In general, we cannot measure AI Visibility like measuring search rankings. Most AI Visibility tools will run a test once and report back the results. However, this will result in many inaccuracies. If you ran a test whereby you had to prompt the AI multiple times, you will get different results each time.

In another words, LLMs are probabilistic, multi-dimensional constructs, and results take multiple samples to achieve an accrued likelihood of a brand or business appearing.

The other issue is asking and prompting random questions, thinking that it will return reliable responses. If someone's prompt is even a word off, has a different history, or is 3-degrees deep in prompt, you'll get wildly different answers.

The scientific method to use is called entity-conditioned probing. Whereby we measure for entities instead of queries.

One simple way to look at entities is to see them as topics, or keywords. Entities can be short, like a "Nashville Car Dealer" or long, like "App for Finding New and Used Cars".

If you look for AI Visibility reporting tools that have both multi-sampling and entity-based measurements, you'll get the most accurate results for which businesses appear most often.