Operations
FinOps frameworks and hardware repatriation metrics to manage spot-GPU usage, local compute breakevens, and token decay.
Contextual Boundary
Why This Exists
Most AI discussions focus on model capabilities. My work focuses on what happens after deployment. As AI systems become embedded in products, organizations face a new class of problems involving economics, governance, security, reliability, and operational control. The Production AI Governance Framework exists to help organizations understand, measure, and manage those challenges.
Core Analytical Axioms
Forensically proven concepts in this operational boundary.
Agentic FTE Simulator
A model mapping the headcount substitution rate and margin adjustments of deploying AI agents in support and operations.
Organizations deploy customer support bots without calculating the real cost of handling edge cases (human intervention rates), distorting savings projections.
Calculates the real EBITDA payback of automation projects.
Cloud Repatriation Breakeven
The financial math calculating the exit point from cloud databases (AWS, Azure) to private bare-metal configurations.
Companies stay on hyperscalers for high-velocity database access, bleeding up to 60% of operating cash flow on markup pricing.
Recaptures lost cash flow to directly improve EBITDA margins in mature products.
Token Simulation
A model calculating prompt expansion and context decay in multi-step reasoning chains.
Multi-agent workflows run recursive loops that expand prompt sizing exponentially, ballooning execution bills in hours.
Sets clear guardrails to avoid runaway looping bills.
Want to apply this to your organization?
Run a free diagnostic first. If the numbers concern you, book a session to build a remediation plan.
Richard Ewing — AI Economist & Capital Auditor