Answer Engine Optimization (AEO) is the practice of structuring a brand's content, technical access and off-site presence so that AI answer engines — ChatGPT, Perplexity, Google AI Overviews and AI Mode, Gemini and Copilot — select it as a source and present it as the answer to a user's question. Where SEO competes for a position in a list of links, AEO competes to be the single response, or one of the few sources cited beneath it. It is measured in mentions, citations and share of voice across engines, not in keyword rankings.
This page covers the full discipline: the definition, how AEO differs from SEO and GEO, how answer engines pick their sources, a seven-step method, what changes per engine, the tools and metrics, and the mistakes that cost the most time. For the short course, start with AEO 101.
An answer engine is any system that takes a question in plain language, gathers relevant material, and returns one written answer instead of a page of results. ChatGPT with search enabled, Perplexity, the AI Overview at the top of a Google results page, Google's AI Mode, the Gemini app and Microsoft Copilot all work this way. The user reads the answer, perhaps glances at the sources cited under it, and moves on. There is no page two, and often no click at all.
That changes what “optimization” means. In search, ten results share the page and the user chooses between them. In an answer engine, the model has already chosen: it fetched or recalled a handful of sources, decided which passages answered the question, and wrote a response that names some brands and leaves out the rest. AEO is the work of being in the first group. It breaks into three parts, best fixed in this order:
Can the engine reach your pages? The right crawlers allowed in robots.txt, content present in the server-rendered HTML, fast responses, and no login walls on the pages you want quoted.
Can a model lift a clean, self-contained claim from the page and attribute it to you? Answer-first sections, plain definitions, tables and specific facts make that easy; long preambles make it hard.
Does the rest of the web agree with you? Engines weight consensus. If review sites, communities and publications describe you differently from your own site, the model sides with them.
AEO is not a trick for getting a model to say your name. Keyword stuffing, paragraphs addressed to “AI assistants” and hundreds of thin question pages do not produce citations, because retrieval ranks passages on how well they answer a query and models cross-check claims against other sources.
Three terms, two real distinctions. SEO and AEO differ in what they compete for. AEO and GEO differ mostly in emphasis.
| SEO | AEO | GEO | |
|---|---|---|---|
| Competes for | A position in a ranked list of links | Being the answer, or one of the few sources under it | Being a cited source inside a generated answer |
| Where the user ends up | On your page, after a click | Reading the answer; a click is optional | Reading the answer; sometimes following a citation |
| Primary signals | Relevance, links, technical health, intent match | Crawler access, extractable passages, entity clarity, corroboration | The same set, with extra weight on citations and freshness |
| Unit of work | The keyword and the page | The prompt and the passage | The prompt and the source |
| Success metric | Rankings, organic clicks | Mention rate, share of voice, position in answer | Citation rate, referral traffic from AI domains |
Answer engines retrieve candidate pages from a search index — Google's own for AI Overviews and AI Mode, Bing's for Copilot, and their own crawled indexes for ChatGPT search and Perplexity. A page that is not indexed cannot be retrieved, and a slow, duplicated or ambiguous page loses to one that is not. AEO is a layer on that foundation, not a replacement for it.
AEO names the outcome: the engine presents your claim as the answer. GEO names the mechanism: generative engines write answers and cite sources. A brand can be cited in a Perplexity answer without being the recommendation, and can be the recommendation in a ChatGPT reply with no citation at all. Most of the time they coincide, which is why this page and the GEO guide read as two halves of one method. Google AI Overviews are the clearest overlap: a generated, cited answer that is also the answer on the page.
An engine knows something about you either because it retrieved a page while answering or because it absorbed a description of you during training. Retrieved answers can move as soon as the engine re-crawls a page; answers drawn from training data only move when the model is updated. Most consumer answer engines now retrieve by default for anything factual, current or commercial.
The engine turns the question into one or more search queries. Google AI Mode calls this fan-out: one prompt becomes several sub-queries, each retrieved separately.
Each query runs against the engine's index. Pages that are not indexed, or are outranked for that sub-query, never enter the candidate set.
Candidate pages are fetched by an agent such as OAI-SearchBot or PerplexityBot and reduced to text. Content that only appears after JavaScript runs is frequently missed.
Pages are split into passages and scored on how directly and specifically each one answers the sub-query. A paragraph that opens with the answer beats one that reaches it after three lines of framing.
The model writes the answer from the top passages and attaches citations to the claims it used. Claims several sources agree on are stated confidently; claims only your site makes are softened or dropped.
AI user agents fall into two groups with different consequences.
Fetch pages to build a search index or ground a live answer. Blocking these removes you from the answers.
