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Methodology

How Aelo measures brand visibility in AI

Aelo asks the models questions from your category without mentioning the brand name, using a frozen set of queries, and checks whether the brand appears in the response, in what position, alongside which other terms, and which sources the model uses. The measurement covers four models, whilst we measure presence on X using a separate module.
Last updated: 6 September 2026 · reading time: approximately 7 minutes
Measurement principle

A neutral scan: we ask the questions exactly as the client does

Your brand name isn’t included in the search query. We ask questions about the category and only then, in the responses, do we check whether the brand name comes up of its own accord. If we were to ask the model directly about the brand, it would almost always give an answer, and the result would be inflated.
There is one exception, and we address it directly. Alongside spontaneous awareness, we measure comparative awareness, where the brand name is mentioned deliberately in order to contrast it with a specific competitor.
We report both metrics separately and never combine them into a single figure. Spontaneous awareness indicates whether the brand features at all in the responses. Comparative positioning shows how the brand fares when the customer is already familiar with both names.
Spontaneous visibility no brand name in the enquiry
Enquiry

Which companies in Poland are involved in [category]?

Model response
  1. 1Brand A
  2. 2Your brandreference
  3. 3Brand B

The brand has to stand out on its own merits. We look at mentions, positioning and who’s alongside it.

Comparative position The name is used deliberately
Enquiry

What [Your brand] differs from [competitor]?

Model response
Your brand in view of competitor

The result shows how the brand performs in a direct comparison, rather than whether it is visible.

Scan range

Four models in a single run

Each model responds differently, selects brands differently and utilises sources differently, so one alone is not enough to assess visibility. We show the results separately for each model and in aggregate.
ChatGPTIt accesses the internet in real time and gives significant weight to fresh sources.
ClaudeHe draws mainly on the knowledge gained from his training and is cautious in his recommendations.
GeminiIt combines the model’s knowledge with Google search results.
PerplexityHe almost always cites his sources, so he’s best at showing where he’s quoted from.
Module XWe measure the brand’s presence on X separately, outside the scan across the four models. A call there costs many times more than on the other engines, and the responses follow a different logic, so mixing this with the rest would skew the overall result.
We update our set of models as the market evolves. We record any changes to the set in the report, as this affects the comparability of results over time.
First scan A set of queries is generated from the topics in the brand card and is frozen 29 enquiries frozen
Another scan The same set, the same models 29 enquiries comparable
Another scan The same set, the same models 29 enquiries comparable
You’re changing the topics in the brand tab
Scan after the change The set is being built from scratch; the report notes this 12 enquiries a new measure

Illustrative figures. The drop from 29 to 12 queries looks like a regression on the graph, but it is actually a change in the unit of measurement.

Repeatability

We freeze the query set on the first scan

Queries arise from the topics described in the brand profile: what you sell, to whom and where. After the initial scan, the set is locked so that subsequent measurements assess the same elements.
If you change the topics in the brand tab, the set will change accordingly. The report takes this into account, because without this information, the drop in visibility would appear to be a decline, when in fact it is a change in the metric.
We discuss what happens between scans in more detail in the article Why aren’t the changes visible straight away?.
Records

More than one number

We do not reduce visibility to a single metric, as a single figure can be misleading. A brand may be mentioned frequently but never cited, or it may be cited but described incorrectly.
01 References 3 of 18
Do models switch brands in your set of queries, and if so, how often?.
02 Item 2 in reply
Where does the brand rank? The higher it is, the greater the chance that the customer will choose it.
03 Proportion of responses 12%
What proportion of all the mentions relating to the same set are yours?.
04 References 6%
What sources do the models use, and is your domain among them?.
05 Sentiment neutral
Is the brand described in positive, neutral or negative terms?.

Sample data. We report each metric separately for each model and in aggregate.

