About
Founder & Principal Consultant — AI Transformation, Product & Technical Strategy
I help companies turn ambiguous business problems into practical software, AI-enabled workflows, and operating systems that teams can actually use.
My work sits at the intersection of engineering leadership, product ownership, operations, and business process design. As Director of Engineering at a clinical trials technology company, I led a 30-person engineering team supporting a $100M+ revenue business. As VP of eCommerce & Technology at a consumer ecommerce company, I led an 18-person team at a similar $100M+ revenue scale. In both roles I owned technical budgets and custom platform development, and built the operating rhythms — prioritization, delivery review, technical decision-making — that kept engineering work connected to business outcomes.
Increasingly, my focus is on AI implementation — not AI as a slogan, and not AI as a collection of tools, but AI as a practical way to redesign work. That means identifying workflows that consume too much time, money, or organizational attention; defining the product and business requirements; designing the technical architecture; and coordinating the people, systems, and AI agents needed to deliver the work.
I am especially interested in problems where the first question is not "What should we build?" but "What is actually challenging right now?" From there, the work becomes clearer: understand the workflow, define the outcome, decide what should not be automated, design the system, and build toward measurable business value.
I work best with founders, executives, and teams who have real opportunity but need more structure around product, technology, operations, and execution. That can mean acting as a fractional CTO. It can mean bringing definition and structure to product and operations so the team can scale without rebuilding the foundation every quarter. It can mean helping a company find the right AI workflow to implement first. In every case, the goal is the same: create clarity, build useful systems, and move the business forward.
Phoenix RTP is a fractional CTO and AI implementation practice based in Research Triangle Park, North Carolina. I work in person with companies around Raleigh, Durham, and Chapel Hill, and remotely everywhere else. The name also captures the three ideas behind how the work gets done: Rethink, Transform, Progress.
Patterns from the work
AI Implementation
I've seen teams get excited about automation before they'd clearly defined the workflow they were trying to improve. The more useful starting point was mapping where the work waited, where judgment was required, where rework happened, and where the same decisions kept pulling senior people back into the process.
Technical Leadership
In smaller, founder-led environments, the need wasn't always a full-time CTO. The company needed someone senior enough to review the architecture, pressure-test the roadmap, and help leadership understand the technical tradeoffs before they became expensive.
Founder Leverage
I've worked with founders who didn't need a large product organization — they needed help getting from idea to first build. That meant narrowing the MVP, identifying the riskiest assumptions, sequencing the work, and creating feedback loops so the first version produced learning instead of just more software.
How I work
Rethink
Talk through the actual workflow, who owns it, where the handoffs break down, what decisions are being made, what information is missing, and where time or money is being wasted.
Transform
What should be faster? More accurate? Easier to manage? More visible? What should not be automated? What risk has to be controlled? Only then does the technical design make sense — and only then does implementation begin.
Progress
The implementation involves AI agents, software automation, better internal tooling, clearer product requirements, or a new operating cadence. The point is not the newest tool — it is measurable movement, and then back to rethinking what is next.
What I believe
AI is an accelerant, but accelerants do not choose the destination.
AI makes mediocrity faster. The value is not in producing more output. The value is in applying better judgment to the right problem.
The first question should not be "How can we use AI?" The better question is "What problem are we actually solving?"
Good implementation starts with understanding the work — not the tooling.
A useful system has ownership, workflow clarity, business rules, technical architecture, exception handling, and a definition of success.
Founders and executives do not need more noise. They need leverage.
Most real business problems do not fit neatly into one department. The work requires product judgment, technical architecture, operational design, and leadership cadence at the same time.
If your company has a workflow, product idea, technical decision, or operating problem that feels important but unclear, reach out. The first conversation is about understanding what is actually going on.