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Data Reconciliation Analytics — Record Quality Audit, String Normalisation & Two-Measure Correlation
x402
Data Reconciliation Analytics — Record Quality Audit, String Normalisation & Two-Measure Correlation 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 local analytical reports on up to 200 reconciliation rows: a record quality audit, string normalisation, and Pearson/rank correlation between source_total and target_total.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — Record quality audit + String normalisation + Two-measure correlation: Record quality audit + String normalisation + Two-measure correlation 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 for the submitted reconciliation rows: (1) a record quality audit flagging completeness, format, and validity issues; (2) a string normalisation report standardising text fields like dataset_group; (3) a two-measure correlation report quantifying the statistical relationship between source_total and target_total across all rows. All analysis is performed locally with no external lookups.
Example use cases
ETL pipeline data quality check
“I've got 120 rows of ETL output with row IDs, dates from 2025-01-01 onwards, dataset group 'payments', and source and target totals. Can you run the record quality audit, string normalisation, and two-measure correlation reports to see how clean and consistent the data is?”
Monthly reconciliation mismatch investigation
“We have 80 reconciliation rows from our monthly close — group 'ledger-oct', dates in October 2025, source_total from our GL and target_total from our bank feed. Run all three analytics reports so I can understand data quality issues and whether the two totals are correlated.”
Onboarding data integrity validation
“Before we import this vendor dataset into our system, can you audit the 150 rows for record quality issues, normalise any inconsistent strings in the dataset_group column, and tell me the correlation between source_total and target_total? Dates range from 2024-06-01 to 2024-09-30, group is 'vendor-onboarding'.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-204?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled, cost-efficient set of three reconciliation-focused analytical reports (quality audit, string normalisation, correlation) on a batch of up to 200 rows for a flat $0.01 USDC fee. It is ideal for ETL validation, monthly financial close reconciliation checks, or pre-import data integrity reviews where no live external data, predictions, or professional assessments are needed. Prefer it over single-report alternatives on the same platform when all three report types are needed simultaneously.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, source_total, target_total) cause row-level rejection
- Invalid date format (non-YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside ±1,000,000,000,000 are rejected by schema bounds
- Empty rows array (minItems:1 not met) returns a schema validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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