% citing your URL
How often an answer links a claim to a page you own.
Methodology
Every figure Clear Cited publishes carries its method, its variance, the engine set, and a date. This is that method - the canonical reference every "how we measure" link points to.
The engines
We measure exactly five: ChatGPT, Perplexity, Claude, Gemini, and Grok - the assistants B2B and developer buyers actually use. We report consumer-app answers where they differ from API output, and we note which was used. We never blend in engines we don't measure.
The procedure
We measure the category questions your buyers actually ask - not vanity terms. Audits surface the verbatim prompts.
AI answers are non-deterministic, so a single response is noise. We take the median across 10+ runs per engine.
Every share-of-model figure carries a Wilson 95% confidence interval, so you can tell signal from sampling wobble.
The model
"Visibility" isn't one number. We score seven, so you can see why share of model moves - presence, endorsement, citation and position each tell a different part of the story.
How often an answer links a claim to a page you own.
How often the answer names you at all in the category.
Your share of qualifying answers vs the named field - the headline metric.
Whether you're recommended, listed neutrally, or cautioned against.
Mention depth + source quality + data richness behind the mention.
Whether the engine describes you accurately and consistently.
Where you rank in the answer's shortlist against competitors.
Defensibility
The prompt set, the brand universe, and the metrics are fixed before each run, so results can't be cherry-picked after the fact.
Clear Cited excludes its own clients from the public ranked Index. We disclose the policy and never rank a brand we're paid to promote.
If the prompt set or engine set changes between editions, a versioned note marks where two editions stop being directly comparable.
Every leaderboard ships a CSV + JSON under CC BY 4.0; we never publish a figure for an engine that returned too little data, and every number is labelled measured vs modeled.
Honesty
A free teardown applies this exact method to your category.
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