Every enterprise working with AI eventually runs into the same problem. A model can write, summarize, and reason, but it doesn't know a business defines its context. That makes context a design requirement: AI needs access to the meaning behind the data as well as the data itself. AI is only as effective as the business information it can access and understand. Enterprise data is often fragmented across applications, documents, transactions, and operational systems, making it difficult for AI to retrieve the right information and produce accurate, business-relevant Answers.

How Databricks delivers enterprise context

• Lakeflow connects the data estate. Lakeflow provides ingestion, streaming, and ETL capabilities, including connectors for sources such as Salesforce, Workday, NetSuite, and Google Drive. This helps bring structured and unstructured information into a unified data foundation.
• The Lakehouse keeps context close to the data. By unifying data on open formats and supporting batch, streaming, analytics, and transactional workloads, the Lakehouse reduces the need to copy or synchronize data across disconnected systems.
• Genie and Genie Ontology add business meaning. Genie helps users and agents work with business data in natural language, while Genie Ontology organizes enterprise knowledge around business concepts, relationships, definitions, and permissions. This allows AI to reason over the organization’s actual terminology and operating context rather than relying only on generic model knowledge.
• Context is permission-aware. Unity Catalog provides the governance foundation that determines what each user, agent, or application is allowed to see. Context can therefore be relevant without bypassing existing access controls.
• Lakebase supports operational memory. Lakebase provides a serverless Postgres foundation for real-time AI applications, including the memory and transactional state agents need to support mission-critical workflows.

Business result

Databricks turns disconnected enterprise information into governed, usable context for analytics, copilots, agents, and applications. The goal is not simply to give AI more data; it is to give AI the right business context, with the right definitions and permissions, at the right time. For enterprise leaders, the practical starting point is to identify the information an AI application needs to do its job, then make that information accessible, understandable, and governed. The test for context is straightforward: can AI work with the same business definitions and permissions that guide the people using it?