Generative Engine Optimisation
GEO, described honestly
Being retrieved and cited by generative AI is a genuine commercial opportunity — and a field full of overconfident claims. Here is what is known about how it works, the work that pays off under any model, and how to tell evidence from hype.
AI Says audits your website — the foundation every AI platform draws on. To see what ChatGPT, Gemini, Claude, Perplexity or Google AI are saying about you today, our specialists review those platforms directly during a consultation. AI Says is independent and is not connected to any AI platform.
No AI provider publishes a ranking algorithm for generative answers. Anyone who tells you they know how to guarantee a mention in one is guessing, selling, or both.
That does not make the problem unaddressable. It means the honest approach is to work on the conditions that are necessary under any plausible mechanism, measure what can actually be observed, and label the difference between the two. That is what this page describes and what our audit does.
What a generative engine is doing
When a generative system answers a commercial question, several distinct things happen in sequence, and GEO is concerned with all of them. The system interprets the question. It decides whether it needs to retrieve anything, or whether what it already holds from training is sufficient. If it retrieves, it issues its own queries — not your customer’s words — against an index or a search product. It selects a handful of documents from what comes back. It synthesises a short answer from those documents plus its own internal representation of the subject. Then, depending on the product, it may attach citations to some of what it said.
Each step is a separate place you can be excluded. You can be missing from the index. You can be in the index but lose the selection step to a directory page that covers twenty businesses including yours. You can be selected but contribute nothing quotable, so the answer paraphrases a competitor’s page instead. You can be quoted but not cited. And you can be described from the model’s internal representation — which may be months out of date — without any retrieval happening at all.
This is why GEO cannot be reduced to a checklist of on-page changes. A page that is perfectly structured still has to survive a retrieval step it cannot observe, against competitors it did not choose, in a product whose behaviour changes between releases.
What we can say with confidence
A short list, and deliberately short. Generative products demonstrably read structured data, follow crawl directives, and retrieve from indexes that respect conventional discoverability. They demonstrably favour sources that state facts plainly over sources that imply them. They demonstrably rely on independent corroboration when resolving who an entity is — which is why a business with three authoritative profiles is described more confidently than one with none. And they demonstrably cite at different rates depending on the product, with some citing almost everything and others citing nothing.
What nobody can currently say
Which of those factors matters most, and by how much. Whether a given change will produce a mention. How long a change takes to propagate into a model’s behaviour, as opposed to its retrieval. Whether being cited more often in one product has any effect in another. Anyone presenting numbers against those questions is presenting a hypothesis dressed as a finding.
Boundaries
SEO, AEO and GEO — what each one owns
The three overlap enough that they are often conflated. They answer different questions and they fail in different ways.
| SEO | AEO | GEO | |
|---|---|---|---|
| Goal | Rank in a list of results | Be quotable as an answer | Be retrieved and cited by a generative system |
| Scope | Your site plus inbound links | Almost entirely your own pages | Your site plus the whole information environment about you |
| Under your control | Largely | Almost completely | Partially at best |
| Measurable today | Yes — positions and impressions | Yes — every signal is directly testable | Only by repeated observation, not by mechanism |
| Typical failure | You rank on page three | Your page has no extractable answer | A directory or competitor is retrieved instead of you |
| Feedback speed | Weeks | Immediate — re-run the audit | Unpredictable, and different per product |
Where to spend effort
Five things that plausibly influence retrieval
Chosen because they are necessary under every plausible mechanism, measurable, and useful even if generative search stopped growing tomorrow.
- 1
Entity resolvability
A system must be able to decide, without ambiguity, that the business on your site is the business the question is about. Organization schema, a consistent legal name, a stable address and sameAs links to profiles that say the same things are what make that decision cheap. Ambiguity is resolved by skipping you.
- 2
Independent corroboration
Claims that appear only on your own site are weak evidence. Claims repeated by directories, registries, industry bodies, publications and review platforms are corroborated. The audit measures the part of this it can see — your declared profile links and other trust signals — and a specialist researches the rest. It is the single largest thing most businesses neglect.
