---
title: "Five Jev Applications to Build Beyond Chat"
subtitle: "Five proposed, policy-bounded decisions for teams experimenting with typed AI judgment"
slug: "five-jev-applications-to-build-beyond-chat"
type: "Starter resource"
version: "Jev · TypeSafe"
---

# Five Jev Applications to Build Beyond Chat

These are **proposed builds**, not TypeSafe deployments, reported customer examples, or measured Jev results. They deliberately move beyond TypeSafe's published support, invoice, security-alert, and agent-trace workflows.

Jev is designed for compact, typed questions over supplied state. TypeSafe documents the request shape in its [quick start](https://docs.typesafe.ai/introduction/quickstart) and the available [question primitives](https://docs.typesafe.ai/introduction). The important boundary is simple: a typed answer can still be wrong. Let code enforce policy, and keep people responsible for consequential actions.

## The pattern

1. Send only the compact **state** needed for the decision.
2. Ask separate, scoped `Choice`, `Score`, or `Noul` questions.
3. Apply code-controlled rules before and after the returned answer.
4. Route ambiguity, low confidence, or meaningful consequences to review.
5. Measure the workflow against labeled examples and a safe baseline.

`Choice` returns a selected option, distribution, and confidence. `Score` returns a rubric score, distribution, and confidence. `Noul` returns one 0–1 value without a separate confidence field. These probabilities are inputs to a decision policy, not guarantees of correctness.

## 1. Pull-request review traffic controller

![Text-free diagram: pull-request inputs route through a decision node to specialist reviewers, with a separate policy route to human review.](/resources/five-jev-applications-to-build-beyond-chat/pr-review-traffic-controller.png)

**Problem.** A green CI run does not say whether a change to billing, auth, or infrastructure needs a specialist reviewer.

**State.** Changed paths, short diff summary, tests changed, CI result, ownership rules, release proximity, and the author's requested change. Avoid pasting a huge raw diff when a compact structured summary will do.

**Typed questions.**

- `Choice`: Which reviewer specialty is most relevant: billing, security, infrastructure, or general engineering?
- `Score`: What is the change-risk band: routine, sensitive, or critical?
- `Noul`: Does the proposed change cross a stated policy boundary, such as modifying payment-calculation logic?

**Code-controlled action.** Apply `CODEOWNERS` and branch protections first. Add the suggested reviewer, ask for a second reviewer on sensitive changes, and put low-confidence assignments in triage. Jev must never approve or merge the PR itself.

**MVP test.** Take 100 historical PRs with actual reviewer assignments and post-merge fixes. Compare reviewer-routing accuracy and missed sensitive changes against a rules-only baseline.

## 2. Data-contract drift sentinel

![Text-free diagram: expected and incoming schemas enter a comparison gate; a match continues while a mismatch is quarantined.](/resources/five-jev-applications-to-build-beyond-chat/data-contract-drift-sentinel.png)

**Problem.** A pipeline can keep running while new records quietly violate the meaning of a field.

**State.** Expected schema and examples, schema diff, a small redacted sample of incoming records, producer version, recent deployment notes, and downstream consumers.

**Typed questions.**

- `Choice`: Which drift type best explains the sample: renamed field, type change, semantic change, benign new value, or unclear?
- `Score`: What is the likely downstream impact: low, medium, or high?
- `Noul`: Is there evidence that the new value breaks the declared contract?

**Code-controlled action.** Run deterministic schema validation first. Keep a questionable batch in a quarantine table, notify its owner, or request a contract update. Only a known-safe rule can auto-accept.

**MVP test.** Replay known incidents and normal seasonal data changes. Measure true detection, false quarantine, and time to find the responsible producer.

## 3. Onboarding rescue at the moment of friction

![Text-free diagram: repeated errors enter a context card, then route to retry, contextual help, or human support.](/resources/five-jev-applications-to-build-beyond-chat/onboarding-rescue.png)

**Problem.** A user hits the same setup error three times, but a generic chatbot requires them to stop and explain everything again.

**State.** The last few redacted UI events, error code, integration status, current onboarding step, account permissions, and available help paths.

**Typed questions.**

- `Choice`: What is the most likely blocker: missing permission, invalid credential, unclear instruction, external-service outage, or unknown?
- `Score`: How stuck does the sequence appear: mild, repeated friction, or blocked?
- `Noul`: Is there enough evidence to show a specific next step without asking a person to inspect the account?

