Evaluation harnesses, independent model risk validation, and fractional AI leadership for banks, insurers, and health plans. Deep technical assurance built by practitioners who ship production AI systems.
Aligned to Fed SR 26-2, the NAIC AI Model Bulletin, and NYDFS Circular Letter No. 7.
The market is loud and largely unproven, and few organizations can demonstrate their systems perform as claimed. The first federal model-risk guidance for this era landed in April 2026. That gap defines the practice.
A low-risk entry point, a signature offering few firms deliver well, and a recurring seat at the table. Every engagement is fixed-fee with a defined scope.
Stakeholder interviews, a data and systems inventory, and a prioritized use-case roadmap with cost and benefit estimated for each use case. You finish knowing where AI pays off and where it will not.
Golden datasets, task-specific metrics, regression detection, and CI integration, plus a documented definition of what working means for your system. The capability that separates real AI engineering from prompt tinkering.
One to two days a week covering architecture decisions, vendor evaluation, hiring support, roadmap, and code review. Senior AI judgment before you can justify a full-time hire. Retained with a 3 to 6 month floor.
Also available on request: AI System Audits and Second Opinions, Independent Model Risk Validation (SR 26-2 and NAIC), RAG and agentic system builds, and team enablement workshops.
Regulators require that whoever validates a model is independent of whoever built it. That requirement is written into the regulation itself. Here is what the discipline looks like in practice.
We do not build your model and then grade our own work. The value is an arm's-length assessment a regulator will accept.
Every claim is backed by a reproducible test, a documented limitation, and an examiner-ready evidence file built to survive scrutiny.
Ph.D.-level quantitative and causal-inference training aimed at the questions non-experts cannot assess, where quality in an AI system actually lives.
Many AI advisors sell strategy and deliver a slide deck. This practice ships the infrastructure that proves a system works and holds up under a regulator's questions, which is the part few firms can actually do.
The buyers who matter cannot self-serve and cannot afford to be wrong. They need assurance that is independent, technical, and accountable. That is the reason this practice exists.
Start with a paid AI Opportunity Assessment: low risk, defined scope, and a clear view of where AI pays off. Or send a short description of the problem you are facing.