Across eight engines, dozens of buyer questions, and answers that change every time you ask, the real question is simple: are you winning or losing? The AI Visibility Score answers it with a single figure from 0 to 100, then shows you where that figure sits against the competitors you actually face.
No credit card required. Your score in about a minute.
Mentions here, sentiment there, citations on a third tab — all of it spread across eight engines. Every chart is true and none of them answers the only question leadership actually asks.
You can see everything and still not know if you are ahead. The signals that matter get lost next to the ones that do not, and nobody agrees on which is which.
Say your score is 60. Good or bad depends entirely on your category. Without knowing where the leaders sit, the figure tells you nothing you can act on.
You cannot set a goal, judge a campaign, or show the board progress without one stable measure that means the same thing month after month.
One figure, measured the same way every time and placed next to your real competitors, so "how are we doing in AI search" finally has an answer.
The score is not a tab you read off a report. It is the end of a pipeline that takes every answer we capture, judges each one, weighs the parts that matter, and lands on a single figure.
Pull every captured answer across engines, personas and regions.
Rate presence, prominence, sentiment, citations and reach.
Count the moments that matter most, tuned to your category.
Map to a 0–100 scale so any company is comparable.
Stable, repeatable, ready to track.
Many noisy inputs go in. One steady, comparable number comes out.
Turning all of that into a single figure is the real work, and it is not an average of a column of numbers. Each answer carries six separate signals, each engine reaches a different size of audience, and the same prompt returns slightly different replies every time you ask. A plain average would either drown the signals that count or swing from one day to the next for no real reason.
So every part is weighted. Being named first in a ChatGPT answer that millions of buyers see counts for far more than a passing mention buried in a quiet reply. The weights themselves are tuned per category, because what makes a cybersecurity vendor look authoritative is not what makes a project tool look authoritative. Then the result is normalized to 0 to 100, so a five-person startup and a public company can sit on the same axis and the comparison still holds.
The score is a blend, not a single measurement. It asks how often you show up, how high in the answer, how warmly you are described, whether trusted sources back you, and how much of the conversation you own versus your rivals.
Those weights are not fixed. They shift by category, because authority looks different in security than it does in HR software, and the score reflects that.
A metric you cannot trust is worse than no metric. If the figure jumped around every day because a model worded things differently, you could never set a goal against it or report it with a straight face.
Because the answers underneath shift from one run to the next, we sample each prompt many times and smooth the results before they ever reach your score. A single lucky or unlucky reply cannot move the needle on its own. The score changes when the pattern across many answers changes, which means a rise or a drop is real, and worth acting on, rather than noise dressed up as news.
72 sounds healthy until you learn the leaders in your space sit at 88. So the score never travels alone. We place it inside your category and rank it against anonymized data from other brands competing for the same answers, then turn that into a plain statement like 'top 18 percent of cybersecurity vendors.'
A percentile is only as good as the data behind it. With a handful of companies, 'top 15 percent' is a rough estimate. With hundreds, the distribution fills in, the cutoffs tighten, and the same phrase becomes a precise, defensible line.
That is the quiet advantage of a shared index. It is not static. It compounds. The longer the platform runs and the more brands measure themselves on it, the more accurate your own standing becomes, at no extra effort on your part.
| A typical visibility dashboard | The AI Visibility Score |
|---|---|
| Many separate metrics to interpret | One figure your whole team understands |
| A number with no context | Ranked against your real competitive set |
| Swings with day-to-day model noise | Smoothed, so it moves only on real change |
| Treats every mention the same | Weights the moments that actually matter |
| Compares startups and giants unfairly | Normalized so the comparison is fair |
| Gets no better over time | Sharpens as more brands join the index |
The value is not the figure itself. It is everything a clear, trusted figure makes possible.
Leadership does not want ten charts. They want to know if the investment is working. The score gives you one line they instantly understand.
A stable number turns a vague ambition into a measurable goal you can run campaigns against.
"GrackerAI transformed our visibility and helped us reach decision-makers right when they were researching."
When the whole company reads AI search through a single, benchmarked figure, you can set goals, judge campaigns, and report progress without translating a dashboard every time.
The engine room beneath the score. Every answer captured across all eight engines, by persona and region.
See the brands you are benchmarked against and exactly where they are winning citations you are not.
Turn a low component into a ranked list of fixes, so raising the score becomes a clear plan rather than a guess.
Discover your AI visibility gaps and start capturing millions of new product discovery clicks.
