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Company Filing Inventories — Record Quality Audit + String Normalisation + Exact Row Deduplication
x402
Company Filing Inventories — Record Quality Audit + String Normalisation + Exact Row Deduplication 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).
Audits record quality, normalises strings, and deduplicates exact rows for supplied company filing inventory data (available_filings, expected_filings), producing 3 independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Company filing inventories — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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 3 independent analytical reports for the supplied rows: (1) a record quality audit flagging missing values, format violations, and anomalies; (2) a string normalisation report showing standardised versions of text fields; and (3) an exact row deduplication report identifying and removing identical rows. All processing is local with no external lookups.
Example use cases
Pre-submission filing data cleanup
“Before I submit our quarterly compliance report, can you audit and clean this batch of 80 company filing inventory rows? I need you to check record quality, normalise any string inconsistencies in the company_group field, and flag any exact duplicate rows across available_filings and expected_filings.”
Identify duplicate filings across groups
“I have 150 rows of filing inventory records across multiple company groups and I suspect there are duplicates. Can you run exact row deduplication and a quality audit on this dataset so I know which rows to remove?”
String normalisation for filing data merge
“We're merging two filing inventory datasets and the company_group names aren't consistently formatted. Can you normalise the strings and run a deduplication check on these 120 rows covering available_filings and expected_filings so we get a clean merged output?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-17-191?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need offline, deterministic data quality processing for company filing inventory records specifically — covering quality auditing, string normalisation, and deduplication together in a single call at low cost. Prefer it over general-purpose data tools when you have structured filing inventory data with available_filings and expected_filings fields and want three reports in one request without any external lookup or live data dependency.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, available_filings, expected_filings) cause row-level validation errors
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Numeric values outside allowed range (-1T to 1T) cause rejection
- Empty rows array returns an error
- Payment not processed results in 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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