Actor Insights

Insights are the structured data points that Vocalis extracts from each conversation. Instead of just giving you a raw transcript, the AI Actor pulls out the specific information you care about — scores, key answers, and visual breakdowns. You configure Insights in Step 3 (Insights) of the AI Actor setup wizard.

The Insights Configuration table and the Add Insight panel.

Auto-generating insights

Click Auto-generate Insights to let the AI suggest a starting set of insights based on your Actor’s purpose. You can edit or remove any of the generated insights afterward.

Adding an insight manually

Click + Add Insight to open the side panel. Fill in the following fields:

Field	What it means
Insight Name	A label for the data point (e.g., “Skill Level Assessment”).
Purpose	A plain-language description that tells the AI what to extract (e.g., “Assess the candidate’s skill levels across various UI/UX competencies and visualize the scores on a bar chart.”).
Output Type	How the data is displayed — Text, Score, Bar Chart, Line Chart, or Pie Chart.
Factors	The breakdown categories to measure against (e.g., design thinking, user research, interaction design). Only applies to chart output types.
Y Axis (Label)	The measurement label for the vertical axis (e.g., “Skill Level (out of 10)”). Only applies to chart output types.
Show in insights & reports	Toggle whether this insight appears in the meeting results dashboard.

Insight output types

Type	Best for	Example
Text	Open-ended answers, summaries	”AI Summary”, “Salary expectations”
Score	Numeric ratings, years, counts	”Overall Score”, “Years of experience”
Bar Chart	Comparing categories side by side	”Skill Level Assessment” broken down by design thinking, user research, etc.
Line Chart	Trends or progression	”Engagement level over time”
Pie Chart	Distribution or composition	”Technology stack breakdown”

Insights table columns

Each insight in the configuration table shows:

Column	What it means
Insight	The name of the data point.
Data Type	The output type — Text, Score, Bar Chart, Line Chart, or Pie Chart.
Factors	Tags showing the breakdown categories for the insight (e.g., design thinking, user research).
Include	Toggle to include or exclude the insight from reports without deleting it.
Actions	Menu to view, edit, or delete the insight.

Writing good purpose descriptions

The purpose description tells the AI what to look for. The more specific you are, the more accurate the extraction.

Good
Vague

Name: Skill Level Assessment
Purpose: Assess the candidate's skill levels across various
UI/UX competencies and visualize the scores on a bar chart.
Factors: design thinking, user research, interaction design,
visual design, problem-solving
Y Axis: Skill Level (out of 10)

Name: Skills
Purpose: Check skills.

Example insights

Here is a set of insights for a UI/UX interview AI Actor:

Insight name	Data type	Factors
Overall Summary	Text	candidate strengths, +3
Overall Score	Score	design thinking, user research, +3
Important Question with Excerpt	Text	important question, candidate response
Skill Level Assessment	Bar Chart	design thinking, user research, +3
Usability Awareness Evaluation	Pie Chart	usability principles, +3

Start with 4-8 insights. Too few and you miss useful data. Too many and the AI spreads its attention thin. Focus on the data points your team actually uses to make decisions.

Introduction

Getting Started

Setup Guides

How-to Guides

Use Cases

Auto-generating insights

Adding an insight manually

Insight output types

Insights table columns

Writing good purpose descriptions

Example insights

Next steps

Test Actor

Meeting Results

​Auto-generating insights

​Adding an insight manually

​Insight output types

​Insights table columns

​Writing good purpose descriptions

​Example insights

​Next steps

Test Actor

Meeting Results

Auto-generating insights

Adding an insight manually

Insight output types

Insights table columns

Writing good purpose descriptions

Example insights

Next steps