AI search visibility is whether, and how often, AI engines mention and cite your brand in their answers. Track it separately from rankings: pick the questions your customers ask, check each engine for whether it cites you and who else, and act on the gaps.
What is AI search visibility?
AI search visibility is how present your brand is inside the answers AI engines generate, across ChatGPT, Google AI Overviews, Perplexity, Gemini and Copilot. It is not your ranking in the list of links; it is whether the synthesised answer is built partly from your content, with your brand named or your domain cited. As more searches are answered on the page rather than clicked through, this is becoming the visibility that matters.
Why track it separately from rankings?
Because ranking and being cited are two different outcomes. A page can sit at the top of the results and never appear in the AI answer above it, or be quoted in an answer without ranking first. Traditional rank tracking only sees the blue links, so it is blind to the answer box that is increasingly where the attention goes. If you only measure rankings, you can be losing ground in AI answers for months without a single number telling you. AI visibility is the other half of the scoreboard.
What should you track?
Five things tell you where you stand:
- The questions, not just keywords. Track the real questions customers ask, the ones that get answered by an AI engine.
- Whether each engine cites you. Presence is per-engine; you can be cited in Perplexity and absent in AI Overviews.
- Your share of voice. Of the questions that matter, how many name or cite you versus a competitor.
- How you are described. The sentiment and framing of the mention, not just its presence.
- Which of your pages is cited, where the engine links its sources, so you know what is working.
How do you track it?
The manual method is simple, if laborious: take a set of the questions your customers actually ask, run each one through the engines you care about, and record whether you are cited, which page won it, and who else appears. Repeat on a regular cadence so you can see movement rather than a single snapshot. The catch is scale, doing this by hand across dozens of questions and several engines, monthly, is a real job, which is why most teams automate it. Either way, the discipline is the same: a fixed question set, the same engines, on a schedule.
What do you do with the data?
You turn the gaps into a to-do list. Where a competitor is cited and you are not, that is a page to build or improve: a clear, answer-first, well-structured, well-sourced page on that exact question, laid out the way structure for AEO describes. Where you are cited, note the page and do more of it. Then re-measure, so the work is judged by whether your share of voice actually moved. It is the same loop as any optimisation: measure, fix the weakest point, measure again.
How does Limecube help?
Checking every question across every engine by hand does not scale, so Limecube does it for you. Its AI Search Hub tracks whether ChatGPT, Claude, Gemini and Perplexity cite you, alongside Google AI Overviews, and reports your share of voice across all of them in one place, so you can see where you are winning and where a competitor owns the answer. It sits next to your rank tracking, so both halves of the scoreboard are together. The 7-day trial covers it.