HOW PILOTS WORK
Thirty days. One workflow. A number at the end.
THIRTY DAYS. ONE WORKFLOW.A NUMBER AT THE END.
Enterprise AI fails when it starts as a platform decision. We start it as an operations experiment with a measurable outcome — and let the measurement decide what happens next.
STAGE 1 · WEEK 0
Diagnose one expensive workflow
We pick a single workflow where delay, rework or leakage is measurably costing you — and map it end to end.
- Half-day working session with the people who run the workflow — not just their managers
- We quantify the cost of the status quo: hours, delays, rework, leakage
- One workflow is selected; everything else is explicitly out of scope
STAGE 2 · WEEK 1
Establish baseline metrics and governance
Current cost, current cycle time, current error rate — agreed up front, with the approval and audit model.
- Baseline metrics agreed in writing — the numbers the pilot will be judged on
- Governance model fixed up front: who approves, what is logged, where data lives
- Access, security review and data-handling agreed before any system touches your data
STAGE 3 · DAYS 1–30
Deploy a working system within 30 days
Not a proof-of-concept deck. A working system, in your environment, used by your team on real work.
- Working software in your environment by day 30 — not a slide deck about future software
- Your team uses it on real work while we iterate weekly
- Human approval on every consequential action from day one
STAGE 4 · DAY 30
Measure proof and decide whether to scale
The baseline decides. If the numbers move, we scale to the next workflow. If they don't, you've lost 30 days, not a year.
- Results measured against the written baseline — no moving goalposts
- Scale decision is yours, priced and scoped before you commit
- If the numbers don't move, you walk away with the diagnosis and 30 days spent, not a year