Answer: Only if you know how it was measured. Most tools ask each question once per AI and sell the result as a score. Asked once, the same question returns the same list of businesses less than 1 time in 100. A trustworthy number comes from asking many times and showing the range.
This page is the measurement industry marking its own homework. Every finding below carries a named, linked source anyone can check.
Why does the same question give a different answer every time?
AI answers are unstable by nature. In the largest public test of this, about 600 volunteers ran the same prompts through the real consumer apps, 2,961 runs in all. The same prompt returned the same list of brands less than 1 time in 100, and the same list in the same order less than 1 in 1,000 (Source: SparkToro, Jan 2026). This is not sloppiness anyone can switch off. In a separate engineering study, 1,000 identical requests to the same model, at settings meant to be repeatable, still produced 80 different answers (Source: Thinking Machines Lab, Sept 2025).
So one ask is a coin flip. A score built on one ask is a coin flip with a logo on it.
How do most tools actually measure?
The arithmetic is checkable from their own published plan limits: for most tools on the market, the allowance works out to each question being asked exactly once per AI per day. Almost none of them print a margin of error next to the score. The most credible independent voice in the field put it plainly: a ranking position inside an AI answer is not a real number, but how often a business is named across many questions, asked multiple times, is probably a reasonable measurement (Source: SparkToro, Jan 2026).
That last sentence is the whole trick. The honest version of this product is not hard to describe. It is just more expensive to run, so most tools do not run it.
What does a trustworthy check look like?
Five things, all checkable from the outside:
It publishes the questions. The exact buyer questions asked should be visible, not "a proprietary panel".
It asks many times and says how many. Repeated asks on each of the four AIs, ChatGPT, Perplexity, Gemini, and Claude, so the number is real, not a fluke.
It shows a range, not fake precision. "Named 2 times out of 12, so the true rate is probably under about 25%" is honest. "Your visibility is 17%" from one ask is not.
It keeps the AIs separate. The four answer differently and change at different speeds. One blended score hides which one is which, so an honest report never leads with one.
It names its limits. One example worth demanding: a check that asks Gemini without a live web lookup is measuring what the model already knows, and it gets no sources back, so an honest report says that wherever the Gemini number appears. A tool with no stated limits has not looked for them.
What should a score never claim?
A rank ("you are number 3 in ChatGPT"): the order changes run to run, so it is a made up number. A promised improvement: nobody outside the AI companies controls the answers. A month over month "win" smaller than the measurement's own noise: that is the coin flip again, sold twice. A report that does any of these deserves one question back: how many times was each question asked. The answer is usually once.
Where does that leave you?
Skeptical is the right setting. Any tool in this category owes you three answers: what exactly was asked, how many times, and what is the range on that number.
That is the standard we built our own check to meet. We ask ChatGPT, Perplexity, Gemini, and Claude your buyers' real questions, many times each, and show how often each one names you, with the range printed on every number. The first check is free.
Free. No card, no call.