Vary user intent (research, comparison, recommendation), prompt phrasing, and personas. Themes that hold across many prompt variations indicate stable AI narratives; themes tied to specific phrasings indicate prompt-sensitive ones.
Each engine has its own source-weighting pattern. ChatGPT favors training-data plus retrieval with neutral framing; Gemini leans heavily on the Knowledge Graph and Wikipedia; Claude is conservative; Perplexity favors direct citations.
AIQ is built for AI reputation tracking. Profound, Peec, Otterly, and BrandRank are GEO visibility tools. The categories differ in what they measure and which team they serve.
A dashboard summary, peer comparison, theme trends, source-quality assessment, accuracy concerns, and a prioritized list of interventions. Reporting that does not include intervention recommendations is descriptive, not useful.
Regularly polling AI engines with defined prompts about a brand, recording the responses, and analyzing themes, sources, and sentiment over time. It is the foundational diagnostic discipline for AI reputation work.
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.
Single-model monitoring misses critical variation. Different engines cite different sources and frame brands differently. Multi-model monitoring gives a representative picture of AI reputation overall.
Daily for high-profile brands or active situations. Weekly for established brands. Monthly baseline checks with quarterly full audits as a minimum standard.
A measure of whether AI responses about a brand skew positive, neutral, or negative across engines and prompts, typically aggregated by topic, theme, or peer comparison.
Run identical prompts on the same engines for each peer, then compare themes, source attribution, sentiment, and prominence in the responses. Without identical prompts, the comparison is not meaningful.