My Quality-Control Tools

Building automations quickly is easy. Building them quickly without quietly introducing mistakes is the hard part — and a single missed flaw in a system that texts patients or moves money can cause real damage before anyone notices.

I built a set of AI helpers whose only job is to catch my own mistakes before they go live. One sends a team of AI inspectors through my work to hunt for errors, another puts a plan in front of a panel of different AIs to argue over its weak spots, and a third quietly grades the quality of every patient message my systems send. Together they let me ship fast and still sleep at night.

1. The bug hunter splits the work across a team of specialist AI inspectors — each one looks for a different kind of problem (broken logic, security holes, things built but never turned on, things that look fine but quietly fail).
2. A quick scan flags the riskiest files first, so each inspector goes straight to where mistakes are most likely to hide.
3. The plan reviewer hands the same plan to three different AI models from three companies, has each score and critique it independently, then shows me where they agree (act) and where they disagree (stop and decide).
4. It keeps rewriting and re-scoring the plan over a few rounds until it hits a quality bar, with a fresh-eyes pass so a model can't rubber-stamp its own earlier feedback.
5. The nightly quality grader has a second AI silently score every patient reply; if quality slips or the daily cost runs over a cap, it shuts itself off automatically.

I can build and change systems far faster than usual because the checking is automated and thorough instead of slow and manual. Real problems — undocumented live systems, safety gaps, a silent data-loss error — get caught and listed for fixing instead of discovered the hard way.