What is Generative Engine Optimization (GEO)?

Someone planning a weekend in a city they have never visited used to open Google, scan ten blue links, click three, and decide. A growing number of them now type the same question into ChatGPT or Perplexity and read one paragraph that names four places. They pick from those four.

That shift changes what a business is competing for. Position five on a results page still got traffic. Being the fifth-best match for an AI answer that names three places gets nothing at all.

Generative Engine Optimization is the work of making sure your business is one of the ones named.

What an answer engine is doing when it recommends something

It helps to be specific about the machinery, because the tactics fall out of it.

When you ask an assistant for "the best specialty coffee in Lisbon," one of two things happens, and often both.

Retrieval. The system runs one or more searches, pulls back a set of pages, and reads them. Perplexity does this on nearly every query. Google AI Overviews does it against Google's own index. ChatGPT does it when the question looks current or local. The model then writes an answer grounded in what it just read, and usually cites the sources.

Recall. The model answers from what it absorbed during training. No live lookup, no citations, just whatever it has internalised about Lisbon coffee. This is why a model can confidently recommend a café that closed two years ago.

Retrieval is where you have leverage this quarter. Recall is where you have leverage over years, and it is downstream of the same thing: how much consistent, structured, third-party-corroborated information about you exists on the open web.

The four questions a model has to answer about you

Strip away the jargon and every GEO task maps to one of these.

Does it know you exist? If your business has no crawlable page, no structured data, and no mentions on sites the model reaches, you are invisible. Not ranked low. Absent.

Does it know what you are? A model needs to classify you before it can match you to a query. "Restaurant" is not enough. Cuisine, price band, neighbourhood, whether you take walk-ins, whether the kitchen is open past ten. Those attributes are what turn a generic query into a match.

Does it believe the information? Models weight corroboration. One claim on your own site is an assertion. The same claim on your site, your Google Business Profile, a guide that covers your city, and a review platform is a fact. Contradictions between those sources are worse than silence, because they make the whole entity look unreliable.

Can it phrase an answer using you? This is the part most sites fail. If the only description of your hotel is a hero image and the words "an experience like no other," there is nothing for a model to lift. A page that plainly states what you are, where, for whom, and at what price gives the model a sentence it can reuse.

Where GEO and SEO diverge

They overlap more than the marketing around GEO suggests. The differences that matter:

The last row is the one people underestimate. There is no Search Console for ChatGPT. Measuring GEO means asking the models the questions your customers ask, on a schedule, and recording what comes back.

What the work looks like in practice

For a local business the sequence usually runs like this.

Fix the entity record first. One canonical name, one address format, one phone number, one category, applied identically on the website, Google Business Profile, Apple Business Connect, and any industry directory that matters in your sector. Inconsistency here poisons everything downstream.

Publish the attributes in machine-readable form. Schema.org JSON-LD describing the business, its opening hours, its price range, its menu or room types, its location. This is not a ranking trick. It is the difference between a model inferring what you are and a model reading what you are.

Write the pages a model can quote. For each real question a customer asks, a page or section that answers it in the first two sentences, then supports the answer. Question-shaped headings. No burying the point under three paragraphs of atmosphere.

Earn corroboration. Get accurately described on sources outside your control: city guides, local press, niche directories, review platforms, community forums. A model that reads four independent descriptions of you converging on the same facts will use them.

Open the door to AI crawlers. Check that your robots.txt permits the agents you want reading you, and consider an llms.txt file that gives them a plain-text map of your site.

Measure, then repeat. Build a list of the prompts that matter for your business, run them across the major assistants on a fixed cadence, and track whether you appear, how you are described, and who appears instead of you.

The honest limitations

Two things worth saying plainly, because the field attracts overclaiming.

You cannot guarantee a mention. There is no submission form, no index status, no way to confirm placement. What you can do is remove every reason a model would fail to find, understand or trust you, and then measure the outcome across enough prompts to see a real pattern.

And the ground moves. Retrieval behaviour, crawler policies and citation formats have all changed repeatedly since these products launched. Anyone selling a fixed permanent method is describing a snapshot.

What does not move is the underlying requirement: be findable, be legible, be corroborated, be quotable. That has been true of every discovery system so far, and it is true of this one.