Glossary
LLM Visibility
LLM visibility is the degree to which a brand appears — and appears accurately — in the outputs of large language models when users ask relevant questions. It spans mention frequency, recommendation position, sentiment, and citation of the brand's own pages. Unlike search visibility, it cannot be read from a rankings page; it must be measured by querying the models directly.
LLM visibility has several distinct layers, and conflating them leads to bad decisions. Being mentioned is not the same as being recommended; being recommended is not the same as being described accurately; and none of those guarantee the model cites your pages rather than a third party's summary of you.
Visibility also varies by engine and by phrasing. A brand can be the default recommendation on Perplexity (which retrieves live sources) while being invisible on a base model whose training data predates the product's launch. Measuring one engine with one prompt gives a data point, not a picture.
The operational answer is sampling: a fixed set of buyer-intent prompts, run on a schedule across the engines that matter for your market, with results tracked as a trend. That converts visibility from an anecdote ('a prospect said ChatGPT recommended us') into a metric that can go up or down in response to your work.
See what AI currently says about your brand
Perciva runs buyer-intent prompts across ChatGPT, Perplexity, Gemini and Claude and shows you the answers verbatim — including which competitor gets recommended instead of you.
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