AI System for Intelligent Contract Review

Challenge

The client operates a wide network of retail stores across the country and manages its own commercial real estate portfolio. Internally, lease agreements must be reviewed and approved by financial control specialists who manually compare each document to a corporate template. This manual process was time-consuming — especially during peak periods — and exposed the business to financial risk due to human error.

The primary objective was to reduce the time and labor required to verify contracts against standardized templates, without involving legal teams in routine work, while maintaining high accuracy and compliance with legal and business standards.

Solution

We developed a system that automates contract review and removes the need for manual comparison.

Users upload contracts in Word or PDF format. The system automatically recognizes the text, analyzes document structure, and compares it against the approved template (e.g., a commercial lease agreement).

AI algorithms distinguish between editorial edits and legally significant changes, classify deviations by severity, and generate a natural-language report.

The report highlights all differences with specific clause references, provides detailed comments for each change, and suggests an automated recommendation — accept, revise, or reject.

An additional module generates an approval note using templated legal language and visualizes all changes for fast and easy review.

The system is configurable for new contract types and scalable to other approval workflows. Reviewing a single contract takes no more than three minutes, which is critical during high-volume periods.

Results

With the new system in place, full contract review — including upload, text recognition, and analysis — takes under three minutes. The system is deployed on the client’s internal servers and fully complies with corporate data protection standards.

Key benefits delivered:

• Reduced contract review time from several hours to minutes,
• Freed up financial control teams from routine work to focus on exceptions
• Lower error rates from manual review processes,
• Increased transparency and repeatability of contract approvals.

In the MVP phase, the system achieved over 90% accuracy in identifying deviations, and more than 75% accuracy in generating appropriate recommendations and approval notes — exceeding project targets.