In brief: Answer engine optimisation (AEO) is the practice of making content visible and useful to systems that deliver direct answers. Generative engine optimisation (GEO) is work intended to improve website visibility in generative-engine responses. Industry usage overlaps, and there is no agreed boundary. I use both for the same supply-side work inside findable. By themselves they don’t make a company evaluable or actionable.
AEO and GEO are the supply-side work of getting named in those generated answers.
What answer engine optimisation means
Answer engine optimisation is making content visible and useful to AI systems that deliver direct answers.
Ahrefs defines it as being mentioned or cited in AI answers from Google AI Overviews, voice assistants, ChatGPT and Claude, rather than only ranking pages.1 HubSpot frames it as improving how often and how accurately a brand or product appears in AI-generated answers, with structured content for ChatGPT, Perplexity and Gemini.2
The shared point is that a search result used to be a link. Now it can be a sentence that names you, cites you, or leaves you out.
AEO doesn’t replace SEO. Both depend on crawlability, authority and structured content.
The difference is the outcome. SEO aims for a click. AEO aims for the source being the trusted one when a model writes the answer.
Both HubSpot and Ahrefs make this point.21 A page that ranks well and is easy for a model to cite has a better chance of being named.
What generative engine optimisation means
The term “generative engine optimization” was introduced by Aggarwal et al. in a paper titled “GEO: Generative Engine Optimization,” first published on arXiv in November 2023 and later presented at KDD 2024.3
The paper defines GEO as optimising website content for visibility in generative engine responses. The authors treat the generative engine as a black box and propose visibility metrics for measuring how often and how prominently a source appears in generated answers.3
The paper introduced GEO-bench, a benchmark of around 10,000 queries. Its main evaluation used the 1,000-query test split. In those experiments, adding citations, quotations and statistics improved the paper’s visibility metrics, with gains of up to 40% against baseline content.3
Those gains belong to the paper’s systems and metrics. They do not guarantee that a page will be recommended. The finding supports an early GEO focus on sourced claims and quotable passages.
GEO isn’t measured by keyword rank. It’s measured by whether the company appears in the answer and what the model says.
Where they overlap and where they differ
Industry labels overlap. GEO is sometimes called answer engine optimisation (AEO), LLMO, AIO or AI SEO. Wikipedia notes that the terms are often treated as near-synonyms, and there is no consensus on the boundaries.4 The same Wikipedia article lists AEO, AIO (AI Overview optimization) and LLMO (large language model optimization) among the related labels.4
HubSpot draws a line between AEO as a broader umbrella and GEO as more specifically focused on generative outputs, but in practice the terms often describe the same supply-side work.5
Google’s 2026 documentation, as reported by business.com and Wikipedia, treats optimising for generative AI search as still falling within SEO.64 Microsoft’s Bing documentation also uses “generative engine optimization” language while stating that SEO fundamentals continue to support AI search experiences.4
The agent-led growth guide calls out AEO/GEO as supply-side work that sits inside findable. Findable is one part. There’s also evaluable and actionable.
I treat AEO and GEO as the same supply-side work. Both describe getting named in AI answers. That work sits inside the findable part of the funnel.
| AEO / GEO | SEO | ALG (demand side) | |
|---|---|---|---|
| Emphasis | AEO: direct-answer visibility. GEO: attributed source visibility in generative responses. | Rank in traditional search results. | Whether a buyer’s agent can find, judge and act on the product. |
| Primary signal | Whether the company is recommended or mentioned, a source is cited, the mapped claim is present, a competitor is present or the company is absent. | Position in the SERP, clicks, traffic | Findable + evaluable + actionable |
| Surfaces | Systems that deliver direct answers and generative-engine responses | Google, Bing, organic listings | The buyer’s agent, wherever it searches |
| Operator-owned inputs | The company’s published pages and source material | The company’s pages | The company’s pages, docs and tools |
| What it does not control | The model, the buyer’s prompt | The algorithm | The buyer’s agent, the last step |
AEO and GEO are supply-side work. They affect what the company publishes. Agent-led growth is a demand-side frame. It asks whether the buyer’s agent can complete an evaluation before the first call.
