Udyara: Cultivating Women Led Enterprises

Trust-Based AI Agent for Women-Led Startup Policy Navigation

Udyara is a policy-aware AI agent network designed to help women entrepreneurs in India understand, verify, and navigate government startup incentives — starting with the Stand-Up India Scheme.

Built to demonstrate:

• AI agents
• Trust-based decision support
• Retrieval-Augmented Generation (RAG)
• Decentralised policy intelligence

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🚩 Problem Statement

• Government startup policies are complex, fragmented, and difficult to interpret.
• Women entrepreneurs often miss out on benefits.
• This gap results in underutilization of public funds, delayed entrepreneurship, and reduced participation of women in the startup ecosystem.

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💡 Solution

Udyara acts as a Policy Navigator Agent that:

• Retrieves official policy documents
• Understands eligibility criteria
• Guides users through benefits & next steps
• Provides transparent, explainable responses

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🧠 Core Features

• 📄 Policy-aware AI using RAG
• 🔍 Eligibility reasoning
• 🧾 Source-backed answers (no hallucination)
• 🔐 Trust-first architecture

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🏗 Architecture Overview

User Query
   ↓
Frontend (React / Next.js)
   ↓
FastAPI Backend
   ↓
RAG Pipeline
   ├── Policy PDFs
   ├── Sentence-Transformer Embeddings
   ├── FAISS Vector Store
   └── Gemini LLM (response synthesis)

• LLMs are used for interpretation and explanation, not decision-making

🔀 Flow

PDF (policy)
   ↓
Local Embeddings (sentence-transformers)
   ↓
FAISS Vector Store (files on disk)
   ↓
Retriever
   ↓
Gemini LLM (answers)

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🧪 Demo Use Cases

These queries demonstrate real-world policy discovery, eligibility validation, and benefit awareness for first-time women founders.

• "Am I eligible for Stand-Up India as a woman founder?"
• "What documents are required?"
• "What loan benefits are provided?"
• "What are common rejection reasons?"

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🚀 Tech Stack

Layer	Technology Used	Purpose
Frontend	React	User interaction (chat-based UI)
Backend	FastAPI	API handling & orchestration
AI Pipeline	LangChain	RAG workflow management
Embeddings	Sentence Transformers	Policy document vectorization
Vector DB	FAISS	Fast similarity search
LLM	Gemini 2.5	Answer synthesis & formatting
Data Source	Govt Policy PDFs	Trusted knowledge base

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📦 Project Structure

Udyara/
├── backend/
├── frontend/
└── README.md

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⚙️ Setup & Running (Prototype)

Backend Setup

1. Navigate to backend directory

cd backend

2. Create virtual environment

python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Create .env file

GEMINI_API_KEY=your_api_key_here

5. Run the backend

uvicorn app.main:app --reload

6. API available at:

• http://127.0.0.1:8000
• Swagger UI: http://127.0.0.1:8000/docs

Frontend Setup

1. Navigate to frontend directory

cd frontend

2. Install dependencies

npm install

3. Run the frontend

npm run dev

4. Website running at

• http://localhost:5173/

5. Explore frontend

• Try Agent: to ask questions & instructions, interact with the agent
• Make sure backend & frontend are simultaneously running.

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🌱 Future Scope

• Add more women-centric policies (MUDRA, PMEGP, Startup India)
• Multilingual support (Hindi / regional languages)
• Voice-based interaction for accessibility
• State-specific incentive mapping
• Verifiable credentials for document checks