Where does ChatGPT get its information about businesses?
Two layers feed every recommendation. The first is training data: the model has read a snapshot of the public web, so businesses that were well documented before that snapshot exist in its memory. The second is live retrieval: for many questions, ChatGPT searches the web while answering and pulls from the pages it finds in that moment.
You influence the first layer slowly and the second layer quickly. That difference decides where to start fixing your AI visibility.
Why do some businesses appear again and again?
Repetition across sources is the strongest pattern. When the same clinic appears in the city's top directories, holds detailed reviews that mention specific treatments, and gets named in local articles, the model sees agreement between independent sources and treats the recommendation as safe. A business with one strong website but no footprint elsewhere gives the model a single voice to trust, and models hedge exactly there.
Test this against any category you know well: the names that repeat are rarely the biggest advertisers. They are the businesses whose facts agree everywhere the model looks, and that is a property anyone can build deliberately.
Consistency matters as much as volume. If your opening hours, location or service list differ between sources, the safest move for the model is to name someone else.
Does ChatGPT browse the web for every answer?
No, and the split matters. Simple conversational questions are often answered from memory. Recommendation questions with local intent, like "best interior fit-out company in Business Bay", usually trigger a live search. That means your current website, your llms.txt file (where adopted) and your freshest reviews all feed what gets read at answer time. A business that fixed its sources last month can already appear in this month's answers.
What can you do to be recommended more often?
Start with the fastest fix: correct wrong or inconsistent facts everywhere your business is listed, since errors actively disqualify you. Next, earn presence in the directories and review platforms your category draws from, in English and in Arabic. Then publish pages that answer real buyer questions in plain language, one question per page. Last, add the machine-readable layer: structured data on key pages and an llms.txt file at your site root.
The order matters because each layer feeds the next: corrected facts make new listings consistent, consistent listings make your content credible, and credible content earns the citations that models keep returning to.
You don't need to guess at any of this. Run the questions your buyers ask, see who gets named today and why, then close the specific gaps. A free sample audit shows exactly what that evidence looks like before you commit to anything.