We tested ten well-known companies in AI search

We ran AI search scans for ten well-known software companies to see which products the answers named and which pages they cited. The useful comparison was within each scan: when a company was absent, which alternatives appeared?

The sample covered deploy and hosting, work tracking, and payments. Grouping companies by category let us inspect competing products in the answers, although each company’s questions were generated separately.

What we ran

For each company the engine writes 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. Same pipeline, same prompts, same scoring any account gets. Nothing was hand-tuned per company, and nothing was rerun to get a better number.

Three things the answers showed

Being big does not mean being named

Atlassian is the largest company in the set by a wide margin. On its own customer questions it was named in 25% of answers, and four rivals were named more often than it: Linear, Asana, ClickUp and Notion. Linear came up 23 times to Atlassian 15.

That was not a one-off. In four of the ten scans a competitor beat the company on its own questions. Render was named more often than Vercel on Vercel questions, and more often than Railway on Railway questions. Vercel was named more often than Netlify on Netlify questions.

These scans show that a familiar company can be absent from a relevant category answer. They do not isolate the effect of company size or establish why a particular rival was named.

The engines do not agree with each other

Atlassian was named in 33% of its ChatGPT answers and 7% of its Google AI Overviews answers. Keeping those results separate shows a difference that the aggregate cannot explain.

In most rows, ChatGPT named the company less often than the other engines. Railway ranged from 53% to 100% across engines. The split is useful for choosing which answers to inspect; it does not by itself identify a fix.

Your own website is 3% of the citations

Of the 7,801 citation instances recorded across the ten scans, 205 pointed to the website of the company whose customer questions were being asked. That is 2.6%, rounded to 3% here.

Linear’s own site received none of the 714 citations in its scan. Paddle had the highest own-site share, at 5%. A citation count records a linked source; it does not reveal every page an engine may have used.

The most-cited domains across the scans were reddit.com (252), youtube.com (197), northflank.com (185), ones.com (173) and medium.com (144), followed by dev.to (134).

Northflank was not one of the companies scanned, yet its site received 185 citations in the answers. DigitalOcean received 72. For a founder investigating this category, those pages are worth reading to see which comparisons and technical details the answers drew on.

What to do with this

• Read the engine split beside the aggregate. A difference is a reason to inspect the answers, not a diagnosis by itself.
• Record the alternatives named on your questions and check how well each fits the request.
• Keep your own pages accurate and accessible. The low own-site citation share does not measure the effect of improving them.
• Inspect comparison pages and technical resources that the answers cite. Look for a useful contribution your own research or experience could add.
• Review the communities and media that appear in your category’s sources before deciding where to participate.

Why this matters

A recommendation can influence a shortlist before someone visits a vendor’s site. Website analytics will record a referral if the person clicks through, but they cannot reconstruct every answer in which a product was considered or omitted.

In four scans, a rival was named more often than the company being measured. Repeating a stable set of questions would help establish whether those differences persist. This run alone cannot tell us what the companies monitor internally or what customers ultimately chose.