How we measure
Everything below is what actually happens. Where a number has a limit, the limit is stated next to it.
Last reviewed 9 September 2026.
What we ask
Ten questions, written for your category and reviewed by a person before they are assigned. Six are about your neighbourhood, four about your city — or seven and three if you have no city presence when we start.
Your set is then frozen. We do not swap questions month to month, because a changing question set makes month-over-month comparison meaningless. If a question does change, your report says so and the trend for that question restarts.
Who we ask, and how often
Once a day, for every question, on each assistant your plan covers: ChatGPT and Google AI (Gemini), and Claude on Growth.
We only count an answer when the assistant actually performed a web search for it. An assistant answering from memory is describing the internet as it was during training, not as it is today. When a search does not happen we ask once more, and if it still does not, the answer is stored and flagged rather than counted.
We do not list an assistant we cannot genuinely query. Some well-known ones have no public interface that performs a real search, so they are absent from our plans entirely rather than listed and quietly skipped.
What the numbers mean
Every count states what it is out of. “Named in 6 of 10” means six of the ten questions we asked returned your name at least once. A row of five dots is the last five daily checks for that question.
We never reduce this to a single score. A score out of 100 is more shareable and tells you less: it hides which question moved, which is the only part you can act on.
Where a report projects — “if 50 people ask this month” — the number of askers is an assumption you can change, and it is labelled as one. The ratio it is applied to is arithmetic on answers we actually checked.
What this does not tell you
Assistants word answers differently every time, and personalise by location and history. Our counts describe the checks we made. They are a sample, not a guarantee of what any one customer will see.
We cannot tell you how many people asked, or how many became customers. Anyone claiming to is inferring it. Where we show a scenario, it is labelled.
And we cannot guarantee an outcome. We can tell you what assistants say now, what the businesses being named have that you do not, and what to change.
You can check our work
Every answer we count is stored exactly as the assistant produced it, and your scan log lists all of them with the time, the assistant, whether a real search happened, and a link to the raw answer. If a number looks wrong, you can read the thing it came from.