AUTOMATION AT SCALE
Scale your data teams without increasing headcount
Query in natural language
Automate ad-hoc analysis and reporting using natural language, no SQL required.
Ask follow-up questions
Refine reports iteratively with conversational follow-ups—no need to restart.
Get answers without the wait
Generate charts, summaries, and insights instantly with just a question.
DEEP CONTEXT
Enforce consistent semantics, grounding on a single source of truth
Consistent business logic across the org
Cube Cloud’s semantic layer keeps metrics and logic aligned across all tools and teams.
Governed, semantic SQL generation
Agents generate SQL from semantic models and policies, ensuring built-in consistency.
Create new, governed metrics on demand
Agents can derive new metrics based on trusted, existing definitions, even if they're not part of the base model.
FULL VISIBILITY AND CONTROL
Explain every agent’s decision to create confidence in AI outputs
Full control over agent behavior
Agents follow defined rules and permissions, giving teams full control.
Know what’s behind every answer
Every insight explains its SQL, sources, and assumptions for easy validation.
Verify and govern outputs
Admins can certify agent reports, turning them into trusted, reusable assets ready for audit.
INTEGRATIONS
Accelerate insight-to-action with seamless tool integration
Integrate seamlessly into daily workflows
Use A2A, MCP, or iframe embedding to integrate with tools your teams already use.
Accelerate value through agent collaboration
Orchestrate agents to automate end-to-end tasks, speeding up analysis and decisions.
Save, reuse, and act on insights
Save and reuse queries in workbooks or export to other formats.
Built to fit your data stack
Customer stories
Cube becomes our single source of truth for metric definitions and powers everything from customer-facing dashboards to AI-driven quarterly business reviews. CSMs gain back dozens of hours each quarter, enabled by Cube’s semantic layer and agentic analytics.
Anthony Cronander
Senior Analytics Engineer, Drata
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Without Cube, our data analysts might have to write 20 different queries for a single core business metric. With Cube, that metric is defined once in the data model, and every downstream tool uses that definition along with the associated calculation logic.
Dr. Jun Huang
Global Head of Data Science at Alcon
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My first impressions of Cube are genuinely exciting. There’s clearly immense potential; it represents a vast improvement in Cube’s already powerful AI capabilities that will significantly enhance how we derive clear, actionable insights and become an increasingly core element of our multi-agent system. I’m particularly impressed by its advanced ability to help us and our end users dynamically interpret our data, effectively making our existing AI systems more powerful and responsive.
Tim Handley
Chief Product and Technology Officer at Welbee
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With Cube, we’ve been able to speed up time to release a new data model to production by 5x and decrease analytics downtime by 90%.
Alessandro Lollo
Senior Data Engineer at Cloud Academy
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The Use Cases of Cube
Embedded Analytics
Build consistent, secure, and performant embedded analytics.
Real-time Analytics
Trust your real-time data with a stack designed for consistency and speed.
LLM & AI Semantic Layer
Bring context to AI chatbots and LLMs
Modern Cloud OLAP
Bridge the Gap Between your Modern Data Stack and Spreadsheets