Context
A corporate law firm handling M&A transactions needed to review hundreds of contracts per deal. Senior associates spent weeks on due diligence, reading through vendor agreements, employment contracts, and IP licenses.
The Problem
Contract review was tedious and error-prone. Key provisions could be buried in lengthy documents. Inconsistencies between contracts were easy to miss. The firm needed to scale review capacity without proportionally scaling headcount.
Why generic AI wouldn't work
Generic contract analysis tools focus on common provisions in standard contracts. M&A due diligence involves specialized clauses (change of control, IP assignments, non-competes) that vary significantly across industries and deal types. The nuances that matter most are exactly those that generic tools miss.
The System We Designed
- Custom clause taxonomy developed with firm's M&A practice
- Semantic search enabling natural language queries across contract corpus
- Risk scoring based on clause presence, absence, and specific language
- Cross-contract comparison to identify inconsistencies
- Structured extraction of key terms, dates, and obligations
Human-in-the-Loop & Explainability
Associates review system-flagged risks and exceptions. All extracted provisions link to source text for verification. System learns from associate corrections and annotations. Final review and client advice remain fully human-controlled.
Outcomes
- 75% reduction in initial review time
- Standardized risk identification across all associates
- Previously missed issues now consistently flagged
- Associates focus on judgment calls vs. document hunting
Reference available upon request. Some details have been generalized to protect client confidentiality.
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