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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.

Speaker: Ravi Marwaha, COO, CPO & CTO, Arango
Date: Thursday, May 21st
Format: Virtual presentation (zoom) + open discussion

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