A law firm wants an AI contract review assistant. Design the solution and your 4-week engagement plan.

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Start with discovery: which contract types, which review checklist (the firm's playbook), who reviews, and what output they need (issue list, redlines, risk score). Build an extraction-and-comparison pipeline: parse contracts, segment clauses, classify each clause against the playbook (standard, deviation, missing), explain deviations with citations, and suggest fallback language from approved clause libraries. Lawyers review everything. Measure against partner-labelled contracts, and integrate with their document management system and Word.

Architecture

Contracts from the document management system are ingested, parsed and segmented into clauses, classified against playbook categories and compared with the firm playbook, then explained with citations and fallback language from the approved clause library, delivered as a review report and Word add-in for lawyer review, with feedback flowing into evals and playbook refinement.

Key design choices

Playbook as structured data: the firm's positions per clause type (e.g. "liability cap: ≥ 12 months' fees; fallback: 6 months with carve-outs"). The LLM compares against explicit criteria, which improves consistency and explainability.
Citations to the exact clause text, and quotes for every issue.
Missing-clause detection needs a checklist approach, since retrieval alone won't notice what's absent.
Confidentiality: client documents stay in approved environments; strict access controls per matter; no training on client data.
Integration: a Word add-in, because lawyers live in Word, plus DMS integration.

4-week plan

Week	Activities	Deliverable
1	Discovery with partners and associates; pick 1 contract type (e.g. NDAs or MSAs); collect 50 contracts + playbook; security setup	Scope doc, eval set plan
2	Parsing + clause segmentation; playbook encoding; first classification prompts; SMEs label 30 contracts	Baseline accuracy
3	Iterate on errors; explanations and fallbacks; Word output; second labelling round	Accuracy report v2
4	Pilot with 5 associates on live (non-urgent) matters; measure review time; demo to partners	Results + production plan

Metrics: issue detection recall (missing issues is the costly error), precision (noise annoys lawyers), review time saved, associate satisfaction.

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A law firm wants an AI contract review assistant. Design the solution and your 4-week engagement plan.

Architecture

Key design choices

4-week plan

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