What is GEO (generative engine optimisation)?

Generative engine optimisation (GEO) is the practice of making your product likely to be named, described accurately, and recommended inside the answers that generative AI systems produce. A generative engine is any tool that reads a question and writes a synthesised answer rather than returning a list of links: ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews are the common examples. GEO is the work of understanding how those systems assemble an answer, then arranging your content, your presence across the web, and the facts about your product so that you are part of the answer when someone asks what software solves their problem.

If search engine optimisation (SEO) is about ranking a page so a person clicks it, GEO is about being included in an answer the person never clicks away from. The destination changed, so the optimisation target changed with it. This page defines GEO precisely, explains the mechanism that makes it possible, and shows where it sits next to SEO and AEO so you know what to actually do.

How generative engines build an answer

To optimise for a generative engine you have to know how it decides what to say. There are two mechanisms at work, and most modern systems use both at once.

Parametric recall: what the model already learned

A large language model is trained on a very large body of text. During training it absorbs patterns, associations, and facts into its parameters, the internal weights that make up the model. When you ask a question, the model can answer partly from this stored knowledge without looking anything up. This is parametric recall. If your product was discussed widely and consistently across the web at the time the model was trained, the model may already associate your name with a category and recall it when relevant.

A model's learned information may be out of date or incomplete. A new product can be absent from that information, and a product with an old name may still be described in old terms. You cannot edit the model's training data. For a current question, check whether the assistant used search and inspect the sources it cites.

Retrieval: what the model fetches at answer time

Some assistants search the web while answering and show links to sources. That lets a current page appear in an answer even if it was published after a model's training. Search behaviour varies by product and question, so inspect the answer itself before assuming which source was used.

A newly published page may be available to a search-enabled assistant once it is indexed or fetched, but there is no fixed timetable for a mention. Check crawler access, publish a page that answers a real customer question, and save the answer before and after. If the answer changes, inspect its cited sources before attributing that change to your page.

What generative engine optimisation actually targets

You cannot choose which sources an assistant uses. You can make the information it might encounter easier to verify: explain the product on your own site, keep public profiles accurate, and publish detail that answers a specific question. Then test the questions your customers ask.

• A page a person can use. State what the product does, who it serves and what it costs or requires. Make the important text accessible to crawlers and visitors.
• Public records you can check. Keep relevant listings current, then inspect the sources an assistant actually cites for your target questions before prioritising another directory.
• An accurate description across your own profiles. Correct an old category or feature claim wherever you control it. This helps readers compare sources even if the next AI answer does not change.

Keyword repetition cannot substitute for an accurate product description or a useful answer. Put the product name, category and relevant details where a reader can find them, and check whether the resulting page actually resolves the question.

GEO, SEO, and AEO: how they relate

These three terms describe overlapping work aimed at different surfaces, and the distinctions matter because they change what you measure.

SEO optimises for traditional search engines that return ranked links. The unit of success is a ranking position and the click it earns. The reader leaves the engine and lands on your page.

AEO, answer engine optimisation, optimises to be the direct answer to a question: the featured snippet, the voice-assistant response, the boxed answer at the top of a results page. The unit of success is being the single chosen answer, often with no click at all.

GEO optimises to be included in a generated, synthesised answer that may weave together several sources and several products. The unit of success is being named, described accurately, and recommended inside that answer. GEO inherits much of the technical groundwork of SEO, because a page that retrieval engines can fetch and parse cleanly is the same page that traditional crawlers handle well. The difference is the audience being optimised for: a model assembling prose, not a crawler ranking links. In practice the three reinforce each other, and a healthy discoverability programme does all three at once rather than treating them as rivals.

Why this matters for a post-launch founder

A growing share of people researching software now ask a generative engine before they ever open a search results page. They describe their problem in plain language and read the answer they get back. If your product is not in that answer, you are invisible at the exact moment someone is deciding what to use, and you have no link, no impression, and no way to know it happened. The traditional analytics that show you a ranking drop do not show you an answer you were left out of.

For a solo or small-team founder the encouraging part is that GEO rewards clarity and consistency more than budget. A product described precisely, corroborated across a few independent sources, and present in the communities where its users already talk has a genuine chance of being pulled into answers, regardless of company size. The harder part is that it is steady, distributed work across many surfaces, and it is easy to lose track of where you stand.

What to do next

Start by finding out what the engines actually say about you. Ask ChatGPT, Claude, Gemini, and Perplexity the questions your customers would ask, in the words they would use, and read the answers honestly. Note whether you appear, whether the description is accurate, and which competitors are named instead. That baseline tells you whether your problem is recall, retrieval, or simply that the facts about your product are scattered and inconsistent. From there the work is concrete: publish clear pages that state plainly what you do, earn accurate mentions in the directories and communities your users read, and keep the facts consistent everywhere they appear.

If you would rather start from a measured baseline than a manual audit, AfterLaunch runs a free scan that scores your discoverability across seven dimensions, including how you currently show up in AI answers, so you can see where you stand before you decide what to fix first.