AI visibility
An AI visibility sample is a repeatable observation method, not a census of every answer a system can produce. Define the customer questions, platforms, market settings, and review rules before turning mentions or citations into a trend.
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Answers can vary with wording, product version, context, location, timing, and repeated runs. A large-looking percentage can hide a small or changing sample. Preserve enough context to distinguish a real pattern from a measurement change or an isolated response.
Build a balanced prompt set from genuine customer decisions. Include problem exploration, comparison, selection, and implementation questions. Separate branded prompts from non-branded questions instead of blending them into a flattering score.
Record platform, model or product where available, language, market, date, prompt wording, and relevant settings. Use the same procedure across the observation period and annotate unavoidable changes.
Define what counts as a brand mention, a linked citation, a recommendation, or an inaccurate statement. Review ambiguous outputs manually and keep example evidence so reports can be checked.
Measure the useful things
01
Mention and citation rate within a fixed sample
02
Accuracy and context of brand descriptions
03
Relevant AI referrals and downstream outcomes
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Get the practical steps, common mistakes, measurement ideas and primary documentation in the full guide.
Read the complete guideA few good questions
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Ask GavekNot necessarily. Prompt sets, platforms, markets, repetition, and scoring definitions can differ. Compare methodology before comparing the headline percentage.
No. Improve the clarity, usefulness, and accessibility of the evidence you publish, then observe results. AI systems select and compose answers independently, and their behaviour can change.
Your next move starts here
Start with your website. Leave with a clearer picture of what to improve, what to measure and where to focus next.