Building the Contextual Data Layer for Enterprise AI

Your AI agents are answering questions. But are they answering them correctly?

At inference time, a model only reasons from what you give it. If context is missing, it fills the gap. If context is wrong, it follows it confidently. As Ravi Marwaha, CPO & CTO of Arango, puts it: “Models don’t fail gracefully. They fail plausibly.”

This session addresses one of the most overlooked challenges in enterprise AI — not model quality, not prompt engineering, not data volume. Context. What your AI actually sees when it needs to reason, decide, and act.

We’ll cover:

• What is a context layer and what AI agents, assistants, and applications actually need
• Why enterprise AI stalls between pilot and production
• The data architecture requirements for AI that must reason and act in real time
• How a Contextual Data Layer bridges grounds your LLMs with domain specific enterprise data for trusted responses
• Design patterns for managing business context once and reusing it everywhere — across agents, and AI-powered applications

This is a candid, technical discussion for builders, engineers, data architects, and AI practitioners. Sharing knowledge and experience to build solutions for production.