The context
Finance and IT operations both run on cycles — month-end close, quarterly forecast, annual budget. The close is largely assembly: pulling source-system data, reconciling exceptions, posting adjustments, drafting commentary. Cloud cost data sits in a parallel universe owned by engineering, with no shared KPI between the two.
Why it doesn't scale today
Every previous wave of close-automation software was templated and brittle. Cloud cost dashboards are owned by the wrong team to actually act on them. Finance and engineering speak different metrics — and the misalignment compounds.
What we ask in week one
- iWhich steps of your close are model-replaceable, and which need your controller's judgement?
- iiHow do we surface engineering-cost data to your finance team in a form they can use without translation?
- iiiWhat does anomaly detection look like across both your transaction streams and your cloud-spend streams?
- ivWhat's the shared KPI your finance and engineering orgs will both sign up to score against?
What we build
Reconciliation and anomaly-detection agents on the close-cycle critical path. A multi-cloud cost-forecasting layer (Holt-Winters ensemble across AWS, Azure, GCP, Digital Ocean) that finance and engineering both score against. The close compresses; the engineering org earns the cost forecast it can defend in the budget review.
Why we're the right squad
We have built the FinOps lab the squads run themselves. It is the only Finance & Ops surface we know of where finance and engineering both look at the same forecast with the same trust — and the live demo is one click away.