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AI STACK · SYSTEM ONE MODELS

100 decisions Jev can make for you.

Jev makes the small decisions an LLM should not be paying for: which queue, which model, which lead, pass or fail. It returns a typed answer with a calibrated probability, never text. We had Jev rank 151 candidate uses. Ticket routing, model routing and lead scoring came out on top.

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Certified Partner since 2010 · MVP Hall of Fame · 200+ agents in production · UAE and US desks

See the ranking method

Jev · Support ticket routing

Which queue should this ticket go to, and does it need a person now?

Charged twice this month. Payouts failing since Monday.

  • billing0.87
  • technical0.12
  • account0.01
  • other0.00

Noul · escalate 0.95

Choice+Noul · about $0.021 per 1,000 decisions, estimated

Primitives

Three questions. Typed answers.

  • Choice

    One answer from a list you wrote.

    billing, technical, account, other

  • Score

    A position on a scale you defined.

    calm, frustrated, about to churn

  • Noul

    The probability that something is true.

    Should a person take over now? 0.95

The split

The model writes. Jev decides. Code acts.

  1. 01

    The model

    Claude, Gemini or OpenAI writes anything that has to be written.

  2. 02

    Jev

    Takes the closed decision and returns a probability.

    Act at 0.90 confidence or higher. Below that, a person or a larger model.

  3. 03

    Code

    Acts above the line. Anything below it goes to a person.

Top 10

Start with these ten.

The ten Jev ranked highest. Each one has its own page.

All 100

Find the decision that sounds like your week.

Open the one that matches the work. If none do, clear the cut and tell us the decision.

100 of 100

  • 01Support · Cross-industry

    Independent eval

    Support ticket routing

    Every ticket to the right queue in one call. The unsure ones go to a person, not a guess.

    Jev decides
    Choice over 5-50 queues + Noul 'needs a human now' in one call; auto-route above 0.9 confidence
    The LLM does
    LLM drafts the reply only after routing

    about $0.021 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    40 tickets across 9 desks for about $0.0016; TypeSafe eval: customer service 76.0 vs 78.3 best comparator
    apimodels.app/jev-use-cases

  • 02Engineering and AI agents · AI products

    Shipped / named repo

    Model routing

    Jev picks which model answers each request. The cheap model gets the easy work. The expensive one earns its cost.

    Pilot first.

    Jev decides
    Choice model tier per request from complexity/risk
    The LLM does
    The chosen model answers; Jev only picks it

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Jev's two fit passes disagreed here (gap 0.46), so pilot it against your own routing labels.
    Builder's figure. Not reproduced by us.
    Multiple routers; Vercel: about 13% of paid gateway teams used Jev in 24h
    apimodels.app/jev-use-cases

  • 03Sales · B2B SaaS

    Measured build (self-reported)

    Lead scoring against your ICP

    Each lead scored on the criteria you wrote down. Your code applies the weights. Sales sees why.

    Jev decides
    Score per ICP criterion (industry fit, size, intent) + Choice segment; code weights
    The LLM does
    LLM enriches and writes outreach; Jev scores

    about $0.0252 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    700 leads scored in 40s for $0.09 (Gojiberry)
    madewithjev.com/jev-use-cases

  • 04Finance · Accounting/Tax

    Measured build (self-reported)

    Tax document classification

    Every page labelled by form and page type. OCR still reads the numbers. Jev only sorts.

    Jev decides
    Choice IRS form + page kind with confidence gate
    The LLM does
    OCR/LLM extracts fields; Jev labels the page

    about $0.0504 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    $0.001/page, 34x cheaper and 6x faster than prior LLM pipeline (open source)
    apimodels.app/jev-use-cases

  • 05Sales · B2B

    Measured build (self-reported)

    Reply intent in sales email

    Interested, objection, not now, referral, unsubscribe. Every reply sorted before a rep opens it.

    Jev decides
    Choice intent (interested, objection, not now, referral, unsubscribe) on every reply
    The LLM does
    LLM drafts the follow-up

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Thousands of outreach messages in 40s for <$0.20
    apimodels.app/jev-use-cases

  • 06Support · Banking

    Reference example

    Live call intent

    The caller's intent tracked turn by turn during the call. Routing happens while they are still talking.

    Jev decides
    Choice over 51 banking intents on each transcript turn, 90% auto-route rule
    The LLM does
    Speech-to-text + LLM summary after the call

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Advancing Analytics banking demo with 51 categories
    www.advancinganalytics.co.uk/blog/jev-the-smarter-cheaper-way-to-do-enterprise-ai-automation

  • 07Finance · Enterprise

    Independent eval

    Invoice routing: pay, hold, review

    Jev routes the invoice. It does not read the amounts. OCR extracts, rules check the math, a person takes the holds.

    Jev decides
    Choice pay/hold/review + Choice cost centre; extraction stays with LLM/OCR (Jev weakest there)
    The LLM does
    OCR/LLM extracts amounts; rules check math; Jev routes

    about $0.063 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Don't let Jev extract amounts or fields; it scored 61.8 vs 79.1 on invoice processing in TypeSafe's eval. It should only route.
    Builder's figure. Not reproduced by us.
    TypeSafe eval: invoice processing 61.8 vs 79.1 (routing ok, extraction weak); Huryn 50/50 on tricky invoice docs
    aiproplaybook.com/tools/jev

  • 08Sales · B2B

    Measured build (self-reported)

    Outreach message fit

    Each draft checked against the prospect before it goes out. Mismatches come back, not sent.

    Jev decides
    Score predicted reply likelihood + Noul 'message mismatches prospect' before send
    The LLM does
    LLM writes the message; Jev scores fit

    about $0.0294 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Detected lead-message mismatches across 700 leads
    apimodels.app/jev-use-cases

  • 09Support · SaaS

    Pattern (not yet proven)

    Duplicate ticket detection

    Is this the same problem as an open case? Yes or no, with a probability. Fewer tickets worked twice.