OAI-SearchBot — ChatGPT search indexChatGPT-User — fetches when a user asks ChatGPT to read a pagePerplexityBot — Perplexity indexPerplexity-User — user-initiated fetches inside PerplexityClaudeBot — Anthropic's agentGooglebot / Bingbot — feed AI Overviews, AI Mode and CopilotGovern whether content is used to train future models. Blocking these does not affect whether you are cited today.
GPTBot — OpenAI trainingCCBot — Common Crawl, used by many training setsGoogle-Extended — Gemini training; does not affect AI OverviewsApplebot-Extended — Apple model trainingDecide on the two groups separately. A brand that wants to appear in answers allows the first. Whether to allow the second is a content-policy question, not an AEO one. Check your current rules with the robots.txt AI checker.
The order matters. Access problems are cheap to find and invalidate everything downstream, so they come first. Measurement comes second so that every later change can be attributed. Content and structure follow, then off-site work, and the cycle repeats.
Check robots.txt for every AI agent that matters, confirm pages return their full content as server-rendered HTML, and remove interstitials or login walls from the pages you want cited. A crawler that receives an empty shell cannot cite you.
Tool: robots.txt checker. View a page with JavaScript disabled to see what a crawler sees.
Write down thirty to fifty prompts your buyers actually type, run them across ChatGPT, Perplexity, Google AI Overviews and AI Mode, Gemini and Copilot, and record mention, position, sentiment and the sources cited instead of you.
Tools: Brand Visibility Tracker for the baseline; Citation Intelligence for the sources winning instead of you.
Each prompt worth winning should have exactly one page that answers it directly in the first hundred words. Consolidate duplicates and retire thin pages that compete with the canonical one.
Lead each section with a self-contained answer, keep one idea per heading, define terms plainly, use comparison tables for "vs" questions, and make claims specific with names, numbers and dates that can be verified.
Publish JSON-LD for Organization, Article, FAQPage, HowTo and Product where they apply, keep entity data consistent across the site, and add an llms.txt that points AI crawlers at the pages worth quoting.
Tools: JSON-LD generator, JSON-LD validator, llms.txt generator, llms.txt validator.
Get the same facts onto the review sites, communities, directories and publications each engine already cites in your category, through earned coverage rather than paid placement. Models weight consensus.
Earned only. Paid links and directory spam do not build corroboration and can cost you in search. See get cited by AI.
Re-run the prompt set on a schedule, attribute changes to the work that caused them, and connect mention and citation movement to AI referral traffic and pipeline. Fix what the data names, engine by engine.
Tools: AI Search Monitoring for the schedule; AI Traffic Decoder for attribution.
The first pass is a few weeks of work for a small team; the off-site step and the measurement loop are ongoing. See how SEO and AEO teams structure it.
The method holds everywhere. What changes per engine is where candidates come from, how citations are shown, and how much weight freshness and community sources carry.
With search enabled, ChatGPT retrieves live pages through its own index, built by OAI-SearchBot, and shows citations inline and in a sources panel. ChatGPT-User fetches a page on demand when someone pastes a URL. Neither is affected by a GPTBot block. Shopping prompts draw heavily on product pages with clear specifications and on third-party reviews, so keep product data consistent across your site and the retailers that carry you.
Perplexity is retrieval-first for almost every query and always shows numbered sources. It favours recent, well-structured pages and pulls readily from Reddit, forums and review sites, so community corroboration matters more here than anywhere else. PerplexityBot builds the index; Perplexity-User handles fetches a user triggers directly. Because it re-crawls actively, it is usually the first engine to reflect a content change.
Both are built on Google's standard index and crawled by Googlebot, so the technical work is ordinary SEO. Google-Extended is a training control for Gemini and has no effect on whether you appear in an AI Overview. Ranking near the top helps but is not required: AI Mode fans a question out into sub-queries and retrieves for each, so a page that answers a narrow sub-question precisely can be cited without ranking for the head term. Structured data is still parsed.
The Gemini app grounds factual answers with Google Search, so the same index and crawler apply. Entity clarity carries extra weight: a brand that Google's Knowledge Graph recognises unambiguously — consistent name, sameAs links to official profiles, Organization schema — is easier for Gemini to describe correctly and harder to confuse with a similarly named company.
Copilot retrieves from Bing's index via Bingbot and shows citations under its answers. Bing Webmaster Tools and IndexNow give direct control over indexing and freshness, which is unusual among answer engines and worth using. Bing has long weighted clear page structure and entity data, so the schema work above pays off directly.
Platform-specific setup — Shopify, WordPress, Webflow, Framer and the rest — is covered in AEO by platform.
Diagnostic tools check access and structure once; a monitoring platform tracks what the engines actually say, on a schedule. The diagnostics below are free and need no account.
Which AI agents your robots.txt allows and blocks, split by retrieval and training.
Build an llms.txt that points AI crawlers at the pages worth quoting.
Check an existing llms.txt for structure and reachable links.
Produce Organization, Article, FAQPage, HowTo and Product schema.
