Large-Scale Document Intelligence Platform for Legal Operations

We partnered with the client to build a custom AI-powered contract intelligence system tailored for legal use cases. The platform combined natural language processing (NLP), domain-specific risk modeling, and seamless integration into their document workflows.

Our approach followed five key phases:

1. Legal Corpus Analysis & Data Labeling

We began by analyzing thousands of anonymized contracts provided by the client including NDAs, MSAs, SLAs, employment agreements, and more.

We worked with their legal analysts to identify and label:

• Key clauses (e.g., indemnity, termination, jurisdiction, data privacy)

   

• Risky language patterns (e.g., unilateral obligations, broad liability)

   

• Clause variations and custom language that deviated from company templates

   

This domain-specific annotation laid the foundation for a high-accuracy AI model trained on legal context not just general business documents.

✅ Result: Created a labeled dataset of 80,000+ annotated clauses for model training.

2. NLP Model Development & Fine-Tuning

We deployed a series of transformer-based models, including BERT variants fine-tuned on legal corpora. These models were trained to:

• Identify and extract clauses from unstructured legal text

   

• Understand context (e.g., who holds obligation vs. who has rights)

   

• Detect deviations from contract standards

   

• Assign a dynamic risk score based on legal language and precedent

   

We also added OCR capability for scanned documents and a rule-based fallback for edge cases.

✅ Result: Achieved 94%+ precision and 91% recall in clause extraction on test data.

3. Smart Clause Risk Scoring Engine

Rather than just surfacing clauses, we built a risk scoring engine to flag what mattered. Our algorithm assigned a numerical risk score based on:

• Clause content and legal sentiment

   

• Alignment with approved contract templates

   

• Historical legal disputes tied to similar wording

   

• Regulatory requirements by jurisdiction (e.g., GDPR, CCPA)

   

Each score was accompanied by a confidence level and rationale, empowering legal teams to triage documents efficiently.

✅ Result: Legal reviewers could focus on high-risk sections instantly, without scanning 20 pages of boilerplate.

4. Integration with Document Management and Review Tools

To ensure adoption, we integrated the platform with their existing tools:

• Microsoft Word for in-line clause highlighting and comment suggestions

   

• DocuSign and Adobe Sign for contract lifecycle management

   

• SharePoint and internal DMS for centralized contract storage

   

• Slack and email alerts when risky contracts hit the queue

   

✅ Result: No workflow disruption. Lawyers stayed in familiar tools, with AI assisting in the background.

5. Human-in-the-Loop Collaboration

AI didn’t replace legal expertise, it amplified it.

We enabled human-in-the-loop review where lawyers could accept, modify, or reject AI suggestions. Every interaction helped the model retrain and improve over time.

We also added dashboards to track clause frequency trends, risk distributions by region or vendor, and reviewer productivity metrics.

✅ Result: Continuous improvement of model performance and operational visibility for legal leadership.

The Outcome: 6 Days to 1 Hour

The impact was transformative:

• Contract review time dropped from 6 days to under 1 hour for most documents

   

• 90%+ of standard contracts were processed with minimal human intervention

   

• Cost savings from reduced external legal review fees

   

• Improved compliance by ensuring risky clauses never slipped through

   

• Higher morale among legal teams, who could focus on strategic work instead of manual clause review

   

One legal ops manager called it a “game-changer for how we scale legal support across business units without scaling headcount.”