Work with me
I help teams turn AI tools into workflows they can actually run. I think in processes first: I map how your work runs, then direct AI to build the tool that runs it, and own the judgment that decides whether it holds.
I run a one-person shop that designs and ships reliable AI workflows. The hard part is never writing the code, it is knowing what to build, seeing where it breaks, and putting a person in the loop where judgment matters. AI lets me build it fast; the architecture and the reliability are the job, and they are mine. I bring that same discipline to other teams, with the safeguards that keep it dependable when no one is watching.
I turn AI tools into reliable operating workflows.
Most teams already have the models. What they're missing is the part that makes the models safe to lean on: where a human signs off, what gets logged, what happens when a step fails at the wrong moment. That is the work I do. I come in, map how the work actually happens today, and build a path to AI that holds up when real people depend on it.
The engagement is measured by outcomes. You get a workflow your team can run after I leave, and a clear record of what it does and where it stops.
The most practical starting points are lead follow-up, inbox triage and routing, reminders and follow-through, and small dashboards that make the work visible.
Five moves, in order.
However we start, the work runs the same five moves.
- 01 Current-state discovery. I learn your tools, inboxes, folders, handoffs, approvals, the data each step touches, and the real pain points before I recommend anything. No plan survives contact with how the work actually gets done, so I start there.
- 02 Build without your live data. I develop against the shape of your data and my own test accounts, never your real records, and I prove the workflow in a contained spot where a failure costs nothing. Your systems connect only when you plug in your own keys, so your production data never touches my machine.
- 03 Autonomy boundaries and approval gates. I draw the line between what the AI may do on its own and what waits for a person, then put an explicit human gate at every point where a wrong move would cost you something. Most failures come from that line being fuzzy, so I make it sharp, and nothing scales past a gate until it earns the room.
- 04 Failure-mode review. Before anything grows, I go looking for the ways it breaks: stale context, claims with nothing behind them, actions that should never have fired, recovery paths that do not recover. I'd rather find those than have your users find them.
- 05 Hand off and go live. I hand you clean, working code with every integration already proven against its test environment, plus a runbook, a walkthrough, and a test you run. You deploy it and connect your own keys; I never log into your systems or touch your live data. You own it. New versions and changes are paid work when you want them; there is no retainer and no lock-in.
Two shapes, lowest risk first.
- 01 Workflow audit. A focused read of how your work actually happens and where AI can safely carry load. You walk away with a current-state map, risk notes, and a fixed recommendation for what to build first.
- 02 Controlled pilot build. We build one small workflow on sample or approved data, with access kept narrow, and you only pay for the build if it does what we agreed. You walk away with a working piece, a runbook, and a straight verdict on whether it earned the right to grow.
After a build, new versions and changes are paid work when you want them. No retainer, no lock-in. You own what I build and it runs without me.
The fastest way in is one honest paragraph about the workflow that is annoying you. A plain note works best.
Broadcast-grade reliability, applied to AI.
The discipline comes from live broadcast control rooms, where one path going dark at the wrong second means a lot of people see the dark. Years in that chair teach signal flow, redundancy, calm escalation, and proof saved before anything goes wide. AI needs exactly that hand on it. Access to a model is the easy part. The hard part is knowing what happens when it is wrong, who catches it, and how fast they recover. That is the standard every workflow I hand over is built to.
Where I am most useful.
Workflow audits
A clear map of one workflow, where it leaks, what AI should and should not touch, and the safest first build.
Controlled pilot builds
Small human-in-the-loop systems for lead follow-up, request routing, reminders and follow-through, and dashboards that show where things stand.
Ongoing updates
New versions, changes, and improvements as paid work when you want them, with no retainer and no lock-in.
How I work, plainly.
I decide what gets built and review everything that comes back, and I'll be straight with you on the first call about where my strengths lie and what fits.
Where I'm strongest is the systems view: seeing how the whole thing has to fit together, where it will fail under pressure, and what to put in place so it holds. If that's the help you need, we will work well together.
Start a conversation.
If you're turning AI tools into something your team relies on, I'm glad to hear about it. Tell me what you're running, where it's rough, and what you wish it did. Plain is fine. I read it myself.
Vetting me for your IT or security team? The data, security, and ownership detail is on the security and architecture page.
Text me what you're building. Straight to me, not a bot or an assistant, and I usually reply the same day: 365-675-2420.
The fastest way in is one honest paragraph about your situation. A plain note works best.