What is Generative Engine Optimisation?

Generative Engine Optimisation (GEO) is the practice of improving how often a business is named, and how it is presented, inside AI-generated answers - the responses produced by ChatGPT, Google AI Overviews, Perplexity, Gemini, Claude and Copilot. Where SEO competed for position on a results page, GEO competes for a place inside the answer itself.

Why does GEO exist at all?

Because a growing share of discovery now ends inside the answer. When someone asks an AI assistant “who is the best cosmetic dentist near me?” or “which accountant should a small limited company use?”, the engine does not return ten blue links. It returns a short, confident recommendation naming a handful of businesses - and for many users, that is where the search ends.

This changes the economics of visibility. On a traditional results page, position five still earned a look. Inside an AI answer, there is no position five. A business is either named or it is not, and if it is not, the customer never learns it exists. GEO is the discipline that grew up in response: the deliberate work of becoming one of the names the engine gives.

How do AI engines build their answers?

In simplified terms, an answer engine does three things: it retrieves candidate sources relevant to the question, it weighs which of those sources it can trust, and it synthesises an answer - citing or naming the sources it leaned on. Each stage is a filter, and a business can fail at any of them: unreachable to the engine’s crawler, unintelligible when reached, or insufficiently corroborated to be worth recommending.

That is why serious GEO work is layered rather than a single trick. The full sequence Answari uses is published on the methodology page; the logic beneath it fits in two levers.

What are the two levers of GEO?

Machine-readability is the first. The engine should never have to guess what a page, a service or a business is. This is served by structured data (schema markup in JSON-LD), clean semantic HTML, answer-first copy that states the point in the opening lines, real HTML tables rather than styled boxes, and visible freshness dates. The easier a source is to lift accurately, the more often it is lifted.

Entity trust is the second. Engines recommend entities they can verify: one consistent business identity across the web - the same name, address and facts on the website, Google Business Profile, review platforms, the company register and LinkedIn - corroborated by independent third-party mentions. A business that exists as a single, well-attested entity is a safe thing for an engine to put its confidence behind. One that exists as scattered, inconsistent fragments is not.

Every legitimate GEO tactic serves one or both of these levers. Anything that serves neither is decoration.

What does the work actually involve?

Is GEO different from AEO?

The terms overlap heavily and are often used interchangeably. Where a distinction is drawn, Answer Engine Optimisation (AEO) tends to describe optimising for answer-shaped search features and direct question-answering, while GEO describes optimising for generative, synthesised responses. In practice the work is substantially the same, which is why Answari treats them as one discipline with one methodology. The GEO vs SEO guide covers the more useful comparison - what actually changes from traditional search.

What should a business do first?

Measure. Before spending anything on optimisation, find out what the engines currently say when your customers ask - which questions you appear in, which you are absent from, and who is being named instead. That baseline usually contains the strategy: AI visibility is won question by question, and the gaps tell you exactly where the work belongs. It is the reason every Answari engagement starts with the audit, and why the free visibility check exists as step zero.