Founder Matching Platform

Overview

Finding the right co-founder is one of the biggest challenges in launching a successful startup. Many entrepreneurs struggle to connect with compatible partners who share their vision, skills, and commitment.

To solve this, Founder Matching was developed as an AI-powered platform that intelligently connects entrepreneurs based on their skills, business interests, and personal compatibility.

The platform leverages data-driven matching techniques to analyze user-provided data, ensuring optimal matching results. It considers factors such as expertise, industry focus, personality traits, and location.

This application is:

• A comprehensive co-founder matchmaking platform that improves startup success rates.
• Built to evolve with AI and ML-driven ranking models for enhanced matching accuracy.
• Originally developed as part of a client project, the system has the potential to evolve into a widely adopted startup networking tool.

Features

AI-Driven Co-Founder Matching

• Uses data-based analysis to match founders based on skills, interests, and compatibility.
• Analyzes factors such as technical expertise, business acumen, leadership traits, and industry focus.
• Future iterations will integrate Machine Learning (ML) ranking algorithms to enhance match accuracy and provide personalized recommendations.
• Planned improvements include data-driven ranking models that learn from user preferences, feedback, and successful founder connections.

Data-Driven Insights & Recommendations

• Provides real-time recommendations based on user profiles and preferences.
• The platform will evolve with AI-based ranking models to optimize match accuracy and improve user experience.

Secure & Scalable Architecture

• Designed with scalability and security in mind, ensuring safe and seamless user interactions.
• Implements authentication and privacy controls to protect user data.

Tech Stack

Frontend

• Next.js: SSR and SSG for optimized performance.
• Tailwind CSS: Utility-first styling.
• Custom CSS: For complex designs and animations.
• Clerk: Secure authentication with third-party logins.
• Framer Motion: Dynamic animations.

Backend

• Django REST Framework: RESTful APIs and serializers.
• PostgreSQL: Primary database system.
• Redis: Caching and session management.
• JWT Authentication: Secure user authentication.

Hosting

• Vercel: For deploying the frontend with seamless integration.
• Railway: Backend and database hosting.

Installation & Setup

If you wish to clone or contribute to the project, follow these steps:

Prerequisites

• Install Node.js
• Install Python
• Install PostgreSQL for database support

Steps

1. Clone the repository:

   git clone https://github.com/HARDeConstruction/FounderMatching.git
   cd FounderMatching

2. Install dependencies:

   npm install   # Install frontend and backend dependencies
   pip install -r requirements.txt  # Install machine learning dependencies

3. Set up the database: Check the README.md file in the backend directory
4. Start the backend server: Check the README.md file in the backend directory
5. Start the frontend application: Check the README.md file in the frontend directory
6. Open the application in your browser at http://localhost:3000

NOTE: You can find further guidelines in the project's directories README.md files.

Contributions

We welcome contributions to improve the Founder Matching Platform! To contribute:

• Open an issue on GitHub to discuss features or bug fixes.
• Submit pull requests with clear documentation and explanations.
• Reach out via email for collaboration inquiries.

Acknowledgments

• This project was developed in collaboration with VinUniversity Elab as the client. And we are proud to have earned the Best Recognition for our work on this project.
• Special thanks to the professors Hoang for their guidance throughout the project.
• Acknowledging the contributions of the team members (contributors) who played a key role in making this project successful.

License

This project is licensed under the MIT License.