What is AEO? Answer engine optimization, GEO and SEO, defined and measured
Key point. AEO, answer engine optimization, is the work of making a company's pages and facts easy for AI answer engines such as ChatGPT, Google AI Overviews and Perplexity to find, read, trust and cite, so the answers describe the company correctly and recommend it.
An answer engine is any search product that replies with a written answer instead of a list of links: ChatGPT search, Google's AI Overviews and AI Mode, Perplexity, Microsoft Copilot, Claude and Gemini. AEO is to those answers what SEO is to a page of ranked results. This page defines AEO and the three terms it is confused with, GEO, SEO and LLMO, shows how they fit together, and puts our own measurements behind each claim, so you can tell what the work is and what it is worth to you.
Key takeaways
- AEO is getting AI answer engines to read, cite, describe and recommend your company correctly. GEO (generative engine optimization) names the same work and comes from a 2023 research paper. LLMO stresses what a model says from memory. SEO ranks pages in a list of results.
- SEO is the foundation of AEO. An engine can only use a page it can fetch and index, and for Google's AI features Google calls the work "still SEO".
- AEO starts where the ranking ends. In our study of 150 non-branded US Google searches, about 57% of the websites AI Overviews cited were not in that search's top 10 (95% interval 54% to 60%).
- The unit of success changes. SEO counts rankings and clicks. AEO counts citations, mentions, accurate descriptions and recommendations, engine by engine.
- One check is not a measurement. AI answers change between identical asks, so AEO is measured with repeated questions and read as a range.
- Start with your buyers' questions, check which stage of the funnel you drop out at, and fix that stage before buying tactics.
What is answer engine optimization (AEO)?
Answer engine optimization (AEO) is the practice of making a company's pages and facts easy for answer engines to find, read, trust and cite, so that the engines describe the company correctly and recommend it when a buyer asks for options. The goal is to be one of the sources an AI answer is built from, and to be named in the answer itself.
An answer engine works in three steps. It turns the buyer's question into one or more searches, reads a handful of the pages those searches return, and writes one answer from them, often with links to the sources it used. Google calls the multiple searches "query fan-out": "a set of concurrent, related queries generated by the model to request more information and fetch additional relevant search results to address the user's query" (Google Search Central).
So AEO covers every step where a company can drop out. The engine must be able to fetch your pages, retrieve them for the buyer's question, trust them over other sources, describe you as you are today, and choose you when the buyer asks "which one should I use?". Our AI search visibility funnel breaks that path into eight stages, each with its own check.
What AEO is in marketing terms: a share of the answers your buyers read before they ever visit a website. For SaaS and tech companies, fintech, and other growing businesses whose buyers research before they talk to sales, that is where a shortlist now forms.
What are AEO, GEO, SEO and LLMO, in one sentence each?
Four terms describe overlapping work, and each has a one-sentence definition we use consistently across this site:
- SEO (search engine optimization) is the work of ranking a site's pages in a search engine's list of results, so that people click through to them.
- AEO (answer engine optimization) is the work of getting AI answer engines to read, cite, describe and recommend a company correctly in the answers they write.
- GEO (generative engine optimization) is the work of changing content so that generative AI engines are more likely to include and cite it; the term comes from a 2023 research paper, and in market use it means the same work as AEO.
- LLMO (large language model optimization), also called LLM SEO, is the same broad goal with the stress on what a model says about a company from its training data, without searching.
AI SEO is used in two senses, SEO aimed at AI search results and ordinary SEO done with AI tools, so ask which one is meant. Our AI search glossary defines 55 related terms, from citation to noise floor.
What is the difference between AEO, GEO, SEO and LLMO?
The terms differ in the surface they target and in what they count as success. The table puts them side by side.
| SEO | AEO | GEO | LLMO / LLM SEO | |
|---|---|---|---|---|
| Stands for | Search engine optimization | Answer engine optimization | Generative engine optimization | Large language model optimization |
| Where the term comes from | The search industry, late 1990s | Search trade press by 2018, first about voice search and direct answers | Aggarwal et al., arXiv November 2023, published at KDD 2024 | Industry use; no single defining source |
| Target surface | A ranked list of results | Written AI answers: ChatGPT, AI Overviews and AI Mode, Perplexity, Copilot, Claude, Gemini | Generative engines that retrieve sources and cite them | Chat assistants, and the model's own memory |
| Unit of success | Ranking position, impressions, clicks | Citation, mention, accurate description, recommendation | Share of the answer drawn from your source | Mentions and recommendations in chat answers |
| What the buyer sees | Ten links to choose from | One answer that may name you, compare you and cite you | One answer with cited sources | One answer, often with no sources at all |
| Where you measure it | Search Console, rank trackers, analytics | Repeated questions per engine; Search Console's Generative AI report; Bing's AI Performance report | Benchmarks in research; the same tools as AEO in practice | Repeated questions with search switched off |
| What a vendor should tell you | Which keywords and pages | Which engines, which questions, which unit, how many repeats | Whether a tactic holds beyond one benchmark | Training data or live search, and how each is measured |
How do AEO, GEO and SEO relate to each other?
