Post-call analysis

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What analysis produces

• Sentiment — positive, neutral, or negative (overall caller sentiment).
• Success — true / false (or null when success evaluation is off), plus a short reason explaining the verdict.
• Data — a map of the structured fields you defined, keyed by field name.
• Analysis time — when the result was produced.

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Configuring analysis

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Structured extraction types

• Text, Number, Yes / No return a string, a number or a boolean.
• Choice (enum) uses your allowed values. Allow multiple values returns all matches as an array; off returns one value. Under When nothing matches, choose an allowed value to save as the fallback. Without a fallback, an unmatched single choice is empty and multiple choice is an empty array.
• Object defines fixed fields (up to 20). Every key is present; unavailable values are empty. Child fields support Text, Number, Yes / No and Choice, including optional allowed values for Text.
• List defines the same fixed fields for each item. The AI returns one object per entry it finds, up to 100 items, or an empty array when none are found.
• JSON lets the AI choose the structure. Use Object or List when your webhook needs fixed keys. Free-form JSON is limited to 16,000 characters and 8 nesting levels.

{
  "fields": [
    {
      "name": "topics",
      "type": "enum",
      "choices": [
        "pricing",
        "delivery",
        "other"
      ],
      "allow_multiple": true,
      "fallback_value": "other"
    },
    {
      "name": "contact",
      "type": "object",
      "fields": [
        {
          "name": "name",
          "type": "string"
        },
        {
          "name": "callback",
          "type": "boolean"
        }
      ]
    },
    {
      "name": "items",
      "type": "list",
      "fields": [
        {
          "name": "product",
          "type": "string"
        },
        {
          "name": "quantity",
          "type": "number"
        }
      ]
    },
    {
      "name": "details",
      "type": "json",
      "description": "Additional preferences mentioned in the conversation"
    }
  ]
}

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Example configuration

{
  "sentiment": true,
  "success": {
    "enabled": true,
    "criteria": "The caller booked an appointment"
  },
  "fields": [
    { "name": "callback_number", "type": "string", "description": "Phone number the caller wants a callback on" },
    { "name": "appointment_day", "type": "enum", "description": "Requested weekday", "choices": ["mon", "tue", "wed", "thu", "fri"] },
    { "name": "is_existing_customer", "type": "boolean", "description": "Whether the caller is already a customer" }
  ]
}

{
  "sentiment": "positive",
  "success": true,
  "success_reason": "Caller agreed to a Tuesday appointment and gave a callback number.",
  "data": {
    "callback_number": "+493012345678",
    "appointment_day": "tue",
    "is_existing_customer": false
  },
  "analyzed_at": "2026-07-05T09:12:44Z"
}

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Using analysis results

• History filters — filter the call list by sentiment and success to find, say, all negative calls that did not succeed.
• Public API — every call in GET /api/v1/calls and GET /api/v1/calls/{id} carries analysis, sentiment, and success. Filter the list with ?sentiment=negative and ?success=false.
• MCP — the same call fields are exposed through the MCP list_calls / get_call tools.

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AI QA scorecards

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Configuring via the API

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Turn a real call into a simulation test

• POST /api/v1/assistants/{id}/tests/from-call with { "call_id": "…" } (assistants:write; Simulations must be included in the plan)
• MCP tool create_assistant_test_from_call