Use casesClassification
Classification builds lean on Jev Choice, Score and gate-style judgments so apps can accept, reject, label or pick options with typed confidence.
This project introduces a novel approach to Retrieval Augmented Generation (RAG) that replaces traditional embedding methods with a structured, table-of-contents-based navigation system, significantly improving speed and reducing costs by offloading non-generative tasks to Jev.
This project is a PostgreSQL extension that allows SQL queries to directly call TypeSafe AI's Jev for classifying rows within a database table, avoiding the need to export data for external processing.
Jev is what happens when you stop forcing AI to talk. It’s the first public “System One” model from TypeSafe AI, built for fast decisions inside software—not conversations. You give it: • Unstructured state: text,
Jev is a decision-making AI model that provides typed answers with probabilities and confidence scores, trained using RLCD for reliable probability interpretation, and is significantly faster and cheaper than traditional LLMs for specific tasks.
Jev acts as a judgment layer in the development loop, sitting between the developer's assessment of completion and handover to a maintainer, identifying potential issues and inconsistencies.
This project benchmarks the performance of Jev, an AI model that takes a state and typed questions to return probabilities over defined options, comparing its 'pure' and 'hybrid' configurations against heuristic and random strategies in a Battleship game context.
This project rebuilt an AI knowledge graph into a news feed using a decision model for classification, resulting in a significantly cheaper and faster system.
Jev intelligently selects applicable rules for Claude Code based on prompts and file paths, optimizing context token usage and ensuring relevant rule delivery.
This project tested the Jev model's ability to accurately identify specific lines of code that answer questions, comparing its performance and cost against other models like Claude Opus and Haiku, as well as the grep tool.
Jev is a new component integrated into Werkfaden that provides typed probabilistic decisions about project state, rather than generating text.
A bot plays a puzzle game by querying an AI model for tap probabilities before each move, utilizing live API calls for decision-making.
This project integrates TypeSafe AI into a Mac app to provide setup and troubleshooting assistance by reading user questions and referencing the built-in manual to identify relevant documentation or status.
This application uses Jev to determine the optimal landing or docking location for aircraft and ships experiencing emergencies, based on a variety of real-time data and predefined criteria.
Jev is an independent index that automates bookkeeping by performing actions on a desktop accounting application based on a decision log and a dynamically rewritten playbook.
You give it a page and a list of questions, and it sends back a yes, a no or a score for each one, with a probability. 25 judgments about a page take about a third of a second.
