AI search visibility without the snake oil

A customer asks an assistant who to use. The answer is three names. Yours isn’t one of them.

An assistant doesn’t pick the business with the nicest website. It reads what the web says about you, checks whether the sources agree, and repeats what it can trust. We can’t see inside the model. What we can see, and improve, is the public evidence it reads.

Five things have to be true. It can find you. It understands what you are, and where. Other sources corroborate it. There’s enough evidence to trust. Then, and only then, you get named.

Here’s the clinic it skipped. A service page that never says “pregnancy scan”. A profile in the wrong category. A directory with the old number. No structured data. And nothing anyone else has written about it. Each gap is small. Together, the assistant has no reason to be confident.

So we work on the evidence. One clear entity: one name, one category, everywhere. Services and locations spelled out. Structured data that says what the page says. Profiles, registers and directories made consistent. A page worth citing, written to answer the question. Credentials a machine can verify.

Then we ask again. Same questions, same assistants, weeks later. Some answers now include you. Some still don’t. The sources have changed. What remains becomes next month’s work.

We can’t tell an AI who to recommend. We can improve the evidence it uses to decide.