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What is Merrymake?

Merrymake, located in Aarhus, Denmark, serves as a European think tank and research laboratory focused on artificial intelligence. Our goal is to promote a secure and independent shift towards Artificial Superintelligence (ASI). The primary aim of our organization is to make sure that advancements in superhuman intelligence positively impact all forms of life, humanity as a whole, and each individual person. Our team operates at the cutting edge of technology, treating both the development of AI capabilities and the alignment of those capabilities as interconnected engineering challenges that can be addressed. Our research encompasses the entire spectrum of advanced AI development. We are committed to crafting a future that is truly worthwhile, where technological progress enhances human abilities while consistently upholding values such as well-being, dignity, and individual autonomy. By envisioning this tomorrow, we strive to create a world that empowers everyone to thrive.

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.

Media

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Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided

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).

Supported Platforms

Not specified

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Not specified

Training Options

Documentation Hub

Company Facts

Organization Name

Merrymake

Date Founded

2021

Company Location

Denmark

Company Website

merrymake.eu

Company Facts

Organization Name

TypeSafe AI

Date Founded

2024

Company Location

United States

Company Website

typesafe.ai/

Categories and Features

AI Models

Not specified

Categories and Features

AI Decision Models

Not specified

AI Models

Not specified

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