Executive Summary
- Corporate legal teams suffer massive bottlenecks manually reviewing long-form vendor agreements for termination dates and liability limits.
- An extraction pipeline using Vision models and strict JSON schemas completely automates the initial abstraction.
- Reduces manual contract review hours by over 80%.
Abstraction Speed
15 SecPer 100 Pages
Time for the AI to extract and structure the exact liability clauses versus 4 hours manually.
1. The Document Ingestion
A supplier emails a 120-page PDF MSA. A webhook on the shared inbox detects the attachment and uploads it to secure blob storage, triggering the extraction server.
Key Metadata Extraction Accuracy
Human Paralegal92
LLM Structured Parsing99
Security Mandates
Raw MSAs contain highly sensitive negotiated rate data. The pipeline exclusively utilizes secure enterprise private endpoints ensuring data is never used for foundation model training.
2. Structured JSON Output
The LLM is prompted using Pydantic schemas enforcing output strictly formatted as JSON. It searches for `auto_renewal_clause: boolean` and `indemnification_cap: string`.
3. The Audit Dashboard
Once mapped, the variables populate a Notion database or custom React dashboard. Human counsel simply views the extracted variables side-by-side with the flagged clauses to verify before signing.