Turn Documents into Structured Data with Document Understanding API

Yes—it's layout-aware, so it follows the visual structure of the page to keep paragraphs, columns, headers/footers, and nested sections in the right order. You get coherent, sectioned content instead of scrambled text that requires brittle post-processing.

The API is designed for complex tables, including merged cells, spanning headers, and irregular row structures. It outputs clean, structured table data you can use directly in downstream systems—without manual cleanup or guessy CSV conversions.

Yes—multimodal outputs convert visual elements into useful text formats such as Markdown tables or LaTeX, with supporting context. That means your application can reason over figures and formulas instead of silently ignoring them.

You can request JSON Mode, which returns a structured representation of the document rather than a flat text blob. This makes it easy to route content by type (paragraphs, tables, figures) and integrate reliably into pipelines and agents.

Yes—each element can include metadata like page numbers, node types, and coordinates. This gives you the hooks needed for citations, QA workflows, and fast debugging when a user asks, “Where did this answer come from?”

By preserving layout, extracting tables accurately, and translating visual content into usable text, the API minimizes the common failure modes that trigger manual intervention. Teams typically spend less time writing custom parsing rules and more time shipping reliable document-driven features.