**Code-controlled action.** Show the exact help card for a known blocker, offer a retry after an outage, or open a support handoff prefilled with the state. Never expose private account details in the tip.

**MVP test.** Randomize the intervention on one onboarding step. Track successful completion, false or annoying prompts, support escalations, and latency at the actual UI moment.

## 4. Agent permission preflight

![Text-free diagram: a proposed action passes through a lock and routes to allow, human review, or deny.](/resources/five-jev-applications-to-build-beyond-chat/agent-permission-preflight.png)

**Problem.** An agent may propose a tool action that is technically available but outside what the user asked it to do.

**State.** The user's request, proposed tool call and target, the agent's stated reason, explicit grants, resource ownership, and the application's hard permission policy.

**Typed questions.**

- `Choice`: Is the action clearly within scope, ambiguous, or outside scope?
- `Score`: What is the consequence band if executed: reversible, consequential, or irreversible?
- `Noul`: Did the user explicitly authorize this specific type of action?

**Code-controlled action.** Enforce access control and allowlists independently of Jev. Proceed only for permitted, low-consequence actions with adequate evidence. Ask the user about ambiguous actions; require human approval for irreversible ones.

**MVP test.** Build an adversarial set with near-miss permissions, quoted instructions, and tool arguments that differ from the agent's explanation. Prioritize the false-allow rate, not just overall accuracy.

## 5. Research evidence-gap router

![Text-free diagram: a claim and dated sources enter a comparison node, then route to supported, retrieve newer evidence, or human review.](/resources/five-jev-applications-to-build-beyond-chat/research-evidence-gap-router.png)

**Problem.** A content or research agent has a polished claim and citations, yet the cited material may be stale or fail to support the exact sentence.

**State.** One atomic claim, relevant cited excerpts with URLs and publication dates, current date, and whether the claim is time-sensitive. Retrieve the source independently before this step.

**Typed questions.**

- `Choice`: Does the supplied excerpt directly support, partly support, contradict, or fail to address the claim?
- `Score`: How material would an unsupported claim be: minor, meaningful, or publication-blocking?
- `Noul`: Does this claim need a fresh source because it may have changed?

**Code-controlled action.** Attach the supported citation, route a partial claim to rewrite, browse again for stale evidence, or hold a consequential unsupported claim for human review. Do not let Jev invent a source or act as the sole fact checker.

**MVP test.** Use an editor-labeled set of claim–excerpt pairs, including plausible but irrelevant citations. Track missed unsupported claims and unnecessary rechecks.

## One concrete request shape

This is a **proposed** PR-routing payload using TypeSafe's documented API fields. It is an example to adapt, not a verified production call or a claimed Jev output.

```json
{
  "model": "jev-latest",
  "state": "Repository policy: billing/* requires a billing reviewer. PR #482 changes billing/tax.ts and tests/tax.test.ts. CI passes. Diff summary: rounds cross-border tax before currency conversion. Release freeze begins tomorrow.",
  "questions": {
    "reviewer": {
      "type": "choice",
      "instructions": "Which reviewer specialty is most relevant to the behavior change?",
      "criteria": {
        "billing": "Payment, tax, invoicing, or pricing behavior",
        "security": "Authentication, authorization, or secrets",
        "infrastructure": "Deployment, networking, or production runtime",
        "general": "No specialized domain applies"
      }
    },
    "risk": {
      "type": "score",
      "instructions": "Rate the consequence of a defect in this change",
      "criteria": [
        "Routine and easily reversible",
        "Could affect customer-visible behavior",
        "Could change financial or security outcomes"
      ]
    },
    "touches_financial_logic": {
      "type": "noul",
      "instructions": "The PR changes financial calculation behavior"
    }
  }
}
```

For this example, code can require a billing reviewer from the path rule regardless of the model answer. It can also require a second review when the risk rubric is high and send uncertain results to triage. Set numerical thresholds only after checking them against labeled examples from the actual repository.

## When this approach is worth trying

Start where the output is a small decision, inputs are available at decision time, mistakes can be observed, and code has a safe fallback. It is a poor fit for long-form writing or tasks that need extended reasoning and synthesis.

TypeSafe quotes [70–500 ms response times and $0.042 per million input tokens](https://typesafe.ai/blog/introducing-system-one-models-and-jev) for Jev. Verify latency, quality, and cost on your own payloads before claiming a production benefit. TypeSafe's large speed and cost multipliers come from [its selected workflow evaluation](https://evals.typesafe.ai/), which uses model-consensus reference answers rather than independent ground truth.