The core idea behind Jev: Developers define questions and possible answers, and the model scores the options.

Sorting requests instead of answering customers

An example from the documentation shows what this could look like in practice. A customer writes to an online store saying they were charged twice for an order and asks for a refund on the second payment. Before anyone can handle the request, it has to be sorted. Is it about a payment, a delivery, or a return? Does the customer want money back, or just an explanation?

This is where developers connect the store software to Jev and define these questions along with the allowed answers. With each new message, the software hands the customer’s text to Jev. What it gets back isn’t a reply letter but, say, the label “payment issue” and a probability that the customer wants a refund.

The store software can then attach fixed rules. Payment issues go to accounting, refund requests get flagged. When the label is unclear, a staffer reviews the request. Jev provides the assessment, and the software decides what happens based on the programmed rules.

That doesn’t mean the money is back yet. Whether there really was a double charge has to be sorted out later against the actual transactions. A separate chatbot could tell the customer the confirmed status. For the sorting alone, it isn’t needed.

Sub-second response times could open up new uses

TypeSafe says Jev delivers answers in 70 to 500 milliseconds, many times faster than even the fastest current language models. To get there, the model skips step-by-step text generation and, the company says, computes several outputs in parallel. Adding more questions in the same call barely increases the response time.

That speed could allow a check before every response from an AI assistant. Does the draft contradict the conversation so far? Does it claim a refund that isn’t recorded in the customer’s account? TypeSafe describes checks like these in its example workflows. The company lists more possible uses in sales and customer service, like spotting buying intent, sorting requests by topic, or judging when a human should take over.

But regular language models can also output preset categories and data structures. OpenAI offers Structured Outputs for exactly this. A structured response format alone doesn’t set Jev apart from the competition. To win, the model would have to handle these tasks not just faster or cheaper, but with good enough quality.

The published performance tests have limits. TypeSafe compares four workflows it built itself and uses responses from other AI models as its reference, not independently verified correct solutions. GPT-6 Astra is also missing from the evaluations. The model is very cheap, though. TypeSafe lists a price of $0.042 per million input tokens. It says it charges nothing for outputs.

No hallucinations doesn’t mean no mistakes

TypeSafe markets Jev as a model that can’t hallucinate. But that guarantee only covers the allowed output structure. The model won’t produce an answer outside the preset options. A factually wrong choice within those options is still possible.

The model is meant to make many small judgments in the background more economical. Whether those judgments are reliable enough is something companies will have to test for their own tasks. Developers get access first through a waitlist.