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Company Filing Inventories — Record Quality Audit, String Normalisation & Date-by-Category Pivot
x402
Company Filing Inventories — Record Quality Audit, String Normalisation & Date-by-Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).
Runs three local analytical reports on supplied company filing inventory records: a record quality audit, string normalisation, and a date-by-category pivot — for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Company filing inventories — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot for supplied company filing inventories records (available_filings, expected_filings). 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 generated from the supplied rows: (1) a record quality audit flagging completeness, nulls, and anomalies; (2) a string normalisation report standardising company_group and related text fields; and (3) a date-by-category pivot summarising the selected numeric field (available_filings or expected_filings) across company groups and dates. All analysis is performed locally with no external lookups.
Example use cases
Filing completeness audit for compliance team
“I need a quality audit of our company filing inventory — I have 80 rows with row_id, company_group, date, available_filings, and expected_filings. Can you run the record quality audit, string normalisation, and date pivot reports on the available_filings field so I can see where we have data gaps?”
Normalising messy company group labels
“Our filing inventory export has inconsistent company_group names and some dodgy string formatting. Run the string normalisation and quality audit reports on these 120 rows so I can see what needs cleaning before we submit.”
Date-category pivot for filing trend review
“I want to pivot our 200-row filing inventory by date and company group to see how expected_filings are distributed across periods. Can you run all three reports — quality audit, string normalisation, and the date-by-category pivot — using the expected_filings field?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-17-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of company filing inventory data and need all three of a quality audit, string normalisation, and date-by-category pivot in a single low-cost call ($0.01 USDC). It is ideal for offline/batch data quality checks where no live external lookup, prediction, or professional assessment is required. Prefer alternatives if you need real-time filing data retrieval, forecasting, or more than 200 rows processed at once.
Side effects
- Rows array exceeds 200 items — request will be rejected
- Missing required fields (row_id, date, available_filings, expected_filings) on any row — validation error
- Date values not matching YYYY-MM-DD pattern — schema validation failure
- Numeric fields outside allowed range (±1 trillion) — rejected
- Empty rows array — minimum 1 item required
- Invalid field enum value — must be 'available_filings' or 'expected_filings'
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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