Using Extractions in Bolna Dashboard

What are Extractions?

Extractions allow you to automatically capture structured data from call transcripts. Organize extractions into categories and define custom questions to extract specific information like lead quality, appointment details, customer sentiment, and more.

Extractions interface showing categories and extraction templates — Extractions section in Extractions Tab

Configuring extractions via the API instead of the dashboard? The new Extractions feature is powered by the Dispositions API.

Getting Started with Extractions

Step 1: Access the Extractions Tab

Navigate to the Extractions tab in your agent configuration to find the Extractions section.

Empty extractions state with Create First Category button — Empty extractions state

Creating Categories

Categories help you organize related extractions together. For example, “Agent Handover”, “Visit Details”, or “Lead Qualification”.

Click Create First Category or New Category

Start by creating a category to organize your extractions.

Enter Category Name

Choose a descriptive name like “Agent Handover” or “Visit Details”.

Click Create

Your category is now ready for extractions.

Create category dialog with input field — Create new category dialog

Creating Extractions

Within each category, you can create multiple extraction templates to capture different data points.

Extraction Fields

Name

A descriptive name for the extraction (e.g., “Call Outcome”, “Customer Sentiment”, “Agent Handover Needed”).This name will appear in your extraction results and webhooks.

Extraction Prompt

Instructions that guide the LLM on what to extract from the transcript.Example prompts:

What was the outcome of the call? Was the customer satisfied?
Did the agent answer all questions?

Determine whether an agent handover is required based on the
customer's statements.

You can use variables like {name}, {candidate_name}, {email} to reference call-specific data from recipient_data.

Answer Type

Choose how the LLM should structure its response:Free Text

LLM generates a custom answer based on conversation context
Best for open-ended questions and detailed responses
Example: “Describe the customer’s main concern”

Pre-defined

LLM selects from predefined options you configure
Best for categorical data and structured responses
Example: Lead quality (hot/warm/cold), Yes/No questions

Expected Format

When creating extractions with Free Text enabled, you can constrain the response format using the Expected Format dropdown:

Format	Description	Example Value
Text (default)	Any free-form text	`"Customer was satisfied with the demo"`
Timestamp	ISO 8601 date/time	`"2026-04-08T14:30:00"`
Numeric	Integer or decimal number	`"42"`, `"3.14"`
Boolean	Exactly `true` or `false`	`"true"`
Email	Valid email address	`"user@example.com"`
Custom Regex	Matches a custom regex pattern	`"1234567890"`

When Custom Regex is selected, two additional fields appear:

Pattern (required) — The regex the response must match (e.g., ^\d{10}$)
Description (optional) — Human-readable label (e.g., “10-digit phone number”)

Responses are automatically validated against the expected format. Invalid responses are flagged but preserved — the original response is still returned so no data is lost.

Model

Select the LLM model for extraction processing.Default: gpt-4.1-mini (recommended for most use cases)

Answer Types Explained

Free Text Extractions

Use free text when you want the LLM to generate custom responses based on the conversation. Best for:

Summarizing customer concerns
Extracting reasons or explanations
Capturing qualitative feedback
Open-ended questions

Example:

Name: “Customer Concern”
Prompt: “Summarize the main issue the customer raised during the call”
Answer Type: Free Text

Pre-defined Extractions

Use pre-defined options when you want structured, categorical responses.

Edit extraction template showing predefined answer options with conditions — Pre-defined extraction with answer options

Best for:

Yes/No questions
Status classifications
Lead scoring
Outcome categorization

Configuring Pre-defined Answers

Each answer option consists of:

Answer Value - The value to return (e.g., “Yes”, “No”, “hot”, “warm”, “cold”)
Condition - Instructions for when to select this answer

Example: Agent Handover Detection Answer 1:

Value: Yes
Condition: yield if any one of the triggers for handover are satisfied

Answer 2:

Value: No
Condition: yield if none of the triggers for handover are satisfied

Conditions support variables like {name}, {candidate_name} for dynamic evaluation based on recipient data.

Managing Extractions

Edit an Extraction

Click the edit icon (pencil) on any extraction card
Modify the name, prompt, answer type, or model
Click Save Changes

Delete an Extraction

Click the delete icon (trash) on any extraction card
Confirm deletion

Testing Extractions

Before deploying extractions to production, test them against sample or real transcripts to validate accuracy and refine your prompts.

Test Extractions modal showing sample transcripts, transcript input, and extraction results — Test Extractions interface

How to Test Extractions

Click Test Extractions

In the Extractions section, click the Test Extractions button to open the testing modal.

Choose a Transcript

Select from sample transcripts (Sales Call, Support Call, Appointment) or provide your own:

Paste - Paste a transcript directly into the text area
Import - Upload a transcript file

Run Test

Click the Run Test button to process the transcript through all your extraction templates.

