AI mention count
also called MentionsTotal AI-answer mentionsBrand mentions in AI answers
Definition
The number of answer-engine responses in the period that named your brand.
How it's calculated
The sum of your brand's mention counts across the answer-engine responses in the slice. Three differently named semantic-layer measures carry it depending on which source a verb reads, the grid's client-mention sum, the drilldown sources' all-mention sum for your row, and the prompt path's validated visible-mention sum, and all three sum the same underlying per-response counts.
Scope, grain and dimensions
- Grain
- One answer engine, prompt or competitor domain for one tracked segment over one date range.
- Dimensions
- answer engine · prompt · competitor domain · intent · branded vs non-branded
- Required filters
- tracker (unified segment id) or tracked segment id · date range
- Aggregation
- Additive across prompts and answer engines, the prompt-level verbs sum it per prompt to build the mention column and the share-of-voice input.
- Metric type
- raw measure · mentions
Data sources
Where you'll see this
Named reports that normally include this metric.
Skills and analyses that use it
Skills carry the judgment; the analysis verbs do the reading.
Analysis verbs
ai_visibility_overviewai_visibility_competitorsai_visibility_drilldownai_visibility_trend
Method rungs and levers
A rung tells you what a movement here can and cannot explain, read the rungs below it first.
levers L7 AI-answer visibility
Ask Quattr
- "How many AI answers mentioned us last month?" Simulate this →
- "Do we get mentioned without being cited?" Simulate this →
How to read it
Reported beside the citation count, never added to it. The two columns diverging is the normal case rather than a data problem.
Caveats, freshness and failure modes
Three differently named semantic-layer fields feed this output key depending on which source a verb reads, the grid, the drilldown and the prompt-level path each name it differently, and the prompt path prefers validated visible mentions over legacy engine-reported counts where both exist.
Every figure is scoped to a tracked prompt basket, a sample, not a census, so the basket's coverage belongs beside the number it produced.
Engines are non-deterministic: the same prompt can answer differently twice, so this counts observations of a mention rather than distinct facts.
It moves with basket size as well as with performance, so it is read next to the collected-prompt coverage rather than alone.
- Freshness
- ~1 day behind, Daily prompt runs, available the following day.
Common failure modes
- Adding mentions to citations to produce one AI number.
- Reading a rise in mentions as a rise in favourable coverage without the sentiment split.
Not the same as
The confusions that cause the most wrong decisions.
AI mention count AI citation count Compare definitions →
Separate measures kept in separate columns: a mention names your brand in the answer, a citation sources your URL for it. Most citations carry no mention, so summing them double-counts nothing and describes nothing.
AI mention count AI answer presence score Compare definitions →
A count and a weighted points total. Mentions are one input to the score at a weight of 1 against a citation's 1.25; the code guards against filling an absent score with mentions because they are not the same quantity.
AI mention count Brand mentions on the results page Compare definitions →
On-SERP brand mentions are observed in Google search results. AI-answer mentions are counted inside answer-engine responses. A brand can be mentioned constantly on the SERP and never inside an answer.
AI mention count AI mention share Compare definitions →
A share and a count. The share needs a denominator of all tracked brands' mentions; the count is your mentions alone and moves with basket size.
Related metrics and workflows
Verification
A definition is the smallest part of this.
The measurement matters because something acts on it. Here is the rest of the showcase, in the order most people find useful.