One Daily Score for ChatGPT, Claude, Perplexity & Gemini

Key Takeaways

• A single AI visibility score is genuinely useful for spotting trend and reporting to a team, but it has to be built from a stable, repeated, real prompt set, or it's just noise dressed up as a number.
• The right formula is closer to "percentage of your tracked buyer prompts where you were named, averaged across a trailing window," not a one-time snapshot.
• Averaging across engines hides real problems. A score of 50 could mean "moderate everywhere" or "great on ChatGPT, zero everywhere else," and those need completely different fixes.
• Because AI answers are non-deterministic, a score built from single-run checks bounces around meaninglessly. It needs multiple runs per prompt, ideally a trailing weekly average, to be trustworthy.
• A daily number that moves on noise trains you to react to nothing. A weekly-average number that moves on a real 3-4 point shift is the one worth watching.

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Why founders want this number

Once you've manually checked a few prompts across a couple of engines, the next instinct is obvious: turn this into one number you can watch move over time and show a co-founder or investor. That instinct is right, a single trackable score is genuinely more useful than a pile of individual prompt results nobody reviews weekly. Getting the number right is harder than it sounds, though, and a badly-built version is actively misleading.

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What the score should actually measure

The honest version of this metric is: out of your real tracked buyer prompts (not generic category terms, the actual questions your buyers ask, see how to find which competitors ChatGPT recommends over you for how to build that list), what percentage resulted in your brand being named, averaged across engines and across a trailing window of runs.

That's different from "are we mentioned anywhere on the internet about AI" (too vague to act on) and different from a single-prompt, single-run check ("we asked ChatGPT once and we weren't named," too noisy to trust).

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Averaging engines together hides the real problem

A blended score across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews is convenient for a headline number, but it actively conceals what's actionable. A blended 50 could mean you're strong on ChatGPT and invisible everywhere else, or genuinely moderate across the board, and those two situations call for completely different fixes (the first is "do more of whatever's working on ChatGPT elsewhere," the second is "your fundamentals are weak everywhere").

If you're going to track one number, track the per-engine breakdown right underneath it. The headline score is for a Monday morning glance. The per-engine breakdown is where the actual decision happens.

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Non-determinism means single runs lie to you

AI answers vary run to run, this is inherent to how these models generate responses, not a bug in any tracking approach. If your "score" comes from asking each prompt once, a day where you happened to get named 60% of the time and a day where you happened to get named 20% of the time will produce wildly different numbers for the exact same underlying reality.

The fix is repetition: multiple runs per prompt, and a trailing average (a rolling 7-day window is a reasonable default) rather than a single day's snapshot. This is also why a daily number is often the wrong cadence to obsess over, day-to-day movement in a metric built from a probabilistic system is usually noise. A week-over-week shift is the signal worth reacting to.

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What "prompts_observed" and "scans_in_window" should tell you

Any real version of this score needs to show its own confidence alongside the number. If you only ran 2 scans this week across 5 prompts, a score of "40" carries a lot less weight than a score of "40" built from 30 scans across 60 prompts. Treat a score without a visible confidence indicator as a marketing number, not a decision-making one.

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Building it yourself vs. not

You can approximate this manually with a spreadsheet: log every prompt run, every engine, every result, calculate a rolling percentage weekly. It works, and it's a real amount of ongoing manual effort to sustain past a handful of prompts, which is usually the point solo founders start treating this as a weekly time-boxed task instead of an ongoing project.

GetIntel's Visibility Score is built exactly this way on purpose: a trailing 7-day average across your real tracked buyer prompts, run multiple times, across all five major engines, with the per-engine breakdown and confidence numbers shown alongside it, not hidden behind one flattering headline figure.

See your own trailing AI Visibility Score across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.