A new Bayesian statistical approach calibrates automated metrics against human judgments to streamline model replacement. Tested on a system with 5.3M monthly interactions, the method identifies suitable replacements by evaluating correctness and refusal behavior. This framework reduces the manual data burden for enterprise developers migrating production LLMs without sacrificing confidence in output quality.