Jev AI doesn't generate text. Give it an unstructured state and a set of typed questions, and it returns type-safe structured decisions with calibrated probabilities — answers your software can trust directly.
Traditional LLMs generate strings token by token, leaving your code to parse, validate, and constantly worry about hallucinations. Jev AI takes a different path: it abandons string generation and outputs answers that strictly conform to your predefined type schema — each with a calibrated confidence score, all in parallel.
An email, a log line, a support ticket, a chunk of JSON — any unstructured program state can be used directly as input.
Describe the judgments you need as typed questions: enum choices, yes/no probabilities, numeric scores. You define the output structure up front.
A single query returns every answer in parallel: type-safe structured values plus calibrated probabilities and confidence, ready for your code to consume.
import typesafe client = typesafe.Client(api_key="jev_...") decision = client.decide( model="jev-1", state=support_ticket_text, # unstructured state questions={ "intent": ["refund", "bug", "billing", "other"], "needs_human": bool, "urgency": ("score", 1, 5), }, ) # → {"intent": ("refund", p=0.94), "needs_human": (false, p=0.88), # "urgency": (4, confidence=0.91)} · ~120ms end to end
Jev AI is a new class of frontier model — a System One Model, inspired by Kahneman's Thinking, Fast and Slow: fast, intuitive System 1 thinking. It doesn't chat; it makes fast, structured decisions that software can use directly.
| Today's LLMs | System One · Jev | |
|---|---|---|
| Output | Free-form text that needs parsing and validation, with a constant risk of going off the rails | Type-safe structured values — type errors are mathematically impossible |
| Sampling | Sequential token-by-token generation | All results in parallel from a single query, hardware-friendly |
| Training | RLHF / RLVR (human preference, verifiable rewards) | RLCD — reinforcement learning for calibrated decisions |
| Speed | 3–329 seconds end to end | 70–500ms end to end — 40–200× faster at equal intelligence |
| Cost | $0.20–$10 / MTok input, output ~5× the input price | $0.042 / MTok input, output free (too cheap to meter) |
| Confidence | Tends to be overconfident and unstable | Every output carries a calibrated probability: higher confidence means higher accuracy |
| Hallucination | Even the smartest models hallucinate | Cannot hallucinate — the output space is strictly bounded before the call |
Millisecond responses and deterministic outputs let Jev AI live, for the first time, inside systems with hard latency and reliability requirements — just like ordinary code.
Use Jev as a smart if statement: classify, route, score, extract, branch — wherever hand-written rules are too brittle, hand the decision over to calibrated probabilities.
100ms-class responses mean AI can power latency-sensitive UX. In the official demo, Jev plays Doom in real time at 10 queries per second.
Map-reduce over massive datasets: turn petabytes of unstructured data into features and insights, with input costs as low as $42 per billion tokens.
Verify everything: score, judge, validate, and jailbreak-detect LLM prompts, reasoning chains, and outputs — a deterministic brake for generative systems.
Tell us which decisions you want to automate with Jev AI — early access is open now.
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