    Jev decides
    jev_compare-style Choice same_fact / different per candidate incident
    The LLM does
    None; pure decision

    about $0.0336 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern (gradually.ai 'check whether cases are duplicates')
    www.gradually.ai/en/jev-use-cases/

  • 10Support · Cross-industry

    Reference example

    Chatbot to human handoff

    One question on every turn: should a person take over now. The bot stops guessing past its depth.

    Jev decides
    Noul per turn 'escalate to human' with threshold
    The LLM does
    LLM chatbot keeps talking until Jev says hand off

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    TypeSafe quickstart example ('Escalate to a human now?')
    www.jevtypesafeai.com/how-to-use

  • 11Recruiting and HR · Staffing

    Measured build (self-reported)

    Candidate to job matching

    One candidate scored against every open role at once. Mismatches flagged. A recruiter decides.

    Jev decides
    Score fit for one candidate vs every open role, flag mismatches
    The LLM does
    LLM writes the recruiter note

    about $0.0378 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Hiring carries fairness risk. Treat the output as a shortlist aid, not a decision.
    Builder's figure. Not reproduced by us.
    1 candidate vs 400 companies in 12s for $0.0005
    apimodels.app/jev-use-cases

  • 12Support · SaaS

    Reference example

    Ticket urgency and churn risk

    Urgency and frustration scored on every ticket. SLA timers start from the score, not the inbox order.

    Jev decides
    Score (calm to about to churn) + Noul 'SLA breach risk' per ticket, drives SLA timers
    The LLM does
    LLM drafts empathetic reply

    about $0.021 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Cloudflare Workers AI reference example (payouts failing to billing, 0.95 urgent)
    developers.cloudflare.com/ai/models/typesafe/jev/

  • 13Finance · Document-heavy

    Measured build (self-reported)

    OCR page routing

    Jev decides which PDF pages actually need paid OCR. The rest are read locally.

    Pilot first.

    Jev decides
    Noul 'page needs OCR' per page; others extracted locally
    The LLM does
    Paid OCR runs only on flagged pages

    about $0.0252 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Shipped (483 likes); cuts paid OCR volume
    apimodels.app/jev-use-cases

  • 14Data · Research/Media

    Measured build (self-reported)

    Bulk document classification

    A cheap model summarises once. Jev files each item into your taxonomy. Thousands for cents.

    Jev decides
    LLM summarises once; Jev picks 1 of N topics
    The LLM does
    Cheap LLM summarises; Jev picks topic

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    1,018 papers to 24 topics for $0.08; 24,000 HN posts in about 2 min
    madewithjev.com/jev-use-cases

  • 15Security and IT · Cross-industry

    Measured build (self-reported)

    Fraud and phishing email triage

    Jev clears the obvious ones. Anything below the confidence line goes to a larger model, then a person.

    Pilot first.

    Jev decides
    Choice fraud/legit; <95% confidence cascades to an LLM
    The LLM does
    Frontier LLM re-checks low-confidence emails

    about $0.0252 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Cascade low-confidence emails to a bigger model. The published run reached 96/100 only with the fallback.
    Builder's figure. Not reproduced by us.
    100 emails in 1.42s; 96/100 with Kimi fallback; about $0.07 total
    madewithjev.com/jev-use-cases

  • 16Operations and industry · SMB

    Measured build (self-reported)

    Shared inbox triage

    ops@ and info@ sorted by owner and by whether it needs a reply today. Nobody forwards all day.

    Pilot first.

    Jev decides
    Choice owner + Noul 'needs reply today'
    The LLM does
    LLM drafts replies for 'needs reply' mail

    about $0.021 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    500 emails for 3.5 cents; 1,500 own emails tested
    madewithjev.com/jev-use-cases

  • 17Support · Cross-industry

    Pattern (not yet proven)

    QA on every support conversation

    The rubric applied to every conversation, not a two percent sample. Coaching goes where the scores dip.

    Jev decides
    Score per rubric item (empathy, resolution, policy) on 100% of conversations
    The LLM does
    LLM writes coaching notes for flagged calls

    about $0.063 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern; LangChain judge study 500/500 agreement
    apimodels.app/jev-use-cases

  • 18Security and IT · Enterprise

    Reference example

    IT helpdesk routing

    Network, identity, hardware, software, security. The ticket lands on the right desk first time.

    Pilot first.

    Jev decides
    Choice (network, identity, hardware, software, security) + urgency
    The LLM does
    LLM suggests fix for known categories

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Emerjable help-desk example
    www.emerjable.com/how-to-build-ai-decision-systems-with-jev/

  • 19Legal and compliance · Banking/Fintech

    Measured build (self-reported)

    AML alert triage

    Alerts scored on risk and evidence. Only confident false positives close automatically. Investigators get the rest, with the trail.

    Jev decides
    Score risk + evidence quality; auto-close only high-confidence false positives
    The LLM does
    Investigator + LLM narrative for escalated alerts

    about $0.084 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Regulated work. Only auto-close high-confidence false positives, keep full audit trails and validate calibration on your own labels.
    Builder's figure. Not reproduced by us.
    100,000 alerts: <$26K vs >$290K on Opus 5
    apimodels.app/jev-use-cases

  • 20Data · Cross-industry

    Measured build (self-reported)

    Label cleanup after a cheap model

    A cheap model labels first. Jev re-checks every label against your taxonomy. You stop paying twice.

    Jev decides
    Jev re-tags what a cheap LLM labelled first
    The LLM does
    Cheap LLM labels first pass

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    about 2,300 papers re-tagged in about 83s for $0.14
    apimodels.app/jev-use-cases

  • 21Recruiting and HR · Cross-industry

    Pattern (not yet proven)

    Resume screening by competency

    Each competency scored separately against a written role. Your weights, not the model's taste. A person makes the call.

