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.

The proportion of AI responses on a topic in which the brand is mentioned or cited, compared to peer brands across the same prompts and engines.

AI responses across major engines, the sources cited, recurring themes, sentiment per engine, peer comparison, accuracy gaps, and a prioritized list of interventions to shift the narrative.

Run identical prompts on the same engines for each peer, then compare sentiment, theme distribution, source citations, and prominence. The methodology has to be controlled for the comparison to mean anything.

Track sentiment, source quality, theme distribution, peer comparison, and trend over time. AIQ is built specifically for this – pulling daily across multiple engines into a single dashboard.

Against pre-defined goals: improvement in narrative sentiment, accuracy, source quality, and prominence, with correlation to business metrics like recruiting funnel, deal pipeline, and IR meetings over time.

Use a monitoring tool that polls engines on a fixed cadence with consistent prompts, storing full responses for diff and theme analysis over time.

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.