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Invoice Ageing — Keyed Dataset Comparison + Explicit Missing-Value Fill
x402
Invoice Ageing — Keyed Dataset Comparison + Explicit Missing-Value Fill 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).
Compares two sets of invoice ageing records by row key and fills missing values in outstanding_amount or age_days fields, producing two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Invoice ageing — Keyed dataset comparison + Explicit missing-value fill: Keyed dataset comparison + Explicit missing-value fill for supplied invoice ageing records (outstanding_amount, age_days). 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 keyed dataset comparison identifying rows present in one dataset but not the other, and rows where the selected field (outstanding_amount or age_days) differs between the two datasets; (2) an explicit missing-value fill report showing which rows had null values in the target field and what values were imputed using local analytical engines. No external data sources or predictions are involved.
Example use cases
Month-end invoice reconciliation
“I have two invoice ageing snapshots from end of September and end of October, each with about 80 rows including row_id, instrument_group, date, outstanding_amount, and age_days. Compare them by row key and fill any missing outstanding_amount values so I can see what changed.”
Detecting gaps in age_days data
“My accounts receivable export has about 120 invoice rows but a lot of the age_days values are null. Can you run the missing-value fill analysis on the age_days field so I know which records are incomplete and get filled values?”
Reconciling two AR system exports
“We pulled invoice ageing data from two different systems — about 150 rows each — with matching row IDs, instrument groups, dates, outstanding amounts, and age in days. Run the keyed dataset comparison on outstanding_amount to show me where the two systems disagree.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-finance-07-190?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to compare two invoice ageing datasets by matching row keys and identify value differences or fill missing values in outstanding_amount or age_days — especially when both datasets have up to 200 rows and you want two bundled reports (comparison + fill) for a flat $0.01 fee. Prefer this over general-purpose data tools when your data is already structured as invoice ageing records with the exact required fields.
Side effects
- Exceeds 200-row limit — request rejected if either dataset has more than 200 rows
- Missing required fields (row_id, instrument_group, date, outstanding_amount, age_days) — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema validation failure
- Values outside allowed numeric range (±1,000,000,000,000) — rejected
- Empty rows array — minItems:1 constraint violated
- Payment not included or insufficient — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array | |
| method | string |
Tags
x402
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