A picture of use
See which teams use AI, for what, and how often. Connect that activity to its value.
Service
Usage Telemetry
If you cannot see how AI is used, the same problem gets solved ten times, poorly, in ten different chairs.
What this is
Adoption is manageable when the company can see how people use the tools.
We show where AI is being used, where it is stalling, and where the same question is being answered in fragments. That view is what lets you fix a problem once, for the whole company, instead of leaving each person to invent a private workaround. It also shows output quality and where more training, a better workflow, or a different tool is required.
What we build
The visible experience and the system underneath it.
The same problem, once
We mark where people are solving the same thing in private, so the company can fix it in one place.
Quality and support
Weak output and stalled work point to the next training session, workflow, or tool change.
How the engagement moves
A real sequence, from ambiguity to something the team can run.
- 01
Define
Agree on the signals worth collecting and the privacy line around them.
- 02
Implement
Stand up the view and read it against the work, not against license counts.
- 03
Improve
Turn the gaps into training, workflow changes, or support. Then measure again.
A good fit when
- The company pays for AI and cannot explain the return
- The same workaround keeps appearing in different teams
- Training and tooling decisions are still based on anecdotes
What changes
- A shared view of how AI is used
- Problems solved once, company-wide
- A clearer list of where support should go next
Connected services