Brand visibility in ChatGPT, AI Overviews, AI Mode and other generative systems can be measured more effectively today than it could only a year ago. There is still no single equivalent of a traditional rank tracker, however, that can provide a complete and objective picture of a company’s presence across all AI-generated answers.
The most reliable data comes from first-party sources. Google Search Console can now provide visibility data for Google’s generative search features, while Google Analytics can measure real visits and conversions from sources such as ChatGPT. Platforms such as Ahrefs, Semrush and OtterlyAI can add another layer by monitoring citations, brand mentions and selected prompts.
The problem begins when data from prompt tracking is interpreted in the same way as traditional keyword rankings.
AI systems do not behave like a stable search results page with ten identical results for every user, and third-party platforms do not have access to every private conversation taking place inside AI products.[4][6]
What does “visibility in AI” actually mean?
Before comparing tools, we first need to define what is being measured.
AI visibility can refer to at least four different things.
Exposure
The brand or website appears in an AI-generated answer.
Citation
The model references a specific URL as a source.
Brand mention
The company or product is mentioned even if no website link is included.
Traffic and conversion
A user clicks through to the website and completes a measurable action.
These situations are not equivalent.
For example:
- brand mentioned → no link → no session;
- URL cited → no click;
- ChatGPT → click → landing page → enquiry.
This is why the entire topic should not be reduced to a single “AI Visibility Score”.
Four layers of AI visibility measurement
Layer 1 — First-party visibility
Google Search Console
Layer 2 — Real traffic
GA4 / analytics
Layer 3 — Citations & mentions
Ahrefs / Semrush / OtterlyAI
Layer 4 — Controlled prompt panel
a selected sample of prompts analysed in thematic clusters
The higher we are in this model, the closer we are to direct observations from search platforms and users.
The lower we go, the more the result depends on sampling and the methodology of a specific vendor.
That does not make prompt tracking useless.
It means prompt tracking should be treated as one analytical layer, not as a complete market measurement.
Google Search Console – the strongest first-party source for AI visibility in Google
In June 2026, Google began rolling out a dedicated Generative AI performance report in Search Console.[1][2]
The report provides visibility data for Google’s generative search experiences, including AI Overviews and AI Mode.
Depending on report availability, it can be used to analyse:
The key difference between Search Console and a third-party prompt tracker is simple:
Google does not need to estimate whether a website appeared in Google’s own generative search features.
It is reporting its own first-party data.
Google Search Console — pros and cons
Pros
- first-party data;
- no need to manually invent prompts;
- actual visibility data from Google AI features;
- URL-level analysis;
- trend data over time;
- country and device dimensions.
Cons
- the report is not yet available for every property;
- it does not expose full user prompts;
- it does not measure ChatGPT;
- it does not measure Perplexity;
- it does not represent the entire AI search market;
- it is not a traditional prompt rank tracker.
Google is still rolling the report out gradually to a subset of websites.[1]
Therefore, not seeing a Generative AI report in a Search Console property does not automatically mean that the site never appears in AI Overviews or AI Mode.
GA4 – does AI visibility generate real traffic?
Exposure and citation do not necessarily result in a website visit.
The second layer of measurement should therefore focus on real users reaching the site.
OpenAI states that outbound links from ChatGPT use utm_source=chatgpt.com, which helps publishers identify traffic arriving from ChatGPT in analytics platforms.[5]
In GA4, useful dimensions and metrics include:
- sessions from ChatGPT and other identifiable AI sources;
- landing pages;
- engaged sessions;
- enquiries;
- other conversions;
- purchases;
- revenue;
- conversion rate.
Google Analytics 4 — pros and cons
Pros
- measures real users;
- connects traffic to landing pages;
- supports conversion analysis;
- supports revenue analysis;
- not a synthetic AI visibility score.
Cons
- only measures users who actually click;
- cannot measure a citation without a click;
- cannot measure a brand mention without a click;
- some AI influence may later appear as direct or branded search traffic;
- does not capture the full assisted effect of AI visibility.
Why use third-party AI visibility tools?
First-party data has an important limitation.
It cannot always tell us:
- how often a brand appears in a sample of ChatGPT answers;
- which competitor domains are cited next to ours;
- which competing brands are recommended;
- which URLs appear as sources;
- how AI answers change over time.
This is where platforms such as Ahrefs Brand Radar, Semrush AI Visibility Toolkit and OtterlyAI become useful.
They should not, however, be treated as “Search Console for ChatGPT”.
They are systems that monitor model responses across selected or sampled groups of prompts.
The biggest prompt tracking problem: what should we actually monitor?
In traditional SEO, we can use Google Search Console queries, keyword databases and search demand data to decide which keywords matter.
AI search is more difficult.
Assume we monitor the prompt:
The tool asks the selected model every day and checks whether IT Holding appears in the answer.
The measurement itself may be technically correct.
But we do not know how many real users in Poland ask that exact question.
