🔬 ExamLens AI — The Future of Exam Intelligence

The only AI platform that decodes exam patterns, ranks high-yield topics, and generates a personalized study plan — all from your past papers.

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👨‍💻 Founded & Developed by Abhijeet Kangane

"Every student deserves to know what truly matters in their exam — not from luck, but from data."

— Abhijeet Kangane, Founder & Developer

ExamLens AI is a solo-founded, production-grade SaaS product conceived and built by Abhijeet Kangane. It was created out of a frustration with ineffective, broad-spectrum studying, and a belief that AI could finally give every student the same strategic edge that elite coaching centres charge thousands for.

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📌 Project Overview

ExamLens AI is a B2C/B2B SaaS platform that uses Google's Gemini 1.5 Flash AI to perform deep analysis on historical exam papers (PDFs). It identifies recurring topic patterns, maps their frequency across years, ranks them by priority, and generates an actionable, data-driven study planner.

The Core Problem It Solves

Students waste hours studying low-probability topics while high-yield topics — the ones that repeatedly appear on exams — go unnoticed. ExamLens AI solves this by turning historical exam data into a clear, ranked study strategy.

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✨ Key Features

🤖 AI-Powered Paper Analysis

• Upload any past exam paper as a PDF
• Gemini 1.5 Flash extracts and processes all educational topics
• Calculates topic frequency (how many times each topic was tested)
• Assigns a weightage percentage based on historical occurrence
• Results saved securely to your profile for future reference

📊 Interactive Data Visualization

• Topic Frequency Bar Chart — visualize which topics dominate past papers
• Topic Weightage Donut Chart — see proportional distribution at a glance
• Built with Recharts for a smooth, responsive charting experience

📅 Smart Prioritized Study Planner

• Topics automatically ranked from Critical Focus → High Priority → Standard
• See exactly how many times each topic appeared and its % weight
• Color-coded priority system for instant visual clarity

💬 Chat with ExamLens AI (AI Tutor)

• Powered by Gemini 1.5 Flash with streaming responses
• Ask anything about your exam topics, study strategy, or priorities
• AI maintains full context of your uploaded papers and analysis
• Real-time streaming via Vercel AI SDK

🔐 Secure Authentication

• Google OAuth one-click sign-in
• Email/Password registration with confirmation flow
• Protected routes via Next.js Middleware
• Row-Level Security (RLS) — each user can only access their own data

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🛠️ Tech Stack

Layer	Technology	Purpose
Frontend	Next.js 16 (App Router)	Full-stack React framework
Language	TypeScript	Type-safe codebase
Styling	Tailwind CSS	Utility-first dark theme
Icons	Lucide React	Clean icon library
Charts	Recharts	Data visualization
Database	Supabase (PostgreSQL)	Data persistence + RLS
Auth	Supabase Auth	OAuth + Email auth
Storage	Supabase Storage	PDF file hosting
AI Engine	Google Gemini 1.5 Flash	Exam analysis + tutoring
AI Streaming	Vercel AI SDK	Streaming chat responses
PDF Parsing	pdf-parse	Text extraction from PDFs
Deployment	Vercel	Edge-optimized hosting

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📦 Installation

Prerequisites

• Node.js 18.x or later
• A Supabase account (free tier works)
• A Google AI Studio API key (Gemini 1.5 Flash)

1. Clone the Repository

git clone https://github.com/yourusername/examlens-ai.git
cd examlens-ai

2. Install Dependencies

npm install

3. Configure Environment Variables

Create a .env.local file in the root directory:

# Supabase — get from https://app.supabase.com/project/_/settings/api
NEXT_PUBLIC_SUPABASE_URL=https://your-project-id.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key_here

# Google Gemini — get from https://aistudio.google.com/apikey
GEMINI_API_KEY=your_gemini_api_key_here

⚠️ Never commit .env.local to version control. It is already in .gitignore.

4. Set Up the Database

Run the SQL schema against your Supabase project. Go to your Supabase dashboard → SQL Editor and execute the contents of supabase_schema.sql:

# The schema creates:
# - public.users table with RLS
# - public.papers table with RLS
# - Storage bucket 'papers'
# - Auth trigger to auto-create user profiles

5. Create Supabase Storage Bucket

In your Supabase Dashboard → Storage, create a bucket named papers and set it to Public.

6. Run the Development Server

npm run dev

Open http://localhost:3000 in your browser.

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⚙️ API Configuration

Gemini AI (Google)

1. Visit Google AI Studio
2. Create a new API key
3. Add it to .env.local as GEMINI_API_KEY

The platform uses gemini-1.5-flash for both analysis and chat due to its speed and accuracy for structured JSON extraction.

Supabase

1. Create a new project at supabase.com
2. Navigate to Settings → API
3. Copy your Project URL and anon/public key
4. Add both to .env.local

Google OAuth (Optional)

1. Go to Supabase Dashboard → Authentication → Providers → Google
2. Follow the setup guide to configure your Google Cloud OAuth credentials
3. Add the redirect URL to your Google Cloud Console

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🚀 Deployment

Deploy to Vercel

1. Push your code to GitHub
2. Import the repository on Vercel
3. Add all environment variables from .env.local in the Vercel dashboard
4. Deploy — Vercel will automatically build and deploy

Anti-Pause Cron Job

The vercel.json includes a cron job that pings /api/keep-alive every 48 hours to prevent the Supabase free tier from pausing due to inactivity:

{
  "crons": [
    {
      "path": "/api/keep-alive",
      "schedule": "0 0 */2 * *"
    }
  ]
}

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📁 Project Structure

examlens-ai/
├── app/
│   ├── api/
│   │   ├── analyze/route.ts     # PDF ingestion & Gemini analysis
│   │   ├── chat/route.ts        # AI tutor streaming endpoint
│   │   └── keep-alive/route.ts  # Supabase anti-pause ping
│   ├── dashboard/page.tsx       # Main dashboard UI
│   ├── login/page.tsx           # Auth page (login + signup)
│   ├── layout.tsx               # Root layout with Navbar/Footer
│   └── page.tsx                 # Landing page
├── components/
│   ├── Navbar.tsx               # Sticky navigation header
│   └── Footer.tsx               # Footer with founder info
├── utils/
│   └── supabase/
│       ├── client.ts            # Browser Supabase client
│       └── server.ts            # Server Supabase client (SSR)
├── middleware.ts                # Auth route protection
├── supabase_schema.sql          # Database schema + RLS policies
├── vercel.json                  # Vercel cron + config
└── .env.local                   # Environment variables (not committed)

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🔒 Security

• Row-Level Security (RLS): Enforced at the database level. Users can only read/write their own data via auth.uid() = user_id policies.
• Server-side Auth: All sensitive operations (PDF analysis, DB writes) happen in Next.js API routes, never in the browser.
• Environment Variables: All API keys are server-side only. Only NEXT_PUBLIC_* prefixed variables are exposed to the client — and these are safe public keys.
• Input Validation: All API routes validate inputs before processing.

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📝 License

MIT License — see LICENSE for details.

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ExamLens AI · Founded & Built by Abhijeet Kangane

Turning exam history into your competitive advantage.