Ask ChatGPT, Gemini, or Claude the kind of question your buyers used to type into Google — "what's the best project management tool for a small agency?" — and you get one composed answer instead of ten blue links. Some brands are in that answer. Most are not.
That shift created a new acronym pile: AEO (Answer Engine Optimization), GEO (Generative Engine Optimization), LLM SEO, AI SEO. The names differ; the underlying question is the same. SEO asks: do we rank in the list of results? AEO asks: do we appear in the answer itself?
This guide covers what AEO actually is, where it genuinely differs from SEO, where it is the same discipline wearing a new name — and how to measure it, which is the part most articles skip.
The short version. AEO is not a replacement for SEO — AI answers are assembled largely from search-indexed pages, so the SEO foundation is the AEO foundation. What genuinely changes is the unit of success (being named, not ranked), the shape of the query (questions, not keywords), and the fact that there is no console to check: the only way to know where you stand is to ask the assistants your buyers' questions and analyse the answers.
What is AEO?
Answer Engine Optimization (AEO) is the practice of making your brand and content something AI assistants cite, mention, and recommend when they compose answers. The "answer engines" are the systems that respond with an answer rather than a results page: ChatGPT, Gemini, Claude, Perplexity, and Google's own AI Overviews and AI Mode.
You will also see GEO (Generative Engine Optimization) used for the same discipline, sometimes with a shade of difference — GEO leaning toward visibility in generated answers broadly, AEO leaning toward the question-answering surface. In practice, teams use them interchangeably, and the work is identical. We use AEO here.
| Acronym | Stands for | What people usually mean by it |
|---|---|---|
| AEO | Answer Engine Optimization | Being named and cited in AI-composed answers |
| GEO | Generative Engine Optimization | The same discipline, framed around generated output |
| LLM SEO | — | Optimising specifically for chat assistants |
| AI SEO | — | Ambiguous: sometimes the above, often "using AI to do SEO" |
The important mechanical fact: a modern assistant answering a commercial question usually does two things — it draws on what its model already "knows" about your category from training, and it frequently searches the web and reads pages before composing the answer. Those two paths behave completely differently, and confusing them is the source of most bad AEO advice.
| Trained knowledge | Live retrieval | |
|---|---|---|
| Where it comes from | The model's training data | A web search run at answer time |
| What influences it | The whole public record about you, years deep | The pages ranking for that query today |
| How fast it moves | Training-cycle slow — months | Days, once new content is indexed |
| What you can do | Be described consistently everywhere | Classic SEO: be indexable, be the best answer |
| Can you verify it? | Only by asking the model | Only by asking the model |
Both paths run through material that search engines already index. Which is why…
AEO is still SEO underneath
Google said this in so many words in its 2026 AI-optimization guidance: there is no secret AI-specific markup, no special "chunk formatting", no separate submission channel — being indexed and useful in ordinary search is the prerequisite for appearing in AI answers. AI Overviews and AI Mode draw from the standard index. ChatGPT's and Claude's browsing draw on search indexes too.
So the foundation of AEO is exactly the foundation of SEO:
- Crawlable, indexable pages — if a search bot can't fetch it, an answer engine can't read it.
- Semantic HTML with a clear heading structure — assistants extract from pages; clean structure extracts better.
- Content that actually answers questions — original information, first-hand experience, specifics. The models are drowning in generic copy; they quote the page that says something.
- Page experience and speed — fetchers time out like browsers do.
If someone sells you AEO as a replacement for SEO, they are selling you the same work twice.
What genuinely changes
The overlap is real, but so are the differences. Four things behave differently when the "searcher" is a language model composing one answer.
1. The unit of competition is the mention, not the ranking
A results page has ten slots; an answer has a handful of named brands — often three to five. There is no "position seven" to occupy while you build up. You are named, or you are absent. That makes the competitive question sharper: which brands does the model reach for in your category, and what would it take to be one of them?
It also changes what a good week looks like. In SEO, moving from #14 to #9 is progress you can see. In AEO, the equivalent progress is invisible until the moment you cross into the answer — which is why measuring the rate at which you are named, across many questions and many days, matters more than any single check.
2. Questions replace keywords
Nobody asks an assistant "project management tool agency pricing". They ask "what should a 10-person agency use to manage client projects?" — full questions, with context. Optimizing (and measuring) against keyword strings misses how the surface is actually used. The unit of tracking should be the real buyer question, asked verbatim.
3. Consistency across the open web matters more
A model's sense of your brand is assembled from everything it has read: your site, review sites, directories, forum threads, comparison posts. SEO could concentrate effort on your own domain and a link profile. AEO rewards being described consistently everywhere — the third-party pages that mention you are often precisely the pages an assistant reads before answering.
