Mechanical engineering, engineering physics, nuclear risk, Uber-scale operations, and now Phoenix Strategies: the through-line is systems thinking under real-world constraints.
Phoenix Strategies and Prometheus Labs are deliberately separate: one is the client-facing business; the other is the lab bench where the next set of tools, patterns, and instincts gets built.
Phoenix Strategies is the consulting business: the practical vehicle for helping companies grow, clean up messy operations, and turn repeatable judgment into durable systems. It is also the honest, real-world side of the work — the business that supports my family while creating leverage for the teams that hire me.
Prometheus Labs is the evolution of the original Prometheus Project: a living archive of shipped apps, experiments, project logs, prototypes, and whatever AI-enabled build path my brain will not stop pulling on. It is where I explore what modern AI lets one person make, learn, and compound.
Current consulting and labs sit on top of the operating, product, nuclear-risk, and engineering chapters that shaped the work underneath it.
Now running Phoenix Strategies and Prometheus Labs: building AI-enabled operations tools, legal-ops support systems, finance automations, healthcare/mobile products, analytics workflows, and proof-of-work software. Client work includes Chobani, Andela, Ethos, legal-operations teams, and more. AI where it helps; boring software where it has to work.
Spent a decade inside global, high-growth operations: city ops, JUMP, COVID response, Grocery & Retail product operations, merchant migration, GDPR readiness, feedback loops, feature launches, performance systems, and the hard work of making product changes survive contact with the real world.
Developed probabilistic risk models, failure-mode analyses, Monte Carlo-style reasoning, and technical reports for high-stakes energy systems. It was an early lesson in a principle that still matters: when the consequences are real, the model is only useful if the surrounding system is disciplined.
Built the base layer in mechanical engineering and engineering physics: quantitative modeling, systems thinking, and comfort moving between theory, machinery, and messy real-world constraints. That training still shows up in how Sean frames operational problems: define the system, identify the forces, then test what changes the outcome.
