JevStation

Run Jev, Clef, Perplexity Decider and OpenAI Luna Decisions on one key: send a state, ask typed questions, get answers with confidence. Start free. Choice/Score/Noul question types, probabilities + confidence, credit metering where failed runs cost 0, saved threads.

JevStation is an AI-powered decision and evaluation platform designed to help developers, businesses, and technical teams turn text and other information into structured decisions that software can use. Rather than generating long paragraphs that must be interpreted or converted into structured data, the platform uses Jev, a System One decision model developed by TypeSafe, to evaluate supplied information against explicitly defined questions. It returns typed answers, probability distributions, and confidence values for supported question types, making it easier to incorporate AI-based judgments into software workflows.

JevStation is designed for situations in which an application needs to classify information, assign a score, assess risk, or choose an action. For example, a customer-support system might need to determine which department should receive a ticket, whether an issue is urgent, and whether it requires human attention. A review-moderation system might need to classify a customer comment as positive, mixed, or negative. Instead of asking a conventional text-generating model to produce an answer that another program must parse, developers can define the decision they need and receive a structured result.

The platform’s central feature is its typed-question system. It supports three question types: Choice, Score, and Noul. Choice questions select from predefined options, making them useful for classification and routing. Score questions evaluate information against a defined scale, supporting tasks such as rating complexity, assessing quality, or scoring a lead. Noul questions return a probability-oriented judgment for situations that do not fit a conventional choice or numeric score. The exact output depends on the question type and its configured criteria.

JevStation allows users to provide a piece of information, referred to as the state, and ask multiple questions about it in a single evaluation. The state could be a support ticket, product review, technical specification, document excerpt, prompt, or JSON record. Users define the questions and the criteria that determine valid answers. Each evaluation can contain up to 12 typed questions, subject to the platform’s input-size and credit limits. This approach allows several related judgments to be made against the same information without submitting an entirely separate request for every decision.

An important part of the platform is its use of probability distributions. Rather than returning only a single label or score, Jev provides probabilities associated with possible answers. For supported Choice and Score questions, it also supplies a confidence value. These outputs can help developers establish thresholds for automated routing, escalation, review, or other downstream actions. For example, a ticket may be automatically routed when the selected category has sufficiently high confidence, while uncertain cases can be sent to a human reviewer. Confidence values should not be treated as guarantees of correctness, and important decisions still require suitable testing and oversight.

JevStation includes a browser-based playground where users can experiment with the model before integrating it into an application. A user can paste a sample of text, define the questions to be evaluated, run the assessment, and inspect the resulting typed answers. This makes it possible to refine question wording and criteria before implementing them in code. The playground also helps users understand the platform’s response format and estimate credit consumption.

The platform supports saved evaluation threads. Each evaluation can be stored against the user’s account with a title derived from its content, allowing the user to return to previous work and reuse context. Users can also pin standing instructions or reference material to a thread so that subsequent evaluations can use the same background information. This is useful when repeatedly evaluating similar records under a consistent rubric, such as assessing customer tickets against the same escalation policy or reviewing documents against a fixed set of requirements.

For developers, JevStation provides an API that allows evaluations to be called from application code. Its documented endpoint is POST /api/v1/systemone, authenticated using an API key. The API returns structured results according to the same typed-question contract used in the browser playground. Developers can create and test a question set in the interface, then implement the same logic in a backend service or automation pipeline. API usage consumes credits from the associated account, so applications should account for evaluation size, question count, and expected request volume when planning costs.

JevStation also provides a Model Context Protocol (MCP) server that makes its evaluation capabilities available to compatible AI clients and development environments. The documented tools support tasks such as making decisions, running question sets, listing question sets, routing between models, guarding tool calls, moderating content, and checking available credits. This makes the platform relevant to developers building AI agents that need a separate decision layer to evaluate an action before it is executed. API keys should be stored securely because anyone who obtains a usable key may be able to perform evaluations and consume the account’s credits.

Another part of the service is its community catalog of tools, playbooks, and integrations. Users can explore published examples, submit their own question sets, and reuse workflows created by other people. These resources demonstrate how the decision model can be applied to common technical and operational tasks. Examples include support-ticket triage, review sentiment classification, lead qualification, prompt-injection detection, model routing, content moderation, citation checking, and evaluating answers generated by other AI systems. The catalog helps users move from a general idea to a more specific implementation.

JevStation is particularly useful for structured decision tasks in which the output must be consumed by another program. In support operations, it can help classify tickets and estimate urgency. In marketing and sales workflows, it can score leads or categorize customer feedback. In software development, it can evaluate model outputs, assess proposed tool calls, and help select an appropriate model for a task. In document workflows, it can apply a consistent rubric to specifications or other text. These examples represent decision-support applications rather than guarantees that every judgment will be correct.

The platform uses credit-based billing instead of a conventional recurring subscription. New accounts receive free credits, and users can try preset evaluations in the playground without signing up, subject to the site’s daily limit. Standard evaluations consume one credit, while larger evaluations consume three credits. Failed evaluations caused by provider errors are refunded automatically according to the service’s published terms. This makes the cost of a run easier to estimate than a workflow in which every request may involve multiple retries and additional parsing steps.

JevStation is not intended to replace a general-purpose writing assistant. Its main purpose is to make judgments and classifications available in a predictable, typed format. It is most appropriate when an application needs a decision that can be used in code, rather than a long explanation intended primarily for a human reader. Users who need extensive content generation, creative writing, or open-ended conversation may still require a conventional language model alongside JevStation.

The platform should be evaluated carefully before it is used in high-impact workflows. Its outputs are probabilistic judgments, not verified facts or professional advice, and confidence scores do not guarantee correctness. Developers should test question sets on representative examples, examine failure cases, monitor performance as inputs change, and keep human review for decisions where mistakes could have significant consequences. With those safeguards, JevStation can provide a useful structured-decision layer for applications, automation pipelines, and AI-agent systems.

Pricing

JevStation uses a freemium, credit-based pricing model with one-time credit purchases rather than recurring subscriptions.

• Free access: $0. The playground offers up to three preset evaluations per day without an account.

• Free account: 50 credits for new accounts, valid for 30 days. No payment card is required.

• 5,000-credit pack: $9.90, one-time payment.

• 12,000-credit pack: $15.90, one-time payment.

• Standard evaluation: 1 credit for a state of up to 8,000 characters and up to 5 questions.

• Large evaluation: 3 credits for a state of up to 24,000 characters and up to 12 questions.

• Credit validity: Purchased credits are valid for 24 months.

• Failed evaluations: Provider-error failures are automatically refunded according to the published terms.

Prices are in US dollars and may change.

Review

JevStation is a specialized decision-support platform for developers who need structured AI judgments rather than generated prose. Its typed questions, probability distributions, confidence values, API, and MCP server make it useful for integrating classification, scoring, moderation, and routing into software systems.

Its main strength is that developers can test decisions in a browser-based playground before integrating them into an application. The credit-based pricing model also offers a relatively affordable way to experiment, with free credits for new accounts and no recurring subscription requirement.

However, JevStation is not a general-purpose chatbot or content-writing platform. Its value depends on the quality of the questions and criteria users define, and its probabilistic outputs must be tested before being used in important decisions. Developers also need to manage API keys securely and monitor credit consumption.

Verdict: JevStation is worth considering for developers, automation builders, and technical teams that need structured, confidence-aware decisions in AI pipelines and applications.