AI Document Processor

The Problem

A legal-tech firm was spending 40+ hours per week manually extracting key clauses and metadata from contracts before they could be analyzed. The documents varied wildly in format and language — a rules-based system had already failed once.

The backlog was growing, and the manual process introduced errors that occasionally had significant downstream consequences.

The Solution

I designed and built an end-to-end document processing pipeline:

Stage 1 — Ingestion and preprocessing

A Python service handles document ingestion from multiple sources (email attachments, S3, manual upload). It normalizes PDFs using PyMuPDF, handles multi-column layouts, and detects document language.

Stage 2 — Extraction pipeline

Rather than a single large prompt, the extraction uses a multi-step pipeline:

1. Document classification (what type of contract is this?)
2. Structure detection (where are the key sections?)
3. Clause extraction (targeted extraction within each section)
4. Validation (cross-check extracted values for consistency)

This structured approach dramatically reduced hallucination rates compared to a single end-to-end prompt.

Stage 3 — Human review interface

A Next.js interface lets reviewers verify extractions, flag errors, and approve documents for downstream processing. Reviewer corrections feed back into the fine-tuning dataset.

# Simplified extraction pipeline
def process_document(doc: Document) -> ExtractionResult:
    doc_type = classify_document(doc)
    sections = detect_sections(doc, doc_type)
    
    extractions = []
    for section in sections:
        clauses = extract_clauses(section, doc_type)
        validated = validate_clauses(clauses, section)
        extractions.extend(validated)
    
    return ExtractionResult(
        document_id=doc.id,
        doc_type=doc_type,
        extractions=extractions,
        confidence=calculate_confidence(extractions),
    )

The Outcome

80% of documents now processed without human intervention

Processing time reduced from 40+ hours/week to ~4 hours/week (for edge cases and review)

Extraction accuracy of 96.3% on the validation set after fine-tuning

ROI achieved within 3 months of deployment

The system now processes over 500 documents per month and has been expanded to handle two additional document types beyond the original scope.

Tech Stack

• AI: OpenAI GPT-4, fine-tuned GPT-3.5-turbo, LangChain
• Backend: Python, FastAPI, Celery, Redis
• Frontend: Next.js, TypeScript, Tailwind CSS
• Infrastructure: AWS (Lambda, S3, RDS), Docker