VentureSignal

AI-powered B2B SaaS startup screener built for venture capital deal-sourcing.

VentureSignal ingests ~2,300 B2B startups from the YC-OSS API, enriches them by scraping company websites, scores each one via Claude AI against a configurable VC investment thesis, and surfaces results through an interactive React dashboard.

Tech Stack

Layer	Technology
Backend	Python 3.11 + FastAPI
LLM	Anthropic Claude API
Database	SQLite (dev) / PostgreSQL (prod)
Frontend	React 18 + TypeScript 5
Scraping	httpx + BeautifulSoup4
Infra	Docker + docker-compose

Quick Start

Prerequisites

• Python 3.11+
• Node.js 20+
• An Anthropic API key

Backend

cd backend
cp .env.example .env
# Edit .env with your ANTHROPIC_API_KEY
pip install -r requirements.txt
uvicorn src.main:app --reload --port 8000

Frontend

cd frontend
npm install
npm run dev

Open http://localhost:5173 to view the dashboard.

Using the Pipeline

1. Ingest companies — click "Ingest Companies" in the dashboard or POST /api/ingest
2. Enrich websites — click "Enrich Websites" or POST /api/enrich
3. Score with AI — click "Score Batch (20)" or POST /api/score?batch_size=20

Docker

docker compose up --build

• Backend: http://localhost:8000 (API docs at /docs)
• Frontend: http://localhost:3000

API Endpoints

Method	Path	Description
GET	/api/companies	List companies with filters
GET	/api/companies/{id}	Company detail + scores
POST	/api/ingest	Trigger YC-OSS data ingestion
POST	/api/enrich	Trigger website enrichment
POST	/api/score	Trigger LLM scoring batch
GET	/api/stats	Dashboard summary stats

Scoring Dimensions

Each company is scored 1-10 across five dimensions:

• Thesis Fit — B2B SaaS or AI infrastructure alignment
• Market Timing — Growing or emerging market
• Product Clarity — Clear, differentiated value proposition
• Team Signal — Team size relative to stage suggests traction
• Overall Signal — First meeting recommendation

Project Structure

venturesignal/
├── backend/
│   └── src/
│       ├── main.py          # FastAPI app
│       ├── config.py         # Settings
│       ├── models/           # Pydantic + SQLAlchemy models
│       ├── services/         # Ingest, enrich, scorer
│       ├── api/routes.py     # REST endpoints
│       ├── db/database.py    # DB setup
│       └── prompts/thesis.txt
├── frontend/
│   └── src/
│       ├── App.tsx
│       ├── components/       # Dashboard, Table, Card, Filters
│       └── services/api.ts
└── docker-compose.yml

License

MIT