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Data Reconciliation — String Normalisation + IQR Outlier Flags
x402
Data Reconciliation — String Normalisation + IQR Outlier Flags 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).
Normalises string fields and flags IQR-based outliers in source/target reconciliation row data, producing two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags 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
Two independent analytical reports covering: (1) string normalisation results for the dataset_group and related string fields across the supplied rows, and (2) IQR-based outlier flags identifying which rows have source_total or target_total values that fall outside the interquartile range. Reports are computed locally on the supplied data with no external lookups.
Example use cases
Financial ledger reconciliation outlier check
“I have 50 rows of daily ledger reconciliation data with source_total and target_total values across two groups — 'GL' and 'SubLedger' — covering January 2025. Can you normalise the string fields and flag any IQR outliers in source_total so I can see which dates have anomalous figures?”
ETL pipeline data quality audit
“After running our ETL job, I've got 120 rows comparing source database totals to target warehouse totals across three dataset groups. Run string normalisation and IQR-based outlier flagging on the target_total column and give me both reports so I can spot which rows need investigation.”
Monthly sales reconciliation anomaly detection
“We're reconciling monthly sales figures across regional groups for Q3 2024 — about 90 rows with source_total from our CRM and target_total from our ERP. Can you normalise the group label strings and flag statistical outliers in source_total using the IQR method?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-032?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost ($0.01 USDC) local statistical analysis of reconciliation row data — specifically string normalisation combined with IQR outlier detection — without requiring external data lookups, ML predictions, or professional assessments. Ideal for ETL validation, financial reconciliation audits, and data quality checks on up to 200 source/target row pairs where two bundled analytical reports are needed in a single call.
Side effects
- Fewer than 1 or more than 200 rows supplied — request rejected
- Missing required fields (row_id, dataset_group, date, source_total, target_total) — validation error
- Date strings not matching YYYY-MM-DD format — schema validation failure
- Numeric values outside ±1,000,000,000,000 range — schema rejection
- Invalid field enum value (must be 'source_total' or 'target_total') — error response
- Insufficient data variance for meaningful IQR computation (e.g. all identical values)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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