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Company Record Completeness Audit — Record Quality, String Normalisation & Adjacent Row Changes
x402
Company Record Completeness Audit — Record Quality, String Normalisation & Adjacent Row Changes 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 local analytical reports (record quality audit, string normalisation, and adjacent row changes) on up to 200 company completeness records containing populated_fields and required_fields metrics.
Price / call$0.01
Success—
StatusUnknown
How this service works
Company record completeness — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes 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 flagging issues such as nulls, out-of-range values, and structural problems; (2) a string normalisation report identifying inconsistent or malformed string values in company_group and other text fields; and (3) an adjacent row changes report showing value differences between consecutive rows for the selected numeric field (populated_fields or required_fields). All processing is local — no external lookups or live data enrichment.
Example use cases
CRM completeness health check
“I've got a batch of company records from our CRM — each row has a company_group, a date, and counts for how many fields are populated vs how many are required. Can you audit these 120 rows for record quality issues, string normalisation problems, and flag any adjacent row changes in the populated_fields numbers?”
Data quality gate before warehouse load
“Before we load this company dataset into the warehouse, run all three reports — quality audit, string normalisation, and adjacent row changes — on these 80 company completeness rows focusing on the required_fields column, so we can catch any anomalies first.”
Weekly completeness drift detection
“Every Monday I want to check whether our company record completeness scores shifted week-over-week. Can you take these 200 rows of company group completeness data and run the adjacent row changes report along with the quality audit on populated_fields?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-01-200?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a batch of up to 200 company completeness records (with populated_fields and required_fields counts) and need all three analytical perspectives — quality audit, string normalisation, and adjacent row change detection — in a single low-cost call. It is ideal for offline, no-external-lookup audits where the data is already collected and you want structured diagnostic reports without any live enrichment or prediction. Prefer alternatives if you need real-time CRM lookups, ML-based anomaly scoring, or batch sizes exceeding 200 rows.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, company_group, date, populated_fields, required_fields) on any row causes rejection
- Invalid date format (not YYYY-MM-DD) triggers a schema validation failure
- Numeric values outside the allowed range (-1,000,000,000,000 to 1,000,000,000,000) are rejected
- Empty rows array or missing rows property returns a bad request error
- Payment of 0.01 USDC not included or rejected results in a 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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