    Jev decides
    Score per competency (Python depth, leadership, system design); code applies role weights; human decides
    The LLM does
    LLM summarises CV; human makes the hiring decision

    about $0.063 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review.
    Watch out
    Hiring carries legal and fairness risk. Jev scores dimensions and a human makes the decision. Keep audit logs.
    Builder's figure. Not reproduced by us.
    TypeSafe composite-scoring pattern; keep human in loop
    www.ayautomate.com/blog/jev-use-cases

  • 22Operations and industry · Insurance

    Reference example

    Insurance claims triage

    Complexity, missing information, and fraud signals on every first notice of loss. Clean claims move. The rest go to an adjuster.

    Jev decides
    Score complexity + Noul missing info + Noul fraud indicators to straight-through vs adjuster
    The LLM does
    LLM summarises claim for adjuster

    about $0.063 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    TypeSafe docs industry example; Advancing Analytics UK insurers
    www.advancinganalytics.co.uk/blog/jev-the-smarter-cheaper-way-to-do-enterprise-ai-automation

  • 23Legal and compliance · Social/Communities

    Measured build (self-reported)

    Content moderation by severity

    Allow, warn, review, block. Severity and confidence decide the band. Moderators take the middle.

    Jev decides
    Score severity + confidence to allow / warn / review / block; strong in Japanese
    The LLM does
    Human moderators handle review band

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    kiarina labs: 36 vs 292 misses in Japanese moderation
    anandi1989.github.io/awesome-jev-usecases/

  • 24Engineering and AI agents · AI products

    Measured build (self-reported)

    Confidence-gated model cascade

    Jev answers when it is sure. Below the line, the larger model takes over. You pay frontier prices only for hard cases.

    Jev decides
    Jev answers when confident; <threshold to LLM
    The LLM does
    Big LLM handles only uncertain cases

    about $0.021 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Fraud cascade 96/100; madewithjev core pattern
    madewithjev.com/jev-use-cases

  • 25Security and IT · SaaS

    Reference example

    Incident severity and on-call routing

    Severity scored and the owning team picked from the alert text. The page goes to the right person.

    Jev decides
    Score severity + Choice owning team from alert text
    The LLM does
    LLM drafts incident summary

    about $0.0294 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Box CEO demo: incident reports triaged by severity/impact
    apimodels.app/jev-use-cases

  • 26Support · SaaS

    Pattern (not yet proven)

    Help article matching

    Jev picks the article family. The LLM writes only when no article fits.

    Jev decides
    Choice over article families, then LLM writes only if needed
    The LLM does
    LLM personalises the article answer

    about $0.0252 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 27Finance · SMB/Accounting

    Pattern (not yet proven)

    Transaction categorisation

    Every bank line to a GL account, with a flag for the accountant when it is unsure.

    Jev decides
    Choice GL account (≤255) + Noul 'needs accountant review'
    The LLM does
    Accountant reviews flagged lines

    about $0.0084 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    For GL charts over 255 accounts, use hierarchical Choice.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 28Marketing and SEO · Paid media

    Measured build (self-reported)

    Ad pre-testing on synthetic personas

    Stop or scroll, asked of every ad across every persona. A read before the budget goes out.

    Jev decides
    Noul 'would stop scrolling' across N personas × M ads
    The LLM does
    LLM writes ad variants

    about $0.021 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    723 ads × 30 personas = 21,690 decisions for $0.22
    www.jevtracks.com/

  • 29Operations and industry · Providers

    Pattern (not yet proven)

    Patient message triage (admin only)

    Billing, refills, scheduling, clinical. Routing only. Anything clinical goes straight to a clinician.

    Jev decides
    Choice queue (billing, refill, scheduling, clinical) + Noul 'route to clinician now'; never clinical decisions
    The LLM does
    Clinicians handle anything clinical

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Admin routing only. Never make clinical judgments. PHI residency: Jev is hosted.
    Builder's figure. Not reproduced by us.
    Pattern (pingax, advancing analytics)
    pingax.com/what-is-jev-ai-typesafe-system-one-model/

  • 30Sales · B2B

    Pattern (not yet proven)

    Account signals from news and hiring

    Every news item scored for relevance to your territory. Reps start the week with the accounts that moved.

    Jev decides
    Score trigger-event relevance for each news item across territory
    The LLM does
    LLM researches and writes the angle

    about $0.0252 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern; 384 news stories scored for brands in 25s
    www.ayautomate.com/jev-builds

  • 31Data · E-commerce

    Measured build (self-reported)

    Product catalogue tagging

    Category and attributes on every SKU. Incomplete listings flagged before they go live.

    Jev decides
    Choice category/attributes + Noul incomplete listing
    The LLM does
    LLM writes descriptions

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    3,000 products scored on several criteria in 28s for $0.11
    apimodels.app/jev-use-cases

  • 32Data · Healthcare/Academia

    Measured build (self-reported)

    Systematic review screening

    Include or exclude per criterion on every abstract. Reviewers adjudicate. Recall measured on your protocol.

    Jev decides
    Noul include/exclude per PICO criterion; humans adjudicate
    The LLM does
    Reviewers adjudicate includes

    about $0.021 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Humans adjudicate includes. Measure recall on your own protocol.
    Builder's figure. Not reproduced by us.
    95% recall at 16,645 records (tiab-review-plugin)
    anandi1989.github.io/awesome-jev-usecases/

  • 33Security and IT · Cross-industry

    Independent eval

    SOC alert triage

    Alert type on every event. Jev is weaker here, so the auto-close line sits high and containment stays human.

    Jev decides
    Choice (benign, recon, brute force, exfil) + stricter threshold for containment
    The LLM does
    Analyst + LLM investigate escalations

    about $0.042 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Jev is weaker on security incidents (61.7 vs 66.2). Only auto-close at a strict threshold, and keep containment with a human.
    Builder's figure. Not reproduced by us.
    TypeSafe eval: security incidents 61.7 vs 66.2. needs human thresholds
    aiproplaybook.com/tools/jev

  • 34Marketing and SEO · Consumer

    Pattern (not yet proven)

    Social mention triage

    Complaint, praise, crisis, lead, spam. The crisis flag reaches a person in minutes, not days.

