A plain definition, how it differs from SEO mechanically, and what the work actually consists of.
Answer Engine Optimization (AEO) is the practice of making a company retrievable, quotable and attributable so that AI assistants name it inside generated answers. Where SEO competes for a position in a list of links, AEO competes for a sentence inside the response the user actually reads.
The shift it responds to is simple: a growing share of buyers now ask an assistant a question and act on the answer without visiting a search results page at all. If your company is not in that answer, you were not considered — and nothing in your analytics records the omission.
A search engine returns documents. A language model returns a composed answer, and to compose it, it must do three things your SEO was never designed to support:
robots.txt.Failing any one of the three costs the citation. This is why sites that rank on page one routinely fail to appear in AI answers, and why an SEO audit will not find the problem.
| Layer | The question it answers | Typical work |
|---|---|---|
| Access | Can the model fetch the page at all? | Named AI crawler permissions, llms.txt, render and status checks, JavaScript dependency |
| Extractability | Is there anything worth quoting? | Literal buyer questions as headings, short direct answers, comparison tables, definitions |
| Entity | Does the model know who you are? | Organization and Person schema with stable identifiers, consistent naming across the web, disambiguation from similarly named things |
| Corroboration | Does anyone else confirm it? | Presence in the third-party sources models already trust for your category |
| Measurement | Did any of it work? | A fixed prompt set, repeated runs, citation share tracked over time |
AI agents fall into two classes, and blocking them has very different consequences. Training crawlers — GPTBot, ClaudeBot, Google-Extended, CCBot and others — affect whether a model learns your brand exists over time. Retrieval agents — OAI-SearchBot, ChatGPT-User, PerplexityBot — fetch your page while the model is answering a live question.
Blocking retrieval is the expensive mistake: the model wants to cite you and physically cannot open the page. Blocking training is common, frequently accidental, and much less damaging. Any audit that reports “AI crawlers are blocked” without naming which agent and which class has not done the work. The full distinction, with what we found across 137 UK B2B sites →
It matters most where buyers research before they make contact and where a single customer is worth a meaningful amount — B2B software and technical services with a considered, multi-week purchase. It matters least where the purchase is impulsive, local or driven by price comparison.
Ask an assistant the question your best-fit buyer would ask — not your company name, the category question. If competitors are named and you are not, that is the gap, and it is measurable rather than a matter of opinion. Our full measurement protocol is published, including the run counts and the limits of what it can prove.
No. SEO optimises for position in a ranked list of links. AEO optimises for inclusion in a generated answer. The underlying work overlaps — both need a crawlable, well-structured site — but a page can rank first on Google and still never be cited by an AI assistant, because ranking and citation are decided by different mechanisms.
In practice the two terms describe the same work. GEO (Generative Engine Optimization) came out of academic research and tends to emphasise content phrasing; AEO (Answer Engine Optimization) came out of industry and tends to emphasise retrievability and entity clarity. Anyone selling one is selling the other.
Technical fixes apply immediately — unblocking a retrieval agent works the day it ships. Citation share moves more slowly: expect eight to twelve weeks before a fixed prompt set shifts meaningfully, because models have to re-encounter and re-trust the pages.
No, and nobody can. Model outputs are non-deterministic, vary by session and change with every model update. What can be done is to measure citation share against a fixed prompt set, fix the things that demonstrably block or weaken citation, and re-measure on the same protocol.
No. The same work affects Google AI Overviews and AI Mode, Perplexity, Microsoft Copilot and Claude. They differ in which index they retrieve from — Copilot leans on Bing, for instance — which is why crawler access has to be checked per named agent rather than in general.