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The Output Schema defines the structure of the response returned by your AI/ML model. This ensures that predictions or insights received from the model are properly formatted, mapped, and usable within downstream components like Data Apps, feedback mechanisms, or automation triggers. AI Squared allows you to specify each expected field and its data type so the platform can interpret and surface the response correctly.

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Why Output Schema Matters

  • Standardizes how model results are parsed and displayed
  • Enables seamless integration into Data Apps or embedded tools
  • Ensures feedback mechanisms are correctly tied to model responses
  • Supports chaining outputs to downstream actions

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Defining Output Fields

Each field you expect from the model response must be described in the schema: 📸 Placeholder for: Screenshot of output schema configuration UI

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Example Output Payload

Your output schema should include: churn_risk_score → Float prediction_label → String confidence → Float This structure ensures consistent formatting across visualizations and workflows.

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Tips for Defining Output Fields

Make sure field names exactly match the keys returned by the model. Use descriptive names that make the output easy to understand in UI or downstream logic. Choose the right type — AI Squared uses this for formatting (e.g., number rounding, boolean flags, etc.).

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What’s Next

You’ve now connected your source, defined inputs, optionally transformed them, and configured the expected output. Next, you can:
  • Test Your Model with sample payloads
  • Embed the output into Data Apps
  • Set up Feedback Capture