Answer Engine Optimization
Short answer
Answer engine optimization (AEO) is the practice of getting a company named and cited inside AI-generated answers rather than only ranked in a list of links. It works on three levers: an entity that resolves and is corroborated, content formatted so a passage can be extracted intact, and presence on the third-party sources answer engines already trust.
What is AEO?
Search engines return a list and let you choose. Answer engines choose for you and return a paragraph. That single change moves the competition from "which page is most relevant" to "which company does the model believe is the right one to name" — and those are answered by different mechanisms.
An answer engine resolves entities before it resolves documents. Asked which AI agency in Greece to use, it is not scanning pages; it is retrieving what it holds about named organisations and their categories. A company that describes itself as one thing on its homepage and another on its service pages does not get ranked lower. It gets classified into neither, cleanly, and drops out of the shortlist.
AEO vs SEO vs GEO vs LLMO
The terms overlap and are used loosely. Here is how we use them, consistently, across this site.
| Term | What it optimises for | Primary unit | How you measure it |
|---|---|---|---|
| SEO | Position in a ranked list of links | The page | Position for a keyword |
| AEO | Being named inside a written answer | The passage and the entity | Mention and citation rate |
| GEO | Inclusion in generative results broadly, including AI Overviews | The source | Presence in generated summaries |
| LLMO | What a language model holds and repeats about you | The entity | Share of voice across prompts |
In practice the work overlaps enough that the labels matter less than the sequence: measure, fix the entity, make the content extractable, then earn corroboration.
The four things that actually move AI citations
In rough order of impact, and the order we work in:
- Crawlable, server-rendered HTML. The major AI crawlers do not reliably execute client-side JavaScript, so a site that renders in the browser may be retrieved with little or no content. This is the most common serious fault we find, and it usually appears on sites that look excellent to a human visitor.
- A resolvable, corroborated entity. One organisation node, one named person, one consistent name and address across every surface, declared in structured data and echoed on independent profiles.
- Extractable answer formatting. A 40–60 word self-contained answer under the heading, question-form H2s, comparison tables, one concrete fact per section. Passages are what get lifted, not pages.
- Third-party corroboration. Directories, review platforms, community discussion, coverage. This is the slowest lever, the one outside your control, and the reason honest timelines are measured in quarters.
Schema markup and llms.txt help machines parse you once they have decided to read you. They do not create the decision. That distinction is worth holding onto, because most of what is sold as AEO is entirely in the second category.
What Google says officially, and what follows from it
Google's own documentation on AI features is unambiguous: to appear as a supporting link in AI Overviews or AI Mode, a page must be indexed and eligible to appear with a snippet, and there are no additional technical requirements and no special optimisations required. It states plainly that no new machine-readable files, AI text files or bespoke markup are needed.
Two conclusions follow, and we state both because the second one costs us work we could otherwise sell.
- Most of what is marketed as "AI SEO" is technical SEO with a new invoice. If a vendor's AEO deliverable is a crawlability fix and a schema block, that is worth doing and it is not a new discipline.
- The genuine differentiators sit outside the page. Whether a model names your company depends on entity resolution and on corroboration across sources you do not own. That is slower, harder to sell, and the actual work.
Google also describes a "query fan-out" technique, where a single question triggers multiple related searches across subtopics. That is the mechanism behind a page being cited for a question it does not rank first for — and the reason comprehensive, well-structured coverage of a topic outperforms a page optimised for one keyword.
How we run an AEO engagement
- Baseline
- A fixed prompt set run across ChatGPT, Gemini, Perplexity, Claude and AI Overviews, with verbatim answers stored and dated. You can never recreate a clean before-state once the work starts.
- Entity build
- The JSON-LD graph, the person node, and the external profiles that corroborate both.
- Content engineering
- Direct-answer blocks, question-form headings, tables and glossary blocks on the pages that carry commercial intent.
- Monthly re-measurement
- The same prompts, the same engines, the same format. The delta is the report, and over time the delta becomes publishable data.
Prices for each of those are on the pricing page.
Frequently asked
What is answer engine optimization?
Answer engine optimization is the practice of making a company likely to be named and cited inside answers generated by AI systems such as ChatGPT, Gemini, Perplexity, Claude and Google AI Overviews, rather than merely ranked in a list of links. It works on three things: a resolvable entity, content formatted so passages can be extracted, and corroboration on sources those systems already trust.
Is AEO just SEO with a new name?
They share a foundation and diverge above it. Crawlable server-rendered HTML, clean information architecture and genuine subject-matter depth serve both. The divergence is in what is rewarded: search rewards relevance and links, answer engines reward entity clarity, quotable statements and agreement across independent sources.
The practical test is simple. If your page ranks first and no assistant names you when asked the same question, the gap is AEO, not SEO.
How long does an AEO project take?
The technical and entity layer is a matter of weeks and is fully within your control. Being named more often is slower, because it depends on corroboration across sources you do not own — directories, reviews, community discussion, third-party coverage. Plan on a quarter for the first clear movement in a soft market and considerably longer in a contested one.
Does llms.txt help?
Publish it — it costs twenty minutes and it is a reasonable bet on agentic retrieval. Do not buy it as a ranking lever. Google has stated on the record that it does not use it, and crawler-log analysis shows the major AI bots overwhelmingly fetch HTML directly instead. Anyone selling llms.txt as an AI ranking factor is telling you something about their reading, not about the file.
How do you measure whether AEO worked?
A fixed prompt set, run monthly across five answer surfaces, with every response stored verbatim and dated. The metrics are mention rate, citation rate and share of voice against named competitors. Traffic is a poor proxy here, because an assistant can recommend you without sending a click.
What does AEO cost?
Prices for the audit, the build and the ongoing monitoring retainer are published on the pricing page. We publish them because the cost question is one of the highest-intent queries in this category and almost nobody answers it.
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