From web applications to the factory floor.

I started as a full-stack engineer and spent a decade shipping scalable, data-heavy applications. For the past few years my work has moved onto the shop floor: integrating PLC and SCADA systems, building IIoT pipelines, and developing the MES that carries machine data up to the ERP.

Most industrial software fails in the same place — the seam between operational technology and information technology. A machine knows what it just did; the business system needs to know it in a form it can act on. Closing that gap, reliably, in environments that cannot stop running, is the work I find genuinely interesting.

That backend, data, and cloud foundation is exactly what industrial AI runs on. I'm now building in that direction — predictive maintenance, digital twins, and anomaly detection on the real telemetry my systems already produce.

Alongside the engineering, I write. Industrial software is badly documented and rarely explained well, so I publish what I learn — architecture write-ups, integration post-mortems, and practical documentation for people doing the same work. That same instinct is what I bring to developer-tool support: understanding a system deeply enough to explain it to someone who is stuck.

The goal: manufacturing systems that don't just report what happened, but anticipate what's next.