    Jev decides
    Choice (complaint, praise, crisis, sales lead, spam) + Noul 'crisis' on every mention
    The LLM does
    LLM drafts community replies

    about $0.0084 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 35Marketing and SEO · Creators/LinkedIn/X

    Measured build (self-reported)

    Post scoring before you publish

    The draft scored against your rubric. The LLM rewrites. Jev scores again. Stop when it stops improving.

    Jev decides
    Score 8-61 rubric questions per draft; LLM rewrites, Jev re-scores until it peaks
    The LLM does
    LLM rewrites; Jev re-scores

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    61 questions/post in about 1s for $0.0004 (SuperX)
    madewithjev.com/jev-use-cases

  • 36Engineering and AI agents · Product/SaaS

    Measured build (self-reported)

    Session replay to bug fix

    Rage clicks and errors grouped by issue type. The coding agent writes the fix for the clusters that matter.

    Pilot first.

    Jev decides
    Choice issue type per replay event cluster; LLM writes fixes
    The LLM does
    Coding agent writes the fix PR

    about $0.0336 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    3M replay events to 213 fix PRs in 40s for $2
    www.jevtracks.com/

  • 37Engineering and AI agents · Cross-industry

    Measured build (self-reported)

    Browser agent next action

    At every step, which element to click. A small model types. The agent stops waiting on a large one.

    Pilot first.

    Jev decides
    Choice next element from DOM each step; small LLM only to type
    The LLM does
    Small LLM types free text

    about $0.126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    A small LLM still has to type free text. Treat the DOM text as untrusted and screen it for injection.
    Builder's figure. Not reproduced by us.
    Browser Use: flight search 7s, $0.0039 (8,953 likes)
    apimodels.app/jev-use-cases

  • 38Operations and industry · Commercial insurance

    Reference example

    Underwriting submission triage

    Appetite fit and line of business on every submission. Underwriters price the ones worth pricing.

    Jev decides
    Score risk appetite fit + Choice line of business
    The LLM does
    Underwriter prices the risk

    about $0.084 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Advancing Analytics underwriting example
    www.advancinganalytics.co.uk/blog/jev-the-smarter-cheaper-way-to-do-enterprise-ai-automation

  • 39Support · Consumer/Retail

    Pattern (not yet proven)

    Customer review tagging

    Topic and sentiment on every review. The voice of the customer from the whole set, not a sample.

    Jev decides
    Choice over topic taxonomy + Score sentiment for every review
    The LLM does
    LLM writes the VoC summary

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern (gradually.ai, madewithjev 'sort a pile')
    madewithjev.com/jev-use-cases

  • 40Engineering and AI agents · AI products

    Independent eval

    Judge for agent evaluations

    Pass or fail on every agent trace against your rubric. Consistent run to run. The LLM explains failures when asked.

    Jev decides
    Noul pass/fail + Score rubric per trace
    The LLM does
    LLM explains failures when needed

    about $0.126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    LangChain: 500/500 human agreement, 0.44s, $0.34 vs $28.17 Claude
    apimodels.app/jev-use-cases

  • 41Finance · B2B

    Pattern (not yet proven)

    Collections prioritisation

    Likelihood to pay and the next action on every open account. Finance calls the right ones first.

    Jev decides
    Score likelihood-to-pay from notes/emails + Choice next action
    The LLM does
    LLM drafts dunning email

    about $0.0252 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 42Security and IT · Cross-industry

    Shipped / named repo

    Log triage before analysis

    Severity and a novel-error flag on every log group. The expensive analysis runs only on what is new.

    Jev decides
    Choice severity + Noul 'novel error' per log group; LLM only on survivors
    The LLM does
    LLM analyses only novel errors

    about $0.0336 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    jevlogs
    jevnotes.com/projects/

  • 43Legal and compliance · Consumer

    Shipped / named repo

    Plain-language feed filter

    Write the rule in a sentence. Jev applies it to every post. Hidden before you scroll past it.

    Jev decides
    Noul per post against user's plain-language rule; hide in real time
    The LLM does
    None

    about $0.0084 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Browser extension hides X posts by natural language; 'insanely cheap'
    madewithjev.com/jev-use-cases

  • 44Engineering and AI agents · AI coding

    Shipped / named repo

    Model routing for Claude Code and Codex

    Per turn, which model and how much reasoning. Hard turns get the big model. Routine turns do not.

    Pilot first.

    Jev decides
    Choice model + reasoning effort per turn (Claude Code, Codex, OpenCode)
    The LLM does
    Selected model does the coding

    about $0.063 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    jev-router, jev-codex-router, opencode orchestrator
    jevnotes.com/projects/

  • 45Recruiting and HR · Cross-industry

    Pattern (not yet proven)

    Interview feedback, normalised

    Free-text notes scored per competency, with a flag on biased language. The hiring manager decides.

    Jev decides
    Score each competency from free-text notes; Noul 'contains bias-risk language'
    The LLM does
    Hiring manager decides

    about $0.0336 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Hiring carries fairness risk. A human makes the decision.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 46Operations and industry · Manufacturing/Retail

    Pattern (not yet proven)

    Supplier email and document routing

    PO, invoice, ASN, quality, dispute. Each one to its owner before the inbox fills.

    Pilot first.

    Jev decides
    Choice (PO, invoice, ASN, quality, dispute) + owner
    The LLM does
    LLM extracts PO/ASN fields

    about $0.0252 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    aiadoptionagency.com/typesafe-ai-jev/

  • 47Data · Media/Ops

    Measured build (self-reported)

    Image library classification

    OCR or a caption reads the image. Jev files it. Jev never sees pixels.

    Jev decides
    OCR/caption text to Choice category
    The LLM does
    OCR/caption model reads the image first

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Jev reads text only. OCR or caption the images first.
    Builder's figure. Not reproduced by us.
    about 900 images in 40s
    madewithjev.com/jev-use-cases

  • 48Marketing and SEO · Media/Brands

    Measured build (self-reported)

    Style guide check on every draft

    One yes or no per rule in your guide. The LLM rewrites only what failed.

