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Business Directory Coverage — Record Quality Audit + String Normalisation + IQR Outlier Flags
x402
Business Directory Coverage — Record Quality Audit + 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 2026-10-16).
Audits record quality, normalises strings, and flags IQR-based outliers in business directory coverage data (listed_entities, verified_entities) for up to 200 rows across 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Business directory coverage — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied business directory coverage records (listed_entities, verified_entities). 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 for the supplied rows: (1) a record quality audit highlighting completeness and validity issues, (2) a string normalisation report cleaning company group and other text fields, and (3) an IQR outlier flagging report identifying statistically anomalous rows in the selected numeric field (listed_entities or verified_entities). No external lookups or live data are involved.
Example use cases
Audit directory pipeline data quality
“I've got a batch of up to 150 business directory coverage records from our data pipeline with listed_entities and verified_entities counts per company group. Can you run the record quality audit, string normalisation, and IQR outlier flagging reports on these rows for the listed_entities field?”
Detect anomalies in verified entity counts
“We have 80 rows of business directory data with verified_entities counts across several company groups. Can you analyse them for IQR outliers, clean up the string formatting, and give me a full record quality audit — use the verified_entities field for the outlier analysis?”
Validate monthly directory coverage export
“Our monthly export from the business directory has 200 rows with listed_entities and verified_entities per group and date. Please run all three reports — record quality audit, string normalisation, and IQR outlier flags — on these records using listed_entities as the target field.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-14-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined data quality workflow — record auditing, string normalisation, AND IQR-based outlier detection — applied specifically to business directory coverage data (listed_entities, verified_entities) in a single low-cost call. It is ideal for batch validation of up to 200 rows without needing any live lookups, external enrichment, or predictions. Prefer alternatives if you need only one of the three report types, need more than 200 rows, or require live data enrichment.
Side effects
- Rows array exceeds 200 items — request rejected
- Missing required fields (row_id, date, listed_entities, verified_entities) in any row — validation error
- Date field does not match YYYY-MM-DD pattern — schema error
- Invalid field enum value (must be listed_entities or verified_entities) — schema error
- Empty rows array — minimum 1 item required
- Null values in both numeric fields may reduce outlier detection accuracy
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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