Confirm the schema on a page parses and carries the fields engines read.
Mention rate, position and sentiment across every major engine, over time.
The exact sources each engine cites in your category.
Scheduled prompt runs with alerts when a mention appears, disappears or changes tone.
Separate AI referral traffic from the rest and connect it to conversions.
All four are part of one platform; see pricing or the solutions hub for how teams combine them.
Keyword rankings do not describe what an answer engine does, so AEO needs its own measures. All of them start from the same input: a fixed set of prompts, run on a fixed schedule, across the engines your buyers use. Change the prompt set and the numbers stop being comparable.
The share of prompt runs in which your brand is named at all. Reported per engine, because the engines disagree.
Your mentions as a proportion of all brand mentions across the prompt set, against named competitors.
How often a page on your domain is cited as a source, whether or not you are named in the answer text. The purest measure of extractability.
What the engine says about you, and whether it is true. Stale pricing and retired products show up here first.
The share of the prompt set where you appear at all. Low coverage with high share of voice means you win a narrow set of questions.
Sessions from chatgpt.com, perplexity.ai, copilot.microsoft.com, gemini.google.com and similar, and the pipeline they touch. Under-reported by default analytics; separate it deliberately.
Report the first five per engine and the last alongside your organic channel. Worked examples are in the case studies.
A blanket disallow written for training crawlers also removes OAI-SearchBot, PerplexityBot and the rest. Split the rules and decide on each group.
If the answer is assembled by JavaScript after load, the fetch agent may receive a shell. Server-render the pages you want quoted.
A section that opens with history or a rhetorical question loses the passage ranking to one that opens with the answer. Lead with the claim, then support it.
Access, entity data and off-site corroboration decide as much as the copy does. Rewriting pages a crawler cannot reach changes nothing.
A page can rank first and never be cited, or be cited without ranking. Track mentions and citations directly, per engine.
Stale prices and outdated positioning repeat until the sources change. Correct the sources — yours and the third-party ones — rather than waiting for the model to update.
AEO stands for Answer Engine Optimization: making a brand's content, technical access and off-site presence easy for AI answer engines such as ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot to retrieve, trust and present as the answer to a question.
An answer engine replies to a question with one synthesised answer and a few cited sources instead of a list of links. AEO is the work of becoming one of those sources: making sure the engine can fetch your pages, can lift a clear, self-contained answer from them, and finds the same facts corroborated elsewhere on the web.
SEO competes for a position in a ranked list of links the user chooses from. AEO competes to be the answer itself, or one of the few sources cited under it. SEO is measured in rankings and clicks; AEO in mentions, citations and share of voice. They overlap because most answer engines retrieve from a web index, so a page that cannot be indexed cannot be cited.
AEO leans toward being the answer an engine returns. GEO (Generative Engine Optimization) leans toward being a cited source inside a generated answer. The tactics are almost identical — crawler access, extractable content, structured data, off-site corroboration — and most teams run them as one programme.
They describe the same discipline from different angles. "LLM SEO" and "AI SEO" are umbrella labels for optimising for large language models. AEO names the goal — being the answer — and GEO names the mechanism — generative engines that cite. The playbook is the same.
Yes. ChatGPT search, Perplexity, Copilot and Google AI Overviews all retrieve candidate pages from a search index before a model writes the answer. Indexability, crawlability, speed and clear structure remain prerequisites; AEO adds quotable passages, entity data and third-party corroboration on top.
The engine rewrites the question into search queries, fetches candidate pages from its index, splits them into passages, ranks the passages on how directly they answer the query, and writes an answer that attributes claims to the passages it used. Pages win when they are fetchable, answer-first, specific and consistent with other sources.
Separate training from retrieval first. GPTBot, CCBot, Google-Extended and Applebot-Extended govern training data; blocking them does not remove you from answers. OAI-SearchBot, ChatGPT-User, PerplexityBot, Perplexity-User and ClaudeBot fetch pages to build or ground answers; blocking those removes you from the answers those engines give.
Retrieval-based engines such as Perplexity and ChatGPT search can reflect a page change as soon as they re-crawl it, often within days. Answers drawn from a model's training data lag until the next model release. Track each engine separately rather than expecting one timeline.
Run a fixed set of the prompts your buyers actually ask across each engine on a schedule, and record whether you are mentioned, with what sentiment, and which sources are cited instead of you. Report mention rate, share of voice, citation rate and prompt coverage, then connect them to AI referral traffic.
Two kinds: diagnostic tools that check access and structure — a robots.txt checker for AI crawlers, an llms.txt generator and validator, a JSON-LD generator and validator — and a monitoring platform that tracks mentions, citations and share of voice across engines over time. Asva offers the first set free and the second as a product.
Baseline your mention rate and share of voice across ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot, and see which sources are cited in your category instead of you.