    Jev decides
    Nouls per style rule (passive voice, banned words, claims without source)
    The LLM does
    LLM rewrites flagged passages

    about $0.063 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Every: judged everything written in 0.7s, 25x faster, 600x cheaper than a frontier judge
    madewithjev.com/jev-use-cases

  • 49Engineering and AI agents · Ops/RPA

    Shipped / named repo

    Web form field mapping

    Which of your fields fills which box on someone else's form. The LLM writes only the free-text answers.

    Jev decides
    Choice which profile field fills which form input
    The LLM does
    LLM generates free-text answers

    about $0.0336 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Stagehand Act/Extract/Observe uses Jev
    apimodels.app/jev-use-cases

  • 50Legal and compliance · Cross-industry

    Pattern (not yet proven)

    Contract clause review

    Clause type and playbook deviations flagged clause by clause. A lawyer on the redline.

    Jev decides
    Choice clause type + Noul per playbook deviation per clause
    The LLM does
    Lawyer + LLM redline

    about $0.042 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern; legal RAG cookbook
    docs.typesafe.ai/cookbooks/

  • 51Operations and industry · Airlines/OTAs

    Pattern (not yet proven)

    Travel disruption triage

    Rebook, refund, or information. Priority travellers flagged. Agents work the right queue during the storm.

    Jev decides
    Choice (rebook, refund, info) + Noul priority traveller
    The LLM does
    Agent/LLM executes the rebooking

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern (TypeSafe primitives doc refund/rebook/information example)
    learnjev.com/tutorials/three-primitives

  • 52Recruiting and HR · High-volume hiring

    Pattern (not yet proven)

    Knockout question screening

    One check per knockout criterion. Near the line goes to a recruiter. Nobody is rejected on a guess.

    Jev decides
    Noul per knockout criterion (work authorisation stated, certification present)
    The LLM does
    Recruiter reviews edge cases

    about $0.0336 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Hiring carries fairness risk. Send knockouts near the threshold to a recruiter.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 53Legal and compliance · Law firms/In-house

    Pattern (not yet proven)

    Legal intake routing

    Practice area and urgency on every inbound request. Paralegals stop reading everything first.

    Pilot first.

    Jev decides
    Choice practice area + Score urgency
    The LLM does
    Lawyer triage for high urgency

    about $0.0252 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 54Operations and industry · Manufacturing

    Pattern (not yet proven)

    Maintenance ticket triage

    Anomaly class and severity on every ticket and log line. Engineers see the ones that stop a line.

    Jev decides
    Choice anomaly class + Score severity on streaming logs
    The LLM does
    Engineer diagnoses

    about $0.021 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern (pingax manufacturing)
    pingax.com/what-is-jev-ai-typesafe-system-one-model/

  • 55Recruiting and HR · Enterprise

    Pattern (not yet proven)

    HR ticket routing

    Payroll, benefits, leave, IT, employee relations. Sensitive cases flagged to a person immediately.

    Jev decides
    Choice (payroll, benefits, leave, IT, ER) + Noul 'sensitive ER issue'
    The LLM does
    HRBP handles ER cases

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern (advancing analytics: HR ticket routing)
    www.advancinganalytics.co.uk/blog/jev-the-smarter-cheaper-way-to-do-enterprise-ai-automation

  • 56Marketing and SEO · Cross-industry

    Pattern (not yet proven)

    Keyword intent classification

    Intent and funnel stage on every keyword in the list, not the first five hundred.

    Pilot first.

    Jev decides
    Choice intent (informational, commercial, transactional, navigational) + Choice funnel stage per keyword
    The LLM does
    LLM writes briefs for target keywords

    about $0.0042 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern; jev-seo CLI/MCP
    jevnotes.com/projects/

  • 57Marketing and SEO · Publishing

    Measured build (self-reported)

    Newsletter story selection

    Every story scored for your audience. The LLM writes blurbs for the few that make the cut.

    Jev decides
    Score newsworthiness per item for the audience
    The LLM does
    LLM writes the blurbs

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    384 news stories scored for brands in 25s
    www.ayautomate.com/jev-builds

  • 58Data · Cross-industry

    Measured build (self-reported)

    A judgment on every spreadsheet row

    A plain-language question answered for every row as you type. No export to a chat window.

    Jev decides
    Score per row about 100ms per keystroke
    The LLM does
    None

    about $0.0084 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    @dabit3 about 100ms per keystroke
    apimodels.app/jev-use-cases

  • 59Operations and industry · Revenue cycle

    Pattern (not yet proven)

    Medical code family pre-sort (admin)

    Jev narrows the code family. A certified coder confirms every code.

    Jev decides
    Choice candidate code family (hierarchical) for coder confirmation
    The LLM does
    Certified coder confirms

    about $0.063 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    A certified coder confirms every code. Jev only pre-sorts.
    Builder's figure. Not reproduced by us.
    Pattern; hierarchical classification cookbook
    docs.typesafe.ai/cookbooks/hierarchical_classification

  • 60Sales · B2B

    Pattern (not yet proven)

    Sales objection tagging

    Every call snippet mapped to your objection library. Enablement writes to what is actually said.

    Jev decides
    Choice over objection library per call snippet
    The LLM does
    Enablement writes talk tracks

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 61Data · Construction/AEC

    Measured build (self-reported)

    Construction plan-set classification

    Every sheet in the set labelled by type. The bill of materials starts from the right drawings.

    Jev decides
    Choice sheet type across plan set
    The LLM does
    LLM builds bill of materials

    about $0.0336 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    26 sheets in 2.9s for $0.0052; matched GPT-4.1 100%, 17-21x cheaper
    madewithjev.com/jev-use-cases

  • 62Engineering and AI agents · ML/Robotics

    Measured build (self-reported)

    Training label QA

    Does the label match the content? Asked of every item. Annotators fix only what fails.

