Regenera Ledger: AI-Powered ESG Intelligence & Carbon Transparency

Regenera Ledger makes corporate carbon footprints visible, verifiable, and actionable. Companies upload ESG reports; our AI detects greenwashing, uncovers hidden Scope 3 supply chain emissions, calculates the true carbon gap, and connects carbon-heavy companies directly with verified regenerative farmers for offset projects — all powered by Google Gemini 2.0.

The Problem We Solve

Corporate carbon footprint data is largely self-reported and unverified. Scope 3 emissions (supply chain) are routinely underreported. There is no efficient marketplace connecting companies that need carbon offsets with Indian regenerative farmers who can generate them. Regenera Ledger fixes all three problems in one platform.

Carbon Footprint Awareness Features

Feature	What It Does
Forensic ESG Auditor	Upload any ESG PDF — Gemini detects greenwashing, scores carbon risk 0-100, flags vague claims and missing disclosures
Scope 3 Whistleblower	Cross-references shipping manifests with ESG reports to expose undisclosed supply chain emissions
Carbon Gap Calculator	Calculates gap between reported emissions and AI-estimated actual emissions in tCO2e
Carbon Credit Marketplace	Matches companies to verified regenerative farmers based on carbon gap size, location, and sequestration capacity
AI Farmer Estimation	Gemini estimates each farmer's sequestration capacity, credibility score, and yearly credit potential from soil/crop/location data
Compliance Audit Log	Every analysis action is logged with timestamp and user ID for regulatory accountability

How It Works

1. Company uploads their ESG report PDF
2. Gemini 2.0 performs forensic analysis — greenwashing score, risk flags, extracted emissions
3. Optional: upload shipping manifest for Scope 3 cross-document analysis
4. Carbon gap is calculated (actual vs reported tCO2e)
5. Company is matched to regenerative farmers who can offset their gap
6. Farmers receive AI-estimated carbon credit valuations and appear in the marketplace

📁 Project Structure

GEMINATHON/
├── backend/            # FastAPI Backend
│   ├── ai/             # Gemini API Client
│   ├── data/           # PDF/Manifest parsers & processing logic
│   ├── middleware/     # Auth & Error handlers
│   ├── prompts/        # Gemini prompt engineering templates
│   ├── routes/         # API Endpoints (ESG, Farmer, Matching, etc.)
│   └── utils/          # File upload & Audit logging
├── frontend/           # React Frontend (Vite)
├── database/           # Firestore schema & security rules
└── README.md           # You are here!

🚀 Getting Started

1. Prerequisites

• Python 3.10+
• Node.js 18+
• Google Gemini API Key (AI Studio)
• Firebase Service Account Key (Firebase Console)

2. Backend Setup

1. Navigate to the backend folder:

   cd backend

2. Install dependencies:

   pip install -r requirements.txt

3. Place your firebase-service-account.json in the backend/ directory.
4. Create a .env file (refer to .env.example) and add your keys:

   GEMINI_API_KEY=your_key_here
   JWT_SECRET=some_random_secret

5. Seed the database with demo data:

   python seed.py

6. Start the server:

   uvicorn main:app --reload

3. Frontend Setup

1. Navigate to the frontend folder:

   cd frontend

2. Install dependencies:

   npm install

3. Start the dev server:

   npm run dev

👥 Builders

• Suneev Kundu: AI/Backend
• Shaun Joseph: Data/Matching Logic
• Ayaan Saju: Frontend
• Tanmay Nair: DevOps

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.