AI workflow audit and QC sprint

The problem you already suspect

Your team can get a model to do something impressive in a demo. That’s the trap. The gap between a demo that wows and a system people actually trust at work is where most AI adoption dies. The output sounds right and is wrong often enough that nobody can lean on it. The workflow gets automated before anyone checked whether it was the right workflow. And the quality bar lives in someone’s head instead of in a test you can run.

The failures that stall AI adoption are specific and measurable.

• Confident wrong answers. The model is fluent and sure, and a fraction of the time it is flatly wrong, so a human re-checks everything and the time savings evaporate.
• The wrong thing automated. Effort goes into a flashy workflow nobody needed while the real bottleneck sits untouched.
• No quality gate. There is no runnable check that says this output is good enough to ship, so quality drifts and no one catches it until a customer does.
• Judge false-pass. When an LLM grades the output of another LLM, it waves through broken work because it is grading its own family.

You cannot fix what you cannot see, and a vibe check in a meeting will not surface any of these.

What I do

I work in three moves, and you can stop after any one of them.

• Audit. I map your workflow and find the steps actually worth handing to AI, ranked by payoff and risk. You get a short list of where to start and where not to.
• Build. I build the agentic workflow that does the highest-value piece, wired to check itself before a human sees the output.
• QC. I build a runnable harness that measures whether the output holds up, catches the failure modes above, and keeps catching them after your next model swap.

The harnesses are public, so you can watch them work before you pay. See how I audit a workflow, a workflow that reviews itself, and the judge-trust proof of concept.

What you get

• A workflow audit with a ranked shortlist of AI-ready steps and the ones to leave alone.
• A working build of the highest-value piece, running in your stack.
• A QC harness you keep, so the same failure cannot return after a model or prompt change.
• A readout your product, ops, and leadership teams can all act on.

Pricing

Fixed scope. No hourly meter, no open-ended retainer. We set scope and price on the call.

Why me

I build the systems and I check whether they work, and I learned that discipline where being wrong had a cost. I spent more than fifteen years in K-12 education, including K-8 curriculum and assessment QC at an AI-education company, watching where a model’s confidence and a real person’s reality came apart. Most people who can build an agentic workflow have never had to prove it was safe to put in front of anyone. That proof is where I spend the work, instead of treating it as an afterthought.

The harnesses are method-from-synthetic and fully my own. No prior employer’s content is used or required.

Next step

A 20-minute call to scope one workflow against your stack. If it is a fit, work starts the same week.