What is an intranet search engine, and what should modern enterprise search actually do?

This post describes the fundamental architecture and operational principles of an enterprise search engine's crawler. It details the four-step process: crawling internal sources, indexing and tagging content, matching queries, and ranking results. Key technical aspects highlighted include the importance of cross-app connectivity and permission enforcement, the use of a self-learning language model for semantic understanding, and the integration of an Enterprise Graph for relationship-based ranking. It contrasts intranet search with internal/site search and enterprise search, emphasizing the need for comprehensive, permission-aware indexing. The post also outlines what modern intranet search should achieve: unifying knowledge, personalizing relevance, supporting AI answers, and enabling action from insights. It addresses common challenges like information overload, inconsistent tagging, incompatible systems, outdated content, and irrelevant results, proposing solutions rooted in advanced ranking and graph-based approaches.