EN
Don't chat.
Decide.
Jev turns context into structured decisions for classification, routing and scoring. Version 1.14 brings model improvements, with experimental image input, Box outputs and long context.
Structured decisions for your code
Define outputs, provide context and use the results in your workflow.
Defined outputs
Return values in the structure your application expects.
Confidence scores
Use confidence scores to help route decisions for review.
Code integration
Use classification and scoring results in ordinary application code.
Image input · experimental
Use PNG or JPEG images as context in Jev 1.13 and 1.14.
Box · experimental
Jev 1.14 can return a bounding box and confidence score for an image target.
Long context · experimental
Opt in to a 262k-token limit in 1.13 or 1.14; longer requests may be slower and less accurate.
Choose a model for the task
Compare how each approach fits your workflow.
From state to decision in 3 steps
Drop Jev into your code like a function call.
Define Your Output
Return values in the structure your application expects.
Send State
Define outputs, provide context and use the results in your workflow.
Get Calibrated Answers
Use classification and scoring results in ordinary application code.
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 MoreWorking with Jev
Build workflows around defined outputs and confidence scores.
Defined outputs
Return values in the structure your application expects.
Confidence scores
Use confidence scores to help route decisions for review.
Code integration
Use classification and scoring results in ordinary application code.
Price shown on this site
The price shown on this site is $0.084 per million input tokens, with free output.
Certified & Compliant with
Frequently asked questions
Can't find what you're looking for? Reach out to our team.
What changed in Jev 1.14?
The release brings broad model improvements, especially for multilingual tasks. Image input and long context are experimental in both 1.13 and 1.14; Box requires 1.14.
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.
Does a structured output guarantee a correct decision?
The output follows a defined structure. The decision can still be wrong; confidence scores help you decide how to handle it.
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?
The price shown on this site is $0.084 per million input tokens, with free output.
Start building with Jev
Jev is currently in early access. Tell us about your use case and we'll get you set up.