Classification
Map an email, event or record to one of the labels your application already understands.
Automation patterns
What can Jev do?
Jev fits tasks where software must make a bounded judgment from messy context, preserve uncertainty and continue through ordinary code.
Routing
Choose the next queue, agent, workflow or tool based on state and declared options.
Scoring
Apply a domain rubric to quality, urgency, relevance or risk as one component of a decision.
Verification
Estimate whether a statement or condition holds, then escalate uncertain cases.
AI safety gates
Judge prompts, reasoning traces or outputs before allowing a downstream action.
UX decisions
Support latency-sensitive experiences where a several-second generative response is too slow.
Example: support request routing
State: “I subscribed to Pro last week and noticed a duplicate charge today.”
Question: Which team should handle this? [billing, technical, sales, abuse]
Illustrative result: billing — probability 0.96
Question: Which team should handle this? [billing, technical, sales, abuse]
Illustrative result: billing — probability 0.96
This example shows the interface, not a measured model result. In production, the application might route high-confidence billing decisions automatically and send low-confidence cases to a general review queue.
Example: content quality gate
Instead of asking one vague question—“Is this content good?”—decompose the task into atomic judgments: factual support, policy compliance, clarity and audience fit. Combine the resulting signals with explicit weights and thresholds in code.
Where Jev is a poor fit
Do not use a bounded decision interface for tasks whose primary value is producing new language, rich explanation, code or open-ended creative work. Use a generative model for that part of the workflow.
Try the output shapes
Use the browser-only concept playground. It requires no key and clearly labels simulated results.
Open concept playground