How do you implement AI-powered search integration across enterprise data sources?

Enterprise AI search unifies diverse data sources behind a natural language interface. Architecture: connectors ingest content from multiple sources (SharePoint, Confluence, Google Drive, Slack, databases, internal wikis), an indexing pipeline processes and embeds content, a search engine combines keyword and semantic search, and an AI answer generator synthesizes results into direct answers. Connector implementation: build source-specific crawlers that handle authentication, pagination, incremental updates, and access control propagation. Processing pipeline: extract text from various formats (PDF, DOCX, PPTX, HTML), chunk content semantically, generate embeddings, and store in a hybrid search index (BM25 + vector). Search flow: user enters natural language query, query is processed (intent detection, entity extraction, query expansion), hybrid search retrieves relevant chunks across all sources, reranker refines results, and the LLM generates a synthesized answer with citations. Access control: maintain per-document ACLs from source systems, filter search results based on the searcher's permissions, and implement security trimming at query time. Commercial options: Microsoft Copilot (Office 365 data), Glean (enterprise search), and Coveo (commerce/support). Build-your-own: use Elasticsearch/OpenSearch with vector search, pgvector for PostgreSQL-native, or dedicated vector databases. Metrics: search relevance (MRR, NDCG), answer accuracy, user engagement (clicks, query refinements), and time-to-information.