We scanned 8 B2B SaaS companies across 5 categories. ChatGPT named the same 12 brands in every answer.

On 22 May 2026 the Bersyn scan engine ran 200 buyer Conversations across four AI Surfaces — ChatGPT, Claude, Perplexity, Gemini — for eight B2B SaaS companies in five different categories. The pattern that came back is the most important data we have published this month, so we are publishing it here in full. Receipts and named names.

The eight companies, the categories, and the scores.

Average score across the seven scanned companies: 1.7 / 10.

That is not a typo. The average B2B SaaS company in this sample is mentioned in fewer than two of every ten high-intent buyer Conversations across AI Surfaces.

The same brands win every category

The competitors named in the AI assistants' answers — when the seven companies above were skipped — show a clear pattern. Across all 200 Conversations, twelve brands accounted for the overwhelming majority of mentions.

When a buyer asks any of the four AI assistants which tool to use in any of these five categories, the answer points at a name from this list. The newer entrants — the actual customers Bersyn was scanning — are essentially invisible in the same Conversations.

ChatGPT is the worst Surface for newer brands

Across seven scans and 175 ChatGPT Conversations (5 questions × 4 Surfaces × 7 brands, ChatGPT's quarter being 35 per brand), ChatGPT had the lowest hit rate for the scanned company in seven of seven cases.

Perplexity is the most retrieval-driven of the four; it does live web fetching at query time and picks up newer entrants relatively faster. Claude does some live retrieval too but is heavily training-data dependent. ChatGPT and Gemini lean hardest on training data and on entity-injection patterns — the kind of behaviour Lee 2026 documented in their fan-out query study, where 99.4 percent of brand entity injection by ChatGPT comes from training data, not from retrieval.

That pattern is exactly what this small sample reproduces.

What it means in practice

A newer B2B SaaS company in a category dominated by an established brand has one shared problem and four different fixes.

The shared problem: the established brand has accumulated enough mentions across the open web (review sites, Reddit, comparison articles, LinkedIn posts, Hacker News threads, Wikipedia, Crunchbase) to land inside the training data of all four AI Surfaces. The newer entrant has not.

The four different fixes — one per Surface, because each fails differently:

• ChatGPT. The lever is third-party authoritative mentions in the next training cut. Reddit threads, Hacker News submissions, dev.to articles, LinkedIn posts that cite the brand by name in the category context. ChatGPT will not learn a new brand exists from your own marketing pages alone.

• Gemini. Gemini blends Google Search results with the underlying model. The lever is domain-level SEO presence in the category — Wikidata, Crunchbase, LinkedIn-company, AI tool directory listings. Anything that strengthens the brand's standing in Google Knowledge Graph compounds.

• Claude. Slowest to update. Claude conservatively avoids guessing about unfamiliar brands, so the failure mode is Omitted rather than Misclassified. The lever is the same as ChatGPT — third-party mentions in training data — but with a longer lag.

• Perplexity. Fastest to update because of live retrieval. The lever is SERP presence for the buyer questions buyers actually ask. If your site ranks in the top 5 for "best [category] tool", Perplexity will pull you in.

Each Surface needs a different Patch. A single piece of content can hit one Surface and miss the other three.

What this means for any new SaaS brand

If you are a B2B SaaS company under five years old, in a category with three or more established competitors, there is a non-trivial chance the four AI assistants do not yet know you exist. If a buyer asks Claude or ChatGPT which tool in your category to use, you will not be the answer.

That is the gap Bersyn was built to diagnose, classify, and close. The free scan shows the receipt. The platform generates the Patches. The audit at audit.bersyn.com delivers a one-time $49 PDF with copy-pasteable text suggestions tied to specific pages on your site.

Want this scan run on your company

Two paths.

Run a free scan at bersyn.com. Two-minute setup. Same scan engine that produced the table above. You see the receipt for your own brand.

Buy the one-time audit at audit.bersyn.com. $49. PDF arrives by email, usually within a few minutes. No subscription, no learning curve. Apply the patches on your own schedule.

We will publish more of these as the data accumulates. If you are running a category we have not scanned yet and you want to see the receipt, reply to this post with the category name. We are running scans daily through 30 June.

Data from Bersyn scans run 22 May 2026 between 13:46 and 14:15 UTC. Raw JSON for each of the seven scanned companies is in the public marketing repository. All scan questions, prompts, and responses available on request. Bersyn does not claim truth about which tool is "best" — the data above is a measurement of which brands the AI Surfaces have learned to name, not a quality ranking.