Stop copy-pasting between systems.
Reports, data entry, monitoring, document handoffs. If your team does it every day and hates it, that's probably where we start.
AI implementation
I'll automate the work your team keeps doing by hand.
I build AI tools for real workflows, not demos. Pick the right model, wire it into your systems, train your people, and hand the whole thing over. 15+ years in IT and healthcare means I've seen what breaks when you skip the boring parts.
Last updated
- Client-owned delivery
- Human review by design
- Runbooks and recovery
Work
Three things I do well.
I don't take on everything. These are the problems I'm good at solving, and the ones where my background actually matters.
Build the tool nobody sells.
You know that thing your team does with a spreadsheet and three browser tabs? I turn that into software they actually want to use.
Roll out AI without the chaos.
Pick the right tools, draw clear lines around your data, train people on their actual work, and keep a human in the loop where mistakes are expensive.
Delivery
How an engagement works.
We start small. One workflow, one problem. I build it, make it safe, document it, and hand it over. You run it from there.
- 01Map
Find the one thing that hurts.
What takes too long, who does it, and what goes wrong when they rush it.
- 02Pilot
Build a version that works.
One workflow, one clear way to tell if it worked. No scope creep until this part is solid.
- 03Harden
Make it safe to run unsupervised.
Human review where it matters, logs, recovery, and a way to re-run without breaking anything.
- 04Hand off
Hand it over and get out of the way.
You get the source, the accounts, and the documentation. I train your people, then it's theirs.
Proof
Don't take my word for it.
I ship in public. The projects are on GitHub, the apps are running live, and the whole pipeline that builds this site is something you can read. Here's what that looks like.
Built the site you're reading.
This portfolio runs 30+ automated audits on every build: accessibility, CSP, bundle budgets, schema validation, link integrity, and search index correctness. A nightly task refreshes live GitHub data, deploys to the VPS, and smokes the edge headers. The system catches regressions before they ship, not after.
That pipeline is the kind of operational automation I build for clients: identify the manual step, script it, gate it, and hand it over with documentation. Parker AI runs the consulting offer itself as a live production system.
- 217+ Public projects
- Shipped and documented across Windows, web, Android, desktop, and browser extensions.
- 22 Live apps
- Working browser-based systems you can open and test now.
- 15+ yrs Operations experience
- Enterprise IT and healthcare systems experience before the AI tooling.
Questions
Questions I get asked a lot.
Honest answers. If any of these don't cover what you're wondering about, just ask.
- What does an AI implementation engagement actually deliver?
- A working system your team owns, not a slide deck. I find one painful workflow, build a working version, make sure it handles failures safely, then hand you the source, the accounts, and the docs. You keep everything.
- Where does my data go when you add AI?
- Wherever you want it to. We figure that out first. Some data can go to a cloud model, some should stay on your machines. I'll tell you which is which and set it up accordingly.
- How do you keep AI from making costly mistakes?
- By planning for what goes wrong, not just what goes right. Every workflow I build has a human check where it matters, logging so you can see what happened, and a way to re-run it safely if something breaks.
- How small can a first project be?
- Really small. One report, one document flow, one data-entry step that someone does every week and wishes they didn't. We get that working first. If it goes well, we talk about what's next.
Start small
Got a workflow that's driving you nuts?
Tell me what it is, who does it, and where it breaks. I'll be honest about whether I can help.