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US Brand Signal Policy Simulator
x402
US Brand Signal Policy Simulator is a paid API for AI agents from api.timzinin.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-14).
Simulates and compares multiple brand-safety scoring policies against buyer-supplied signal scores, returning accept/review/reject classifications and cross-policy divergence counts for US brand signals.
Price / call$0.05
Success—
StatusUnknown
How this service works
Pay-per-call data & tool APIs
Output
Returns a JSON object with per-policy classification counts (accepted, reviewed, rejected) and IDs, pairwise cross-policy comparison metrics (changed signal count, decisions only in A vs B), row/input/result digests for auditability, and an attribution note clarifying the simulation makes no winner recommendation.
Example use cases
Pre-launch policy threshold calibration
“I have 6 US brand signals scored between 20 and 95. Run them through two policies — a conservative one (accept at 85, review at 60) and a permissive one (accept at 65, review at 25) — and tell me how many signals end up in each bucket and where the two policies disagree.”
Auditing divergence between brand review teams
“My brand safety team uses a balanced policy (accept at 80, review at 50) but a partner wants to use a broad policy (accept at 70, review at 30). Simulate both against these 5 brand signal scores and show me exactly which signals get classified differently between the two approaches.”
Stress-testing strict brand acceptance criteria
“Run a policy simulation on 8 US brand signals with a strict policy (accept at 90, review at 70) and a standard policy (accept at 75, review at 45) — I want to see how many signals we'd lose from accepted to rejected if we tighten our thresholds.”
Technical details
Endpoint
POSThttps://api.timzinin.com/api/us-brand-signal-policy-simulatorWhen to prefer
Use this endpoint when you need to compare the downstream effects of different brand-safety threshold policies on a known set of scored signals before committing to a policy in production. It is ideal for buyers who already have their own scored signals and want to stress-test or audit policy choices without any external truth-claim or recommendation — the simulator is purely deterministic against buyer-supplied inputs.
Side effects
- Invalid or missing signal scores cause validation error
- Threshold values out of range (e.g. accept threshold below review threshold) may return an error
- Malformed policy definitions cause a 400-level response
- Empty signal list returns zero-count results with valid structure
- Network timeout on heavy input sets (run_ms may exceed SLA)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| policies | array | |
| schemaVersion | string |
Output
Schema · Response
{
"type": "json",
"example": {
"meta": {
"run_ms": 1200,
"settled": true,
"item_count": 1
},
"results": [
{
"counts": {
"rows": 4,
"policies": 3,
"comparisons": 3,
"sensitivityRows": 4
},
"totalRows": 4,
"reportType": "us_brand_signal_policy_simulator",
"rowsDigest": "911caadd6dc1c410ed601d2b580171dbb229935e8832de4b6a72a6747fcac4a5",
"attribution": {
"type": "buyer_supplied_scores_and_policies",
"meaning": "Rows and thresholds were supplied by the buyer; this Actor simulates the stated policies only and makes no truth claim, recommendation, or winner selection."
},
"comparisons": [
{
"policyA": "balanced",
"policyB": "broad",
"reviewOnlyInA": 0,
"reviewOnlyInB": 1,
"acceptedOnlyInA": 0,
"acceptedOnlyInB": 0,
"rejectedOnlyInA": 1,
"rejectedOnlyInB": 0,
"sameDecisionCount": 3,
"changedSignalCount": 1
},
{
"policyA": "balanced",
"policyB": "strict",
"reviewOnlyInA": 1,
"reviewOnlyInB": 1,
"acceptedOnlyInA": 1,
"acceptedOnlyInB": 0,
"rejectedOnlyInA": 0,
"rejectedOnlyInB": 1,
"sameDecisionCount": 2,
"changedSignalCount": 2
},
{
"policyA": "broad",
"policyB": "strict",
"reviewOnlyInA": 2,
"reviewOnlyInB": 1,
"acceptedOnlyInA": 1,
"acceptedOnlyInB": 0,
"rejectedOnlyInA": 0,
"rejectedOnlyInB": 2,
"sameDecisionCount": 1,
"changedSignalCount": 3
}
],
"inputDigest": "cb1a39d72bddee8ad1a44f025db9f4a6a1f513ea6a86d094f1d0fe483444d291",
"resultDigest": "1be9ae6e4daebcd67c8960b9aa6f2bbd14d6121f497d7f7635d120d2fe97a493",
"policyResults": [
{
"counts": {
"total": 4,
"review": 1,
"accepted": 2,
"rejected": 1
},
"policyId": "balanced",
"reviewIds": [
"signal-003"
],
"acceptedIds": [
"signal-001",
"signal-002"
],
"rejectedIds": [
"signal-004"
],
"acceptThreshold": 80,
"reviewThreshold": 50,
"classificationDigest": "f40bc496a84f4d6504946cdaa8825468a26066d320a1fc6fed3fdbe3b1dbc0ff"
},
{
"counts": {
"total": 4,
"review": 2,
"accepted": 2,
"rejected": 0
},
"policyId": "broad",
"reviewIds": [
"signal-003",
"signal-004"
],
"acceptedIds": [
"signal-001",
"signal-002"
],
"rejectedIds": [],
"acceptThreshold": 70,
"reviewThreshold": 30,
"classificationDigest": "84aa5bc4a72f7a5e0517a46eb9873a9f86d5aa4a5fd8a56a60dd7657a5717be5"
},
{
"counts": {
"total": 4,
"review": 1,
"accepted": 1,
"rejected": 2
},
"policyId": "strict",
"reviewIds": [
"signal-002"
],
"acceptedIds": [
"signal-001"
],
"rejectedIds": [
"signal-003",
"signal-004"
],
"acceptThreshold": 90,
"reviewThreshold": 70,
"classificationDigest": "7af0d1e326b8c9fae5ab550d3d8d25cda1b83bc51c5e2ea81ae2d45b054312a5"
}
],
"schemaVersion": "1.0",
"totalPolicies": 3,
"policiesDigest": "ab9be549456b60293338f5088097494f3481886c57e6490058b49a070df01156",
"comparisonsDigest": "c8bc231bd36307ed00b100f7dfc77d9b0f8f6a2df72b062915fc9f6816db160f",
"sensitivityDigest": "20936e7f4ab8fabdd4af527ca46c766b60ad94576ee583874bd1e2aac1decce1",
"sensitivitySummary": [
{
"signalId": "signal-001",
"decisionSpread": 1,
"reviewByPolicyCount": 0,
"acceptedByPolicyCount": 3,
"rejectedByPolicyCount": 0
},
{
"signalId": "signal-002",
"decisionSpread": 2,
"reviewByPolicyCount": 1,
"acceptedByPolicyCount": 2,
"rejectedByPolicyCount": 0
},
{
"signalId": "signal-003",
"decisionSpread": 2,
"reviewByPolicyCount": 2,
"acceptedByPolicyCount": 0,
"rejectedByPolicyCount": 1
},
{
"signalId": "signal-004",
"decisionSpread": 2,
"reviewByPolicyCount": 1,
"acceptedByPolicyCount": 0,
"rejectedByPolicyCount": 2
}
],
"policyResultsDigest": "e4a33873c0ca737d4e419ab0e22c0e954d1742922fe965ab6e709d52a14b1523"
}
]
}
}Tags
x402
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