What is answer engine optimization, and is it different from GEO?

Answer engine optimization, or AEO, is the practice of getting a brand into the direct answer an engine gives rather than into a ranked list of links. The term is older and came out of featured snippets and voice assistants. Some people reserve AEO for engines that retrieve an existing answer and GEO for engines that generate a new one, but the same engines now do both in one response, so in practice the two terms describe the same work.

Does GEO replace SEO?

No. It builds on it. Generative engines reach the web through crawlers, so a page that cannot be fetched, rendered or parsed cannot be quoted regardless of how good it is. Speed, clean structure, structured data and internal linking all still apply. What is new sits on top: stating your entity unambiguously, writing passages that can be quoted on their own, planning around whole buyer questions, earning corroboration outside your own site, and measuring presence instead of position.

Which AI engines should a brand care about?

The ones your own buyers use, which is worth checking rather than assuming, because it moves by market and by category. The distinction that matters more for the work is between an engine answering from training alone and one running a live web search while it answers. The second kind is the one your pages can influence this quarter, because those answers depend on pages that exist and are crawlable right now. Scayla measures ChatGPT and Gemini today, and more engines plug into the same interface.

How do you measure whether GEO is working?

With a fixed set of buyer questions, asked on a repeating schedule, where presence is detected in code by matching brand names and domains rather than by asking a model to grade the answer, and where a failed request is recorded as unmeasured instead of as a negative result. The useful number is the share of that question set where the brand is named or cited, tracked over time and scored for competitors on the same questions so it has a scale.

Can you block AI crawlers and still appear in Google's AI answers?

Not with Google-Extended, which is the token most people reach for. Google documents that Google-Extended governs training for its Gemini models and grounding inside Gemini Apps, and states that it does not affect a site's inclusion in Google Search. The AI answers Google shows inside Search are a Search feature running on Googlebot, so blocking Google-Extended does not remove you from them, and allowing it is not what puts you in. There is currently no robots.txt token that keeps a site in Google Search while taking it out of Google's AI answers.

How long does GEO take to show results?

The fastest wins are technical and usually land within weeks: unblocking AI crawlers, fixing an ambiguous entity, and publishing pages that answer questions nothing on the site answered before. Anything that depends on corroboration outside your own site, such as reviews, directories and press, moves on a scale of months. Because answers vary between runs, judge the trend across several weekly measurements rather than any single answer.

What is generative engine optimization?

Generative engine optimization, defined

Generative engine optimization (GEO) is the practice of getting a brand named, described accurately and cited as a source in the answers that AI assistants generate. The assistants in question are ChatGPT, Gemini, Claude, Perplexity and the AI answers Google shows above its own results.

A generative engine does not hand back a list of ten links for the reader to sort through. It reads, decides and writes an answer, then links the handful of sources it leaned on. On a results page there were ten positions and a brand could live at number six and still get found. An answer is shorter than that. Sometimes it names two or three options, sometimes it runs to a numbered list of eight for a shortlist question, but the tail below that is gone. A brand is either in the answer or it is not there at all.

So the target of the work changes. Classic SEO optimises for a position. GEO optimises for presence: whether the engine names you, whether what it says about you is correct, and whether it points at your pages as the evidence.

GEO, AEO and AI SEO: three names for one job

Answer engine optimization (AEO) is the practice of getting a brand into the direct answer a search engine or assistant gives, instead of into a ranked list of links. The term is older than GEO. It came out of featured snippets, voice assistants and question-and-answer boxes, where a single short answer already replaced the list years before generative models arrived.

People sometimes draw a line between the two: AEO for engines that retrieve an existing answer, GEO for engines that generate a new one. The distinction is real but it has stopped being useful, because the same engines now do both in the same response. A third label, AI SEO, is used for the same territory again, usually by people who want to stress that this is not a separate discipline from search.

The three names describe one job. The work behind all of them is having pages an engine can reach, quote, trust, and find echoed somewhere other than your own site. This guide says GEO throughout and means all three.

Why it became urgent

None of this would matter if people still clicked. Three measured numbers explain why the ground moved.

