A Primer on Generative Engine Optimisation
In an earlier companion piece we shared that agentic commerce has arrived, that buyers and their agents are increasingly beginning their journeys inside AI assistants, and that the answer an assistant returns is becoming the new shelf. That argument leads naturally to an uncomfortable question, and it is one that every brand ought now to be putting to itself: when a customer, or a customer's agent, asks an AI assistant about your category, does your brand appear in the answer at all? For a growing share of commercial discovery, visibility has quietly become a binary condition. Either you are mentioned, or you are invisible, and there is remarkably little middle ground in between.
What generative engine optimisation is
Generative engine optimisation, usually shortened to GEO, is the discipline that has grown up to address precisely that question. Where search engine optimisation concerned itself with ranking a page within a list of links, generative engine optimisation concerns itself with whether, and how, a brand is discovered, understood and cited inside the answers that AI systems generate, across ChatGPT, Gemini, Claude, Perplexity and Google's AI Overviews among others. The distinction matters rather more than it might at first appear, because the two are no longer the same game played on a different pitch. Research circulating within the field suggests that the overlap between the sources Google ranks at the very top and the sources that AI systems actually choose to cite has fallen from somewhere around seventy per cent to below twenty, and the gap appears to be widening as the models develop their own preferences about which sources to trust. Ranking well on Google, in other words, no longer guarantees that an assistant will mention you, and it is entirely possible to be cited prominently by the models while sitting nowhere near the top of a conventional results page.
Why it now matters
The reason this has moved so quickly from a marketing curiosity to a matter for the board is the sheer pace at which discovery is migrating. Gartner has forecast a meaningful decline in traditional search volume as users shift towards AI answer engines, a majority of buyers now report beginning their purchasing journey in an AI chatbot rather than at a search box, and, as we have already noted, agentic commerce will increasingly see the agent itself choose between options on the buyer's behalf. In each of these cases the underlying mechanism is the same. The model compresses a great deal of the web into a single synthesised answer, and a brand that does not form part of that synthesis effectively does not exist for the buyer at the moment of decision.
The consequences differ by context but point in the same direction. For a consumer brand, presence in the answer determines whether it reaches the shopper at all. For a business-to-business firm, it determines whether it makes the shortlist that a buyer, or a buyer's procurement agent, assembles. And for any organisation preparing for agentic commerce, it is the precondition upon which everything else depends, since an agent cannot transact with a brand it has never surfaced in the first place. Being absent from the answer is no longer a soft marketing disadvantage; it is a hard commercial exclusion.
What you can actually do about it
The encouraging news is that generative visibility can be worked at deliberately, and the levers, while different from those of traditional SEO, are no great mystery once they are set out plainly.
The first, and the most frequently overlooked, is simply ensuring that AI systems are able to read your pages at all. A surprising number of sites inadvertently block AI crawlers, whether through their robots file or through content-delivery configurations that have lately begun to deny AI bots by default, and a brand that has unknowingly shut the door cannot be cited however good its content may be. Beyond that foundation, the work falls into a handful of reasonably clear areas. Content ought to be structured in the way the models prefer to consume it, with clear entity statements, named sources, specific figures, and concise passages that pose a question and then answer it directly, so that a model can lift and attribute them cleanly. The brand's entity authority, by which is meant the consistency and richness of the established facts about it across the wider web and the knowledge graph, needs to be strong enough that the models describe it accurately and with confidence rather than vaguely or not at all. Editorial and expertise content that genuinely addresses the questions buyers ask gives the models something authoritative to draw upon. Product feeds, for commerce brands, require the same structured clarity that the agents themselves demand. And all of this should be directed by evidence rather than by guesswork, which means auditing the actual prompts your audience uses, observing which brands the models cite in response, and identifying with some precision the gaps where competitors appear and you do not.
Measure what the models actually say, not what you hope they say
That final point is the one most often skipped, and it is precisely the one that separates real generative engine optimisation from wishful thinking. It is not enough to optimise in the general hope of improvement. A brand needs to measure how it genuinely appears, in real answers drawn from real models, across several engines rather than a single one, at the level of individual prompts rather than through some vague aggregate figure, and to track that picture as content and authority change over time. Simulated visibility estimates are of very little use here. What matters, in the end, is what the models actually say when a buyer asks.
How the GEO Scorecard works
It was this need that led us to build our own GEO Scorecard application. Its purpose is to give a brand an honest, evidence-based picture of how it truly appears across the frontier models, and then to turn that picture into a prioritised plan of action.
At the heart of the Scorecard sits an AI testing engine that runs real queries against the live frontier models, among them ChatGPT, Gemini and Claude, and captures their actual responses rather than relying on proxies or estimates. From those responses it establishes whether the brand is cited, in what position, how frequently it appears relative to named competitors, and in what tone, so that share of voice and sentiment are measured rather than merely assumed.
Around that live testing, the Scorecard assesses a brand across eight dimensions which together determine its generative visibility: the clarity of its brand narrative, its visibility within AI model outputs, its entity and knowledge-graph authority, the quality of its structured content and schema, its competitive GEO position, its domain authority and backlink profile, the strength of its editorial and expertise content, and the weight of its social and public-relations signals. Each dimension is scored and placed within a maturity band, the brand receives an overall score and grade, and the whole is set against a benchmark of its competitors, so that the resulting picture is relative as well as absolute. The output is not a bare number but a client-ready report, generated automatically, that pairs each finding with specific and prioritised recommendations describing what to change and why it will help.
Used well, the Scorecard does three things in sequence.
It establishes a baseline, telling a brand where it genuinely stands today across the models that matter to it.
It directs effort, by showing which of the eight dimensions is most holding the brand back and which interventions will move the position furthest.
And it allows progress to be tracked honestly, since re-running the assessment once changes have been made reveals whether generative visibility has actually improved, in the only terms that ultimately count, which is to say in what the models now say.
The foundation for everything that follows
Generative engine optimisation is, in the final reckoning, the groundwork upon which the rest of this rests. Being present in the answer is fast becoming what being on the shelf once was, the precondition for being chosen at all, and it is the foundation on which any serious agentic commerce strategy must be built. The brands that come to treat their presence in AI answers as something to be measured and managed, rather than something left quietly to chance, will be the ones that the models, and increasingly the agents acting on the models' recommendations, choose to put forward.
WeBuild-AI built the GEO Scorecard to give brands a clear, evidence-based view of how they are represented across the frontier models, together with a prioritised plan for improving that representation. If you would like to see how your brand currently appears in AI-generated answers, we can run a baseline assessment and talk you through what it reveals.
