Most AI projects fail on scope, not capability. The useful version starts with the handful of tasks that happen every week, costs nothing to get wrong, and can be checked by the person who already does them.
We wire the model to your own data with boundaries that hold, build a prompt library the team actually reuses, and put a human in front of anything that leaves the building.
What the work is
- Use-case triage — what to automate first, and what to leave alone
- Data boundaries and tenant grounding before a single prompt ships
- A prompt library written for your work, not a generic cheat sheet
- Agents wired into the systems the work already lives in
- Measurement against hours returned, reported monthly
What you end up with
- A short list of jobs that now run themselves
- A human approval step on anything that reaches a client
- A number you can take to the board, and the workings behind it
- Copilot
- Azure OpenAI
- Power Automate
- Graph API
- Claude
- Power Platform