The operator perspective
My career has been built at the intersection of technology strategy and execution — leading engineering organizations, architecting platforms, and operationalizing AI at enterprise scale. I don't advise from the sideline. I build, ship, and operate the systems I teach about.
That experience shapes everything about confluencegroup.ai. When I help a small business owner understand AI workflows, I'm drawing from production systems I've built. When I work with students, I'm teaching the same concepts I apply every day at the enterprise level.
Why Element451 Current Role
Element451 has built one of the most advanced agentic AI platforms in higher education. Their Bolt AI Agents don't just answer questions — they pursue goals, take actions, and operate across the full student lifecycle: admissions, marketing, enrollment, and student success. I joined as Chief Technology Officer because this is the rare company where agentic AI isn't a roadmap slide — it's production reality.
The platform serves 250+ institutions and the team has helped partner schools reclaim the equivalent of 67+ years of staff time. This is agentic AI operationalized — purpose-built agents working alongside staff across chat, email, text, voice, and web in over 100 languages, backed by $175M in growth investment from PSG Equity.
Working alongside a team that's this far ahead on agentic AI is what keeps confluencegroup.ai grounded in what's real — not what's theoretical.
Why this exists
There's a widening gap between how enterprises adopt AI and what everyone else has access to. Students are graduating without practical AI skills. Educators are trying to figure out AI policy when they should be learning how to use it. Small businesses hear "AI" and think it's not for them — or that it requires an engineering team to get started.
That gap is the problem. And it's solvable — not with more awareness campaigns, but with practical, hands-on capability building led by someone who actually does this work.
What I've built
At Revenue.io, I served as Chief Delivery Officer, owning Product Engineering, Data & AI, and Support. I transformed generative AI from a feature into a scalable revenue engine — 70% attach rate, roughly 20% of core license revenue, approximately 90% gross margin. I built a multi-model generative platform across OpenAI, Anthropic, and Google that scaled to millions of jobs monthly with zero failures over 12 months. I re-architected how the engineering organization works with AI, reducing cycle time by roughly 50% and expanding team capacity by 30%.
At Proof, I served as Chief Technology Officer leading a 100+ person Product Engineering organization — re-architecting the cloud platform from Heroku to AWS, achieving SOC 2 and ISO 27001 certifications, and establishing the product governance and delivery cadence to support enterprise growth.
At Alteryx, I served as Chief Technology Officer leading a global organization of 500+ engineers with an $80M+ operating budget. I architected and launched the Alteryx Analytics Cloud, led the migration of four products to a unified cloud architecture, integrated four acquisitions, and modernized engineering operations from quarterly releases to multiple deployments per day.
Earlier, I spent 15 years at Avanade — the Accenture/Microsoft joint venture — in roles spanning Chief Technology Officer, Vice President/General Manager, and VP of Corporate Strategy for a $1B North American division. Before that, I was an Enterprise Architect at Accenture.
The thread across all of it: building technology capability at scale, and making it work in practice.
Why community
I live in North Idaho. It's a long way from the tech hubs where AI fluency is assumed. The students here, the teachers, the local businesses — they deserve access to the same practical AI knowledge that enterprise teams get. Not the watered-down version. The real thing, taught by someone who builds it.
confluencegroup.ai is how I close that gap. The community work is the primary focus. The advisory work — selective engagements with organizations applying AI — is real, and it's what keeps the community work grounded in current practice.
The name
A confluence is the point where streams converge — where separate flows become something stronger together. It's the right metaphor for what this is: AI capability meeting practical need, operator experience meeting community access, technology meeting the people who use it.
