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Company Headcount Observations — Record Quality Audit + String Normalisation + Grouped Numeric Summary
x402
Company Headcount Observations — Record Quality Audit + String Normalisation + Grouped Numeric Summary 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).
Runs three analytical reports on company headcount observation rows: a record quality audit, string normalisation, and a grouped numeric summary for headcount or open_roles data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Company headcount observations — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + Grouped numeric summary 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 returned together: (1) a record quality audit flagging issues such as nulls, out-of-range values, or malformed dates in the headcount observation rows; (2) a string normalisation report cleaning and standardising company_group label values; (3) a grouped numeric summary aggregating the selected field (headcount or open_roles) by company_group. All computed locally with no external lookups.
Example use cases
HR data quality audit before dashboard refresh
“I've got a batch of headcount observation rows from the past quarter — can you audit the record quality, normalise the company group strings, and give me a grouped numeric summary of headcount across company groups so I can trust the data before refreshing our HR dashboard?”
Open roles pipeline review by segment
“We have 150 rows of headcount observations with open_roles data across several company groups. Can you run all three reports — quality audit, string normalisation, and grouped summary — focusing on the open_roles field, so I can see hiring gaps by segment?”
Cleaning up messy company name strings in headcount exports
“My headcount export has inconsistent company_group labels and I need to know if there are data quality issues too. Can you do a record quality audit, normalise the company group strings, and produce a grouped headcount summary for these rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-12-194?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 company headcount observation rows and need three bundled analytical operations — quality audit, string normalisation, and grouped numeric summary — in a single low-cost call. It is ideal for pre-processing or validating HR/workforce datasets before downstream analysis or dashboard ingestion, and when you do not need live data lookup, predictions, or external enrichment. The $0.01 flat price for all three reports makes it highly economical compared to calling separate cleaning and summarisation services.
Side effects
- Rows array is empty or exceeds 200 items — request rejected with validation error
- Invalid date format (not YYYY-MM-DD) causes row-level errors in the quality audit
- Missing required fields (row_id, company_group, date, headcount, open_roles) causes schema validation failure
- Null numeric values for both headcount and open_roles may reduce usefulness of numeric summary
- Invalid field enum value (not 'headcount' or 'open_roles') causes rejection
- Extremely large numeric values near the ±1e12 boundary may produce edge-case audit flags
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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