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Bundle Sales String Normalisation and Keyed Dataset Comparison
x402
Bundle Sales String Normalisation and Keyed 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 2026-10-26).
Performs string normalisation and keyed dataset comparison on two sets of bundle sales records (bundles_sold, component_units) for up to 200 rows each, returning 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Bundle sales — String normalisation + Keyed dataset comparison: String normalisation + Keyed dataset comparison for supplied bundle sales records (bundles_sold, component_units). 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 2 independent reports: (1) a string normalisation report covering the store_group and related text fields across the submitted rows, and (2) a keyed dataset comparison report identifying matching, differing, and missing rows between the two supplied datasets based on row_id keys, with field-level discrepancies in bundles_sold and component_units highlighted.
Example use cases
Reconcile POS vs warehouse bundle exports
“We pulled bundle sales data from our POS system and our warehouse separately. Both have row_id, store_group, date, bundles_sold, and component_units. Can you compare them row-by-row to find any discrepancies in bundles_sold or component_units, and also normalise the store_group strings in case there are naming inconsistencies?”
Audit end-of-month bundle sales figures
“I have our end-of-month bundle sales report and last month's actuals — both as keyed rows with store_group, date, bundles_sold, and component_units. Run a keyed comparison and string normalisation on both datasets so I can spot which rows changed or don't match up.”
Validate bundle data before merging into master
“Before I merge two bundle sales files into our master dataset, I need to normalise the store_group field and compare them by row_id to catch any duplicate or conflicting bundles_sold and component_units values. Each file has around 150 rows — can you run both reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-15-036?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation and keyed row-level comparison on bundle sales data (bundles_sold, component_units) in a single low-cost call. It is purpose-built for retail bundle data reconciliation and returns two independent reports for up to 200 rows at $0.01 USDC. Prefer it over generic data tools when your data specifically contains bundle and component unit fields keyed by row_id and store_group.
Side effects
- Exceeding 200 rows in either array returns a validation error
- Missing required fields (row_id, store_group, date, bundles_sold, component_units) causes row rejection
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Submitting only one dataset without the other may produce an incomplete comparison report
- Numeric values outside the allowed range cause validation errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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