AI Document Processing Engine
Intelligent OCR, Entity Extraction & Automated Document Routing
Project Overview
A legal services firm was spending 40+ hours per week manually reviewing and routing contracts, NDAs, and client intake forms. Staff extraction accuracy was around 87% — too low for legal-grade work. They needed an automated pipeline that could exceed human accuracy and cut turnaround from hours to minutes.
The Challenges
- 1
Varied document formats: PDFs, Word docs, and low-resolution scanned images
- 2
Legal entity extraction required domain-specific NER beyond standard models
- 3
Routing rules depended on extracted content — meaning accuracy was non-negotiable
- 4
GDPR compliance required PII to be masked before any cloud storage
Our Approach
We built a multi-stage pipeline: AWS Textract for OCR on scanned documents, a custom Hugging Face NER model fine-tuned on 8,000 labelled legal documents for entity extraction, a rules engine for classification and routing, and FastAPI webhooks delivering results to the firm's existing case management system.
Key Features & Metrics
Multi-format ingestion: PDF, Word, and scanned images via AWS Textract
Custom NER model fine-tuned on 8,000 legal documents across 14 entity types
96% extraction accuracy — surpassing the 87% manual baseline
Automated routing based on document type and extracted party names
GDPR-compliant: PII masked with AES-256 before storage
Processing time cut from 40+ hours per week to under 8 hours
Results & Business Outcome
Weekly document processing time dropped from 40+ hours to under 8. Extraction accuracy improved from 87% to 96%. Paralegal time freed up added ~$60K annual revenue capacity.
When AI handles the reading and routing, your experts can spend 100% of their time on the thinking that actually requires human expertise.
Let's Build Something Intelligent Together
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