- 3
Specificity over breadth
Generative retrieval is triggered by narrow questions. A page that answers one specific question thoroughly is more retrievable than a page that mentions twelve services in passing. This is the opposite of the instinct to consolidate everything onto a services page.
- 4
Extractable structure
Answer-first paragraphs, question-shaped headings, FAQ markup, short self-contained statements. If the useful sentence in your page depends on three paragraphs of context, it will not survive extraction.
- 5
Freshness that is visible
Dated content, updated pages and declared modification times help a system prefer your current position over its own stale internal representation of you. An out-of-date description is often an artefact of nothing newer being available.
What we deliberately do not do
- Promise a mention, a placement or a citation.
- Report a “GEO rank” as though a ranked list existed.
- Attribute an observed change to a specific action without evidence.
- Present an estimate as an observation.
In our assessment
Where GEO shows up in the AI Readiness Score
There is no single GEO number, because there is no single GEO mechanism. What the free audit does is measure the preconditions — the ones that are necessary under every plausible mechanism — and score them across the five readiness categories.
Being AI-ready doesn’t automatically make you AI-visible — but you can’t become visible without it. AI Says helps you build the foundation. Our experts help you understand how you compare, where your competitors are gaining ground, and what to do next.
Technical Readiness
25% of the scoreWhether AI systems and search crawlers can reach, read and index your pages at all.
Entity & Brand Signals
25% of the scoreHow clearly your site states who you are, what you do and where you operate, in a form machines can extract.
Content Authority
20% of the scoreWhether there is enough substance and topic coverage for an AI system to treat you as a credible source.
Answer Readiness
20% of the scoreWhether your content is shaped as answers to the questions customers actually ask.
Trust Signals
10% of the scoreThe proof-of-existence signals — real contact details, a real address, verifiable profiles elsewhere.
The AI Readiness Score measures how well your website is prepared to be understood, trusted and cited by AI-powered search — the foundations you control. It is measured from your website, not from AI platform answers. Readiness is what makes visibility possible; to see your actual visibility on each platform, book a consultation with a specialist.
Questions
Questions about GEO
GEO (Generative Engine Optimisation) is the practice of improving how generative AI systems retrieve, cite and describe your business — and the problem it addresses is very real. These systems draw on a small number of sources, and which ones they choose has commercial consequences. The term is young, so treat any GEO service that promises guaranteed placement in AI answers with the same scepticism you would apply to a guaranteed number-one ranking.
Generative answers favour sources that are retrievable, unambiguous about who they are, corroborated elsewhere and written to answer the question directly — though the exact selection logic is unpublished and varies by product and model version. The reliable way to learn more is observation: asking a defined set of questions repeatedly, recording which sources are cited, and looking for patterns, while treating patterns as evidence rather than proof of a mechanism.
Focus on the signals that matter under any plausible selection mechanism: be retrievable, be unambiguous about who you are, state your facts in machine-readable structured data, be corroborated by independent sources, and write content that answers specific questions directly. None of these depends on one model's quirks, and all of them are measurable — which is why they form the basis of the AI Says audit.
AEO works on your own pages; GEO works on the wider information environment around you. AEO asks whether a machine can read a page and lift a clean answer from it. GEO asks whether the system reaches for your page at all, whether it cites you, and what other sources say about you that shape the answer. You can complete an AEO programme on your own site, while GEO also involves earning mentions on sources you do not control.
SEO remains essential, because many generative products retrieve from a conventional search index before they generate an answer. The more accurate framing is that SEO has become one of several inputs to a process that also weighs entity clarity, corroboration and answerability — so strong SEO is the foundation GEO builds on.
GEO best practice will sharpen as platforms publish more about retrieval and as larger bodies of controlled, repeated observation accumulate. Until then, the sound approach is to invest in what can be verified — the readiness signals on your own site — label estimates as estimates, and have a specialist review the platforms directly rather than relying on guesswork.
Start with what can be measured
The free check covers every signal on your side of the problem, with the evidence shown.