LLMs are fluent but tone-blind. Simasia adds the missing judge: freeze a large embedding model, train one small logistic-regression head on a brand’s voice, and you can rate any reply from 0 to 1 for a few kilobytes of weights per brand.
Because the judge is a classifier, not a writer, everything else follows cleanly. It explains a score with the closest on-brand and off-brand examples — no language model needed. It can train from raw text, a file, or a list of URLs, and even manufacture off-brand negatives from on-brand copy alone. And it closes the loop: hand it a generator and it rewrites until the reply clears your threshold. Shipped as an installable package with a config-file CLI for people who don’t write code.