TypeSafe AI

What is TypeSafe AI?

TypeSafe AI is a San Francisco AI lab that trains models for software instead of chat. Its first public model, Jev, takes a block of state plus typed questions and returns choices, scores and truth values with calibrated probabilities, so code can branch on the result without parsing text.

What can TypeSafe AI do?

• 01

  Typed decisions

  Returns choices, scores and truth values instead of generated text.

• 02

  Calibrated confidence

  Every answer carries a probability distribution and a confidence score.

• 03

  Three question primitives

  Choice, Score and Noul can be mixed in a single API call.

• 04

  Parallel evaluation

  All questions run at once against one shared state per request.

• 05

  Sub-second responses

  Published end-to-end latency of 70ms to 500ms per call.

• 06

  Token-based pricing

  $0.042 per million input tokens; output tokens are not billed.

• 07

  REST API and SDKs

  POST /v1/systemone with official Python and JavaScript clients.

Use Cases

• “AI automation software — Code keeps control of the flow while Jev handles the semantic judgements, so a workflow can run in the background millions of times without a person in the loop.
• “Search and retrieval — Teams replace or supplement embeddings in a RAG pipeline by scoring query-to-candidate relevance and reranking results with pairwise comparisons.
• “LLM guardrails — Semantic checks sit on every LLM input, output and tool call to catch jailbreaks, prompt injection and policy violations at a fraction of the LLM's own cost.
• “Recruiters and hiring managers — Resumes, applications and interview feedback are scored against explicit job-related criteria, and candidates are matched to roles and routed onward.
• “Scientific discovery — Researchers screen papers against inclusion and exclusion criteria for systematic reviews and check whether cited passages actually support a claim.