Automating Complex Contract Analysis Across 50,000+ Enterprise Documents with Zero Data Retention.
Architected a private hybrid RAG system using pgvector, Python FastAPI, and Claude 3.5 Sonnet that parses 150-page master service agreements in 8 seconds with verified citations.
Review time per 100-page contract reduced from 4.5 hours down to under 40 minutes.
Zero hallucinated clause numbers; every flagged risk links directly to source document coordinates.
Zero-retention enterprise agreements verified by third-party security auditors.
The Context & Initial Architecture
An enterprise compliance consultancy spent thousands of billable attorney hours reviewing cross-border commercial vendor contracts, looking for indemnity risks, breach clauses, and non-standard warranty language.
The Technical Bottleneck & Constraints
Strict client non-disclosure agreements prohibited sending raw contract documents to public AI models without private tenancy isolation, zero data training guarantees, and 100% verifiable clause citations.
System Design & Implementation Flow
We built a multi-stage document ingestion pipeline that OCRs scanned PDFs, splits texts with semantic chunking, indexes embeddings in PostgreSQL pgvector, and coordinates autonomous audit agents.
1. Layout-Aware OCR & Semantic Chunking
Extracts hierarchical section headings, tables, and clauses into structured markdown with page/coordinate bounding boxes.
2. Hybrid Semantic & Lexical Vector Search
Queries pgvector using reciprocal rank fusion (RRF) combining dense text embeddings with BM25 keyword matching for surgical legal clause retrieval.
3. Grounded LLM Evaluation with Citations
The AI agent generates risk scorecards, quoting verbatim clauses with exact page citations so counsel can verify findings with one click.
“The speed and accuracy of the contract audit pipeline Onesign delivered allowed our team to take on 3x more clients without expanding head count.”
David Sterling
Managing Partner, Sterling & Associates Legal Group
Technologies Utilized
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