From Crawl to Answer: How to Build AI Search That Stays Fresh as Your Website Changes

Effective AI website search requires more than crawling and embedding pages because answers can become inaccurate when website content changes, especially for high-impact information such as pricing or policies. A production system should extract clean, structured content rather than raw HTML, apply a content policy that excludes duplicate, irrelevant, private, or outdated pages, and combine keyword, semantic, and reranked retrieval to handle both exact terms and differing user language. Freshness should be managed according to page volatility through scheduled recrawls, change detection, or live retrieval for time-sensitive questions, while generated answers should include inspectable source citations. Public-facing search must enforce access boundaries before content reaches the model, separating public, authenticated, tenant-specific, and internal information. Search queries, failed results, low-confidence answers, and user behavior can also reveal documentation and product-information gaps, creating a feedback loop for improving content and retrieval. The proposed architecture is a continuing cycle of discovery, extraction, filtering, indexing, retrieval, freshness verification, evidence-based answering, and learning rather than a one-time crawl-and-index process.

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