AI analytics agents provide instant, data-backed answers to business questions, but they need deep context from metadata intelligence platforms to work reliably.

Context engineering is the discipline of assembling and structuring the information an AI system needs in order to operate correctly within its intended operational environment.

AI Metadata Management transforms static data catalogs into intelligent systems, automating tagging, enhancing discovery, and building trust for enterprise AI, accelerating analytics and reducing risk.

Active metadata is dynamic, real-time metadata used to drive action, automation, and intelligence across the data stack for modern data management.

Self-serve analytics empowers non-technical users to access, explore, and analyze data independently.

Column-level lineage traces individual data columns from source to destination, revealing transformations, dependencies, and usage.