A full-stack web application that matches startup founders with potential investors based on industry, funding stage, risk tolerance, and investment amount.

• HTML5 - Semantic structure
• CSS3 - Modern styling with CSS Grid/Flexbox
• Vanilla JavaScript - Interactive functionality
• Responsive Design - Mobile-first approach

• Python Flask - RESTful API
• Flask-CORS - Cross-origin resource sharing
• Redis-Caching - For fast data fetching
• NoSQL MonogoDB - For fast and easy data storage
• Google Sheets - To store scraped investor data
• GEMINI - For search results

• Python 3.7+
• pip (Python package manager)
• Modern web browser

1. Clone/Download the files

   mkdir recon
   cd recon

2. Save the files

   • Save app.py (Flask backend)
   • Save requirements.txt
   • Save index.html (Frontend)

3. Install Python dependencies

   pip install -r requirements.txt

4. Run the Flask backend

   python app.py

   The API will be available at http://localhost:5000

5. Open the frontend

   • Open index.html in your web browser
   • Or serve it via a simple HTTP server:

   # Python 3
   python -m http.server 8080
   
   # Then visit http://localhost:8080

Find Matches:

curl -X POST http://localhost:5000/api/match \
  -H "Content-Type: application/json" \
  -d '{
    "industry": "fintech",
    "funding_stage": "seed",
    "risk_tolerance": "high",
    "investment_amount": 500000,
    "company_name": "MyStartup"
  }'

The scoring system awards points based on alignment:

• +3 points - Industry match
• +2 points - Funding stage overlap
• +1 point - Risk tolerance alignment
• +1 point - Investment amount within range

Maximum Score: 7 points

• Multi-step wizard interface
• Real-time form validation
• Loading states and animations
• Responsive design
• Professional UI matching the reference
• Interactive match results

For production deployment:

1. Input validation - Add server-side validation
2. Rate limiting - Prevent API abuse
3. HTTPS - Secure data transmission
4. Database - Replace in-memory data with persistent storage

• Real-time matching with WebSocket connections
• Machine learning for improved matching accuracy
• Messaging system between founders and investors
• Analytics dashboard for platform insights

• Use the setup instructions above

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 5000
CMD ["python", "app.py"]

1. Implement user authentication (JWT tokens)
2. Add email notifications for matches
3. Add investor application workflow

This is a MVP implementation.