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Company Record Completeness — Record Quality Audit + String Normalisation + IQR Outlier Flags
x402
Company Record Completeness — 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 2026-10-15).
Runs three analytical reports (record quality audit, string normalisation, and IQR-based outlier detection) on up to 200 rows of company record completeness data (populated_fields, required_fields).
Price / call$0.01
Success—
StatusUnknown
How this service works
Company record completeness — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied company record completeness records (populated_fields, required_fields). 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 or malformed values, (2) string normalisation results for text fields like company_group, and (3) IQR-based outlier flags for the selected numeric field (populated_fields or required_fields), all derived from the supplied rows without any external lookups.
Example use cases
CRM data quality health check
“I have 120 rows of CRM record completeness data with company_group, date, populated_fields, and required_fields — can you audit the quality, normalise the strings, and flag any IQR outliers in populated_fields so I can see which records are problematic?”
Identifying under-populated company groups
“Run the record quality audit and IQR outlier analysis on these 80 rows of company completeness records — I want to know which company groups have an unusually low or high required_fields count compared to the rest.”
Pre-pipeline data validation for completeness metrics
“Before we load this batch of 200 company record completeness rows into our data warehouse, can you do a quick quality audit, string normalisation, and outlier flag pass on the populated_fields column to catch anything weird?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-01-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, low-cost, three-in-one data quality pass on a batch of company record completeness rows — specifically when you want string normalisation, record quality auditing, and IQR-based outlier flagging in a single call. It is ideal for pre-ingestion validation of CRM or ERP completeness metrics. Do not use it when you need live external data lookups, predictive modelling, or analysis of more than 200 rows at once.
Side effects
- Fewer than 1 or more than 200 rows submitted — rejected with validation error
- Invalid date format (not YYYY-MM-DD) — row rejected or error returned
- Missing required fields (row_id, company_group, date, populated_fields, required_fields) — validation failure
- Non-numeric values where numbers expected for populated_fields or required_fields — schema rejection
- IQR outlier detection may be unreliable with very small row counts (fewer than ~10 rows)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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