What is Generative Engine Optimization (GEO)?

TL;DR. Generative Engine Optimization (GEO) gets your brand correctly named and cited inside AI-generated answers from engines like ChatGPT, Gemini, Claude, Google AI Overviews, AI Mode and Copilot when buyers ask category questions. GEO shares its technical foundation with SEO and adds entity consistency, third party references, original data, and prompt-based measurement.

GEO is what you do to make sure your brand shows up, and shows up accurately, inside those AI answers. Zaraftis is the AI visibility and GEO platform that measures whether it does.

If you have been in a marketing meeting in the last six months, you have probably heard somebody say a sentence like “we need a GEO strategy” with the same confidence that, a year earlier, they would have said “we need a TikTok strategy.” The room nods, nobody asks what GEO is, and everyone assumes everyone else knows.

This piece is the reference you can send the next person who needs to know, without making them watch a forty-minute webinar to get there. It’s a working definition of what GEO is, where the term came from, what changes when you do it, and what does not.

What is Generative Engine Optimization?

Generative Engine Optimization gets your brand named, and named accurately, when an AI engine answers a buyer’s category question. Everything else is implementation detail. The term comes from a 2023 research paper, “GEO: Generative Engine Optimization” by Aggarwal et al., published at KDD 2024 (arXiv).

The AI engines in question are the systems that have moved into the part of the buyer journey that used to belong to a Google search results page. ChatGPT in search and browsing modes. Google’s AI Overviews and AI Mode. Microsoft Copilot. Gemini. And others that draw from the same open web. The shared property is that these engines no longer return a list of ten links and let the buyer pick. They return one synthesized answer, in prose, with a small number of brands named and cited inside it. GEO is how you become one of those brands, and how you make sure the engine describes you accurately when it does.

Is GEO the same as AEO?

GEO and AEO get used interchangeably, but they shouldn’t be. They describe overlapping but distinct disciplines.

AEO, or Answer Engine Optimization, is the older term. AEO came out of the voice-search and featured-snippet era, and it targets systems like Alexa, Siri, Google Assistant, the Featured Snippet, and the “People Also Ask” box. AEO work is structural: FAQ schema, How-To schema, concise direct answers right under question-shaped H2s, lists and tables an extractor can lift cleanly. The success state is a single short answer surfaced verbatim.

GEO, or Generative Engine Optimization, is the newer term, and the one most operators have settled on for the LLM era. GEO targets ChatGPT, Gemini, Claude, Copilot, Google’s AI Overviews and AI Mode, and similar systems: anything that synthesizes a multi-sentence prose answer from many sources and cites some of them by name. GEO work is broader. Structured data and extractability still matter, but so does unique data, original research, third party citations, entity consistency across the open web, and the small number of distinctive claims a model can confidently attribute to you.

If you want a one-liner: AEO optimizes for extraction; GEO optimizes for citation. Good GEO work tends to include the AEO fundamentals, but the inverse is not true.

How does a generative engine produce an answer?

Before talking about how to optimize for these engines, it helps to know roughly what generative engines do. Almost every generative engine in production today works in three stages.

1. Retrieve. When a user asks a question, the engine pulls a candidate set of documents that might be relevant. Retrieval is some combination of a live web search, a vector search across an embedded corpus, and the model’s own pre-trained knowledge.
2. Rank. A smaller, faster model orders the candidate set by likely usefulness for the specific question.
3. Synthesize. The main language model is shown the top documents alongside the question and asked to produce a single prose answer that cites some of the sources by name.

Two things follow from the retrieve-rank-synthesize pipeline that are worth holding in your head, because most of GEO is downstream of them.

First, a page can be in the retrieval pool and still not be cited in the answer. Retrieval gets you in the room, and synthesis decides who gets quoted. They’re separate problems with separate fixes.

Second, the model produces the answer it can most confidently attribute given the documents in front of it, which is often a different thing from the “best” answer. The brand whose claims are stated explicitly, in language that matches the question, in a place the model can find them, gets cited. The brand that buries the same information in a five-paragraph hero section gets summarized away.

(For a deeper read on the synthesis step, see How AI engines decide which brands to cite.)

How is GEO different from SEO?

GEO and SEO overlap, but they aren’t the same. A large share of the underlying work is the same and some of it is new. Knowing which is which is the difference between a sensible GEO program and a “we just renamed our SEO deck” one.

What is the same

Crawlable HTML, page speed, topical authority, internal linking and schema all still count. A site with broken fundamentals will not be cited in AI answers, in the same way a site with broken fundamentals would not have ranked. If your SEO is bad, your GEO will be too.

What is new

Five things, mostly:

• Generative engines read structure differently. A real <table> with semantic headers, a clean FAQPage schema with real questions, an Organization block with a consistent sameAs array. Google says none of this is required for its AI features and there’s no special schema, so treat it as making your facts unambiguous, not as a ranking trick (Google). (GEO inherits the AEO toolkit here.)
• Entity consistency matters more now. The model is matching your brand against everything else the model has read about you. If your LinkedIn description, your about page, and your G2 listing disagree, you are a fragmented entity, and a fragmented entity gets summarized weakly or not at all.
• Third-party reference content matters more. In one academic study of US car-buying queries, 81.9% of AI search citations went to third-party sources, against 45.1% in Google search (Chen et al., 2025). The brand with the strongest footprint in independent listicles, comparison articles, and category roundups wins.
• There are more crawlers to allow. OAI-SearchBot (ChatGPT search), Claude-SearchBot (Claude search), PerplexityBot and others now sit alongside Googlebot. Training controls such as GPTBot, ClaudeBot and Google-Extended are separate tokens: blocking GPTBot or Google-Extended doesn’t remove you from ChatGPT search or Google Search (OpenAI; Anthropic; Google). Check that a 2023-era “block AI scrapers” rule didn’t take a search crawler with it.
• The unit of measurement is the answer. You measure visibility prompt-by-prompt, not keyword-by-keyword, because the engine no longer returns a list you can have a position on.

