Enterprise Context Explained

Enterprise context is the knowledge a person needs to do work correctly inside a specific company. It includes facts, but also the links between them: who is responsible, which customer is involved, what was tried before and what the team agreed.

A new hire learns this context over months, by asking colleagues and reading old threads. An AI model starts without it. It must be given this context for each task.

• Entities: people, teams, customers, products, projects and systems.
• Relationships: ownership, reporting lines, dependencies, account history.
• Decisions: what was decided, by whom, when and for what reason.
• Process: how work actually moves through the company, which is often different from the written policy.
• Time: when each fact became true, and whether it is still true.
• Access: who is allowed to see each fact.

Enterprise context is spread across the tools where people work: email, chat, documents, tickets, CRM records, code and meeting notes. Much of it is never written down in one place. A decision may live in a chat thread, its reason in a meeting note and its owner in a ticket. This is the problem of enterprise knowledge fragmentation.

General models know a lot about the world and nothing specific about your company. Without enterprise context, an assistant gives generic answers and an agent makes decisions on partial facts.

Companies capture enterprise context for AI by connecting their source systems into a context graph, and serving it to every AI tool through an enterprise context layer.

See how East River Labs captures enterprise context, or talk to us.