What is Generative Engine Optimization?

A buyer used to type a question into Google and get ten blue links. Now they type the same question into ChatGPT, Perplexity, or Gemini and get one answer — usually with a small number of vendors named inside it.

Generative Engine Optimization, GEO, is the practice of making sure your product is one of the vendors named in that answer.

Why this isn't just SEO with a new name

Search engine optimization gets a page to rank on a results page a human then has to read, compare, and click through. Generative Engine Optimization gets a product recommended directly, inside an answer the buyer already trusts enough to act on.

The mechanics are different too. Search rankings are driven by links, on-page signals, and crawl behavior a site controls directly. AI answers are driven by what third-party sources — review platforms, comparison pages, community threads, case studies — say about a product, and how citable that content is to a model doing retrieval. A page can rank first on Google and never once get mentioned by an AI engine, because the model isn't reading the page — it's reading what other sources say about the company behind it.

The two kinds of AI visibility

Not all AI citations are equal. There's a real difference between:

• Brand-name visibility — the model can describe your product correctly when a buyer already knows to ask for it by name.
• Category-level visibility — the model reaches for your product on its own, when a buyer describes their problem instead of naming a solution.

Brand-name visibility is easy and doesn't generate new demand: it only helps buyers who already found you somewhere else. Category-level visibility is where new pipeline comes from, and it's the harder, more valuable number to move. We published a full breakdown of exactly this gap in our Zakro case study.

How it's measured

We measure GEO with a single number: Share of Answer — the percentage of a buyer's real questions where a product gets named, run live against the major AI engines, in fresh threads with no memory, with every answer logged.

Because AI answers vary from run to run, Share of Answer isn't a one-time score. It's a number you track on a schedule, the same way you'd track search rankings — except the ranking page doesn't exist anymore. The answer is the page.

What actually moves the number

Three things, in order of leverage:

1. Presence on the sources models already trust for a given question — comparison sites, review platforms, community discussions relevant to the category.
2. Content shaped to be quoted — clear claims, real numbers, direct answers to the exact questions buyers ask, not marketing copy that reads well to humans but has nothing a model can lift.
3. A consistent, unambiguous entity — the same name, description, and facts about the company everywhere a model might encounter them, so there's nothing to resolve incorrectly.

None of this is guesswork. It's measurable, it's reproducible, and — like everything else on this blog — we'll keep publishing the actual numbers as we go.