Neither term is geo-targeting. The GEO abbreviation has nothing to do with location-based advertising or local search.
Neither term is agentic marketing. An agent running inside a company’s own systems, drafting content or routing leads, is a different conversation. AEO and GEO focus on being visible to someone else’s agent on public pages.
A citation count is not the goal. A mention is not a recommendation. A citation the buyer cannot open is not proof. When price, limits or proof are missing, the page does not make the product evaluable. I record any missing or invented answer, and whether another source or competitor appears.
What I would actually do
I wouldn’t start with a GEO audit tool. I don’t start by rewriting every page.
The first checks use pages and claims that already exist.
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Run the buyer questions. I take the questions from sales calls and the message map. I ask them in ChatGPT, Perplexity, Claude and Google AI Overviews. For each question, I record whether the company is recommended or mentioned, whether a source is cited, whether the mapped claim appears, whether a competitor appears and whether the company is absent. That’s the baseline.
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Check the source. I compare the answer with the cited page. If the page is wrong or unclear, I fix it. If the answer does not match the page, I record the mismatch. If the answer names a competitor, I look at what that competitor’s page says. I then check the page the agent is already reading for the claim that should appear and the proof that backs it up. I work one question at a time.
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Put the answer-first block in place. I put the sentence I want quoted in the first short block, with a source a person can check. I use the claim I want repeated rather than burying it in the third paragraph. If the page leaves out the price, the limit or who the product is not for, it does not make the product evaluable.
A few practices appear across the source guidance and the early GEO research, but the sources support different claims.
Answer-first quotable blocks. The page should open with a sentence an AI can cite verbatim. That’s the text most likely to be quoted.12
Sourced claims. The paper tested citations, quotations and statistics as separate methods for improving its visibility metrics.3 Any external claim needs a URL. A competitor comparison needs a date. An industry benchmark needs a source a person can check. If I publish a number, I link or cite the source. hcd.ai points to a Columbia Journalism Review test in which more than half of the AI search engine responses had incorrect attributions.7 A sourced figure gives the person reviewing the answer a reference. If the claim only exists on a slide or a call, the public page does not give an agent a checkable source.
Entity consistency. The company name, product names and founder names should appear the same way across the site, the documentation and third-party profiles. A model that sees three different product names may treat them as three different products. Inconsistency makes the model less confident about which entity it found.12
Third-party mentions. Reviews, analyst mentions, comparison pages and earned media give the model evidence beyond the company’s own claims. A claim repeated only on the company’s site is weaker than one cited elsewhere.12
Machine index. This site publishes /llms.txt as a voluntary index of its live pages.8 It’s not a ranking signal, and it does not replace the claim on the page.
I keep a fixed list of buyer questions. These come from the message map, from sales calls, and from the searches a competitor already ranks for. I run the list on a schedule. The cadence depends on how often content changes and how competitive the category is. Monthly is a reasonable start.
I keep those fields separate because a recommendation or mention can appear alongside a citation. I check evaluability separately. A page may be cited while still leaving the agent unable to judge price, limits or proof.
If the answer does not match the cited page, I record the mismatch rather than treating the page and the answer as the same result.
Where it stops
Getting named in an AI answer is the top of the funnel. A lot of the AEO and GEO conversation stops there.
The buyer’s agent doesn’t stop there. It still has to judge: is this the right product, at what price, for what problem, with what proof? If those answers aren’t on the page, the product is not evaluable there. A run may return no answer, an inaccurate one, another source or a competitor; I record which happened.
After judgement comes action. Can the agent start a trial, call an API, book a meeting, or submit a request that arrives intact? If the honest next step is a security review, that conversation is the action.
