This benchmark measures brand visibility inside AI answers across the five most-used answer engines. We ran a fixed panel of category-defining prompts through each model on a recurring schedule and scored whether brands appeared, where they ranked, which sources were cited, and how favorably each brand was described.
Why a cross-model benchmark
A single blended visibility score hides more than it reveals. The same brand can lead on one surface and be absent on another, because each model retrieves and cites differently. Measuring per surface is the only way to see where presence is concentrated versus genuinely broad.
Key findings
- Citation share is concentrated. Across most categories, the top three cited sources accounted for a majority of all citations. The long tail of brands appeared rarely, if at all.
- Structure predicts citation. Pages with clean semantic HTML, explicit schema, and direct answers were cited disproportionately relative to their traffic.
- Surfaces disagree. Brands that led on Perplexity were frequently mid-pack on Google AI Overviews, underscoring that coverage must be measured and earned per surface.
- Sentiment drifts quietly. Favorability shifted between cycles without any change on the brand's own site, driven by third-party sources the model retrieved.
What separates the cited from the invisible
The brands that consistently appeared shared three traits: machine-readable architecture, a structured and sourced body of facts, and authoritative coverage across the full set of category prompts rather than a handful of hero pages.
Methodology
Prompts were category-specific and held constant across cycles. Each response was scored for brand presence, rank position within the answer, cited sources, and sentiment. Scores were benchmarked against the category average so every figure carries context.
This benchmark is updated each cycle. For a category-specific cut of the data, request a demo and we will run your prompts against every tracked surface.