Blog
How AI search works, and how to measure it
Written from our own measurements. Where a number appears, it comes from a run we did, and the method is written out.
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llms.txt
llms.txt is a plain-text file describing what your site is and pointing to its main pages in Markdown. It is a proposal, not a standard, and no major assistant has confirmed reading it. It costs an hour, it is harmless, and it is not a ranking factor — good housekeeping rather than a lever.
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Schema markup for AI search
Schema markup tells a machine what the things on your page are. It reduces parsing mistakes and makes your facts checkable against other sources. It does not make a page authoritative, and if assistants are not naming you, markup is rarely the reason.
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Gemini and Claude visibility
Gemini and Claude answer the same buying questions differently from each other and from ChatGPT, and a business visible in one is frequently invisible in another. There is no separate playbook for either; there is a reason to measure them separately instead of reporting one number that is wrong about most of them.
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Perplexity optimization
Perplexity cites its sources on screen every time, so you can see exactly which pages decided the answer. That makes it the easiest surface to work on and the least representative: what works there does not prove what works in ChatGPT, Gemini or Claude.
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How to get recommended by ChatGPT
There is no way to buy or request a mention in ChatGPT's answers. What works is the ordinary work: be described accurately in the sources it draws on, answer the buying question plainly on your own pages, and measure whether the share of answers naming you moved. Anyone offering guaranteed placement is guessing.
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How to track brand visibility in Google AI Mode
AI Mode is Google's conversational search surface, and it is not covered by rank tracking and only partly by Search Console. Tracking it means asking the buying questions yourself, repeatedly, and recording whether you were named, who was named instead, and what was cited.
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Google AI Overviews optimization
AI Overviews are the generated summaries above Google's results. They cite sources, which makes them the most measurable part of AI search and the most familiar to optimise. Being cited there is not the same as being named by an assistant asked directly, and the two move independently.
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How to improve brand visibility in AI search
Improving AI visibility is mostly not a content exercise on your own website. Measure where you are absent, find what the assistants read instead, and get your business described accurately in those sources. Order matters: without a baseline you cannot tell a change from drift.
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GEO vs SEO
SEO gets you into a list of links; GEO gets you named inside a written answer. Most of the underlying work is shared, but the metric, the volatility and the pages that decide the outcome are different. Keep the SEO you have, measure where you stand in answers, and spend the marginal effort where you are absent.
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What is answer engine optimization?
Answer engine optimization is the work of being the source an engine uses when it answers directly instead of returning links. The hard part is that a mention often comes with no link at all, so the event you care about happens somewhere you have no logs — which is why it has to be measured by asking, not by analytics.
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What is generative engine optimization?
Generative engine optimization is the work of getting named inside answers that are written rather than listed. Three things are genuinely different from SEO: there is no position, the answer changes between identical asks, and it is assembled from several pages at once. Everything else is SEO.
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What is AI visibility?
AI visibility is how often an assistant names your business when someone asks it for a recommendation. It is not a ranking and it cannot be read off a search report: assistants answer with a handful of names, differently each time, and differently from each other. You measure it by asking the questions your buyers ask, asking each one several times per assistant, and recording the share of answers that named you.
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Fundamentals · · updated
AI search optimization
AI search optimization is the work of becoming one of the names an assistant gives when someone asks it for a recommendation. It overlaps with SEO, because assistants read the open web, but the target is a share of answers rather than a ranking, and an answer is assembled from several descriptions of you at once rather than from your homepage alone. You measure first, find the question types you are absent from, and start with your own pages.
