Measuring AI visibility
How do I measure share of voice in AI answers?
Why share beats a raw count
A raw mention count tells you nothing about the competitive picture. Being named in fifteen of forty answers sounds strong until you learn one competitor was named in thirty-eight.
Share of voice also survives changes to the prompt set better than absolute counts, which makes it the more honest number to track over time.
Weighting for position
Being named first is worth more than being listed sixth. A simple weighting — first mention counts double, for instance — captures that without over-engineering.
Whatever scheme you pick, keep it fixed. Changing the weighting mid-programme makes every prior measurement incomparable.
See where you actually stand
We ask AI engines the questions your buyers ask and show you whether you were named, who was named instead, and which sources they cited. Free.
Related questions
Ask the engines the questions your buyers ask, then record whether you were named, in what position, who was named instead, and which sources were cited. Run the same prompt set on a fixed schedule so results are comparable. Ad-hoc spot checks feel informative but cannot show movement.
Because the engine can find more, and more specific, independent evidence about them. Typically they hold review profiles you lack, appear in comparison articles you are absent from, or state concrete claims — pricing, audience fit, certifications — that give a model something quotable. Recommendation follows evidence, not product quality.
On a 0–100 scale, most established brands land between 40 and 65. Above 70 usually means an engine names you unprompted in open category questions; below 35 means you are found only when someone already knows your name. The number matters far less than which underlying signals are dragging it down.
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Last reviewed 2026-07-30.