What Is Answer Engine Optimization (AEO)?

Answer engine optimization (AEO) is the work of making your brand, product or page the thing an AI system uses when it answers a question. Instead of competing for a blue link, you aim to be named, quoted or linked inside the answer that ChatGPT, Perplexity, Gemini, Copilot or Google's AI Overviews writes. AEO builds on SEO: the same crawlability and clear pages matter. But you measure it in mentions, citations and recommendations, not rankings, and what other sites say about you counts as much as your own pages.

On this page

• Where the term came from
• How an answer engine decides what to say
• AEO vs SEO at a glance
• From MentionX data: why AEO needs its own measurement
• The five levers of AEO
• What good AEO looks like on a page
• Worked example: Ledgerline's first month
• How AEO is measured
• Where to start this week
• Who should own AEO
• What AEO is not
• Sources

Updated 31 October 2026. Anything marked "Our view" is MentionX's opinion, not documented engine behaviour. The worked example uses a fictional company.

Where the term came from

The phrase predates ChatGPT. Marketers used it for featured snippets and voice assistants, where Google or a smart speaker read out one answer instead of ten links. The idea was the same: structure content so a machine can lift a clean answer from it. Google still documents featured snippets as short excerpts it shows when its systems decide that format helps users (Google Search Central).

Large language models raised the stakes. A featured snippet quoted one page. A generative answer can summarise five sources, recommend two brands and leave everyone else out. AEO today means being one of those sources and one of those brands.

You'll also hear it called GEO (generative engine optimization), a term popularised by a 2023 research paper that tested ways to make content more visible in generated answers (Aggarwal et al.), or LLM SEO. The names overlap heavily. We compare them in AEO vs SEO vs GEO.

How an answer engine decides what to say

There are two routes, and you need to plan for both.

1. From memory. The model answers from what it learned in training. You influence this slowly, by being widely and consistently described across the web before future training runs.
2. From retrieval. The engine runs searches, reads a handful of pages and writes an answer grounded in them, often with links. OpenAI says ChatGPT search typically rewrites a question into one or more targeted queries and may follow up with more specific ones (OpenAI Help). Google says a page must be indexed and eligible to show with a snippet to appear as a supporting link in AI Overviews or AI Mode, with no additional technical requirements (Google Search Central).

Retrieval is where most teams get early wins, because you can change the pages that get read. The research behind retrieval-augmented generation describes the same split: a model's built-in "parametric" knowledge plus documents fetched at answer time (Lewis et al.). The training data vs live retrieval page goes deeper.

AEO vs SEO at a glance

Our view: treat AEO as an extension of SEO, not a replacement. Crawlable, indexable, helpful pages are the entry ticket for both. AEO adds two things SEO teams often under-weight: the off-site sources engines retrieve, and measuring answers rather than positions.

From MentionX data: why AEO needs its own measurement

Method. We ran read-only aggregate queries over completed MentionX runs from 22 August to 29 October 2026 (UTC): 2,154 runs across 18 projects, mostly B2B software. Engines were called through their APIs with web search or grounding on, which can differ from the consumer apps. Brands are anonymised. Perplexity, Copilot and DeepSeek rows come from a single day (26 October).

Engines disagree. The share of runs that mentioned the tracked brand:

The question matters more than the engine. Mention rates were 100% on brand-specific prompts and 92% on comparisons, but 29% on recommendation prompts, 27% on best-of prompts and 6% on problem prompts. The prompts that show whether new buyers find you are the ones where brands were least visible.

Being cited isn't the same as being named. 39.2% of runs had no citations at all. In 25.8% of all runs the tracked brand was mentioned with no citation about it, from its own site or anyone else's. Citation tracking alone would miss those mentions.

How to read it. A brand that looks fine when you ask "what is [brand]?" in one engine can be nearly invisible on the questions that drive discovery. That's why AEO needs a fixed prompt set, several engines and repeated runs.

Caveats. 18 projects, uneven engine coverage, API rather than app answers. Your rates will differ. The pattern, not the exact numbers, is the point.

The five levers of AEO

1. Be retrievable. Allow the search crawlers answer engines use, such as OAI-SearchBot for ChatGPT search (OpenAI) and PerplexityBot (Perplexity). Blocking GPTBot (training) doesn't remove you from ChatGPT search, but blocking OAI-SearchBot can. Make sure key pages render without JavaScript and aren't blocked by a CDN rule.
2. Be extractable. Write answer-first. Use question headings, tables and numbered steps. Engines often lift passages, so each section should make sense on its own.
3. Be cited elsewhere. Engines lean on review sites, forums, comparison articles and news. If the pages that answer "best X" never mention you, the AI answer usually won't either.
4. Be consistent. Use the same name, one-line description and category everywhere, from your homepage to your review-site profiles. Mixed signals produce vague or wrong answers.
5. Be measured. Track a fixed set of prompts over time. Answers vary between runs, so look at rates across many prompts, not one screenshot.

