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Supplier Qualification Data Quality Audit (Record Quality + String Normalisation + IQR Outlier Flags)
x402
Supplier Qualification Data Quality Audit (Record Quality + String Normalisation + IQR Outlier Flags) is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Runs three analytical reports on supplied supplier qualification records: a record quality audit, string normalisation, and IQR-based outlier detection for checked_criteria and met_criteria fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Supplier qualification — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied supplier qualification records (checked_criteria, met_criteria). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports covering: (1) a record quality audit identifying missing, malformed, or inconsistent records; (2) string normalisation results for text fields like company_group; and (3) IQR-based outlier flags on the selected numeric field (checked_criteria or met_criteria), all applied to the supplied rows (up to 200).
Example use cases
Pre-onboarding supplier data cleanse
“I've got 120 supplier qualification records with company_group, date, checked_criteria, and met_criteria columns. Before I load them into our system, can you run a full record quality audit, normalise any string inconsistencies, and flag any IQR outliers in the checked_criteria field?”
Quarterly vendor evaluation data review
“We just pulled our quarterly vendor evaluation data — about 80 rows with row_id, company_group, date, checked_criteria, and met_criteria. Can you audit the record quality, normalise the strings, and flag any outlier values in met_criteria using IQR?”
Procurement data quality investigation
“Something looks off in our supplier qualification dataset — 35 rows covering group-a and group-b suppliers. Run a record quality audit, string normalisation, and IQR outlier detection on checked_criteria so I can identify which rows have data issues.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-02-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled, cost-efficient data quality pass on supplier qualification records specifically — covering string normalisation, completeness auditing, and numeric outlier detection in one call. Prefer it over building custom scripts when you have up to 200 rows with checked_criteria and met_criteria fields and want three complementary quality reports for $0.01 USDC. Not suitable if you need live external lookups, predictive scoring, or records exceeding 200 rows.
Side effects
- More than 200 rows submitted — request rejected due to row limit
- Missing required fields (row_id, company_group, date, checked_criteria, met_criteria) on any row — validation error
- Date field not matching YYYY-MM-DD pattern — schema validation failure
- Invalid field enum value (not 'checked_criteria' or 'met_criteria') — rejected
- Empty rows array — rejected due to minItems:1 constraint
- Numeric values outside allowed range (-1e12 to 1e12) — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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