GEO is the practice of making AI systems understand, trust, and recommend your business when someone asks a question you should be the answer to. Here is what that actually involves, and how models decide.
Traditional SEO rests on a simple premise: help a crawler understand a page so it ranks that page in a list. Everything follows from it — title tags, internal links, page speed, keyword targeting.
A language model does something categorically different. Asked who the best option is in a city, it is not returning a ranked list of documents. It is drawing on what it has absorbed about entities in that category, weighing how confidently it can describe each one, and composing a recommendation in prose.
That breaks the assumption underneath most SEO work. A perfectly optimised meta description helps a crawler decide where to file your page. It does very little to help a model decide whether your business is the one worth naming. Those are different jobs, and only one of them is being done for most companies right now.
The uncomfortable part: you can hold a strong ranking position and still be entirely absent from the answer that appears above it.
This is the framework the audit scores against and the engagement works through, in the order that matters.
01
Before a model can recommend you, it has to be confident you exist as a distinct thing with stable attributes: what you do, where you operate, who you serve, what credentials you hold. That confidence is built by consistency across directories, review platforms, professional bodies, publications, and your own structured data. Inconsistency is not a small problem here — it is the problem.
Signal: consistent, corroborated facts
02
Models quote passages that are safe to quote: a specific claim, attached to a source, expressed plainly enough to lift without distortion. Most marketing content fails this test not because it is badly written but because it asserts things no model would risk repeating. Depth, specificity, and sourcing beat volume and keyword density every time.
Signal: quotable, sourced passages
03
Say what you do in language a model can parse without inference. Name your services, your markets, and your qualifications explicitly rather than implying them through brand voice. Clear hierarchy, logical order, and plain statements of fact do more for machine comprehension than any amount of clever copy.
Signal: explicit, unambiguous claims
04
Run the questions your buyers ask, on a schedule, across the models they use. Record who is named and how. Without that loop you are optimising blind, and blind optimisation in a channel this new is indistinguishable from guessing.
Signal: tracked citation rate
The questions that come up when teams dig into this.
The label is new and the hype around it is considerable. The underlying work is not new at all: entity clarity, accurate structured data, genuine expertise, and third-party corroboration have been recognised quality signals for years.
What is genuinely new is the measurement surface and the optimisation target. You can now ask the systems directly whether they recommend you, and you can work on how confidently they describe you. Treat GEO as an extension of search fundamentals rather than a new set of tricks, and it holds up.
Much of it overlaps, which is the good news. Structured data, clean architecture, real expertise, and consistent business information serve both.
The divergence is in emphasis. GEO cares far more about off-site consistency and third-party corroboration than most SEO programmes do, and far less about link volume for its own sake. It also changes how content is written — toward claims that can be lifted and attributed rather than pages built to hit a keyword target.
Roughly: entity recognition, authority signals, contextual relevance, quotability, and consistency. Does it recognise you as a distinct entity with clear attributes? Is your business corroborated by sources it trusts? Does what it knows match the question being asked? Can it quote you safely? Is the information consistent and reasonably current?
No one outside the labs has the exact weighting, and it changes between model versions. Anyone claiming a precise formula is guessing with confidence.
To a degree — Perplexity leans heavily on live citation and rewards clear sourcing, while ChatGPT draws more on absorbed training data where long-run entity consistency matters more. Those differences are real and worth accounting for.
But building for one model is a poor bet. They update constantly, and the work that helps across all of them is the same underlying work: be a clearly defined, well-corroborated, quotable entity.
Then you are early, which is the advantageous position. Entity authority accrues slowly and compounds, so building it before your category becomes contested is materially cheaper than trying to displace an incumbent answer later.
The audit will tell you honestly whether your category is being answered by assistants yet. If it genuinely is not, we will say so rather than sell you an urgency that does not exist.
The audit scores your business on all four and tells you which gap is costing you most.
