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Supplier Qualification Data Quality Suite — Record Audit, String Normalisation & Dataset Comparison
x402
Supplier Qualification Data Quality Suite — Record Audit, String Normalisation & Dataset Comparison 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 local analytical reports on supplier qualification records: a record quality audit, string normalisation, and keyed dataset comparison between two supplied datasets (up to 200 rows each).
Price / call$0.01
Success—
StatusUnknown
How this service works
Supplier qualification — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison 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
Returns three independent analytical reports: (1) a record quality audit highlighting missing values, nulls, outliers, and structural issues in the supplier qualification rows; (2) a string normalisation report showing standardised versions of text fields like company_group; and (3) a keyed dataset comparison report diffing the primary and reference datasets row-by-row using row_id as the key, identifying added, removed, or changed records.
Example use cases
Pre-ingestion supplier data validation
“Before we load this batch of supplier qualification records into our system, can you run a quality audit on them and flag any rows with missing or malformed checked_criteria or met_criteria values? I have about 80 rows with row_id, company_group, date, and the two criteria fields.”
Detecting drift between two supplier datasets
“I have two versions of our supplier qualification data — one from this quarter and one from last quarter — both with the same row_ids. Can you compare them side by side and tell me which rows have changed in their checked_criteria or met_criteria values?”
Standardising supplier group names before reporting
“Our supplier qualification export has inconsistent company_group labels — some have trailing spaces, mixed casing, or slight spelling differences. Can you normalise those strings across all 120 rows so we get clean, consistent group names for our report?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-02-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to perform three supplier-qualification-specific analytical operations in a single low-cost call: quality auditing, string normalisation, and keyed dataset comparison. It is best suited for offline/batch validation of up to 200 rows of supplier qualification data (checked_criteria, met_criteria) without needing any external data lookups, predictions, or professional assessments. Prefer it over generic data quality tools when your records conform to the supplier qualification schema (row_id, company_group, date, checked_criteria, met_criteria) and you need all three reports bundled at $0.01 USDC.
Side effects
- More than 200 rows submitted — endpoint rejects with an error as maxItems is 200
- Missing required fields (row_id, company_group, date, checked_criteria, met_criteria) cause validation failure
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- row_id values exceeding 80 characters are rejected
- No live or external data is returned — only analysis of supplied data; users expecting external lookups will be disappointed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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