TypeSafe
TypeSafe AI and decision models
TypeSafe builds decision models: models that answer typed questions with probabilities instead of producing text. Its first model, Jev, is available on OpenRouter.
The idea is borrowed from the distinction between System One (fast, intuitive judgment) and System Two (slow, deliberate reasoning). Most operational decisions in software are System One problems, yet teams often route them through large System Two models.
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Why decision models exist
Language models are general, but that generality has a cost. To classify a ticket with an LLM you write a prompt, ask for JSON, validate the output, handle refusals and retries, and pay for output tokens you don't really need.
A decision model skips all of that. You declare the possible answers up front and receive a probability for each. The output is always well-formed, and the probabilities let you set thresholds and route uncertain cases to people or larger models.
Decision model vs. LLM
A practical way to decide which to use:
- Use a decision model when the answer is one of a known set: a category, yes/no, or a level on a scale.
- Use an LLM when you need generated text, extraction of arbitrary fields, multi-step reasoning or tool use.
- Combine them: let the decision model triage and gate, and send only the cases that need it to the LLM.
Where ClassifierHub fits
ClassifierHub turns TypeSafe's Jev into a product for teams: templates for email, leads, support, contact forms, documents and agent guardrails, plus an API, MCP server and integrations. It is provider-agnostic, so the decisions you define stay the same as more models become available.
ClassifierHub is an independent product and is not affiliated with TypeSafe or OpenRouter.
Frequently asked questions
Related guides
Last updated 2026-09-25. ClassifierHub is an independent product built on top of the Jev decision model, accessed through OpenRouter. It is not affiliated with or endorsed by TypeSafe or OpenRouter.