Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Jev is TypeSafe AI’s first public System One Model, a class of AI designed to make fast, structured decisions that software can consume directly. Instead of generating arbitrary strings like a traditional large language model, Jev produces predefined type-safe values accompanied by calibrated probabilities and confidence estimates. Its architecture generates outputs in parallel rather than autoregressively producing one token at a time, allowing the model to prioritize speed and computational efficiency. TypeSafe trains Jev using Reinforcement Learning for Calibrated Decisions, an approach intended to optimize for accurate uncertainty estimates and consistent structured outputs. The model can be embedded into conventional software as an intelligent decision layer for classification, scoring, routing, extraction, branching, and other tasks where hand-written rules would be too rigid. Jev can also be used to judge, verify, guardrail, or detect problematic behavior in outputs from other AI systems. TypeSafe reports typical end-to-end response times between 70 and 500 milliseconds and positions the model for applications where low latency is important. The company also emphasizes schema guarantees, meaning Jev’s outputs are constrained to the structures defined by the application rather than requiring developers to parse and validate unrestricted generated text. Jev is aimed at developers and organizations building automation, real-time software, large-scale data workflows, and production systems that require dependable structured AI decisions.
Description
SubQ Snap is a comprehensive hosted multimodal decision-making framework aimed at developers who are creating AI agents and enterprise workflows. By utilizing Subquadratic’s API, applications can transmit both text and image data along with specific inquiries, receiving organized responses that may include labels, choices, scores, and probabilities. This functionality aids in various tasks, such as selecting tools and files, routing decisions, triaging information, implementing guardrails, ranking items, classifying data, and verifying whether a test or result aligns with predetermined criteria. Capable of processing up to 384K tokens of context and accommodating image inputs, the model is specifically crafted for making bounded decisions rather than generating open-ended text, making it an effective complement to language models used for planning or content creation. Furthermore, the public-preview API documentation provides detailed examples of requests, information on accessing the model, and various decision formats that users can leverage. Overall, this innovative approach streamlines the decision-making process for developers, enhancing the efficiency of AI-driven applications.
API Access
Has API
No
API Access
Has API
Yes
Integrations
OpenAI
No
Pricing Details
Input: $0.042 / 1M tokens
Input tokens: $0.042 / 1 million tokens ($42 per billion tokens).
Output tokens: FREE (too cheap to meter).
Output tokens: FREE (too cheap to meter).
Free Trial
No
Free Version
No
Pricing Details
$0.05/1M input tokens
$0.05 per 1 million input tokens; output tokens are free. SubQ Snap is in public preview, with access subject to capacity limits.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
TypeSafe AI
Founded
2024
Country
United States
Website
typesafe.ai/
Vendor Details
Company Name
Subquadratic
Founded
2025
Country
United States
Website
subq.ai/