AI-Powered Contract Intelligence for a Global Legal Enterprise
What was at stake
The legal enterprise employed 600+ attorneys across 14 practice areas, each reviewing contracts manually using inconsistent templates and subjective risk assessments. Average review time was 4.5 hours per contract, backlogs were growing, and the firm was losing competitive bids because of slow turnaround. They needed an AI-driven platform that could handle the nuance and variability of legal language at enterprise scale.
How we delivered
Legal Corpus & Taxonomy Development
Partnered with senior attorneys across all 14 practice areas to build a proprietary taxonomy of 2,400+ clause types, risk categories, and compliance obligations — creating the labeled dataset foundation for model training.
Custom LLM Fine-Tuning & NLP Pipeline
Fine-tuned a large language model on 850,000+ legal documents, building a multi-stage NLP pipeline for document ingestion, clause extraction, semantic classification, and risk scoring with 96.3% extraction accuracy.
Review Workflow Integration
Integrated the AI engine into the firm's existing document management system, building a review dashboard that presents extracted clauses, risk flags, and suggested redlines — allowing attorneys to approve, modify, or override AI recommendations in context.
Continuous Learning & Governance
Implemented a human-in-the-loop feedback system where attorney overrides automatically feed back into model retraining. Established an AI governance committee with quarterly model audits and bias assessments.
Measurable impact, verified by the client
Technologies we used
“Their AI platform processes 10,000+ legal documents a day and cut our manual review time by 85%. We saved $3.2M in the first year alone. The ROI case wrote itself after the first quarter.”
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