AEO and GEO: How They Differ From SEO — and Why the Window Is Still Open

Everyone is writing about AEO and GEO right now, and nearly everyone is paraphrasing everyone else. We decided to measure instead: we took 39 queries in Ukrainian, ran them through Google AI Overview, and logged every domain it cited. Below: what AEO and GEO actually are, what the measurement showed, and what practically follows for the article you publish tomorrow.

What AEO and GEO are — and how they differ from SEO

The short version: SEO competes for a click, AEO for a citation, GEO for a mention. Classic SEO optimises a page to rank high in a list of links, then relies on a human to click. AEO (Answer Engine Optimization) optimises a specific paragraph so that an answer engine — Google AI Overview, Perplexity, ChatGPT's search mode — drops that paragraph into its generated answer and attributes it to you. GEO (Generative Engine Optimization) works one level up: it makes a generative model know your company as an entity and name it when asked "who does this," even when it is not retrieving any page of yours at that moment.

The distinction is not cosmetic. It changes the unit of work: in SEO you optimise a page for a query, in AEO a paragraph for a question, in GEO an entity for a topic.

How we measured

We assembled 39 Ukrainian-language queries — the ones a person actually types when looking for a vendor or trying to understand a topic — and ran each through Google AI Overview in June 2026. For every answer we recorded the full list of domains the engine cited. The queries span three areas of our work: 22 concern customer-facing AI agents, 9 internal AI operations, and 8 AI-driven growth.

The cover image of this article is that result. Each dot is one domain; its size equals the number of answers that cited it. There is no centre in the picture, and that is not an artistic choice.

What the data showed: there is no consensus

The headline finding is that this niche has no source the engine treats as the standard answer. Citations are smeared across a long tail of one-off domains rather than concentrated around a few recognised authorities.

Sit with that last number. The domain cited more often than any other in the niche shows up in fewer than one answer in five. Four times out of five, the engine picks somebody else.

In five of the 39 answers our analysis explicitly noted that Aceverse was not mentioned. We did not start this measurement from the position of a leader — which is precisely why we needed it.

Why this is a window, not chaos

The absence of consensus is not disorder; it is unclaimed ground. When a niche already has a source the engine cites in 60–70% of answers, you are fighting accumulated authority, and that is a long road. When the strongest source takes 18%, nobody holds anything — and the question collapses into whose paragraph is easiest to drop into an answer.

And that ease is an engineering property of the text, not a consequence of domain age. A model does not retrieve a brand; it retrieves a fragment. It is looking for a short, self-contained chunk that answers the question and does not fall apart when torn out of context. You can build that deliberately — which is exactly why structure currently outweighs authority.

How to structure an article so it gets cited

There is one rule: write so that any paragraph could be pulled out of the text, shown to a stranger, and still be true and comprehensible. Everything else follows from it.

• An H2 is a question people actually ask. Not "Our approach to integration" but "Can an AI agent connect to your CRM."
• The first paragraph under an H2 is the complete answer. Two to four sentences answering the heading immediately, with no run-up. Elaborate afterwards.
• One fact per paragraph. The paragraph is the retrieval unit. Two facts in one paragraph means the model cites it half-right, or not at all.
• Numbers with a source and a date. "245 domains, Google AI Overview, June 2026" gets cited. "Many domains" does not.
• Comparisons go in a table. A table row is a ready-made citation.
• FAQPage and Article markup. It relieves the machine of guessing which part is the question and which is the answer.
• llms.txt. A file where you explicitly list your authoritative material. It is a declaration for crawlers, not a guarantee — do not confuse the two.
• Hide nothing. An answer behind a form, a login, or a learn-more button does not exist as far as the crawler is concerned.

And a caveat you will not hear from anyone selling "guaranteed placement in ChatGPT": citation is non-deterministic. The same answer to the same query a week later may carry a different set of sources. Structure raises the probability; it does not buy the slot.

What does not work

The most expensive mistake is writing an introduction. Three paragraphs about "the rapid evolution of technology" before the first useful sentence means the model retrieves exactly those three paragraphs, because they sit under the heading. Then, in order: keywords without an answer to the question; "learn more on our blog" in place of the answer itself; and invented market figures — a model cross-checks claims against other sources, and a contradiction discredits the whole page, not just the one sentence.

Limits of this measurement

We are showing one snapshot, not a law of nature. It is 39 queries, not the whole market. It is June 2026 — and Google AI Overview changes month to month. It is one language and one locale: in the English-language segment the picture is almost certainly denser, because that niche has long been worked. AI Overview itself is non-deterministic: the answer depends on session, geolocation and personalisation, so any such measurement is a snapshot that has to be repeated. And most importantly: a citation does not equal traffic. The engine can cite you and send no clicks at all.

We publish these limits deliberately. An article that never names the boundaries of its method is marketing material, not research — and the model sees that too.

What to do about it

Start with your own measurement. Take 20–40 questions your customer genuinely asks before buying, run them through AI Overview and ChatGPT, and write down the domains. You will get a map of who the engine treats as the answer in your niche — and you will see immediately whether a consensus exists or the window is still open. Then rewrite your pages around the questions on that list: one H2, one question, one self-contained answer.