The problem
Contract review is expensive, slow, and error-prone in exactly the way machines aren’t: finding defined terms, dates, obligations, and non-standard clauses across thousands of near-identical documents. The banks proved the economics years ago; LLMs have since made the extraction dramatically better.
The system
A pipeline that ingests your contract estate, extracts clauses and key attributes with citations to the exact source passage, compares against your playbook to flag deviations, and queues only genuine judgment calls for counsel. Accuracy is measured against a lawyer-labeled sample — because in legal work, an uncited claim is worthless.
How it's built
- Layout-aware ingestion of the contract estate (PDFs, scans, amendments)
- Clause/attribute extraction with mandatory citations to source passages
- Playbook-deviation flagging tuned on your standards, not generic risk lists
- Lawyer-labeled eval set; precision and recall reported per clause type
Delivery
Sprint runs on a few hundred of your real agreements and reports per-clause accuracy; Build scales to the estate.
What to expect
- Portfolio-wide answers (change-of-control, renewals, indemnities) in hours, not quarters
- Review effort concentrated on flagged deviations
- Every extracted fact carries a citation a lawyer can check
Documented results in the wild
Independent, published deployments of this class of system — cited as market evidence that it works at scale. These are not our clients.
- JPMorgan Chase COiN contract-intelligence software eliminated 360,000 hours of annual lawyer and loan-officer review across ~12,000 agreements a year. Bloomberg / ABA Journal, 2017 ↗
- LawGeex benchmark AI hit 94% accuracy spotting NDA risks vs 85% for 20 experienced lawyers — in 26 seconds vs 92 minutes. Artificial Lawyer, 2018 ↗
- BT (via Deloitte) AI contract analysis standardized 4,500 documents across 14 entities in two weeks — an estimated 50% time saving. Legal Dive, 2023 ↗