We scanned ten companies nobody has heard of

Our first small study covered well-known software companies. We then ran the same pipeline on ten companies from recent Y Combinator batches to see what the answers looked like for a less established group.

The companies had live products and teams of three to fifty. Each received customer questions generated from its own site, so the two groups share a measurement pipeline but do not answer an identical test.

What we ran

For each company the engine generates the questions a real customer in that category would ask, puts them to the leading AI search engines, and records every product named and every source cited. Nothing was hand-tuned per company and nothing was rerun to get a better number.

The earlier ten-company sample provides a comparison group. It is useful context for this pilot, although the groups differ in category, size and age.

Half of them were never named once

Five companies were never named in the answers returned for their scans. Each scan contained roughly 58 recorded answers to generated customer questions. This is an observation about those question sets, not every question someone could ask about the products.

The scans still returned many citations: roughly 740 per company. For five companies, none pointed to the company’s own site. The absence of a mention or link does not show whether a page was retrieved or why it was left out.

The exception, and it is one company

One company in the cohort was named in 91% of its answers. That is higher than nine of the ten household names. It is the largest and longest-established in the cohort, and it sells into a narrow technical niche with a lot of documentation written about it.

The result suggests a question for a larger study: do category specificity and the available documentation help explain variation? This cohort cannot separate those factors from age, size or the wording of the questions.

The engines agree about obscurity

The earlier sample of well-known companies showed a 15.6 percentage-point spread between aggregate engine mention rates.

In this smaller-company sample, the aggregate engine rates were within 1.2 percentage points. Similar aggregate rates do not mean the engines named the same companies on the same questions.

The contrast warrants a larger, matched comparison. It does not establish that engines behave identically for young companies, or that there is no value in inspecting their answers separately.

We predicted the opposite

We recorded predictions before the run. The first was that the smaller companies would be named less often than the well-known group; the observed gap was in that direction.

The second was wrong. We expected that when an unknown company did get named, the engine would link to its own website more often than it does for a household name, on the reasoning that with no third-party coverage there is nothing else available to cite. The opposite happened. Own-domain citations were 0.82% for the unknown cohort against 2.63% for the household names, about a third rather than more.

The own-site citation share was lower in the smaller-company sample. That contradicts the prediction, but does not establish the retrieval mechanism behind it. A follow-up would need to separate whether a company was mentioned from whether its own domain was cited.

What we would do with this

• Save a baseline of answers to questions customers actually ask. A zero in that sample is a reason to inspect the answers, not proof that every page about you is absent.
• Check the cited sources for relevant comparisons, documentation and discussions. Consider what accurate information you can contribute.
• Keep results separate by engine even when aggregate rates are similar. Different answers can produce the same average.
• Record which alternatives appear and why they fit the question. This can reveal a product-positioning issue as well as a source-coverage issue.

How we measured this, and what it cannot show

This pilot covered ten companies from recent YC batches and ten well-known companies measured earlier. It used the same scan pipeline with company-specific question sets and retained the returned answers and citations.

Both groups contain only ten companies and were not matched on category, size or age. The observed difference is large, but the design cannot tell us how much each factor contributed. Treat the smaller subgroup differences as exploratory.

We report the smaller-company results in aggregate. The purpose is to examine the sample’s distribution, and company names are unnecessary for that comparison.