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Data Reconciliation — Record Quality Audit + Keyed Dataset Comparison
x402
Data Reconciliation — Record Quality Audit + 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 2026-10-19).
Runs two independent analytical reports on supplied reconciliation data: a record quality audit and a keyed comparison between source_total and target_total across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — Record quality audit + Keyed dataset comparison: Record quality audit + Keyed dataset comparison 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 independent analytical reports for the supplied data: (1) a record quality audit flagging issues such as nulls, out-of-range values, or malformed entries across the rows, and (2) a keyed dataset comparison highlighting where source_total and target_total diverge, grouped by dataset_group and date. No external data is fetched; all analysis is performed locally on the supplied rows.
Example use cases
Monthly finance close reconciliation check
“I have two sets of 80 rows from our accounting close — each row has a row ID, a date in January 2025, a dataset group like 'accounts-payable' or 'accounts-receivable', and both a source_total from our ERP and a target_total from our data warehouse. Can you audit the record quality and compare the keyed datasets to find any mismatches?”
ETL pipeline output validation
“After running our nightly ETL job, I have 120 rows with source_total from the raw database and target_total from the transformed output, grouped by pipeline stage and dated 2025-03-10. Run a record quality audit and dataset comparison so I can see which rows have bad data or discrepancies.”
Cross-team dataset discrepancy investigation
“My team's dataset and the analytics team's dataset both have totals for the same 60 records dated throughout Q1 2025, grouped by product line. Can you compare them as source_total vs target_total and also audit the record quality to flag any nulls or anomalies?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-018?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two simultaneous analytical outputs — a quality audit and a keyed comparison — on a single batch of up to 200 reconciliation rows with source and target totals. It is ideal for ETL validation, financial close reconciliation, or any scenario where you need to both check data quality and compare two numeric columns keyed by row ID, date, and group. It does not perform live lookups, predictions, or external data enrichment — all analysis is purely local and deterministic on the supplied data.
Side effects
- More than 200 rows submitted — request exceeds the maxItems limit and will be rejected
- Missing required fields (row_id, dataset_group, date, source_total, target_total) on any row — validation error returned
- Date values not matching YYYY-MM-DD pattern — schema validation failure
- source_total or target_total outside allowed range (-1T to +1T) — value rejected
- Empty rows array (minItems: 1 violated) — error returned
- Malformed JSON payload — request rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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