Inference API

Inference API

You only need this page if you want to make the API request yourself. If you leave autoFetch enabled, the SDK does the request for you and places the result in result.apiResponse.

Manual request flow

1. The SDK finishes the consent flow.
2. Your onComplete callback receives result.token and result.apiUrl.
3. Your app sends a POST request to that apiUrl with the bearer token.

const handleComplete = async (result) => {
  const response = await fetch(result.apiUrl, {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer ' + result.token,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      Input: {
        item1: {
          text: 'Product description',
          category: 'fashion'
        }
      }
    })
  });

  const data = await response.json();
  console.log(data);
};

Request body

If the returned API URL supports inference, send your items inside Input.

Field	Type	Required	Description
Input	Object	Only for inference requests	The items you want the model to score
includeLlmData	Boolean	No	Include LLM summary data when the user approved that access
topicFilter or domain	String	No	Filter returned traits to a topic or domain

const response = await fetch(apiUrl, {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer ' + token
  },
  body: JSON.stringify({
    Input: {
      item1: { text: "Product description", category: "fashion" },
      item2: { text: "Another item", category: "lifestyle", img_url: "https://..." }
    }
  })
});

const data = await response.json();

---

What response should you expect?

Most integrations only need to think in three response patterns. Your app should look for the fields it needs instead of depending on one fixed response shape.

Response pattern	When you will see it	Main fields to use
Traits profile	Your app asks for profile and personalization signals	traits, userTraits, connectedPlatforms, metadata
Inference scores	Your app sends inferenceData for content scoring	InferenceResult.output
Combined response	Your app needs both profile data and scoring results	InferenceResult, traits, connectedPlatforms, sometimes user and metadata

Response examples

Traits only

{
  "success": true,
  "traits": {
    "positive_traits": {
      "Creative Problem Solving": 92,
      "Community Engagement": 85
    },
    "traits_to_improve": {
      "Routine Consistency": 32
    },
    "user_summary": "You are curious and idea-driven...",
    "top_traits_explanation": "Your strongest signals came from...",
    "archetype": "Creative Explorer",
    "nudges": [
      { "text": "Turn one of your ideas into a small weekly habit." }
    ]
  },
  "connectedPlatforms": ["reddit", "youtube"],
  "metadata": {
    "format": "standard"
  }
}

Inference only

{
  "InferenceResult": {
    "output": [
      [[0.9998]],
      [[0.0013]]
    ]
  }
}

Score	Meaning
0.9+	User will highly engage/like this content
0.5-0.9	Moderate positive response
<0.5	User likely won't engage

These scores come from the MIND1 model, which is the per-user preference model trained on the user's positive and negative interaction data.

Combined traits and inference

{
  "InferenceResult": {
    "output": [
      [[0.9998]]
    ]
  },
  "traits": {
    "positive_traits": {
      "Creative Problem Solving": 92
    },
    "traits_to_improve": {
      "Routine Consistency": 32
    },
    "user_summary": "You are curious and builder-minded...",
    "top_traits_explanation": "You repeatedly engage with...",
    "archetype": "Creative Explorer",
    "nudges": [
      { "text": "Use your strongest interests as the starting point for your next project." }
    ]
  }
}

The trait generator internally produces a DataAnalysis structure, but the public response payload reshapes that output. When you build against the API, follow the response you actually receive.