How AI Search Works
Written for people first, and structured so the AI engines that now answer these questions describe you accurately.
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How do AI models handle companies that operate under multiple brand names?
Multi-brand entities often fragment in AI engines: the parent company gets one description, operating brands get unrelated descriptions, and executives attach to one entity but not the others. Fixing it takes schema markup with sameAs links across all owned properties, aligned Wikipedia and Wikidata entries across the brand family, explicit parent-subsidiary statements in structured data, and independent coverage that names the relationships.
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How do AI models weight different types of sources when discussing companies?
AI engines weight sources first by authority (a domain's reputation, how often other authoritative domains cite it, and structural signals such as clean schema), then by recency, topical relevance, and how consistently multiple credible sources say the same thing. These signals add up, so strengthening a handful of the right sources usually moves the engines more than any single page does.
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How does Perplexity AI decide what it says about my fund when investors ask about us?
Perplexity runs a live web search when a query comes in and writes a citation-backed response. What it says about your fund depends on which authoritative pages it retrieves for that prompt.
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What is the role of corporate blogs in influencing AI search results?
Most corporate blogs fail to influence AI engines because they recycle marketing copy. The ones that get cited share specific traits: substantive original analysis, regular updates, question-format structure with schema markup, named expert authorship with bio context, and engagement with the wider third-party source ecosystem.
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How do AI search engines handle time-sensitive vs evergreen queries about brands?
AI search engines handle queries in two modes: time-sensitive queries (breaking news, recent events, current status) trigger retrieval, pulling from live web search, news APIs, and recently-indexed pages; evergreen queries (what a company does, who an executive is, background definitions) draw mostly from the training corpus and Wikipedia. A reputation program has to address both layers to avoid visible gaps in AI answers.
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Services for How AI Search Works
The expertise behind these answers, put to work for your brand.
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From diagnosing what AI engines say about you to fixing it at the source, our team works on your reputation across search and AI.
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