Enterprise AI
AI systems built to work inside an organization — grounded in its own data, acting through its own tools, governed by its own access rules — where the value comes from what surrounds the model rather than from the model alone.
Production AI is a system: context, tools, permissions, traces, evals, and feedback loops around the model.
What it is
Enterprise AI is the category Group e-media works in: AI systems that earn their place because they understand an organization's own information — data, documents, conversations, operational records — and act through governed tools. Anyone can call a frontier API. The work is connecting that API to the data substrate, tool surface, and operating discipline of a real business.
Why it matters
Most AI value inside an organization is decided by what's around the model: retrieval quality, tool governance, evals, and feedback loops. Treating those as the work — with the model as a routing decision — is what separates a production system from a pilot.
How we build it
Services that stack — from AI readiness and data foundations through agent workflows, assistants, and benchmarks to conversation intelligence and self-optimizing agents. Four research tracks: AI Persona, Agent Memory, Closed-Loop Knowledge, Skill Distillation. The combination is the enterprise AI stack.
Related resources
The storage and ingestion sub-service under Data Foundations. A lakehouse on open table formats with streaming and CDC ingestion, lineage, dead-letter handling, and retrieval indexes. One substrate for analytics, AI, and operational tools.
One operating layer for the AI models, tools, and data your teams use. Cost, quality, and governance you can see. Provider choices — hosted, local, or self-hosted — you can change without rewriting the system.
A navigable map of every system your data lives in — schemas, documents, code, tickets, events, owners, and permissions — so an AI agent can find the right source and respect the right access boundary.
How an AI system gets durably better at its job — not by being smarter, but by routing every production failure into either a knowledge update, an eval case, a workflow patch, or a documented exception with a named owner.