> DeepSeek (prod) Jev > 4-way accuracy 83.7% 80.6% > signal-vs-noise 85.7% 83.7% > recall on "problem" 86.0% 93.0% ← >

> DeepSeek (prod) Jev > 4-way accuracy 83.7% 80.6% > signal-vs-noise 85.7% 83.7% > recall on "problem" 86.0% 93.0% ← > noise precision 91.8% 100.0% ← > avg latency 932ms 307ms > tokens / item 2,610 843 > agreement between them 84.8% > Same accuracy ballpark. 3x faster, 3x cheaper. But the interesting part is the error patterns are complementary: > 🔸 Jev almost never loses a real problem (93% recall). Where our cheap classifier says "noise 0.85, discard forever," Jev often finds the buried complaint — like the Trustpilot review that's 90% praise with one real grievance inside. That's the exact failure mode that hurts a product team most. > 🔸 When Jev says "noise," it's right (100% precision) — it just doesn't say it often enough to be the sole filter. > So we're not replacing anything. We're wiring Jev as a second-opinion gate: every time the cheap classifier votes "noise," Jev gets a look. If it disagr