Generative engine optimisation, or GEO, is the practice of making your business more likely to appear and be cited when people get answers from AI systems: ChatGPT, Google's AI Overviews, Gemini, Perplexity and the other generative search tools people now use instead of, or alongside, traditional search engines.
Where traditional SEO optimises for a list of blue links, GEO optimises for being named in an AI-generated answer. When someone asks an AI assistant which accounting firm is best for a small business, or what the best CRM is for a trade, or how much a marketing consultant charges in Australia, the AI composes an answer from sources it trusts. GEO is the work of making sure your business is one of those sources.
This matters because the way people search is changing. A growing share of product research, comparison questions and professional recommendations now starts in an AI chat window, and the businesses that are visible in those answers will capture demand that never reaches the classic search results page. The businesses that are not named are simply absent from the conversation.
AI systems do not invent answers from nothing. They draw on the text they were trained on, and increasingly on live sources they retrieve at the moment of the question. Understanding what makes a source attractive to these systems is the core of GEO.
Three things matter most. The first is clarity: AI systems prefer sources where the entity, the business, its services and its claims are stated explicitly and consistently. A website that never clearly says what it does, where it operates and who it serves is hard for an AI to cite with confidence.
The second is corroboration: AI systems are more likely to cite a fact or a business when the same information appears across multiple independent sources, not just on the business's own website. Consistent mentions across directories, industry sites, reviews and press all reinforce the picture.
The third is structure: content that is well organised, with clear headings, direct answers and self-contained passages, is easier for AI systems to extract and quote. A wall of undifferentiated prose is harder to use than a page with a clear definition, a direct answer and specific facts an AI can lift cleanly.
It is worth saying plainly what GEO is not. It is not paying to be included in AI answers, because that marketplace barely exists yet and where it does, the same rules of trust apply. It is not prompting an AI to recommend you in a chat, because that does not change what it tells the next person. And it is not a shortcut that lets a business skip the hard work of being genuinely credible. The businesses AI systems cite are the businesses that are easy to understand, consistently described and independently referenced. GEO is the discipline of making that true on purpose rather than by accident.
The disciplines overlap, and it helps to see how they fit together.
SEO is the established practice of improving visibility in traditional search engine results. It covers technical foundations, content, authority and user experience, and it remains the foundation of online visibility.
AEO, answer engine optimisation, is the narrower practice of structuring content so answer engines can extract and present it directly, often as a featured snippet or a short answer at the top of results.
GEO sits alongside them, aimed at the newer generative systems. It includes elements of both, because AI systems use traditional search signals and content structure, but it adds its own focus: entity clarity, source corroboration, being present in the places AI systems draw from, and content written to be cited rather than merely clicked.
None of this replaces SEO. GEO builds on it. A business with weak SEO foundations will not become citable just because its pages are well structured. The practical sequence is: get the SEO foundations right, then work specifically on being the source AI systems choose.
AI systems need to know exactly who you are, what you offer, where you operate and who you serve. That means consistent business information across your website and the wider web, clear service descriptions, and structured data that states the facts in a form machines can read. Entity work is the foundation of being citable.
When an AI answer includes a number, the number came from a source it trusted. Original data, published research, transparent methodology and content that other reputable sites reference all make your material more attractive to generative systems. Content that merely repeats what fifty other sites say offers an AI nothing worth citing.
llms.txt is a proposed standard that lets a website provide a plain-text file describing its content specifically for AI systems and language models. It is an emerging tactic, and it is one piece of the picture rather than a magic switch, but for businesses that want to be AI-friendly it is a low-cost signal worth having in place.
Schema markup tells machines what your pages mean: that you are a business, what services you offer, where you operate, what questions you answer. Generative systems retrieve and parse structured data more reliably than unstructured prose, so clean markup improves your chances of being understood and cited correctly.
Content that AI systems can cite is direct, specific and self-contained. A page with a clear definition at the top, an answer-first structure, headings that describe what follows and facts that stand on their own is far easier for a generative engine to use than a page that buries its point in a slow build-up. This is good writing for humans and good structure for machines at the same time.
The work starts with an audit of how visible your business already is in AI answers. We ask the generative systems your customers actually use, across the questions that matter to your business, and record where you appear, where you are absent, and who the AI cites instead.
Then we fix the foundations: entity clarity, consistent information, structured data and content structure, the same fundamentals that make a business citable. From there the work extends to the factors that build corroboration: earning consistent mentions across the sources AI systems trust, creating original material worth citing, and keeping the whole picture aligned.
This is not a set of tricks to manipulate AI systems into recommending you. The approach is honest: become genuinely well-documented, clearly described and consistently referenced, and let the AI systems that reward those qualities find you. Where the work intersects with SEO, I do that too, because the two belong together.
If you want to know whether your business appears in AI answers, and what it would take to become the source AI systems cite, book a free 30-minute Strategy Session. We will check your current visibility and map the work. No pitch deck, no obligation.
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