AI answers your team — and your customers — can trust. Grounded in your semantic model, governed end to end.
Cube grounds Analytics Chat, workbooks, and dashboards on a single governed model — so every answer ties back to the same numbers.
Embedded Analytics
Brex, Webflow, and 100+ other SaaS companies ship AI-powered customer-facing analytics on Cube — multi-tenant, governed, and built around the semantic layer.
Build a fully custom AI analytics experience — agent-to-agent capable via MCP.
Analytics Chat and Dashboard iframes — the fastest drop-in path.
Full workbook and dashboard creation, embedded inside your app — your customers build their own.
Maximum control at the data layer; build any UI on top.
Your colors, your branding, your agent name — Cube's embedded surfaces disappear into your product.
Multi-tenant by construction. Governance flows from your model through to your customers' permissions.
Brex chose Cube over dbt Semantic Layer and LookML — the semantic layer is what makes the AI useful at scale.
Business Intelligence
Cube's semantic layer keeps your definitions consistent across every surface — chat, workbooks, dashboards, Claude, ChatGPT, Slack. The same question gets the same answer wherever it's asked.
Ask questions in plain English. Cube's Analytics Chat builds queries against your semantic model — so answers are accurate, governed, and the same regardless of who asks.
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 CronanderSenior Analytics Engineer, DrataRead the Story
We integrated Cube Cloud smoothly with ClickHouse, leveraging both for fast query execution while maintaining the abstraction needed for different teams to access data without diving into database-specific complexities.
Rocio Delgado
Senior Engineering Manager at Webflow
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
Cube really stood out as a great fit for our use case. We were able to level‑up our data infrastructure without needing to build a full‑blown and expensive data pipeline.
Jc Weinrich
Architect at Jobber
See how Cube can help you get the most of your data
