Decisions, Not Strings.
The Machine-Native Decision Engine.
Generative LLMs produce conversational text for humans. Jev ingests raw program state and returns typed, calibrated decisions with exact confidence probabilities in 70–500ms. Zero hallucinations, zero JSON parsing failures, and up to 97% lower operating costs.
$0.042 per million tokens
Parallel sampling overhead is unmetered
P50 ~114ms, 40x to 190x faster than LLMs
Mathematically guaranteed type boundaries
The Dual-System Paradigm: Why Agents Need Fast and Slow Brains
Inspired by Daniel Kahneman's Thinking, Fast and Slow, robust agent design separates instant perception and decision making from deliberate reasoning. Monolithic LLMs are overkill for routine decisions, while Jev provides the missing machine-native operational layer.
Decision Engine & Gating
Evaluates program state, inspects tool parameters, filters incoming streams, scores relevance, and validates safety constraints. Runs with calibrated probabilities in under 150ms.
- ✓ Parallel option sampling
- ✓ Exact confidence estimates (0.0 to 1.0)
- ✓ Strict typed choices (Enum, Score, Boolean)
- ✓ Zero token hallucinations
Deliberate Synthesis & Generation
Invoked only when Jev confidence is below threshold or when freeform output is required. Handles long-form writing, complex code generation, multi-hop architectural refactoring, and user chats.
- ✓ OpenAI GPT-4o / GPT-5
- ✓ Anthropic Claude 3.5 Sonnet / Opus
- ✓ DeepSeek V3 / R1
- ✓ Google Gemini 1.5 / 2.0
Frontier LLM Integrations with Jev
Select an integration below for architectural guides, benchmarks, and code snippets.
Jev + OpenAI GPT-4o / GPT-5
Dual-Brain Agent Routing and 97% Cost Reduction on High-Frequency Decisions
Jev + Anthropic Claude 3.5 Sonnet / Opus
Zero-Hallucination Tool Selection, Terminal Agent Shielding, and Cost-Effective Evaluation
Jev + DeepSeek V3 / R1
High-Throughput Content Triage at 780 Decisions Per Second with Zero Token Waste
Jev + Google Gemini 1.5 / 2.0
Multimodal State Embedding, Live Search Grounding, and Real-Time Web Routing
Jev + xAI Grok 2 / Grok 3
Real-Time Social Stream Triage and High-Velocity Sentiment Filtering
Jev + Moonshot Kimi & Open Weights (Qwen / Llama)
Edge Deployment, Local GGUF Decision Engines, and Hybrid Privacy Architectures
Verified Community Insights & Real-World Tests
Real posts and benchmarks sourced directly from developers testing Jev with frontier models.
"Astra + Jev cooperation defeated the Ender Dragon in 8 minutes and 43 seconds for under $1 total cost. Previously, GPT-6 Astra playing Minecraft alone took 141 hours, got wiped by creepers, and farmed potatoes for hours. This is the new agent standard: two distinct brains working together."
Context: Agent benchmark comparing monolithic GPT-6 Astra against a dual-brain architecture with Jev acting as the real-time action selector.
"If Jev makes high-frequency decisions this fast and cheap, the standard production pipeline is clear: Jev handles filtering, classification, and routing. Only truly ambiguous problems get handed to frontier LLMs like GPT-5.6 SOL."
Context: Architecture breakdown on reducing enterprise LLM operational costs by 95% using Jev as a deterministic ingress gateway.
"423 news items across 15 desks given to Jev and DeepSeek for the same filtering criteria. Jev does not output paragraphs; it outputs structured decisions directly. Speed: 780 judgments/sec with P50 latency of 476ms. When Jev finished, DeepSeek had completed only 14% of the batch."
Context: Independent side-by-side throughput benchmark evaluating high-volume automated editorial triage.
"Paired Jev as the decision maker and router with Gemini web search, Exa, and parallel pipelines to decide and structure final responses. Extremely reliable for autonomous workflows."
Context: Production implementation combining Google Gemini web retrieval grounding with Jev calibrated decision gating.
Understanding System One Decision Infrastructure
Answers to key architectural and operational questions.
Q1.What is a System One Model and what is Jev?
A System One Model is a new class of AI model designed for autonomous decisions inside software, inspired by Daniel Kahneman's concept of fast intuitive thought. Jev is TypeSafe AI's flagship System One model, evaluating unstructured program state to return typed decisions with calibrated probabilities in 70 to 500 milliseconds.
Q2.Why are Jev output tokens free?
Unlike traditional autoregressive language models that generate text one token at a time in sequence, Jev evaluates all possible output options in parallel within a single query pass. Because output sampling incurs virtually zero additional compute overhead, TypeSafe provides output tokens free of charge.
Q3.How does Jev eliminate hallucinations?
Jev does not generate freeform text strings. Its output schema (such as boolean flags, finite choice enums, or normalized confidence scores) is constrained in advance. Schema validation is mathematically guaranteed, eliminating JSON parsing errors and hallucinatory format deviations.
Q4.How does Jev pair with frontier LLMs like GPT and Claude?
In a dual-system architecture, Jev functions as the high-frequency decision engine and gatekeeper (System 1). It handles state classification, tool parameter checks, and routing. When a decision falls below the confidence threshold or requires open-ended prose generation, the agent escalates to deliberate reasoning models like GPT-4o or Claude 3.5 (System 2).
Ready to explore dual-system architecture in your codebase?