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Data Reconciliation — Record Quality Audit + String Normalisation + Secondary-Measure Weighted Mean
x402
Data Reconciliation — Record Quality Audit + 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 2026-10-19).
Runs three bundled analytical reports on up to 200 reconciliation rows: a record quality audit, string normalisation, and a secondary-measure weighted mean across source_total and target_total fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean for supplied data reconciliation records (source_total, target_total). 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
Returns three independent analytical reports: (1) a record quality audit flagging issues in the supplied rows, (2) a string normalisation report cleaning text fields, and (3) a secondary-measure weighted mean computed over the selected field (source_total or target_total). All three reports are based solely on the supplied data with no external lookups.
Example use cases
Monthly financial reconciliation quality check
“I need to audit the quality of our monthly reconciliation records between the source and target systems — can you run a record quality audit, string normalisation, and weighted mean on these rows for November 2026 across our 'revenue' and 'expense' dataset groups using source_total as the field?”
ETL pipeline data validation
“After our ETL run, I have up to 150 rows with source_total and target_total for dataset groups 'orders' and 'refunds' dated between 2026-09-01 and 2026-09-30 — please run the record quality audit, string normalisation, and secondary-measure weighted mean reports on them using target_total as the field.”
Dataset group discrepancy investigation
“Something looks off between our source and target totals for the 'inventory' and 'adjustments' groups in Q3 2026 — can you run all three reports (quality audit, string normalisation, weighted mean) on these 80 reconciliation rows so I can see where the issues are?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-205?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled local analytical operations — record quality audit, string normalisation, and weighted mean — on reconciliation data in a single cheap call ($0.01 USDC). It is ideal for batch validation of up to 200 source/target reconciliation rows without needing live lookups, predictions, or external data enrichment. Prefer it over separate analytics calls when all three report types are needed simultaneously on the same dataset.
Side effects
- Rows array exceeds 200 items — request will be rejected
- Missing required fields (row_id, dataset_group, date, source_total, target_total) — validation error
- Date fields not matching YYYY-MM-DD pattern — schema validation failure
- source_total or target_total outside ±1,000,000,000,000 range — value rejected
- Empty rows array (minItems: 1 not met) — request rejected
- Invalid field enum value (not 'source_total' or 'target_total') — schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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