Jev directory
Jev for Customer Support
184 curated Jev resources. Support, helpdesk, and ticketing workflows.
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All 184 Jev AI model resources for Customer Support
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A small distilled sibling of Perplexity's pplx-decider-v1.2: a Qwen3.5-4B decider that reads a state plus typed choice, noul and score questions in one pass, keeping most of the 27B model's Decision Index.
Jev Models & Reimplementations#community#open-models#multimodal
Community
mradermacher's static GGUF quants of Omartificial-Intelligence-Space/ara-jev-Faisal, an Arabic intent, policy and routing classifier; Q4_K_S and Q4_K_M are marked fast and recommended, under a non-commercial CC-BY-NC-4.0 licence.
Jev Models & Reimplementations#community#open-models#local
Community
Perplexity's 27B decision model rebuilt on Qwen3.8-27B with a joint request format that answers a whole request's questions in one sequence, reporting 68.09 on Decision Index 0.3 and 77.43 on vision.
Jev Models & Reimplementations#community#open-models#multimodal
Community
A Laya fine-tune from Convai Innovations on the 1,200-case typed-decisions benchmark: it reports 0.777 accuracy on the official 400-case test set, above TypeSafe Jev 1.13.0 at 0.727 and the teacher ceiling at 0.735.
Jev Models & Reimplementations#community#open-models#local
Community
A specialist full fine-tune of mistralai/Ministral-3-3B-Reasoning-2512 by rony for the LocalLLaMA/typed-decisions benchmark: it reads a case, question and lettered options and softmaxes next-token logits, reporting 0.808 accuracy and 0.0773 KL on all/test.
Jev Models & Reimplementations#community#open-models#local
Community
J33.AI's small System One decision model: an EmbeddingGemma 2 encoder plus a 0.59M scorer, 271.6M parameters total, reporting 75.2% accuracy and ECE 0.019 on 2,000 typed-decisions test decisions while deciding alone on 39.9%.
Jev Models & Reimplementations#community#open-models#calibration
Community
A 27B decision model from torchcast-ai that answers choice, noul and score questions together in one forward pass over the TypeSafe-compatible /v1/systemone format; the card reports Decision Index 0.2.1 of 65.00, +7.09 over Jev 1.13, at 33 ms per yes/no question.
Jev Models & Reimplementations#community#open-models#local
Community
FRIDA-Decisions answers Russian text with structured decisions in a single encoder pass (choice, ordinal score, yes/no or ranking) and scores 0.893 on the 735-item razvilka benchmark, effectively level with TypeSafe Jev's 0.897 (McNemar p = 0.84).
Jev Models & Reimplementations#community#open-models#russian
Community
The Ollama build of iapp's OpenThai-SystemOne: a 0.8B Thai and English decision model whose v0.3 weights are fine-tuned for Ollama's /v1/systemone letter readout, reporting a 74.3 macro across 13 public subsets for Q8_0.
Jev Models & Reimplementations#community#open-models#local
Community
A 27B decision model from StartLux Labs that takes text, JSON or images up to 262,144 tokens and returns a probability per option through the TypeSafe /v1/systemone format, reporting Decision Index 0.2.1 63.88 and 208 of 231 on JevBench public.
Jev Models & Reimplementations#community#open-models#local
Community
The Q8_0 8-bit GGUF build of StartLux-Decision-v1-27B, 28.60 GB for llama.cpp, served through a decision wrapper that the card measures at 100% same decisions as the original and 209 of 231 on JevBench public.
Jev Models & Reimplementations#community#open-models#quantized
Community
The Q8_0 8-bit GGUF build of StartLux-Decision-v1-9B, 9.53 GB for llama.cpp, with the card reporting 100.0% same decisions as the original weights and 200 of 231 correct (86.6%) on JevBench public.
Jev Models & Reimplementations#community#open-models#quantized
Community
InternLM's multimodal structured decision model, fine-tuned from Qwen/Qwen3.5-4B: a shared state, a schema of named questions and optional images go in, and every question returns an answer distribution from one forward pass. The card reports a 90.02 seven-suite average.
Jev Models & Reimplementations#community#open-models#multimodal
Community
InternLM's 0.8B multimodal structured decision model, fine-tuned from Qwen/Qwen3.5-0.8B: a state, named questions and optional images produce an answer distribution per question in one forward pass. Self-reported seven-suite average 79.38, Brier 0.530.
