CTO / platform builder / applied AI product engineer
I build infrastructure, products, and support paths for software that carries a lot of history and cannot afford to lose it.
Most of my work starts when a problem crosses product, platform, and operations. I like the part where a one-off fix becomes something the next person can run.
Issue 14 / Career archaeology
My LinkedIn profile still says "tech junkie that thrives under pressure." Underneath that line is a long attempt to unload responsibility correctly: scripts, abandoned interfaces, GitOps, an internal testbed, and finally a platform I could teach to the development team.
Read this issueA local Docker build audit showed why I do not want to guess where workspace startup time goes. One .dockerignore pass cut the build context from about 8.9GB to about 2.4GB. The larger startup question still needs its own timeline.
The useful signal was not which agent looked best on visible tests. It was which one kept Archibus's boring rules intact after the hidden checks showed up.
KeyControl had to leave my memorized WebCentral setup and still install, run, and pass checks for someone else.
The container is the easy part. Real Archibus workspaces also need databases, licenses, restore paths, startup scripts, IDE access, diagnostics, and support evidence.
Archibus delivery lab
ArchiBot turns high-context WebCentral support work into repeatable environments, safer diagnostics, and AI-assisted tooling that stays close to the source material.
Cloud environment platform
The workspace work is about turning fragile local rebuilds into productized environments with identity, templates, billing, support paths, and visible operating evidence.
Data exploration product
Dexaga was the product path from consulting knowledge to a tool: identify available data, query it, aggregate it, and make related operational context visible.
Small tools and experiments
Most of these start as a minor annoyance. If the same step keeps coming back, I give it a name, an interface, and just enough structure for somebody else to use it.
Workflow infrastructure
The useful agent system is less like a crowd of autonomous workers and more like a disciplined sidecar workflow with clear ownership, evidence, and final review.
A plain fit check.
I am most useful when the work has enough context that a generic implementation plan starts lying: Archibus upgrades, managed workspaces, applied AI support paths, platform rescue, and developer tools that need to survive real users.
Start a conversationUpgrade readiness, repeatable test/UAT environments, performance triage, and support paths around real WebCentral constraints.
Agent workflows, context boundaries, token-cost discipline, and human review loops for teams that need answers they can defend.
Cloud workspace platforms, deployment visibility, auth boundaries, billing surfaces, and operational feedback loops.
Editors, CLIs, dashboards, worktree workflows, and automation that reduce handoff friction instead of adding another ceremony.
The long version.
This is the current chapter: taking high-context facilities management software work and turning it into systems teams can actually run, support, and improve.
Dexaga helps users identify, query, aggregate, and understand data already available in operational databases without needing to know SQL.
This period connects the Counterpointe R&D work to Dexaga: taking messy operational systems, building tools around them, and making the result usable by people who are not sitting inside the code.
This was the period where I learned to stop accepting slow enterprise software as inevitable and started building tools that changed the shape of the work.
This is where the Archibus and facilities management software domain became the base layer for the rest of the work.
This was lower-level systems work: hardware support, test routines, simulators, version-control discipline, and Unix-like operating environments.
This was early mobile work with the whole release path attached: application code, database work, security concerns, signing, and publishing.
School and the tool drawer.
Computer Science, with the useful parts happening both in class and in the lab.
Managed a student computer lab with 50+ machines and helped keep real users working.
Built cross-platform VM networks, Xen Ubuntu hypervisor labs, and bridged web, app, and LDAP services.
Worked on firewall upgrade ideas, intrusion prevention, and a GPS-based lane-departure warning system.
If the work sounds familiar, send me the messy version of the problem.