Beyond RAG: Building Enterprise AI Assistants That Users Actually Trust

As organizations increasingly adopt AI for enterprise data operations, they face a critical challenge: while the potential of AI assistants is immense, the barrier to successful deployment and sustained adoption remains dauntingly high. User tolerance for AI failures is remarkably low - a few wrong answers can lead to permanent abandonment of AI tools. Organizations typically find themselves in one of three scenarios: struggling with lengthy development cycles using existing tech stacks, acting as restrictive gatekeepers due to concerns about accuracy and governance, or dealing with disappointing adoption rates as users lose confidence after encountering inconsistent results.

Join us to discover PromptQL, a revolutionary approach that addresses these challenges head-on by making AI assistants more reliable, transparent, and consistently accurate. We'll explore how PromptQL transforms AI-data interactions through:

• Dynamic Query Planning: Move beyond static embedding retrieval to intelligent, adaptive query plans that evolve based on intermediate results and user instructions.
• Universal Data Access: Connect AI systems to any data source - from SQL databases to SaaS platforms - under a unified schema with proper governance.
• Advanced Computation & Memory Management: Handle complex calculations and maintain context across large datasets without the typical limitations of LLM context windows.
• Enterprise-Grade Security: Implement fine-grained authorization and access controls that ensure your AI applications meet strict enterprise standards.

We'll compare PromptQL with other approaches to building AI assistants and agents - from simple RAG implementations to function calling frameworks and proprietary agent architectures - highlighting where each approach excels and where PromptQL offers unique advantages for enterprise use cases.

Through live demonstrations and real-world use cases, you'll see how PromptQL enables high-trust AI applications by delivering four critical properties: transparency, steerability, verifiability, and repeatability. Experience firsthand how PromptQL ensures consistent, accurate responses - ask the same question multiple times and get the same reliable answer, unlike traditional systems that may provide varying results.

This session is ideal for data engineers, architects, and technical leaders who want to unlock the full potential of AI while maintaining enterprise-grade reliability and trust.