Tasks

System One Model Call

Evaluates a state against one or more structured questions using a System One model. Returns typed judgments — choices, scores, and probabilities — that can be used directly in automation logic.

Unlike a general-purpose LLM call, System One models do not generate free-form text. Each question produces a structured, typed answer with a confidence score, making the results directly usable in task logic without parsing or prompting.

See Models for a full list of supported System One models.

# Use cases

• Route an inbound message to the correct department based on its content.
• Score customer sentiment or urgency before deciding which workflow to trigger.
• Verify that a piece of text meets a condition before continuing a process.

# Basic usage

# Parameters

# model

required

string

The System One model to use. Currently supported:

• jev-latest: Always resolves to the latest stable Jev model.

See Models for a full list of supported models.

# state

required

string | object | array

The content for the model to evaluate. All questions in the request see the same state.

State must contain text only. Images, audio, and video are not supported.

# questions

required

object

An object of named questions to evaluate against the state. Each key becomes the corresponding key in response.answers. All questions are evaluated independently in one request.

Each question has a type that determines its structure and the shape of its answer.

# questions[name].type

required

string

The question type. One of noul, choice, or score.

# questions[name].instructions

required

string

A natural language description of what the model should evaluate.

# questions[name].criteria

object | array

Defines the valid answer space for choice and score questions.

• For choice: an object where each key is a valid answer and each value describes it.
• For score: an array of strings describing each point on the scale, ordered from lowest to highest.

Not used for noul questions.

# Result

# Properties

# response.model

string

The resolved model version that processed the request.

# response.answers

object

An object keyed by each question name provided in questions. Each answer's shape depends on the question type.

# response.answers[name].noul

number

For noul questions. A float between 0 and 1 indicating the probability that the condition described in instructions is true.

# response.answers[name].choice

string

For choice questions. The key from criteria that the model selected.

# response.answers[name].confidence

number

For choice and score questions. A float between 0 and 1 indicating the model's confidence in its answer.

# response.answers[name].probabilities

object

For choice and score questions. The full probability distribution across all options.

# response.answers[name].score

number

For score questions. The zero-based index of the selected point on the scale defined in criteria.

# response.answers[name].legend

object

For score questions. Maps each score index back to the label from criteria for easy reference.

# response.usage

object

Token usage for the request. Contains input_tokens and output_tokens.

# Examples

# Routing and sentiment in one call

Evaluate urgency, department routing, and frustration level together in a single request.

# Structured state

Pass related context as an object so the model can evaluate the full picture.