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SignalHarness AI Search Result Synthesis Audit
x402
SignalHarness AI Search Result Synthesis Audit is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.03/call, status unknown (last checked 2026-09-15).
Audits and analyzes caller-supplied AI search result synthesis data, returning structured analysis with warnings and evidence scope metadata.
Price / call$0.03
Success—
StatusUnknown
How this service works
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Output
Returns a structured JSON object containing: an analysis_json field with the audit findings, a list of warnings (e.g. data reliability caveats), an evidence_scope label (e.g. 'caller_supplied_data'), the service_id, execution status, and a full receipt with payment details, latency, and a SHA-256 hash of the result for verification.
Example use cases
Audit AI research synthesis output
“I just ran a web search and had an AI synthesize the results into an answer — can you audit that synthesized JSON and tell me if there are any warnings or issues with the evidence it used? Here's the synthesis: {"topic":"climate policy","summary":"Carbon taxes are the most effective tool","sources":["https://news.example.com/climate"]}”
Validate search synthesis before publishing
“Before I publish this AI-synthesized search result to my users, can you run an audit on it to check for problems? The synthesis JSON is: {"query":"COVID vaccine efficacy","answer":"Studies show 90% efficacy","evidence":["https://pubmed.example.com/12345"]}”
QA check on agent-generated search answers
“My agent generated a synthesized answer from search results — I need you to audit the synthesis JSON and highlight any warnings about the evidence scope or reliability before I trust it. Here it is: {"question":"Who won the 2023 election?","synthesized_response":"Candidate X won with 52%","citations":[]}”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/ai_search_result_synthesis_audit/invokeWhen to prefer
Choose this endpoint when you need to programmatically audit or validate AI-synthesized search results before acting on or publishing them, especially in agentic pipelines where hallucination risk or evidence quality matters. Prefer this over generic validation endpoints when your input is specifically AI search synthesis output and you need structured warnings plus a cryptographic receipt for auditability.
Side effects
- Invalid or malformed request_json string (too short, not valid JSON-like content)
- Input exceeds 65536 character limit
- Payment failure or insufficient USDC balance
- Service returns warnings that caller-supplied data could not be independently verified
- Execution timeout resulting in non-succeeded status
- Network or blockchain settlement errors reflected in receipt
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_search_result_synthesis_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_search_result_synthesis_audit",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "30000",
"resultSha256": "6c606c9e5b5074cf020bbf7b1fe6f06ca0d33bfe575ecf1d8bfdc904d71f8be6",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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