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AI Text Classifier (Custom Labels + Confidence)
x402
AI Text Classifier (Custom Labels + Confidence) is a paid API for AI agents from x402-seller-0ay3.onrender.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-14).
Classifies any text into one of your own custom labels with a confidence score, powered by Claude Haiku 4.5
Price / call$0.01
Success—
StatusUnknown
How this service works
Text classification (sentiment analysis, topic tagging, intent detection, moderation labels...) into one of your own labels, with a confidence score, via Claude Haiku 4.5. JSON body: {text: string (max 8000 chars), labels: string[] (2 to 20 labels)}.
Output
A JSON object with two fields: `label` (one of the labels you provided) and `confidence` (a float between 0 and 1 indicating how certain the model is). Example: {"label": "negative", "confidence": 0.94}
Example use cases
Sentiment analysis on product reviews
“I have this product review and I want you to classify it as 'positive', 'negative', or 'neutral' with a confidence score: 'Battery life is amazing but the screen scratches way too easily.'”
Support ticket intent detection
“Classify this support message into one of these intents — 'billing_issue', 'technical_problem', 'feature_request', or 'general_inquiry' — and tell me how sure you are: 'I was charged twice this month and I can't figure out why.'”
Content moderation label assignment
“I need you to label this user-submitted comment as either 'safe', 'offensive', or 'spam' and give me a confidence score: 'Click here to win a free iPhone, limited time only!!!'”
Technical details
Endpoint
POSThttps://x402-seller-0ay3.onrender.com/api/ai/classifyWhen to prefer
Choose this endpoint when you need fast, cheap, zero-setup text classification into your own arbitrary label set without training a custom model. Ideal for ad-hoc labeling tasks, dynamic label sets, or when you want LLM-quality understanding (nuance, sarcasm, context) rather than a simple keyword or regex approach. At $0.01/call it's cost-effective for moderate volumes. Prefer it over general-purpose LLM calls when you want a structured {label, confidence} response guaranteed every time.
Side effects
- Text exceeds 8000 characters — request rejected
- Fewer than 2 or more than 20 labels provided — validation error
- Empty text or empty label array — bad request
- Service temporarily unavailable on cold start (Render.com free tier spin-up delay)
- Model returns ambiguous classification when text is highly mixed or short
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | Text to classify (max 8000 chars). |
| labels | array | List of possible labels (2 to 20). |
Output
Schema · Response
{
"type": "json",
"example": {
"label": "negative",
"confidence": 0.94
}
}Tags
x402
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