Describe your AI workflow in plain English. See which steps fit Jev, an LLM, or ordinary code — then generate an implementation-ready Decision Spec for it.
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Write your AI workflow in plain English. No API keys, no setup — just describe what you want to build.
The analyzer maps your task to a workflow graph, showing which steps fit Jev, which need an LLM, and which are ordinary code.
Each Jev node comes with an editable Decision Spec and implementation-ready code in TypeScript, Python, and cURL.
Jev is a model from TypeSafe AI that returns structured answers — a Choice, a Noul, or a Score — instead of free-form text. It handles routing, gating, and scoring deterministically, with lower latency and cost than an LLM.
It does not write essays or translate languages. It decides.
When you need to pick from a known set of options — teams, categories, priorities — Jev Choice returns a deterministic answer.
When you need to check whether something passes a threshold — escalate, approve, flag — Jev Noul gives you a typed yes/no.
When you need to rate something against a rubric — satisfaction, relevance, risk — Jev Score returns structured numbers.
Jev does not write essays, translate text, or summarize documents. Those tasks still need an LLM. The analyzer tells you which is which.
Pick one option from a fixed set. Replaces LLM routing with a deterministic, typed decision.
Answer a single yes/no question based on weighted criteria. Lower latency than an LLM for binary gates.
Rate a state against a defined rubric with multiple dimensions. Returns a structured numeric result.
Route tickets, check escalation, and score satisfaction — all with Jev primitives instead of LLM calls.
Detect policy violations (yes/no) and decide on an action (remove, warn, approve) with deterministic primitives.
Score the applicant, decide approve/deny/review, and calculate the interest rate — Jev for decisions, code for math.
Paste an existing LLM prompt and see how it could be restructured with Jev primitives.
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See pricing →No. Jev is a System One model for bounded, typed decisions. It returns a structured answer — a Choice, a Noul, or a Score — instead of generating free-form text, the way an LLM does.
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