Jev Models & Reimplementations#community#open-models#multimodal
Community
InternLM's 2B multimodal structured decision model, fine-tuned from Qwen/Qwen3.5-2B: a state, named questions and optional images produce an answer distribution per question in one forward pass. Self-reported seven-suite average 84.68, Brier 0.437.
Jev Models & Reimplementations#community#open-models#multimodal
Community
Standard Thinking's merged BF16 3B checkpoint of Standard One: it scores choice, noul and score questions and returns probabilities through POST /v1/systemone with no generated text. The v2.2 card reports 100.00% JevBench public easy and 79.97% on many-option questions.
Jev Models & Reimplementations#community#open-models#multilingual
Community
The v2.2 PEFT LoRA adapter for Standard Thinking's Standard One 3B decision model, shipped with a merge recipe for Ministral-3-3B-Instruct; serving requires the merged checkpoint and the shared server code in the StandardOne-8B repository.
Jev Models & Reimplementations#community#open-models#local
Community
Fastino's 287M multilingual classification model in the GLiNER2.5 family: pass any label set at call time and get labels back in a single forward pass, with no prompt template and no generated tokens. It averages 56.7% exact-match on the Fast Decisions suite.
Jev Models & Reimplementations#community#open-models#local
Community
A Laravel-ready PHP SDK for Laya, a self-hosted multilingual decision engine described as a Jev AI alternative: classify text in 100+ languages and get typed enums, ints and bools back without paying per-token fees.
Jev Repos & SDKs#community#github#php
Community
marcreichel/laya-phpPHP
marcreichel/laya-phpPHPA proof-of-concept HTTP service for constrained decisions with a local MLX model: send text or JSON plus typed choice, score, or noul questions and it returns probabilities over the answers you declared; no generated text, no parsed model JSON.
Jev Tools & CLIs#community#github#python
Community
snellingio/system-onePython
snellingio/system-onePythonA Python library that turns Jev-style decision models into ordinary function calls: check returns a bool, classify picks one option, label picks every option that applies, and score rates on a scale, with probabilities behind every answer.
Jev Repos & SDKs#community#github#python
Community
jlowin/vibecheckPython
jlowin/vibecheckPythonA Chinese-language e-commerce customer-service demo on FastAPI, LangGraph and React: adaptive retrieval routing with hybrid BM25-plus-vector search, and intent classification switchable between TypeSafe Jev's structured choice interface and DeepSeek.
Jev Demos & Experiments#community#github#python
Community
gbh1234/Adaptive-ragPython
gbh1234/Adaptive-ragPythonA Bangla-focused adaptation of Laya, the non-autoregressive typed-decision model: targeted data preparation, fine-tuning and evaluation for classification, routing and scoring on Bangla, Banglish and code-switched text, with the checkpoint still to come.
Jev Repos & SDKs#community#github#python
Community
shamspias/laya-banglaPython
shamspias/laya-banglaPythonKorean-language Fastcampus seminar repo that teaches attaching Jev to a DeepAgents (LangGraph) shopping-mall support agent across 13 notebooks and a React sample app: tool selection, guardrails, risk gates and grounded RAG.
Jev Cookbooks#community#github#korean
A Chinese-language decision model that returns a probability for each fixed candidate option in one forward pass, with no generated text or chain of thought. It is a LoRA fine-tune of Qwen3.5-0.8B on 13,048,000 CJ-Dataset rows, benchmarked on CJ-Bench against Chinese-Jev.
Jev Models & Reimplementations#community#github#python
Community
yanqiangmiffy/chinese-decisionPython
yanqiangmiffy/chinese-decisionPythonThree open-weights 4B decision models for English (Deem-4B), Turkish (Karar-4B) and German (Wähler-4B) that choose between options and return a probability for each. The server implements the Jev API, POST /v1/systemone, so existing Jev clients work by changing the endpoint.
Jev Models & Reimplementations#community#github#python
Community
mertkayacs/jevaltPython
mertkayacs/jevaltPythonA TypeScript-first toolkit billed as the standard library for System One models: ready-made classify, extract, pickOne, rank and verify methods, a service-agent runtime that runs conversations turn by turn, and a one-message order parser. A Python port is in preview and the project is in alpha.
Jev Repos & SDKs#community#github#typescript
Community
Engid/question-kitTypeScript
Engid/question-kitTypeScriptA Python repository whose name and README heading promise customer support triage built with Jev. As of 2026-10-17 the repository has only a title README, but it is notable for its 6 forks against 4 stars.
