AI should amplify human expertise,
not replace it. I build systems that prove it.

I build AI systems that make people more capable, not obsolete — and I've built the architectural foundation to do it well at enterprise scale.

Most recently, I designed and built a multi-tenant SaaS foundation with formal architectural decision records, rehearsal evidence, and integrated LLM workflows in the product layer. CMMC Level 2-aligned security posture. Tenant isolation that holds up under real audit pressure. The kind of platform that an enterprise customer can adopt without the architecture review becoming the gating event. It's the foundation underneath whatever AI-amplified vertical applications I build next, and the design discipline behind it is what I want the rest of my career to look like.

The reason I can build at that level of rigor is that I've spent eighteen years doing it for other people's companies. A decade of that has been at Salesforce, where I led GTM integration for major acquisitions — Slack, Tableau, MuleSoft, Own, Zoomin — and where the cost of getting an org consolidation wrong was measured in eight figures and the kind of executive attention nobody wants. I've architected mission-critical platforms for defense and regulated public sector customers operating under CMMC Level 2 controls, where downtime isn't a metric on a dashboard, it's a phone call from someone who is already very unhappy. I've sat across from CIOs and senior engineers who needed an answer that worked, not a presentation about why the answer was hard.

That work is what taught me the discipline I bring to AI now. Architectural decisions that survive contact with reality. Security and compliance treated as design constraints from day one rather than retrofitted afterward. Customer presence — the kind where you fly to the customer's city, sit in their war room, and own the technical relationship through the messy parts. AI-native companies need senior people who can build the system and hold the customer relationship at the same time. That's the role I've been training for, in one form or another, for two decades.

I'm also writing Service Intelligence, a practitioner's framework for the AI era, forthcoming in 2026. It argues the thesis that informs everything I build: as AI handles more of the execution, the human premium skyrockets, and the companies that win the next decade will be the ones that use AI to make their people more present, more skilled, and more trusted — not less needed. The technology is interesting. The technology applied to make humans more capable is the work.

The work I want to do next is bringing twenty years of architectural discipline, customer presence, and enterprise stakes to AI-native companies building customer-facing technical systems.