The methodology, the frozen question set and the categories are published on this page as of , before any measurement. The first report covers Q4 2026. There are no results here yet, and we would rather say so than show a chart of something we have not run.
Nearly every claim published about AI search is an assertion. So is nearly every claim published by every competitor. The consequence is that the same small number of third-party studies get cited over and over, including the one we lean on ourselves below.
That existing work found the ten most-cited domains carry 35%+ of B2B SaaS citations, with Reddit first and G2 second. It is genuinely useful and it does not break the result down by assistant, which is the question a marketing team actually needs answered: if we can only work on one source type this quarter, which one, and for which platform?
So we are running it. Original data is the fastest route to citations because it is the one thing a model cannot get anywhere else, and it retro-fits credibility onto everything else we publish.
Source: Goodie, The Most Cited B2B SaaS Domains in AI Search. 5.7 million citation links analysed across B2B SaaS prompts.
Every question template and every category is on this page now, before any measurement. A benchmark whose questions were chosen after the answers were seen is an advertisement.
Community, review platform, publisher, analyst, video, vendor and other. The taxonomy is fixed in advance so a surprising result cannot be reclassified into a tidier one.
Model outputs are non-deterministic. A single run tells you nothing, and a benchmark built on single runs would report sampling noise as a finding.
Personalisation would make the result a description of our own browsing rather than of the platform.
The entire point is that they differ. Averaging them would destroy the only finding worth having.
Answers change between releases without notice. A series that cannot explain its own discontinuities is not usable.
The complete response set, so anyone can reclassify the sources themselves and disagree with our scoring. A benchmark whose working is private is a claim.
The benchmark measures source types, not brands, so client work cannot influence the finding. Where a client's brand appears in a raw response, it is left in the data and not used as an illustration.
The benchmark measures which source types each assistant draws on, so the questions are templated rather than written per category. Where each type is expected to behave differently, that expectation is stated in advance so the result can contradict it.
The question a buyer starts with, before any brand is in mind. The purest test of which sources an assistant reaches for when it has nothing to anchor on.
Head-to-head and alternatives queries. We expect review platforms and community threads to dominate here, and the benchmark exists partly to test that expectation.
The narrow questions a buyer shortlists with. We expect vendor domains to perform relatively better here than anywhere else in the set.
A buyer describing a problem rather than naming a category. Included because it is how a large share of real queries are phrased, and because it tends to surface a different source mix.
Cadence: Quarterly. Metric definitions follow the published measurement standard.