Independent. Not affiliated with TypeSafe AI, the maker of Jev. Disclosures

Decision LayerUnofficial

Try “routing”, “confidence”, “leads” or “noul”.

    Use cases · 12 so far

    What you can hand Jev, and what to watch

    Each use case says what Jev decides, which question types it uses, how good a fit it is, and the catch. None of these are our test results yet; when we have run one, it moves to Tests and says so.

    Strong fit short text, fixed options, cheap mistakes  ·  Test first promising, prove it on your data  ·  Person in the loop decisions about people or money: Jev sorts, a person decides

    SupportStrong fit

    Support ticket routing

    Which team gets the ticket, how urgent it is, how upset the customer sounds.

    ChoiceNoulScore
    Why it fits
    Short text, a fixed list of teams, and a cheap mistake: a misrouted ticket gets bounced, not lost. TypeSafe’s own quick start uses this example.
    Watch for
    Send low-confidence tickets to a person. TypeSafe’s routing pattern escalates below 0.5 confidence; set your own line from your data.
    Sources
    Guide in progressAgency guide
    InsuranceTest first

    Insurance lead scoring

    How well an inbound lead fits, what they are shopping for, and whether they are ready to talk now.

    ScoreChoiceNoul
    Why it fits
    Lead generation is on TypeSafe’s use-case list (scoring against an ideal customer profile and detecting intent). Agencies get many short web forms and call notes, which suits Jev.
    Watch for
    Never score on protected traits. Jev handles raw numbers and dates poorly, so do age, ZIP and renewal-date math in code and give Jev words.
    Sources
    Guide in progressAgency guide
    InsurancePerson in the loop

    Claims intake triage

    Claim type, how complex it looks, and whether it should go to a senior adjuster for review.

    ChoiceScoreNoul
    Why it fits
    TypeSafe lists insurance claims (classifying loss reports and adjuster notes, flagging complexity and fraud indicators, prioritizing for review). We have found no published worked example yet.
    Watch for
    Use it to sort the queue, never to deny or pay a claim. Many states have adopted the NAIC’s model bulletin on insurers’ use of AI; a person makes every decision about a claim.
    Sources
    Guide in progressAgency guide
    SupportTest first

    Call QA scorecards

    Did the rep cover each required point: greeting, disclosures, needs questions, next step.

    NoulScore
    Why it fits
    A QA rubric is a set of narrow yes/no and 1-to-5 questions, which is exactly how TypeSafe says to build: many small questions in one request, combined in code.
    Watch for
    Long calls hit the 32k-token state limit and Jev gets less accurate on long, noisy input. Score the transcript in sections.
    Sources
    Guide in progressAgency guide
    InsurancePerson in the loop

    Marketing compliance checks

    Does this ad, email or script make a claim on your prohibited list, or leave out a required line?

    NoulChoice
    Why it fits
    TypeSafe’s list includes detecting missing clauses and prohibited claims. Cheap enough to check every piece of copy, not a sample.
    Watch for
    Jev answers the question you wrote, not the one you meant. Write one question per rule, and keep a compliance person as the final sign-off.
    Sources
    Guide in progressAgency guide
    AI engineeringTest first

    Guardrails for AI agents

    Is this command, tool call or output safe to run, or should it stop for review?

    NoulChoice
    Why it fits
    Vercel replaced OpenAI’s Luna with Jev for a command-safety classifier and, per TechCrunch, got results five to 18 times faster with greater accuracy.
    Watch for
    TypeSafe says Jev does not treat adversarial content as hostile by default. Ask about it explicitly.
    Sources
    Guide in progress
    AI engineeringTest first

    Model routing

    Is this request easy enough for a cheap model, or does it need a stronger one?

    ChoiceScore
    Why it fits
    On TypeSafe’s use-case list. A routing call that costs a fraction of a cent can decide whether to spend dollars on a large model.
    Watch for
    Measure the whole pipeline’s quality, not just the router’s.
    Sources
    Guide in progress
    AI engineeringStrong fit

    Re-ranking search results

    Which of these passages actually answers the question.

    ScoreChoice
    Why it fits
    Official cookbook. Choice questions accept up to 255 options, so one call can rank a full results page.
    Watch for
    TypeSafe notes a bias toward the first option in a Choice. Shuffle candidates before asking.
    Sources
    Guide in progress
    OperationsPerson in the loop

    Candidate screening

    Which applicants make the shortlist for a person to review.

    Score
    Why it fits
    TypeSafe’s one published case study: Jack & Jill replaced Gemini 3.1 Flash Lite for first-pass screening; cost per 1,000 candidates fell from $0.755 to $0.092 with similar accuracy.
    Watch for
    That case study is the vendor’s own write-up, and its accuracy gain was not statistically significant. Hiring decisions about people need human review and bias checks.
    Sources
    Guide in progress
    OperationsTest first

    Content moderation

    Allow, warn, send to review, or block.

    ChoiceScore
    Why it fits
    On TypeSafe’s use-case list. One Choice question maps straight onto your moderation actions.
    Watch for
    Adversarial users will probe it. Test with real abuse samples before trusting it.
    Sources
    Guide in progress
    Real timeTest first

    Real-time game and UI decisions

    The next move, from a text description of the current state.

    Choice
    Why it fits
    TypeSafe’s Doom demo makes about 10 decisions a second; the company quotes 70 to 500 milliseconds end to end.
    Watch for
    Text state only: Jev takes no images. The speed numbers are TypeSafe’s own.
    Sources
    Guide in progress