QuickScore - Pre-Seed Startup Analyzer MVP

A comprehensive AI-powered platform for analyzing pre-seed startups, extracting insights from pitch decks, and providing investment recommendations.

Flow Diagram

Streamlit Dashboard

QuickScore Dashboard

Features

• Document Processing: Extract and analyze pitch decks, financial documents
• AI-Powered Evaluation: OpenAI-based scoring across 5 key dimensions
• Web Scraping: Intelligent data collection from LinkedIn, websites, GitHub
• ML Analytics: Feature engineering and predictive modeling
• Batch Processing: Analyze multiple startups simultaneously
• Investment Reports: Generate professional analysis reports

Quick Start

Prerequisites

• Python 3.11+
• PostgreSQL 15+
• Redis 7+
• Node.js 18+ (for frontend)

Installation

1. Clone and setup backend:

git clone <your-repo-url>
cd quickscore

# Create virtual environment
python -m venv venv
venv\Scripts\activate  # Windows
# source venv/bin/activate  # Linux/Mac

# Install dependencies
pip install -r requirements.txt
playwright install

2. Setup database:

# Start PostgreSQL and Redis (using Docker)
docker-compose up -d postgres redis

# Run migrations
alembic upgrade head

3. Configure environment:

cp .env.example .env
# Edit .env with your API keys and database URLs

4. Start development servers:

# Backend API
uvicorn app.main:app --reload --port 8000

# Celery worker (new terminal)
celery -A app.worker worker --loglevel=info

# Celery beat scheduler (new terminal)  
celery -A app.worker beat --loglevel=info

API Usage

Create Startup

curl -X POST "http://localhost:8000/api/v1/startups" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "TechCorp",
    "website": "https://techcorp.com",
    "industry": "SaaS",
    "description": "AI-powered project management tool"
  }'

Trigger Analysis

curl -X POST "http://localhost:8000/api/v1/startups/{startup_id}/analyze" \
  -F "pitch_deck=@pitch_deck.pdf" \
  -F "founder_linkedin=https://linkedin.com/in/founder"

Get Results

curl "http://localhost:8000/api/v1/analyses/{analysis_id}"

Architecture

• FastAPI: REST API with async support
• SQLAlchemy: Database ORM with Alembic migrations
• Celery: Background task processing
• OpenAI: LLM-powered evaluation
• Playwright: Web scraping with anti-detection
• scikit-learn: Feature engineering and ML models

Scoring Framework

The system evaluates startups across 5 dimensions:

1. Team (40%): Founder experience, domain expertise, track record
2. Market (25%): TAM, timing, competition, growth potential
3. Product (15%): Differentiation, technical feasibility, UX
4. Traction (10%): Early metrics, user feedback, partnerships
5. Pitch Quality (10%): Clarity, storytelling, presentation

Deployment

Production with Docker

docker-compose up --build

Deploy to Railway

railway login
railway up

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=app tests/

# Run specific test categories
pytest tests/unit/
pytest tests/integration/

Development

Code Quality

# Format code
black app/ tests/
isort app/ tests/

# Lint
flake8 app/ tests/

Database Migrations

# Create migration
alembic revision --autogenerate -m "Add new feature"

# Apply migrations
alembic upgrade head

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

Contributers

Mehardeep Singh Sandhu

GitHub