D. Brian Letort, Ph.D.

I lead enterprise AI programs at global scale. I build governed platforms, operating models, and data foundations. I publish research on the systems layer that makes enterprise AI reliable. I teach — through Pluralsight and graduate faculty appointments — the next generation of engineers and architects doing this work.

I operate from a simple premise: the AI platforms that earn enterprise trust are the ones governed from day one, and those are the ones used at the top of the house. Everything else eventually stalls inside legal review. That premise shapes how I architect, how I write, and how I advise.

At Northrop Grumman, across a 20-year tenure, I served as Northrop Grumman Fellow, Chief Enterprise Architect, and Chief Data Scientist. I aligned a $1.5B IT portfolio to mission architecture, contributed to $13B in proposal architectures, developed production machine learning models, trained thousands of employees, and delivered approximately $4.5M in annual savings. I held a Top Secret clearance during that era. The posture I built there — careful, auditable, mission-first — is the operating default I bring to every program I touch.

In my current role, I serve as Head of Corporate Data Office & AI / Enterprise Architecture Leader, with a mandate spanning AI strategy, governance, platform architecture, data governance, Master Data Management, reporting, portfolio management, operating-model design, and AI value realization. The work includes AI portfolio views across 200+ opportunities, AI FinOps, token economics, model routing, private AI strategy, GPU capacity planning, and reusable architecture patterns for governed AI delivery.

Across my career I have taught 5,000+ students through graduate and doctorate coursework in computer science, data science, machine learning, and enterprise AI. That teaching discipline is where I formed the conviction that the clarity of the teacher is the clarity of the architect.

My research program asks a practical question: why is enterprise AI so unreliable, and which layer of the stack is missing?

The answer I am writing toward, through one foundational paper and a three-paper trilogy on Zenodo, is Context Compilation Theory — the discipline of treating the assembly of context for a model the way software engineering treats the compilation of source code. Retrieval tells you what to read. Reasoning tells you what to say. Context compilation is the governed, measurable, optimizable layer between them. It is where reliability lives, where provenance is enforced, and where token economics becomes a design surface rather than an accounting line.

The trilogy around the foundational paper addresses the three dimensions an engineering discipline needs to stand on its own: representation (a Context IR and compiler passes), runtime (paged context memory and evidence blocks), and precision-aware optimization (quantized context and mixed-precision assembly). A companion paper on Cybernetic Software Delivery extends the frame to the governance of engineering work produced by autonomous agents.

This is not academic extracurricular. It is the through-line of the work. Every platform I build is an implementation of the theory. Every outcome I publish is evidence for it. I believe context compilation is the next durable discipline inside AI engineering, and I am writing the framework for it in public.

I live and work on the Mississippi Gulf Coast, in Gulfport. I write, teach, and operate under the banner Operate. Publish. Teach. I accept a small number of advisory, board, and keynote mandates per year.

• Advisory, board, or committee engagements · brian@brianletort.ai
• Press, podcasts, and keynote requests · brian@brianletort.ai
• Research and speaking history · brianletort.ai