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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What is the role of news aggregators and syndication in AI search results?
Wire and aggregator syndication distributes a single origin story across many domains. Because AI engines synthesize answers by pulling from multiple indexed sources, wide syndication increases the likelihood that the wire version shapes the final response. The mechanism is symmetric: it works for accurate, on-message content and for errors or off-message framing in equal measure.
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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 broader third-party source ecosystem.
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What does it mean that AI models are citing us wrong?
When an AI engine asserts something inaccurate about your brand, the fix is diagnostic, not argumentative: AIQ identifies which source is feeding the error, then the remediation targets that source specifically, a Wikipedia edit request, a press correction, a structured-data fix, or an owned-content addition.
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How do we control what ChatGPT says about us?
Not directly. No company can control ChatGPT's output: the model is proprietary, and asking it to change an answer has no lasting effect because it doesn't remember the conversation and rebuilds every answer fresh from the sources it trusts. What works is shaping those sources: Wikipedia, mainstream news, structured data, and your own properties, and monitoring continuously so drift is caught early.
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How do AI search engines handle time-sensitive vs evergreen queries about brands?
AI search engines split queries into two behavioral modes: time-sensitive queries (breaking news, recent events, current status) trigger retrieval-first behavior, pulling from live web search, news APIs, and recently-indexed pages; evergreen queries (what a company does, who an executive is, background definitions) draw primarily from the training corpus and Wikipedia. A reputation program needs 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.
Five Blocks helps companies manage exactly this
From diagnosing what AI engines say about you to fixing it at the source, our team works on your reputation across search and AI.
