Raj Joseph

AI agents are only as reliable as the data they consume. This talk explains how AI-native observability, automated quality validation, and business context prevent hallucinations, detect data drift, and build trust in enterprise AI. Real examples will show how organizations improve AI accuracy while reducing operational risk.

AI is accelerating, but most data foundations are still manual, fragmented, and reactive. Many organizations try to bolt agents onto legacy stacks, hoping automation will solve observability, trust, quality, and governance. It won’t. Agents layered on brittle systems inherit the same blind spots, context gaps, and operational delays.

In an era where data complexity and scale challenge every organization, manual intervention can no longer keep pace. Prizm by DQLabs redefines the paradigm—offering a no-touch, agentic data platform that seamlessly integrates Data Quality, Observability, and Semantic Intelligence into one self-learning, self-optimizing ecosystem. Unlike legacy systems Prizm is AI native, it is Agentic by Design, built from the ground up around a network of intelligent, role-driven agents that observe, recommend, act, and learn in concert to deliver continuous, autonomous data trust. Join us at Big Data London to Discover how Prizm’s agent-driven anomaly detection, data quality enforcement, and deep semantic analysis set a new industry standard—shifting data and AI trust from an operational burden to a competitive advantage that powers actionable, insight-driven outcomes.