About Me

I started my career by buying a retail business at 22 — which is a fast way to learn that technology is either the thing that saves your business or the thing that sinks it. There's not much in between.

From there I spent 27 years at Nella Cosa Corporation, where I walked in as employee number three and walked out when it was an $8M multi-channel e-commerce operation. I didn't just watch that growth happen — I built the systems that made it possible. The inventory management that handled 20,000+ SKUs. The marketplace integrations I coded before commercial tools existed. The infrastructure that went from crashing twice a day to 99.9% uptime. The crisis response that captured $1M+ in 48 hours when COVID turned the entire business upside down.

Somewhere along the way I realized I wasn't just a technologist. I was the person who absorbed complexity so everyone else could focus on their actual jobs. The person executives called when they needed a problem to disappear — not just get fixed, but genuinely stop being their problem.

When Nella Cosa transitioned in 2025, I made a deliberate choice: instead of immediately jumping into the next role, I'd go deep on the technology I believe will define the next decade. I've been building AI-powered SaaS tools, shipping iOS apps, designing AI agent architectures, and getting my hands dirty with the kind of work that most people only talk about at conferences.

How I Think

This is probably the part that actually matters, more than any list of technologies or job titles.

I tend to sit with problems longer than most people are comfortable with. Not because I'm slow — because the first answer is usually wrong, and the real problem is usually hiding behind the obvious one.

The SQL Server that crashed twice a day? Everyone assumed it was a hardware issue or a bad update. I spent months investigating until I traced it to corrupted characters in an unused database field — and discovered that the daily restart "fix" was actually making things worse by triggering more corruption. The real solution took an afternoon once I understood the actual problem.

The pricing model everyone assumed was optimized? I built custom queries that factored in labor costs alongside margins and discovered something counterintuitive: fewer orders at higher margins generated more total profit than chasing volume. That insight drove $1.2-2M in revenue uplift.

I don't just ask "how do we fix this?" I ask "are we even looking at the right problem?" And then I build systems that make the fix permanent — not patches that need re-patching every quarter.

That same approach applies to everything I do now. Every AI project starts with "what's the actual problem?" before I write a line of code. Every system I design considers how the pieces interact, because optimizing one component at the expense of the whole is the most expensive mistake in technology.

My Technical Philosophy

Pragmatic with a side of paranoia. I build things that work reliably, but I also assume everything will eventually break in the most inconvenient way possible — so I plan for that too.

I believe the best technology disappears into the background. If people notice the technology, something has gone wrong. My job is to make it invisible while it does the heavy lifting.

And I still believe that devices aren't hardware-limited — it's always software. Give me consumer-grade equipment and enough stubbornness and I'll make it do things the manufacturer never intended (this philosophy saved $17,000 on a single server purchase and delivered 10x the processing power at a fraction of the typical cost).