We also do not know whether they more commonly ask:
- “recommend an SEO company”;
- “who should I hire for SEO?”;
- “who does good SEO for small businesses?”;
- “I need an SEO agency in Warsaw”;
- “which SEO company should I choose?”.
Semantically, these are similar needs.
Textually, they are different prompts.
This is one of the most important distinctions in AI visibility measurement.
A list of 20 prompts can produce a highly detailed dashboard.
That does not mean those 20 prompts represent the real behaviour of the entire user population.
Why is prompt measurement more difficult in smaller markets such as Poland?
The problem is not that AI visibility tools are technically unable to send prompts in Polish.
Several platforms already support Polish-language tracking and Poland as a location.
The bigger problem is the quality and depth of data available for estimating how important individual prompts actually are.
Semrush includes Poland in parts of its AI Visibility Toolkit and Prompt Research.[8]
At the same time, Topic Volume data in Prompt Tracking is currently available only for selected countries and does not include Poland.[9]
This illustrates the difference between:
- “we can track a prompt in Polish”;
- and: “we know how much real demand exists for that prompt”.
-
01
Less demand data
Some AI visibility products have richer demand-related data for the US and English-speaking markets than for Poland.
-
02
High language variation
The same intent can be expressed in dozens of different ways.
-
03
No complete public prompt volume
OpenAI does not provide a public equivalent of a traditional report showing: prompt → monthly search volume in Poland.
Prompt monitoring should therefore be treated as a controlled sample, not an exact measurement of total AI demand.
Ahrefs Brand Radar – broad discovery beyond manual prompts
Ahrefs attempts to reduce the “what should we track?” problem by maintaining a large index of questions derived partly from search data.
Brand Radar uses so-called search-backed prompts sourced in part from People Also Ask questions and Ahrefs’ keyword database.[6]
The platform can be used to analyse brand presence across multiple AI systems and also supports custom prompts.[6]
Ahrefs Brand Radar — pros and cons
Pros
- large prompt dataset without manually creating the entire list;
- part of the prompt set is backed by search data;
- brand mentions;
- citations;
- competitor analysis;
- AI Share of Voice;
- custom prompts;
- multi-platform analysis.
Cons
- still based on sampling;
- keyword data is not the same thing as real ChatGPT prompt data;
- Ahrefs cannot see private user conversations;
- refresh frequency depends on the dataset;
- broader monitoring may become expensive.
Semrush AI Visibility Toolkit – combining traditional SEO and AI monitoring
Semrush currently uses two related but distinct approaches.
The first is its broader AI Visibility Toolkit, which uses Semrush’s own database of prompts, topics, model responses and competitor data.[8]
The second is Prompt Tracking, where the user defines a custom set of prompts and the platform regularly monitors selected AI engines.[9]
This distinction matters.
Prompt Research helps answer: “which topics may be worth monitoring?”
Prompt Tracking answers: “what is happening to the prompts we selected?”.
Semrush AI Visibility Toolkit — pros and cons
Pros
- combines traditional SEO and AI visibility data;
- large proprietary dataset;
- mentions;
- citations;
- competitor analysis;
- Prompt Research;
- custom Prompt Tracking;
- some features available for Poland.
Cons
- AI Visibility Score is a Semrush proprietary metric;
- custom tracking depends heavily on the quality of the prompt library;
- does not provide access to all real user conversations;
- some prompt-volume-related data is not available for Poland;
- metrics should be treated as directional rather than as a complete market measurement.
Semrush itself notes that due to the dynamic and personalised nature of AI responses, its metrics should be interpreted as directional signals rather than an exact representation of total AI visibility.[8]
OtterlyAI – straightforward custom prompt monitoring
OtterlyAI follows a more traditional prompt-tracking model.
Prompts can be:
- added manually;
- imported;
- generated using Prompt Research.
The platform supports tracking in multiple languages, including Polish, and supports Poland as a location.[10]
Other platforms, such as Peec AI, offer a similar model for monitoring selected prompts, but this article focuses on the tools covered in the source documentation below.
OtterlyAI — pros and cons
Pros
- simple workflow;
- supports Polish;
- Poland available as a location;
- multiple AI platforms;
- citations;
- brand mentions and coverage;
- regular monitoring of selected prompts;
- easy-to-understand reporting.
Cons
- results depend heavily on the quality of the prompt library;
- monitoring begins only after a prompt is added;
- no full historical backfill before tracking begins;
- the same prompt can generate different responses across sessions;
- the monitored response may differ from what an individual user sees.
Which tool should you use?
| Tool | Main advantage | Main limitation | Best use |
|---|---|---|---|
| Google Search Console | first-party data | Google AI only | real exposure in Google AI features |
| GA4 | real traffic and conversions | only after a click | business impact of AI traffic |
| Ahrefs Brand Radar | broad search-backed prompt discovery | still sampled | discovery and broad brand analysis |
| Semrush AI Visibility | combines SEO and AI data | proprietary metrics and PL data gaps | competitor and topic monitoring |
| OtterlyAI | simple custom prompt tracking | depends strongly on prompt selection | controlled prompt panel |
Should individual prompts be monitored?
