How do AI assistants decide which companies to recommend?

What do AI assistants look at before recommending a company?

They look for companies that clearly match the question and that several trustworthy sources agree about. With web search on, they read a handful of pages, often review sites, lists, directories and company websites, and recommend the names that come up repeatedly with a clear reason attached.

A company that says plainly what it does, for whom and where, is easy to match to a question. A company described only in vague marketing language is hard to match, even if it is the better choice.

Reviews and ratings feed in too. When an assistant explains a recommendation, it often repeats what customers praise in reviews, such as speed, price or a particular service. A business with few reviews gives it little to say, so it tends to name someone else.

Does training data or live search matter more?

Both matter, in different ways. Training data shapes what an assistant already believes about your category and the well-known names in it. Live search decides which pages it reads for this particular question. Most assistants now search for recommendation questions, so being findable and quotable today counts for a lot.

Training data changes slowly and you cannot edit it directly. Live search responds to changes you make within weeks: a clearer page, an accurate listing or a mention on a site the assistant already trusts.

Why do assistants keep recommending the same few companies?

Because those companies appear in the sources assistants read most: comparison articles, "best of" lists, review platforms and encyclopedia entries. When many sources name the same companies, an assistant treats that agreement as evidence and repeats it.

This compounds. A company that is recommended gets written about, and what gets written about gets recommended. Breaking in means appearing in the same places the current favourites appear.

Does the biggest brand always win?

No. Size helps because big brands are written about more, but assistants try to match the question. A specific question like "best payroll software for restaurants with under 20 staff" often surfaces a specialist that describes exactly that, ahead of a larger general provider.

The more specific the question, the more a clear, specific page counts and the less raw fame does. That is where smaller companies have their best opening.

What can a smaller company do to get recommended?

Make it easy for an assistant to match you and to confirm it. State what you do, for whom and where in plain words on your site. Let AI crawlers read it, keep your name and details consistent across listings, and get mentioned on the review and comparison sites in your category.

Start by finding out where you stand. Mentioned's report shows which of your customers' questions you are missing from, which companies are named instead, and a fix list ordered by impact, so the first afternoon of work goes where it counts.