DocPipeline
A document intelligence system for insurance workflows that combines OCR, LLM extraction, confidence checks, and human review.
Problem
Manual extraction from premium and loss summaries was slow, inconsistent, and expensive. Analysts spent 15 to 30 minutes per document while backlogs kept growing.
Architecture
A staged pipeline handles ingestion, layout analysis, OCR, semantic extraction, confidence scoring, review routing, and structured persistence. The system is optimized for reliability over raw model theatrics.
Impact
Processing time dropped to a few minutes per document, accuracy improved materially, and the output became useful for downstream reporting instead of a manual re-entry task.
Stack
An applied product experiment that became a serious production system by focusing on confidence thresholds, review workflows, and architecture discipline.
The long-form write-up for this case study is being folded into the site’s new publishing flow. For now, this page keeps the project framing visible: what the system had to solve, how the architecture was shaped, and why the operational result mattered.