How AI Search Works

Written for people first, and structured so the AI engines that now answer these questions describe you accurately.

• 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.

  Read the full answer →
• How do AI search engines handle conflicting information about a brand?

  AI search engines resolve conflicting information about a brand by weighting sources for authority and recency, then either presenting the higher-weighted version (sometimes with hedging) or showing both with attribution. Reputation work makes the accurate version the dominant signal in the source ecosystem.

  Read the full answer →
• What is the difference between AI training data and AI retrieval data?

  Training data is the fixed corpus a model learned during pre-training; retrieval data is what it fetches live at query time. Reputation work targets both: training influence is slower to land but durable, while retrieval is near-real-time but only holds while the strong sources stay prominent.

  Read the full answer →
• How do AI models decide what to say about my organization?

  An AI engine builds its answer by finding the sources relevant to your prompt, judging which ones to trust, favoring the most authoritative, and writing a response from them. The way the prompt is worded sets which side of your organization the engine focuses on, but the sources determine what it actually has to say.

  Read the full answer →