AI visibility — we ship the fix in code and content
What the market calls AI search optimization, AI SEO or AI visibility, we call entity architecture and machine readability — because showing up in ChatGPT, Gemini or Claude answers is not a campaign but engineering work. AVE Studio builds that layer for European companies from Budapest, and because we run our own measurement platform, we don't assert the change, we show it.
Request the AssessmentAssessment · a fixed fee, credited to the work
Updated ·
Why aren't you visible in AI answers?
There is rarely a single cause. In practice three layers tend to be missing, each fixed differently — which is why AI search optimization, as we practise it, starts by finding out which one is your bottleneck.
The AI can't tell who you are
Your company name fits several organisations, or there is no single referenceable definition of you anywhere. The answer engine isn't leaving you out — it cannot identify you with confidence, so it names someone it is sure about instead. The fix is entity architecture: one canonical definition, consistent naming, and external identifiers linked up.
Your content was written for human eyes, not for AI visibility
If your claims live in an image, a JS-loaded block or a paraphrase, the extraction layer finds no quotable sentence. What isn't in the served HTML doesn't make it into the answer. The fix: statements that stand on their own, plus the structured data that describes them.
The access layer quietly excludes you
Robots rules, bot management and provider-level protection often block the very clients that collect sources for answer engines — without anyone having decided that. We measure this per engine and state plainly which exclusion is deliberate and which is accidental.
What does AVE Studio build for your AI visibility?
We break the work into shippable items. Each lands in code or content, not in a slide deck.
- Entity architecture: a canonical definition, consistent naming, external identifiers linked up.
- Machine-readable content: claims that stand on their own, plus the structured data for them, in the served HTML.
- Access layer: a per-engine review of robots and bot-management rules, and the unblocking of unintended exclusions.
- Measurement: a baseline and a repeat run using the same method, so the change isn't a matter of opinion.
How do you know AVE Studio's work affected your AI visibility?
Because we measure the same thing twice. Before the work we record a baseline — which questions, which engines, how often the answer names you — and after the fixes we repeat it with the same method. Results come as a range, not a single number: generated answers vary between runs, and a point value would hide that. We do not compute a composite score.
The measurement methodology in full
The full diagnosis: nine causes, each with a check and a fix →
Why AVE Studio for AI visibility?
AVE Studio works to get your brand named and cited by AI answer engines. We do not advise on it — we deliver: entity architecture, machine readability, the access layer and answer-ready content, in code, into your system. That is the GEO, AEO and KEO work.
We start with measurement, because without it nobody knows what worked — but measurement is the instrument, not the goal. We do not guarantee placement, because in a generated answer nobody can. What we do commit to: a baseline measurement, a checkable intervention in code, a re-measurement by the same method, and the raw data stays yours. Our methodological requirements are fixed in a public, 18-point standard.
Why AVE Studio measures this way — the reasoning behind the methodPublic measurements
How do I know whether I'm currently visible in AI answers?
Only by measuring. Running one question once is not enough: the same question can return a different answer on the next run, so a screenshot proves neither presence nor absence. You need a repeated, fixed question panel, run per engine. At AVE Studio the entry point is a free assessment — not a self-service tool, we run it on request — and a priced measurement gives the full picture.
Why isn't a good Google ranking enough?
Because answer engines do not read your Google ranking out loud. They work from a different index, cite different sources, and assemble part of the answer without searching at all, from the model's memory. A good ranking helps but is not a sufficient condition — in our measurements we regularly see companies with strong Google presence that the answer engine never names. That is why AI search optimization became its own discipline: it is the answer engines' own layer that needs fixing, not your ranking.
How long does AI visibility take to change?
We do not promise a deadline, because we do not control index and model refresh cycles. Retrieval-side fixes — indexability, access, machine readability — tend to show up in the measurement within weeks; entering the model's memory is considerably slower and does not always happen. The difference between the baseline and the re-measurement shows it, not an estimate.
What does AI visibility work cost?
We price AI search optimization per instrument: every item is a self-contained, fixed-scope instrument rather than a flat retainer. Measurement is the entry point, remediation builds on it, and continuous measurement carries its own monthly fee. The itemised list and current prices live on the GEO service page; we quote from the result of the measurement, not from an estimate.
Frequently asked questions about AI visibility
Twelve questions about AI visibility: what it is, what it depends on, and which parts of it can actually be influenced.
What does an AI need to see and understand my website?
Three things: unambiguous identity, text that stands on its own, and open access. The first comes from entity architecture, the second from machine-readable content and structured data, the third from your robots and bot-management rules. If any one is missing, the other two are worth less.
Which AI engines are worth being visible in?
The ones your buyers actually ask — we measure that rather than guess it. Today ChatGPT, Google's AI overviews, Gemini, Claude and Perplexity account for most Hungarian and European B2B questions. The engines draw on different sources, so measurement runs per engine, and their results cannot be collapsed into one number.
Can you build AI visibility without measuring it?
You can build it; you cannot know it. Much of the work is defensible on its own — a clean entity definition or an open access layer is correct whether or not anyone measures it. But whether the work had an effect is unanswerable without measurement, because answer-engine output varies on its own. That is why we start with a baseline.
Does structured data — schema.org — matter for AI visibility?
It matters, but it is not a magic word. Structured data makes explicit who you are, what you sell and what you relate to — that is the precondition for being identifiable. On its own it does not make you quotable: if the text cannot stand without its surrounding context, or access is closed, the markup counts for little. All three layers have to be right together.
Do I need an llms.txt file on my site?
It is useful, but neither required nor sufficient. llms.txt is a proposed, not yet standardised file that shows language models the structure of a site — cheap, and it does no harm. Actual access, however, is decided by robots.txt and by how the server treats bots, and in measurements those are what cause real losses, not a missing llms.txt.
Should I block or allow AI crawlers?
That is a business decision, and it has to be taken separately for training and for search. If you want the answer engine to cite you, its search crawlers must be allowed — blocked, your presence is not partial but zero. Crawlers used for model training are a separate question, and there are legitimate reasons to exclude them. AVE Studio surveys this as a robots matrix, bot by bot, so the decision is not made by accident.
What is a Wikipedia or Wikidata presence worth?
A lot for identification, little for visibility on its own. A Wikidata item is a stable anchor for which company is meant — which matters especially if your name is confusable. But Wikidata does not make you recommended: an answer engine names you when it finds a usable source for the question. The two solve different problems.
Who does this work, and what does AVE Studio do differently?
More and more players: classic SEO agencies are extending their offering, and studios specialising in AI visibility have appeared. We publish no competitor ranking, because we run the measurement — we do not rank ourselves. What sets AVE Studio apart is that it also builds after measuring: the fix lands in code — entity architecture, the access layer, answer-ready content — not in a slide deck. Measurement is the instrument for that, not the product. If you pick a different provider, ask them for both: measurement, and a fix delivered in code.
Further reading
Why start now? Two of the three layers — identifiability and access — are one-off work: once they are right, they keep working. The third, the model's memory, builds over months, and it can only build from what the machine finds about you today.
Let's talk
Write or call. A short conversation usually shows which layer is your bottleneck — including when the honest answer is that it isn't worth starting yet.