    Jev decides
    Noul 'label matches content' per item
    The LLM does
    Annotators fix flagged labels

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    58,643 robot-training labels in <3 min for $0.90
    apimodels.app/jev-use-cases

  • 63Legal and compliance · Banking

    Pattern (not yet proven)

    Adverse media triage

    Is this article about this person, and what kind of risk is it. An analyst confirms the true matches.

    Jev decides
    Noul 'article is about this person' + Choice adverse category
    The LLM does
    Analyst decides true matches

    about $0.042 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Regulated work. An analyst confirms true matches.
    Builder's figure. Not reproduced by us.
    Pattern (financial-crime alerts)
    www.ayautomate.com/blog/jev-use-cases

  • 64Operations and industry · Marketplaces

    Pattern (not yet proven)

    Fake and incentivised review flags

    Every review checked before it shows. Trust and safety reviews the flags, not the feed.

    Jev decides
    Noul incentivised/fake + Choice topic
    The LLM does
    Trust & safety reviews flags

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 65Finance · Enterprise

    Pattern (not yet proven)

    Expense policy check

    Jev judges the policy wording. Code checks the amounts, dates, and limits. A person approves the exceptions.

    Jev decides
    Noul per policy rule on each expense line + receipt text
    The LLM does
    Rules engine checks amounts/dates

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Check amounts, dates and limits in code. Jev only judges the semantic policy rules.
    Builder's figure. Not reproduced by us.
    Pattern (emerjable invoice/expense)
    www.emerjable.com/how-to-build-ai-decision-systems-with-jev/

  • 66Engineering and AI agents · SaaS

    Shipped / named repo

    Forms that choose the next question

    The next question picked from your catalogue based on the answers so far. No if-then maze.

    Pilot first.

    Jev decides
    Choice next question from catalogue given answers so far
    The LLM does
    None

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    JevForm (json-render + xstate)
    madewithjev.com/jev-use-cases

  • 67Engineering and AI agents · Agents

    Measured build (self-reported)

    Grok with Jev

    Grok reasons and acts. Jev makes the small decisions between steps. The loop stops stalling.

    Pilot first.

    Jev decides
    Grok executes; Jev makes step decisions between actions
    The LLM does
    Grok reasons and executes

    about $0.0336 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    20,472 decisions in 15.7s for $0.41; setup in 5-7 min
    www.jevtracks.com/

  • 68Marketing and SEO · DTC/Paid media

    Measured build (self-reported)

    Competitor ad teardown

    Hook, format, offer, call to action, and awareness stage on every ad in the category.

    Jev decides
    Choice hook, format, offer, CTA, awareness stage + Noul LP mismatch for every ad
    The LLM does
    LLM writes the creative brief

    about $0.021 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    724 ads / 37 brands in 40s for $0.09; 1,891 ads in 19s for $0.12
    apimodels.app/jev-use-cases

  • 69Engineering and AI agents · Software

    Measured build (self-reported)

    Pull request risk checks

    Secrets, auth changes, deleted tests, blast radius. Many checks, one call. Anything uncertain goes to a reviewer.

    Jev decides
    14 Nouls (secrets, SQL injection, touches auth, deletes tests…) to block / security review / nits / merge; 0.35-0.65 to human
    The LLM does
    Reviewer/LLM comments on flagged PRs

    about $0.168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    6 PRs, about 0.5s and $0.00007 per PR
    apimodels.app/jev-use-cases

  • 70Finance · Investing

    Shipped / named repo

    Earnings call signals

    Guidance direction and tone tagged per paragraph across the coverage list. The analyst writes the view.

    Jev decides
    Choice guidance direction + Score tone per paragraph across coverage universe
    The LLM does
    Analyst writes the thesis

    about $0.0336 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern; AI Hedge Fund added Jev (100x faster/cheaper claim)
    apimodels.app/jev-use-cases

  • 71Operations and industry · Logistics

    Pattern (not yet proven)

    Shipping exception classification

    Exception type and customer impact on every carrier event. Ops calls the customers who matter first.

    Jev decides
    Choice exception type + Score customer impact
    The LLM does
    Ops/LLM notifies customer

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern (aiadoptionagency)
    aiadoptionagency.com/typesafe-ai-jev/

  • 72Data · Cross-industry

    Pattern (not yet proven)

    Record matching

    Same entity or not, on candidate pairs your blocking step finds. Merges stay with a person.

    Jev decides
    Noul 'same entity' on candidate pairs from blocking
    The LLM does
    Blocking/fuzzy match finds candidate pairs first

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 73Marketing and SEO · Cross-industry

    Shipped / named repo

    Brand tracking in AI answers

    How ChatGPT, Gemini, and Perplexity describe you, scored answer by answer.

    Jev decides
    Score sentiment + Choice list position for each AI answer captured
    The LLM does
    LLM captures answers; Jev judges them

    about $0.0336 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Notra: Jev judges brand sentiment and list position
    jevlist.ai/

  • 74Data · Research/HR

    Pattern (not yet proven)

    Survey open-text coding

    Every open answer coded to your codebook. The researcher writes the insight, not the tally.

    Jev decides
    Choice codebook theme + Score sentiment per response
    The LLM does
    Researcher writes insights

    about $0.0084 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 75Sales · B2B SaaS

    Pattern (not yet proven)

    RFP and security questionnaire routing

    Each question to its owner, with a flag if the library already answers it. The LLM drafts only the gaps.

    Pilot first.

    Jev decides
    Choice owner (security, legal, product) + Noul 'answered in library'; LLM drafts only gaps
    The LLM does
    LLM drafts answers for gaps

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 76Support · E-commerce

    Pattern (not yet proven)

    Refund request pre-check

    Is it a refund request, is damage claimed, are there fraud signals. The rules engine owns the amount and the window.