Jev Demos & Experiments#community#github#python
A learning lab for decision models: TypeSafe Jev and Microsoft Decision-1 route support tickets in under a second for about $0.02 per 1,000 calls, while Claude handles the hard cases. It ships an interactive site, a benchmark, calibration tests and docs.
Jev Demos & Experiments#community#github#benchmark
A Kafka Connect sink connector that sends each input record to TypeSafe AI's Jev with questions you define, then publishes an Enriched Record holding the original message, Jev's answers and the evidence to trace and deduplicate each evaluation.
Jev Integrations & Adapters#community#github#kafka
Community
smatiolids/kafka-jev-connectorJava
smatiolids/kafka-jev-connectorJavaA LangGraph agent for the Forward Deployed Engineer role that classifies incoming support tickets with a Jev decision node, auto-closes basic issues to Tier 1 and sends technical ones to an LLM summarizer. The README notes Jev is mocked by a standard LLM in this demo.
Jev Agents & MCP#community#github#python
A benchmark that races Jev against a frontier chat model on the same job (routing 40 customer-support tickets into four categories) and reports time, tokens and cost per ticket, with an animated replay site and simulated demo results.
Jev Benchmarks & Evaluations#community#github#typescript
Community
stevenjb5M/JevTypeScript
stevenjb5M/JevTypeScriptA TypeScript starter for Jev called through the Vercel AI Gateway with the AI SDK. It wraps evaluate calls in an askJev helper and ships a support-ticket triage example using choice, score and boolean questions.
Jev Demos & Experiments#community#github#typescript
Community
1leetadmin/JEVTypeScript
1leetadmin/JEVTypeScriptA small FastAPI demo that triages customer support tickets with Jev as the decision layer: one request returns typed answers for department, urgency, refund intent and human-review need, while Python rules route and LangChain can draft a reply.
Jev Demos & Experiments#community#github#fastapi
A Quarkus proof of concept that dispatches support tickets with Jev: one call answers urgency as a noul, the best-placed team as a choice and severity as a score, and low-confidence tickets go to review instead of being assigned automatically.
Jev Demos & Experiments#community#github#java
Community
darkwings/jevvingJava
darkwings/jevvingJavaAn MIT-licensed case study with two working Python programs pairing Jev and Claude Code: a support-triage app with Jev inside it, and a Claude Code safety hook where Jev judges the agent's own shell commands. Tests and evals run offline, free.
Jev Practices & Patterns#community#github#python
A Next.js workbench for evaluating AI ticket triage with TypeSafe AI: classify, route and score support tickets through structured schemas, then inspect outputs, latency, token use and estimated cost in a three-column view.
Jev Demos & Experiments#community#github#typescript
Community
netflix2023/jevTypeScript
netflix2023/jevTypeScriptA Python web app that sends every support ticket to Jev (typesafe/jev-1.13) and to configured comparison models through OpenRouter, showing team routing, urgency, severity, sentiment and draft replies side by side with latency and cost.
Jev Demos & Experiments#community#github#python
Community
nikojim/jevPython
nikojim/jevPythonA two-app collection built with Jev models: a visual bug-triage demo that asks Jev to analyze incoming support issues and a GitHub pull-request review dashboard that evaluates changes against user-defined rules.
Jev Demos & Experiments#community#github#typescript
The Jev architecture rebuilt from Qwen3-0.6B: a calibrated, type-safe decision model on an open backbone. No decoding loop: the state is encoded once, questions are scored in one parallel pass, and answers are a distribution over options declared before the call.
Jev Models & Reimplementations#community#github#python
Community
sahasrarjn/system-onePython
sahasrarjn/system-onePythonA standalone proof of concept showing Jev as a structured decision layer for support-ticket routing: fixed-option routing, 1–5 urgency scoring and probabilities for sensitive data, with a deterministic confidence and safety policy deciding auto-route versus human review.
Jev Demos & Experiments#community#github#streamlit
A pilot build of the Parking Operations Decision Layer PRD: the support desk, agent guard and shared decision layer send each item to Jev as typed questions in one call, then rules and a confidence gate route it to AUTO, REVIEW, HUMAN or BLOCKED with an audit log.
