In a single search experience, you can discover data and AI assets org-wide and instantly discover AI models, datasets, and related data artifacts, spanning projects and regions while adhering to IAM permissions. You can also augment assets with business context and enrich AI artifacts with business metadata for informed decision-making, such as ownership, key attributes, and relevant context.

Critical business knowledge often remains locked in unstructured sources like design docs, wikis, PDFs and images. You can instantly turn thousands of PDF contracts, design docs, and wikis into a structured knowledge graph. AI agents can then query this to answer complex questions—like "What are the common liability clauses in our 2025 vendor agreements?"—with grounded, traceable facts.

Move beyond simple tables to create data products: self-contained units of intelligence that include built-in intent, SLAs, and governance constraints. By automatically inferring relationships across the data estate, Knowledge Catalog packages these assets into data products so they can be easily distributed and scaled across cross-functional AI teams and agents.

Data engineers define structure in technical schemas while analysts define meaning in BI tools, creating a gap for AI agents that can lead to untrusted results. Knowledge Catalog helps you build a shared semantic layer with strict enterprise governance. Whether a human asks a question or an agent performs a task via API, the answer is always based on the same trusted enterprise truth.

Knowledge Catalog is deeply integrated with Google Cloud's Lakehouse and integrates its catalog to support governance policies that are centrally defined and enforced across multiple engines like BigQuery and Google Cloud Managed Service for Apache Spark. Knowledge Catalog also enriches governance across Lakehouse by supporting semantic search, data lineage, profiling, and quality checks, providing a managed foundation for your open data lakehouse.