Measurement
Your AI visibility score says 36%. ChatGPT never named you.
By Arnav Mukherjee, founder of TofuBofu · July 16, 2026
A founder posted a small experiment in the r/aeo community last week that I have not stopped thinking about. He took one buyer question, ran it eighty times across four AI engines, and counted how often his brand got named. On ChatGPT, across all eighty answers, the count was zero. Not low. Zero. Yet if you blended the whole run into a single visibility percentage, the way a lot of tools do, it would have read somewhere around 36%. A number that looks like partial success, sitting on top of a total absence on the one engine most of his buyers use.
I recognized it instantly, because I had just scanned my own company and watched the opposite happen: an honest, ugly, flat zero. Same reality, two very different numbers, depending entirely on whether the tool tells you the truth or averages it into something more comfortable. This post is about that gap, and how to read an AI visibility score so it cannot fool you.
What a blended score hides
A blended AI visibility score takes several separate measurements and rolls them into one figure: how often you are named, across how many engines, how prominently, sometimes weighted by sentiment. The appeal is obvious. One number is easy to track, easy to put on a slide, easy to watch go up. I understand why the category reaches for it.
The problem is what averaging does to a zero. If you are named well on two engines and never on a third, the blend quietly carries the two strong results over the weak one, and the composite lands somewhere reassuring. The math is correct. The message is wrong, because the two facts hidden inside that composite, you are doing fine here and you do not exist there, demand completely different responses, and the single number erases the distinction. You cannot act on an average. You act on the breakdown.
A zero on one engine is a signal, not an outlier
The instinct is to treat one engine's zero as noise, a fluke to be smoothed over. It is the opposite. The engines are built differently enough that a hard zero on one of them is telling you something specific. A search-grounded engine like Perplexity retrieves live pages, so it can find you as soon as your content and citations exist. A training-based engine answering more from memory may not carry you at all until a model update. Add the fact that these systems are non-deterministic, the same question returns different answers on different runs, and you get brands that are named repeatedly on one engine and never on another.
Which is exactly why the identity of the zero matters. A zero on a low-traffic engine is a minor gap. A zero on ChatGPT is a different order of problem, because that is where the largest share of buyer research now begins. G2's 2026 research puts 51% of B2B buyers starting on an AI chatbot, and ChatGPT is the biggest single door into that. Averaging that zero into a blended 36% does not shrink the problem. It hides it behind a number that lets you feel fine about the surface you can least afford to be missing from.
See your real number on each engine, not one blended average
A free scan shows your presence per engine across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, and Bing Copilot, so a zero can never hide inside an average.
Run your free scanThe numbers that survive disaggregation
Break the blend apart and a handful of measurements are left standing, the ones you can actually act on. Read your score through these, not through the composite.
1. Presence rate per engine
Not the average across engines, the rate on each one separately. This is the number that exposes a zero the moment it appears, and it is the first thing to look at. If any engine reads zero, start there before you celebrate the others.
2. Weight by where your buyers are
Not every engine deserves equal billing. A gap on the engine that carries most of your category's research costs more than the same gap on a minor one. Rank your engines by real buyer traffic and read the presence rates in that order, so the score reflects consequence, not just arithmetic.
3. Recommended versus merely mentioned
Being named in a list is not the same as being the answer. A tool that counts any mention the same as a genuine recommendation inflates its own score. Separate the two: appearing is table stakes, being recommended is the win, and only the split tells you which you have.
4. Prominence and per-query detail
Where you appear in an answer, first line or buried at the end, changes how much the mention is worth, and which specific buyer queries you win or lose is the map for what to fix next. A blended number has neither. The per-query, per-engine grid is where the work actually gets assigned.
The honest version is less flattering, and more useful
When I scanned my own company, the flat zero stung precisely because there was nowhere to hide. But that is the version I would rather ship. A blended 36% would have let me nod, file it under work in progress, and miss that ChatGPT did not consider us a candidate at all. The zero forced the real question. This is the whole reason we show presence per engine before any composite: the number that makes you flinch is usually the one worth acting on, and a score designed to keep you comfortable is a score designed to keep you still.
Frequently asked questions
What is a blended AI visibility score?
It is a single number that rolls several measurements into one: how often you are named, across how many engines, how prominently, and sometimes with what sentiment. The convenience is real, you get one figure to track. The risk is that averaging flattens the detail, so a strong result on one engine can mask a total absence on another, and the one number never tells you which.
Can my AI visibility score look good while ChatGPT never mentions me?
Yes, and it is common. If a tool averages your presence across four or five engines, being named often on two of them can pull the blended figure into respectable territory even when ChatGPT, the highest-traffic surface, names you zero times. The score reads as partial success while your most important engine reads as invisible. The average is arithmetically true and strategically misleading.
Why does one AI engine show zero when others mention me?
Because the engines are built differently. A search-grounded engine that retrieves live pages can find you the moment your content and citations exist, while a training-based engine answering from memory may not know you until a model update, or may simply never surface you for a given query. Different retrieval, different training data, and non-deterministic sampling all mean a brand can be named repeatedly on one engine and never on another.
Which AI visibility number should I actually watch?
Watch presence rate per engine, not the blend. Then weight by where your buyers actually are: a zero on ChatGPT matters more than a zero on a low-traffic engine because more research starts there. Add whether you are recommended or merely mentioned, and whether you appear early or late in the answer. Those disaggregated numbers tell you where to act. The single blended figure is a headline at best.
Is a single AI visibility score useless?
Not useless, just insufficient. One number is fine for tracking direction over time, is this going up or down, and for a quick shared reference. It stops being useful the moment you need to decide what to do, because the action always lives in the breakdown: which engine, which query, mentioned or recommended. Use the blend as a headline and never as the whole story.
How should a tool present AI visibility instead of one blended number?
It should show the per-engine breakdown first, so a zero on any single engine is impossible to hide. It should separate being recommended from being merely named, show prominence, and break results down per buyer query so you can see exactly where you win and lose. A blended headline is fine on top, as long as the disaggregated truth is one glance away underneath it.
Should I worry more about a low average or a single zero?
A single zero on a high-traffic engine usually deserves more attention than a mediocre average, because absence and weakness are different problems. A low-but-nonzero score means you are in the running and need to climb. A hard zero means the engine does not consider you a candidate at all, which is a category or corroboration problem, and no amount of averaging changes that you are simply not in that answer.
Sources and further reading
- G2 2026 AI Search Insight Report: 51% of B2B buyers start research on an AI chatbot, which is why a zero on the biggest engine cannot be averaged away
- The AI visibility metrics that actually matter: the four measurements worth tracking, and the vanity ones to ignore
- How often should I check my AI visibility?: why answers wobble run to run, and why non-determinism means a single reading is a snapshot