Jev Demos & Experiments#community#github#typescript
Cloudflare's 9B multimodal decision model on Ollama: text, JSON, or images in one non-autoregressive forward pass, Apache-2.0, and the lowest measured latency of any decision model in Cloudflare's Decision Index runs at 38.8 ms median.
Jev Models & Reimplementations#community#open-models#local
Community
Convai Innovations' 421M non-autoregressive decision model on Ollama: fine-tuned from ModernBERT-large, Apache-2.0, answering one question in under 10 ms on an M5 Max with 0.060 in-task calibration error.
Jev Models & Reimplementations#community#open-models#local
Community
Bespoke Labs' 9B decision model on Ollama, fine-tuned from Qwen3.5-9B: Apache-2.0, up to 64 typed questions per call, answers in under 100 ms on a MacBook Pro with an M5 Max, and 75.7% mean accuracy across 13 human-labelled datasets.
Jev Models & Reimplementations#community#open-models#local
Community
Together AI's experimental decision family on Ollama, fine-tuned from Qwen3.5 at 4B and 0.8B: 93K downloads, up to 64 questions per call, an 88.0% development accuracy on 1,000 decisions, and MIT-licensed dataset builders and training scripts.
Jev Models & Reimplementations#community#open-models#local
Community
ActBro's 2B calibrated System 1 decision model on Ollama, built on OpenBMB's MiniCPM5-2B: 78.79% on JevBench among sub-2B open-weight models, 86.41% on the 10-source decision-v7 set, and one output token per decision via /v1/systemone.
Jev Models & Reimplementations#community#open-models#local
Community
The Laya family mirror on ModelScope: a 421M non-autoregressive System 1 decision model, Apache-2.0, with a pip-installable package, more than 100 languages claimed at about 33 ms per pass, and 16,713 downloads.
Jev Models & Reimplementations#community#open-models#local
Community
Start here with the Jev AI model
Featured entries for customer support.
Autonomous agent platform with 187,504 stars for building and running agents from plain English or a visual builder. Its platform features first-class TypeSafe Jev decision blocks for typed routing, filtering, scoring and confidence-gated next-action dispatching.
Jev Integrations & Adapters#community#autonomous-agents#platform
Community
Significant-Gravitas/AutoGPTPython
Significant-Gravitas/AutoGPTPythonA monorepo of roughly 20 latency-focused demos: moderation firehoses, rerankers, inbox triage, macOS launchers, and closed-loop control simulators, each with p50/p95 telemetry.
Jev Repos & SDKs#community#demos#typescript
Community
dabit3/jev-experimentsTypeScript
dabit3/jev-experimentsTypeScriptJev AI model articles
Original guides that go deeper on Jev customer support.
Voice agents need decisions in milliseconds. How turn detection, intent, and escalation work in real time, and where a fast decision layer fits.
Jev Practices & Patterns#voice#realtime#agents
Read article
Synthetic labels can train a decision model, but they can also bake in a teacher's errors. How to generate and audit them.
Jev Practices & Patterns#synthetic-data#training#distillation
Read article
Model deprecations and price changes are operational risks. How to pin versions, read contracts, and plan a migration before you need one.
Jev Guides & Articles#operations#vendor-risk#sla
Read article
From the Jev community
Watch walkthroughs, read the threads, and see what builders are posting.
YouTube: Ryan Vogel runs Jev on 100 and then 1,000 emails: category, priority, spam, and reply predictions.
YouTube: AICodeKing tests support routing, refund detection, prompt-injection resistance, and browser automation.
YouTube: Retriever AI tests Jev on real browser tasks: where it worked, where it struggled, and what comes next.
Hacker News: Pick Verify: a single decision at the warehouse pick station catches confusable picks.
Show HN: Verifying robot picks with a decision modelRead the thread on Hacker NewsHacker News: Decision Index 0.2.1 scores open reproductions of Jev on a frozen suite.
jackbravo4 pointsDecision Index 0.2.1 – 70 open reproductions of TypeSafe Jev's Decision ModelRead the thread on Hacker NewsHacker News: Software Bombe and live Enigma demo where Jev ranks cribs and judges plaintext.
ag_212 pointsShow HN: Jev as the Bletchley Park Analyst: A Software Bombe on EnigmaRead the thread on Hacker NewsHacker News: Show HN for the decide CLI: brew install, file context, and multi-question shell scripts.
vsekhar2 pointsShow HN: Decide – Jev decisions in the shell, scripts, and agent skillsRead the thread on Hacker News