What is answer engine optimisation (AEO), and how it differs from GEO

What is answer engine optimisation (AEO)?

Answer engine optimisation is the practice of structuring your content so an AI answer engine extracts it as the direct response to a user's question. Where classic search aims for a ranked link, AEO aims to be the passage the model quotes or summarises when someone asks a question your content can answer.

An answer engine is any system that returns a synthesised answer rather than a list of links. Google's AI Overviews, ChatGPT, Perplexity, and Gemini all behave this way. The user reads the answer and acts on it, and in most cases never clicks through. Pew Research Center found in 2025 that when Google shows an AI summary, people click through to a website just 8% of the time. For most queries the answer now ends the journey, so the click that search depended on never happens.

That changes what content has to do. A page can no longer earn its keep by ranking and waiting for a visit. It has to be readable by a model that is choosing, right then, which sentence to quote back to the buyer.

How answer engines pick a source

An answer engine assembles its response from sources it can parse cleanly and trusts to be correct. It favours content that states a claim plainly and sits in a structure the model can isolate, such as a direct definition placed under a matching question or a clear table. These get pulled more reliably than long, hedged prose where the answer is buried three paragraphs down.

Two signals do most of the work. The first is extractability: how easily the model can lift a self-contained answer without dragging in surrounding context that muddies it. The second is trust: whether the source carries the references, structured data, and corroboration that mark it as reliable. A passage can be perfectly worded and still go unused if the domain behind it has earned no standing in the model's training and retrieval.

For a buyer asking AI which provider suits their situation, this is the layer that decides whether your name appears at all. Seer Interactive reported in 2025 that organic click-through to the top result falls by around 60% once an AI answer sits above it. The traffic that used to reach you by ranking now stops at the answer. If you are not in the answer, the buyer never reaches the point of considering you.

AEO vs GEO: one discipline, two emphases

Answer engine optimisation and generative engine optimisation are the same discipline with two emphases, and most explanations fudge the difference. AEO is about being the extracted answer: the passage a model quotes when it responds to a question. GEO is about being the recommended name: the brand a model puts forward when a buyer asks who is best. AEO earns the citation; GEO earns the recommendation. You need both, because the question that extracts your definition is often the same buyer who later asks the model who to hire.

The table below sets out where the emphasis sits.

Dimension	Answer engine optimisation (AEO)	Generative engine optimisation (GEO)
Primary goal	Be extracted as the direct answer	Be recommended as the named provider
Typical query	"What is X" / "How do I do X"	"Who is the best X for my situation"
Unit that wins	A citable passage	A trusted brand name
Format that helps	Clear definitions, tables, factual passages	Authority, references, consistent named presence
Metric	Citation rate across questions	Named-in-answer rate across buyer prompts
Buyer stage	Earlier, informational	Later, decision-forming

AEO and GEO sit on a spectrum rather than either side of a line. The two jobs blend rather than divide. The work that makes a passage extractable, such as clean structure and plain claims, is also the work that teaches a model to trust your name. The shared foundation is content a machine can parse and a domain a machine has reason to trust. The emphasis shifts as the buyer moves from learning to deciding.

Where AEO sits relative to SEO

AEO and classic search optimisation share their foundations and split on their target. Both need content that is crawlable, structured, and authoritative. SEO then optimises for a position in a list of links and measures success by the click. AEO optimises for inclusion in a synthesised answer and measures success by the citation. A page can rank first and still be ignored, because the AI summary above it has already answered the question. The work that earns a ranking is not the same work that earns a citation, and that gap is where most B2B teams are currently exposed.

For a fuller treatment of the ranking-versus-citation distinction, see is GEO the same as SEO. For the parent concept that AEO sits inside, see what is generative engine optimisation (GEO).

What makes content extractable

Content earns extraction by answering one question cleanly in a place the model can find it. The strongest pattern is a question stated as a heading with a direct answer of forty to sixty words sitting immediately beneath it, written so the passage makes sense on its own. A model can lift that block whole and trust it to hold up out of context. Long preambles, qualifiers, and answers that only surface after three paragraphs of build-up get skipped, because the engine cannot isolate the part that responds to the query.

