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DR
▸ current focus
targeting · Q3–Q4 2026

I architect production-grade, scalable AI systems that survive contact with reality.

I build AI capability the way a platform leader should: tied to business outcomes, grounded in infrastructure reality, and designed to survive production. Fifteen-plus years across LLM systems, agentic AI, DevSecOps, and cloud — most recently building enterprise AI at ATA LLC, before that running my own AI automation consultancy and leading DevSecOps on a $500M defense program at Lockheed Martin. I work fluently across hosted and local model stacks, from frontier assistants like ChatGPT, Claude, Gemini, and Codex to self-hosted runtimes such as Ollama, vLLM, Hermes, and Diffusers-based systems.

The core value I bring is not just model familiarity. It is the ability to translate AI ambition into production architecture: retrieval, orchestration, evaluation, infrastructure, security, approvals, and the workflows that let teams actually depend on the system after the demo is over.

Director-level scope
Strategy, architecture, and execution in the same seat.
Production-grade architecture
I architect AI systems for scale, governance, and operational reality.
Platform mindset
Reusable AI infrastructure beats one-off prompt hacks.
$250K+
annual AI efficiency
$1.2M
savings · platform play
+30%
team productivity
80%
AI-driven QA coverage
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▸ ai stack
tools I use in practice

Tools I actively ship with, not just experiment with.

assistants
Codex ChatGPT Claude Gemini
local / self-hosted
Ollama vLLM Hermes Diffusers
runtime / orchestration
LangGraph FastAPI pgvector WebSockets
delivery patterns
RAG Agent workflows Structured extraction Eval-minded QA

I am not tied to a single model vendor or workflow. My work spans hosted assistants, local inference stacks, retrieval systems, agent orchestration, document intelligence, and the platform work needed to make those systems reproducible, governable, and useful.

▸ how I build AI systems
operating principles
Prototype fast. Production deliberately.

The shortest path to a proof of concept is rarely the right path to a reliable system.

Human approval where it matters.

Autonomy is useful, but governance, auditability, and kill switches matter in real organizations.

Evaluate before you trust.

I prefer measurable behavior, regression checks, and observable workflows over vibes.

Local-first when privacy or performance matters.

I build across hosted APIs and self-hosted stacks depending on the constraints.

executive impact
What the work changed.
operator + platform + business
$250K+
annual AI efficiency gains
document intelligence and automation
$1.2M
annual savings
platform automation on defense program
150+
engineers influenced
cross-functional technical leadership
80%
test coverage
AI-driven QA automation framework
leadership surface area
AI strategy platform architecture agent systems DevSecOps modernization governance and approvals build-vs-buy decisions
▸ featured systems · 5 entries
2022 — 2026

Start here for the clearest picture of how I architect scaled AI solutions: platform systems, governance-aware agent workflows, and productized tooling that can move from experimentation to repeated use.

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OpenDuck
governed agent platform
IntellePrep
ai-assisted product platform
WebbDuck
local-first generative platform
DNADuck
dataset and identity pipeline
PhoenixDraw
productized creative ai loop
23 — 2026
The full record includes public OSS, private product systems, internal tooling, and experiments.
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