AEO · GEO · AI Visibility
Answer Engine Optimisation: Getting Your Brand Into the Answer
Answer engine optimisation (AEO) — also called generative engine optimisation, GEO, or simply AI SEO — is the work of getting a brand named and cited inside the answers AI assistants give. This guide covers what actually moves those answers, what does not, and how to measure any of it.
Why this became a channel
For twenty-five years the deal was simple: rank in a list, earn a click, measure the click. Assistants broke that arrangement. A buyer asks a question, gets a composed answer naming three or four companies, and often never visits a search results page at all. If your brand is not in the answer, you were not considered — and nothing in your analytics will tell you it happened.
Most companies find the channel by accident. A customer mentions offhand that ChatGPT suggested them, and it turns out a referral stream has been running unmanaged for months. The uncomfortable version of that story is the one where a competitor is being named instead and you never find out.
AEO, GEO, AI SEO — the naming
These terms are used interchangeably by almost everyone doing the work. Answer engine optimisation emphasises the output, generative engine optimisation emphasises the system producing it, and AI SEO is what most buyers actually type into a search box. Some practitioners draw a line between direct answers and generative ones, but the levers are the same, so the distinction is mostly branding. Do not let a pitch turn it into a differentiator.
How it differs from SEO
| Traditional SEO | Answer engine optimisation | |
|---|---|---|
| What you win | A position in a list of links | A mention inside the answer |
| Runner-up value | Position 10 still earns clicks | Not being named earns nothing |
| Main levers | Your pages, links, technical health | Third-party sources the model retrieves and cites |
| Measurement | Rank tracking, impressions, clicks | Prompt testing; share of mentions |
| Attribution | Referrer data in analytics | Largely invisible — you have to ask buyers |
The overlap is real, though. A page that is well structured, genuinely useful and easy to parse tends to do well in both worlds. Google's own documentation on AI features in Search describes how AI Overviews select and link sources, and its helpful content guidance still describes the substance assistants tend to quote.
What actually influences an answer
You cannot edit the model. You can change what it finds. In rough order of leverage:
- The sources already being cited in your category. Pull the citations behind the answers assistants give today and you have a target list — usually review sites, industry directories, comparison posts and a handful of publications. Everything else is guesswork until you have this.
- Third-party comparisons and roundups. "Best X for Y" pages are quoted constantly. Being absent from the ones that rank in your category is the single most common reason a brand never gets named.
- Directory and profile entries. Structured, current, consistent listings are cheap to fix and disproportionately retrievable.
- Original data worth quoting. Benchmarks, surveys, pricing studies. Assistants cite numbers; they rarely cite adjectives.
- Entity consistency. If your own site, your listings and your press coverage disagree about what you do or where you are, the model has no confident fact to state.
- Your own marketing pages. Last, and further down than most brands expect.
How to measure it
Rank tracking does not apply here. The working method is a prompt set:
- Write the ten to twenty questions a buyer would realistically ask before choosing a provider in your category. Real phrasing, not keyword phrasing.
- Run each across ChatGPT, Claude, Gemini and Perplexity, plus Google's AI Overviews.
- Record four things: were you named, in what position, how were you described, and which sources were cited.
- Re-run monthly. Answers drift on every model update, so a single snapshot tells you almost nothing.
The citation column is the one that pays. It converts a vague ambition into a list of specific pages to go and influence.
What does not work
- Keyword stuffing for robots. Assistants summarise meaning, not phrase density.
- Thin "AI-optimised" pages published at volume. More pages is not the lever; being cited by better sources is.
- Prompt injection tricks hidden in page text. Fragile, ineffective at scale, and a reputational risk if noticed.
- Anyone guaranteeing a placement. Not possible. Treat it as a disqualifier.
Doing it yourself, or hiring
The baseline prompt test is genuinely a DIY job — an afternoon, no budget. What is hard to do internally is the sustained part: earning placements in the sources that matter, producing citable data on a schedule, and keeping a monthly measurement habit alive once the novelty wears off. That is the point at which most teams bring in help.
Frequently asked questions
What is answer engine optimisation (AEO)?
AEO is the practice of getting a brand named and cited inside the answers AI assistants give, rather than ranked in a list of blue links. When someone asks ChatGPT, Claude, Gemini or Google's AI Overviews a question in your category, the assistant composes an answer and names a few sources. AEO is the work of becoming one of them.
Is AEO different from GEO?
Mostly not. Generative engine optimisation (GEO) and answer engine optimisation (AEO) describe the same work and are used interchangeably by most practitioners. Some people reserve GEO for generative assistants and AEO for featured-snippet-style direct answers, but the levers are identical, so the distinction rarely matters in practice.
Does AEO replace SEO?
No, and anyone selling it that way is overreaching. Classic SEO still drives the clicks you can measure, and much of what makes a page rank also makes it retrievable by an assistant. AEO is an additional layer aimed at a channel where there is no list to rank in and usually no click to attribute.
How do I know if AI assistants mention my brand?
Ask them. Write down the ten questions a buyer would realistically type before choosing a provider in your category, then run each one across ChatGPT, Claude, Gemini and Perplexity and record who gets named and which sources are cited. That prompt set is your baseline. Re-run it monthly, because answers drift whenever a model updates.
What actually influences an AI answer?
The sources the model can retrieve, plus whatever it absorbed in training. In practice that means independent coverage, comparison and roundup pages, directory and profile entries, structured data, and original figures worth quoting. Your own marketing pages matter far less than most brands expect, which is why so much of the work happens off your site.
Can results be guaranteed?
No. Nobody can guarantee a placement inside a specific model's output, and a guarantee is the clearest sign someone does not understand how these systems work. What a competent provider can commit to is a measured baseline, a defined body of work on the sources that shape your category's answers, and honest monthly reporting on movement.
Where to go next
If you are choosing a provider, how to choose an AI search agency covers the questions worth asking and what the work should cost. To compare providers directly, see the AI search agency listings. If you are an agency doing this work, you can list your agency, and Get Matched puts a shortlist in front of buyers who describe their problem. The glossary defines the surrounding terms.