Enterprise Search (AI)

The average enterprise employee switches between 9–11 different SaaS tools daily, each with its own search interface, each returning results only from its own data silo. A sales rep preparing for a customer meeting must search Salesforce for the account history, Google Drive for the proposal deck, Slack for the last conversation, and Jira for open support tickets — independently, manually, in four separate tools. AI enterprise search solves this by creating a unified semantic index across all connected systems.

The technical architecture has three components: (1) **Universal Connectors** — pre-built integrations that continuously index and permission-sync content from SaaS tools, cloud storage, email, code repositories, and databases; (2) **Semantic Retrieval** — vector embeddings and hybrid search (semantic + BM25 keyword) that match queries based on meaning, not just keyword overlap; (3) **Answer Synthesis** — an LLM layer that reads the top retrieved results and generates a direct answer with citations, rather than presenting raw search results.

The differentiation from consumer search is critical: enterprise search must respect permissions (never show content to users who lack access to the source system), handle multi-modal content (PDFs, spreadsheets, images, meeting transcripts), and provide answer provenance so employees can verify and navigate to the source. The ROI model is compelling: reducing time-to-information by even 15 minutes per employee per day yields measurable productivity gains at scale.