AEO and GEO sit inside findable. They do not make a company evaluable or actionable. The company can appear in an answer and still leave the agent unable to judge the product or complete a next step.
I don’t call a company agent-ready when it only appears in AI answers. I call it findable. The agent-led growth guide covers what evaluable and actionable mean, and where the check goes from there.
Questions I get asked
What is answer engine optimisation?
Answer engine optimisation is making content visible and useful to AI systems that deliver direct answers. The goal is being cited or mentioned in Google AI Overviews, ChatGPT, Perplexity and Claude, rather than only ranking pages.
What is generative engine optimisation?
Generative engine optimisation is work intended to improve website visibility in generative-engine responses. It’s measured by visibility in those responses, not by traditional search rank. The term was introduced by Aggarwal et al. in 2023.3
Is AEO the same as GEO?
In practice, they overlap. AEO emphasises answer engines. GEO emphasises generative AI outputs. HubSpot sometimes treats AEO as the broader umbrella, with GEO focused specifically on generative outputs.5 Wikipedia notes there is no consensus.4 On this site I treat them as the same supply-side work that sits inside findable.
How is AEO or GEO different from SEO?
SEO optimises for position in traditional search results. AEO and GEO optimise for visibility in AI-generated answers. Both depend on crawlability, authority and structured content. Google’s own documentation, as reported by business.com and Wikipedia, treats the work as still part of SEO.64 In practice, AEO and GEO add tactics like quotable blocks, cited statistics and machine-readable surfaces to the existing SEO foundation. They complement SEO. They don’t replace it.
How do AEO and GEO relate to agent-led growth?
AEO and GEO are supply-side. They’re the work of getting named in AI answers. They sit inside findable.
Agent-led growth is a demand-side frame. It asks whether the buyer’s agent can find the product, judge it, and complete an action, often before the first call. A company that is findable but not evaluable or actionable still loses the buyer. The ALG guide covers those stages.
How do I measure AEO or GEO?
Keep a fixed list of buyer questions. Record whether the company is recommended or mentioned, whether a source is cited, whether the mapped claim appears, whether a competitor appears and whether the company is absent. Check evaluability separately, because a cited page may still leave the agent unable to judge price, limits or proof.
I use the same findable, evaluable and actionable check in the Agent-Ready GTM Start kit. Start is the front door.
Sources
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Ahrefs, “What Is AEO (Answer Engine Optimization)?” ahrefs.com (opens in new tab) ↩ ↩2 ↩3 ↩4 ↩5
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HubSpot, “What Is Answer Engine Optimization? How to Optimize for AI Search.” blog.hubspot.com (opens in new tab) ↩ ↩2 ↩3 ↩4 ↩5
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Pranjal Aggarwal et al., “GEO: Generative Engine Optimization,” arXiv:2311.09735, November 2023; presented at KDD 2024. dl.acm.org (opens in new tab) ↩ ↩2 ↩3 ↩4 ↩5
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Wikipedia, “Generative engine optimization.” Notes overlapping terminology (AEO, AIO, LLMO), reports that the terms are often treated as near-synonyms with no consensus on boundaries, and reports Google’s position that optimising for generative AI search remains SEO. en.wikipedia.org (opens in new tab) ↩ ↩2 ↩3 ↩4 ↩5 ↩6
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HubSpot, “AEO vs. GEO: What’s the Difference?” blog.hubspot.com (opens in new tab) ↩ ↩2
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business.com, “What Is Generative Engine Optimization?” Reports Google’s position that optimising for generative AI search remains SEO. business.com (opens in new tab) ↩ ↩2
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hcd.ai, “Get Ready for the GenUI Web,” 1 June 2026. The Columbia Journalism Review attribution finding is reported there. hcd.ai (opens in new tab) ↩
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The site ships a machine-readable summary at
/llms.txt. robbiemaltby.com ↩