AI Jev for clear decisions.

System One AI focuses on fast, bounded decisions. The Jev model evaluates supplied context against defined outcomes and returns probabilities. System Two reasoning focuses on more deliberate, multi-step problem solving. In iajev, you provide a question and choices to explore a probabilistic decision model without writing code.

The Jev AI playground offers a limited anonymous free preview. Start with an example, enter your context, and run a decision. Keep the question and choices unchanged while editing the context to compare probabilities across two runs. Signed-in usage and API requests use paid credits; this is not an unlimited free Jev API.

Choice selects one of your candidate answers. Jev Score describes a level on a scale, while Noul represents a yes-or-no decision. iajev currently exposes Choice only. For a yes-or-no task, use two text choices; the response remains a choice, not a boolean. The lead qualification tool returns categories rather than a numeric score.

Create an iajev API key in account settings and add credits. Send your context, question, and choices to the decisions endpoint with a Bearer token and a unique Idempotency-Key. The cURL example above is a starting point; the API docs cover request fields, responses, billing, and retry behavior. Keep your key on the server.

Yes. The iajev API accepts HTTP requests with JSON, so you can adapt the cURL example to Python or use fetch in server-side JavaScript and TypeScript, including Node.js. Go, Java, and Rust applications can use an HTTP client too. No dedicated Jev SDK is required to call this endpoint.

Call the iajev API from a server route, such as a Next.js backend, and use the returned choice to select the next action. For LangChain, LlamaIndex, Pydantic AI, Vercel AI SDK, or an MCP-based agent, you would write a custom tool or HTTP adapter. iajev does not provide native connectors for these frameworks.

iajev API pricing uses credits: each started 1,000 input tokens costs 1 credit, with a minimum of 1 credit per successful decision. Output tokens do not add a credit charge. The price per credit depends on your subscription or credit pack. See the Pricing page for current plans; use input length and expected request volume to estimate your Jev API cost.

For a Jev vs ChatGPT, GPT, Claude, Gemini, or DeepSeek comparison, start with your task. Chat and reasoning models support open-ended generation; Jev focuses on typed decisions over defined outcomes. Compare alternatives on the same labeled examples, measuring accuracy, latency, and cost. iajev does not publish a head-to-head benchmark for these models.

JSON mode and structured output constrain response format; function calling lets a model request a tool invocation. Jev focuses on decisions with probabilities over specified outcomes. In iajev, those results arrive as JSON. A valid schema alone does not establish whether a decision is correct or its probabilities are well calibrated.

RLCD stands for Reinforcement Learning for Calibrated Decisions, the training approach introduced with Jev. Its goal is to align decision probabilities with correctness. RLHF uses human feedback, while RLVR uses verifiable rewards. These describe different training objectives; iajev lets you inspect decision outputs, not train or fine-tune the model.

Build a Jev benchmark from labeled examples in your own workflow. Measure accuracy, latency, and reliability, then check probability calibration across many cases. The API returns request latency and a separate AI confidence score. One high-confidence answer does not prove calibration, and structured output does not eliminate incorrect decisions or every risk described as AI hallucination.

You can define choices such as approve, reject, or review for verification and content moderation, or low, medium, and high for risk triage. These are custom choice tasks, not dedicated scoring or boolean endpoints. Test them against your own policy and keep human review for consequential or uncertain cases before adding business automation.