Stanford team swaps LLM decision layer for Jev, 193x faster and 444x cheaper
A Stanford team replaced LLM interpretation of every result with a Jev-based decision layer that routes and scores records, making production up to 193x faster and 444x cheaper.
a Stanford team built a system that analyzes 40,000,000,000 data points every 15 minutes v1 used LLMs to interpret every result. it worked, but analyzing millions of records created crippling latency and massive inference costs. then they redesigned the decision layer around Jev. up to 193x faster and 444x cheaper in production. the model shouldn’t decide everything. in this setup: 40B data points → structured buffer → Jev router → typed score & filter (sub-15ms) → anomaly gate → cheapest capable model → frontier reasoner (top 0.001% only) → action the important part is that the decision layer doesn’t generate prose. it routes → scores → blocks → approves. so expensive models only get called when the data actually demands them. that’s the point of Jev Engineering: separate reasoning from decision-making, then make the decision layer measurable, cheap and fast. full breakdown in the article below ↓
This post quotes another post: “Long read: how to build a Jev-powered agent stack”