By making quick, cheap judgments, Jev can route tasks to the appropriate model, select relevant skills from a library, or decide how much "reasoning effort" an LLM needs. This pre-processing step drastically reduces token consumption, cost, and latency for AI agents.

The new Jev model from TypeSafe is not an LLM competitor but a complementary tool. It outputs numbers and confidence scores for specialized tasks like classification, which can then feed into a conversational LLM for user interaction, creating a more efficient and accurate workflow.

Jev, a "judgment model," is for high-volume, low-stakes decisions like classification and rating. Unlike LLMs, it doesn't write or reason but provides fast, cheap "snap judgments," making it ideal for automating micro-decisions in workflows.

Unlike standard LLMs that generate text, Jev is optimized for making choices from predefined options (e.g., yes/no, 1-10 scale, pick from a list). This makes it a "System 1" model, ideal for high-speed classification, routing, and filtering tasks that serve as smart "if" statements within larger applications.

Traditional video models process an entire clip at once, causing delays. Descartes' Mirage model is autoregressive, predicting only the next frame based on the input stream and previously generated frames. This LLM-like approach is what enables its real-time, low-latency performance.

Fast and cheap judgment models like JEV can continuously check unstructured content (text, emails) against predefined rules, much like a code linter flags errors for software developers. This enables real-time quality control, style enforcement, and risk detection for all forms of business communication and documentation.

Jev processes requests in milliseconds for a fraction of a cent (e.g., 1,700 emails for 18 cents). This combination of speed and low cost makes it viable for high-volume, real-time applications like instant lead scoring or support ticket routing, which are often prohibitively expensive with large language models.

Judgment models like JEV make traditional ML techniques like classification and regression more accessible. Companies that currently use expensive LLMs for these tasks can now use a simpler, API-driven approach that is better suited for the job, without needing to build and host complex custom models from scratch.

Jev is a classifier AI that makes probabilistic decisions based on predefined choices (a schema). Unlike LLMs which generate text conversationally, Jev provides structured, type-safe output, making it an "AI decision maker" rather than a chat agent that you "ask" questions.