Engagement snapshot
- Mandate
- Speed up contract review while keeping legal judgment and policy exceptions under explicit human control.
- Timeline
- 8 weeks for the lease workflow, then extension into vendor contracts.
- Team shape
- Legal ops lead, 1 contracts counsel, 2 engineers, and 2 analysts.
The problem
Legal operations reviewed hundreds of clauses each month against internal playbooks, insurance requirements, and local exceptions, which made turnaround slow and costly.
What we built
Built clause extraction, deviation scoring, and redline suggestion workflows with policy references and mandatory human approval on non-standard language.
Operating context
The operator had a growing contract load but did not want to rely on full external review for routine agreements. Internal playbooks were strong, but applying them consistently across large document volumes was labor-intensive.
Key constraints
- Suggested language had to be tied to approved fallback positions and policy references.
- Unusual jurisdictional requirements needed explicit exception handling, not generic scoring.
- Counsel had to retain the ability to override, annotate, and export the review trail.
What we built
Clause segmentation and tagging
Split agreements into reusable clause categories so deviation scoring worked on like-for-like language.
Playbook-aware review engine
Compared extracted clauses to house positions and surfaced risk explanations with approved fallback language.
Approval-bound redlining
Kept all final redline generation behind counsel review while preserving full evidence and version history.
Delivery path
Playbook decomposition
Converted existing legal guidance into clause families, fallback positions, and explicit exception rules.
Lease-first rollout
Started with the most common lease patterns before expanding into less standardized vendor agreements.
Counsel workflow fit
Shaped the interface around redline review and comment workflows so legal staff did not need to change tools.
Why it mattered
Review speed improved because counsel entered the file with a structured first pass instead of a blank page. More deviations were caught because the system consistently checked routine clauses that humans often skipped when volume was high.
Implementation notes
- Clause family design determines more of the outcome than the model itself.
- Approved fallback language is what turns a reviewer into an accelerant instead of just a classifier.
- Counsel adoption improved once the system stopped trying to sound authoritative and started acting like a well-prepared analyst.