AI Reputation describes how a brand is perceived in AI answers, that is how often, how prominently, in what tone and based on which sources it appears. It results from the brand's own website, third-party sources and model behavior.Open glossary entry → goes beyond whether a brand is mentioned. It assesses the quality of the portrayal: Share of Voice in AI answers measures the proportion of mentions a brand receives within a prompt set compared to competitors. It indicates the competitive position in AI answers.Open glossary entry → versus competitors, answer position, Sentiment describes the tone in which a brand is portrayed in AI answers, for example positive, neutral, mixed, negative or uncertain. It measures the quality of a mention, not just its presence.Open glossary entry → and the sources that shape the brand's AI image.
AI Reputation cannot be controlled directly. It emerges from the interplay of the brand's own website, third-party sources, user reviews, search results, Crawling is the process by which a search engine bot fetches a URL and downloads its content. It is the first step before a page can be indexed.Open glossary entry → access, prompt context and model behavior. A critical statement in an AI answer can influence purchase decisions before a user even visits the website.
Professional AI monitoring includes:
- fixed prompt sets and repeated measurement,
- separate evaluation per platform,
- competitor comparison,
- source analysis,
- checking whether answers portray the brand negatively.
RankScan measures mentions, early mentions and sources per AI assistant. It reports critical portrayals as the measure “Negative associations in AI answers”.
AI Reputation is improved through topical authority, clear differentiation, genuine root-cause fixes and consistent, credible signals. Manipulation such as fake reviews is risky and counterproductive in the long run.