AgentUtility Churn Risk Sentiment

x402

AgentUtility Churn Risk Sentiment is a paid API for AI agents from x402.agentutility.ai, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-27).

Analyzes text for churn-risk sentiment, returning an overall sentiment label, numeric score, and confidence level for customer retention use cases.

Price / call$0.01

Seller volume →—

Success—

StatusUnknown

How this service works

817 pay-per-call APIs for AI agents. Every endpoint is POST /<name> and is payable in USDC over x402 on Base and Solana — no signup, no API key, just pay-per-call. GET https://x402.agentutility.ai/ returns the same catalog as machine-readable JSON.

Output

Returns a JSON object with three fields: overall_sentiment (e.g. 'negative'), overall_score (a float from -1 to 1 indicating sentiment polarity), and confidence (a float from 0 to 1 indicating model certainty in the classification).

Example use cases

Example prompt

“Analyze this customer message for churn risk sentiment and give me the overall sentiment label, score, and confidence: 'Your service has been incredibly disappointing lately and I'm seriously considering canceling my subscription.'”

Technical details

Endpoint

POSThttps://x402.agentutility.ai/churn-risk-sentiment?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a churn-specific sentiment signal rather than generic sentiment analysis — it is calibrated for customer retention contexts and returns a structured confidence score alongside the sentiment label, payable per-call in USDC with no subscription required.

Side effects

Empty or missing input text returns a 400 error
Input text too long may be truncated or rejected
Payment failure via x402 returns 402 Payment Required
Ambiguous or very short text may yield low confidence scores
Non-English text may produce unreliable sentiment scores

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "confidence": 0.86,
    "overall_score": -0.72,
    "overall_sentiment": "negative"
  }
}

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