Question 1

How does Canviq use AI to analyze customer feedback?

Accepted Answer

Canviq uses Claude AI (Anthropic's Claude Haiku) to classify the sentiment of open-text survey responses, extract themes and pain points, and group responses into semantic clusters. This runs automatically on every survey response — no manual tagging required.

Question 2

What is auto-segmentation in Canviq?

Accepted Answer

Auto-segmentation groups survey respondents by persona automatically — based on role, company size, product usage level, and engagement patterns. Instead of seeing a single PMF score, you see the score for each segment. This reveals which customer type has already found fit with your product and which has not.

Question 3

What is a High-Expectation Customer (HXC)?

Accepted Answer

A High-Expectation Customer (HXC) is the most demanding user who still finds value in your product — introduced by the Superhuman methodology as a complement to the PMF score. HXCs represent your target market: if you build for them, you build for the people most likely to pay, retain, and refer. Canviq's HXC profiler identifies these users from your survey responses automatically.

Question 4

How does Canviq auto-create GitHub issues from feedback?

Accepted Answer

When a survey response scores below a configurable threshold (e.g., a "somewhat disappointed" or "not disappointed" PMF answer), Canviq automatically creates a GitHub issue in your configured repository. The issue includes the anonymized response text, the respondent's segment, and a link back to the Canviq dashboard for context.

Question 5

Can AI agents query Canviq's feedback analysis?

Accepted Answer

Yes. Canviq exposes an MCP (Model Context Protocol) server that lets Claude, ChatGPT, and other AI agents query PMF scores, read sentiment summaries, and retrieve segment data using natural language. This enables AI-driven product review workflows that do not require a human to open a dashboard.

AI that turns feedback into product decisions

AI that turns feedback
into product decisions