Builder, engineering leader, and customer-focused technology consultant working at the intersection of enterprise transformation and AI-native software.
I build production software systems where AI agents and humans work together — and I build the governance frameworks that make greater autonomy possible. My work focuses on applying agentic AI to consequential domains (healthcare, security, regulated systems) while preserving verification, accountability, and human judgment where it matters most.
I'm a hands-on engineer and architect. I write code, design systems, and ship production software. The governance frameworks I publish aren't theoretical — they emerged from building real systems under real constraints, where business outcomes depend on getting both the technology and the operating model right.
Experiment aggressively. Deploy economically. Use the least expensive, least uncertain technology capable of solving the problem. Reserve frontier intelligence for the problems that actually require it.
From technology to business outcomes
AI transformation isn't primarily a model-selection problem. It is an organizational problem: determining where intelligence creates business value, where deterministic systems remain superior, how scarce domain expertise scales, how governance keeps pace with implementation velocity, and how teams change without abandoning engineering discipline.
My work focuses on that intersection — business strategy, architecture, engineering execution, AI economics, organizational transformation, and customer outcomes. The through-line:
That's pro-autonomy architecture, not bureaucracy. Governance exists to make greater speed possible — not to slow things down.
Before moving into enterprise engineering leadership, Douglas spent 14 years in customer-facing software consulting, working directly with clients to understand business problems, deliver software solutions, and expand relationships through successful outcomes.
That foundation shapes everything: the instinct to start with the customer's problem rather than the technology, to earn the next conversation through delivery, and to treat trust as the real product.
Built and scaled clinical analytics platforms serving millions of patients across hundreds of healthcare organizations. Led engineering teams through platform modernizations involving regulated data, compliance requirements, and high-availability constraints.
The Agentic Stage-Gate-Loop governance framework emerged directly from this production experience: when AI agents become active participants in building consequential software, the engineering operating model has to change. The question isn't whether to adopt AI — it's how mature organizations should function when implementation moves at machine speed.
Veteran-owned small business.
Rethinking Healthcare Velocity: The AI-Native Engineering Advantage
Genzeon × Sharecare · July 2026
AI-native engineering isn’t a tooling change. It’s an operating-model change.