How frequently and in what context a brand or website appears across a defined set of AI platforms and prompts.
AI visibility describes how a brand or website appears in a defined collection of AI responses. A review might examine mentions, linked citations, recommendations or the context in which the business is described. These observations answer different questions and should not be collapsed into an unexplained score.
Define the measurement scope before interpreting results. Record the platforms, prompts, dates, language and relevant settings, along with how many runs were collected. A selected prompt set is a sample chosen for a purpose, not a census of every question customers ask.
Changes in the sample can change the result even without a change to the website. Adding branded prompts, switching products or altering location context can make two reports difficult to compare. Keep a consistent method where possible and explain adjustments rather than presenting every change as a business improvement.
Takeaway: Use AI visibility research to understand relevant answers and identify inaccurate or missing information. Keep it alongside actual referral and customer outcomes, not as a replacement for them.
Example
A supplier tests a fixed group of procurement questions across two AI products during a specified week. It records where its website is cited and where its name is suggested. The findings describe that test, not the supplier’s share of all AI-assisted purchasing decisions.
What to check
Save the underlying responses so counts can be checked. Separate successful responses, failures and unavailable features. Report each observation type clearly and avoid promising visibility because a page meets basic technical requirements.
For more detail, see AI features and your website.