"Measuring quality drift in production: the metrics that move first"
AI features rarely fail loudly in production — they degrade quietly while the golden set stays green. The earliest warnings live in operational metrics: review-queue depth, correction rate, confidence distributions. Which numbers move first, and how to watch them.
Nobody changed the prompt. Nobody swapped the model. Yet the AI feature that was solid at launch is, eight months later, the thing users mention with a sigh — and there's no incident to point at, no deploy that broke it, just a slow slide nobody can date. By the time complaints arrive, the quality has usually been slipping for weeks.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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