They are layers of one job, not rival disciplines. SEO is the foundation, AEO is the layer that starts once an engine has a set of candidate pages, and GEO is a second name for that layer with an academic origin.
SEO gets you into the pool. An engine can only use a page it can fetch, index and retrieve. For Google's AI features, Google says a page must be "indexed and eligible to be shown in Google Search with a snippet" to appear as a supporting link, and that "From Google Search's perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO" (Google Search Central).
AEO decides what happens in the pool. Once the engine has its candidates, it reads a few passages and writes one answer. Which passages it reads, which sources it trusts, how it describes you and whether it recommends you are separate events from your ranking. Our measurements below put numbers on that gap.
GEO and AEO are the same layer. The GEO paper gave the work a precise definition and a benchmark; AEO gave it a longer history and the name most buyers search for. A team selling either will audit crawler access, rewrite pages so a passage answers the buyer's question, work on the third-party sites engines read about you, and report how often you appear.
LLMO adds the memory path. An assistant can answer from its training data without searching, and then it cites nothing and no page edit this week reaches it. What counts there is what the web said about you before the model was trained.
Our measured comparison of the first two layers, AEO versus SEO, measured, goes deeper, and AEO versus GEO versus AI SEO traces where each term came from. Our summary of the GEO paper explains what that paper tested and found.
What do our measurements show about AEO?
Our measurements show that being retrieved, read and cited is a separate contest from ranking, and that the answers move between identical asks. Each finding below is quoted as we published it, with its scope.
In our words: "In our study of 150 non-branded Google searches (US, July 2026), about 57% of the websites cited in AI Overviews did not appear in that same search's top 10 organic results (95% interval 54% to 60%)." The figure describes which sites AI Overviews cited, not the effect of moving up a position.
Rank still helps, which is why SEO stays the foundation: "The website in the first organic position was cited by the AI Overview 56% of the time (95% interval 51% to 61%). Websites in positions 6 to 10 were cited 34% of the time (31% to 37%)." That is 393 answers, an association rather than the value of moving up.
| What we measured | Our finding | Scope |
|---|---|---|
| How crowded an answer is | "A Google AI Overview cites about 6 different websites on average, so a single citation is roughly one sixth of the answer." | Google AI Overviews, 393 answers, 140 searches, 3 captures; an average |
| How stable AI Overview sources are | "When we checked the same 118 searches three times over two days, only 14 of them returned the exact same set of cited websites each time." | Google AI Overviews, US English, non-branded business searches, July 2026 |
| How stable ChatGPT sources are | "Asking ChatGPT the same question three times in a row, the sets of websites it cited overlapped by only about 30% on average, across 100 non-branded questions." | ChatGPT interface through a third-party service; an overlap index over 3,600 pairs, not a share |
| Whether engines use the GEO vocabulary | "When Microsoft's AI cited our pages, 4 of the 73 searches it ran used the GEO paper's own method names, such as 'quotations statistics'." | Bing Webmaster Tools report for our own site, March 8 to September 6, 2026 |
Three practical conclusions follow. A strong Google ranking makes a page more likely to be read, and leaves plenty of room for pages that do not hold the top spot. A citation is usually a share of an answer, not the whole of it. And because the sources change between asks, a single screenshot of an AI answer tells you very little; AEO is measured with repeated questions and read as a range.
What does Google say about AEO and GEO?
Google addresses both terms by name. Its guide to generative AI features says: "'AEO' stands for 'answer engine optimization' and 'GEO' for 'generative engine optimization'. These are both terms you may see used to describe work specifically focused on improving visibility in AI search experiences." It then says that, for Google Search, this work is "still SEO" (Google Search Central, last updated July 10, 2026).
The same guide lists tactics Google Search does not need: llms.txt files and other "special" markup, "chunking" content into small pieces, rewriting content just for AI systems, seeking inauthentic mentions, and overfocusing on structured data. It says "there's no special schema.org markup you need to add", while recommending structured data for rich results.
The popular guides mostly say the opposite. When we read the guide pages Perplexity cited for questions about setting up an AEO program, on October 8, 2026: "Of the 8 guide pages we read, 7 recommended at least one of these tactics as a way into AI answers (7 of 8; 95% interval 53% to 98%), usually schema markup, and none of those 7 mentioned Google's statement." The count covers 8 pages chosen because Perplexity cited them; it is not a survey of the web.
