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Business Location Inventories — String Normalisation + Secondary-Measure Weighted Mean
x402
Business Location Inventories — String Normalisation + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).
Normalises string fields and computes a secondary-measure weighted mean for supplied business location inventory records (location_count, verified_locations) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Business location inventories — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean for supplied business location inventories records (location_count, verified_locations). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two analytical reports: (1) a string normalisation report for the company group field across the submitted rows, and (2) a secondary-measure weighted mean calculation for the selected numeric field (location_count or verified_locations), all computed locally against the supplied data with no external lookups.
Example use cases
Retail chain location audit rollup
“I have 120 rows of retail location inventory data with company groups, dates, location counts, and verified location counts — can you normalise the company group strings and give me a weighted mean of location_count using verified_locations as the secondary measure?”
Franchise footprint weighted summary
“Here are 80 records of franchise location data across multiple company groups and dates. Run string normalisation on the group names and compute the secondary-measure weighted mean for verified_locations so I can compare franchise footprints.”
Multi-brand inventory data clean-up
“I've got 200 rows of business location inventory data with messy company group strings — can you normalise those strings and also compute a weighted mean of location_count weighted by verified_locations so I can get clean summary stats?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-05-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation and a secondary-measure weighted mean computed together on business location inventory data (location_count, verified_locations) in a single low-cost call. It is ideal for offline/batch analytical enrichment of up to 200 rows without requiring live lookups, predictions, or external data — particularly when company group string consistency and weighted location summaries are both needed simultaneously.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, company_group, location_count, verified_locations) causes row rejection
- Invalid date format (non YYYY-MM-DD) triggers a pattern validation error
- Selecting an invalid field value outside the enum causes a schema error
- Null values in the numeric fields may affect weighted mean calculation accuracy
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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