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Business Location Inventories — Record Quality Audit + String Normalisation + IQR Outlier Flags
x402
Business Location Inventories — 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 2026-10-19).
Runs three analytical reports on up to 200 rows of business location inventory data: a record quality audit, string normalisation, and IQR-based outlier flagging for location_count or verified_locations fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Business location inventories — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied business location inventories records (location_count, verified_locations). 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: (1) a record quality audit highlighting missing, malformed, or inconsistent values; (2) normalised versions of string fields such as company_group; (3) IQR-based outlier flags identifying rows where location_count or verified_locations deviate significantly from the interquartile range. All computed locally with no external lookups.
Example use cases
Audit franchisee location data quality
“I have a spreadsheet of 120 rows from our franchisee location inventory with columns for row_id, company_group, date, location_count, and verified_locations — can you run a full record quality audit, normalise the company_group strings, and flag any IQR outliers in the location_count field?”
Detect anomalous verified location counts
“My team submitted 80 rows of verified location data for our retail network. Can you check the verified_locations field for IQR-based outliers, normalise the string fields, and give me a record quality report so I can spot any bad entries?”
Pre-merge data cleanse for location records
“Before we merge these 150 business location inventory records into our main database, can you run the quality audit, string normalisation, and outlier flagging on the location_count field so we catch any dirty or anomalous rows first?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-05-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a batch of up to 200 business location inventory rows and need three simultaneous data quality operations — quality auditing, string normalisation, and statistical outlier detection — in a single low-cost call. Prefer it over manual data cleaning pipelines or general-purpose analytics tools when your data specifically contains location_count and verified_locations fields organised by company_group and date, and you want standardised reports without any live lookup or external data enrichment.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, date, location_count, verified_locations) cause row-level validation errors
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Numeric values outside allowed range cause rejection
- Too few rows to compute meaningful IQR statistics may produce unreliable outlier flags
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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