Official Site In-Depth Architecture
TypeSafe AI Official Homepage →
The official platform introducing Jev, the pioneering System One decision model purpose-built for high-speed software decisions rather than chat generation.
System One Decision Engine · Ecosystem Directory
Jev AI Developer Directory
Curated, verified client SDKs, drop-in adapters, and architecture patterns for TypeSafe AI System One decision models.
Official Site Official Upstream Demo
TypeSafe AI Console & Web Playground
Official developer workspace for generating API keys, inspecting usage quotas, and interactively testing state questions inside the web playground.
Official Docs Official Upstream Demo
TypeSafe Official Documentation Portal
Comprehensive technical documentation covering the System One decision model, state schema design, calibrated probabilities, and API guides.
Official Docs Official Upstream Demo
TypeSafe Machine-Readable Index (llms.txt)
The standardized, curated llms.txt index mapping every documentation chapter, primitive specification, and pattern for automated AI agent retrieval.
Official GitHub In-Depth Architecture
Official TypeScript / JavaScript SDK (@typesafe-ai/sdk) →
Production-ready Node.js and browser SDK supporting choice(), score(), and noul() primitives with end-to-end type safety, retries, and strict error handling.
Official GitHub In-Depth Architecture
Official Python SDK (typesafe-sdk on PyPI) →
Official Python library featuring both synchronous and asynchronous clients (TypeSafeClient / AsyncTypeSafeClient) with native Pydantic validation.
Official GitHub In-Depth Architecture
System One Python Adapter (LLM Fallback) →
Open-source drop-in replacement for TypeSafeClient backed by standard LLM APIs, enabling offline development and staging environment simulation.
Official GitHub In-Depth Architecture
Official Agent Skills Repository →
Ready-to-use agent skill definitions and instructions for teaching AI coding assistants how to construct System One questions and parse responses.
Official Docs In-Depth Architecture
Architecture Pattern: Intent Routing →
Classify incoming user requests with low latency and route each to deterministic code, specialized LLMs, or human operators based on calibrated confidence.
Official Docs In-Depth Architecture
Architecture Pattern: Speculative Fan-out →
Send dozens of typed speculative questions in a single API call; host code inspects confidence scores to execute only high-certainty branch operations.
Official Docs In-Depth Architecture
Smart Home Assistant (Official Reference Demo) →
Verified official reference implementation demonstrating speculative fan-out across HVAC, lighting, and security sub-systems with video walkthrough.
System One Engineering
What Makes Jev Different from Generative LLMs?
Standard large language models generate sequential human tokens (System Two). Jev is TypeSafe’s flagship System One model engineered for instant, deterministic software decisions. By evaluating runtime state against typed questions (Choice, Score, Noul), Jev returns calibrated probabilities and confident decisions in milliseconds.