Organizations are deploying AI
faster than they can govern it.
My research focuses on the economic, engineering, operational, and security systems required to keep AI sustainable after deployment.
The Production AI Governance Framework
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.
The Convergence Model
Five operational disciplines converging into a single runtime enforcement layer.
Economics
Distill inference margins, synthetic COGS, and R&D opex/capex capitalization.
Explore →Product
Govern features, manage product debt indices, and prevent margin collapse.
Explore →Engineering
Mitigate vibe coding, calculate technical insolvency dates, and scale platforms.
Explore →Security
Secure agent execution boundaries, implement kill switches, and block shadow AI.
Explore →Operations
Evaluate cloud TCO, repatriate workloads, and run token usage simulations.
Explore →Runtime Governance
Deterministic boundary execution. This is where frameworks convert to runtime physical control. (Exogram Platform)
Deploy Controls →Integration Mesh
Explore how research, diagnostics, academy courses, and software controls interact.
The Production AI Governance Ecosystem
Every resource on this site is a node in a single multi-year research program exploring AI operational limits.
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