Resources
Tools and case studies to help you identify epistemic gaps in AI governance and high-stakes AI deployments.
Case Studies
Real consulting engagements showing how upstream problems manifest—and how to catch them before they cause failures.
When the AI Said "Done" But Nothing Happened
How interface confidence obscures system reality. A data security platform's chatbot claimed to configure critical controls—but the underlying system never executed them. What this reveals about validation gaps in AI-enabled tools.
Practical Guides
Analysis and frameworks for evaluating AI implementations, governance architecture, and vendor claims.
Why Your AI Governance Framework Will Fail Audit
Even with comprehensive documentation and internal approval, most governance frameworks can't answer the questions external auditors actually ask. The five questions your framework probably can't address—and what that reveals about upstream problems.
The Three Questions Your AI Vendor Can't Answer
Standard vendor evaluation asks about accuracy, integration, and compliance. These questions can't reveal whether the system will work in your environment. What to ask instead—and why most vendors struggle to answer.
How to Tell If Your AI Implementation Has Upstream Problems
Five warning signs that reveal architectural failures in AI governance and deployment. If you're seeing three or more of these patterns, you have systemic problems that documentation can't fix.
Research
The Information-Theoretic Imperative: Compression and the Epistemic Foundations of Intelligence
Published research on why compression optimization mechanically yields epistemically valid models. Currently pending review at peer journal.
Whitepapers
Publicly available soon.
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