When a potential customer asks ChatGPT, Gemini, Copilot or another AI assistant to recommend a company in Dubai, does your business appear?
That question is becoming part of online visibility. People can use AI to compare suppliers, understand services and build a shortlist before visiting a company’s website. If your business is absent, described incorrectly or associated with the wrong service, you may never see that activity in a traditional ranking report.
Measuring AI search visibility gives you a clearer picture. It shows where your business appears, what it is recommended for, which sources support the answer and whether that visibility leads to useful website visits or enquiries.
The purpose is not to chase every brand mention. It is to understand whether AI assistants present your business to the right people, for the right reasons.
AI visibility is more than appearing in an answer
A company name in an AI response can mean several different things.
Your business might be listed as a recommended supplier. It might be mentioned only as an example. Your website could be cited as a source without the company being recommended at all. An assistant might also describe your services accurately while leaving out the one you most want to grow.
Those outcomes should not be combined into a single vague claim that your company is “visible in AI.” They answer different business questions.
| Signal | What it tells you |
|---|---|
| Brand mention | Your company appeared somewhere in the answer |
| Supplier recommendation | The assistant presented your company as an option for the customer |
| Website citation | A page from your website was used or displayed as a source |
| Accurate service description | The answer reflects what your business actually offers |
| Website visit or enquiry | Someone took a measurable next step after the AI interaction |
A useful measurement programme keeps these signals separate. Ten citations are not automatically ten recommendations, and ten recommendations are not automatically ten leads.
Measure the questions connected to real business goals
The quality of the measurement depends on the questions you choose.
Suppose a Dubai fit-out company wants more office renovation projects in JLT and Business Bay. A broad prompt such as “recommend a fit-out company in Dubai” may produce interesting results, but it does not fully reflect the work the company wants to win.
More useful prompts would include the service, location and a genuine customer requirement:
- “Which companies specialise in office fit-outs in JLT?”
- “Can you recommend a Dubai contractor for renovating an occupied office?”
- “Which fit-out companies in Business Bay can manage design and construction?”
The company should also test questions about its other priority services, common objections and the languages its customers use. English and Arabic prompts should be treated as separate tests because equivalent customer needs can be expressed differently in each language.
Do not include your company name when testing discovery. Asking “Is this company good?” measures how an assistant describes a business you have already introduced. Asking for suitable suppliers measures whether the business appears without that help.
Establish a baseline before making changes
You can begin with a manual check. Choose a small set of commercially relevant questions and run each one in a fresh conversation. Ask the assistant to search the web and provide sources where the product supports that option.
Use a logged-out or private browsing session when possible, and record the conditions. Private browsing helps separate the test from your usual browser session, but it does not hide your IP address or create a representative view of every customer. Location, product settings and account context can still affect an answer.
For each response, record:
- the exact prompt;
- the AI product and date;
- whether web search was used;
- whether your business was recommended;
- the service and location attached to the recommendation;
- the explanation given;
- the cited pages;
- any inaccurate or outdated information.
Repeat the questions in new conversations instead of relying on one favourable screenshot. AI answers can vary, so the complete set of results is more useful than the best response.
This baseline does not need to be large. Its first job is to reveal a pattern worth investigating. It may show that the company appears for one service but not another, that its pages are cited without generating a recommendation, or that the assistant misunderstands an important part of the offer.
Connect AI answers with platform and website data
Manual tests show what an answer looks like. Platform reports and analytics can show other parts of the journey.
Google Search Console’s generative AI performance report provides information about site-link impressions in Google AI experiences, including AI Overviews and AI Mode. It can help you identify which pages receive exposure in those results. It does not measure every mention of your brand across all AI assistants.
Bing Webmaster Tools’ AI Performance report reports citations and cited pages across supported AI experiences. Microsoft explains that citation counts do not represent a ranking position, so they should be read as evidence that a page was used, not that the business was the top recommendation.
Google Analytics can identify some visits from recognised AI assistant referrers through its AI Assistant channel. Google AI Overviews and AI Mode traffic are classified under Organic Search instead. Google’s channel definitions are important when building a report because one channel will not contain every AI-related visit.
Together, these sources help answer different questions:
- Are AI systems showing or citing pages from the website?
- Is the business being recommended for relevant customer needs?
- Are people visiting the site from recognised AI assistants?
- Are those visits producing suitable enquiries?
No single dashboard answers all four.
Use the results to decide what to improve
Measurement becomes valuable when it leads to a clear decision.
If your business is recommended accurately for an important service, identify the pages and information associated with that result. Keep them current and check whether customers can take the next step easily.
If your website is cited but the business is not recommended, examine whether the cited page establishes who the service is for, where it is available and why the company is a suitable provider. A page can be informative without clearly presenting the business as an option.
If the company appears for the wrong service, review how its offer is described across service pages, directories and profiles. Confirm that old services, locations or package details are not creating confusion.
If the business is absent from relevant answers, study the companies and sources that do appear. This does not reveal the assistant’s full selection process, but it can expose information your own website fails to answer. The next step may involve clearer service content, stronger evidence, better entity information or technical access for search crawlers.
If AI referrals reach the website but do not turn into enquiries, visibility may not be the main problem. Review whether the landing page matches the question, explains the offer clearly and provides an appropriate route to contact the business.
The result should always lead to a specific action. “Improve AI visibility” is not a useful task. “Clarify that our office fit-out service covers occupied workplaces in JLT and add relevant project evidence” is.
Decide whether manual checks are enough
Manual testing is suitable for establishing a baseline and understanding the answers in detail. It becomes difficult to manage when you need to track many services, locations, languages and AI products over time.
An API can support a custom monitoring process. It allows a technical team to save prompts, collect responses and compare results on a schedule. The team must record the model, search configuration and location settings where available. API results should remain clearly labelled because an API setup does not necessarily reproduce the consumer experience in an AI application.
A specialist tracking platform can reduce the work involved in repeated collection and reporting. Before choosing one, check which AI products it covers, whether it uses a consumer interface or an API, how it defines visibility, whether it stores complete answers and citations, and whether you can control prompts, languages and locations.
Whichever approach you choose, keep access to the underlying responses. A percentage without the questions, answers and sources behind it is difficult to interpret and easy to misuse.
Make AI visibility a business measurement
AI search visibility should help a UAE business understand how it is introduced to potential customers.
Start with the services you want to grow. Test realistic questions without naming the company. Separate recommendations from mentions and citations. Connect the answers with website exposure, referral traffic and enquiries. Then use the pattern to choose a specific improvement.
The goal is not to manufacture a perfect score. It is to know whether AI assistants understand your business well enough to present it as a relevant option when a customer needs what you offer.
If you want a structured assessment, Lunasol’s GEO and AEO service includes AI visibility audits, entity work and content structured for AI citation. The audit can help identify where your business appears, how it is described and which improvements deserve priority.
AI Visibility is owned by Lunasol.
