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Commercial Proposal Comparisons — Record Quality Audit + String Normalisation + IQR Outlier Flags
x402
Commercial Proposal Comparisons — 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 2026-10-17).
Runs three analytical reports on commercial proposal comparison data: record quality audit, string normalisation, and IQR-based outlier flagging for quoted_amount or delivery_days fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Commercial proposal comparisons — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied commercial proposal comparisons records (quoted_amount, delivery_days). 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 for the submitted proposal records: (1) a record quality audit highlighting missing, malformed, or inconsistent values; (2) string-normalised versions of text fields such as company_group; and (3) IQR-based outlier flags on the chosen numeric field (quoted_amount or delivery_days), identifying records that fall outside expected interquartile range bounds. Covers up to 200 rows per call with no external lookups.
Example use cases
Procurement outlier detection before award
“I have 80 vendor quotes from our last RFQ — can you audit them for data quality issues and flag any quoted_amount outliers using IQR analysis so I can spot suspiciously high or low bids before we make the award decision?”
Supplier name normalisation across proposal data
“Our proposal comparison spreadsheet has inconsistent company names like 'Acme Corp', 'acme corp', and 'ACME CORP' — can you run string normalisation and a record quality audit on these 50 rows so we can clean up the data?”
Delivery days outlier audit for contract review
“Before we sign contracts, can you check these 150 commercial proposal records for outliers in the delivery_days field and flag any records with data quality problems, using IQR-based outlier detection?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-24-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled data quality reports (quality audit, string normalisation, outlier flagging) on commercial proposal or vendor quote data in a single low-cost call. It is particularly suited for procurement workflows processing up to 200 rows where you want IQR-based outlier detection alongside basic data cleaning, without needing live lookups, predictions, or external integrations.
Side effects
- More than 200 rows submitted — request rejected due to maxItems limit
- Missing required fields (row_id, company_group, date, quoted_amount, delivery_days) on any row — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Numeric values outside allowed range (-1e12 to 1e12) — rejection
- Invalid field selector (must be 'quoted_amount' or 'delivery_days') — enum validation error
- Empty rows array — minItems violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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