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Data Reconciliation — Record Quality Audit + String Normalisation + Duplicate Key Report
x402
Data Reconciliation — Record Quality Audit + String Normalisation + Duplicate Key Report 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 analytical reports (record quality audit, string normalisation, and duplicate key detection) on up to 200 supplied data reconciliation rows comparing source and target totals.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report 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
Three independent analytical reports: (1) a record quality audit flagging missing, malformed, or out-of-range values; (2) a string normalisation report identifying and correcting inconsistencies in text fields like dataset_group; and (3) a duplicate key report listing any row_id values that appear more than once across the submitted rows.
Example use cases
ETL pipeline reconciliation audit
“I've got 50 reconciliation rows from our ETL run for November — each has a row_id, date, dataset_group called 'etl-nov', and source_total vs target_total. Can you run the quality audit, string normalisation, and duplicate key report on them so I can see where the data is broken?”
Financial data duplicate detection
“We're merging two financial datasets and I need to check for duplicate row IDs and record quality issues. Here are 120 rows with source_total and target_total values under dataset_group 'fin-merge-2024' — please give me the quality audit, string normalisation, and duplicate key reports.”
Dataset group name normalisation check
“Our dataset_group labels might have inconsistent casing or spacing across these 80 reconciliation records from our warehouse sync. Can you run the string normalisation and record quality audit on them so we can standardise before loading?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-192?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous analytical reports (quality audit, string normalisation, and duplicate key detection) on a batch of reconciliation records in a single call. It is ideal for ETL validation workflows, financial reconciliation pipelines, and data migration audits where source and target totals must be compared across up to 200 rows. It does not perform live lookups, predictions, or external actions — use it for offline, supplied-data analysis only.
Side effects
- More than 200 rows submitted — request rejected with validation error
- Missing required fields (row_id, dataset_group, date, source_total, target_total) — row-level validation failure
- Date not in YYYY-MM-DD format — schema validation error
- Numeric totals outside allowed range (±1,000,000,000,000) — validation rejection
- Empty rows array — schema minimum items error
- Payment failure or insufficient USDC balance — 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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