4. The answer varies — per model, per day
Ask the same question twice and you can get different brands. Ask three different assistants and you will. Each model has different training data, different search behavior, and different retrieval habits. There is no single "ranking" to check — there is a distribution, and it moves. That is a measurement problem first and an optimization problem second.
SEO vs AEO at a glance
| SEO | AEO | |
|---|---|---|
| Surface | Results page (links) | Composed answer (prose) |
| Unit of success | Ranking position | Being mentioned / cited |
| Query shape | Keywords | Full questions |
| Slots available | ~10 per page | ~3–5 brands per answer |
| Where it's decided | Your pages + links | Your pages + everything written about you |
| Verification | Rank trackers | Asking the models and analyzing answers |
| Feedback loop | Days (crawl + rank) | Mixed: retrieval is fast, training is slow |
| Reporting surface | Search Console | None — you have to generate the data yourself |
How to actually do AEO (without the snake oil)
The honest playbook is short:
- Keep the SEO foundation. Indexable site, fast pages, semantic structure, sitemap, no accidental bot-blocking. Check your robots.txt actually allows the AI crawlers you want (their search bots and fetchers respect it).
- Publish answers to the questions your buyers ask. Not keyword pages — direct, specific, first-hand answers. If you have data nobody else has, publish it; original numbers get cited.
- Tend the third-party record. Directory entries, review profiles, comparison articles — the pages that describe your category are the pages assistants read. Being absent there is being absent from the source material.
- Measure the answers themselves. Ask the real models your buyers' real questions, on a schedule, and track whether you are named, how prominently, and who else is. Everything above is guesswork until you can see the answers moving.
And the things to skip: "AI-optimized" rewrites of existing pages, llms.txt as a strategy (Google has said outright it does not use it — publish one, but expect nothing), schema markup as a magic key (useful for rich results; not required for AI features), and any service promising to "inject" your brand into models.
How do you measure AEO?
You cannot log into ChatGPT's search console — there isn't one. Measuring AEO means doing the only thing that reflects reality: asking the assistants your buyers' questions and analyzing the answers. Done properly, that means:
- asking the consumer-tier models real people use, not developer API defaults;
- asking verbatim, with no system prompt steering the answer;
- letting the model decide for itself whether to search the web, as it would in a real chat;
- scoring every provider's answers with one judge and one rubric, so a score on Gemini and a score on ChatGPT are comparable;
- repeating it on a schedule, because a single ask is an anecdote, not a measurement.
Here is what is worth tracking once you are collecting answers, and what each number actually tells you:
| Metric | The question it answers | How to read it |
|---|---|---|
| Presence rate | How often are we named at all? | The base number. Everything else is conditional on it |
| Prominence | When we're named, how visible are we? | First sentence and top pick beat a footnote mention |
| Share of voice | How much of the answer space is ours? | Compares you to rivals on the same denominator |
| Competitor set | Who else gets named? | Often the biggest surprise in the first report |
| Cited sources | Which pages did the model read? | Your AEO to-do list, written by the model |
| Web-search rate | Did it search, or answer from memory? | Tells you which lever — content or record — applies |
That is precisely what Mentionify does — it asks ChatGPT, Gemini, and Claude your tracked questions daily and turns the answers into presence, share of voice, competitor leaderboards, and one AI Visibility Score. You can read the full methodology on how it works, see the step-by-step manual version in how to check whether ChatGPT mentions your brand, or run the free AI visibility checker on your own brand right now.
FAQ
Is AEO replacing SEO?
No. AI answers are assembled largely from search-indexed content, so SEO is AEO's foundation. What changes is the surface you compete on (mentions, not rankings) and how you verify results.
AEO vs GEO — is there a difference?
Practically, no. GEO (Generative Engine Optimization) and AEO describe the same discipline; usage varies by team. Both reduce to: be present and well-described in the material AI systems read, and measure what they actually say.
How long does AEO take to show results?
Two clocks run at once. Retrieval-influenced answers (where the model searches the web) can change within days of new content being indexed. The model's trained knowledge of your brand moves on training-cycle timescales — months. Track weekly or daily and you will see the fast clock first.
Can you pay to be in AI answers?
Not organically. No major assistant currently sells placement inside composed answers (ads around them are appearing). Anyone selling guaranteed AI mentions is selling something else.
Does schema markup help with AEO?
It helps search engines understand your pages, which helps rich results — and Google has been explicit that structured data is not required for AI features. Add it because it is good housekeeping, not because it is a lever on AI answers.
