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SignalHarness AI Answer Source Audit
x402
SignalHarness AI Answer Source Audit is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.03/call, status unknown (last checked 2026-09-13).
Audits the sourcing and attribution quality of AI-generated answers, flagging unsupported claims and evidence gaps in caller-supplied data.
Price / call$0.03
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object containing an analysis_json field with detailed audit findings, a list of warnings about unverified or caller-supplied claims, an evidence_scope label (e.g. 'caller_supplied_data'), plus a payment receipt with execution metadata including latency, payment ID, and a SHA-256 result hash for integrity verification.
Example use cases
LLM response citation check
“I just got this answer back from our AI assistant and I want to know if the sources it cited actually support the claims — can you run a source audit on it? Here's the answer JSON: {"claim":"Inflation dropped to 3.2% in Q3","source":"Federal Reserve 2023 report"}”
Hallucination detection in AI content
“Our content pipeline generated this article excerpt with citations — can you audit it to flag any unsupported or hallucinated source references? Here's the JSON: {"excerpt":"Scientists discovered a new antibiotic in 2024","citations":["Nature Vol. 612"]}”
AI answer quality gate before publishing
“Before we publish this AI-written FAQ answer to customers, can you check whether the evidence behind it is solid and flag any warnings? The answer data is: {"faq":"Does our product support OAuth 2.0?","answer":"Yes, fully supported since v3.1","evidence":"internal docs"}”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/ai_answer_source_audit/invokeWhen to prefer
Choose this endpoint when you need a lightweight, pay-per-call audit of AI-generated answer quality and source attribution without building a full fact-checking pipeline. It is especially useful for agents that produce or relay LLM outputs and need a fast integrity check before surfacing results to end users. Prefer it over general web search or citation tools when you already have structured answer JSON and want an analysis of its evidentiary basis rather than fresh source retrieval.
Side effects
- Malformed or non-JSON request_json input returns a validation error
- Input exceeding 65,536 characters is rejected
- Ambiguous or incomplete evidence data may result in low-confidence analysis with generic warnings
- Payment failure on the x402 protocol prevents execution
- Caller-supplied data is not independently verified — results reflect only what was provided
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request_json | string |
Output
Schema · Response
{
"type": "json",
"example": {
"replay": false,
"result": {
"warnings": [
"Verify the caller-supplied data before relying on this result."
],
"service_id": "ai_answer_source_audit",
"analysis_json": "{\"example\":\"schema-valid caller-supplied data\"}",
"evidence_scope": "caller_supplied_data"
},
"status": "succeeded",
"receipt": {
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"usage": [],
"status": "succeeded",
"network": "eip155:8453",
"artifacts": [],
"endedAtMs": 0,
"latencyMs": 0,
"paymentId": "example-payment",
"receiptId": "example-receipt",
"requestId": "example-request",
"serviceId": "ai_answer_source_audit",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "30000",
"resultSha256": "99186689a1780324b80359df4ee84368e9598b01fbe389d96f9c7a532d5558a1",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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