Building an Enterprise Legal AI Platform with RAG
How I architected a production RAG system processing 50,000+ French legal documents using Claude Sonnet and Pinecone with 95%+ retrieval accuracy.
The Problem
A French legal tech company needed to process and search 50,000+ legal documents intelligently. Traditional keyword search was failing — lawyers needed semantic understanding, not just keyword matches.
Architecture I Designed
The system needed three core capabilities: intelligent document ingestion, semantic search, and AI-powered answer generation with source citations.
Document Pipeline
Every legal document goes through a multi-stage pipeline. First, text extraction handles PDFs and Word documents. Then a chunking strategy splits documents into 512-token chunks with 50-token overlap to preserve context across boundaries. Finally, Claude Sonnet generates embeddings that capture legal semantic meaning.
Triple-Index Elasticsearch
I built three separate indices for different search patterns. The main semantic index handles natural language queries. A metadata index enables filtering by court, date, and jurisdiction. An appeals index tracks decision chains so lawyers can traverse the full history of a case.
Claude-Powered PII Anonymization
French legal documents contain sensitive personal data. I built an anonymization module using Claude that achieves 98% PII detection accuracy — identifying names, addresses, ID numbers, and financial data before documents are indexed.
Key Technical Decisions
Why Pinecone over pure Elasticsearch for vectors? Pinecone handles billion-scale vector search with sub-second latency. For 50K documents with 512-token chunks, we generate roughly 200K vectors. Pinecone handles this effortlessly.
Why Claude Sonnet specifically? Legal French is complex. Claude Sonnet outperformed GPT-4 on French legal comprehension benchmarks in my testing, particularly for OHADA compliance language.
Hybrid search over pure semantic. Combining BM25 keyword search with vector similarity gave 15% better retrieval accuracy than either alone.
Results
- 50,000+ documents processed and indexed
- Sub 2-second response times end to end
- 95%+ confidence scores on retrieval
- 98% PII detection accuracy
- 80% reduction in document creation time via AI generation module
Lessons Learned
Chunk size matters more than model choice. I tested 256, 512, and 1024 token chunks — 512 with overlap was optimal for legal documents which have dense, structured paragraphs.
Always build an eval set before optimizing. I created 200 legal Q&A pairs with a lawyer and ran every architecture change against it. This prevented several regressions.