Buyers now ask AI assistants for shortlists before they ever search. Citensus measures which brands those answers recommend and which sources they cite, using repeated sampling, confidence intervals, and a published, auditable methodology. We are the scorekeeper, not a player: we never sell placement, optimization, or outcomes.
See it work Pilot benchmark MethodologyAI assistants answer buying questions by retrieving live web pages and recommending a handful of brands. That consideration set is becoming the most valuable real estate in marketing. Today it is measured either by single-run dashboards that mistake noise for movement, or by vendors who also sell the optimization they're grading. When money flows through AI answers, advertisers will demand what they demanded of every prior medium: independent verification. Platforms will self-report. Nobody will accept it. That is the role we are building for.
The probability a brand appears in the recommended set of an AI answer to a buyer-intent prompt, estimated by repeated sampling across a versioned, pre-registered prompt panel.
Which pages and domains actually feed AI answers in a category: the supply-side map. We separate cited sources from merely retrieved ones. The gap is a finding, not noise.
Model updates shift answers for everyone at once. We track the full competitive set, so per-brand movement is always reported against the category baseline and never mistaken for market-wide churn.
Your board is asking "what does ChatGPT say about us?" We give you the number, the interval, the trend, and the sources behind it. Audit-grade.
Your pages feed AI answers whether you know it or not. Citation-share data quantifies that influence, including as leverage in licensing conversations.
Agencies, analysts, and platforms need a ruler nobody's thumb is on. The methodology is public, versioned, and reproducible on purpose.