Intelligent Document
Processing
How we automated DataFlow's document extraction and classification pipeline to process 10,000+ documents daily with 99% accuracy — replacing 24 manual staff positions and cutting costs by 73%.
10K+
Documents processed daily
99.2%
Classification accuracy
85%
Reduction in manual processing
< 3s
Average processing time per doc
The Challenge
Drowning in paper, starving for data
DataFlow, a logistics and supply chain company operating across 14 countries, was processing over 10,000 documents daily — invoices, purchase orders, customs forms, shipping manifests, and insurance claims. A team of 28 staff manually keyed data into their ERP system.
The manual process was slow (4-6 hours per batch), error-prone (3-5% error rate), and couldn't scale. As DataFlow grew, they were hiring data entry staff faster than salespeople. Documents from field offices arrived as low-quality scans with handwritten annotations, making off-the-shelf OCR tools unusable.
They needed a system that could handle the volume, the variety of document types, and the inconsistent quality — while integrating directly with their existing SAP and Oracle ERP systems.
Scope
Document types processed
The Solution
End-to-end processing pipeline
Ingestion & Pre-Processing
Documents arrive via email, API upload, or scanned batch. Our pipeline normalizes formats (PDF, TIFF, JPEG, DOCX), corrects skew and rotation, enhances low-quality scans, and splits multi-page documents into logical units.
OCR & Text Extraction
Custom OCR models trained on DataFlow's specific document types achieve 99.5% character accuracy — even on handwritten fields, stamps, and degraded scans. We extract both printed and handwritten text with layout-aware positioning.
Classification & Routing
A fine-tuned classification model identifies document type, urgency, and department routing in under 200ms. Documents are automatically tagged and sent to the correct processing queue — no human triage needed.
Data Extraction & Validation
AI extracts structured data from unstructured documents — vendor names, amounts, dates, line items, clauses. Cross-references against existing records in DataFlow's ERP to flag discrepancies automatically.
Integration & Output
Extracted data flows directly into DataFlow's ERP, accounting software, and data warehouse via API. Rejected or low-confidence documents are routed to a human review dashboard with pre-filled suggestions.
Problem Solving
Challenges we solved
Challenge
Poor scan quality from field offices
Solution
Built an adaptive image enhancement pipeline that automatically adjusts contrast, removes noise, and corrects perspective distortion. Trained OCR models specifically on degraded document samples from DataFlow's worst-case scanners.
Challenge
Handwritten annotations on printed forms
Solution
Developed a dual-extraction approach: standard OCR for printed text and a specialized handwriting model for annotations. The system identifies handwritten regions automatically and applies the correct model to each zone.
Challenge
Documents with inconsistent layouts
Solution
Instead of rigid template matching, we trained layout-understanding models that recognize semantic fields regardless of position. The system adapts to layout variations within the same document type without manual template updates.
Results
The transformation
Before Moonflower AI
- 28 staff dedicated to manual data entry
- 4-6 hour processing time per document batch
- 3-5% error rate in data extraction
- Documents lost or misfiled weekly
- No real-time visibility into processing status
- $1.2M annual document processing costs
After Moonflower AI
- 4 staff for exception handling only
- < 3 seconds per document, real-time processing
- 0.8% error rate (84% improvement)
- Zero lost documents with full audit trail
- Live dashboard with processing analytics
- $320K annual costs (73% reduction)
“We went from a room full of people manually keying in data to a system that processes our entire daily volume before our team finishes their morning coffee. The accuracy is actually better than our manual process ever was.”
Marcus Rivera
COO, DataFlow
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document processing?
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