Bilbo - Metabase AI Assistant | Natural Language Data Tool Frequently Asked Questions

Bilbo - Metabase AI Assistant | Natural Language Data Tool Frequently Asked Questions. Bilbo - Metabase AI Assistant | Natural Language Data Tool: Bilbo, your ai tool, turns questions into insights. Use Product Name to query in plain English, build fast, visualize instantly. Try free!

Frequently Asked Questions About Bilbo - Metabase AI Assistant

How is my data handled by Bilbo - Metabase AI Assistant?

Your data remains secure at all times. Bilbo does not store, copy, or transmit your raw database content. All queries are processed through encrypted connections and executed within your existing Metabase environment, adhering strictly to your configured permissions and access controls. Your data never leaves your infrastructure, preserving compliance and confidentiality.

Does Bilbo - Metabase AI Assistant work with self-hosted Metabase instances?

Absolutely. Bilbo is fully compatible with both Metabase Cloud and self-hosted installations, including those running on private servers or virtual private clouds. This ensures organizations with strict security, compliance, or data residency requirements can still benefit from AI-driven analytics without compromising control over their systems.

What types of visualizations can Bilbo - Metabase AI Assistant create?

Bilbo supports a wide variety of visual formats, including bar charts, line graphs, pie charts, area charts, scatter plots, heatmaps, and tabular summaries. Simply specify your desired format in natural language—such as “visualize signups by country in a map” or “show retention cohort data as a heatmap”—and Bilbo will generate a professional-grade visualization tailored to your request.

How does shared team context work in Bilbo - Metabase AI Assistant?

Shared team context allows users to define, save, and distribute reusable query logic and metric definitions across the organization. For example, if your marketing team defines “customer acquisition cost” once, others can reference it in future queries with confidence in accuracy and consistency. This fosters alignment, reduces errors, and builds a scalable foundation for enterprise-wide data literacy.