68% of Google searches in the US now end without a single click, measured between January and April 2026. Two years earlier it was 60%. SparkToro, on Similarweb data.
When an AI summary appears, 8% of visits lead to a click on a result. Without the summary it is 15%. Pew Research Center, July 2025.
Traffic from AI assistants to US retail sites grew 693% over the 2025 holiday season compared with the year before. It is still a small share of total traffic, but it is the fastest growing one. Adobe Analytics, January 2026.

The clicks a brand used to earn from a ranked list are shrinking, and the answers replacing them carry only a few names. A brand can hold every ranking it had and still drop out of the shortlist its buyers actually see.

How an AI answer is built, and where a brand can enter it

An assistant draws on two different things when it answers a question about a market, and they behave very differently.

What the model already learned

Some of the answer comes from the model's training: general knowledge about companies, categories and products, frozen at the point the model was trained. A brand cannot edit this directly and cannot wait for it. It moves slowly, mostly when models are retrained or updated, and it carries whatever the open web said at the time, mistakes included.

What the engine fetches while answering

The rest comes from a live search the engine runs in the moment. ChatGPT and Gemini both document that they search the web and ground parts of their answers in what they find, and both show the sources they used. This is the part a brand can influence this quarter, because it depends on pages that exist right now, are crawlable right now, and answer the question being asked.

The practical consequence is that GEO is mostly a publishing problem, not a prompt problem. The lever is what an engine can find and quote about you when somebody asks, not any phrasing trick inside the chat. It is also why the two halves of the work, measuring what the engines say and changing what they can read, only mean something together.

What GEO inherits from SEO

Nearly all of the technical foundation carries over. Generative engines reach the web through crawlers, and a page they cannot fetch, render or parse cannot be quoted no matter how good it is. Everything that made a site legible to a search crawler still applies:

Pages that return quickly and render without needing a browser to execute heavy scripts.
Clean titles, headings and a structure that signals what each page is about.
Structured data that states plainly what the entity is, what it sells and where it operates.
Internal links that let a crawler move from a hub page to everything below it.
No accidental blocks: a robots rule or a firewall that keeps AI crawlers out removes a brand from consideration entirely, silently.

Check that last one before anything else. It is the cheapest item on the list to fix, it never shows up in a marketing dashboard, and it produces exactly the same symptom as having nothing worth quoting. The next section names the crawlers involved.

Which crawlers to let in, by name

"Let the AI crawlers in" is useless advice without the list, so here it is, with each name taken from the vendor's own documentation. They are separate user agents with separate jobs, and blocking one rarely does what people assume.

GPTBot, OAI-SearchBot and ChatGPT-User: OpenAI, split between model training, the ChatGPT search index, and fetches made when a user's own prompt needs a page. OpenAI's bot documentation.
ClaudeBot, Claude-SearchBot and Claude-User: Anthropic, split the same three ways. Anthropic's crawler documentation.
PerplexityBot and Perplexity-User: the search index, and fetches triggered by a user's question. Perplexity's bot documentation.
Applebot-Extended: controls whether your content trains Apple's models, separately from Applebot itself.
Google-Extended: not a crawler at all, and the one people get wrong. See below.

If the intent is to be found, the robots.txt is short:

User-agent: GPTBot
User-agent: OAI-SearchBot
User-agent: ChatGPT-User
User-agent: ClaudeBot
User-agent: Claude-SearchBot
User-agent: Claude-User
User-agent: PerplexityBot
User-agent: Perplexity-User
User-agent: Google-Extended
User-agent: Applebot-Extended
Allow: /

Then check the layer above it. A firewall rule, a bot-protection product or a rate limiter can refuse these agents before robots.txt is ever read, and a permissive robots.txt sitting behind a 403 looks identical to one that works. The only honest test is to request your own pages with each user agent and read the status code that comes back.

The Google-Extended trap

Google-Extended has no user agent of its own. It is a robots.txt token that controls what Google may do with content Googlebot has already fetched. Google documents that it governs training for the Gemini models and grounding inside Gemini Apps, and then states plainly that "Google-Extended does not impact a site's inclusion in Google Search nor is it used as a ranking signal in Google Search".

