It’s layout-aware, so it preserves the correct reading order across multi-column forms, headers/footers, and tightly packed sections. That keeps member, provider, and payer details mapped to the right fields—reducing normalization work and preventing errors that lead to denials or rework.

Yes—complex claim tables are extracted without scrambling rows or columns, including line items, units, charges, adjustments, and totals. This makes billed-amount validation and EOB reconciliation more reliable, so your adjudication rules run with fewer exceptions.

Each output can include structured JSON plus citations like page number, coordinates, and element type. That gives auditors and adjusters a clear trail of evidence and enables fast review by jumping directly to the exact location in the document.

The system runs validation and self-correction loops to catch common extraction failures—like hallucinated values, mismatched totals, or broken formatting—before results are returned. That improves straight-through processing rates and reduces time spent on manual QA.

You receive clean, structured JSON that’s easy to map into your existing pipelines, whether you’re feeding adjudication rules, workflow queues, or analytics. Because the data is consistent and citeable, teams typically spend less time on custom parsing logic and exception handling.

Basic OCR often loses structure—especially in multi-column layouts and tables—leading to mis-mapped fields and downstream errors. By preserving layout and validating outputs, you get more accurate, review-ready data that helps prevent avoidable denials and speeds up processing.