The independent field guide to Jev · not affiliated with TypeSafe AI
What Jev can decide for your business, and what it can’t.
Jev is TypeSafe AI’s new decision model: it reads text and picks an answer instead of writing one. We explain it in plain English, link every claim to its source, and show where it fits support teams and insurance agencies, and where it doesn’t.
Latest releaseOct 14, 2026
New post · Worked example · 10 min readText written to fool it: building a claims fast track that a claimant can’t talk their way intoRead the post › Podcast · Episode 5 · script ready, audio soonText written to fool itWhat a planted sentence in a claim can and can’t do to Jev’s answer, and how to build a fast track for small glass claims so it doesn’t matter.Show notes and script ›
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Three pieces, the quickest way inText written to fool it: building a claims fast track that a claimant can’t talk their way into
02Episode 5: Text written to fool it
0312 things Jev can decide
The feed
Posts and episodes, newest firstWorked example · Oct 14, 2026 · 10 minText written to fool it: building a claims fast track that a claimant can’t talk their way intoJev reads everything you send it as evidence, including a line a claimant wrote to steer it. Here is what new research says that line can and can’t do, and how to design a fast track for small glass claims so it doesn’t matter.Episode 5 · script ready, audio soonText written to fool itWhat a planted sentence in a claim can and can’t do to Jev’s answer, and how to build a fast track for small glass claims so it doesn’t matter.Comparison · Oct 13, 2026 · 10 minJev, its clones and your service inbox: what’s actually under the hoodDecision models are multiplying. Here is what Jev is likely built on, how it differs from a chatbot and from a classifier you train yourself, and why the test you run matters more than the brand. Shown on a made-up email to an insurance agency.Episode 4 · script ready, audio soonJev, its clones and your inboxWhat’s likely inside Jev, how it compares with a chatbot, a classifier you train yourself and the new look-alikes, shown on a made-up email to an insurance agency.Worked example · Oct 12, 2026 · 10 minA function call for underwriters: sorting commercial submissions with JevWhat “unstructured state in, typed probabilistic decisions out” means in practice, shown on a made-up contractor submission at an insurance agency.Episode 3 · script ready, audio soonA function call for underwritersWhat Jev’s founder means by “unstructured state in, typed probabilistic decisions out,” shown on a made-up contractor submission at an insurance agency.Worked example · Oct 11, 2026 · 11 minLabels, not essays: sorting first notice of loss reports with JevA worked example for a claims desk. Four narrow questions, a few lines of routing code, and a person on every decision that touches a payout.Episode 2 · script ready, audio soonLabels, not essays: Jev on the claims deskFortune’s look at why developers took to Jev, and what that means for sorting first notice of loss reports: four small questions, plain routing rules, and a person on every payout.Explainer · Oct 11, 2026 · 7 minWhat Jev is, and what it isn’tA plain-English guide to TypeSafe AI’s decision model: what it hands back, why developers like it, and the five things it is bad at.Episode 1 · script ready, audio soonWhat Jev is, and what it isn’tThe new AI model that never writes a word: what it hands back, why developers are excited, and the five things TypeSafe itself says it is bad at.
Comparison · in the worksJev vs an LLM vs a classifierWhen to use which, with the cost of a million decisions a month.
How-to · in the worksYour first Jev call: routing support ticketsPython and JavaScript, from API key to a working router.
How-to · in the worksConfidence thresholds, explainedWhen to let Jev act on its own and when to send it to a person.
Worked example · in the worksScoring insurance leads with JevA worked example with synthetic data, code and results.
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How we grade sources
MethodologyEvery claim on this site carries one of six labels. TypeSafe’s own numbers are always marked as vendor claims, and quotes are verbatim.
Regulator
A regulator’s own rule, bulletin or guidance, such as the NAIC.
Our test
Something we ran ourselves, published on Tests with its data and code.
Independent reporting
News outlets with editors, such as TechCrunch or The Register.
Official docs
TypeSafe’s documentation and the gateways’ own pages. Good for how things work.
Vendor case study
A customer story written by TypeSafe. Reported, not confirmed.
Vendor claim
TypeSafe’s marketing and blog. Reported, not confirmed.