Most company names are made up. A word that tested well. An acronym nobody remembers the origin of. Something that sounded good in a conference room and stuck.
Ours isn't. Wilde is a family name. It came over with ancestors who immigrated to the United States from Hampshire, England — and it's been carried forward through generations since. Putting it on this company is a nod to that lineage and the legacy of the family that built it here.
My grandmother was the clearest expression of what that name means to me. She was the kind of person who made you feel genuinely at ease — kind, present, warm in a way that didn't ask anything in return. I admired her as a kid. I admired her more as a young adult. The name on the door carries all of that forward.
That's not a marketing story. It's just the truth.
Data has been my career
For over a decade, I've run a consulting company working with some of the largest and most complex organizations in the country — universities, hospitals, global financial institutions, and industries where the stakes of getting data wrong are real and measurable. The sectors change. The discipline doesn't.
The work has always come down to the same thing: large, messy datasets with answers buried inside them. What's actually happening here? Where's the risk? What's the root cause, and what's the fix? Every engagement is a data problem at its core. You learn to find the signal in the noise — and you develop a healthy respect for what rigorous analysis can and can't tell you.
I've spent my career using data and tools to find evidence for root causes and corrective actions. That discipline doesn't change — only the tools do.
What changed recently is what those tools can now do. AI can look at data points and data elements deeper, with more accuracy, and faster than I can. It can hold more context at once, surface connections across dimensions I didn't think to check, and do it in seconds rather than hours. For someone who already thought this way — who already lived in the data — that's not a disruption. It's an upgrade.
Why build apps
The natural question is: why not just keep consulting? The answer is that consulting solves one problem for one client at a time. What I wanted to build was something that could solve a problem once — well, with intention — and put it in the hands of anyone who needed it.
The apps we're building at Wilde DataLabs come from the same instinct that's driven my entire career: find a real problem, understand the data behind it, and build something that produces a useful answer. The difference is scale. Instead of one engagement, one client, one industry — it's a tool anyone can use, anywhere, any time.
A lesson worth teaching
There's one more thread here that matters to me. I have teenage kids, and I wanted them to see something specific: that the gap between having an idea and building something real is smaller than it's ever been. The experience gap that used to take a decade to close? These tools compress it. Curiosity and discipline matter more now than credentials and years.
Wilde DataLabs is, in part, a working demonstration of that — built alongside them, intentionally. What they're learning here will outlast anything we ship.
What comes next
We're building AI-powered apps — each one built around a specific problem worth solving. The background is in enterprise data. The instinct is the same one that's driven every engagement I've ever run. The tools are better than they've ever been.
The name on the door means something. So does everything that goes out under it.
— Wilde DataLabs