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Company Headcount Observations — Record Quality Audit + String Normalisation + Base-100 Series
x402
Company Headcount Observations — Record Quality Audit + String Normalisation + Base-100 Series 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 supplied company headcount time-series data: a record quality audit, string normalisation, and a base-100 indexed series — all processed locally without external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Company headcount observations — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series 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 separate analytical reports delivered together: (1) a record quality audit flagging issues such as nulls, out-of-range values, and structural problems across the supplied rows; (2) a string normalisation report showing cleaned and standardised versions of company group labels; and (3) a base-100 rebase series for the selected field (headcount or open_roles), indexed against the specified base row.
Example use cases
Workforce data quality review
“I have 120 rows of monthly headcount and open roles data across several company groups — can you run a full record quality audit on them and flag any issues, normalise the company group name strings, and give me a base-100 series rebased from the first row for the headcount field?”
Headcount growth indexing for reporting
“Take these quarterly headcount observations for our three business units and produce a base-100 indexed series starting from row 0 so I can compare relative growth across groups on the same scale.”
Cleaning messy HR dataset before analysis
“We've got 80 rows of headcount records pulled from different sources and the company group names are inconsistently formatted — can you normalise those strings and run a quality audit to find any bad or missing values before we analyse the data?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-12-201?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of company headcount or open roles time-series data and need three analytical passes in a single call — record quality auditing, string normalisation of company group labels, and base-100 series rebasing — at a fixed low cost. Prefer it over building custom pipelines when you need rapid, offline (no external lookup) data quality and normalisation checks on workforce observation datasets without requiring live HR system integration.
Side effects
- Fewer than 1 or more than 200 rows supplied — request rejected
- Date values not matching YYYY-MM-DD format — validation error
- base_index out of range (0–199) or beyond actual row count — error returned
- Null or missing required fields (row_id, company_group, date) — quality audit may flag or reject rows
- Numeric values outside allowed range — flagged or rejected
- Payment not confirmed via x402 protocol — request not processed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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