Skip to content

TypeSafe AI ships Jev, a decision layer to replace LLM calls

Short answer

TypeSafe AI launched Jev, a non-LLM decision model that answers yes/no, scoring or multiple-choice questions with a confidence level in 70-500ms, costing $0.042 per million input tokens with free output. It targets routing, classification and screening steps — like support ticket triage or CV screening — where LLM calls are too slow or expensive, though its accuracy sits at 67.8%.

What this means for operators

For a support or ops team that currently pays an LLM to tag ticket urgency, screen resumes or decide which model handles a request, Jev is a much cheaper intermediate step: it returns a structured score or choice with a confidence value instead of free text, so you can auto-route high-confidence cases and send low-confidence ones to a human. The catch is the 67.8% accuracy figure and the lack of any explanation for how it reached a decision, so it's a triage layer, not a replacement for judgment on anything with real downside if it's wrong — and adversarial inputs (a crafted support message, a doctored CV) can still hijack its reasoning without strong guardrails around it.

TypeSafe AI has launched Jev, a decision-making AI model the company frames as a "System One" model — fast and instinctive rather than slow and deliberate, borrowing the terms from Daniel Kahneman. Unlike a large language model, Jev doesn't generate text, can't chat, and according to TypeSafe cannot hallucinate in the structural sense: it always returns output in the same typed format, even if the content of that answer is wrong.

Jev works with three question types — Noul (yes/no, scored 0 to 1), Score (rated against a rubric), and Choice (pick one option from a set) — applied against a "state," whatever text you're asking about. Each answer comes with a confidence level. In a CV-screening example run by Zapier, Jev scored a candidate's LLM experience, technical depth and career-progression pattern, each with its own confidence score, from 78% to 100%.

On speed and cost, TypeSafe reports Jev returns a decision in 70-500ms regardless of how many questions are attached, at $0.042 per million input tokens with output priced at effectively zero. In a WikiRace demo navigating from the Wikipedia page for Baseball to Sun using only on-page links, Jev completed the task in 0.419 seconds for $0.047, versus Claude Sonnet 5 (3.724 seconds, $3.31), Claude Haiku 4.5 (4.975 seconds, $1.03) and GPT-5.6 Terra (9.453 seconds, $2.04 cents, with one hallucination).

Suggested uses include routing requests to a cheaper or stronger LLM before inference, screening support messages for topic, urgency and sentiment, labeling large batches of documents or proposals, checking AI tool outputs for harmful or low-quality calls, and gating actions in a network based on a safety judgment.

TypeSafe discloses Jev's accuracy at 67.8%. The model lacks multi-step reasoning, can't explain how it reached a conclusion, only processes text (audio, image and video must be converted to descriptions first), and remains vulnerable to adversarial prompts — an area TypeSafe says it is still working on.

Jev is available now through TypeSafe's API and playground, with $5 in free monthly credits. There's no native Zapier integration yet; Zapier's writeup describes wiring it in through API by Zapier and routing the output with Filter by Zapier.

Source: Zapier Blog · In the Atlas: Zapier →

The tools in this story

  • Claude - price, who uses it, alternatives
Next step

Visibility Analyzer

An AI news item will not tell you how assistants see your own site. The Visibility Analyzer checks that on your live site: which answers cite you, which pages an engine cannot retrieve, and what to fix first. Free to run.

Run a free visibility audit

Free to run. No card.

Fee
Free
Length
One run, minutes

Free tier: two analyses a day, no card required.