AtScale + Distillery: Building Trusted NLQ Experiences with Model Context Protocol

Learn how enterprise organizations are using semantic layers and MCP to scale Natural Language Query (NLQ) across large language models (LLMs) and chat interfaces, ensuring accuracy, security, and consistency across every question asked.

Join AtScale and Distillery to explore how semantic governance makes NLQ work—across multiple tools, chatbots, and GenAI platforms.

In this 60-minute session, we’ll walk through:

• Escape Vendor Lock-In with Open Semantics
  Why open semantic approaches beat proprietary solutions from Snowflake and Databricks.
• Universal Chatbot Compatibility via MCP 
  How MCP lets your semantic layer work with Claude, ChatGPT, Slack, Teams, and any AI assistant.
• Governance That Actually Works
  How AtScale enforces consistent data definitions across every conversational interface.
• From Demo to Production
  What it takes to move beyond chatbot prototypes to enterprise-ready AI analytics.

Who should attend?
This webinar is designed for:

• Data Leaders looking to scale AI analytics without compromising governance
• Platform Engineers evaluating open vs. proprietary AI infrastructure
• GenAI Enthusiasts interested in production-ready conversational analytics
• Anyone tired of chatbot demos that never make it to production

Why it matters:
Most enterprises are stuck choosing between vendor lock-in solutions or building everything from scratch. This webinar reveals a third path: leveraging open protocols and semantic layers to create flexible, governed AI experiences that work with your existing tools and any approved LLM.

You’ll walk away with a clear roadmap for implementing trusted natural language query across your organization—without the usual compromises on security, consistency, or compatibility.