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Data Reconciliation — String Normalisation + Secondary-Measure Weighted Mean
x402
Data Reconciliation — 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 2026-10-24).
Normalises string fields and computes a secondary-measure weighted mean across supplied source/target reconciliation rows (up to 200 rows, 2 reports per call).
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean for supplied data reconciliation records (source_total, target_total). 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 per call: (1) a string-normalised view of the submitted reconciliation rows and (2) a secondary-measure weighted mean computed over the chosen field (source_total or target_total), grouped across the provided dataset_group and date dimensions. No external lookups or predictions are included — all computation is local and based solely on the supplied data.
Example use cases
Monthly finance reconciliation across regions
“I have 150 rows of source and target revenue figures across our EMEA and APAC dataset groups for September 2026 — can you run string normalisation on them and compute the weighted mean report on the target_total field so I can see where the discrepancies are?”
Audit source vs target inventory counts
“We're comparing source and target inventory totals for 80 SKU rows dated between 2026-09-01 and 2026-09-30, grouped into 'warehouse-a' and 'warehouse-b' — please run the string normalisation and secondary-measure weighted mean on source_total and give me the two analytical reports.”
Cross-system sales data validation
“Can you reconcile 50 rows of daily sales data where source_total is from our CRM and target_total is from our ERP, across dataset groups 'online' and 'retail' for Q3 2026? I need the weighted mean computed on source_total with string normalisation applied.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need local, offline-style string normalisation combined with secondary-measure weighted mean analytics on reconciliation data (source vs target totals) without any external API calls, live lookups, or AI predictions. It is well-suited for batch reconciliation jobs of up to 200 rows where you want two standardised analytical reports in a single $0.01 call. Prefer alternatives if you need row counts above 200, live data enrichment, or prediction-based anomaly detection.
Side effects
- Exceeds 200-row limit — request rejected with validation error
- Missing required fields (row_id, dataset_group, date, source_total, target_total) — row rejected
- Date field not matching YYYY-MM-DD pattern — validation failure
- source_total or target_total outside ±1,000,000,000,000 range — schema rejection
- Empty rows array — minimum 1 item required
- Invalid field enum value (not 'source_total' or 'target_total') — schema error
- Payment failure or insufficient USDC balance — call not processed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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