The AI visibility tool that shows whether AI names your brand when buyers ask.
Compare mention rates, original answers and competitor share of voice across the AI engines currently supported by InsightWonder.
What AI Visibility helps the team understand.
Compare mention rates, original answers and competitor share of voice across the AI engines currently supported by InsightWonder.
Cross-engine view
Compare mention rates for the same questions across every supported model.
Question-by-model matrix
See exactly where your brand is absent, mentioned or recommended.
Original answer drilldown
Open the answer behind every result instead of trusting a score alone.
Keep every result connected to evidence and action.
The product explains the result, preserves its source context and makes the next review explicit.
The rules ai visibility follows
A feature list is easy to copy; the rules behind the numbers are not. Each of these is enforced in the product, and most of them were added after getting it wrong once.
| Rule | Why it exists |
|---|---|
| Questions containing your brand name are excluded from scoring. | Ask an assistant whether a named company is any good and it will of course name it. In one real account a reported 13% visibility came entirely from a single brand-named question; the true unprompted figure was zero. |
| Unmeasured is never rendered as 0%. | A model that timed out produced no evidence either way. Counting that cell as a non-mention drags the percentage down, turns a normal month into an apparent decline, and can fire an alert about a drop that never happened. |
| Every result is reported per engine before any average. | In our own benchmark one model named 3.5 providers per answer and another 2.1. A single cross-model number describes neither situation and hides where the work is. |
| Every stored run carries a methodology version, and runs on different versions are never compared. | Enabling web search once moved a site from 3% to 13% while the site did nothing. Presented as a trend, that is an internal change sold to a customer as their own improvement. |
Move from summary metrics into the underlying question.
Sample data demonstrates the evidence depth of a formal project.
Visibility evidence
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Questions about AI Visibility
Does this feature run from the public website?
No. Marketing pages explain the capability. Formal data collection begins only after a user enters the App and confirms the project scope.
Can snapshot results become part of this workflow?
Yes. A valid snapshot can be claimed as an initial sample, while onboarding still confirms markets, competitors and the formal monitored question set.
Are all metrics comparable across AI engines?
Core answer states can be normalized, but citations, search assistance and answer formats still need engine-specific evidence.
How often is the data refreshed?
Measurement runs on the schedule your plan allows, and monitoring quota is shared at the account level. Alerts require two consecutive runs to confirm, so model sampling noise does not become a false alarm.
Can the original AI answer be inspected?
Yes. Every result drills down to the answer that produced it, question by question and model by model, together with the citations found in that answer.
What happens when a model is unavailable?
That run is marked partial rather than being silently filled in. The remaining engines still complete, and the gap stays visible in the result.
Put AI Visibility into a complete GEO workflow.
Create a project, confirm your market and start measuring the questions that matter.