Three services, one underlying method: audit first, document everything, migrate without dropping a connection.
Independent assessment of where AI actually fits your operations — and where it doesn't. I audit current workflows and systems the way I'd audit a physical network, then map a realistic path: what to automate, what to leave alone, and what order to do it in.
Connecting AI tools into the systems you already run — data pipelines, documentation workflows, reporting, field operations software — without breaking what already works. Every integration point gets tested and documented like a splice record.
Moving legacy processes and platforms onto modern, AI-capable systems with zero-drop continuity. This is tier 1/2 testing discipline applied to software: verify before cutover, verify after, keep a rollback path the whole way through.
This is the architecture I use to register and audit municipal fiber networks. It works just as well for auditing a company's data and AI stack.
Map the existing system end to end: data sources, tools, workflows, and where they actually connect.
Build the integration or migration plan, sequenced by risk and dependency — not by what's easiest first.
Execute the plan in staged cutovers, with verification at every handoff point.
Leave behind a system of record — not tribal knowledge — so the work holds up after I'm gone.
Most engagements start as an audit — that conversation is free.