Read Google's statements as statements about Google Search, AI Overviews and AI Mode. They say nothing about how ChatGPT, Perplexity, Claude or Copilot choose sources. Our engine guides cover each one, starting with how AI search engines choose their sources .
Is AEO real, or just SEO with a new name?
AEO is real in the sense that matters to a buyer: it measures something a rank report cannot show. A rank report tells you where your page sits in a list. It cannot tell you whether an AI answer read your page, how it described your company, or whether it put you on a shortlist, and our data shows those outcomes diverge from rank on most searches.
Where the "new name" view is right is the foundation. Crawlable, indexed pages that answer the question plainly serve both, and a vendor who sells AEO without them is selling the top floor of a building with no ground floor. Google's own view, for Google, is that the work is SEO.
The practical answer is to keep your SEO and add the parts it never covered: each engine's search crawler, the description engines give of you, the independent sites they read about you, and measurement that repeats every question.
Do I still need SEO if AI search is taking over?
Yes, you still need SEO, because AEO depends on it: every engine that searches the web retrieves from an index, and pages that cannot be crawled or indexed are invisible to both. AEO does not replace SEO, and SEO is not dead. What has changed is that a ranking is no longer the end of the story: the buyer may read an AI answer that cites your page and never click, or cites a competitor's page that ranks below yours.
So the budget question is a split, not a swap. Keep the SEO work that gets pages fetched, indexed and ranked. Add the AEO work that checks what happens next, engine by engine. Our post on whether AI will replace search engines looks at the longer trend.
What does AEO work involve?
AEO work follows the path an answer takes, from the engine's crawler to the buyer's click. Each stage has its own check, and fixing the wrong stage changes nothing.
| Stage | The question AEO asks | Typical work |
|---|---|---|
| Fetch | Can each engine's search crawler reach your pages? | robots.txt and firewall rules per crawler; Google's Search generative AI setting |
| Retrieve | Is your page among what the engine reads for the buyer's question? | Pages that answer the sub-questions a buyer's question breaks into |
| Trust | Does the engine lean on your site or on others? | Facts about you stated on the independent sites the engine already cites |
| Describe | Does the engine describe what you do today? | One consistent description on your site and on the profiles engines read |
| Recommend | When a buyer asks for options, does the engine pick you? | Plain statements of who you are best for, in the buyer's own category words |
| Click and convert | Does the answer send a visit, and does it convert? | Landing pages for the question the visitor arrived with |
The detail for each stage, with the check and the fix, is in the AI search visibility funnel. Two live guides cover common first tasks: how to do prompt research for building the question list, and what an AI visibility audit covers for a first diagnosis.
How do you measure AEO?
You measure AEO by asking the questions your buyers ask, on each engine separately, several times, and recording four things per answer: whether you were cited, whether you were named, whether the description was right, and whether you were recommended. Each of those can move without the others.
- Write the question list in the buyer's words, and keep non-branded questions (buyers who do not know you yet) apart from branded ones.
- Ask every question at least three times, and repeat on another day before you believe a change.
- Never average engines together. They search different indexes and cite different sources.
- Use the engines' own reports where they exist. Google Search Console's Generative AI performance report counts how often links to your site were shown in AI Overviews and AI Mode; Bing Webmaster Tools reports citations in Microsoft's AI experiences.
- Call a change real only when it beats the run-to-run variation you measured before you changed anything.
Our research measurements work this way. For a first reading of your own company, Lenz, our AI visibility platform, shows how ChatGPT and Gemini answer questions about your category: see how Lenz works.
How do I start with AEO this week?
Start with the questions your buyers ask and the cheapest stage to check, then work down the funnel.
- List 20 to 40 buyer questions: what you do, which category you are in, whether you are credible, how you compare, and "best X for Y" in your category.
- Check each engine's search crawler in your robots.txt: Googlebot, Bingbot, OAI-SearchBot, Claude-SearchBot and PerplexityBot, and ask your CDN whether bot protection blocks any of them.
- Confirm Google's Search generative AI setting in Search Console is on include, which is the default.
- Ask each question three times on two engines, logged out, and record citations, mentions, description and recommendation.
- Find the first stage where you drop out and fix that one before anything else.
- Repeat the same questions in four weeks and compare against the spread you measured, not a single answer.
Frequently asked questions
What does AEO stand for?
In marketing, AEO stands for answer engine optimization. The same letters also stand for Authorized Economic Operator, a customs and trade status, which is why a search for "aeo" alone returns mixed results.
What is the equivalent of SEO for AI?
Answer engine optimization (AEO), also called generative engine optimization (GEO). It builds on SEO and adds the work of getting AI answers to read, cite, describe and recommend you correctly, measured engine by engine.
Is AEO the same as GEO?
