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Anicca x402 Intent Router
x402
Anicca x402 Intent Router is a paid API for AI agents from x402-agents-production.up.railway.app, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-13).
Classifies free-text input against a caller-supplied list of intent labels, returning the best-matched intent with confidence score and extracted entities, paid per-call in USDC via x402.
Price / call$0.005
Success—
StatusUnknown
How this service works
Intent router for AI agents
Output
Returns a JSON object containing the matched intent label and a unique intent ID, a confidence score (0–1), an optional secondary intent with its own confidence, a reasoning string explaining the classification, a list of extracted entities (type and value), and the detected language of the input.
Example use cases
Chatbot support ticket routing
“I have a customer message: 'My order hasn't arrived and I want a refund.' Route it against these intents: ['refund_request', 'order_status', 'complaint', 'general_inquiry', 'cancel_order'] and tell me the top match with confidence.”
Appointment rescheduling detection
“Classify this patient message — 'Can I move my appointment from Monday to Wednesday afternoon?' — against ['book', 'reschedule', 'cancel', 'confirm', 'no_show'] and pull out any date entities mentioned.”
E-commerce action intent detection
“Figure out what this shopper wants: 'Do you have the blue version in size medium? I'd like to grab two.' Match it to one of these intents: ['product_search', 'add_to_cart', 'check_inventory', 'checkout', 'track_order'] with a confidence score, in English.”
Technical details
Endpoint
POSThttps://x402-agents-production.up.railway.app/intent-routerWhen to prefer
Choose this endpoint when you need lightweight, per-call intent classification with entity extraction that is billed micropayment-style in USDC via x402, making it ideal for AI agent pipelines that must pay only for what they use. It suits scenarios where you supply your own intent taxonomy (2–20 labels) and need structured routing decisions with confidence scores rather than a fixed, pre-trained classifier. Prefer it over heavyweight NLP APIs when you want zero infrastructure, multilingual support, and transparent per-call cost.
Side effects
- Fewer than 2 intents provided — request rejected
- Text exceeds 2000 characters — validation error
- Unsupported language code provided — enum mismatch error
- Payment not settled via x402 — 402 Payment Required response
- Low confidence match when input is ambiguous across intents
- Context string exceeds 500 characters — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | |
| context | string | |
| intents | array | |
| language | string |
Output
Schema · Response
{
"type": "json",
"example": {
"entities": [
{
"type": "date",
"value": "Friday"
}
],
"intent_id": "int_a1b2c3",
"reasoning": "The user asks to move an existing appointment.",
"confidence": 0.97,
"matched_intent": "reschedule",
"secondary_intent": "book",
"language_detected": "en",
"secondary_confidence": 0.08
}
}Tags
x402
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