Fordel StudiosContract Analysis and Clause Extraction Pipeline
This case study describes a real engagement. Client identity, proprietary details, and specific metrics are anonymized or approximated under NDA.
What needed
solving
Manual contract review averaging 6+ hours per document. Obligation tracking error rate at 12%, representing material risk exposure on every engagement. The review team processed 80–120 contracts per month with no structured tooling.
Legal language is structurally adversarial — subtle differences in phrasing carry significant legal weight, and standard NLP models trained on general text perform poorly on defined terms, conditional clauses, and non-standard section numbering. Building accurate extraction across four document schemas without a monolithic prompt required a classification layer and per-type extraction chains. Citation fidelity was a hard requirement: every extracted clause summary had to reference the exact section and page number from the source document. Low-confidence extractions needed explicit flagging rather than silent pass-through, which required a calibrated confidence threshold per clause type rather than a single global threshold.
How we
built it
- 01
Catalogued the firm's four primary contract types and built extraction schemas for each — commercial leases, shareholder agreements, service agreements, and joint ventures — rather than a generic universal extractor that performs poorly on all of them.
- 02
Trained clause identification on the firm's historical contracts, including edge cases and non-standard drafting, to ensure the model recognised the firm's document vocabulary rather than only textbook clause structures.
- 03
Built a playbook deviation scoring system that mapped extracted clauses against the firm's standard positions and flagged deviations by severity — giving reviewers a prioritised list rather than a full re-read.
- 04
Validated extraction accuracy at 93% on a holdout set of 200 contracts across all four types before moving to production, with explicit human review required for clauses below a confidence threshold.
This engagement was scoped around four distinct contract types — commercial leases, shareholder agreements, service agreements, and joint venture documents — each with different clause taxonomies and risk profiles. The system was built as a multi-stage pipeline using LangGraph, with document-type classification routing each contract to the appropriate extraction prompt chain. A lightweight review interface was built alongside the pipeline so that extracted clauses are visible in context of the source document, enabling rapid human verification rather than blind trust in AI output. The system was tested against 200 held-out contracts with known ground truth before production deployment.
What we
delivered
AI-powered extraction pipeline that identifies key clauses, flags risk terms, produces structured summaries with citation references, and generates obligation timelines for import into matter management systems.
Measurable
outcomes
- Contract review time reduced from 6+ hours to under 90 minutes per document — an 82% reduction on the primary workflow.
- Obligation tracking error rate fell from 12% to 3.1%, materially reducing risk exposure on every engagement without adding headcount.
- Review throughput increased 2.8× enabling the team to take on significantly more M&A and commercial work without hiring.
“We were spending six hours per contract on review work that should have been automated years ago. The extraction accuracy is high enough that our lawyers now start from the AI output and spend their time on the obligations that actually require judgement.”
— Managing Associate, Corporate Practice GroupReady to build
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