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Jev, System One and AI routing, explained
Practical guides and honest comparisons to decide whether Jev belongs in your workflows, and how to put it there.
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What is Jev, the decision model from TypeSafe AI?
Jev by TypeSafe AI returns typed decisions with probabilities and confidence. Use cases, limits, pricing, versions, languages and data handling.
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System One models: what System 1 and System 2 mean for AI
What TypeSafe calls a System One model, the Kahneman analogy and its limits, calibration, and when you still need a generative LLM.
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Jev vs LLM: which one should route and classify?
Jev or an LLM (Claude, GPT, Gemini) as a router or classifier? Cost per decision worked out, latency, output format, confidence, hybrid setup.
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Jev vs other text classifiers: rules, fine-tuning, zero-shot and more
Text classification without training data? Rules, fine-tuned BERT, embeddings, zero-shot NLI and LLM routers compared fairly with Jev.
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Jev in n8n: route tickets with a confidence threshold
Call Jev by TypeSafe AI from n8n with the HTTP Request node, route on confidence, fall back to your LLM. Tested workflow to import.
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Jev in LangChain: a LangGraph router with a confidence threshold
Replace an LLM classifier with Jev in LangGraph: a Jev node, a conditional edge on confidence, a fallback to your current LLM. Tested code.
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Jev in CrewAI: a Flow @router with a confidence threshold
Route tickets in a CrewAI Flow with Jev: the @router picks the branch on confidence, and your current LLM takes over otherwise. Tested code.
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Jev in Temporal: an activity, a retry policy and a deterministic workflow
Call Jev from a Temporal workflow in Python: an activity with a retry policy for 429 and 529, a branch on confidence, a fallback to your LLM.