From Experimental Musician to AI Director

I built a full enterprise AI capability from zero in 2.5 years at a traditional industry company—starting as an army of 1, now leading a team with production AI systems in customer-facing use.

My background combines 20+ years of experimental music with AI/ML engineering since 2016. The pattern recognition from improvisational music practice informs how I approach systems thinking and architecture, but the results come from production experience building AI in constrained environments.

Background

AI/ML Experience:

  • Director of AI at a traditional manufacturing company (2.5 years building from scratch)
  • AI/ML infrastructure at Wikipedia (content moderation, tooling)
  • BlackCrow AI (bespoke ML models for D2C brands, predictive analytics)
  • AI/ML work since 2016

Unique Perspective:

  • Experimental music background (20+ years) - Pattern recognition from improvisational practice informs systems thinking and architecture
  • Traditional industry credibility - Not FAANG theory, real production constraints
  • Proven track record - 0 to full AI capability in 2.5 years
  • Foundation-first approach - Infrastructure before innovation

What I Do Now

I help mid-market traditional companies build AI capabilities through:

  • AI Readiness Assessments - Evaluate infrastructure, team, and processes
  • Vendor Selection & RFP Support - Navigate the AI tooling landscape
  • Team Building & Leadership Coaching - Hire and scale AI teams strategically

Why Work With Me?

Most AI consultants bring FAANG playbooks to traditional companies and wonder why they fail.

I bring:

  • Real production experience in traditional industry constraints
  • Practical lessons from failed POCs and successful pivots
  • Strategic approach: prove value before asking for big investments
  • Understanding that organizational readiness matters more than technical capability

Philosophy

Foundation first. I spent 2 years building infrastructure before scaling the team—because trying to hire a big team on Day 1 would have failed. Traditional industries have different constraints than tech companies, and that’s okay. Embrace them.

Use AI yourself first. We used Claude Code and agentic coding tools to modernize our own legacy systems before recommending AI to stakeholders. Practice what you preach.

Timeline expectations matter. 2-2.5 years from zero to production AI capability is realistic. Anyone selling you faster is selling hype.

Connect

Living on the Oregon Coast. Trail runner, experimental musician (guitar, alternate tunings), AI systems builder. I believe time sovereignty is the real wealth.