What good AEO looks like on a page

Take a question buyers really ask, such as "what's the best AP automation tool for a 300-person company?" A page that tends to get used in answers:

• Opens with a direct answer in the first two sentences.
• Uses headings phrased as the follow-up questions a buyer would ask next.
• Puts comparisons in a table with clear criteria.
• States facts (prices, limits, integrations) with a date.
• Names who the product is for, and who it isn't for.
• Is reachable by crawlers and not hidden behind scripts or logins.

A page that rarely gets used buries the answer under 400 words of scene-setting, hides pricing behind "contact us" and says "industry-leading" without proof.

Google's guidance on helpful, people-first content applies to the pages its AI features read. Google also says the same SEO best practices apply to AI features as to Search overall (Google). There is no separate trick.

Worked example: Ledgerline's first month

Illustrative example. Ledgerline is a fictional AP automation company, and the numbers are invented.

Ledgerline's marketing lead collects 20 buyer questions from sales-call notes and review sites. They include "best AP automation for NetSuite", "how to stop duplicate invoice payments" and "Ledgerline vs CompetitorA". She runs each in ChatGPT, Perplexity and Google, twice.

The baseline: Ledgerline is mentioned in 14 of 120 answers. Twelve of those are the branded and comparison prompts. On the eight discovery prompts, it appears twice, both times low in a list with no reason given.

She logs every cited source. Three domains appear again and again: a large review site, an industry newsletter's "best AP tools" roundup, and a competitor's integration guide. Ledgerline's own pages appear once.

The logs show OAI-SearchBot fetching the homepage but getting 403s on the pricing page, because of a bot rule added last year. The integrations page names NetSuite only in a logo image.

Month one actions:

1. Remove the bot rule for verified search crawlers, and confirm 200s in the logs.
2. Rewrite the integrations and pricing pages answer-first, with dated facts in HTML text.
3. Update the review-site profile to use the same one-line positioning as the homepage.
4. Email the newsletter author a factual correction about Ledgerline's NetSuite support.

On day 30 she re-runs the same 20 prompts with the same settings. The point isn't a particular number. The point is that each action ties to something she saw in the baseline: a blocked page, a missing fact, a source that kept being cited.

How AEO is measured

Three outcomes matter, from weakest to strongest:

Turn these into rates. Of 50 answers, in how many were you mentioned, cited or recommended? Compare against two or three named competitors. The mention rate, recommendation rate and AI share of voice entries cover the formulas.

Report by engine and by intent, with sample sizes. Keep branded prompts separate, or they'll inflate your average.

Where to start this week

1. Write down 20 questions buyers ask before they buy. Take them from sales calls, support tickets and review sites, not keyword tools.
2. Run each in ChatGPT, Perplexity and Google (look for an AI Overview), at least twice. Log whether you're mentioned, cited or recommended, and which sources appear.
3. List the third-party pages that show up again and again. Those are your off-site targets.
4. Check crawler access in your logs for your most important pages.
5. Pick the three pages closest to those questions and rewrite their openings to answer first.
6. Re-run the same prompts in 30 days.

The GEO 101 course walks through each step.

Who should own AEO

AEO touches several teams. Web engineering controls crawler access. Content owns the pages. Product marketing owns positioning. PR and partner teams influence third-party sources. Analytics owns measurement.

Our view: one person should own the prompt set and the monthly report, usually in SEO or content. Without a single owner, fixes get scattered and nobody can say whether anything worked.

What AEO is not

AEO isn't stuffing pages with questions, buying fake reviews or publishing hundreds of near-identical pages. Google's spam policies cover scaled content abuse and other manipulative practices (Google). Thin pages rarely get cited, and when they are, they make your brand look worse.

It also isn't a guarantee. OpenAI says placement in ChatGPT search results isn't guaranteed (OpenAI Help). You're improving your odds across many prompts, not booking a slot.

Sources

• Google Search Central, AI features and your website
• Google Search Central, Featured snippets
• Google Search Central, Creating helpful, reliable, people-first content
• Google Search Central, Spam policies
• Google, Verifying Googlebot and other Google crawlers
• OpenAI, Overview of OpenAI crawlers
• OpenAI Help Center, ChatGPT search
• Perplexity, Perplexity crawlers
• Aggarwal et al., GEO: Generative Engine Optimization (arXiv, 2023)
• Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (arXiv, 2020)
• MentionX aggregate scan data, 22 August to 29 October 2026 (UTC), 2,154 engine runs, 18 projects. Anonymised and read-only.