AI, ML and LLMs

Reliable AI Agents for Business Analytics

Name: Reliable AI Agents for Business Analytics
Uploaded: 2026-01-16
Duration: 59 min 39 s
Description: MotherDuck cofounder, Ryan Boyd demonstrates how to close the LLM accuracy gap for business analytics using MotherDuck's MCP server, semantic modeling, and hypertenancy architecture for AI agents.

2026/01/16

In this webinar, MotherDuck co-founder Ryan Boyd and DevRel engineer Jacob Matson demonstrate how recent advances in LLMs have transformed AI-powered business analytics from experimental to production-ready.

Key Takeaways

The LLM Accuracy Gap Has Closed Just seven weeks before this webinar, text-to-SQL accuracy was a major concern. With the release of Claude Opus 4.5 and similar models, LLMs now understand business problems 3x better and can generate SQL queries that match expert-level quality. The introduction of interleaved thinking with tools allows models to iteratively refine their analysis.

Connect Your Data in 30 Seconds MotherDuck's hosted MCP server enables instant connection between Claude (or other LLMs) and your data warehouse. The demo shows connecting to MotherDuck directly from Claude's UI—no code required—and immediately querying business data with natural language.

From "What Happened" to "What Should We Do" Jacob demonstrates an AI analytics workflow that goes beyond simple reporting. Starting with revenue analysis, the AI drills down into product categories, identifies seasonality patterns, and recommends specific actions—all through conversational queries against a MotherDuck database.

Improving SQL Generation Quality Ryan covers practical techniques to improve LLM accuracy:

Adding business definitions to the context window (e.g., "annualized weekly revenue = last week × 52")
Using semantic modeling tools like Cube, Omni, or Malloy
Budget approaches: SQL views and column comments that LLMs can reference
Providing golden queries as examples for joins and calculations

Hyper-Tenancy for AI Agents MotherDuck's architecture provisions isolated compute per user or agent. This means AI-powered analytics can be embedded in customer-facing applications where each customer queries only their own data with dedicated resources—demonstrated live with a multi-tenant e-commerce analytics dashboard built in under an hour.

Real-World Validation Sales reps at MotherDuck now use natural language queries daily to analyze their pipeline. Quote from the team: "What used to take hours as a data analyst now takes seconds." Even MotherDuck's most skeptical co-founder is now convinced the technology is production-ready.