BetterUp helps people and teams do their best work through coaching, with human coaches and AI. More than 750 organizations use it, and its network has 4,000+ coaches in 70+ countries. Behind all of that is an analytics team that provides insights on growth and engagement to help organizations, and keeps everyone inside BetterUp working from the same numbers.
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Where BetterUp started
As BetterUp grew, more people wanted answers from its data: customers checking their reports and teams inside BetterUp looking at dashboards. Keeping all of those answers consistent took real work.
Plenty of the people asking didn't write SQL, and the analytics team couldn't keep up with the demand for analytics and insights.
Why they chose Cube
The team ran hands-on trials with large and small vendors. Cube stood out on three things: how mature its semantic modeling was, an AI roadmap built around the semantic model, and the security and governance controls BetterUp needs for its customers.
We now have a single source of truth for our metrics and our logic housed in a semantic layer that is headless. So regardless of the rest of your stack, any tool will be able to read from that and give you a consistent, governed analytics and insights experience.
How it works
Cube now holds BetterUp's metrics and logic in one place, and everything else reads from it. BetterUp connects Claude to Cube through the MCP server, so people can chat with the data from Claude Desktop. Internal teams also chat and build dashboards with the Cube agent.
BetterUp built its own reporting app for customers on Cube's APIs, so the numbers customers see match the numbers inside the company. Other AI tools, like the ones sales and marketing use, read from the same model. Jeff's take:
"It allows end users to consume the data using the AI tool of their choice, and giving us that governance layer on top."
What changed
Customer success managers now answer their own questions before a customer call, instead of sending a last-minute request to the data team. Jeff sums it up simply: CSMs "can answer their own questions just by chatting with the data."
It reaches well beyond analysts. A network operations manager, who is very non-technical, built one of BetterUp's most-used dashboards with Cube and Claude, and teams can use the AI tool of their choice. And with routine questions handled, the data science team spends more of its time on predictive work.
On reliability, as Jeff puts it:
"You can chat with the data and it will give you reliable results, even if you don't know exactly the right question to ask."
What this means for your agents
Agents don't open dashboards. They reach data through MCP and APIs. When definitions and access rules live in one semantic layer, every agent gets the same governed answer. In Cube's paired LLM benchmark, adding a semantic layer lifted answer accuracy by +17 to 23 percentage points.
If you're working out where semantics should live for your own agents, get a free architecture review.
BetterUp is one of 400+ companies, including Brex, Maersk and Patagonia, that run on Cube, the semantic layer for AI.
Give every team and every AI agent the same governed answers
Cube is the semantic layer for AI. Get a free architecture review to map where your metrics and logic should live, or start building on Cube yourself.
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