Measuring AI Mentions

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

• How do you build an AI reputation monitoring dashboard?

  An AI reputation monitoring dashboard should track sentiment, source quality, theme distribution, peer comparison, share of voice, and trend over time - aggregated across the major AI engines, not a single one. The dimensions are only as good as the underlying data, which has to poll every engine with consistent prompts.

  Read the full answer →
• 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.

  Read the full answer →
• 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.

  Read the full answer →
• What tools exist for monitoring AI narratives?

  AIQ is built for AI reputation tracking; Profound, Peec, Otterly, and BrandRank are GEO visibility tools. The two categories differ in what they measure - mention presence versus narrative quality - and which team they serve.

  Read the full answer →
• How do different AI models – ChatGPT, Gemini, Claude, Perplexity – differ in how they talk about brands?

  Each engine draws on a different source mix, and that shapes how it talks about brands: ChatGPT leans on its broad training corpus plus Search with neutral framing, Gemini leans on Google's Knowledge Graph and Wikipedia, Perplexity is citation-first, Copilot emphasizes the Bing/enterprise index, Grok pulls heavily from X, and Claude tends toward cautious, caveated phrasing.

  Read the full answer →