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Quick Questions
We define which AI initiatives the analysis covers and what decision it must support — fund, defer, or stop. Together with your stakeholders we agree the benefit hypotheses, the baselines they will be measured against, and the time horizon of the case.
We build a total-cost-of-ownership model covering data preparation, development, licensing, infrastructure, inference at production volumes, retraining cycles, and the operating team. Build-versus-buy alternatives are costed side by side so the comparison is honest.
Each claimed benefit — hours saved, conversion uplift, error reduction, churn avoided — is tied to a measurable baseline from your own data. Where a baseline does not exist yet, we define how to establish one before money is committed.
We stress-test the case against the variables that actually move it: adoption rates, model accuracy, usage volumes, and token or compute prices. The output is a range of outcomes with explicit best, expected, and worst cases rather than a single seductive number.
Costs, benefits, and risks are consolidated into payback period, NPV, and break-even calculations per use case. Initiatives are ranked so leadership sees not just whether each one pays off, but in which order to fund them.
We present the findings in a decision-focused session with the financial model handed over, not just a PDF. Your team keeps a working workbook it can update as assumptions change — turning a one-off analysis into a reusable investment tool.
We believe in radical transparency. You'll always know where your project stands and what comes next.
Progress reports every week
Communicate with your team
Clear deliverable checkpoints
Complete technical handoff
Let's begin with a conversation about your project goals.