Fordel StudiosThis case study describes a real engagement. Client identity, proprietary details, and specific metrics are anonymized or approximated under NDA.
Contract Analysis and Clause Extraction Pipeline
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.
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.
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.
The Challenge
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
Document corpus analysis and schema design (Weeks 1–2): We reviewed 80 anonymized contracts across the four document types to map the clause taxonomy for each: universal clauses present in all types (parties, governing law, payment terms), document-type-specific clauses (CAM charges in leases, anti-dilution provisions in shareholder agreements), and risk indicator patterns (uncapped liability, automatic renewal without notice windows, unusual indemnity language). This produced a structured extraction schema and a risk flag taxonomy that governed all subsequent model work.
Pipeline architecture and extraction implementation (Weeks 3–5): We built a multi-stage Python pipeline using LangGraph. Stage 1 parses PDFs and Word documents into logical sections with preserved section numbers, headers, and page references. Stage 2 classifies document type and routes to the appropriate extraction chain. Stage 3 runs structured extraction using Anthropic Claude against each section, returning JSON with clause text, section reference, page number, and confidence score. Stage 4 runs the risk flag pass, checking extracted clauses against the risk taxonomy and producing a severity-ranked summary of flagged items. Low-confidence extractions are surfaced for human review rather than silently included.
Obligation timeline generation (Weeks 6–7): A secondary extraction layer identifies all date-sensitive obligations — notice periods, renewal windows, payment schedules, conditional triggers — and resolves them to absolute calendar dates where the execution date is known. The output is structured JSON that can be ingested directly into matter management systems, enabling automated calendar reminders without manual data entry. Relative dates that cannot be resolved (e.g., "30 days after regulatory approval") are flagged with the dependency clearly labeled.
Review interface, testing, and deployment (Weeks 8–10): We built a lightweight Next.js review interface where extracted clauses are displayed alongside the source document with highlights synchronized to the source text. Reviewers can correct extractions with a single interaction; corrections are logged to a feedback dataset for future fine-tuning. The system was validated against 200 held-out contracts before production deployment. Post-launch, all new contracts run through the pipeline as standard intake, with the team reviewing structured output rather than starting from a blank document.
What We Delivered
Per-contract review time dropped from an average of 6.5 hours to 82 minutes — an 82% reduction. The team processes the same monthly volume in roughly one day of collective effort rather than a full week. Review throughput increased 2.8x without adding headcount.
Extraction accuracy on key clause identification reached 93% in post-deployment validation against ground-truth contracts. The obligation tracking error rate fell from 12% to 3.1%, reducing the risk surface on every engagement. The reduction in missed obligations has been directly attributed to the system in post-deployment reviews of near-miss events that would previously have gone undetected.
The review interface has changed how junior team members are trained. Rather than learning clause extraction from scratch, they validate AI-generated output — a faster skill to develop that produces consistent output quality earlier in their tenure. The correction feedback loop built into the interface means extraction quality improves continuously without additional engineering intervention.
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