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Company Headcount Observations — Record Quality Audit + String Normalisation + IQR Outlier Flags
x402
Company Headcount Observations — Record Quality Audit + String Normalisation + IQR Outlier Flags 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 record quality, normalises strings, and flags IQR-based outliers across up to 200 company headcount observation rows, returning 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Company headcount observations — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied company headcount observations records (headcount, open_roles). 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
Three independent analytical reports covering: (1) a record quality audit identifying missing, malformed, or inconsistent fields; (2) string normalisation results for company_group and other text fields; and (3) IQR-based outlier flags indicating which rows contain anomalous headcount or open_roles values. All analysis is performed on the supplied data with no external lookups.
Example use cases
Pre-pipeline headcount data quality check
“Before I load this month's headcount snapshots into our data warehouse, can you run a full record quality audit, normalise the company_group names, and flag any IQR outliers in the headcount field across these 120 rows?”
Spotting anomalous open roles in HR data
“I have 80 rows of company staffing observations with headcount and open_roles values — can you check for record quality issues, clean up the string fields, and flag which open_roles values are outliers according to IQR?”
Workforce data cleanup for reporting
“We're preparing a workforce report and I need to audit these 45 headcount observation records for data quality problems, normalise the company group labels, and identify any headcount figures that look like outliers — can you run all three checks?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-12-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 company headcount observation rows and need three simultaneous analytical passes — quality auditing, string normalisation, and IQR outlier detection — in a single low-cost call. It is ideal for pre-ingestion data validation, HR data pipeline hygiene, or workforce reporting prep where all three checks are needed together rather than separately. Prefer sibling endpoints if you only need one or two of these operations, or if you need deduplication, distribution analysis, or base-100 indexing instead.
Side effects
- Rows exceeding 200 items will be rejected — ensure input array has at most 200 entries
- Missing required fields (row_id, company_group, date, headcount, open_roles) per row will cause validation errors
- Dates not matching YYYY-MM-DD pattern will be flagged or rejected
- Numeric values outside allowed range (±1 trillion) will fail schema validation
- Empty rows array (minItems: 1 not met) will return an error
- Invalid field enum value (must be 'headcount' or 'open_roles') will cause a bad request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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