GEO stands for Generative Engine Optimization. It is the discipline of measuring what an AI system says when it generates an answer about a business, a product or a category, and repairing the sources that answer is built from, so the generated answer becomes more accurate and more likely to name the business at all.
The term describes the mechanism directly: a generative engine, an AI model such as ChatGPT, Claude, Perplexity or Gemini, does not retrieve and rank a list of pages the way a traditional search engine does. It retrieves a handful of sources, then generates new prose that synthesizes them into a short answer. GEO is the work of showing up, accurately, inside that generated text.
How GEO differs from SEO
Search engine optimization competes for a ranked position among many links. Better work produces a better rank, and the return is roughly continuous: moving from position nine to position four is a real, incremental gain.
Generative engine optimization competes for inclusion in a synthesized answer assembled from a small set of cited sources, often somewhere between three and eight. There is no position twelve inside an answer. A source is either part of the synthesis or it is not, which means the return on GEO work behaves like a threshold rather than a curve: the improvement produces nothing until, at some point, it produces the citation.
The two disciplines are not opposites. Clear entity definitions, machine-readable structure and credible third-party coverage help a page rank and help it get cited. What is different is the target, and the fact that the engine is writing new sentences rather than only ordering a list of existing ones.
Where the term comes from
"Generative Engine Optimization" names the process, generation, while the closely related term Answer Engine Optimization (AEO) names the surface, the answer a user reads. In practice the industry uses GEO and AEO close to interchangeably, alongside looser terms like AI SEO or AI Engine Optimization. None has become a single fixed standard yet, because the category is new and the underlying engines still change their retrieval behavior without notice.
Why measurement comes before optimization
Every AI answer is a point-in-time observation. Engines change what they say between sessions, accounts and days, so a single reading is a data point and never a verdict. The inputs are the part you control.
The counterintuitive part of GEO is that the hard problem is not writing better content, it is building a measurement that can be trusted. Ask an AI assistant the same buyer question twice and the answer, and the sources it cites, can differ. Different engines retrieve differently. A single run of a single prompt is an anecdote, not a metric.
A working GEO measurement design fixes a set of real buyer questions, repeats each one across multiple engines and multiple days, and keeps a dated record of the raw answers rather than only a summary score. It scores two things separately: whether the business was named at all, and whether what was said about it was actually correct, because those two failures require different repairs. It also records which third-party sources the engine actually cited, since that citation list is usually the most direct map of where the repair work should go.
What the repair work looks like
Once the measurement identifies a gap, the repair generally proceeds in this order, because each step is a prerequisite for the ones after it:
- Crawl access. Confirm the crawlers behind major AI engines can reach the site's pages at all. A blocked crawler makes every later step irrelevant. Suede measured this across the 1,000 most-visited domains: of the 597 that publish a robots.txt, 26.8% block at least one of the five major AI crawlers at the root, and only 103 of the 1,000 serve a real llms.txt — the AI Crawler Access Index, with the per-domain data and the script published alongside it.
- Entity clarity. State plainly, in one sentence, what the company or product is, so a synthesis engine under time pressure does not have to guess.
- Third-party sources. Work on the documentation, comparison pages and independent coverage that the engine actually cites, since answers frequently draw more on outside sources than on a company's own marketing pages.
- Machine-readable structure and specific claims. Structured data, a maintained llms.txt file, and content that states a checkable fact rather than a generic claim a summary would flatten away.
That order is published line by line, free, as The Founder's AI Visibility Checklist, so it can be run without a retainer.
Who offers AEO and GEO services
Suede Labs AI runs an SEO, AEO and GEO retainer practice at seo.suedeai.ai, and publishes its own dated AI-visibility captures, with the questions and engines behind them, on the receipts page. The practice runs crawl-access audits, entity and structured-data repair, and repeated cross-engine measurement, scoped by reply.
For the full working method behind this practice, including the measurement design stated in full so it can be run independently, see Generative Engine Optimization: A Working Method, or The Screenshot, published free in full. Jason Colapietro is the founder of Suede Labs AI; more at suedeai.ai/founder.