AI ROI Forecasting
Don't fund AI projects on gut feelings. We build rigorous financial models that simulate the compute costs, development hours, and API fees against the projected operational savings or revenue lifts, giving your CFO a clear ROI forecast before a single line of code is written.
Core Features
Compute Cost Estimation
Calculating the exact cloud costs (AWS/GCP) for training models and the ongoing API token costs for inference.
Time-to-Value Modeling
Simulating how long the development will take and the exact point in time when the project will break even.
Sensitivity Analysis
Running Monte Carlo simulations to see how ROI changes if API prices increase, or if user adoption is lower than expected.
Business Case Creation
Packaging the financial models into a compelling business case required for securing board-level funding.
Our Process
Cost Baseline Establishment
Week 1Auditing your current operational costs (e.g., 'How much does a human customer service rep cost per ticket?').
Architecture Scoping
Week 2Working with engineers to determine the exact tech stack required (OpenAI vs Open-Source, Vector DB size, Data Pipeline complexity).
Financial Modeling
Week 3Building the Excel/Python models that factor in CapEx (Development) and OpEx (API fees, maintenance, compute).
Benefit Quantification
Week 4Calculating the financial upside, whether it's 'hours saved per week' multiplied by employee salary, or predicted conversion rate lifts.
CFO Presentation
Week 5Delivering the finalized business case, complete with risk matrices and sensitivity curves, to the finance team.
Technologies We Use
FAQ
How do you estimate API costs when we don't know how much users will use it?
Can you prove that AI will actually save time?
What is the biggest hidden cost in AI?
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