Search used to end with a list. You typed a question, you got ten links, and your job was to be one of them. That contract is quietly being rewritten. A growing share of questions now end with a written answer that names two or three brands and cites a handful of sources, and most people never scroll past it.
Answer Engine Optimization is the work of making sure your brand is one of the ones named, and your pages are among the ones cited.
What AEO actually means
AEO covers everything you do to influence how AI answer engines describe, recommend and cite your brand. The engines in scope are the ones your buyers actually use: ChatGPT, Google AI Overviews, Gemini, Claude, Perplexity, Copilot, Meta AI, Mistral, and in Chinese speaking markets DeepSeek, Qwen and Kimi.
You will also see the term GEO, generative engine optimization. In practice the two are used interchangeably. AEO emphasises the answer, GEO emphasises the generative model producing it. Pick one and be consistent internally.
A useful test: if a buyer asked an AI to recommend a tool in your category today, would your brand appear, and would the answer describe you the way you would describe yourself? AEO is the discipline of turning both answers into yes.
How AEO differs from SEO
Search results
Ten links, and the reader decides who to trust
AI answer
One answer, two brands named, and the decision is mostly made
The two share a lot of plumbing, and most teams should run them together. But four differences change how you work.
- The unit of success. SEO wins a position. AEO wins a sentence, and there is usually only room for two or three brands in it.
- The query shape. People type keywords into search boxes and full sentences into AI. The phrasing you need to cover is longer, messier and more conversational.
- The source set. An answer engine may cite a forum thread, a review site or a competitor's comparison page rather than your homepage. Your visibility depends on pages you do not own.
- The feedback loop. There is no rank tracker that shows this by default, and no referral header for a mention that never became a click.
How answer engines pick sources
No engine publishes its exact selection logic, and anyone claiming otherwise is guessing. What is observable, by watching thousands of answers, is that a few properties keep showing up in the pages that get cited.
- They can be fetched. The crawler is not blocked, the content is not locked behind script rendering, and the page returns quickly.
- They can be quoted. A clear claim sits in a clear sentence under a clear heading, rather than buried in a paragraph of positioning language.
- They are corroborated. The same fact appears somewhere the engine already trusts, which is why earned mentions and community discussion matter more here than in classic SEO.
- They are specific. Concrete numbers, named limits and plain comparisons survive summarisation. Adjectives do not.
This is the uncomfortable part: a meaningful share of what AI says about you is assembled from pages you did not write. You can influence that, but you cannot edit it directly.
What actually moves the needle
Most of the practical wins fall into three buckets, roughly in order of effort.
- Remove the blockers. Check that AI crawlers can reach the pages you care about, that key claims are in text rather than images, and that structure is machine readable. This is cheap and often the reason a good page is invisible.
- Answer the question directly. For each prompt that matters, make sure one page answers it in the first hundred words, with the specifics an answer would need to quote.
- Earn corroboration. Get the same claim represented where the engines already look, through documentation, comparisons, reviews and genuine community presence. This is slow, and it is the part competitors cannot copy quickly.
How to start, in five steps
- Write down the questions. List the twenty to forty prompts a buyer would actually type before choosing in your category. Use their words, not your product names.
- Get a baseline. Run those prompts across the engines your market uses and record what comes back, including which brands are named and which URLs are cited.
- Find the gaps. Separate prompts where you are absent from prompts where you appear but are described wrongly. These need different fixes.
- Fix in priority order. Start with blocked or unquotable pages, then the prompts closest to a purchase decision.
- Re-measure on a schedule. Answers drift as models update, so a single audit ages badly. A weekly or daily baseline is what makes the work reportable.
Five mistakes worth avoiding
- Optimising for keywords instead of questions. Nobody types a three word keyword into an AI assistant.
- Publishing thin pages at speed. Answer engines summarise, so volume without substance gives them nothing to quote.
- Ignoring pages you do not own. If a review site or forum thread is shaping your answer, that is where the work is.
- Checking manually and calling it data. Answers vary by session, phrasing and region, so one screenshot proves very little.
- Treating it as a one off project. This is a monitoring discipline, closer to uptime than to a redesign.
How to measure it
If you cannot put a number on it, it will lose budget to channels that can. Four metrics cover most of what a team needs.
- Presence, how often you appear at all across tracked prompts.
- Rank inside the answer, because being mentioned last is not the same as being recommended.
- Citation share, how many of the cited links point to a domain you own.
- Share of voice, the same measures for your competitors, so the number has a context.
Okaeo exists to make those four measurable without manual checking, but the framework holds whatever you use to collect it.