DataOS is the data activation layer for AI, applications, and analytics. It sits on top of an organization’s existing data stack and unifies semantics, governance, quality, lineage, and access into a single, reusable operating capability, without requiring rip-and-replace. Built to serve not only human analysts but also AI agents and automated workflows, DataOS makes data consistent, trusted, and ready for action at machine speed. DataOS delivers AI-ready data through four core pillars: 1) Context Data becomes usable when meaning travels with it. DataOS aligns shared definitions for entities, metrics, dimensions, and relationships so dashboards, apps, and agents interpret information the same way. 2) Trust AI systems require runtime confidence. DataOS combines centralized policy decisions with distributed enforcement, declarative data quality checks, and end-to-end lineage for auditability and debugging. 3) Action Different consumers need different interfaces. DataOS provides a unified query layer and standardized access patterns such as SQL, APIs, and agent-ready interfaces, with guardrails like intent and result validation. 4) Productization DataOS treats reusable data products as the unit of scale. These bundle semantics, governance, quality signals, ownership, documentation, lifecycle management, and versioning, making data scale the way software does.