Jev Use Cases: Decisions Across Real Workflows
Map agents, support, RAG, moderation, verification, and other workflows to typed decisions and practical tutorials.
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Start with one bounded decisionKeep the operating policy outside the modelChoose the smallest pilotWhen to split a use caseStart with one bounded decision
An industry name is not a model task. Identify the exact judgment, define the answer space, and decide who owns mistakes. The examples below describe possible decision boundaries; they are not claims that a model is qualified to make final legal, hiring, financial, or safety decisions.
| Workflow | Decision | Primitive | Example / tutorial |
|---|---|---|---|
| Agents | Tool risk | Choice + Score | Decide allow, confirm, or block |
| Customer support | Ticket triage | Choice + Score + Noul | Route and flag a refund request |
| RAG & search | Evidence selection | Score + Noul | Keep relevant safe passages |
| Moderation | Policy check | Noul | Review possible policy violations |
| Verification | Citation support | Choice | Supports, contradicts, insufficient |
| Sales | Lead dimensions | Score | Rank fit, intent, and urgency |
| Recruiting | Evidence match | Choice | Select evidence for a stated requirement |
| Legal | Claim support | Choice | Check a passage against a claim; lawyer reviews |
| E-commerce | Customer intent | Choice | Route delivery or billing issues |
| Gaming | Support intent | Choice | Route account and gameplay tickets |
| Risk | Action review | Score + Choice | Escalate consequential tool proposals |
| Knowledge graphs | Relation evidence | Choice | Classify support for a proposed edge |
Keep the operating policy outside the model
A hiring evidence match should not autonomously decide employment. A legal source match should not replace legal review. A risk score should not grant permissions. Jev can supply a bounded semantic signal while the application enforces verified policy and human responsibility.
Choose the smallest pilot
Start with shadow-mode recommendations on historical examples. Compare the recommendation with the actual human outcome and examine disagreement. Add a review route before automating low-risk decisions. Measure quality and workload together: a system that sends everything to review has not solved the operational problem.
When to split a use case
A workflow may contain several judgments that share state but require different primitives. Keep those atomic. Separate exact extraction and calculation from semantic interpretation so each stage can be tested and replaced independently.