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.
Jev processes tasks up to 400x cheaper than LLMs, with costs as low as cents for thousands of complex queries. This economic shift makes it feasible to analyze entire archives (emails, ads) for deep insights, a task previously too expensive or time-consuming.
Instead of A/B testing on live audiences, marketers use Jev to simulate how different buyer archetypes (e.g., "gym owner") would react to thousands of ads. This provides a cheap, rapid way to generate hypotheses and refine campaign angles before launch.
Jev can audit an entire website for internal linking opportunities by treating it as a large-scale classification problem. For every pair of pages, it answers a simple question: "Does this page have a real reason to link to that one?" This is far cheaper and faster than using a full LLM.
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.
Unlike prompting an LLM with a complex request, using Jev effectively requires a mental shift. You must break down a large judgment (e.g., "is this a good lead?") into its constituent, simple questions (industry fit? company size? intent?) and run them in parallel.