Legal Document AI Assistant

Review contracts in minutes, not hours

OBJECTIVES

Reduce contract review time by 70%+Catch all deviations from standard termsGenerate plain-language issue summaries

PROJECT TYPE

AI Assistant

A mid-size law firm spent 40% of associate time on contract review—tedious work that didn't leverage their expertise.

Build Your Legal AI

15 MIN · NO PREP REQUIRED

On-premise LLM (fine-tuned Llama)—RAG system for firm standards—NetDocuments API integration—PostgreSQL for review history—React dashboard for management—Python for document processing—

OVERVIEW

A mid-size law firm spent 40% of associate time on contract review—tedious work that didn't leverage their expertise. We built an AI assistant that reviews contracts against firm standards, flags non-standard clauses, and generates revision suggestions. Associates now focus on judgment calls, not clause hunting.

THE PROBLEM

The firm handled 200+ contracts monthly—NDAs, MSAs, vendor agreements, employment contracts. Each required associate review against firm standards.

Experienced associates could review a contract in 2-3 hours. Junior associates took 4-5 hours. At $300/hour billing rates, this added up—but clients increasingly balked at paying for routine review.

Worse, fatigue caused mistakes. After reviewing the tenth NDA of the day, eyes glazed over. Non-standard indemnification clauses slipped through. Unfavorable liability caps went unnoticed.

The firm needed to maintain quality while reducing the time burden—letting associates focus on negotiation strategy and client counseling rather than clause-by-clause comparison.

CONSTRAINTS

Must handle 15+ contract types with different standards
Cannot store client documents on external servers
Must explain reasoning, not just flag issues
Output must meet attorney work product standards
Must integrate with NetDocuments DMS
Partners must be able to customize firm standards

DELIVERABLES

What we shipped.

FIG. 02

AI contract review assistant with on-premise deployment

Firm-specific standards library with version control

Issue detection with severity classification

Revision suggestion engine with explanation

NetDocuments integration for seamless workflow

Admin portal for standards management

Training program for attorney adoption

KEY DECISIONS

How we solved it.

FIG. 03

Cloud AI or on-premise deployment?

On-premise with secure API calls

Client confidentiality required on-premise document processing. We deployed the inference model locally, with only anonymized queries to cloud services for ambiguous language interpretation. Full audit trail maintained.

Flag issues only or suggest revisions?

Both, with confidence scoring

Flagging issues is helpful but still requires attorney work to draft revisions. AI-suggested revisions with confidence scores give attorneys a starting point. Low-confidence suggestions come with reasoning for attorney judgment.

Rigid standards or learning from corrections?

Learning with partner approval

Firm standards evolve. AI learns from attorney corrections, but changes require partner approval before affecting future reviews. This prevents drift while enabling improvement.

OUTCOMES