Enterprise POC: AI Document Classifier for Insurance Claims
A 7 day proof of concept that demonstrated 91% accuracy classifying insurance claim documents, securing executive approval for a $120K full build.
Client: Mid-size insurance carrier (NDA protected)
Insurance document classification POC
The Challenge
The carrier processed 2,000 claim documents per day across 12 document types. Manual classification took an average of 4 minutes per document. Two full time employees did nothing but sort documents. The VP of Claims wanted to automate this but needed proof the AI could handle their specific document types before the board would approve the $120K budget for a production system.
Our Approach
We built a focused POC that answered one question: can an LLM classify their 12 document types with at least 85% accuracy? We used a sample of 500 real documents (anonymized), built a classification pipeline with Claude, and measured accuracy against human labeled ground truth. No UI, no database, no authentication. Just the classification engine and a results report.
What We Built
Delivery Timeline
Day 1: Data Audit
Received 500 anonymized documents, assessed quality, identified 12 document types, built ground truth labels.
Day 2-3: Pipeline Build
Built classification pipeline with Claude, tested prompt variations, optimized for accuracy across all 12 types.
Day 4-5: Benchmarking
Ran full benchmark against labeled dataset, generated confusion matrix, identified weak spots (handwritten notes).
Day 6: Cost Model
Built cost projection comparing AI vs manual classification at 2,000 docs/day, including error correction overhead.
Day 7: Presentation
Delivered executive report with accuracy data, cost projections, limitations, and full project proposal for production build.
Tech Stack
Architecture
ai
Claude Sonnet for document classification with structured JSON output
pipeline
Python processing pipeline with parallel document handling
analysis
Pandas for accuracy metrics, confusion matrix, and cost modeling
Security
data
All documents anonymized before processing, PII stripped in preprocessing step
access
POC ran on isolated environment, no data left our infrastructure
The Results
“The POC gave us exactly what we needed to present to the board. Seven days and $2,500 to unlock a $120K project that will save us $400K per year in labor costs.”
Key Takeaways
A focused POC answers one question definitively. We did not build a UI or a dashboard. We built a classification engine and measured its accuracy.
Real data matters. Synthetic test data would not have revealed that their handwritten medical notes were the hardest document type to classify (78% vs 95% for typed documents).
The deliverable is a decision, not software. The POC code was throwaway. The value was the accuracy report and the go/no go recommendation that let executives make an informed decision.
Deliverables
FAQ
Frequently Asked Questions
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