That cuts both ways, and people get it wrong in both directions. Blocking Google-Extended does not take you out of the AI answers Google shows inside Search, because those are a Search feature running on Googlebot. Allowing it is not what puts you into Search either. If your goal is to stay in Google Search but out of its AI answers, this token is not the lever, and there is currently no token that does that job.

What is genuinely new

Once the destination is an answer rather than a ranking, parts of the job change shape.

Entity clarity beats keyword coverage

An engine has to decide which company you are before it can recommend you. If your name is ambiguous, if nothing on your site states plainly what you do and where you are, or if another organisation shares your name, engines will merge the two and answer with whichever description is better documented. Stating the entity explicitly, in the visible copy and in structured data, is now foundational work rather than housekeeping.

Passages get quoted, not pages

Engines lift a sentence or a short paragraph, not a whole page. Writing that assumes the reader has already read the three paragraphs above it does not survive extraction. Self-contained passages that answer one question completely do.

The question is the unit of work

Buyers type whole questions into an assistant, not two-word keywords. The useful unit of planning is the set of questions that precede a purchase in your category, and whether a page of yours actually answers each one in its own words.

Corroboration counts

Engines assemble an answer from several sources. A claim that appears only on your own site is weaker than the same claim reflected in places outside your control: directories, reviews, documentation, press, marketplace listings. Be clear-eyed about it in both directions. Corroboration is gamed constantly, through review farms, paid placements and syndicated press, so an engine reading it is reading a signal that can be manipulated. Building it honestly runs on a timescale of months, not weeks.

Presence replaces position as the metric

There is no rank to report. The only honest measurement is how often you are named or cited when the questions that matter are asked, which has to be sampled rather than looked up.

How to measure whether GEO is working

The obvious method, opening an assistant and asking about yourself a few times, tells you almost nothing. Answers vary between runs and personalise to the account asking, so the same site can look strong and weak an hour apart. A measurement you would put in front of someone else has to hold four things steady.

A fixed question set. Decide the buyer questions that matter, write them down and keep asking the same ones. If the questions move, the trend means nothing.
A repeating schedule. One measurement is an anecdote. A weekly series is evidence, and it is the only way to separate a real change from the model's natural variation.
Detection in code, not judgement. Whether an answer named your brand or cited your domain should be decided by matching text and URLs, not by asking a model to grade the result. A model marking its own homework is not a measurement.
Honest gaps. When a request fails or an engine is unavailable, that has to be recorded as unmeasured. Writing it down as "not mentioned" invents a bad result and corrupts the trend.

With those four in place the useful number is simple: the share of your question set where the brand is named or cited, tracked over time, with the same questions run against competitors so the number has a scale.

Where to start, in order

Check that AI crawlers can reach you, using the list above and testing the status code each agent actually gets. Absence caused by a block looks identical to absence caused by weak content, and it is far cheaper to fix.
Fix the entity. Make sure a stranger, and a machine, can tell from your homepage and your structured data exactly what your company is, what it sells and who it serves.
Write down the questions. Twenty to fifty real buyer questions in your category, in the words a buyer would use.
Measure before you change anything. Establish the baseline, otherwise you will never be able to prove the work did something.
Answer the questions that have no page. Publish pages that answer them directly, in self-contained passages, and link them to the relevant category and product pages.
Re-measure weekly and keep the log. Match what you shipped against what moved.

Where Scayla fits

Scayla is an AI SEO platform for brands and agencies that runs the loop above as a product rather than a project. It measures a fixed set of buyer questions every week against ChatGPT and Gemini, detects presence in code by matching brand names and domains, and keeps 26 weeks of history with the same questions scored for competitors. Then it does the work on your own site: researched and fact-checked articles, technical fixes, category and FAQ content, and internal links. Every change waits for your approval and can be rolled back in one click, and the reporting shows what shipped against what moved.

It runs as an official app on the Shopify App Store and an official plugin on WordPress.org. You approve. It ships.