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Data Reconciliation: Base-100 Series + Two-Measure Correlation
x402
Data Reconciliation: Base-100 Series + 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).
Generates a base-100 index series and a two-measure correlation report for up to 200 rows of source/target reconciliation data using local analytical engines.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — Base-100 series + Two-measure correlation: Base-100 series + Two-measure correlation 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 analytical reports: (1) a base-100 index series rebasing the chosen field (source_total or target_total) relative to a specified row, and (2) a two-measure correlation report quantifying the statistical relationship between source_total and target_total across the supplied rows. No external data is fetched; all computation is performed on the submitted data using local analytical engines.
Example use cases
Monthly budget vs actuals reconciliation
“I have 60 rows of monthly budget figures — each row has a row_id, date, dataset_group called 'budget-ops', a source_total (planned spend), and target_total (actual spend). Can you rebase the source_total series to 100 starting at row 0 and also compute the correlation between the two measures so I can see how closely actuals track the plan?”
Cross-system sales data alignment
“We pull sales totals from two systems each day and I want to know if they're diverging. I've got 90 rows with row_ids, dates, dataset_group 'sales-reconcile', source_total from system A, and target_total from system B. Please build a base-100 index on source_total from the first row and show me the correlation between the two series.”
Inventory count source-target drift analysis
“Can you run a base-100 normalization and a two-measure correlation on my inventory reconciliation data? I have 120 rows — each has a row_id, date, dataset_group 'warehouse-east', source_total as the ERP count, and target_total as the physical count. Start the index at row 0 on source_total.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-165?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have already collected source and target numeric totals and need two offline analytical outputs — a base-100 time series and a correlation coefficient — without any external data fetching, prediction, or professional assessment. It is ideal for reconciliation workflows where you want to normalize a series to a common baseline and simultaneously quantify how closely the two measures track each other, all in a single low-cost call for up to 200 rows.
Side effects
- Fewer than 1 or more than 200 rows submitted — request rejected
- base_index value exceeds the number of supplied rows — index out of bounds error
- Null values in source_total or target_total may affect correlation calculation
- Dates not matching YYYY-MM-DD pattern — schema validation failure
- row_id or dataset_group fields exceeding 80-character limit — validation error
- Values outside the ±1,000,000,000,000 range — rejected by schema bounds
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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