    Jev decides
    Noul 'customer clearly requests refund', Noul 'damaged/missing item claimed', Noul 'fraud indicators'; rules engine owns amounts
    The LLM does
    Rules engine owns amounts and windows

    about $0.021 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    A rules engine owns refund amounts and windows. Jev can't reliably do arithmetic or compare dates.
    Builder's figure. Not reproduced by us.
    Pattern: aiadoptionagency + emerjable refund designs
    aiadoptionagency.com/typesafe-ai-jev/

  • 77Support · Voice AI

    Pattern (not yet proven)

    Voice agent turn-taking

    Has the caller finished the thought? Asked every few hundred milliseconds. The agent stops talking over people.

    Jev decides
    Noul 'speaker finished their thought' on partial transcript every about 300ms
    The LLM does
    LLM generates the spoken reply

    about $0.0063 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    awesome-jev-usecases voice pillar (no canonical repo yet)
    anandi1989.github.io/awesome-jev-usecases/

  • 78Engineering and AI agents · Software

    Pattern (not yet proven)

    Bug report triage

    Component, severity, and duplicate check on every new issue. Engineers start on the right bug.

    Jev decides
    Choice component + Score severity + duplicate check
    The LLM does
    Engineer fixes

    about $0.0252 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern (Perplexity function mapping)
    www.anthropic.com/research/economic-index-june-2026-report

  • 79Data · Cross-industry

    Pattern (not yet proven)

    Data quality alert triage

    Real issue or expected change, on every alert. Analysts chase the real ones.

    Jev decides
    Choice anomaly type + Noul 'real issue vs expected'
    The LLM does
    Analyst investigates real issues

    about $0.021 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern (Perplexity function map)
    www.anthropic.com/research/economic-index-june-2026-report

  • 80Data · Cross-industry

    Measured build (self-reported)

    Filter database rows by meaning

    A plain-language condition inside the SQL query. No export, no embeddings.

    Jev decides
    jev() SQL function: Noul/Choice/Score per row in Postgres/DuckDB
    The LLM does
    None

    about $0.0084 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    129 rows in about 1s for $0.0009; duckdb-jev
    madewithjev.com/jev-use-cases

  • 81Engineering and AI agents · AI products

    Independent eval

    Reranking for retrieval

    Every retrieved passage scored against the question. The LLM answers from the best ones.

    Pilot first.

    Jev decides
    Score relevance per (query, passage) pair
    The LLM does
    LLM answers from reranked passages

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Cookbook: top-1 5% to 18%, top-10 38% to 62%; nDCG@10 0.692 vs Cohere 0.691
    anandi1989.github.io/awesome-jev-usecases/

  • 82Legal and compliance · Litigation

    Pattern (not yet proven)

    E-discovery first pass

    Relevant or not, privileged or not, per document. Attorneys review what is responsive.

    Jev decides
    Noul relevant + Noul potentially privileged per document
    The LLM does
    Attorneys review responsive docs

    about $0.063 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern; legal passage rerank cookbook (40 queries)
    www.ayautomate.com/blog/jev-use-cases

  • 83Marketing and SEO · Marketplaces

    Pattern (not yet proven)

    Programmatic SEO quality gate

    Thin content and duplicate intent flagged before a page is indexed.

    Jev decides
    Score thin-content + Noul duplicate-intent before indexing
    The LLM does
    LLM rewrites thin pages

    about $0.042 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 84Operations and industry · Fashion

    Measured build (self-reported)

    Real-time outfit selection

    What the shopper says, matched to an item in the catalogue while they talk.

    Pilot first.

    Jev decides
    Choice item from closet/catalogue from voice transcript
    The LLM does
    Renderer shows the outfit

    about $0.0168 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    about $0.0011 and about 620ms per decision
    madewithjev.com/jev-use-cases

  • 85Operations and industry · Knowledge workers

    Measured build (self-reported)

    Personal inbox triage

    A label and a needs-my-reply flag on every email. The LLM drafts only what you will send.

    Jev decides
    Choice label + Noul 'needs my reply' on every email
    The LLM does
    LLM drafts replies

    about $0.021 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    500 emails for 3.5 cents
    madewithjev.com/jev-use-cases

  • 87Marketing and SEO · Cross-industry

    Pattern (not yet proven)

    Persona segmentation

    Every contact placed in a persona from title and activity. Messaging follows the persona.

    Pilot first.

    Jev decides
    Choice persona per contact from title + activity
    The LLM does
    LLM personalises messaging

    about $0.0084 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 88Sales · Cross-industry

    Pattern (not yet proven)

    CRM account deduplication

    Same company or not, on candidate pairs. RevOps merges. Nothing merges on its own.

    Pilot first.

    Jev decides
    Noul 'same company' on candidate pairs; Choice correct industry/segment
    The LLM does
    RevOps merges

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 89Marketing and SEO · Knowledge work

    Shipped / named repo

    Research source ranking

    Every source scored for relevance and credibility before anyone reads it.

    Jev decides
    Score source relevance/credibility; pick queries & windows
    The LLM does
    LLM synthesises the answer

    about $0.021 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    JevSearch often surfaces URLs outside initial top results
    www.jevtracks.com/

  • 90Engineering and AI agents · AI coding

    Independent eval

    Context compaction for coding agents

    Keep or drop, per tool result, instead of a summary. Evidence is mixed, so benchmark it on your budget.

    Jev decides
    Score each tool call/result keep vs drop; content stays verbatim
    The LLM does
    Agent continues with pruned context

    about $0.126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review.
    Watch out
    Mixed evidence: the Nous eval kept its existing approach. Benchmark recall at your token budget.
    Builder's figure. Not reproduced by us.
    fast-jev-compaction; Nous eval: 75.5% recall at 115K vs 78.9% at 55K (mixed)
    apimodels.app/jev-use-cases

  • 91Legal and compliance · Healthcare/All

    Reference example

    PII gate before an LLM

    Contains PII or not, per chunk, before it leaves your boundary. Regex stays in place too.

