How The Governance
System Emerged.
The intellectual evolution mapping the progression from foundational AI unit economics up to deterministic runtime enforcement.
Recent Published Works & Laboratory Briefings
How to Reduce LLM API Token Costs in Production ↗
Deploying semantic vector caching with cosine similarity thresholds (0.85-0.92) alongside edge regex pre-filtering cuts production LLM API token OpEx by 50%+ and reduces query latency to <20ms, protecting SaaS gross profit margins from linear token burn.
How to Reduce LLM Costs in Production: The Inference Dividend Model ↗
Serving AI features with un-monitored model calls erodes traditional 80% SaaS gross margins into low-margin territory as user activity scales linearly with API token burn. Capturing the Inference Dividend through edge pre-validation, semantic intent caching, and task-based model tiering slashes token OpEx by over 50% while reducing cache response latencies under 20ms.
Salesforce and SAP are putting AI agents inside your workflows. Who tells them no? ↗
Enterprise SaaS providers (Salesforce, SAP, Oracle) are embedding autonomous AI agents directly into transactional workflows with authority to issue refunds, alter contract terms, and spend corporate capital - creating a critical breakdown in corporate signing matrices and shadow delegation that bypasses internal executive approval controls.
Growth Is Not Your Cost Problem - Your Architecture Is ↗
Shrinking software margins during user base growth stem from underlying LLM architecture flaws, not growth itself. Placing semantic caching and sub-millisecond edge filtering in front of frontier models slashes runtime API spend by over 50% without quality degradation.
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.
Economics
Distilling the unit economics of LLM inference, indexing raw engineering throughput, and auditing R&D capital allocation.
Governance
Establishing the Product Debt Index (PDI) to convert undocumented technical debt into boardroom-ready exit valuation metrics.
Operational AI
Solving the Cost of Predictivity. Modeling AI margin collapse points, cloud FinOps repatriation breakevens, and small model alternatives.
Agent Security
Identifying security liabilities in autonomous systems. Mapping jailbreaks, shadow AI data leaks, and sandbox evasion vectors.
Runtime Governance
Shifting from passive observability to deterministic physical control boundaries. Building state-verification engines.
Exogram
Deployment of the sovereign Exogram runtime interceptor. The physical proxy layer enforcing zero-trust governance.
Ecosystem Alignment Map
Every publication, tool, and software system mapped back to the core research program.
The Production AI Governance Architecture
Every resource on this site is a node in a single multi-year research program exploring AI operational limits.
Frequently Asked Questions
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