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Warehouse Bins — String Normalisation + Date by Category Pivot
x402
Warehouse Bins — String Normalisation + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-31).
Runs two local analytical reports on warehouse bin records: string normalisation of text fields and a date-by-category pivot for occupied_volume or available_volume, across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Warehouse bins — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied warehouse bins records (occupied_volume, available_volume). 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
Two analytical reports: (1) a string-normalised version of the text fields (row_id, store_group) across the submitted rows, and (2) a date-by-category pivot table for the selected numeric field (occupied_volume or available_volume), grouped by store_group across dates — all computed locally with no external lookups.
Example use cases
Warehouse bin occupancy pivot by store
“I've got 150 rows of warehouse bin data with store groups like 'group-a' and 'group-b', dates ranging from October 2026, and occupied_volume figures — can you normalize the store group strings and pivot the occupied_volume by date and category so I can see trends per group?”
Available volume cleanup and summary
“My warehouse dataset has some messy store group labels and I need a clean date-by-category breakdown of available_volume across all 80 rows — can you normalise the strings and run the pivot report on available_volume?”
Retail bin utilisation reporting
“I have a week's worth of warehouse bin records for two retail store groups, about 56 rows total, with occupied and available volumes recorded daily — please normalise the text fields and give me a date-category pivot for occupied_volume so I can spot which group is filling up fastest.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-13-035?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have warehouse bin records (with store_group, date, occupied_volume, available_volume) and need both string normalisation of categorical text fields and a date-by-category pivot in a single low-cost call. It is ideal for batch preprocessing of up to 200 rows offline with no need for live data lookups, predictions, or external integrations. Prefer it over general-purpose analytics tools when the data fits the warehouse bin schema exactly and cost predictability ($0.01 USDC for two reports) matters.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, store_group) cause row rejection
- Invalid date format (not YYYY-MM-DD) fails schema validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array returns an error
- Malformed JSON body causes a 400-level response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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