GenAI Document Intelligence System
Legal Tech Startup
The Problem
Law firm needed to process 10,000+ legal documents for case research. Traditional keyword search was insufficient, producing too many irrelevant results. Lawyers spent 3+ hours per research task manually reviewing documents, extracting clauses, and identifying precedents. System needed to handle complex legal language, understand context, and provide accurate summaries with proper citations.
What We Built
Implemented end-to-end generative AI pipeline with custom GPT-4 fine-tuning and RAG architecture. Built document ingestion system processing PDFs, extracting text with OCR where needed, and chunking intelligently by legal structure. Fine-tuned GPT-4 on domain-specific legal corpus. Implemented vector embeddings using Pinecone for semantic search. Built C++ inference optimization layer to handle high concurrency with sub-200ms response times. Created React frontend with citation tracking, clause extraction, and precedent identification. Integrated seamlessly with existing case management platform via REST APIs.
Tech Stack
Results
- ✓Legal research time: 3 hours → 25 minutes (85% reduction)
- ✓Inference latency optimized to <200ms at 500 concurrent users
- ✓Processing 10,000+ documents with semantic understanding
- ✓Accuracy rate: 94% for clause identification, 91% for precedent matching
- ✓System handles complex multi-document queries with proper citations
Client Feedback
"Their RAG system cut our legal research time from 3 hours to 25 minutes. The C++ inference layer handles 500 concurrent users with sub-200ms latency. Exactly what we needed."
— CTO, Legal Tech Startup