We're seeing this pattern across the board: agents reason well over clean text but fall apart when faced with real enterprise documents. A few months ago, Databricks AI Research released OfficeQA, a benchmark based on real-world enterprise document workflows. We found that even highly capable frontier agents scored below 50% accuracy on document reasoning tasks. The bottleneck wasn't reasoning — it was reading.

That's why we’re excited to announce Document Intelligence, built on three core pillars: research-backed accuracy, enterprise scale, and end-to-end simplicity.

Document processing is the accuracy ceiling for every agent. To get this right, the Databricks AI Research team set out to build specialized systems designed for the messy reality of what enterprises actually deal with: inconsistent layouts, nested tables, images, and handwriting.

This research powers a set of chainable AI Functions that break document processing into composable steps: ai_parse_document (now Generally Available) converts raw scans into layout-enriched structured text, while downstream, ai_classify routes documents correctly, and ai_extract pulls the key structured insights that matter most. Together, they form a document intelligence pipeline you can assemble with ease: parse once, then classify, extract, and re-extract without reprocessing the original document.