VeracIQ
The Problem
Current AI governance frameworks measure whether a system is internally consistent. None of them measure whether it is correct.
That distinction matters more than most organizations realize. A model can be coherent, stable, and fully compliant with every applicable framework while its outputs have quietly drifted from operational reality. Standard audits won't catch this — not because auditors aren't looking, but because the measurement methods are endogenous to the model itself. They confirm that the system agrees with itself. They don't confirm that the system agrees with the world.
By the time that gap becomes visible, the failure has already happened.
The Distinction
VeracIQ detects epistemic drift by orienting AI systems against external ground truth — not internal model self-report. This is a structural difference in method, not a refinement of existing approaches.
The theoretical foundation for this approach is formalized in information-theoretic terms: compression dynamics in AI systems create irreversible information loss that accumulates over time. Governance frameworks built on regulatory checklists don't account for this. VeracIQ does.
The result is the ability to identify, before deployment, where a system will fail to hold under regulatory scrutiny — and why.
What VeracIQ Identifies
Where governance frameworks assume invertible processes that AI systems cannot provide. Compression dynamics create information loss that conflicts with regulatory traceability requirements — not as an implementation failure, but as a structural constraint that wasn't visible during architecture decisions.
Where the technology is appropriate, the governance framework is appropriate, and the combination systematically fails. Information flow analysis reveals these incompatibilities before they become expensive to unwind.
Where systems appear to meet requirements while compressing away information that auditors will eventually demand. These gaps only become visible when you understand both how AI systems actually process information and what regulatory frameworks actually verify.
Regulated Domains
FDA/EMA submission workflows, clinical trial oversight, post-market surveillance. AI-assisted decisions require evidence chains the architecture must be designed to preserve from the start.
Model risk management, algorithmic accountability, SR 11-7 compliance, AML/KYC systems. Audit-trail integrity under Basel IV and SEC/FINRA scrutiny requires governance that accounts for compression efficiency losses.
Clinical decision support, diagnostic AI. Patient safety requirements must be reconciled with how the system actually processes information — compression dynamics affect safety bounds in ways standard validation doesn't surface.
IRB processes, cross-departmental AI governance, environments where technical and compliance teams are working from incompatible assumptions about what the system can and cannot do.
Engagements
Engagements are structured around pre-deployment assessment. The objective is to identify structural problems before they are built in, not to document them afterward.
Assessment of current or planned AI governance approach using information-theoretic methods to identify structural issues and map epistemic drift risk before deployment.
Documentation of where the architecture conflicts with regulatory requirements, where compression dynamics create compliance risk, and what changes prevent failure.
Specific recommendations for governance systems designed to maintain information flow integrity and survive regulatory scrutiny — including frameworks that don't yet exist for the problem you're solving.
A limited number of ongoing advisory mandates for organizations implementing AI in high-consequence environments. Currently accepting inquiries for 2026.
Theoretical Foundation
The information-theoretic framework underlying VeracIQ is formalized in The Information-Theoretic Imperative: Compression and the Epistemic Foundations of Intelligence, co-authored with Christian Dittrich and available at arXiv:2510.25883, currently under review. The paper formalizes the relationship between compression dynamics, epistemic drift, and the conditions under which AI systems maintain or lose their orientation to ground truth.
VeracIQ is grounded in 20+ years of research operations, risk, and compliance at Harvard University, combined with independent theoretical work in information theory, causal structure, and the epistemics of machine learning systems.
Work Together
For architecture reviews, implementation guidance, or to discuss a specific regulatory challenge — implementation methodology is discussed under NDA.
VeracIQ is patent pending. Provisional No. 63/858,627.