Yes, but a single prompt should not become the primary KPI.
Model responses can vary between requests, sessions, locations and user contexts.
For that reason, the trend across a thematic group of prompts is more useful than the position of a brand in one individual answer.
- which SEO agency is good?
- which company should I choose for SEO?
- who provides SEO services?
- which SEO agency is suitable for a small business?
- which SEO agency operates in Warsaw?
- who should I hire for an SEO audit?
Then analyse what share of answers across the cluster mentions the brand, rather than: “did the brand appear in one prompt today?”.
How to build your own prompt panel
Prompt selection should not start with: “generate 100 AI prompts”.
A better starting point is real user intent.
- Google Search Console — use real search queries.
- People Also Ask — collect related questions.
- Sales conversations / CRM — review what real prospects ask.
- Internal site search — use this where the website has its own search function.
- Competitor analysis — identify brands and services users compare.
- Prompt discovery — Ahrefs, Semrush or OtterlyAI.
- Group by intent — do not group only by exact wording.
How should prompts be grouped?
- Informational — e.g. how does SEO work? how long does SEO take?;
- Commercial — e.g. which SEO agency? which SEO company should I choose?;
- Problem / solution — e.g. why is my website losing traffic? how can I recover rankings?;
- Brand — e.g. IT Holding reviews, IT Holding SEO;
- Competitor — e.g. IT Holding vs [competitor];
- Transactional — e.g. who can perform an SEO audit? SEO agency in Warsaw.
Analyse results primarily at cluster level, not at single-prompt level.
See also: how to check whether your website is ready for AI search and AI Overviews and AI Mode.
Which KPIs are actually useful?
| KPI | What it measures | Reliability |
|---|---|---|
| GSC AI impressions | exposure in Google’s generative features | high |
| URLs visible in GSC AI | which pages appear in Google AI features | high |
| AI referral sessions | real clicks from AI platforms | high |
| AI conversions | measurable business impact | high |
| Brand mentions | brand presence in a monitored sample | medium |
| Citation coverage | how often the domain is cited in a monitored sample | medium |
| Cluster visibility | visibility across a prompt group | medium |
| One-prompt result | one response to one question | low |
| Vendor AI Visibility Score | proprietary synthetic metric | supporting only |
What should not be treated as the primary KPI?
“Position in ChatGPT”
Generative answers do not have a stable equivalent of traditional positions 1–10 that can be interpreted exactly like a search ranking.
One AI Visibility Score
It can be useful for measuring trend within the same platform.
It is not a universal measure of the entire AI search market.
A handful of randomly chosen prompts
A small arbitrary sample may produce a precise-looking dashboard while still failing to represent actual user behaviour.
AI crawler visits alone
A crawler visit does not automatically mean: citation, mention, recommendation or click.
Citations alone
Citation can increase brand visibility, but without additional data it does not tell us the business impact.
How often should AI visibility be analysed?
Prompt-tracking platforms may collect data daily.
That does not mean every daily movement should trigger a strategic change.
Practical model:
- Data collection: daily or according to tool availability;
- Operational review: weekly;
- Reporting and strategic decisions: monthly trend.
Can AI visibility ROI be measured?
Partially.
The strongest measurable scenario is:
AI referral → landing page → conversion → conversion value.
That can be analysed using standard analytics.
A much more difficult scenario is:
AI mention → user remembers the brand → several days later searches for the company in Google → enters through branded search.
Standard analytics may not attribute that outcome to the earlier AI exposure.
For that reason, separate direct measurable effect from assisted / unobservable visibility.
Do not assign a precise financial value to every ChatGPT mention.
More on the foundations of visibility in new search systems: SEO & AI.
How to measure AI visibility in practice
- Google Search Console — measure actual exposure inside Google AI features.
- GA4 — measure real visits, landing pages and conversions.
- AI visibility tool — choose one platform for monitoring: mentions, citations, competitors.
- Prompt panel — build a representative set of questions.
- Clusters — analyse groups of intent, not single responses.
- Monthly trend — compare performance over time.
- Business data — compare visibility with enquiries, sales and website performance.
When running a technical audit, it is also worth checking whether the site is losing visibility signals for other reasons — for example through an SEO audit.
Summary
Measuring visibility in AI search is possible today, but it requires more caution than traditional Google rank tracking.
The strongest data comes from first-party sources.
Google Search Console can show actual exposure in Google’s generative search features, while GA4 can show whether users actually arrive from AI platforms and whether those visits convert.
Tools such as Ahrefs Brand Radar, Semrush AI Visibility Toolkit and OtterlyAI add another layer by monitoring citations, mentions and selected prompts.
They do not, however, provide access to the complete set of conversations users have with AI systems.[4][6]
Prompt tracking is therefore best treated as a controlled sample.
The most useful signal is not whether a brand appeared in one specific question today, but the broader trend across a thematic cluster of prompts combined with real traffic and conversion data.