I build the systems, processes, and infrastructure that let digital-first organizations scale with clarity and precision.

My work sits at the intersection of technology and strategy, translating organizational goals into the platforms, workflows, and teams that actually carry them out.
A cross-functional operator with a background spanning systems administration, product management, and digital infrastructure, with deep technical range and a track record of building durable systems that scale.
I hold an MBA in Sustainable Enterprise and bring 15+ years across the full stack of operations, from configuring the platform to leading the team that runs on it.
Most organizations bought the tools first and went looking for the problem second. I work the other direction. Every place I've put AI to work started with something that was already costing us time, money, or quality, and the tool was the last decision, not the first.
The result is not a pile of subscriptions. It is a smaller, faster operation that ships things it could not ship before.
I use AI to design, code, and ship features that customers actually touch. Internal ideas that once died in a backlog waiting for engineering capacity now get built and tested. The distance between spotting a gap in the customer experience and doing something about it has collapsed.
Usage reports, spend data, and platform metrics usually sit unread because pulling meaning out of them takes hours nobody has. I use AI to interrogate that data and bring leadership the answer they need. In practice that has meant right-sized licenses, renegotiated contracts, and bandwidth costs brought back under control.
Automation exposes every assumption nobody wrote down. When a workflow resists being automated, it is telling you the process underneath is broken, and fixing that is usually worth more than the automation itself. Document it, clean it up, then hand off the repetitive parts.
Transcription, video and audio editing, chaptering, and metadata across a large and growing content library. Work that used to consume hours of contractor time per asset now moves in a fraction of that, which changes what a lean team can realistically take on.
Accessibility standards and transcript accuracy are not optional, and at volume they are expensive to hold to a high bar by hand. AI-assisted review makes consistent quality achievable across an entire library instead of only on the newest releases.
When a long-tenured person leaves, the real loss is everything that lived in their head and never got written down. I use AI to turn scattered notes, threads, and recordings into documentation the next person can actually work from.
Some decisions need a person. Judgment calls, sensitive communication, and anything where being wrong is expensive and hard to detect. Part of integrating AI well is being clear about where it earns its place and where it quietly introduces risk you will not notice until much later. That line is an operations decision, not a technology one.