> ## Documentation Index > Fetch the complete documentation index at: https://docs.oumi.ai/llms.txt > Use this file to discover all available pages before exploring further. # OVERVIEW > Get started using decision models in production ## WHAT IS A DECISION MODEL A decision model answers narrow, multiple-choice questions instead of generating open-ended text. You give it a state that describes the current situation and a set of questions about that state. It returns a probability for each answer option, scoring all questions in parallel. A generative LLM produces its answer one token at a time. That makes it flexible, but slow and expensive when all you need is a choice between a few options. A decision model skips text generation and only scores the options, so each decision is faster and cheaper. A decision model can't write code, compose messages, or explain its reasoning. Use it as a fast decision layer next to your LLM, not as a replacement. The LLM sets up the state and acts on the result; the decision model makes the call. ### WHEN TO USE A DECISION MODEL Decision models fit high-volume decisions where speed and cost matter: * **Agent guardrails:** check each action an agent proposes, such as exposing secrets, deleting data, or sending data outside your network, before it runs. * **Content moderation:** classify large volumes of user content against your policies. * **Routing:** pick which tool, workflow, or model handles a request. * **Automated testing and navigation:** choose the next action in a game, UI, or browser session. ## API INTERFACE You send each decision as one request. The request holds the state to evaluate and the questions to answer about it. The response holds one answer per question. | | Value | | - | - | | **Endpoint** | `POST https://inference.oumi.ai/v1/systemone` | | **Auth header** | `X-API-Key: $OUMI_API_KEY` | > [!TIP] > > You can start right away with our pretrained Iris model. Set `model` to > `projects/{project_id}/models/iris-latest`, using the ID of any project you > own. No training or deployment needed. ### REQUEST | Field | Type | Required | Description | | - | - | - | - | | `model` | string | Yes | The decision model to call, as a path that includes your project: `projects/{project_id}/models/{model_name}`. For example, `projects/FOOBAR-123/models/iris-latest`. | | `state` | string, object, or array | Yes | The content to evaluate: a plain string, or a JSON object or array of related context. | | `questions` | object | Yes | The questions to answer, keyed by a name you choose. That name is the key for the answer in the response. | Each question has a `type`, `instructions` that state the question, and `criteria` that describe the possible answers. There are three question types: | Type | Use it for | `criteria` format | | - | - | - | | `noul` | Yes or no questions | An object with a `true` and a `false` description. Optional. | | `choice` | Picking one option from a set | An object that maps each option name to its description. | | `score` | Placing the state on an ordered scale | An array of descriptions, ordered from lowest to highest. | `instructions` and each criteria description can be a plain string, or a JSON object or array of structured guidance. ```bash curl theme={null} curl -X POST https://inference.oumi.ai/v1/systemone \ -H "Content-Type: application/json" \ -H "X-API-Key: $OUMI_API_KEY" \ -d '{ "model": "projects/{project_id}/models/iris-latest", "state": { "customer_tier": "enterprise", "ticket": "My checkout page shows a blank screen after I click Pay. I have tried two browsers." }, "questions": { "is_bug": { "type": "noul", "instructions": "Is the customer reporting a software defect?", "criteria": { "true": "The customer describes broken or unexpected product behavior.", "false": "The customer is asking a question or requesting a feature." } }, "team": { "type": "choice", "instructions": "Which team should own this ticket?", "criteria": { "account": "Login, permissions, or profile issues.", "frontend": "Rendering, layout, or browser compatibility issues.", "payments": "Checkout, billing, or payment processing issues." } }, "urgency": { "type": "score", "instructions": "How urgent is this ticket?", "criteria": [ "Can wait for the next release", "Should be fixed this week", "Blocking revenue right now" ] } } }' ``` ### RESPONSE | Field | Type | Description | | - | - | - | | `model` | string | The model path from your request. | | `answers` | object | One answer per question, keyed by the question names from your request. | | `usage` | object | `inputTokens` and `outputTokens` for the request. | The fields in each answer depend on the question type: | Type | Fields | | - | - | | `noul` | `noul`: the probability that the answer is `true`, from 0 to 1. | | `choice` | `choice`: the most likely option. `probabilities`: the probability of each option. `confidence`: how sure the model is of its pick. | | `score` | `score`: the expected position on the scale, from 0 to the last index. `probabilities`: the probability of each position. `legend`: maps each position to its criteria description. `confidence`: how sure the model is of its pick. | ```json Response theme={null} { "model": "projects/{project_id}/models/iris-latest", "answers": { "is_bug": { "type": "noul", "noul": 0.9252490387264224 }, "team": { "type": "choice", "choice": "payments", "confidence": 0.9057838757165146, "probabilities": { "account": 0.011615685025438532, "frontend": 0.05119506449688511, "payments": 0.9371892504776764 } }, "urgency": { "type": "score", "score": 1.8715208986248113, "confidence": 0.8411583967409364, "probabilities": { "0": 0.022584699202479703, "1": 0.08330970297022941, "2": 0.8941055978272909 }, "legend": { "0": "Can wait for the next release", "1": "Should be fixed this week", "2": "Blocking revenue right now" } } }, "usage": { "inputTokens": 334, "outputTokens": 0 } } ``` In this example, the model is 93% sure the ticket is a bug, routes it to the payments team, and rates it as blocking revenue. ## AVAILABLE MODELS > [card] [Iris](https://docs.oumi.ai/guides/decision-models/iris.md) > > Call and fine-tune the Iris decision model. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.