Intelligent Document Processing: 10× Faster Contract Review for a Top-5 Law Firm

The Problem

India's top-5 law firms review thousands of contracts monthly. A single M&A due diligence can involve 800+ documents. Associates were spending 60% of their time on mechanical extraction work — highlighting dates, payment terms, liability caps, termination clauses.

Not only is this expensive (₹800–1,200/hr associate time), it introduces human error and inconsistency across reviewers.

The Solution Stack

We built a three-stage pipeline:

Stage 1: Document Ingestion & OCR

Multi-format ingestion (PDF, DOCX, scanned images) → PDFPlumber + Tesseract OCR for scanned documents → layout-aware text extraction preserving table structure.

Stage 2: Clause Extraction with Claude

Claude 3.5 Sonnet processes each document page with a structured extraction prompt, returning typed JSON:

import anthropic

client = anthropic.Anthropic()

def extract_clauses(document_text: str) -> dict:
    response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=4096,
        messages=[{
            "role": "user",
            "content": f"""Extract the following from this contract. Return structured JSON:
            - parties (list of entities)
            - effective_date
            - termination_clauses (with conditions)
            - payment_terms
            - liability_cap (amount if present)
            - governing_law
            - risk_flags (potential issues with explanation)

            Contract text:
            {document_text}"""
        }]
    )
    return json.loads(response.content[0].text)

Stage 3: Risk Scoring

A fine-tuned XGBoost classifier assigns risk scores (Low / Medium / High) based on clause patterns across 50,000 historical contracts. High-risk documents are flagged for senior review.

Handling 40+ Document Types

The system handles NDAs, employment contracts, vendor agreements, lease deeds, shareholder agreements, and more. We maintain a clause taxonomy of 180+ clause types, with extraction prompts specialized per document category.

Multilingual support covers English, Hindi, and regional languages via a translation pre-step.

Results

• ▸Processing speed: 4 min/document → 24 sec/document (10× faster)
• ▸Extraction accuracy: 98.3% on held-out test set
• ▸Risk recall: 96.1% of flagged risks correctly identified
• ▸Manual hours saved: 2,400/month across 12 associates
• ▸ROI: 8-month payback on full implementation cost