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Company Headcount Observations — Record Quality Audit & String Normalisation
x402
Company Headcount Observations — Record Quality Audit & String Normalisation 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).
Audits and normalises up to 200 company headcount observation records (headcount, open_roles) producing two independent quality and string normalisation reports using local analytical engines.
Price / call$0.01
Success—
StatusUnknown
How this service works
Company headcount observations — Record quality audit + String normalisation: Record quality audit + String normalisation for supplied company headcount observations records (headcount, open_roles). 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 independent reports: (1) a record quality audit flagging issues such as missing values, out-of-range numbers, malformed dates, and duplicate row IDs across the supplied headcount/open_roles rows; (2) a string normalisation report detailing transformations applied to text fields like company_group. Both reports cover up to 200 input rows and are generated locally without external lookups.
Example use cases
HR data pipeline quality gate
“I'm loading headcount and open_roles observations into our data warehouse. Can you run a record quality audit and string normalisation check on these 150 rows before ingestion, so I can catch bad dates, nulls, or inconsistent company_group strings?”
Monthly workforce snapshot validation
“We pulled our monthly company headcount snapshot — 80 rows with company_group, date, headcount, and open_roles. Please audit the record quality and normalise any messy string fields so the data is clean for our BI dashboard.”
Open roles data cleansing for reporting
“Our open_roles and headcount data has come in from three different sources and the company group names are inconsistent. Can you run a string normalisation and quality audit on these 200 rows and tell me what needs fixing?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-12-001?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined record quality audit and string normalisation pass on company headcount observation data (headcount, open_roles) in a single cost-effective call. It is ideal for data pipeline validation gates, pre-ingestion cleansing, or periodic data health checks where no external lookups or predictive models are needed — just local analytical engines on up to 200 rows for $0.01 USDC.
Side effects
- Input array is empty or exceeds 200 rows — rejected with validation error
- Missing required fields (row_id, company_group, date, headcount, open_roles) in any row — row-level error reported
- date field does not match YYYY-MM-DD pattern — flagged as malformed in quality audit
- Payment not provided or insufficient USDC — request rejected before processing
- headcount or open_roles values outside ±1,000,000,000,000 range — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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