About
A major insurance carrier processed 50,000+ documents monthly with 40+ FTE hours of daily manual entry and 8%+ error rates. UppLabs built an IDP pipeline using GPT-4 Vision and fine-tuned BERT for 15+ document types with human-in-the-loop review. Result: 85% reduction in manual review, 99.2% extraction accuracy, 40x faster processing, $2.1M annual savings in 6 months.
Challenge, approach, and impact
Diverse Document Formats Across 15+ Types
Insurance documents arrived in 15+ formats including structured forms, unstructured letters, handwritten notes, faxed images, and multi-page medical records with inconsistent layouts. A single model approach was insufficient to handle this variety at production accuracy.
Domain-Specific Accuracy Requirements
Generic OCR and NLP models failed on insurance terminology, medical codes including ICD-10 and CPT, and policy-specific fields. Achieving production-grade accuracy required custom domain adaptation using fine-tuned models trained on insurance-specific data.
Compliance and Full Audit Trail
Every extracted data point needed a traceable link to its source document location. Regulatory requirements demanded full audit trails for all automated decisions, making transparency and traceability non-negotiable design constraints.
Legacy System Integration Complexity
The client operated a 15-year-old claims management system with limited API surface. Bridging a modern AI pipeline with legacy enterprise software required custom middleware architecture with guaranteed delivery and retry logic.
How we built
Testimonials
Confidental C.
Confidental
“Processing 50,000 documents a month manually was costing us 40+ FTE hours daily and generating error rates above 8%. UppLabs delivered an intelligent document processing pipeline in 6 months that now handles the same volume with 99.2% accuracy at 40x the speed. The $2.1M in annual savings was the headline result but the deeper value was eliminating compliance risk from manual data entry errors. Their GPT-4 and BERT ensemble approach was exactly the right architectural decision for our document complexity.“
Team structure
Client team
C C
Chief Operations Officer
Project stakeholder
J J
Head of Digital Transformation
Project stakeholder
The client stakeholders were working closely with the team at UppLabs
Agency team
2 x ML Engineer
Governance
2 x Backend Developer
Governance
1 x QA Engineer
Governance