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Data Reconciliation — IQR Outlier Flags + Keyed Dataset Comparison
x402
Data Reconciliation — IQR Outlier Flags + 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 2026-10-24).
Runs two analytical reports on supplied reconciliation data: IQR-based outlier flagging on a chosen numeric field, and a keyed row-by-row comparison between two datasets, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — IQR outlier flags + Keyed dataset comparison: IQR outlier flags + 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: (1) an IQR outlier flags report identifying rows where the chosen numeric field (source_total or target_total) falls outside the interquartile range bounds, and (2) a keyed dataset comparison report showing row-by-row differences between the primary and secondary dataset, matched by row_id, dataset_group, and date.
Example use cases
Monthly finance close reconciliation check
“Here are my source and target ledger totals for October — 80 rows each keyed by row_id and dataset_group. Flag any IQR outliers in source_total and compare the two datasets side by side so I can see where they diverge.”
ETL pipeline output validation
“I ran two ETL jobs and got slightly different output datasets — both have around 120 rows with source_total and target_total. Can you run IQR outlier detection on target_total and do a keyed comparison between the two outputs so I can find which rows are off?”
Vendor billing discrepancy investigation
“I have our internal billing records as the source dataset and the vendor's invoice totals as the target, about 60 rows matched by row_id. Please flag any outliers using IQR on source_total and compare both datasets key-by-key to highlight any billing mismatches.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-179?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two complementary analytical views on reconciliation data in a single call: statistical outlier detection (IQR-based) on a numeric field, plus a structured keyed comparison between two dataset versions. Prefer this over single-purpose tools when you want both anomaly flagging and diff-style comparison without making two separate requests. Best suited for financial reconciliation, ETL validation, or audit workflows involving up to 200 rows of source/target paired data.
Side effects
- Missing required fields (row_id, dataset_group, date, source_total, target_total) returns a validation error
- Exceeding 200 rows in either dataset returns a rejection
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Null values in both source_total and target_total on the same row may produce incomplete outlier analysis
- Mismatched row_id keys between primary and secondary datasets result in unmatched rows in the comparison report
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array |
Tags
x402
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