    Jev decides
    Noul 'contains PHI/PII' per document or chunk as a gate before an LLM
    The LLM does
    Redaction tool masks flagged spans

    about $0.0336 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Pair it with deterministic PII regex. Jev is hosted-only, so check data residency.
    Builder's figure. Not reproduced by us.
    TypeSafe Rust playground PHI detection; emerjable PII screening
    mrjev.com/projects/

  • 92Recruiting and HR · Job seekers/Staffing

    Measured build (self-reported)

    Careers page crawling

    Which link next, and does this job match the profile. Seconds per site, not minutes.

    Pilot first.

    Jev decides
    Choice next link + Noul 'job matches profile'
    The LLM does
    LLM writes the application

    about $0.063 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    about 20s vs about 5 min with an LLM
    apimodels.app/jev-use-cases

  • 93Operations and industry · Government

    Pattern (not yet proven)

    Citizen request routing

    Department and urgency on every 311 request. Staff respond. The sorting is done.

    Pilot first.

    Jev decides
    Choice department + Score urgency
    The LLM does
    Department staff respond

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.gradually.ai/en/jev-use-cases/

  • 94Engineering and AI agents · Assistants

    Shipped / named repo

    Tool calling without an LLM

    The tool and its arguments picked from fixed options. No text generated.

    Jev decides
    Choice tool + args from option sets
    The LLM does
    Tools return the answer

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Chatbot with no LLM: web search, Wikipedia, weather, Todoist, Home Assistant
    madewithjev.com/jev-use-cases

  • 95Engineering and AI agents · AI agents

    Reference example

    Skill and tool selection for agents

    Which skill fits this task, and whether any skill applies at all.

    Jev decides
    Choice best skill + Noul 'any skill applies'
    The LLM does
    Agent executes the skill

    about $0.0336 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    TypeSafe skill-suggestion cookbook; Slack agent 2x faster
    docs.typesafe.ai/cookbooks/skill_suggestion

  • 96Operations and industry · Telecom

    Pattern (not yet proven)

    Network alarm correlation

    Same root cause or not, across alarm pairs. The NOC works one incident, not forty alarms.

    Jev decides
    Noul 'same root cause' across alarm pairs + Choice severity
    The LLM does
    NOC engineer resolves

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model. Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    aiadoptionagency.com/typesafe-ai-jev/

  • 97Operations and industry · Retail

    Pattern (not yet proven)

    On-site search reranking

    Every candidate product scored against the query. The search engine still retrieves.

    Pilot first.

    Jev decides
    Score relevance per (query, product)
    The LLM does
    Search engine retrieves candidates

    about $0.0084 per 1,000 decisions, estimated

    Details
    Confidence rule
    Set the threshold on the score. Low confidence goes to review.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern; rerank cookbook
    anandi1989.github.io/awesome-jev-usecases/

  • 98Engineering and AI agents · SaaS

    Pattern (not yet proven)

    Experiment readout classification

    Stats computed in code. Jev classifies the outcome for the readout. The PM decides.

    Jev decides
    Choice outcome class from stats summary (code computes stats)
    The LLM does
    Stats computed in code; PM decides

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern
    www.anthropic.com/research/economic-index-june-2026-report

  • 99Operations and industry · Government

    Pattern (not yet proven)

    Public consultation coding

    Stance and theme on every comment. Analysts write the report from the whole set.

    Jev decides
    Choice stance + theme per comment
    The LLM does
    Analysts write the report

    about $0.0126 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.90 confidence or higher. Below that, a person or a larger model.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    Pattern (survey coding)
    www.gradually.ai/en/jev-use-cases/

  • 100Marketing and SEO · Cross-industry

    Shipped / named repo

    AI writing tell linter

    One check per tell. It flags, it never rewrites. You keep your voice.

    Pilot first.

    Jev decides
    One Noul per AI-tell rule; never rewrites text
    The LLM does
    You (or an LLM) rewrite

    about $0.042 per 1,000 decisions, estimated

    Details
    Confidence rule
    Act at 0.85 or higher. Block at 0.15 or lower. Between 0.35 and 0.65, a person decides.
    Watch out
    Write your house rules into the request. When rules were left out, Jev got 5 of 24 right with high confidence (Huryn). Validate thresholds on labelled samples.
    Builder's figure. Not reproduced by us.
    snifftest prose linter
    anandi1989.github.io/awesome-jev-usecases/

The honest no

Where Jev is the wrong tool.

Jev does not write. It does not do arithmetic, compare dates, or count. It is weak at pulling fields out of invoices. It will be confidently wrong about rules you did not write into the request, and text you feed it can carry instructions. If the job is any of those, we will say so in the first week.

Method

How we ranked these.

  1. 01151 candidate uses, gathered from public builds, vendor docs, and how teams use ChatGPT, Claude, Codex and Grok at work.
  2. 02Jev scored every one on five things: fit, published proof, business value, search demand, and how far a demo would travel.
  3. 03Jev chose its own weights: fit 28, value 23, proof 20, demand 17, reach 12.
  4. 04Fit was scored twice with different wording. Where the two passes disagreed, the card says pilot first.
  5. 05Every headline figure was checked against its source. The figures belong to the people who published them.
  6. 06Search demand is Jev's judgment, not keyword volume. We say so.
Download the full table (CSV)

Last updated

Questions

What we actually say.

What is Jev?
A System One model from TypeSafe AI. It answers typed questions about text you give it and returns probabilities, not prose.
Is Jev an LLM?
No. Pair it with one. The LLM writes, Jev decides.
What does Jev cost?
About $0.042 per million input tokens, and output is free. A support ticket costs roughly two cents per thousand decisions.
Where is Jev the wrong tool?
Writing, arithmetic, dates, invoice field extraction, and rules you left out of the request.
Can Mindcat put Jev on our process?
Yes. One named decision, a threshold, and a person on the exceptions. The first week is in the org.

The brief

Name the decision your team makes a thousand times a day.

We read the process and write down where Jev fits, where it does not, and where the line sits.

Where are you?

Certified Partner since 2010 · MVP Hall of Fame · 200+ agents in production · UAE and US desks

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