Jev blog
Deeper dives behind What is Jev? — integration, question design, guardrails and cost — each linked to a use case with copyable code.
How to integrate Jev into Python and JavaScript pipelines
Call Jev from Python or Node.js in one HTTPS request, read typed answers, gate on confidence, batch questions and handle errors. Copyable code for Flatkey.
Read →Oct 21, 2026Jev vs traditional text classifiers: when each one wins
How Jev compares with trained text classifiers on labels, data needs, calibration, latency and cost, and a practical rule for choosing between them.
Read →Oct 20, 2026Jev's three question types explained: Choice, Score and Noul
How Jev's Choice, Score and Noul questions work, the exact JSON schema for each, how to read probabilities and confidence, and how to pick the right type.
Read →Oct 19, 2026Using Jev for content moderation and AI guardrails
Build moderation and guardrails with Jev: policy categories, severity scores, PII and grounding checks, agent action gates, and confidence-based human review.
Read →Oct 18, 2026Support-ticket routing with Jev: a step-by-step design
Design a support-ticket router with Jev: team Choice, urgency Score, refund Noul, confidence-based fallbacks, evaluation, and copyable Python and Node.js code.
Read →Oct 17, 2026Jev speed, cost and calibration: what RLCD means for your thresholds
What TypeSafe reports about Jev's 70–500 ms latency and $0.042 per million input tokens, how RLCD calibration works in practice, and how to set confidence thresholds.
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