For twenty years the goal was a blue link near the top of a page of ten. The buyer scanned, clicked, and landed on your site. AEO breaks that loop. When someone asks ChatGPT or Perplexity for the best tool in your category, the model returns a short answer naming two or three brands and a handful of sources. There is no page of ten. There is a paragraph, and you are either in it or you are not.
Answer Engine Optimisation (AEO) is the work of making sure your brand appears, accurately and favourably, inside the answers that AI engines write. That covers what the answer says about you, whether you are named at all, which of your pages get cited as sources, and how you compare to the competitors named beside you. The same discipline goes by GEO (Generative Engine Optimisation) and sometimes LLMO; the names differ, the job is identical.
The unit of traffic changed
In search, the unit was a click. In answer engines, the unit is a mention. A model can recommend you, summarise you, and send a buyer to a decision without ever sending a click you can see in analytics. That is the part most teams miss: you can lose the sale and never see the loss, because the visit never happened.
This is why AEO reporting looks different from SEO reporting. There is no position 4 to screenshot. The honest metrics are: for a fixed set of buyer prompts, how often is the brand named, what does the answer actually say, which sources does the engine cite, and what share of those answers do competitors own. Track those over time and you have a real picture; track anything else and you have a feeling.
Why SEO habits transfer only halfway
Some of it carries over. Clear pages, real answers, and structured data still help, because models read the same web crawlers index. But three things are new.
- Ranking is per-answer, not per-keyword. The same brand can be named for one prompt and invisible for the next.
- Sources matter as much as your own site. Models lean on third-party pages, so being cited elsewhere is leverage you do not get from on-page SEO alone.
- There is no rank tracker for a sentence. You have to actually run the prompts and read what comes back.
If your client is not in the shortlist, they are not losing rank. They are absent from the conversation entirely.
How answer engines choose the brands they name
Two mechanisms feed every answer. The first is the model's training: a compressed memory of the web as it existed months ago. If your brand was widely and consistently described back then, the model already "knows" you. The second is live retrieval: for fresh or comparative questions, the engine searches the web while the user waits, reads a handful of pages, and writes its answer from those. ChatGPT with browsing and Perplexity both work this way, and they show their sources.
You cannot edit the training data, so the leverage sits in retrieval. The engine tends to trust the same shortlist of page types: comparison roundups, review platforms, community threads where practitioners speak plainly, and documentation that answers a question directly. If those pages agree that you belong in the category, the answer usually says so too. If they are silent about you, the model has nothing to repeat.
How AEO and SEO fit together
AEO is not a replacement for SEO, and treating it as a rival budget line is a mistake. Retrieval engines discover pages through the same crawling and the same signals of authority that Google uses, so a site that ranks tends to get read. The difference is what happens next: SEO wins when the page earns the click, AEO wins when the page earns the quote. In practice the strongest programmes run both from one content plan — every important page written to rank AND to be liftable, sentence by sentence, into an answer.
What AEO work looks like week to week
- Run a fixed prompt set against the engines your buyers use, on a schedule, and log who gets named and cited.
- Rewrite the pages that should be winning answers so the conclusion comes first and can be quoted standalone.
- Ship structured data (FAQPage, Product, Organization) on the pages the prompts point at.
- Earn mentions on the third-party pages the engines keep citing — roundups, reviews, community threads.
- Re-run the prompts, compare against the baseline, and report the delta in mentions and share of voice.
Where to start
Pick the ten questions a buyer asks before they choose. Run them through the engines your buyers actually use. Log who gets named, who gets cited, and where you are missing. That baseline is the whole job. Everything after it is closing the gaps you found.
Concretely: if you sell project management software to agencies, the list starts with prompts like "best project management tool for agencies", "Asana alternatives for client work", "how do agencies track client projects", and "is Monday.com good for agencies". Ten of those, run against ChatGPT, Perplexity and Gemini, read honestly, will tell you more about your AI visibility than any dashboard you could buy — and it costs one afternoon.
What's the best way to improve my visibility in AI search engines?
Answer the question first, then check whether it worked. Those are the two halves, and most advice only covers the first. Pick the questions your buyers actually ask, write the answer plainly on a page a crawler can read without running JavaScript, and put the conclusion in the opening sentence instead of the last paragraph.
The half that gets skipped is the check. You cannot tell from your own page whether an engine changed its mind about you — that only shows up in the answers, and they drift from week to week. Re-run the same prompts on a schedule, watch the median rather than any single result, and treat a rival appearing in your place as the specific gap to close. That loop is what AnswerPeek automates across ChatGPT, Perplexity, Gemini and AI Overviews. The loop matters more than the tool, though: a spreadsheet and a monthly reminder still beat no measurement at all.
