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We are publishing the results of our own measurements here brand visibility in AI responses. The first study covers one brand, a set of 18 queries that have been put on hold and surveys from June, July and September 2026. Key finding: the brand has gone from zero references to be the most common the source cited in its category, and recommends only one of the four models has it.
Author: Patryk Pietrzyk · Last updated: 25 October 2026 · reading time: approximately 7 minutes
Method

What did we measure in the AI brand visibility study?

The survey covered one brand: The „Węże” lime works in Działoszyn, a regional producer of building materials and fertilisers, which sells mainly within a single province. Set 18 purchase enquiries was frozen before the first measurement and did not change between scans.
Between the first and last measurements, the set of engines and the method used to calculate the average visibility index changed. We have therefore not included visibility figures as percentages here: the values from June and September are not comparable. We have retained only those indicators which we have calculated in the same way throughout.
All three are simple calculations: how many queries mention the brand, in how many models is its domain referenced, and how many search engines recommend the brand without hints. We describe the method of calculation in methodology.
Study parameters
BrandOne. A regional producer of building materials and fertilisers, operating within a single province.
Enquiries18, frozen prior to the first measurement, identical in every scan.
MeasurementsJune, July and September 2026. In the intervening period, the brand worked on raising its profile.
EnginesFour in the latest survey. The sample changed over the course of the survey, which limited the comparability of some of the indicators.

We are also describing this case, along with the company’s name (with its consent), in a series of posts about the lime manufacturer.

Results

How have mentions, citations and recommendations changed?

Mentions rose from 0 to 18 out of 18 queries, domain citation from 6% to 94%, and the recommendation appeared in only one engine. Two curves rose together, whilst the third barely moved.
Study procedure; measurements taken in June, July and September 2026
Search queries mentioning the brand Search queries containing the brand’s domain name
100% 66% 33% 0% June July September 0 out of 18 17 out of 18 18 out of 18

Both figures are calculated using the same set of 18 queries in each measurement. The vertical axis shows the proportion of queries, not the average visibility metric.

Brand mentions0 out of 18 → 17 out of 18 → 18 out of 18In June, the brand did not appear even once, although the models discussed its category at length. In September, it appears in every query in the set.
Citing your own domain6% → 50% → 94%In the latest survey, the brand’s domain is the source for 17 out of 18 queries and is cited more often than any other domain in the category.
Recommendation0 → 0 → 1 engineIn the first two surveys, no driver recommended the brand of their own accord, in the third measurement One of the four did it. Barrier The description given in each report was the same.
At that time, the brand was operating on its own domain: it was updating product pages, she added company details and organised the content into shopping-related questions. She did not achieve anything during that period independent reviews or rankings. The first two indicators responded to this work, but the third did not.
Conclusion

Does visibility in AI imply a recommendation?

No. Working on our own domain was enough for models to start mentioning and quoting the brand, but it wasn’t enough for them to start recommending it.
01
The fact that there are no mentions does not mean the topic is dead In the first survey, the models discussed the category at length and named specific suppliers. The brand simply wasn’t among them. The absence of any mention constitutes information about the brand, not about an interest in the subject.
02
A custom domain triggers two out of three indicators Organising the content and data describing the company has resulted in mentions and citations within one month. It’s the fastest option available.
03
Being the most frequently cited source does not constitute a recommendation In the latest survey, models visit the brand’s website more often than any other source in the category, yet they still do not recommend it. Citation is proof that the content is available, not an indication of quality.
04
The barrier was the same in every report In every report, the models highlighted the same shortcoming: a lack of independent rankings, comparative tests and external reviews. No changes made to their own website have altered this.
We describe this condition separately in the article known, but not recommended. The study shows how much work can be done in this state without moving a single step.
Observation

Why does the same brand come across differently in different AI chatbots?

