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

Quick answer

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.

An AI narrative audit produces a structured read of where the brand stands across the engines and what to do about it. The sections are consistent: full responses across the eight major engines for a defined prompt set; source attribution showing which sources each engine is citing for the prompts that matter; theme analysis identifying the recurring framings the engines apply; sentiment classification per engine and aggregated; peer comparison against a named set running the same prompts on the same engines; accuracy gaps where the engines are stating something incorrect; risk areas where the engines are weighting a problematic source heavily; and a prioritized intervention list mapping each finding to a specific action at the source layer. The deliverable is built to be acted on. A CCO reading it should know which three or four source-layer interventions will produce the most movement and on what timeline.

Last reviewed: 19/05/2026

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