TL;DR
- One mention is not a pattern. Among prompts where the tracked brand appeared at least once, only 5.5% included it in every analyzed answer.
- Variation happens within the same model. About 29% of question-and-model combinations with ten or more answers produced a mix of mentions and omissions.
- Absence needs context. Just over half of tracked prompts never surfaced the brand, partly because broad and adjacent questions were tracked on purpose.
- Measure a rate, not a screenshot. Track a stable set of questions under comparable conditions, separate results by model, and look for sustained changes.
You ask ChatGPT a buying question. Your brand appears. You take a screenshot.
But if you asked again, would you still be there?
At Visibly AI, we analyzed close to 100,000 AI answers for 120 brands across nine industries, including technology, beauty and personal care, and food and beverage.
Among prompts where the tracked brand appeared at least once, only 5.5% included it in every analyzed answer.
For the remaining 94.5%, the brand appeared sometimes and disappeared other times.
These results combine answers from OpenAI and Gemini. They describe what happened in our sample, rather than guaranteeing what either engine will say next.
That’s the difference between knowing your brand can show up and understanding how consistently it does.
One appearance tells you less than you think
Imagine two brands checking their visibility for a relevant shopping question.
Each gets mentioned. Each has a screenshot showing it made the list.
But over 40 comparable answers, one brand appears four times. The other appears 32 times.
That’s an illustrative example, but it captures the problem: a single check can make very different levels of visibility look identical.
An individual answer either mentions you or doesn’t. Across repeated answers, your visibility becomes a rate.
The same question can produce a different result
For example, imagine asking the same AI model “What are the best moisturizers for sensitive skin?” ten times under the same settings. Your skincare brand might appear in four answers and be absent from the other six.
That’s an illustrative example, but we observed this pattern in our data. Among question-and-model combinations with at least ten analyzed answers, roughly 29% produced a mix of mentions and omissions.
That calculation includes combinations where the brand never appeared. Unlike the opening 5.5% figure, it is not limited to questions that produced at least one mention. It describes the share of tested combinations showing variation, not the average chance of a brand being mentioned.
The key finding: variation also occurred within the same model, answering the same question. It wasn’t simply a difference between OpenAI and Gemini.
A missing mention needs context
Most answers in our sample did not mention the tracked brand. Just over half of the tracked prompts never surfaced it in any analyzed answer.
That needs context. At Visibly AI, we deliberately track a broad range of prompts, including adjacent questions and some outside a brand’s core offering, to understand the boundaries of its visibility.
Absence from a loosely related question means something different from absence when a shopper describes exactly the problem your product solves.
Brand mention rates across all 2,630 tracked prompts, combining OpenAI and Gemini answers. Of the 1,270 prompts with at least one mention, only 70 included the brand in every analyzed answer—the 5.5% highlighted above. Source: Visibly AI, July 16–September 22, 2026.
An overall mention rate therefore needs to be read alongside the questions being measured. Changing the prompt mix can change the score without any improvement or decline on existing questions.
Variation doesn’t make visibility unmeasurable
Think about a website’s conversion rate. One visitor buys; another leaves. You need enough observations to understand the pattern.
Brand mention rates work similarly. Repeating comparable checks gives you an estimate of how often your brand appears for a particular question.
More observations can make that estimate more precise. They don’t guarantee the next answer or make the rate permanent. Model updates and changing source material can also affect results over time.
The practical goal is to track a stable set of questions under comparable conditions, separate results by model, and look for sustained changes. Small movements based on a handful of answers deserve less confidence than patterns repeated across many checks.
That gives marketers something more useful than an isolated result: a baseline they can compare over time.
What marketers should do differently
For marketing teams, start with the buying situations that matter most. Measure how often your brand appears across relevant needs, budgets, and use cases.
For AEO and SEO agencies, report repeated observations alongside examples. Keep the questions comparable over time and show the sample behind the rate. One missing answer shouldn’t automatically become a performance alarm.
For ecommerce managers, investigate prompts where your brand appears intermittently. Which products appear alongside yours, and what reasons do the answers give for including them? This can help prioritize work, although it doesn’t prove which content change will improve visibility.
And remember that a mention isn’t necessarily a recommendation. Read the answer to understand whether your product is being suggested, compared, or simply referenced.
Measure when you show up—and how often
This is why Visibly AI repeats buying questions across ChatGPT and Gemini and tracks the results over time.
You can see which prompts surface your brand, how frequently it appears, and which competitors appear alongside it. That helps you identify gaps, prioritize relevant opportunities, and assess whether your visibility is improving.
A screenshot is a useful example. A measured mention rate gives it context.
See how consistently your brand appears with Visibly AI.
About the research
Visibly AI analyzed 92,510 answers across 2,630 tracked prompts for 120 brands in nine industries between July 16 and September 22, 2026. Prompts averaged 35 analyzed answers, ranging from 4 to 80. The 5.5% finding combines engines and describes consistency within the observed sample, not a guarantee of future answers. The same-model analysis covered 5,360 prompt-and-model combinations with at least ten answers each; 1,550 showed both mentions and omissions. Sentiment prompts that named the brand in the question were excluded. These findings describe the studied brands and prompts, not an industry-wide benchmark.