Because Each model selects brands differently and sources: in September 2026, Claude and Perplexity mentioned the brand in 100% responses, ChatGPT in 56%, and Gemini in 44%. The data comes from a single measurement; the four engines were given an identical set of queries on the same day.
Proportion of responses mentioning the brand, measured in September 2026
Claude
100%average position 1.8
Perplexity
100%average position 11.2
ChatGPT
56%average position 10.3
Gemini
44%average position 12.9

Mentions and rankings are two separate things. Both search engines mention the brand in every result, but one places it in the top two, whilst the other ranks it only around eleventh place.

The practical implication concerns the way the search works. If someone types a query about their category into a chat and sees their own brand at the top of the results, they will single-engine performance, rather than a picture of visibility. The same brand, in a different model, appears in less than half of the responses on the same day. We explain how to check this for yourself in the article how to find out what AI says about a company.
To be honest

What are the limitations of this study?

This is a single brand in a single category; the methodology changed partway through, and without a control group, it is impossible to prove causality. You need to be aware of this before applying these figures to your own category.
It’s one brand, not a trialThe results describe a specific case within a specific category. They do not provide a basis for making claims about the market or about how another sector might behave.
This category is specificA technical product, regional sales, competition comprising several large manufacturers. In a saturated market, the pace would be different.
The method changed during the course ofBetween measurements, the set of engines and the method used to calculate average visibility changed. We are therefore only showing the figures that have been calculated in the same way from the start.
This is not proof of causationWe can see that the metrics have increased following work on our own domain. As we do not have a control group, we cannot rule out the possibility that part of the change is due to updates to the models themselves.
Future studies will cover more brands and categories. Until then, please treat these figures as an example of a single case rather than as the norm.
FAQ

Questions about this survey

Questions about the methodology, the data and what this implies for other categories.
How many brands were included in this survey?

Just one. This is a single-case study conducted over three months using a fixed set of 18 queries, not a sample-based study. The results describe a mechanism, not a market distribution.

Why don’t you give the visibility index as a percentage?

This is because, between the measurements, the set of engines and the method used to calculate the average changed. The figures from June and September do not represent the same quantity, so plotting them on the same graph would be misleading.

Did the brand pay to have these results published?

No. These are data from measurements carried out as part of routine maintenance, published with the customer’s consent.

How do we know that the increase was due to work on the website?

It is not known for certain. We can see a temporal correlation, but without a control group, it is impossible to distinguish the effect of the work from the updates to the models themselves. We therefore describe this as an observation, not as proof of causation.

Will it be the same in my sector?

It doesn’t have to. The pace depends mainly on how saturated the category is. The brand in question is performing well in a regional niche, where the models had few specific indications. We expect that, in a category with dozens of recognisable players, the first mentions will come later.

What needs to be done to ensure that the model starts recommending, rather than just listing?

Obtain independent quality endorsements from outside your own sphere: reviews, comparative tests, rankings. We discuss this in the article known, but not recommended.

How long did it take for the first recommendation to come in?

After 11 weeks: in full scan On 10 September 2026, the brand was recommended by one of its four engines. Mentions and citations began to appear just one month later.

How often did you repeat the measurement?

Three times: 24 June, 27 July and 10 September 2026, always using the same set of queries. The target frequency is once a month, and we explain why this is the case in the article how often to measure visibility.

Can I measure the same thing for my brand?

Yes. The measurement in Aelo works in exactly the same way as in this study: a neutral scan, a fixed set of queries, and mentions, citations and recommendations shown separately in report.

Will there be further studies?

Yes, as we gather data from more brands and categories. Further publications will appear on this page.

What sources is this text based on?

See where your brand stands

Free Account No card required: you get the following to start with 250 AeloCoins and a full report on your brand’s visibility on ChatGPT, Claude, Gemini and Perplexity. You’ll see mentions, citations and brand recognition figures separately, just as in this study. Plans and modules you’ll find in pricing.
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Related: known, but not recommended, methodology, how often to measure visibility, Why does AI recommend the competition?, Visibility.
Aelo
Aelo (aeloapp.io) measures brand visibility on ChatGPT, Claude, Gemini and Perplexity.

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