The Input Schema defines the structure of the data passed to your AI/ML model during inference. This ensures that inputs sent from your business applications or workflows match the format expected by your model endpoint.

AI Squared provides a no-code interface to configure input fields, set value types, and ensure compatibility with model requirements.

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Why Input Schema Matters

  • Ensures data integrity before reaching the model
  • Maps business inputs to model parameters
  • Prevents inference failures due to malformed payloads
  • Enables dynamic or static parameter configuration

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Defining Input Fields

Each input field includes the following:

FieldDescription
NameThe key name expected in your model’s request payload
TypeThe data type: String, Integer, Float, or Boolean
Value TypeDynamic (changes with each query/request) or Static (fixed value)

📸 Placeholder for: Screenshot of input schema editor

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Static vs. Dynamic Values

  • Static: Hardcoded values used for all model requests. Example: country: "US"
  • Dynamic: Values sourced from the business application or runtime context. Example: user_id passed from Salesforce record

📘 Tip: Use harvesting (covered later) to auto-fetch dynamic values from frontend apps like CRMs.

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Example Input Schema

{
  "customer_id": "12345",
  "email": "user@example.com",
  "plan_type": "premium",
  "language": "en"
}

In this example:

customer_id and email may be dynamic

plan_type could be static

Each key must align with your model’s expected input structure

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Next Steps

Once your input schema is defined, you can:

Add optional Preprocessing logic to transform or clean inputs

Move forward with configuring your Output Schema