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Legal 5 months

RAG Knowledge Base for LexCorp Legal Services

Built a RAG-powered legal research assistant that reduces document review time by 75% across 10M+ legal documents.

RAG Applications AI Agent Development Custom Software Development
Verified Results NDA Signed US-Based PM Fixed Price
-75%
Document Review Time
10M+
Documents Indexed
94%
Search Accuracy
+40%
Billable Hours Recovered
-75%
Document Review Time
10M+
Documents Indexed
94%
Search Accuracy
+40%
Billable Hours Recovered

The Challenge

LexCorp associates spent 60% of billable hours manually searching through 10M+ legal documents for case precedents and regulatory guidance. Search was keyword-only with no semantic understanding — relevant documents were routinely missed.

Our Solution

We built a RAG pipeline on LlamaIndex that indexes 10M+ legal documents with hybrid vector + keyword search. Associates ask questions in plain English and get cited answers with source documents in under 10 seconds.

Our Approach

We used a chunking strategy optimized for legal document structure, hybrid embedding with text-embedding-3-large, and a re-ranking model for precision. The system includes citation verification and confidence scoring to prevent hallucination.

"

Our associates can now research a complex case in 2 hours instead of 2 days. The RAG system TechVerse built is the most impactful technology investment we've made in a decade.

P
Patricia Moore
Managing Partner, LexCorp Legal Services

Project Details

ClientLexCorp Legal Services
IndustryLegal
Timeline5 months
Team Size6
ROI520

Technologies Used

Python FastAPI LlamaIndex OpenAI Pinecone PostgreSQL React AWS Docker Celery
3 slots open · AI-first development

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