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· Baleybots

Design a document AI API with structured output

A document AI endpoint is easier to integrate when callers know exactly what they can send and what success returns. Keep the document's transport separate from the extracted object's schema.

Define the caller's contract

Specify accepted document types, size limits, and whether the endpoint accepts bytes or a URL. A remote URL introduces fetching and access rules that an uploaded file does not. Then define the output object, nullable fields, and error responses. A caller should be able to distinguish unsupported input, failed interpretation, and a request paused for review.

Workflow at a glanceDefine the caller's contractUse this sequence as a small fixture before you scale the work.
  1. 1Choose one supported input representation and test…Choose one supported input representation and test it end to end.
  2. 2Validate extracted fields against a JSON SchemaValidate extracted fields against a JSON Schema.
  3. 3Document asynchronous completion and human-review responsesDocument asynchronous completion and human-review responses.

Try it on a small example

  1. Choose one supported input representation and test it end to end.
  2. Validate extracted fields against a JSON Schema.
  3. Document asynchronous completion and human-review responses.

What to verify

Baleybots' callable document-run API requires a pipeline with a supported processor output and an object output schema. Its errors distinguish invalid documents, blocked document URLs, unsupported MIME types, and missing schemas. Preserve those distinctions in your client rather than collapsing everything into extraction failed. Run document fixtures that are readable, malformed, and ambiguous before relying on the endpoint for downstream writes.

Product details checked against the Public API reference on October 4, 2026. These guides describe documented behavior; availability depends on your account and the deployed service. Baleybots is in invite-only beta.