From Paper Chaos to Intelligent Automation — AI for Document Processing
[Financial Services Firm] — 10,000+ documents/month, 15 document types
THE CHALLENGE
A financial services company was spending hundreds of person-hours each month manually reviewing, classifying, and extracting data from client documents — loan applications, tax returns, bank statements, identity documents, and insurance forms. The manual process was slow, error-prone, and could not scale with the company's growth.
OUR APPROACH
We built DocMind — an end-to-end document intelligence pipeline that automatically classifies incoming documents by type, extracts key data fields with high accuracy, validates extracted data against business rules, and flags anomalies for human review. The system handles 15 different document types and integrates directly with the client's loan origination system.
TECHNICAL HIGHLIGHTS
- Document classification model with 98.5% accuracy across 15 document types
- Custom OCR pipeline optimized for poor-quality scans and handwritten content
- Named entity recognition for financial data extraction (amounts, dates, account numbers)
- Business rule validation engine with automated anomaly detection
- Human-in-the-loop workflow for edge cases and quality assurance
RESULTS
- 85% reduction in manual document processing time
- 98.5% classification accuracy
- 94% extraction accuracy (up from 72% with previous OCR-only solution)
- Handles 10,000+ documents per month with 2 FTE (previously required 12)
- $380K annual savings in operational costs
TECHNOLOGIES USED
- Python
- Hugging Face Transformers
- Tesseract OCR
- spaCy
- FastAPI
- React
- PostgreSQL
- MinIO
- AWS (Textract, Lambda, S3)