Your AI visibility score looks fine. Here's how it hides the questions you're losing.

Questions this guide answers

• Why is my AI visibility score misleading?
• Should branded prompts count in an AI visibility score?
• Why did my AI visibility score drop after I added prompts?
• Should I track AI visibility by prompt or by topic?
• How do I compare AI visibility across different prompts and topics?

Direct answer

An AI visibility score is an average. It is the share of AI answers that name your brand, taken across every prompt and engine you track. An average can look healthy while most of the questions your buyers ask never mention you, because a few strong prompts carry the rest.

Four things move a blended score without changing what buyers see. Branded prompts that name you in the question lift the floor. Adding or removing prompts moves the number while every answer stays the same. Averaging across engines hides an engine where you are absent. And counting mentions credits answers that name you without recommending you.

Read the score per prompt instead. Report branded prompts on their own line, sort the rest from lowest to highest, read each engine separately, and compare only prompts measured in both periods. The prompts at zero are where buyers build a shortlist without you, and they are the ones to work on first.

What a blended score actually averages

Most AI visibility scores start from a prompt set: a fixed list of questions, written the way buyers ask them, that a tool runs on each engine on a schedule. Every run produces answers, and each answer either names your brand or doesn't. The score divides one by the other.

That makes it an average across 3 things at once: prompts, engines, and runs. Each choice about how to combine them changes the number. Does every prompt count equally, or does a prompt with 100 answers outweigh one with 20? Does every engine count equally, or by how much your buyers use it? Do branded prompts count at all? Two tools can disagree for these reasons alone, before any difference in the answers they collected.

None of this makes the score wrong. It makes it a summary, and a summary answers "how are we doing overall?" It can't answer "which buyer questions are we losing?", and that is the question that tells your team what to do next.

A worked example: 19% that is really zero

Illustrative scenario only. A mid-market payroll software vendor tracks 30 prompts on 4 engines, with roughly the same number of answers for every prompt. Its dashboard says the brand is named in 19% of AI answers. The table shows where the 19% comes from.

What the example hides

The 19% reads like "we show up in about 1 answer in 5." The real picture is that on 20 of the 30 questions, the ones buyers ask before they know the brand exists, no answer on any engine named it. Take the 5 branded prompts out and the rate falls to 4%.

Nothing in the blended number is false. It is just answering a different question from the one the marketing team needs answered. The 20 category prompts at zero are where AI engines leave the brand out of the shortlist, and the score gives no sign of them.

4 ways a blended score misleads

Each of these can move or inflate the score with no change in what a buyer sees. All 4 use the illustrative numbers above.

Prompt-level or topic-level: use both, for different jobs

Practitioners keep asking whether to track AI visibility by individual prompt or by topic, in threads like "Do you track AI visibility by individual prompts…" and "AI visibility scores are confusing" on r/aeo. The answer is both, because they do different jobs.

Topic views are for deciding. Grouping prompts into topics such as pricing, integrations, or use cases shows leadership where the brand is strong and weak, and helps decide where to spend the next month. They inherit the same weakness as the blended score at a smaller scale: a topic average can still hide a question at zero.

Prompt views are for acting and verifying. A fix targets a specific question: a page that answers it, a source the engine cites for it, a fact the answer gets wrong. You can only check whether the fix worked by asking the same question again, on the same engines, and comparing. A topic average can move for reasons unrelated to the fix.

So decide at topic level, then act and verify at prompt level. Keep the topic groups stable, so a topic's trend isn't also a change in which prompts it contains.

How to read your score per prompt: a 6-step routine

This takes about 10 minutes on a 30-prompt set, and it works in any tool that shows results by prompt and engine.

• Pull branded prompts out. Report them on their own line, as an accuracy check.
• Sort the rest from lowest to highest. Count the prompts where no answer named you. That count is often more useful to report than the average.
• Read each engine separately. Note any engine where a group of prompts sits at zero while other engines name you.
• Compare like with like. Use only prompts measured in both periods, and note the date of every change to the prompt set.
• Start with demand. Among the prompts at zero, work first on the ones buyers ask most often.
• Read the answers behind the zeros. For each one, note which brands the answers name and which pages they cite. That is the start of the diagnosis: the sources that shape the answer are the ones to be present on.

How SolCrys shows it

In SolCrys, Prompt Analysis lists every tracked question with its estimated volume (High, Medium, or Low), how many answers named your brand, which other brands the answers named, and how well they recommend you. Switch between a view by prompt and a view by category, filter by engine and time range, and expand a row to read the answers themselves. The headline Overall Mention figure is weighted by the engine mix you configured, so it is still an average; Prompt Analysis is where the distribution shows.

From there, the work follows the Measure → Diagnose → Execute → Verify loop. A Workspace Deep Analysis takes up to 10 of the prompts you are losing and returns a ranked plan: what holds the brand back, with the answers that show it, and the actions to take. Your team approves what ships. Then the same prompts run again on the same engines, so you can see whether the answer moved.

To see your own prompts at zero, Start Free: 10 ChatGPT prompts and 1 content audit, free, with email verification and no credit card.

What we don't claim

The numbers in the worked example are illustrative, chosen to make the arithmetic easy to follow. The 4 effects are arithmetic, though, and they apply to any score built as an average.

A per-prompt reading is noisier than an average, because each prompt has fewer answers behind it. A single run at zero is one sample, not a measurement. Read a prompt over a window of several runs before acting on it, and see how many runs an AI-visibility number needs for the sample-size evidence.

And a prompt-level view doesn't fix anything by itself. It tells you, more honestly, where the work is.

Sources

• r/aeo: "Do you track AI visibility by individual prompts…" (Sept. 30, 2026)
• r/aeo: "AI visibility scores are confusing" (Oct. 9, 2026)

FAQ

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