Don't chat.
Decide.
Jev is the first System One Model — a new class of AI built for machines, not conversations. Unstructured state in, type-safe probabilistic decisions out. No tokens generated. No hallucinations. Just calibrated answers in 70–500 ms.
A new kind of intelligence
Jev gives up text generation to gain superpowers that LLMs cannot match.
Calibrated Confidence
Every answer ships with an epistemically honest probability. Higher confidence means higher accuracy — consistently.
Zero Hallucinations
Outputs are pre-defined type-safe values. The model cannot go off the rails because it never generates free-form text.
70–500ms Latency
Parallel sampling generates all outputs in a single query. 40–200× faster than frontier LLMs on System One tasks.
Type-Safe by Design
Output structures are defined in advance. Type errors are mathematically impossible.
RLCD Training
Reinforcement Learning for Calibrated Decisions — optimized for honest probabilities, not human chat preferences.
Software-Native
Think of Jev as a frontier-intelligence function call. Slot it into ordinary code as a smart if-statement.
LLMs vs Jev
Two orders of magnitude faster, cheaper, and more reliable on structured decisions.
From state to decision in 3 steps
Drop Jev into your code like a function call.
Define Your Output
Declare the type-safe structure of the decisions you need — classifications, scores, routes, or branches.
Send State
Pass unstructured context — a paragraph, a log, a user profile — as input. No prompt engineering required.
Get Calibrated Answers
Receive type-safe values with confidence scores in milliseconds. Compose them into any workflow.
Built for automation, not conversation
Wherever software needs a fast, reliable decision, Jev fits.
Fuzzy decision rules
Replace brittle if-chains with calibrated AI decisions.
Intelligent routing
Route tickets, leads, or requests with confidence scores.
Data extraction
Pull structured fields from unstructured text.
Quality scoring
Score any input against your own criteria.
Smart Workflows
Classify, route, score, extract, and branch where hand-written rules are too brittle.
Learn MoreMathematical guarantees, not promises
Jev's reliability comes from its architecture, not from guardrails bolted on top.
Zero Type Errors
Output structures are pre-defined. Type errors are mathematically impossible — not just unlikely.
No Hallucinations
Jev never generates free-form text. It cannot make things up because it doesn't generate strings.
Epistemically Honest
When Jev says 95% confident, it is right 95% of the time. Trained with RLCD for honest probabilities.
Consistent by Design
Similar inputs produce similar answers. Deterministic behavior your systems can depend on.
Built by a ChatGPT Co-Inventor
Diogo Almeida co-invented the methods behind ChatGPT — then spent 2 years building what comes next.
Transparent Pricing
$0.042 per million input tokens. Output tokens are free — too cheap to meter.
Certified & Compliant with
Frequently asked questions
Can't find what you're looking for? Reach out to our team.
How is Jev different from ChatGPT or other LLMs?
Jev is not a chatbot. It's a System One Model built for structured decisions. It never generates text — it returns type-safe values with calibrated probabilities. That makes it 40–200× faster, far cheaper, and immune to hallucinations.
What does "System One" mean?
Borrowed from psychology: System 1 is fast, intuitive thinking; System 2 is slow, deliberate reasoning. Jev automates the fast, repetitive decisions that software makes millions of times a day — leaving slow reasoning to humans or LLMs.
Can Jev replace my LLM?
No — and that's the point. Jev handles structured decisions (classify, route, score, extract). Your LLM still handles open-ended generation. They complement each other: LLM for chat, Jev for automation.
How does Jev avoid hallucinations?
Jev's output space is pre-defined as type-safe values. It never generates free-form strings, so it cannot produce information that doesn't fit the declared structure. Type errors are mathematically impossible.
What training method does Jev use?
RLCD (Reinforcement Learning for Calibrated Decisions) — a new training method optimized for epistemically honest probabilities rather than human chat preferences.
How much does it cost?
Input tokens cost $0.042 per million ($42 per billion). Output tokens are free. For the same intelligence level, Jev is two orders of magnitude cheaper than frontier LLMs on System One tasks.
Start building with Jev
Jev is currently in early access. Tell us about your use case and we'll get you set up.