Most AI visibility tools hand you a single number. That number is a noisy sample of one on a model that answers differently every time you ask. Nadelio measures across several runs and reports the score with its confidence interval, so you know whether a result is real or just noise.
You type one brand. Nadelio identifies your competitive set, then measures two territories side by side:
The GEO score combines three equally weighted parts, each on a 0 to 100 scale: your Google share of voice, your coverage in AI answers, and the quality of your AI rank.
Ask an AI assistant the same question ten times and you will not get the same answer ten times. The models are non deterministic by design. A tool that runs your query once and prints a score is reporting a coin flip as if it were a fact.
So Nadelio runs the AI measurement several times and looks at the spread of the results, not just the average.
From the runs we compute the average score and a 95% confidence interval around it: the plain standard error of the mean, times 1.96. Explained in one line to anyone: the true value is very likely within this band.
We report it as 42 ± 6, never as a bare 42. And we refuse to put an interval on a single run: with one measurement, we say so plainly rather than inventing a false precision.
The paid Deep Audit runs more measurements, which tightens the interval. The free audit shows an honest but wider band. Nothing is hidden behind the paywall except the number of samples.
The interval drives one honest label, so you know at a glance how much to trust the number:
This is also why a lead can be an illusion. If your score is 44 ± 9 and a rival sits at 48 ± 11, the bands overlap: the rival is not really ahead, the gap is noise. Nadelio says so. A tool showing bare numbers would call it a loss.