My background is enterprise technology consulting — cloud migration strategy, infrastructure rationalization, post-M&A assessments, TCO modeling for Fortune 500 clients. That work funded the time to go deep on applied AI, and the two things feed each other now. The advisory work makes the AI more grounded. The AI work makes the advisory more defensible.
I don't just recommend things. I build them, deploy them, and operate them in production — mostly solo, on tight budgets, where the architecture has to be right because there's nobody else to fix it. That constraint is actually useful. It produces systems that are simpler, cheaper, and more maintainable than what you get from a team with unlimited scope.
The 30 years covers the full arc: on-premises datacenters, storage area networks, VMware and Hyper-V virtualization, Windows and Linux server environments, enterprise networking, and the first wave of cloud migration starting in the mid-2010s. AI is the most recent chapter, not the whole story. That matters because most of the hard problems in enterprise AI aren't model problems — they're infrastructure and data problems, and I've been solving those for a long time.
The consulting piece ran in parallel. For several years at a global systems integrator, I led cloud advisory engagements across industries — insurance, banking, healthcare, retail, CPG, hospitality, government. The work was CRAs, TCO models, cloud business cases, application rationalization, post-M&A assessments. I've helped clients identify over $57M in infrastructure savings across 14 engagements, executed 10 SOWs, and scoped migration programs ranging from a 5-week banking CRA to a 5-month Fortune 500 DC exit with 60,000+ servers. The Selling section of this site documents those engagements in detail, all anonymized.
Since 2024 I've been building applied AI systems — not as a hobby project, but in production. Park Whisperer is my personal AI platform: real-time theme park intelligence running 24/7 across Azure, GCP, and AWS, processing satellite weather data, training ML models, running a multi-agent knowledge RAG, and publishing daily AI-generated content to social platforms. Solo, under $100/month. I use it to stress-test architectural patterns before recommending them to anyone else. The Portfolio and Clients pages document how those patterns translate into client work.
Before a model is called, I've already decided: what context is strictly necessary, what can be cached or pre-computed, which model tier matches the actual reasoning demand, and what value the output delivers per dollar spent. That trade-off is explicit in the architecture — not discovered after the bill arrives.