Measure AI search visibility by tracking whether a brand is mentioned and whether its pages are cited across a stable set of relevant questions. Keep the exact prompts, answer context and observation dates so the results can be compared. Website visits are a separate outcome, because an answer can mention a business without sending a click.
As of 27 October 2026, this guide proposes a repeatable observation method rather than an industry-standard score. The worked percentages below use invented counts to explain the calculation. No visibility results for Anteomnio or a competitor are claimed.
What should you count?
Separate a brand mention from a link to the brand's website. Also distinguish a favourable recommendation from a neutral reference or warning. A brand appearing first in an answer is not automatically a useful outcome.
| Measure | Definition for this worksheet |
|---|---|
| Mention rate | Valid observations containing the brand ÷ valid observations |
| Direct citation rate | Valid observations linking the brand's site ÷ valid observations |
| Context | Human-reviewed description of how the brand appears |
| Referral outcome | Visits and useful actions attributable to identifiable referrals |
Count each brand once per answer for the mention rate, even if it appears repeatedly. Use the same rule for every competitor. Record unsuccessful collection attempts separately instead of quietly treating them as answers with no mention.
Separate three kinds of evidence
Record context, including negative references.
Inspect the cited page and supported claim.
Track identifiable referrals separately.
How do you build a useful prompt set?
Start with questions customers actually ask when learning about a problem or choosing a service. Include informational and commercial questions, but report those groups separately. A brand's absence from a broad educational answer means something different from its absence from a supplier shortlist.
Keep the original wording, intended audience, language and location context. Avoid adding your own brand name to every question; that would measure recognition under a brand cue rather than unprompted inclusion.
Maintain a stable core set for trend comparisons. You can add exploratory questions, but do not mix them into the historical series without noting the change. Percentages do not make different prompt pools automatically comparable.
How should observations be recorded?
Record the engine or interface, date, prompt, visible answer and cited URLs. Where available, record model or mode, search settings and whether the session had prior conversation context. Different settings can change the result.
Repeat observations across your chosen review periods. Preserve enough evidence to inspect a surprising change. If a tool provides a score without the underlying answer or collection method, ask how the score is produced before relying on it.
Keep the observation reproducible
- 01PromptPreserve wording and intent.
- 02ContextRecord engine, mode and session conditions.
- 03AnswerSave the response and cited URLs.
- 04ReviewApply the same counting rules.
How do you calculate the rates?
Suppose a fixed set produces 40 valid observations. If 10 mention the brand, the mention rate is 10 ÷ 40 = 25%. If six link to the brand's site, the direct citation rate is 6 ÷ 40 = 15%. These are hypothetical teaching figures, not measured performance.
For repeated runs, state whether the denominator counts unique prompts or all prompt-run observations. This worksheet uses observations. A prompt asked three times contributes three observations, which should be disclosed so readers can interpret the result.
Compare competitors using the same collected answers. Multiple brands can appear in one answer, so their mention rates do not need to add to 100%. Avoid presenting this measure as an exclusive market-share allocation.
What can Google Search Console tell you?
Google's AI features documentation explains that its AI search features are included in overall Search Console web-search reporting. Treat that reporting as a view of search performance, not a complete cross-engine mention tracker.
The same Google guidance says standard SEO practices remain relevant and that no additional special optimisation is required for AI Overviews or AI Mode. Technical access, useful content and a clear site structure remain practical work. Inclusion is not guaranteed by adding a special label or schema type.
Keep referral analytics beside your observation series. Referrals can show some visits and outcomes, but they will not capture every path where someone sees a brand and returns later through another route.
A hypothetical observation set
Count each brand once per answer.
Count answers linking the brand website.
How do you connect changes with observations?
Maintain a change register with the page, the substantive edit and the actual publication or update date. Record the baseline before making the change, then review the same prompt set afterward.
An increase after an edit is an association worth investigating. It does not prove the edit caused the increase. Model changes, source changes, competitors and collection conditions may also affect answers. Even a stable competitor trend does not remove all those explanations.
Inspect cited passages and unanswered questions to choose the next content improvement. Give readers clear definitions, useful examples and evidence for claims. Keep important information in readable page text as well as visual artifacts.
What should a monthly report contain?
Include the prompt-set version, valid observation count, mention and direct-citation rates, representative answer context and the change register. Explain missing data and method changes. Add referral outcomes separately where attribution is available.
The SEO agency guide can help define reporting responsibilities. Anteomnio's search and AI visibility service connects technical access, content and repeatable observation without promising placement in generated answers.
The mention-and-citation tracking framework is informed by the GEO content-engineering material based on Eugen Ullrich's work under CC BY 4.0. The definitions, hypothetical example and caution about causal attribution above state the method used in this guide.
Sources and calculation notes
The counts and rates are hypothetical: 10 of 40 observations is 25%; six of 40 is 15%. No live tracking study was conducted. The method defines observations explicitly and does not claim causal proof from a before-and-after change.
- AI features documentation — Google describes its AI search features, standard SEO guidance and Search Console reporting.
- Eugen Ullrich's work — Attribution for the underlying GEO content-engineering framework supplied in the installed skill; not a source of measured visibility results.
External references checked on 27 October 2026.