Extraction output format and results

Every extraction produces a structured result on the call execution, under extracted_data.

Extraction output format

Each extraction result is a JSON object with the following fields:

Field	Type	Description
`subjective`	`string \| null`	Free Text LLM response. `""` if no relevant info found; `null` if `is_subjective` is `false`
`objective`	`string \| null`	Selected Pre-defined value. `null` if not configured or no condition matched
`confidence`	`float`	LLM confidence score from `0.0` to `1.0`
`confidence_label`	`string`	Human-readable label: `"High"` (≥ 0.8), `"Medium"` (≥ 0.5), or `"Low"` (< 0.5)
`reasoning_subjective`	`string \| null`	LLM’s reasoning for the free-text response. `null` if `is_subjective` is `false`
`reasoning_objective`	`string \| null`	LLM’s reasoning for the pre-defined selection. `null` if `is_objective` is `false`
`validation`	`object \| null`	Post-LLM validation result for typed free-text responses. `null` for plain `text` type or when `is_subjective` is `false`

Results are nested by category and extraction name under extracted_data:

{
  "extracted_data": {
    "Category Name": {
      "Extraction Name": {
        "subjective": "Free text response from LLM",
        "objective": "Pre-defined value or null",
        "confidence": 0.92,
        "confidence_label": "High",
        "reasoning_subjective": "LLM's explanation for the free-text answer",
        "reasoning_objective": "LLM's explanation for the selected option",
        "validation": null
      }
    }
  }
}

Complete Example:

{
  "extracted_data": {
    "Lead Quality": {
      "Call Outcome": {
        "subjective": "Customer expressed strong interest and asked about enterprise pricing options.",
        "objective": "interested",
        "confidence": 0.92,
        "confidence_label": "High",
        "reasoning_subjective": "Customer asked about enterprise pricing and requested a follow-up demo.",
        "reasoning_objective": "Customer explicitly expressed interest and agreed to a next step.",
        "validation": null
      }
    },
    "Contact Info": {
      "Customer Email": {
        "subjective": "user@example.com",
        "objective": null,
        "confidence": 0.95,
        "confidence_label": "High",
        "reasoning_subjective": "Customer clearly provided their email address during the call.",
        "reasoning_objective": null,
        "validation": {
          "is_valid": true,
          "expected_type": "email"
        }
      }
    },
    "Agent Handover": {
      "Agent Handover Needed": {
        "subjective": "",
        "objective": "No",
        "confidence": 0.88,
        "confidence_label": "High",
        "reasoning_subjective": null,
        "reasoning_objective": "Customer did not request to speak with a human agent at any point.",
        "validation": null
      }
    }
  }
}

Understanding the output

subjective field

Contains the free text response generated by the LLM based on the extraction prompt.

Returns a string with the LLM’s analysis
Empty string "" if no information found
"null" (string) if extraction wasn’t applicable

Example: "The customer expressed interest and agreed to a demo appointment"

objective field

Contains the pre-defined value selected by the LLM from configured answer options.

Returns the configured answer value (e.g., "Yes", "No", "hot", "warm", "cold")
null if pre-defined answers aren’t configured
null if no matching condition was satisfied

Example: "No" (from answer options “Yes” or “No”)

confidence and confidence_label

Every extraction result includes a confidence score explaining how certain the LLM was about its answer.

Field	Type	Description
`confidence`	float	Score from `0.0` to `1.0` — higher means more certain
`confidence_label`	string	`"High"` (≥ 0.8), `"Medium"` (≥ 0.5), or `"Low"` (< 0.5)

Use these to build confidence-based routing — for example, flag "Low" results for human review.

reasoning_subjective and reasoning_objective

Brief explanations from the LLM explaining why it produced each answer.

reasoning_subjective — present when is_subjective is true; explains the free-text response
reasoning_objective — present when is_objective is true; explains the pre-defined selection

Both are null when their respective answer type is disabled. Useful for auditing unexpected results.

validation field

Present when a Free Text extraction has an Expected Format constraint (anything other than plain text). Contains:

{
  "is_valid": true,
  "expected_type": "email"
}

is_valid: false means the LLM’s response didn’t match the expected format — the original response is still returned in subjective so no data is lost
null for plain text type or when is_subjective is false

Empty vs Null

Different empty states have different meanings:

Value	Meaning
`""` (empty string)	No relevant information found in transcript
`"null"` (string)	Extraction wasn’t applicable to this call
`null` (JSON null)	Field not configured OR no condition matched

Accessing extraction results

Extracted data is part of the call execution result and is available as extracted_data in every execution response. You can access it in the following ways:

Via the Executions API

Fetch any execution by ID using GET /executions/{execution_id} or list all executions for an agent using GET /v2/agent/{agent_id}/executions. The extracted_data field is returned in the response body.

{
  "execution_id": "abc123",
  "status": "completed",
  "transcript": "...",
  "extracted_data": {
    "Agent Handover": {
      "Agent Handover Needed": {
        "subjective": "Customer asked to speak with a human.",
        "objective": "Yes",
        "confidence": 0.95,
        "confidence_label": "High",
        "reasoning_subjective": "Customer explicitly said they want to talk to a person.",
        "reasoning_objective": "Customer requested a human agent.",
        "validation": null
      }
    }
  }
}

Via Webhooks

If you’ve configured a webhook, the extracted_data field is included in the post-call webhook payload, the same execution object sent to your endpoint after every call.

Via the Dashboard

Open any call record from Monitor → Call History in the dashboard to see extraction results alongside the transcript (and the call summary, when the agent’s Call Summary toggle is on).

Via Batch Executions

For batch campaigns, extracted_data is returned in each execution record when fetching batch execution results.

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Extraction output format and results