Guide · 4 chapters

GEO measurement framework

Define stable questions, engines, markets, evidence and review cadence before the first run.

Start with a stable measurement question

A useful GEO program begins with the decisions buyers ask AI to help them make. Broad brand questions create awareness signals; comparison and purchase questions expose commercial gaps.

The unit of analysis is not a keyword. It is a question, an answer state and the evidence behind that answer.

Build a consistent framework

Confirm the brand entity, markets, competitors and prompt taxonomy before the first formal run. Keep a shared core prompt set across engines, then add engine-specific questions where behavior differs.

Preserve evidence, not only scores

Every result should connect back to the prompt, answer, engine, market, cited URL and competitor context. Scores summarize; evidence explains what to do.

Improve, assign and rerun

Turn gaps into owned actions across technical access, primary content, structured facts and third-party authority. Preserve the original baseline and rerun the same set after meaningful changes.

Keep exploring

FAQ

Questions teams ask before they start

Is GEO different from SEO?

They overlap on technical access and content quality, but GEO measures the answer itself — whether the brand is named, recommended and cited — instead of a ranking position.

How often should results be remeasured?

Keep the question set stable and rerun on a fixed cadence, then compare like with like. Changing questions and cadence at the same time makes results incomparable.

Does being cited mean being recommended?

No. A page can be cited as background while a competitor is the one actually recommended. The two are reported separately.

Is this written by AI?

Drafts can be generated, but every published piece goes through human review. Nothing reaches the site automatically.

How does this relate to traditional SEO?

It overlaps but is not the same. Both reward clear, credible content; only one of them is measured by whether a model names you in an answer.

Where do the definitions come from?

From how the measurement actually works in the product, so the vocabulary on the site and the numbers in the workspace mean the same thing.

From signal to action

Turn AI visibility into a repeatable growth system.

Create a project, confirm your market and start measuring the questions that matter.

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