In market use, yes: both describe getting a company cited and recommended in AI-written answers. GEO comes from a 2023 research paper with a precise definition; AEO is older and is the term most buyers search for. Choose a provider on method, not on the label.
Is AEO worth it?
It is worth it when your buyers use AI answers to build a shortlist and you can measure where you drop out. Start with a baseline of your buyers' questions; if engines already describe and recommend you correctly, your budget belongs elsewhere.
How long does AEO take to show results?
It depends on the stage. OpenAI says a robots.txt change takes about 24 hours to reach its search results, and Google says a change to the Search generative AI setting generally takes a few days. A fix that depends on other websites rewriting what they say about you can take months.
Does schema markup help AEO?
For Google's AI features, Google says structured data is not required and there is no special markup to add, though it still helps with rich results. Other engines have not documented how they use it. Our schema markup guide covers what still works.
Do I need an llms.txt file for AEO?
Not for Google: Google Search ignores llms.txt, so it "will neither harm nor help" your visibility there. Our llms.txt guide explains what the file is and when to keep one.
What is the difference between strategic AEO and mass AI content?
Strategic AEO starts from the questions your buyers ask, finds the stage where you drop out and fixes that stage, then measures the result. Mass AI content publishes many pages and hopes some get cited. Google's spam policies list "using generative AI tools or other similar tools to generate many pages without adding value for users" as scaled content abuse, so volume alone can work against you.
How do AEO services differ from traditional content agencies?
A content agency is usually judged on what it publishes: posts, pages and traffic. An AEO service is judged on what AI answers do with your company: whether engines read your pages, cite them, describe you correctly and recommend you, engine by engine. Writing can be part of the work, but often the fix is a crawler rule, a profile or one clear page.
Can anyone guarantee that ChatGPT will recommend my company?
No. No one controls what an engine writes. What can be measured is where you drop out, and whether a change moved that stage by more than normal variation.
Where should I go next?
This page is the hub for the definitions and the debate. Each guide below goes deeper on one question.
| Guide | The question it answers |
|---|---|
| Generative engine optimization: what the GEO paper shows | Where the term GEO came from and what its tests found |
| How to do prompt research for AEO | Which buyer questions to track |
| What an AI visibility audit covers | What a first diagnosis should include |
| Schema markup for AEO | Which structured data still matters |
| Google has removed FAQ rich results | What changed for FAQ markup in 2026 |
| AEO versus SEO, measured | Where good SEO does the AEO work, and where it stops |
| AEO versus GEO versus AI SEO | Where each term came from and what to ask a vendor |
| AI search glossary | 55 terms, each in one sentence |
| How to choose an AEO agency | Criteria, questions and red flags for hiring help |
| The AI search visibility funnel | The eight stages where a company drops out |
Work with us
If you want the AEO work done for you, our managed AEO services take your buyers' questions through every stage with your team: Book a call. If you would rather see where you stand first, Lenz gives you a free analysis of how ChatGPT and Gemini answer your buyers' questions: Get your free AI visibility report.
How this page was made
Soroush Mehri wrote this page with the help of an AI writing assistant (Claude, made by Anthropic), working from the sources listed below and from our own measurements. Before writing, we read the pages that rank and are cited in Google's AI Overview for "what is AEO" and "answer engine optimization" on October 13, 2026, and set out to cover what they cover and add the measured evidence they do not have. Findings of ours appear only as we have published them. Mojtaba Ebadi reviewed the page, and Soroush Mehri approved it for publication.
Sources
- Google Search Central, "Optimizing your website for generative AI features on Google Search", last updated July 10, 2026: developers.google.com
- Google Search Central, "AI features and your website": developers.google.com
- Google Search Central, "Google Search's guidance on using third-party SEO tools, services, and advice", last updated June 5, 2026: developers.google.com
- Google Search Central, "Spam policies for Google web search" (scaled content abuse), last updated August 28, 2026, verified October 20, 2026: developers.google.com
- Google Search Console Help, "Generative AI performance report (Search)": support.google.com
- Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan and Deshpande, "GEO: Generative Engine Optimization", KDD 2024: arXiv 2311.09735
- OpenAI, "Overview of OpenAI Crawlers": developers.openai.com
- Elementera, measurements of Google AI Overviews on non-branded US searches (July 2026), of ChatGPT through a third-party service (100 non-branded questions), and Bing Webmaster Tools AI Performance export for elementera.com (March 8 to September 6, 2026).
- Elementera, reading of 8 guide pages cited by Perplexity for AEO program questions, October 8, 2026.
Ex-Microsoft. 10+ years of experience in AI/ML, MSc from Université de Montréal (Mila). US patent holder.
Sr. Engineering Leader with 15+ years of experience. Ex-ApplyBoard. AWS Certified Solutions Architect.
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