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Ownership Record Completeness Audit — Record Quality, String Normalisation & Dataset Comparison
x402
Ownership Record Completeness Audit — Record Quality, String Normalisation & Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
Audits ownership record completeness by running record quality checks, string normalisation, and keyed dataset comparison on supplied documented_links and required_links data (up to 200 rows, 3 reports).
Price / call$0.01
Success—
StatusUnknown
How this service works
Ownership record completeness — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied ownership record completeness records (documented_links, required_links). 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 supplied ownership record data: (1) a record quality audit highlighting completeness issues, null values, and anomalies in documented_links and required_links; (2) a string normalisation report standardising company_group and other string fields; and (3) a keyed dataset comparison report showing row-level differences between the primary and secondary datasets, keyed on row_id.
Example use cases
Corporate ownership compliance gap check
“I need to audit our ownership records for Q4 — can you run a record quality audit and keyed comparison on these two datasets? The primary has 8 rows for company groups EMEA and APAC, each with documented_links and required_links counts from 2026-10-01 onwards, and I want to see where our documentation is incomplete.”
String normalisation before board reporting
“Before I submit our ownership records to the board, can you normalise the string fields across our 50-row dataset (company groups like 'Group A', 'group-a', 'GROUP A') so they're consistent, and also run a quality audit on the documented_links versus required_links figures?”
Cross-dataset ownership link discrepancy detection
“We have two versions of our ownership records — the current one and last quarter's — both with about 120 rows covering company groups North and South. Can you do a keyed dataset comparison to flag any rows where documented_links or required_links differ between the two versions?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-21-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to validate, normalise, and compare ownership record completeness data entirely offline — no live lookups or external data sources are involved. It is ideal when you have two versions of an ownership dataset (current vs reference) and need a structured audit of string consistency, record quality, and link count discrepancies across up to 200 rows. Prefer this over general-purpose data tools when your use case specifically involves documented_links vs required_links metrics organised by company_group and date.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, company_group, date, documented_links, required_links) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers schema rejection
- Numeric values outside the allowed range cause field-level errors
- Empty rows array (minItems: 1 not met) returns an error
- Mismatched row_ids between primary and comparison datasets may result in unmatched keys in the comparison report
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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