Jev

Jev is a specialized AI model designed specifically for machine-native decision-making in automation and data processing workflows. Unlike conversational models that generate natural language, Jev produces structured, type-safe outputs with calibrated confidence scores. Each decision is accompanied by a confidence score, enabling systems to automatically reject low-confidence results instead of acting on them. Built using reinforcement learning for calibrated decisions (RLCD), it offers a fundamentally different approach to AI than traditional language models.

Highlights

• Type-safe outputs that code can process and act upon directly
• Confidence scores calibrated to eliminate hallucinations
• 193x faster than traditional LLMs on structured decision tasks
• Token-based pricing at $42 per billion tokens
• Designed for autonomous workflows and automation pipelines

Perfect for teams building autonomous agents, data processing systems, or any workflow where AI decisions must be reliable and actionable by software. The API-first design integrates seamlessly with production systems. Pricing is transparent token-based with no minimums, making it practical for scaling automation infrastructure.