Made myself a little image classifier with OCR + Jev It was able to categorise ~900 images in 40 seconds Pretty cool
API & ModelsZDR
Zero data retention
The enterprise data-handling option: Jev is not trained on customer requests or responses, and ZDR is available for enterprise accounts.
- Category
- API & Models
- Also known as
- ZDR
- Related terms
- 3
- Directory entries
- 9
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
The models page states that Jev is not trained on customer requests or responses and points to the legal pages for the Data Processing Agreement, the Privacy Policy, and zero data retention for enterprise customers.
It matters for regulated workloads: the decision contract can be inspected, and the data path can be contracted, separately from the model's accuracy characteristics.
Tagsapilegal
Related terms
Definitions that connect to this one.
TypeSafe AIThe company behind Jev and the System One API, including the official SDKs, cookbooks, console, and published workflow evals.Core ConceptsAPI keyThe bearer credential for the TypeSafe API, created in the console and read from TYPESAFE_API_KEY by the official SDKs.API & Modelsmodel fieldThe required request field that selects which model handles the call; accepts versioned IDs and aliases.API & Models
From the directory
The model card for jev-1.13.0: $42 per billion input tokens with free output, a 64k context, 250k tokens per second, and how to list the models your account can call.
Sites & GuidesDocs#official#docs#models
Official
TypeSafe AI's product site for Jev, its first System One Model: typed decisions with calibrated confidence, performance and pricing claims, a FAQ, and links to the docs, console, workflow evals, and launch post.
Sites & GuidesDocs#official#docs#models
Official
The hub for TypeSafe's legal and policy documents, plus the data commitments attached to the API.
Sites & GuidesDocs#official#docs#privacy
Official
8GitHub stars
A browser tool that checks whether a cited paper supports the sentence citing it: code matches references and verifies quotes, Claude proposes an evidence passage, and Jev scores supports, contradicts, or says nothing.
Tools & Integrations#community#javascript#browser
6.5kGitHub stars
An Android overlay that reads visible WeChat, QQ, and X conversations through accessibility, asks Jev for intent, danger, and the best action, then drafts three replies and fills one in without ever sending it.
Tools & Integrations#community#kotlin#android
Community
jev-chat/jev-chat-jarvisKotlin
jev-chat/jev-chat-jarvisKotlin2GitHub stars
An experimental iOS app that turns an on-device Qwen3-VL model into a multiple-choice decision tool: it reads next-token logits for A/B/C and scores 2-26 supplied options without generating any text.
Cookbooks & Demos#community#swift#ios
Community
NullPo-jp/PocketJevSwift
NullPo-jp/PocketJevSwift3 more matching entries in the full directory.
From the community
Posts from builders shipping with Jev right now.
900 images in 40 seconds
Intent-based search in Gmail
Jev is really good at intent-based search! How it looks in Gmail: (for a huge inbox you'd prob let semantic search / embeddings pull first but still much better experience)
@dabit3
Another crazy @typesafeai Jev example: Predictive spreadsheets Spreadsheets recalculate numbers, not meaning. Jev reads intent. Type "Urgency" at the top of a column and, as you type, it figures out you want each row rated from "no follow-up needed" to "urgent" in ~100 ms.
End-to-end tests run by agents
e2e + jev from @typesafeai ⚡ I'm building an open-source framework for running e2e tests with agents. supports web, mobile (and more!) available soon: tester.army/e2e
An always-on assistant with no wake word
Ai is evolving. Jev can be armed at all times. I can speak freely and it knows ( from probabilities ) if im asking my computer to do something or blaberring away at something else. no wake word. speed + affordability + intelligence is getting to the point where an always Show more
400 companies matched to one candidate
JEV is INSANE. We gave it 400 companies and one candidate profile. In 12 seconds, it predicted which jobs the candidate had the highest chance of getting, assigned a confidence score and detected job-candidate mismatches. All for just $0.0005 It can also score Show more
Getting started: install the skill
Unsure how to get started with Jev? Install the skill: npx skills add typesafe-ai/skills --skill typesafe-ai Then prompt in your project: use /typesafe-ai to see how Jev can be used to replace slow, expensive LLM usage and find possible new features it would enable for users.