Most of GEO follows from that last change: the question moves from “where do I rank for this keyword” to “what do the AI engines say when somebody asks this question about my category”. (More on the rank-to-answer shift in The end of keyword rankings.)

What KPIs should you track for GEO?

The KPIs aren’t exotic. You can measure most of them already with the right tool. What’s new is putting them in the leadership review.

AI visibility, by engine

Across a defined set of buyer-intent prompts, the percentage of answers that mention your brand at all. Track it per engine, because the engines disagree more than people expect. A brand can be named often in Gemini and rarely in ChatGPT, and the combined number hides both.

Share of voice

Of the answers that mention any vendor in your category, your share of those mentions. Share of voice is the competitive number.

Citation share

When the engine cites a source link alongside the answer, how often the source is one of yours. Citation share is closer to the old “is your content the standard reference” question, but measurable per prompt rather than per keyword.

Mention sentiment

When your brand does appear, whether the surrounding language is positive, neutral, or quietly damning. A high visibility number with net-negative sentiment around it isn’t a win.

What does GEO work actually look like in practice?

Reasonable people will disagree about the order, but the shape of a GEO program tends to look the same. There is a technical foundation, a content layer, an off-site layer, and a measurement layer. You need all four, and all four are doable.

Technical foundation

Allow the search crawlers you need in your robots.txt (OAI-SearchBot for ChatGPT search, Googlebot for Google). OpenAI says a robots.txt change takes about 24 hours to reach ChatGPT search. Server-render your high-intent pages. Validate your structured data. Make sure your Organization, Product or SoftwareApplication, and Article schema agree with the visible page content. Use real semantic HTML on data-heavy pages. It’s dull and cheap, and it’s the best-value sprint most teams can run. (Twelve specific fixes in The AI-readability checklist.)

Content layer

Write the answer in the first paragraph. Use real comparison tables on competitor pages. Give your numbers, frameworks, and definitions their own sentences so the model can latch onto them. Publish original data, benchmarks, or opinions the model cannot find verbatim anywhere else. Generative engines reward information gain. Cut FAQ blocks that read as generated; Google asks for unique, non-commodity content.

Off-site layer

Tighten your entity description across the open web (your about page, LinkedIn, Crunchbase, G2, your most-cited press piece). Earn real third party mentions in the kinds of structured listicles and comparison articles that AI engines lean on. The off-site layer takes the longest.

Measurement layer

Define a prompt set of the buyer questions that matter most, worded the way your buyers ask them, not the way marketers group them. Start with a few dozen and grow from there (Zaraftis plans track 25 to 150). Run it weekly on the engines your buyers use. For most B2B audiences that means ChatGPT, Google AI Overviews, Gemini, Copilot, Google AI Mode and Claude. Track AI visibility, share of voice, citation share, and sentiment per engine, because the engines disagree and an average hides the gaps. Put the trend lines in your monthly marketing review. Until somebody on your team is judged on the AI visibility numbers, the program will not get the attention it needs.

Is GEO just SEO with a new name?

Partly. The fundamentals overlap, the work shares vocabulary, and a competent SEO team has a real head start on GEO. But ranking and being named in an AI answer are different outcomes, and the gap between them usually sits in the GEO-specific work: structured data, third party references, entity consistency, content extractability, and distinctive claims a model can attribute to you. If your only investment is in classic SEO, you will leave most of your potential AI visibility on the table, and you will not know it because your dashboard does not measure it.

Do AI mentions actually convert?

AI mentions convert later, through a path your funnel software cannot see. A buyer asks an AI engine about a category. The engine names two or three brands. The buyer notes your name, looks the brand up directly a week later, and the visit lands as direct or branded organic. By the time the lead enters your CRM, the AI conversation is invisible. The “AI mentions don’t convert” argument is the same argument people made against measuring share of voice in offline advertising, and the argument was wrong then for the same reason. Brand effects are real even when your attribution model cannot see them. GEO is, structurally, a brand metric. You measure it because brands that win on GEO tend to win later in the funnel.

What is GEO not?

GEO isn’t a set of “AI prompts to put in your robots.txt to bribe the model.” Those tricks don’t work. It isn’t a prompt-engineering trick on your meta description, or a service you hire once and forget. It isn’t voice search re-skinned either, although voice search and featured snippets are the AEO lineage that GEO inherits some of its toolkit from.

GEO is also not a replacement for SEO. Teams that approach GEO as a replacement for SEO underinvest in the foundations and overinvest in clever-sounding tactics, and then wonder why the numbers do not move. GEO sits on top of competent SEO, the same way SEO sits on top of a working website.