GenAI is moving fast, but most enterprise deployments still struggle with consistency, accuracy, and explainability. Models can generate fluent answers yet deliver the wrong revenue number or contradict BI dashboards. The problem isn’t creativity. It’s context.
AtScale CTO & Founder David Mariani joined Data Science Connect’s GenAI Demo Day to showcase how AtScale’s MCP Server connects LLMs like Claude to governed enterprise metrics. By grounding models in semantic definitions, enterprises get trustworthy, transparent outputs every time.
During a live 10-minute demo, Dave proved the difference:
• Ask a business question without MCP → ungrounded, inconsistent answer
• Ask through MCP → accurate response backed by lineage and governed BI logic
Panelists:
- Haley Massa — Senior AI/ML Architect, Applied Field Engineering, Snowflake
- Rob Dominguez — Engineering Manager, PromptQL
- Kanchana Patlolla — Technical Solutions Manager, Google
- David P. Mariani — CTO & Founder, AtScale
- Teo Parashkevov — AI Solutions Manager, B EYE
What You’ll Learn
• Why GenAI fails without semantic context
• How MCP allows LLMs to query governed metrics directly
• How semantic layers deliver traceable, explainable AI outputs
• Practical patterns for moving from experimentation to production
• How to unify BI, analysts, and GenAI under one trusted model
Why Watch
Enterprises don’t need to “fix” GenAI—they need to anchor it. This session shows how semantic context transforms AI from flashy demos to real business results.
