Was this newsletter forwarded to you? Sign up to get it in your inbox.

Not every language model is destined to be a chatbot. Companies need fast, cheap, reliable AI workflows for processes that run in the background: deciding where to route a customer service request, checking that invoices match a purchase order, or classifying transactions as potential fraud.

Today, TypeSafe—a new AI lab—launched Jev, its new model designed to produce structured answers that your code can use directly. You pose questions in plain English—even fuzzy or subjective queries—and it returns probabilities for yes/no answers or any other categories you define.

Think of it as a smart if-then statement that determines what happens next when you’re automating a workflow. Say you’re building software that prioritizes customer service requests, and you write code that asks the model, “Does this customer sound angry?” Jev might answer 0.9, which means there’s an estimated 90 percent probability that the answer is yes based on what the model learned in training. You could also provide categories you define, like “annoyed,” “irritated,” “offended,” “furious,” and “enraged,” and learn that the customer was 60 percent likely to be classified as furious, with only a 10 percent probability of being enraged.

With an answer of 0.9 (very likely to be angry), the software might automatically proceed to escalate the customer concern to a manager, or if it answers 0.1 (not likely to be angry) that request might be deprioritized. However, chatbots are trained to respond with flowery text—like, “You’re absolutely right, this customer does sound very angry. Would you like me to compose a draft email response in a friendly, supportive tone?”—not numbers. This text response would cause the program you’re building to crash because it was expecting a number between 0 and 1, not an essay. Historically I’ve used DSPy, a Python framework for programming models, to coax LLMs into following instructions on what types of outputs to return, but Jev does all that work natively, which makes it insanely fast and cheap. Just how well it gets the job done is still an open question.

Want to sponsor Every? Click here.