B2B SaaS

ML-Powered Document Processing

Automating data extraction from 1M+ documents monthly

NLPOCRAutomationEnterprise

8 months (0 to 1)

Team of 10

Lead Product Manager

🎯The Problem

Enterprise customers were spending 100+ hours weekly manually extracting data from invoices, contracts, and forms. Issues included: - Error rate of 12% in manual data entry - Processing delays causing downstream workflow bottlenecks - High operational costs ($500K+ annually per large customer) - No way to handle 10x document volume spikes

🔍My Approach

Designed 0-1 product for intelligent document processing: 1. **Customer Discovery** - Shadowed 10 operations teams processing documents - Identified 15 document types with highest volume - Mapped current workflows and pain points 2. **Technical Feasibility** - Evaluated OCR engines (Tesseract, Google Vision, AWS Textract) - Tested NER models for entity extraction - Prototyped with 1000 sample documents 3. **Product Strategy** - Started with invoice processing (highest volume, clear ROI) - Built template-free extraction (works on any format) - Human-in-the-loop for quality assurance - API-first design for easy integration

💡The Solution

Launched end-to-end ML document processing platform: **Core Features** - Multi-format support (PDF, images, scanned docs) - Automatic field extraction using NER + custom ML models - Confidence scoring with human review queue - Validation rules and business logic - Real-time API and batch processing **ML Pipeline** 1. Document classification (identify document type) 2. OCR and text extraction 3. Layout analysis (tables, headers, line items) 4. Entity extraction (dates, amounts, names, addresses) 5. Validation and confidence scoring 6. Human review for low-confidence predictions **Integration** - RESTful API for real-time processing - Batch upload via web interface - Webhooks for async notifications - Export to ERP systems (SAP, Oracle, Workday)

Technologies Used

PyTorchTesseractspaCyAWS TextractDockerKubernetesPostgreSQLFastAPI

📈Impact & Results

-92%

Processing Time

From 5 minutes to 24 seconds per document

96%

Accuracy

Improved from 88% (manual) to 96% (automated)

$8M

Cost Savings

Saved across customer base in first year

1M+

Volume Handled

Documents processed monthly

$5M

ARR

Product revenue in first 12 months

💭Key Learnings

•Template-free was right bet - customers had thousands of document variations
•Human-in-the-loop essential for trust - started with 30% review, now at 5%
•Model retraining from corrections created flywheel - accuracy improved 8% over 6 months
•Table extraction was hardest - complex layouts required custom models
•API-first design enabled land-and-expand - integrations drove adoption

Want to Learn More?

I'd be happy to discuss this project in more detail, share additional insights, or answer any questions.

Let's Connect