> We benchmarked TypeSafe's Jev against our production LLM classifier — with real customer data and human ground truth.

> We benchmarked TypeSafe's Jev against our production LLM classifier — with real customer data and human ground truth. Here's what happened. 🧵 > > Hey! I'm Brian, building Atisbo — a product intelligence platform that turns raw customer feedback (support chats, reviews, social, calls) into a ranked list of what to build next. Evidence comes in, clusters into problems, gets scored, and PMs decide from there. We dogfood it on ourselves daily. > https://www.atisbo.dev > > Every piece of feedback entering Atisbo gets classified (problem / idea / question / noise) by a generative LLM. > High-confidence noise gets auto-filtered; the rest goes to human triage. > That classification decision is load-bearing: a false "noise" silently deletes a real customer pain from the roadmap. > So when I found TypeSafe, the question wasn't "is this cool" — it was "can a System One model beat or complement our production pipeline on REAL data?" > >