Structure carries the rest of the weight. Tables get pulled reliably because the relationship between items is explicit and the model does not have to infer it. A clear factual statement, supported by a named source, reads as more reliable than the same claim left unattributed. Schema markup tells the engine what a passage is, which helps it match content to a question. None of this is a trick. It is writing that is precise about what it claims and tidy about where it sits, which happens to be what a machine reads best and what a careful human reader prefers too. For the practical detail, see how to get cited in Google AI Overviews and AI answers.

The traits that earn extraction are the same ones that, repeated across a domain, build the standing behind a recommendation. A site that answers its buyers' questions cleanly, again and again, with sources and structure, teaches the model both to quote it and to trust the name attached to it. That is why AEO and GEO run as one programme. The content that wins citations is also the substrate that wins recommendations.

What this looks like for a B2B buyer

A compliance lead at a funded scale-up needs a vendor and opens ChatGPT. She types a plain question about the best provider for her regulated sector. The model returns a short answer with two or three named companies and a sentence on each. She reads it in fifteen seconds and starts there. She never visited a homepage or scanned a results page. The shortlist formed inside the chat, drawn from whichever names the model had seen cited and trusted most often.

That is both jobs at work in one session. AEO put the explanatory passages into the answer she read on the way in. GEO put the names into the recommendation she acted on. If your company answered neither, the decision moved forward without you, and your analytics recorded nothing because no one ever arrived.

How to know where you stand

You can measure your position today without guessing. The method is to ask the answer engines the questions your buyers ask, record which name they return, and score it. Run the same question set monthly and you can watch the citation rate and the named-in-answer rate move question by question. This is the practice behind both jobs, and it is the only honest way to know whether the work is landing. For the method in full, see how to measure how often AI recommends your brand.

This is the work we run at Forge, as a programme rather than a one-off project. We move B2B companies from absent to recommended in the AI answers their buyers actually read. The method runs in four steps. We audit where you stand across the questions your buyers ask. We build the content the models draw on. We earn the references and structured signals that teach the models to trust your name. Then we re-score every month so you can see the movement.

The results follow the same arc across the programmes we run. A UK digital identity platform we work with grew its monthly AI reach from around 39,000 to 2.9 million in six months and won a seven-figure contract directly through AI search. A healthcare compliance platform saw its AI audience grow 36% month on month, with its largest contract arriving through ChatGPT. Other programmes are earlier in the curve. A regulated GovTech records platform started named in none of the 28 unbranded questions its buyers were asking AI. That category sits wide open, and the work to put the platform into it is under way.

Frequently asked questions

What does AEO stand for?

AEO stands for answer engine optimisation. It is the practice of structuring content so AI answer engines such as Google AI Overviews, ChatGPT, and Perplexity extract it as the direct answer to a user's question, rather than returning a ranked link the user has to click.

Is AEO the same as GEO?

AEO and GEO are the same discipline with two emphases. AEO focuses on being the extracted answer a model quotes. GEO focuses on being the recommended brand a model names when a buyer asks who is best. The foundations overlap, and most B2B teams need both because the same buyer asks both kinds of question.

Is AEO just SEO with a new name?

No. AEO and SEO share foundations: crawlable, structured, authoritative content. They split on the target. SEO optimises for a ranked position and measures the click. AEO optimises for inclusion in a synthesised answer and measures the citation. You can rank first and still be left out of the AI answer that sits above your listing.

What is an answer engine?

An answer engine is any system that returns a synthesised answer instead of a list of links. Google AI Overviews, ChatGPT, Perplexity, and Gemini are examples. The user reads the answer directly, and in most cases never clicks through to a source. Being inside the answer is what now decides whether a buyer ever sees you.

How do I measure AEO performance?

Measure AEO by asking answer engines the questions your buyers ask, recording which sources and names they return, and scoring your citation rate across that question set. Re-run the same questions monthly to track movement. The named-in-answer rate is the metric that shows whether AI is putting you forward to buyers.

Does AEO matter for B2B?

AEO matters for B2B because buyers now form shortlists inside AI chats before visiting any website. Seer Interactive reported in 2025 that an AI answer above the top result cuts its organic click-through by around 60%. If your content is not the answer, you are absent from the moment the buyer decides who to consider.

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Sources: Pew Research Center, 2025 (click-through behaviour when an AI summary is shown). Seer Interactive, 2025 (organic click-through with AI answers above the result).