Automate Sorting and Routing with Document Classification API

The API performs layout-aware page segmentation, identifying stable regions like headers, footers, sections, and multi-column flows. That means you can classify based on consistent semantic areas (e.g., “Invoice Summary” or “Patient Info”) even when formats vary across vendors or versions.

You get AI-ready JSON with cleanly separated sections, paragraphs, and tables instead of a single flat text blob. This makes it easy to map documents into your label taxonomy using consistent fields and reduces downstream parsing and edge-case handling.

Yes—extracted elements can include verifiable metadata like page numbers, spatial coordinates, and confidence signals. This gives you auditable evidence for each label, supports targeted human review for low-confidence cases, and speeds up debugging when a classification is disputed.

It uses multimodal visual understanding to interpret tables, charts, images, and form structure as part of the document’s meaning. That’s especially useful for distinguishing document types that look different even when the text is similar, such as statements, claim forms, and lab reports.

By classifying from structured, layout-grounded regions rather than brittle keyword matches, the system stays stable across new templates and minor formatting shifts. You can also use confidence signals and citations to continuously audit outcomes and tighten your review workflow where it matters.

Most teams plug it in as a preprocessing step: send a document, receive structured JSON plus traceability, then run your rules or ML model on predictable fields. This approach typically shortens implementation time because you spend less effort cleaning text and handling layout-specific exceptions.