Jev AI: The Model That Cannot Talk
TypeSafe's first model does not write prose or code. It returns a typed decision with calibrated confidence in under half a second — and costs $42 per billion tokens, not per million.
70–500ms
Response Latency
$42
Per Billion Tokens
20–200×
Faster Than LLMs
$0
Output Token Cost
What Jev AI actually does
Every model you have used before Jev AI answers a question by writing. It produces tokens, one after another, and whatever meaning you need has to be parsed back out of that text. Jev AI went the other way. It takes a question and a schema you define, and hands back a single typed value that already fits the shape your code expects — no parsing, no retries on malformed JSON, no prompt engineering to coax the format into line.
TypeSafe calls Jev AI a System One model, and the whole design follows from that one decision. Because the answer is a value rather than a sequence, generation happens in parallel instead of token by token, which is where the sub-second latency comes from. Because the value must live inside your schema, Jev AI cannot invent something that is not there. And because Jev AI output is tiny, TypeSafe does not charge for output at all.
Jev AI cannot generate text at all
Not a limitation that was switched off — Jev AI was never trained to produce prose or code. It returns a typed value: one option from a list, a position on a scale, or a probability. That is the entire output surface.
Jev AI cannot hallucinate
Because output is constrained to your schema, Jev AI cannot invent a value that does not exist in the options you defined. It can still return the wrong valid answer — but never a fabricated one.
Jev AI is priced per billion, not per million
Jev AI charges $0.042 per million input tokens, which works out to $42 per billion. Output tokens are not metered at all — the founder's phrase is that they are too cheap to meter.
Jev AI was built by a ChatGPT co-inventor
Diogo Almeida helped build the methods behind ChatGPT at OpenAI, then spent two years in stealth on a different question: why superhuman chat never produced superhuman automation.
Jev AI samples in parallel, not token-by-token
Traditional models emit one token at a time. Jev AI generates all outputs in a single query through a parallel sampler, which is where the 70–500ms latency and the speed multiple come from.
Calibrated confidence on every answer
Every Jev AI response includes an honest probability, trained through RLCD. You set a confidence floor in your own code and decide what threshold justifies acting without human review.
The three question types
The entire Jev AI API surface is three shapes. You pick one, declare its parameters, and that is the contract. Anything you ask has to be expressed as one of them — which sounds restrictive until you notice how much production automation is exactly this and nothing more.
| Type | Returns | Typical use |
|---|---|---|
| Choice | One option from up to 255 | Routing, classification, triage, picking a category or an action |
| Score | A position on a 2 to 10 level scale | Severity ranking, quality grading, priority assignment |
| Noul | A yes/no probability | Binary gates, eligibility checks, policy and safety decisions |
Each Jev AI response also carries a calibrated confidence figure. That number is not decoration — it is the product of the training method, and it is what lets you decide in code which answers are safe to act on automatically and which should go to a person.
Where Jev AI fits in a real system
The useful framing is not "does Jev AI replace my language model" but "which parts of my pipeline are decisions rather than writing". Those parts are usually the high-volume ones, and they are usually the ones where per-million pricing made blanket coverage unaffordable.
Support ticket routing
A Jev AI Choice query picks the right queue from up to 255 options in under half a second, at a cost where routing every ticket is cheaper than a single human glance.
Content moderation gates
A Jev AI Noul query returns the probability that a submission violates policy. Set a confidence floor, auto-approve above it, and route the ambiguous middle to a human.
Lead and document scoring
A Score query grades on a 2–10 scale. Because pricing is per billion tokens, scoring an entire database in one pass stops being a budget decision.
Agent step validation
Before an agent acts, a Noul query checks whether the step is safe or in policy. At 70–500ms the check is cheap enough to run on every step.
Data pipeline classification
Structured classification at ingest time rather than in batch, since latency is low enough to sit inline in a streaming pipeline.
Eligibility and policy checks
Rules too fuzzy for if-statements but too high-volume for an LLM. Jev AI confidence lets you encode risk tolerance as a number in your own code.
What Jev AI will not do
Jev AI will not write your code. It will not draft an email, summarise a document, answer a customer in prose, or hold a conversation. Those are not Jev AI features waiting in a future version — text generation is absent from the architecture, and adding it would undo the speed and the pricing that make the thing interesting in the first place.
Jev AI also will not save you from being wrong. Constrained output means fabrication is impossible, but Jev AI can still pick the wrong option from a list you gave it. The difference matters: a wrong choice is something you can measure, threshold against, and improve. A hallucinated one is not.
And the questions have to fit Jev AI. A Choice tops out at 255 options. A Score runs on a scale of two to ten levels. If your problem genuinely needs a free-form answer, Jev AI is the wrong tool, no amount of schema design will squeeze it into those shapes, and you should reach for a language model instead.