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What is an Output Schema?

An output schema defines the exact JSON structure of the verification results returned by Omni. Instead of receiving raw engine outputs, you get results formatted precisely the way your systems need them.

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Defining an Output Schema

Output schemas are defined as JSON structures in the workflow configuration. Each field maps to data extracted and verified by the workflow engines.

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Schema writing guide

When you configure the output schema in workflow creation Step 4, the UI applies the following rules (whether you use the Field Builder or JSON input).
  • Standard: Schemas follow JSON Schema Draft-07, plus the additional constraints below.
  • Root shape: The document must start with "type": "object" and define fields under "properties".
  • Field types: You can use string, number, integer, boolean, object (nested object), and array (list).
  • Nesting depth: Field nesting is limited to 2 levels (for example, an object inside another object at the root).
  • Duplicate names: Duplicate field names are not allowed.
  • description: Adding a description on each field helps the AI produce more accurate structured results.
  • Sync: Field Builder (table view) and JSON input (code view) stay automatically synced—edits in one are reflected in the other.
Output schema Field Builder empty state with Schema Writing Guide

Field Builder tab — empty state with the Schema Writing Guide panel (same rules as above).

JSON Schema Input with Schema Writing Guide

JSON Schema Input tab — valid Draft-07 schema with the guide below the editor.

Example output schema for KYB:

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Verification status

Every analysis response carries a top-level verificationStatus assigned by the system, with one of three values: verified, pending_review, or rejected. The system decides this value regardless of your output schema.
You may define a field with the same name inside your schema’s decision block, but that is a user-defined value and is separate from the system verdict. Aligning it with the same enum keeps downstream branching simple.

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Schema Best Practices

Design the schema to match what your backend or compliance systems expect. This eliminates the need for post-processing transformations.
Always include a top-level decision field with result, verificationStatus (verified / pending_review / rejected), and reasons. This is a recommended pattern, not a structure the system enforces. Matching the system enum keeps downstream routing simple.
Simpler schemas are easier to maintain and integrate. Only nest when the data naturally requires it.

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Ontology Mapper

The Ontology Mapper validates that engine outputs conform to your defined schema. If an engine returns data in a different format, the mapper normalizes it to match your schema before delivering results.