⚖️ LAW-AI: Enterprise Legal Intelligence Platform

Next-Gen Legal Assistant powered by NVIDIA AI, RAG (Retrieval-Augmented Generation), and Supabase Realtime. Built for advocates, law firms, and legal tech innovators.

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🚀 Why This Project Leads the Industry?

LAW-AI is engineered as a production-ready legal operating system, solving the core challenges of legal tech: latency, accuracy, and data privacy. It leverages a modern, event-driven architecture to deliver sub-200ms responses and verifiable legal citations.

🌟 Core Capabilities

• 🧠 Expert Legal Reasoning: Powered by Llama 3.3 Nemotron (70B) via NVIDIA NIM, specifically fine-tuned for complex reasoning.
• ⚡ Instant Streaming: Custom streaming pipeline using Vercel AI SDK ensures zero-wait interactions.
• 📚 RAG (Document Intelligence): Upload PDFs/DOCX. The system vectorizes content using NVIDIA embeddings and stores in Supabase pgvector for semantic search.
• 🎙️ Voice Lawyer: Real-time voice consultation with Indian accent support (TTS/STT).
• 📝 Smart Drafting: Context-aware document generation ([OFFER_DRAFT] system). Ask for a notice, get a structured draft instantly.
• 🔒 Enterprise Security: Role-Based Access Control (RBAC), IP Rate Limiting, and JWT authentication via Supabase.

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🛠️ Technology Stack

Component	Technology	Description
Frontend	Next.js 14 (App Router)	React Server Components, Streaming SSR
Styling	Tailwind CSS + Shadcn UI	Responsive, dark-mode ready design system
Backend	Next.js API Routes	Edge-compatible serverless functions
Database	Supabase (PostgreSQL)	Relational data + Realtime subscriptions
Search	pgvector	Vector similarity search for RAG
AI Engine	NVIDIA NIM	High-performance inference for Llama 3.3 & DeepSeek
Auth	Supabase Auth	secure email/password & OAuth (Google)
Storage	Supabase Storage	Secure document hosting with RLS policies
Payments	Razorpay	Subscription management (Pro/Enterprise plans)

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🏗️ Architecture Overview

graph TD
    User[User / Client] -->|HTTPS| CDN[Vercel Edge Network]
    CDN -->|Next.js App| App[Application Server]
    
    subgraph "AI Pipeline"
    App -->|Stream| NV[NVIDIA NIM API]
    NV -->|Llama 3.3| Model[LLM Engine]
    NV -->|Embeddings| Vector[Vector Model]
    end
    
    subgraph "Data Layer"
    App -->|Auth/Data| DB[(Supabase Postgres)]
    App -->|Uploads| Storage[Supabase Storage]
    DB -->|pgvector| VectorStore[Vector Index]
    end
    
    subgraph "Realtime"
    DB -->|CDC Changes| Realtime[Supabase Realtime]
    Realtime -->|WebSocket| User
    end

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⚡ Quick Start Guide

1. clone the Repository

git clone https://github.com/raghavx03/lawaiv2.git
cd lawai-main

2. Install Dependencies

npm install
# or
yarn install

3. Environment Setup

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

# Supabase Configuration
NEXT_PUBLIC_SUPABASE_URL=your_project_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_anon_key
SUPABASE_SERVICE_ROLE_KEY=your_service_role_key

# AI Configuration (NVIDIA NIM)
NVIDIA_LLAMA_API_KEY=nvapi-...

# Payment Gateway (Razorpay)
NEXT_PUBLIC_RAZORPAY_KEY_ID=rzp_test_...
RAZORPAY_KEY_SECRET=...
RAZORPAY_WEBHOOK_SECRET=...

# App Config
NEXT_PUBLIC_APP_URL=http://localhost:3000

4. Database Initialization

Push the schema to your Supabase instance:

npx prisma generate
npx prisma db push

5. Run Development Server

npm run dev

Visit http://localhost:3000 to see the application.

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📂 Project Structure

/src
  /app              # Next.js App Router (Pages & API)
    /admin          # Secured Admin Dashboard
    /ai-assistant   # Main Chat Interface
    /api            # Serverless API Endpoints
    /auth           # Authentication Pages
    /dashboard      # User Dashboard
  /components       # React Components (Shadcn UI)
  /lib              # Core Utilities (AI, DB, Auth)
    ai-service.ts   # NVIDIA Integration Logic
    rag.ts          # RAG Pipeline Implementation
  /types            # TypeScript Definitions
/prisma             # Database Schema
/public             # Static Assets

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🛡️ Security Features

• Middleware Protection: middleware.ts adds security headers (HSTS, X-Frame-Options) and route guards.
• Row Level Security (RLS): Database policies ensure users only access their own data.
• Input Sanitization: All AI inputs are sanitized to prevent injection attacks.
• Rate Limiting: Custom implementation using Redis/Upstash (optional) or in-memory map.

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🤝 Contributing

We welcome contributions! Please check CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📄 License

Distributed under the MIT License. See LICENSE for more information.

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📞 Contact

Raghav Shah - raghavshahhh@gmail.com

Project Link: https://github.com/raghavx03/lawaiv2