Review Results

View extraction results organized by category. Each extraction shows:

SUBJECTIVE - Free text responses generated by the LLM
OBJECTIVE - Pre-defined values selected by the LLM

Understanding Test Results

Extraction results are displayed hierarchically by category. Each extraction shows its full result including confidence and reasoning:

CATEGORY NAME
  ├─ EXTRACTION NAME
  │   ├─ SUBJECTIVE: [Free text response]
  │   ├─ OBJECTIVE: [Pre-defined value or null]
  │   ├─ CONFIDENCE: 0.92 (High)
  │   ├─ REASONING (SUBJECTIVE): [LLM explanation]
  │   └─ REASONING (OBJECTIVE): [LLM explanation]

Example:

VISIT DETAILS
  ├─ RESCHEDULED VISIT TIME
  │   ├─ SUBJECTIVE: 14:00
  │   ├─ OBJECTIVE: null
  │   └─ CONFIDENCE: 0.89 (High)
  ├─ RESCHEDULED VISIT DATE
  │   ├─ SUBJECTIVE: 24/03/2026
  │   ├─ OBJECTIVE: null
  │   └─ CONFIDENCE: 0.91 (High)

null appears when an extraction type isn’t configured or no matching value is found. Low-confidence results (below 0.5) are worth reviewing manually.

Testing Best Practices

Test with Multiple Transcripts

Test your extractions against various conversation types to ensure they work across different scenarios.

Validate Both Answer Types

If using both free text and pre-defined answers, verify both are extracting correctly.

Refine Based on Results

If results aren’t accurate, edit your extraction prompts and conditions, then test again.

Use Real Transcripts

For production validation, test with actual call transcripts from your agent.

Sample Transcripts

The Test Extractions interface provides three sample transcript types:

Sales Call - Conversation about products and services
Support Call - Customer support interaction
Appointment - Scheduling and booking conversation

These samples help you quickly validate extraction logic without needing your own transcripts.

Working with Categories

Rename a Category

Click the edit icon next to the category name to rename it.

Delete a Category

Click the delete icon next to the category name. This will remove the category and all extractions within it.

Deleting a category is permanent and cannot be undone. All extractions in the category will be deleted.

Add Extractions to a Category

Click Add Extraction to [Category Name] button at the bottom of each category section.

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 the Call History tab in the dashboard to see extraction results alongside the transcript and call summary.

Via Batch Executions

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

Common Use Cases

Use Case	What to extract	What it enables
Agent Handover	Did the caller ask for a human?	Route to a live agent in real time without manual review
Lead Scoring	Budget, timeline, decision-maker	Push hot leads to your CRM or Slack before the rep hangs up
Appointment Sync	Confirmed date, time, location	Write bookings to Google Calendar or Cal.com automatically
Churn Prevention	Sentiment, unresolved complaints	Flag unhappy customers and trigger a follow-up workflow
Compliance Auditing	Disclaimer read, consent given	Structured Yes/No record across thousands of calls
Call Intelligence	Objections, outcomes, cancellation reasons	Spot trends and measure what’s working without reading transcripts

Best Practices

Write specific prompts : Clearly define what to capture and how to interpret the conversation. Avoid vague or multi-part instructions.
Pick the right answer type : Use Pre-defined for categorical data like Yes/No or status fields, and Free Text for open-ended responses.
Keep extractions focused : Split complex logic into multiple simple extractions rather than one long prompt.
Test before deploying : Run your extractions against real or sample transcripts to catch issues early.

Next Steps

Extractions API

Access extractions programmatically

Webhooks

Receive extraction data in real-time

Call History

View extraction results for past calls

Extractions Tab

Configure other post-call tasks

​What are Extractions?

​Getting Started with Extractions

​Step 1: Access the Extractions Tab

​Creating Categories

​Creating Extractions

​Extraction Fields

​Answer Types Explained

​Free Text Extractions

​Pre-defined Extractions

​Configuring Pre-defined Answers

​Managing Extractions

​Edit an Extraction

​Delete an Extraction

​Testing Extractions

​How to Test Extractions

​Understanding Test Results

​Testing Best Practices

​Sample Transcripts

​Working with Categories

​Rename a Category

​Delete a Category

​Add Extractions to a Category

​Extraction Output Format

​Understanding the Output

​Accessing Extraction Results

​Common Use Cases

​Best Practices

​Next Steps

Extractions API

Webhooks

Call History

Extractions Tab

What are Extractions?

Getting Started with Extractions

Step 1: Access the Extractions Tab

Creating Categories

Creating Extractions

Extraction Fields

Answer Types Explained

Free Text Extractions

Pre-defined Extractions

Configuring Pre-defined Answers

Managing Extractions

Edit an Extraction

Delete an Extraction

Testing Extractions

How to Test Extractions

Understanding Test Results

Testing Best Practices

Sample Transcripts

Working with Categories

Rename a Category

Delete a Category

Add Extractions to a Category

Extraction Output Format

Understanding the Output

Accessing Extraction Results

Common Use Cases

Best Practices

Next Steps