Landing Page
Registration
AI Rankings
Dashboard
Chatbot
Scholarships Directory
Roadmap
Applications
Profile

About the Project

Inspiration

With rising tuition costs and recent cuts to programs like OSAP, many students face increasing barriers to higher education. At the same time, thousands of scholarships go unclaimed each year simply because students never discover them.

We created ScholarMatch to change that. ScholarMatch uses a smart recommendation engine to connect students with scholarships tailored to their background, achievements, and goals, turning a fragmented search into a personalized pathway to funding.

What It Does

ScholarMatch helps students discover scholarships that best match their profile.

Students provide information such as:

Academic interests
Location
GPA or achievements
Extracurricular activities

The system then recommends scholarships they are most likely to qualify for.

Key features include:

Personalized scholarship recommendations
Automated scholarship discovery
AI-powered chatbot for guidance and Q&A
Automated scholarship application filling

How We Built It

Backend

Python – backend logic and data processing
FastAPI – API framework
BeautifulSoup – web scraping
Docker & Docker Compose – containerization
Vultr – cloud deployment

Frontend

JavaScript (JSX)
React – frontend framework
Tailwind CSS – styling
React Three Fiber + Three.js – 3D landing page canvas
Vite – frontend build tool

Database

Supabase (PostgreSQL) – database and backend hosting

AI & Services

Google Gemini API – AI match explanations, resume parsing, scholarship readiness scoring, chatbot
Supabase REST API
Auth0 – authentication & user management
Cloudinary – resume & file uploads

Challenges We Ran Into

Data aggregation – scraping and standardizing scholarships from multiple sources.
Matching relevance – tuning the AI recommendation engine for accurate results.
Frontend complexity – integrating React Three Fiber + Three.js while keeping performance smooth.
Authentication & security – implementing Auth0, JWT verification, and async requests.
Deployment – orchestrating Docker, Vultr hosting, backend, AI services, and database.
Time constraints – prioritizing a functional prototype over extra polish.

Accomplishments We're Proud Of

Built a working AI-powered scholarship recommendation engine in hackathon time.
Created a 3D landing page using React Three Fiber + Three.js.
Developed automated scholarship scraping and parsing pipelines.
Integrated secure authentication with Auth0 and JWT.
Enabled smart scholarship application autofill.
Deployed a full-stack system accessible online.
Added an AI chatbot for guidance and Q&A.

What We Learned

Full-stack development with Python, FastAPI, and React.
Web scraping and data processing using BeautifulSoup and JSON.
AI integration for recommendations, resume parsing, and chatbot functionality via Google Gemini API.
Secure authentication and user management with Auth0 and JWT.
Deployment and containerization using Docker, Vultr, and Supabase.

What's Next for ScholarMatch

Scale the platform to include scholarships from more countries and institutions.
Leverage AI for predictive insights – analyze trends to recommend scholarships even before students search.
Partnerships with schools and organizations to increase adoption and provide verified opportunities.
Continuous AI improvement – refine matching algorithms and chatbot responses based on real user feedback.