RB2B

Named in 8 judge labels across 1 category by 4 of 6 models in the September 2026 Edition. 1 first choice, 1 negative label. Every number here is derived from the raw labels under vendor table v2026-09-08.5.

Standing

8 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. RB2B was named 8 times in Intent data, where G2 Buyer Intent led with 25%. The labels and the evidence are below, counted exactly.

Standing by category

Every category where a model named RB2B. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Vertical	First choices	Rank	Negative rate	Labels	Quadrant
B2B intent data providers	Sales	4%	7 of 40	12%	8	under 10 labels · led by G2 Buyer Intent at 25%

By model

How each model treated RB2B across every prompt where it was named. Six models, six prompts per category.

Model	First choice	Alternative	Mention	Negative	Labels
Claude Opus 5	1	2	1	0	4
Claude Opus 4.8	0	1	0	0	1
GPT-6 Astra	0	0	0	0	0
GPT-5.6 Sol	0	1	0	1	2
Gemini 3.1 Pro	0	1	0	0	1
Perplexity Sonar Pro	0	0	0	0	0

What the models said for it

Verbatim evidence the judge attached to positive labels.

“RB2B or Warmly for first-party + Clay for signal enrichment, total under ~$1k/month” Claude Opus 5 · Intent data · budget prompt · first choice

“RB2B is better if “intent” primarily means identifying visitors already on your website” GPT-5.6 Sol · Intent data · budget prompt · alternative

“RB2B is unusual in resolving to individuals (US only, for privacy reasons).” Claude Opus 5 · Intent data · comparative prompt · alternative

“Start free with Apollo.io (or a visitor-ID tool like Leadfeeder/RB2B)” Claude Opus 4.8 · Intent data · budget prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

“RB2B says its person-level identification is limited to US traffic... Person-level identification creates substantially more privacy and reputation risk” GPT-5.6 Sol · Intent data · negative prompt · soft negative

Names read as RB2B

What the judge wrote, as written, with how often. The vendor table decides that these count as RB2B; a claim can dispute any of them.

Every label used the canonical name.

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page gets a verified contact who is told when each edition publishes; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as RB2B, applied by version and listed in the change log; and a logo and one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or quadrants, any preview, or any say over which quotes appear. Sponsorship is separate: a sponsor funds categories or buyer dimensions and is named on what it funded, and the rules are the same for every sponsor.