What Is a GEO Score? How AI Visibility Scoring Actually Works

A GEO score is a single number some tools use to summarize how visible your brand is inside AI-generated answers: essentially one figure meant to capture how often, how prominently, and how favorably assistants like ChatGPT, Gemini, and Perplexity mention you when someone in your category asks a question. There is no industry standard behind it. Every tool that publishes a “GEO score” calculates it a different way, with its own mix of inputs and its own scale, which means a score from one tool is not directly comparable to a score from another.

What a GEO score is trying to compress

The underlying idea is reasonable: measuring AI visibility by hand, question by question, produces a pile of individual data points, not a single trend line. A score is an attempt to fold that pile into one number so it can be tracked over time and compared across competitors at a glance. The problem is not the goal, it is that “fold it into one number” requires a series of judgment calls about what to weight and how, and different tools make those calls differently without always saying so.

Why the sample behind the score matters as much as the formula

A score is also only as trustworthy as the sample it was built from. Twenty four answers, the size of FoundCite’s public teardown, is enough to show a real, reproducible pattern (the most-named brand not being the best-positioned one), but it is still a modest sample, and a single unusual answer can move a ratio like “14 of 24” more than the same single answer would move a ratio built from a much larger run. A tool that computes a GEO score from a handful of questions, run once, is reporting something real, but it is reporting it with wider error bars than the clean single number on the dashboard tends to suggest. Ask what the sample size behind any published score actually is before treating small week-to-week changes in it as meaningful.

What usually goes into one

Most versions draw from some combination of the following, in varying proportions:

• Mention frequency. How often the brand is named across a set of questions or a sample of answers.
• Position within the answer. Whether the brand is named first, or buried after several competitors.
• Sentiment. Whether the mention is a straightforward recommendation, a neutral mention, or a caveat-laden one.
• Source authority. Whether the pages the assistant cited to reach that mention are themselves credible, independent sources.
• On-page signal strength. Whether the brand’s own site gives a model clean, structured facts to work with in the first place.

The frequency-versus-position tension is where this gets concrete, not theoretical. In FoundCite’s public help desk teardown, 24 real answers to 8 buyer questions, Zendesk was the most-named brand, appearing in 14 of 24 answers. But two free, open source projects, Zammad and osTicket, averaged better positions when they were named, 2.5 and 3.5 respectively, against Zendesk’s average of 4.6. Score purely on frequency and Zendesk wins. Score purely on position and the free tools win. A single “GEO score” has to pick a weighting between those two signals, and the vendor rarely publishes which one it chose. The full breakdown, including a third contender, Help Scout, at 12 of 24 mentions and an average position of 3.8, is in the raw teardown file.

Why it is not standardized

Unlike, say, a credit score, which has a small number of well-known models (FICO, VantageScore) with roughly documented inputs, there is no equivalent body defining a GEO score. It is a young, fast-moving space, and most of the tools competing in it treat their exact formula as a product differentiator rather than something to publish. Search for the term today and what comes back is mostly other tools’ product pages, each promising to calculate one for you, rarely explaining the formula in enough detail to reproduce it. That is not a criticism of any one vendor so much as a description of where the category is right now: early, fragmented, and short on shared methodology.

The practical consequence is that a GEO score is most useful as a relative measure inside one tool, tracked over time for one brand, or compared against competitors measured by that same tool with the same method. Treating it as an absolute, portable grade, the way a credit score travels between lenders, is asking more of the number than most of them are built to deliver.

Two scores can also disagree simply because they were built from different inputs by design, not because either one made a mistake. A score built from one assistant’s answers alone measures something narrower than a score built by averaging across several assistants, the way FoundCite’s own method blends ChatGPT, Grok, and Perplexity into one count. Neither approach is wrong. They are answering slightly different questions, and a brand can legitimately look stronger in one than the other without either number being broken.

What counts as a “good” score

There is no universal cutoff worth repeating here, and any specific threshold a tool gives you (“above 70 is strong”) is only meaningful inside that tool’s own scale. A more durable way to judge it: measure yourself against direct competitors using the identical method and the identical question set, in the same run. In the help desk example, being named in 14 of 24 answers is only informative next to the fact that a competitor was named in 12 of 24 and another in 2 of 24, all counted the same way, in the same run, on the same day. The ratio matters far less on its own than it does relative to the field it was measured against.

How to actually raise it

Whatever the exact formula behind a given score, the levers pointing in the right direction are consistent across tools:

• Get named on the pages assistants already trust. Independent comparisons, documentation, and review pages feed both the frequency signal and the position signal at once, since they are the pages most likely to get read and quoted in the first place.
• Write plain, quotable sentences. A model has nothing concrete to lift from marketing language that talks around what a product does. State what it does, for whom, and how it differs from the obvious alternative, in sentences a person could paste directly into an answer.
• Keep titles, descriptions, and structured data accurate and specific. That is the first layer a model or the crawler feeding it reads, before it ever gets to your prose. The meta tag generator is a fast way to check that layer on a page you already have live.
• Measure before and after, with the same method. A score moving is only informative if you know it moved because of something you changed, not because the sample behind it shifted. Re-running the identical question set before and after a change is the only way to tell the difference.

None of that is a shortcut to a specific number, and no article can hand you one that means anything outside the tool that produced it. It is the same underlying work described in what LLM SEO actually involves, aimed at the same goal as AI visibility itself: more mentions, in better positions, across more of the questions your buyers actually ask, measured the same way every time so a change in the number means something.