
  - 25 seats
  - 30 agents
  - Custom MCP tools
  - Compliance review
  - Priority support
- **Enterprise** ($20,000+/mo) — Enterprise revenue orgs and regulated verticals
  - Unlimited seats
  - All 54 agents
  - Custom workflow design
  - SOC2 + GDPR
  - Dedicated CSM

## Roadmap
### Phase 0: Principles & stack _(done)_
- 8 non-negotiable principles
- Locked tech stack
- Phased plan
### Phase 1: Orchestration core _(progress)_
- LangGraph setup
- Temporal plugin
- MCP scaffolding
- Sourcing squad
### Phase 2: Agent squads _(planned)_
- Enrichment, scoring, copywriting, sequencing squads
- Compliance agents
### Phase 3: Production _(planned)_
- Migration ada

... (truncated)

Auto-generated from product configuration data

Watch the Agentic LeadGen promo

A 30-second overview of the product vision and value proposition

Try the Agentic LeadGen demo

An interactive mock demo with realistic data, dashboards, and an AI assistant. No real backend — just the experience of using the product.

Review investor details

Mock data only · No source code exposed · Safe to explore

Interested in this product?

Contact about Agentic LeadGen

Reach out for a deeper walkthrough, partnership terms, or investment details. Your message is sent directly to the portfolio owner and tagged with this product so they can reply with the right context.

🚀 Agentic LeadGen🎯 Prototype📈 35% complete

One-click inquiry

Click Send an inquiry to compose a message — the subject is pre-filled with this product so the owner knows exactly what you're asking about.

Keep exploring

Related products

Products with similar domain, stage, or partnership potential.

Quick demo launch:

# Agentic LeadGen ~54 AI agents across 9 squads — orchestrator-worker topology ## Overview A production-grade agentic lead-gen platform powered by ~50 specialized AI agents operating in parallel under a central orchestrator. LangGraph + Temporal for durable orchestration, FastAPI backend, Next.js frontend, Postgres + Redis + Qdrant + Neo4j data plane, MCP servers as the unified tool interface. ## Vision B2B lead-gen is a fragmented pipeline. Agentic LeadGen replaces it with a swarm of 54 specialized agents — sourcing, enriching, scoring, sequencing, and converting in parallel, governed by compliance agents. ## Problem ### Fragmented lead-gen Sourcing, enrichment, outreach are separate tools. - No orchestration - Data silos - Manual handoffs ### No durable execution Long-running multi-touch sequences crash on failure. - Lost work - No retries - No human-in-loop waits ### Compliance afterthought GDPR/CAN-SPAM patched in, not designed in. - Legal risk - DNC lists manual - No audit trail ## Solution ### 54-agent swarm 9 squads (sourcing, enrichment, scoring, copywriting, sequencing, sending, reply-classification, CRM-sync, compliance). ### Durable orchestration LangGraph (cognitive graph) + Temporal (durable workflows). ### MCP tool boundary All external tools (Apollo, Hunter, LinkedIn, CRM) as MCP servers — swappable, observable. ### Compliance by construction Suppression + Compliance Review agents enforce GDPR/CCPA/CAN-SPAM. ## Key Features - **54 AI Agents**: 9 specialized squads - **Orchestrator**: Master DAG decomposition - **Durable Workflows**: LangGraph + Temporal - **MCP Tools**: Universal tool boundary - **Compliance Agents**: GDPR/CCPA/CAN-SPAM built-in - **Multi-DB Plane**: Postgres + Redis + Qdrant + Neo4j ## Architecture **Pattern:** Orchestrator-worker, LangGraph + Temporal, MCP tools, multi-DB - **Frontend** (Next.js 15 + tRPC): Type-safe control center - **Backend** (FastAPI + Pydantic v2): Async-first API - **Orchestration** (LangGraph + Temporal): Cognitive graph + durable workflows - **Tools** (MCP servers): Apollo, Hunter, Clearbit, LinkedIn, CRM - **Data Plane** (Postgres + Redis + Qdrant + Neo4j): Leads, hot state, vectors, graph - **Observability** (OpenTelemetry + LangSmith): Traces, evals, costs ## Tech Stack ### Frontend - Next.js 15 - tRPC - Tailwind - shadcn/ui ### Backend - FastAPI - Pydantic v2 - asyncio ### Orchestration - LangGraph - Temporal - MCP servers ### Data - PostgreSQL 16 - Redis 7 - Qdrant - Neo4j - Kafka/Redpanda ### LLM - Claude Opus 5 - Sonnet 5 - Haiku ### Infra - Docker Compose - Kubernetes - Terraform ## Business Model - **Enterprise SaaS**: Per-seat with volume tiers - **Agent compute**: Metered LLM + tool usage - **MCP marketplace**: Third-party tool integrations ## Target Audience - B2B SaaS sales teams - Revenue ops - Enterprise marketing - Outbound agencies ## Competitive Advantages - 54-agent swarm - Durable Temporal workflows - MCP universal tools - Compliance by construction ## Market Potential B2B lead-gen ~$12B, AI-native growing 50% YoY ## Pricing $2K–$20K / month enterprise ### Pricing Tiers - **Team** ($2,000/mo) — B2B SaaS sales teams and RevOps - 5 seats - 10 agents - Temporal workflows - MCP basics - Standard support - **Business** ($8,000/mo) — Enterprise marketing and outbound agencies - 25 seats - 30 agents - Custom MCP tools - Compliance review - Priority support - **Enterprise** ($20,000+/mo) — Enterprise revenue orgs and regulated verticals - Unlimited seats - All 54 agents - Custom workflow design - SOC2 + GDPR - Dedicated CSM ## Roadmap ### Phase 0: Principles & stack _(done)_ - 8 non-negotiable principles - Locked tech stack - Phased plan ### Phase 1: Orchestration core _(progress)_ - LangGraph setup - Temporal plugin - MCP scaffolding - Sourcing squad ### Phase 2: Agent squads _(planned)_ - Enrichment, scoring, copywriting, sequencing squads - Compliance agents ### Phase 3: Production _(planned)_ - Migration ada ... (truncated)