TypeSafe AI launched Jev, a structured decision model that returns probabilities instead of free-form text, claiming 70–500ms response times and significantly lower costs than large language models. The company raised $40 million in seed funding led by DCVC, though independent performance verification remains limited.
Jev is a specialized AI model that produces only structured output rather than free-form text, enabling it to generate responses in parallel rather than autoregressively. This architecture delivers dramatically faster latency (70-500ms versus seconds for standard LLMs) and enables new use cases like real-time video game playing, suggesting structured output could become a fundamental computational primitive for AI systems.
A new DuckDB extension enables users to classify rows in CSV, Parquet files, or DuckDB tables using Jev. It processes approximately 1,000 rows in 10 seconds, offering faster performance than LLMs and better ergonomics than traditional classifiers for data analysis workflows.
TypeSafe AI introduced Jev, a specialized 'System One' model designed for fast, bounded decisions in production systems. Unlike general-purpose LLMs, Jev outputs typed probabilistic decisions directly rather than generating text, achieving significantly lower latency and cost for operational tasks like incident routing and severity assessment.
A reverse-engineered model mimics Jev, TypeSafe's commercial system for selecting from multiple text options in a single pass. The repository includes implementations for Doom and chess games, with training and evaluation tools using attention-based scoring across option-context pairs.