The two figures that raise the most questions

How we calculate share and citations

Proportion of responsesWe take your set of search queries, count the mentions of your brand and your specified competitors in the models’ responses, and check what proportion of all mentions belong to you. The conditions are identical for every brand, as the questions and models are the same, so the comparison is fair. We track changes in market share over time, as trends reveal more than a single measurement.mentions of your brand ÷ mentions of all the brands being compared
Citation indexWe collect the sources that the models cite in their responses and group them by query and model. You can then see which domains dominate in your category and whether your website appears as a source at all. This is separate from mentions: a brand may be mentioned by name, yet never actually cited.replies mentioning your domain ÷ total number of replies
Measurement frequency

A full scan once a month

A single measurement is simply a snapshot of the situation at a given moment. It is the history that provides the real value, which is why we record every result and show the trend.
Day 1Full scan Day 30Another full scan You implement tasks · the models process the changes
What does a full scan provide? A more in-depth analysis of citations, sources, barriers and competition. This forms the basis for a report and a list of tasks.
Why not more often? Changes on the website and signals from outside it take time before the models pick them up.
What would a more frequent scan measure? Variations in the models, not the result of your work. A single result should still be interpreted as a point within a trend.
The cycle repeats every month, and each result is recorded
We explain how much time the models need in this article Why aren’t the changes visible straight away?, and the selection of frequencies in the article How often should visibility be measured?.
To be honest

What this measurement does not reveal

We make a distinction between two things: whether we diagnose accurately and whether we change the outcome. The diagnosis has been verified. We have not proven its effectiveness, and we will not pretend otherwise.
The models are not deterministicThe same query can produce a slightly different result, and the models change with each update. That is why we treat individual measurements with caution and look at the trend.
We do not control the responsesNo one controls what the model generates. We measure the change in the number of mentions between the pre-implementation scan and the post-implementation scan, with no promised timeframe and no percentage.
Visibility is not a recommendationA brand may be recognisable yet still not recommended. Recognition is built through content on the brand’s own website, whilst recommendations are driven by signals from outside it, and this takes longer.
In a nutshell

Key points about the method

  1. Neutral scan: we ask the same questions as the customer, without mentioning the brand name.
  2. Four models, with results shown separately for each one and in total. Attendance at X is measured by a separate module.
  3. A frozen set of queries, with every change to it recorded in the report.
  4. Metrics: mentions, ranking, share of responses, citations and sentiment.
  5. A full scan once a month. It’s the trend that counts, not a single reading.
FAQ

Questions about the method

The questions we’re asked most often when discussing measurements.
Does Aelo provide the models with the name of my brand?

Not when measuring spontaneous awareness. The questions relate to categories, and the brand name must come up of its own accord. The name is mentioned deliberately only in a comparative context, which we report separately.

Which models are you looking at?

ChatGPT, Claude, Gemini and Perplexity. We measure a brand’s presence on X using a separate module, as making a request there costs many times more, and the responses follow a different logic.

Where do the queries in the scan come from?

They are based on the topics outlined in the brand brief: what you sell, to whom and where. After the initial scan, the set is locked so that subsequent measurements assess the same criteria.

What will happen if I change the themes in the brand tab?

The set of queries will change along with them, and the report will reflect this. Comparability with previous scans is then lost, as it is the metric that changes, not the result.

How do you calculate the proportion of responses?

We divide the number of mentions of your brand by the number of mentions of all the brands being compared, using the same set of search queries and the same models.

What is the difference between a reference and a quotation?

A mention is when a brand is named in the body of a reply. A citation is when your domain is cited as a source. A brand is sometimes mentioned but never cited. We collect these terms in dictionary.

How often do you run a full scan?

Once a month. There’s no point in doing it more often, as changes to the website and signals from outside it take time before the algorithms pick them up. We go into this in more detail in the article Why aren’t the changes visible straight away?.

Why can results vary?

The models are not deterministic: the same query may yield a slightly different result, and the models themselves change with each update. That is why it is the trend across multiple scans that matters, not a single result.

Does Aelo guarantee an increase in visibility?

No. We measure the change between the pre-implementation scan and the post-implementation scan, without promising a deadline or a specific percentage. No one controls what the model generates.

How does GEO differ from SEO?

SEO optimises a website to improve its ranking in search results, whilst GEO determines whether the model mentions the brand in its response. We explain the differences in this article SEO, GEO and AEO.

Measure your visibility in AI

You start with 250 AeloCoins, without a card. That’s enough to start the measurement and test the modules. Once you’ve used them up, you’ll switch to a paid plan. Measurements work straight away, and a full report on your brand is included from the Starter plan onwards. The methodology is the same across all plans.

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