AI answer presence score
also called AI answer presence
Definition
A weighted points total for how present your brand is in answer-engine responses: each citation counts 1.25 and each brand mention counts 1, summed over the tracked prompt basket for one answer engine.
How it's calculated
(1.25 × your citations) + (1.0 × your mentions), summed over the slice. It is the numerator of AI share of voice, whose denominator applies the same weights to every tracked domain's citations and mentions.
Scope, grain and dimensions
- Grain
- One answer engine, plus a cross-engine overall, for one tracked segment over one date range.
- Dimensions
- answer engine · intent · branded vs non-branded
- Required filters
- tracker (unified segment id) for segment-scoped reads · date range
- Aggregation
- Additive points, so the cross-engine figure is the same quantity whether the semantic layer re-aggregates it or the verb sums the per-engine scores, both reduce to 1.25 × all your citations + 1.0 × all your mentions. It is a points total, not a ratio, so it is not comparable across segments or periods with different basket sizes.
- Metric type
- score · weighted points (citations weighted 1.25, mentions weighted 1)
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.
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
- "What is our AI answer presence score?" Simulate this →
- "Which engine has the strongest presence score?" Simulate this →
How to read it
Read it as the raw weight behind share of voice, not as a level with a ceiling, it has no upper bound and rises with basket size. The comparable figure is the share of voice built from it; the score itself is for ordering engines and periods with the same basket.
Caveats, freshness and failure modes
A points total with no ceiling: it grows with the size of the tracked prompt basket, so two periods with different baskets are not comparable on this number.
The country- and device-filtered path returns it empty. The filtered source does carry its own presence-score measure, but the verb does not request it there, this is a gap in what the verb asks for, not a gap in the data.
The code carries an explicit guard against substituting mention counts for this score when it is absent, they are different quantities on different scales, and a card missing the score must show it missing.
Answer-engine citations and Google's on-SERP AI Overviews are different surfaces, on different datasets, measured separately and never rolled together.
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.
- Freshness
- ~1 day behind, Daily prompt runs, available the following day.
Common failure modes
- Substituting mention counts or citation counts when the score is absent.
- Comparing a score from the unfiltered grid against a country- or device-filtered card, where the verb leaves it empty.
- Reading it as a percentage or a 0 to 100 index, it is an unbounded points total.
- Comparing scores across periods whose prompt baskets differ in size.
Not the same as
The confusions that cause the most wrong decisions.
AI answer presence score AI mention count Compare definitions →
A weighted points total and a raw count. Mentions are one of the two inputs to the score, weighted 1 against a citation's 1.25, so the score moves when either input moves; the code guards against filling an absent score with mentions because they are not the same quantity.
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.