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What is Jev?

Jev is a low-latency System One Model from TypeSafe AI built for structured decision-making rather than open-ended text generation. TypeSafe describes System One Models as a new class of frontier models intended to make fast decisions that can be consumed directly by application code. Jev takes unstructured information as input and returns predefined typed outputs with probabilities and confidence scores instead of generating unrestricted natural-language strings. Its parallel sampling architecture produces outputs in a single query rather than sequentially generating one token at a time, which is designed to reduce latency and inference cost. The model is trained with Reinforcement Learning for Calibrated Decisions, which focuses on producing well-calibrated probabilities for structured System One tasks. TypeSafe positions Jev as a software-native intelligence layer for tasks including classification, routing, scoring, extraction, decision branching, and other workflows that benefit from probabilistic logic. It can also be applied to model evaluation and safety workflows by scoring, judging, verifying, guardrailing, or detecting jailbreaks in prompts, reasoning traces, and generated outputs. The company reports service response times of roughly 70 to 500 milliseconds and says this can make Jev suitable for interactive and real-time applications where traditional frontier models may introduce too much latency. Jev is designed to return consistent schemas without type errors, allowing its results to be incorporated into code without the same parsing and validation steps commonly required for free-form LLM responses. TypeSafe also highlights map-reduce style processing over large datasets as a potential use case, where the model can turn large amounts of unstructured information into structured features and insights.

What is Hypertune?

Hypertune is a remarkably flexible platform that specializes in the management of feature flags, A/B testing, analytics, and application configuration. It boasts a robust architecture that ensures end-to-end type safety and incorporates a version control system inspired by Git, along with the ability to conduct local, synchronous, in-memory evaluations of flags. You can create type-safe, customized input parameters like the current User or Organization to refine feature flag rules, which helps in precisely targeting the intended audience. Additionally, the platform supports the development of reusable variables, such as user segments, which can be applied across different feature flags, thereby simplifying the debugging process for specific users. Hypertune offers an array of options including A/B testing, percentage-based rollouts, multivariate tests, and machine learning loops, enabling a seamless process for rolling out, testing, and optimizing new features. You also have the capability to log analytics events using type-safe custom payloads and can generate dynamic funnels and charts on the dashboard for evaluating the impact of each feature release. Furthermore, the SDK allows for initialization with only the essential feature flags, permitting partial evaluations of flag logic at the edge, which significantly boosts both performance and security. This diverse set of features positions Hypertune as an exceptional tool for developers who are eager to innovate and enhance their applications, ensuring they can adapt swiftly to changing requirements. With its user-friendly interface and powerful functionality, Hypertune empowers teams to make informed decisions based on real-time data.

Media

Media

No images available

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Input: $0.042 / 1M tokens
Input tokens: $0.042 / 1 million tokens ($42 per billion tokens).

Output tokens: FREE (too cheap to meter).

Pricing Information

$0
Free Version
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training
On-Site Training

Company Facts

Organization Name

TypeSafe AI

Date Founded

2024

Company Location

United States

Company Website

typesafe.ai/

Company Facts

Organization Name

Hypertune

Company Location

United Kingdom

Company Website

www.hypertune.com

Categories and Features

AI Decision Models

Not specified

AI Models

Not specified

Categories and Features

Feature Management

Not specified

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