Every month, we dive into what it takes to build and scale Reliable AI — the kind that survives edge cases, earns trust in production, and becomes mission-critical. From identifying critical reliability challenges in AI, to showcasing practical solutions and their impact, this is where leaders working on AI projects come to learn, share insights, and stay ahead on making AI reliable for the enterprise.

Data readiness is a myth! Don’t let it hold back your AI initiatives.

Organizations routinely delay mission-critical AI deployments by months while trying to get data "AI-ready" — an unrealistic expectation in complex organizations with diverse systems and tribal knowledge.

Join us for the first edition in the Reliability Calls series, where we'll explore how the Agentic Semantic Layer can help enterprises dramatically accelerate reliable AI adoption.

Working with messy, distributed organizational data demands a reliability breakthrough — and that's exactly what this innovative approach delivers by autonomously building semantic understanding on-the-fly, enabling immediate reliability on mission-critical tasks without months of preparation.See how enterprises have used this approach to achieve >95% accuracy on mission-critical AI tasks within days of implementation.

This session will feature:

• How the Agentic Semantic Layer Makes AI Understand Your Business Data We will demonstrate an approach that eliminates months of preparation typically required before AI can deliver value, enabling immediate reliability on mission-critical tasks without demanding perfectly prepared data. The Agentic Semantic Layer represents a fundamental shift in AI reliability by autonomously building a unified view of enterprise data on-the-fly. It introspects existing schemas, documentation, and code to bootstrap understanding, then continuously improves through interactions—turning messy, distributed data into a coherent knowledge graph that AI can reliably reason about.

• PromptQL Reliability Score

How PromptQL evaluates and ensures reliability when working with imperfect enterprise data from heterogeneous sources, providing AI project leaders with confidence in AI-driven insights and recommendations.