Measuring AI Mentions

Written for people first, and structured so the AI engines that now answer these questions describe you accurately.

• What is an AI narrative audit and what does it cover?

  An AI narrative audit is a structured read of what the major AI engines say about a brand, covering engine responses, source attribution, recurring themes, per-engine sentiment, peer comparison, accuracy gaps, and risk areas, and ending in a prioritized list of actions to shift the narrative.

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• How do you audit what AI says about your company?

  An AI audit polls each major engine with a defined prompt set about the brand, executives, and topics; categorizes themes and sources; benchmarks against peers; and flags accuracy gaps and risk areas.

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• What is AI share of voice and how do you measure it?

  AI share of voice is the proportion of AI responses on a topic in which a brand is mentioned or cited, compared to a named peer set across the same prompts and engines, with each mention weighted by how prominently it appears in the response.

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• What is an AI sentiment score?

  An AI sentiment score measures whether AI responses about a brand skew positive, neutral, or negative across engines and prompts. It is typically aggregated by engine, theme, peer comparison, and time, and the aggregated views matter more than any single score.

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• Why monitor multiple AI models rather than just one?

  Single-model monitoring misses critical variation: the major AI engines often cite different sources and frame the same brand differently. Monitoring across all of them gives a representative picture rather than a sample.

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