What happens before an AI recommends a company

If your brand does not appear when a buyer asks ChatGPT to recommend a supplier, you probably assume one of two things. Either the machine has never heard of you, or your website is not good enough.

We captured 12,264 of the searches AI systems write for themselves, across two markets and three engines: 177 category questions, each run five times, on ChatGPT, Claude and Gemini. In many of those traces the machine generated searches for a brand by name, or described the brand accurately when asked about it directly, and still recommended somebody else.

That gap, between being investigated and being chosen, does not show up in any visibility score, whoever sells you the score.

In the recommendation questions we tested, the engine rarely ran the user's words as a search. It wrote its own searches, usually several, and ran those instead.

Those self-written searches are the interesting part, because they contain brand names nobody typed. On 52% of the 89 category questions we tested in one market, and 66% of the 88 in the other, the engine's own searches named companies the user had never mentioned. The generated searches suggest the engine can arrive at retrieval with candidate companies already named.

You can have an excellent page and still never be one of the candidates competing to be in the answer.

One company in our dataset shows this more clearly than any theory could. It is the same company, the same website, the same people, selling the same service either side of a border.

In its home market it is the machine's first thought. It appears in the engine's own searches on 45 of 88 category questions, 222 times across repeated runs. In the neighbouring market it enters the machine's own searches once, across 89 questions and five runs of each.

The engine knows the firm in both places. Ask about it by name in the weaker market and you get an accurate, complimentary description. It almost never surfaces in the machine's own research when the category question is asked.

A second company, in a completely different industry, showed the same home-market pattern: present in the engine's own searches on 36 of 65 questions, 235 times across repeated runs. For that company we have no second market to compare, so the border contrast rests on the first firm alone.

The machine's picture of the company is fine in both markets. What decides the answer is whether the company comes to mind when the buying question is asked.

We then took every case in our data where an engine's own searches introduced a brand, investigation visibly followed, and the brand was absent from the final answer. There were 56 such brand-question pairs. In the one dataset where we computed the rate, that was 13% of the brands the machine itself had brought into its research.

We reconstructed five of them end to end and three patterns came up. Five is a small sample, so read these as early patterns rather than settled findings.

It looked, and found nothing worth carrying. One of the world's best-known professional services firms was probed directly: the engine wrote a search into that firm's own website, looking for evidence about a specific service. The firm's site then appears nowhere in the citations, and the firm is absent from the answer. The engines built their answers from smaller specialists whose pages state that service plainly.

It compared, and the brand did not survive the comparison. A large, listed company was swept into comparison searches alongside genuine specialists, on a question that asked for specialists. The comparison searches ran, the final answer omitted the brand, and the answer itself states the filter it applied: smaller specialist firms rather than large integrators.

It read the pages, cited them, and still recommended someone else. Worry about this one. On a question about a specific kind of trip, a well-known operator was investigated by name, and its own pages appear in the answer's list of sources. The brand is named in no answer. The competitor that wins the question offers something built precisely for what was asked; the investigated brand's pages described something more general.

Being cited is not being selected. The machine used the pages and then named a rival.

Most AI visibility measurement answers one question. Does the brand appear, yes or no. Appearing is the last step of a much longer sequence, so a no tells you nothing about which step failed.

Here is the sequence we can now watch. We call it the Decision Trace.

Failing at candidate formation and failing at selection look identical in a score, and they need opposite responses. No amount of on-page work will fix a brand the machine never nominates, and that is the weaker market's problem exactly. The operator whose pages were cited and passed over has a different problem: the pages were found and read, and the answer chose a rival whose pages matched the question more exactly. The first is a positioning problem, the second an evidence problem, and the same zero in a visibility score covers both.

None of the interventions this points to are new. Positioning, authority, evidence, content that answers the buyer's actual need: good search strategists have argued for all of it for years. What is new is being able to observe enough of the decision to know which one is actually required. Search work has always had to infer the diagnosis from outcomes. A Decision Trace reads it from the decision itself.

We can observe investigation and selection. We cannot inspect the model's reasoning, so every explanation we offer is an inference from how the machine worded its searches, what it cited, and how the answer explains its own choice. We label them that way.

There are limits here, and the two biggest work against our own argument. Our candidate-formation measure undercounts: in one market, 30 of the 43 questions where the brand made the answer showed no brand-named search at all: the brand was selected without an observable brand-specific search. And answers move on their own. We rescanned 65 questions a few weeks apart having changed nothing ourselves, and six changed anyway, roughly 9%, over a period when competitors published, indexes updated and the models themselves were upgraded. We treat that as background movement any improvement claim has to beat, ours included. Our September test is pre-declared for that reason: treated and untreated questions, with pass and fail conditions written down before anything ships. We will report what it finds, including if it finds nothing.

Your brand can be well known, accurately described, actively investigated by the machine, cited in the answer's own sources, and still left out of the recommendation.

Those are different failure points, and they need different responses. A visibility score of zero looks identical across all of them.

So the question worth asking is not "do we appear?". It is "where are we losing the decision?". We built the AI Decision Review to answer it: take the buyer questions that matter commercially, establish where in the sequence the brand drops out, and match the intervention to the failure instead of guessing.

Treat the score as the first question you ask, never the last.