Jev turns LLM inputs into cheap, typed probability decisions
Jev gives teams a cheaper way to classify and rank text: send a document and questions to its API, then get probabilities, choices, or scores instead of generated prose. Its output is free, while input costs $0.042 per million tokens, and questions run in parallel. That suits spam detection, labeling, prioritization, and search reranking, though the model is currently weak with numbers, dates, and adversarial content.