Case Study
DocuFlux AI Extraction
An AI-driven extraction pipeline for 2,500 daily claim files, combining automated document understanding with human validation on low-confidence cases.
OCRLayout ModelRule EngineHuman-in-the-loopSAPOracle ERP
Project Details
The insurance claims operation handled about 2,500 files per day with 5-9 documents per file. Manual classification and data entry created a persistent 10-15% error band.
We implemented DocuFlux Extraction with OCR, layout-aware extraction models, a rule engine, and human approval for low-confidence outputs. Validated outputs were integrated into SAP/Oracle ERP workflows and object storage.
+Kodcuk Approach
Architecture decisions were shaped around scale, reliability, and operational clarity.
Key Delivery Layers
+Confidence-based routing separated fully automated and human-reviewed records.
+Rule policies enforced consistency checks on policy IDs, amounts, dates, and identity fields.
+Approved records were written with traceable audit history across downstream systems.
Measured Outcomes
File processing time: 18 minutes to 95 seconds
Error rate: 12% to 1.8%
