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

Metadata intelligence turns raw metadata into an operational layer. It generates insights, orchestrates governance workflows, and enriches agentic analytics with reliable, query-time context

AI-powered data lineage provides clarity on data flow, helping data teams understand origins, transformations, and impacts with ease.

Enterprise data modeling creates a unified data structure, ensuring consistent definitions and reliable analytics across your entire organization.

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