Jev 做客服工单分流省下 2.4 万美元
把 100 万工单先过 Jev 做分类与置信度判断，只让 20% 难题进前沿模型，账单从 3 万降到 6 千。
HOLY SH*T, 1M SUPPORT TICKETS WENT FROM $30,400 TO $6,480 AND NOBODY BOUGHT A SMARTER LLM. CHECK THESE 5 THINGS BEFORE YOU SEND EVERY REQUEST TO A FRONTIER MODEL. Jev returns decisions, not text. you send state plus typed questions and it answers all of them in one parallel pass. i'd treat it as the front door, not the replacement for the model behind it. 1. the pass › one ticket in, three typed answers out. department, frustration score, urgency. one round trip, about 400 ms, zero lines of parsing code. 2. the gate › confidence decides whether to act, not the answer. under 0.5 goes to a human. the bar rises with the cost of being wrong. 3. the cascade › 80% of tickets end in plain code and never reach an LLM. only the hard 20% go to a frontier model or a person. 4. the bill › $0.0004 on every ticket, frontier price on the 20%. that is the whole gap between $30,400 and $6,480. 800,000 tickets answered in under half a second. 5. the honest part › it can still pick the wrong valid value. it writes no text, code or summaries. it reads your instructions literally. the speed numbers are self-reported by TypeSafe. zero type errors is not zero mistakes. a 0.95 can still be wrong. the quoted article gives you the three question types, the threshold code and what people already built with it. use the numbers as a starting point, not a benchmark. keep this map beside your pipeline. bounded answers, high volume, a confidence gate. that is where Jev fits.
这条是引用着另一条写的：x.com/0xaigoat/status/2102027640752177195