A clear look at how large language models choose which businesses to name, and what that means for getting recommended.
AI recommendations feel like magic, but the logic is understandable. Models repeat what is specific, consistent and corroborated. Here is how that works.
A language model does not have preferences. When asked to recommend, it reproduces the strongest, most consistent pattern it has seen about a category. If your brand is described the same accurate way across many credible sources, that pattern becomes the answer.
If a competitor has more accurate, consistent, corroborated content in your category, the model has more to say about them and less about you. The gap is not quality of business, it is quality and quantity of information the model can find.
That is precisely what CitedAgain publishes every month, built on real research, not spun text.
No, and you would not want that. We build real, accurate information so the true story becomes the strongest pattern.
Because old content lingers. Refreshing accurate facts helps models cite the current story.
Weeks for first signs, three to six months for broad, consistent change.
Give us your website and a real person will check how ChatGPT, Claude and Google describe you today, and tell you exactly what can be done. Free, no obligation.