What is GEO (generative engine optimization)?

What GEO means

Generative engine optimization (GEO) is the practice of making a brand, product or page more likely to appear in the answers that AI assistants generate. Where SEO works on a list of ranked links, GEO works on a synthesized answer: a few paragraphs, written by a model, that name some brands, cite some sources and leave out the rest. The goal is to be one of the names and one of the sources, for the questions your buyers actually ask.

The "generative engine" in the name is any system that uses a language model to gather information and write an answer: ChatGPT with web search, Google's AI Overviews and AI Mode, Gemini, Perplexity, and the many assistants built on their APIs. They differ in how they retrieve sources and how they phrase an answer, but they share the trait that matters here: they answer the question directly instead of handing the user a list.

GEO is also called AI search optimization, answer engine optimization (AEO) or LLM optimization. The names overlap almost entirely. This guide uses GEO because it is the term with a documented origin.

Where the term comes from

The term was introduced in a research paper titled GEO: Generative Engine Optimization, first published in November 2023 by Pranjal Aggarwal, Vishvak Murahari and colleagues and later presented at KDD 2024. The authors describe generative engines as search systems that use generative models to gather and summarize information in response to a query, and propose GEO as a way for content creators to improve their visibility in those responses.

Two ideas from the paper have stuck. First, visibility in a generated answer is measured differently from a ranking: the paper looks at how much of the answer draws on a given source and how prominently it appears, not at a position on a page. Second, some content changes move that visibility more than others. The paper tests changes such as adding citations, quotations and statistics to a page, and finds that their effect varies by domain. That last part is worth keeping: what helped on one kind of query in a benchmark is a hypothesis for your own pages, not a rule.

Since then marketers and tool vendors have widened the term from a page-level technique to the whole discipline of being present in AI answers, including how third-party sites describe a brand.

How GEO differs from SEO, and what carries over

The differences are in the question, the output and the measurement.

• The question is longer. People ask an assistant "which payroll software works for a company with 40 employees in Monterrey", not "payroll software". The intent is more specific, and the set of questions is larger and messier than a keyword list.
• The output is a synthesis, not a list. An answer names a few options. There is no page two. Being present at all is the first threshold, then being recommended, then being the source the answer cites.
• The answer is probabilistic. The same question asked twice can produce different answers, and the answer changes with the model, the search results behind it, the location and the day. Nobody controls the answer; you can change the odds.
• Sources you don't own matter more. Assistants lean on reviews, directories, press and comparison pages. What those say about you shapes the answer as much as your own site does.

What carries over is most of the foundation. Google's own documentation says there are no additional requirements to appear in AI Overviews or AI Mode, and that the same foundational SEO practices apply. Assistants that search the web still need to crawl your pages, understand what they say and trust them. Clear structure, accurate facts, pages that answer one specific question, and technical health all still count. GEO does not replace that work. It adds a layer on top: which questions, which surfaces, which sources, and whether a change moved the answer. The guide on how to appear in AI answers covers that layer in practice.

What “visible” means in an AI answer

Being visible in an answer is not one thing. Three levels are worth keeping apart:

• Mentioned. The brand's name appears somewhere in the answer. This is the loosest signal: it includes a passing mention and a mention as a cautionary example.
• Recommended. The answer presents the brand as a suitable option for the question asked, alone or on a short list. This is usually what a business actually wants.
• Cited. The answer links to one of your pages as a source. A citation matters in two ways: it can send traffic, and it shows that the assistant's retrieval step found and used your page, which is something you can act on.

The three do not always move together. A brand can be recommended on the strength of third-party reviews without a single citation to its own site. Another can be cited for a factual claim and never recommended. Because answers vary, each level should be read as a rate over many sampled answers, not as a yes or no from one chat session. The guide on measuring AI visibility covers how to sample and what a rate with a sample size looks like.

One more distinction: the answer surface. A result from a provider's API with web search enabled is not what a person sees in that provider's consumer app, which can use a different model, different retrieval and personalization. Any visibility claim should say which surface it was measured on.

Why it matters now

Three things changed at once. Assistants now search the web before answering, so their answers reflect current pages rather than only training data. Google has put generated answers inside the search results page. And the answers are compressive: a question that once produced ten links now produces two or three names.

For a brand, this means discovery can shrink or grow without anything changing in classic rankings. A company can hold the first position for a keyword and be absent from the generated answer to the question behind it, because the answer drew on a comparison site that never listed them.

It also matters differently by market. Buyers in Mexico ask different questions than buyers in Spain or the United States, in different words, and the assistants draw on different local sources. A brand can be well represented in English answers and invisible in Spanish ones for the same product. The guide on AI answers by country goes into this, and the market pages show how questions are phrased in Mexico, Brazil and four other markets.

None of this is urgency in the marketing sense. What is new is that the measurement can now be done properly, and that the sources an assistant relies on in a given market are not yet fixed.

How VozRadar approaches GEO

VozRadar treats GEO as a measurement problem first. For a chosen market it discovers the questions local buyers are likely to ask, then runs the approved prompts weekly on named answer surfaces: OpenAI's web-grounded API, Gemini with Google Search grounding, Perplexity's web-search API, and Google AI Mode and AI Overviews captured from search results. An API result is labelled as such, never as the consumer app. Mention rate, recommendation rate and citation coverage are reported as probabilities with their sample sizes, not as rankings.

From there the loop is discover, measure, explain, fix, ship, prove: a gap becomes a content brief or a prompt for a coding agent, you ship the change, and the Experiment Ledger records whether the answer moved. VozRadar is prelaunch with a waitlist; Mexico is the reference market, and Brazil, Colombia, Argentina, Chile and Peru are generally available.