One outcome number per Qube
We don't arrive with a roadmap to "transform finance with AI." We agree on a specific, measurable result e.g., the cost per invoice processed, the time to close a period, or the defect escape rate. We're accountable to that number from day one. If we can't name it upfront, we don't start.
Built to outlive the tool
Models will improve. Vendors will change. We build the capability (the process logic), the data structures, and the outcome framework. So, it survives the technology that runs it today. No dependency lock-in. No migration crisis when something better arrives.
Embedded where work actually happens
Inside the ERP screen. Inside the support tool. Inside the planning system. Not in a standalone AI interface that competes for attention. When AI lives in the workflow your people already use. This way, adoption is no longer a problem.
Governance built in, not bolted on
Before anything goes live, we define exactly what the AI can decide (guardrails), what it flags for human review, and what it never touches. Every action is logged. Every exception has an owner. When the stakes are real, humans stay in the loop – by design.
What’s covered:
- Use-case fit and feasibility
- Data readiness review
- First-Qube scope definition
- Outcome number agreed upfront
Which AI pilot in your enterprise deserves to become real?