KiHustle Hallucination Risk Scorer

x402

KiHustle Hallucination Risk Scorer is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-10).

Scores a given text for the likelihood that it contains AI hallucinations or fabricated information

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

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Output

Returns a JSON object with a 'result' field indicating the hallucination risk assessment outcome and a 'status' field confirming successful processing. The risk score or classification indicates how likely the input text is to contain hallucinated or fabricated content.

Example use cases

Fact-checking AI research summaries

“I just got a research summary back from my AI assistant about quantum computing breakthroughs in 2023 — can you score it for hallucination risk before I use it in my report?”

Validating AI chatbot responses before publishing

“Before I push this AI-generated FAQ answer to our help center, can you check it for hallucination risk? Here's the text: 'Our product was founded in 2015 and has served over 10 million customers across 45 countries.'”

Quality-gating LLM outputs in an automation pipeline

“I'm building an AI pipeline that generates product descriptions — can you score each output text for hallucination risk so I can automatically reject anything above a high-risk threshold?”

Technical details

Endpoint

POSThttps://kihustle.tech/agents/api/v1/hallucination-risk-scorer?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a quick, low-cost ($0.002 per call) automated check for hallucination risk in AI-generated text, especially within pipelines that produce LLM outputs that need quality gating before downstream use. It is well-suited for single-text evaluation rather than bulk batch jobs. Prefer it when you want a lightweight signal about fabrication likelihood without running a full fact-verification pipeline.

Side effects

Empty or missing 'text' field returns an error or undefined behavior
Extremely long text inputs may exceed processing limits
Non-string input types may cause schema validation failures
Network timeouts if the scoring model is slow to respond
Payment failure via x402 protocol if USDC balance is insufficient

Input

Schema · Request body

Field	Type	Description
text	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "result": "processed",
    "status": "success"
  }
}

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