JevStation

What JevStation Does

JevStation is a specialized AI decision platform built around Jev, TypeSafe's System One model. Unlike traditional language models that generate prose, Jev answers typed questions about your data and returns structured, machine-readable values with complete probability distributions and confidence scores. Instead of parsing AI-generated text, you get direct answers in the exact format your code expects.

Who It's For & Key Problem Solved

JevStation is designed for developers and teams who need reliable AI judgment calls without the overhead of prompt engineering, output parsing, or handling malformed responses. It solves the core problem of turning unstructured data—support tickets, reviews, specifications, JSON records—into actionable decisions that code can branch on immediately.

Key Features & Capabilities

• Three Question Types: Choice (categorical answers), Score (numerical ratings), and Noul (yes/no judgments)
• Structured Output: Every answer returns as a typed value with its full probability distribution and calibrated confidence score
• Saved Threads: All evaluations are automatically stored with derived titles for easy reference
• Reusable Context: Pin standing instructions or reference material to threads for consistent evaluation criteria
• Credit Metering: Predictable costs with 1 credit per standard evaluation (up to 8,000 characters, 5 questions) or 3 credits for larger runs
• Automatic Refunds: Failed runs cost zero credits with automatic refund on provider errors
• Shared Playbooks: Publish and reuse question sets across your team through the open catalog

Typical Workflows

Common use cases include support ticket triage (urgency + complexity scoring), review moderation (sentiment classification with probability routing), rubric-based document scoring, and team playbook sharing. Each evaluation completes in one round trip with no token streaming or output reassembly required.

What Makes It Different

JevStation deliberately trades text generation for judgment accuracy. You're limited to 12 typed questions per evaluation, but you get calibrated confidence on every answer, predictable costs regardless of retries, and output that never needs parsing. Your data remains private by default and is never used for model training.