NextGenVDR - AI-Powered Virtual Data Room

NextGenVDR is a modern Virtual Data Room (VDR) platform that revolutionizes due diligence processes through AI automation. Built for M&A transactions, fundraising, and other confidential document sharing scenarios, it combines secure document management with intelligent AI-powered analysis.

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🎯 What It Achieves

Core Mission: Transform traditional due diligence from a manual, time-intensive process into an intelligent, automated workflow that accelerates deal completion while maintaining security and compliance.

Key Benefits:

• AI-Powered Analysis: Automatic document processing, intelligent Q&A, and context-aware insights
• Role-Based Access: Granular permissions for buyers and sellers with secure document sharing
• Smart Q&A Workflow: Natural language queries across uploaded documents with source attribution
• Scalability: Handle multiple deals simultaneously
• Insights: Benchmark against comprehensive deal database

🚀 Getting Started

Prerequisites

• Python 3.11+
• Node.js 18+
• pnpm package manager

Backend Setup

1. Navigate to project root

   cd nextgenvdr

2. Install dependencies with uv

   uv pip install -r backend/requirements.txt

3. Set up environment variables

   cp .env.example .env
   # Edit .env with your OpenAI API key and other configurations

4. Start the FastAPI backend server

   .venv/bin/python backend/main.py

   The backend API will be available at http://localhost:8000

   • API Documentation: http://localhost:8000/docs
   • Alternative docs: http://localhost:8000/redoc

Frontend Setup

1. Navigate to frontend directory

   cd frontend

2. Install frontend dependencies

   pnpm i

3. Start the Next.js development server

   npx next dev

   The frontend application will be available at http://localhost:3000

🧠 AI-Powered Features

The backend includes advanced RAG (Retrieval-Augmented Generation) capabilities:

• Document Processing: Automatic chunking and embedding of financial documents
• Agentic RAG: LangChain-powered intelligent document retrieval
• Chat Interface: Natural language queries across uploaded documents
• Smart Q&A: Context-aware question answering with source attribution

API Endpoints

Key backend endpoints include:

• POST /auth/register - User registration
• POST /auth/login - User authentication
• POST /documents/upload - Document upload with AI processing
• POST /ai/rag-chat - Interactive chat with document context
• POST /ai/process-document-for-rag - Process documents for vector search
• GET /ai/rag-status - RAG system health check

Architecture

• Frontend: Next.js 15 with React 19, TypeScript, shadcn/ui, Tailwind CSS
• Backend: FastAPI with SQLAlchemy, OpenAI integration, LangChain RAG
• Database: SQLite (development), PostgreSQL (production ready)
• AI/ML: OpenAI GPT-4o-mini, text-embedding-3-small, FAISS vector database