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What Gather Insights Does

Gather Insights uses AI after each call to evaluate the transcript, extract structured information, and answer the specific questions you care about. This is your LLM-as-a-judge layer for quality scoring, categorization, and field extraction. Access: In your agent editor → Analytics tab → Post-Call Analytics section

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Analytics Language

Expert Mode Set the language for AI evaluation: Access: In your agent editor → Analytics tab → Settings section → Analytics Language Select the language the AI uses to evaluate goals and insights when analyzing transcripts and answering insight questions. You can also set the Analysis Model in the same Settings section.
This setting applies to both goals and insights.

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Default Insights vs Custom Insights

Default insights — Pre-configured by itellicoAI:
  • Cannot be edited or archived
  • Toggle on or off with the Active switch
  • Optimized for common use cases
Custom insights — Created by your team:
  • Full control over name, description, and type
  • Can be edited, archived, and restored
  • Tailored to your own workflows
Default insights give you a starting point. Add custom insights for your own reporting, QA, or extraction needs.

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How To Decide What To Add

Use this when you want…Best fit
a ready-made common quality signalDefault Insight
a custom question tied to your own workflowInsight
a yes/no business outcomeGoal

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Setup

1

Access Post-Call Analytics

In your agent editor → Analytics tab → Post-Call Analytics section → Add → Add Insight
2

Configure the insight

Name: Internal label
Example: Customer Satisfaction Score
Description: The exact question AI should answer
Example: On a scale of 1-5 stars, rate the customer's overall
satisfaction based on their tone, language, and whether their
issue was fully resolved during the call.
More specific descriptions produce more reliable AI judgments.
3

Choose the answer type

Yes/No — binary answer
Example: Was the customer's issue fully resolved?
Open — free text answer
Example: What was the main topic discussed?
Data Point — one extracted value
Example: What order number did the caller provide?
Single Choice — one option from your list
Example: Which product line was discussed?
Multiple Choice — one or more options from your list
Example: Which objections did the caller mention?
Rating — 1-5 stars
Example: How professional was the agent?
4

Save

Click Add Analysis to save.

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Use Insights For Structured Extraction

Use Data Point or Open insights when you want the AI to pull a specific field or summary out of the call. This is the clearest post-call equivalent of “variable extraction”. Good extraction-style prompts:
  • What is the customer's order number? Return only the order number or "not provided".
  • What date and time did the customer request for the appointment?
  • What is the main cancellation reason? Return a short phrase only.
  • Summarize the exact next steps agreed on in 1-2 sentences.
If you want clean operational data, tell the AI exactly how to answer: category only, short phrase only, specific date/time, or "not provided" when missing.

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Question Types

Binary answer questions for clear metrics.Best for:
  • Compliance checks
  • Quality verification
  • Feature usage detection
Examples:
  • Was payment information collected?
  • Did the agent follow the script?
  • Was the caller transferred?
  • Did the customer agree to the offer?
For most teams, a strong first insight set is:
  1. Customer satisfaction as a rating
  2. Issue resolved as yes or no
  3. Primary call topic as single choice or open text
  4. Customer sentiment as a rating
Start small. Once those answers are operationally useful, add more.

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Examples

Name: Customer Satisfaction ScoreType: Rating (1-5 stars)Description:
Rate the customer's overall satisfaction based on their tone,
language, and responses (1-5 stars):

5 stars = Very satisfied, enthusiastic, all needs met
4 stars = Satisfied, positive interaction
3 stars = Neutral, basic needs met
2 stars = Somewhat dissatisfied, some frustration
1 star = Very dissatisfied, angry, unresolved issues
Name: Issue Fully ResolvedType: Yes/NoDescription:
Was the customer's issue, question, or request fully resolved
by the end of the call? Answer Yes only if the customer confirmed
satisfaction or explicitly agreed the issue was resolved.
Name: Requested Appointment DateType: Data PointDescription:
Extract the appointment date requested by the caller.
Return only the date in YYYY-MM-DD format.
If no date was requested, return "not provided".
Name: Primary Call TopicType: OpenDescription:
Identify the main topic or reason for this call. Choose from:
- Billing inquiry
- Technical support
- Product information
- Order status
- Complaint
- Cancellation request
- General question

Return only the category name. If multiple topics were discussed,
return the one that took the most conversation time.

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Insights vs Goals

Use both together for complete conversation tracking:
FeatureGoalsInsights
PurposeTrack conversionsExtract insights and judge quality
ExampleAppointment bookedHow satisfied was the customer?
Answer TypeAchieved / Partially achieved / Not achievedYes/No, text, data point, choice, or 1-5 rating
Use ForSuccess metricsQuality scoring, categorization, extraction
Best ForBusiness outcomesDetailed reporting
Example combination:
  • Goal: Appointment booked — was the conversion achieved?
  • Insight: Customer Satisfaction — how well was it handled?
  • Insight: Primary Call Topic — what did they want?

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Viewing Results

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Individual Conversations

  1. Go to Conversations
  2. Click a conversation
  3. Open the Gathered Insights section
  4. Review each question and AI answer

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Dashboard

Go to Dashboard to see insight trends and metrics. Learn more about Dashboard →

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When To Review Insight Results

Review them when you want to:
  • compare quality across agents or campaigns
  • understand why a goal is being missed
  • route quality issues into follow-up work
  • export structured call insights for reporting or downstream workflows

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Best Practices

The AI uses your description to answer. Be detailed.❌ Was the call good?✅ Did the agent provide accurate information, address all customer questions, and maintain a professional tone throughout the call?
For rating questions, always define what each level means.
If you want reusable data, constrain the output:
  • category only
  • one short sentence
  • one date/time
  • not provided if missing
Start with 3-5 critical insight questions, not 20.

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Next Steps

Conversation Goals

Track conversions and outcomes

Post-Call Automation

Trigger emails and tasks from insights

Dashboard

View aggregate insights

Prompt Guide

Align live behavior with what you measure