When a buyer asks an AI engine to recommend a vendor, a product or an agency, the answer comes back as a short, confident list — and most brands have no idea whether they are on it, or why. That question is what GEO, or generative engine optimisation, exists to answer. If SEO was about where you rank on a results page, GEO is about whether you appear inside the answer itself, and AI search visibility is fast becoming something boards ask about. I run Optimizer360.ai, an organic marketing company whose first product is built around GEO, so this is a subject I work on daily. Here is a plain explanation of what GEO is, how AI engines actually decide what to say about brands, and what a sensible first step looks like.
What is generative engine optimisation?
Generative engine optimisation is the practice of improving how your brand is represented in the answers that AI systems generate — assistants, chatbots, and the AI answer layers inside search engines. The output of GEO is not a ranking position. It is presence and accuracy: whether the engines mention you when the question is in your category, whether what they say about you is correct and current, and whether they draw on sources that frame you fairly.
That difference matters because an AI answer is a synthesis, not an index. A results page shows ten options and lets the reader judge. An AI engine reads widely, compresses, and gives a recommendation in its own words. Being on page one of search and being absent from the AI answer are now two different problems.
How do AI engines decide which brands to recommend?
No one outside these companies knows the full recipe, and anyone who claims to is selling something. But the broad mechanics are understandable. AI answers draw on two layers: what the underlying model learned during training, and what the system retrieves live — web pages, news, reviews, directories, documentation — when it answers a query. Both layers reward the same underlying thing: clear, consistent, verifiable information about who you are and what you do, published in places the engines read.
In practice, a few patterns show up again and again:
- Engines prefer brands they can describe confidently. If your own site explains what you do in plain language, with specifics, you are easier to summarise — and easier to recommend.
- Consistency across sources matters. When your website, your directory listings, your press coverage and third-party reviews all tell the same story, confidence rises. When they conflict, you get hedged or omitted.
- Third-party corroboration carries weight. Engines lean on independent sources — comparisons, reviews, industry coverage — not just your own claims about yourself.
- Structure helps. Pages that answer real questions directly, in a form a machine can lift cleanly, are more likely to be used as a source.
How GEO differs from SEO — and how it doesn't
The two disciplines overlap more than vendors admit. Good GEO still starts with the fundamentals SEO practitioners have preached for years: a crawlable site, clear positioning, genuinely useful content, legitimate third-party mentions. If your SEO foundation is weak, there is no GEO trick that compensates for it.
The differences are real, though. SEO optimises for a click; GEO optimises for being quoted and characterised correctly, because the reader may never visit your site at all. SEO has mature measurement; measuring AI search visibility means systematically asking the engines the questions your buyers ask and recording what comes back — tedious, but doable, and far better than anecdotes from whichever executive tried it last weekend. And GEO raises the stakes on accuracy: an engine repeating something outdated or wrong about you is not a rankings problem, it is a reputation problem, and fixing the sources it learned from takes deliberate work.
Where to start: an honest first audit
You do not need a platform to take the first step. You need a spreadsheet and discipline.
- Write down the ten to twenty questions a real buyer in your category would ask an AI engine — including the ones where you would expect a competitor to be named.
- Ask those questions across the major engines, more than once, and record the answers verbatim: who gets named, what is said about you, what is wrong or missing.
- Trace the errors to their sources. Outdated descriptions usually live somewhere specific — an old page, a stale directory entry, a years-old article.
- Fix what you control first: your own site's clarity, your product descriptions, your public documentation.
- Repeat the exercise on a schedule, because answers shift as models and sources update.
That loop — ask, record, trace, fix, repeat — is the core of GEO work. Tooling makes it scale; it does not replace it.
What to be sceptical about
Because the field is young, it attracts big promises. Be wary of anyone who guarantees you a place in AI answers — nobody controls these engines, and the honest version of this work is about raising probability and accuracy, not buying outcomes. Be wary of tactics that amount to spamming the sources engines read; the platforms keep getting better at discounting that, and the reputational downside lands on you, not on the vendor who suggested it. And be wary of treating GEO as a one-time project. It is closer to reputation management than to a campaign: ongoing, measurable, never quite finished.
The takeaway is simple. Your buyers are already asking AI engines about your category, and the engines are already answering — with or without you, accurately or not. GEO is the discipline of finding out what they say, understanding why, and doing the patient, verifiable work that improves it. Run the audit above this quarter; whatever you find, you will know more about your AI search visibility than most of your competitors know about theirs. I write more about GEO and organic marketing at abhaydesai.ai and at optimizer360.ai.