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Company Headcount Observations — Record Quality Audit, String Normalisation & Exact Row Deduplication
x402
Company Headcount Observations — Record Quality Audit, String Normalisation & Exact Row Deduplication 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, normalises, and deduplicates up to 200 company headcount observation records, returning three independent analytical reports using local engines — no external lookups or predictions.
Price / call$0.01
Success—
StatusUnknown
How this service works
Company headcount observations — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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 reports covering: (1) a record quality audit flagging malformed, missing, or out-of-range values in headcount and open_roles fields; (2) a string normalisation report standardising company_group and other string fields; and (3) an exact row deduplication report identifying and removing fully duplicate rows — all generated locally from the supplied data, with no external lookups.
Example use cases
Pre-analysis headcount data cleanup
“I have 120 rows of company headcount observations with company_group, date, headcount, and open_roles columns, and I want to clean them up before I run any analysis — can you audit the record quality, normalise the company name strings, and remove any exact duplicate rows?”
HR data pipeline validation step
“Before we load this batch of headcount data into our warehouse, can you check it for quality issues, normalise the company group labels, and flag any duplicated rows? It's about 80 records with row_id, company_group, date, headcount, and open_roles.”
Spotting bad data in open roles feed
“Our open_roles feed has been acting weird and I suspect there are duplicates and inconsistent company name formatting — can you run the record quality audit, string normalisation, and deduplication reports on these 60 headcount observations?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-12-191?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined data-quality operations — record auditing, string normalisation, and exact deduplication — on company headcount observation data in a single API call and want purely local, deterministic analysis without external lookups, predictions, or professional assessments. It is especially useful for data pipeline validation steps before loading headcount time series into a warehouse or analytics system.
Side effects
- Submitting more than 200 rows returns an error as the batch limit is exceeded
- Missing required fields (row_id, company_group, date, headcount, open_roles) cause validation errors on the input schema
- Dates not matching YYYY-MM-DD pattern are rejected by schema validation
- row_id values exceeding 80 characters are rejected
- Numeric values for headcount or open_roles outside the allowed range cause schema rejection
- Payment of 0.01 USDC must be included; missing payment results in 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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