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Jev, explained in plain English
What Jev does, how to use it, and whether the claims about it hold up. Every source is linked and labeled, and each post shows the date we last checked them.
Text written to fool it: building a claims fast track that a claimant can’t talk their way into
Jev 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.
- 02Jev, 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. · Oct 13, 2026 · 10 minComparison
- 03A 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. · Oct 12, 2026 · 10 minWorked example
- 04Labels, 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. · Oct 11, 2026 · 11 minWorked example
- 05What 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. · Oct 11, 2026 · 7 minExplainer
Coming next
- 06Jev vs an LLM vs a classifierWhen to use which, with the cost of a million decisions a month.Comparison
- 07Your first Jev call: routing support ticketsPython and JavaScript, from API key to a working router.How-to
- 08Confidence thresholds, explainedWhen to let Jev act on its own and when to send it to a person.How-to
- 09Scoring insurance leads with JevA worked example with synthetic data, code and results.Worked example