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Procurement Research — Adjacent Row Changes + IQR Outlier Flags
x402
Procurement Research — Adjacent Row Changes + 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 2026-10-19).
Analyzes up to 200 procurement records to detect adjacent-row value changes and flag IQR-based outliers in quoted_amount or quoted_units fields, producing two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Procurement research — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied procurement research records (quoted_amount, quoted_units). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent analytical reports: (1) an adjacent row change report showing delta values between consecutive procurement rows for the selected field, and (2) an IQR outlier flagging report identifying which rows fall outside the interquartile range bounds — both computed locally without external lookups or predictions.
Example use cases
Vendor quote anomaly audit
“I have 80 procurement quotes from our vendors with dates, company groups, and quoted_amount values. Can you run adjacent row change analysis and IQR outlier flagging on the quoted_amount field to identify any suspicious price jumps or statistical outliers I should review?”
Procurement unit quantity spike detection
“We've got 150 procurement records and I'm worried some quoted_units values look way off compared to neighbors. Can you run the adjacent row change and IQR outlier analysis on the quoted_units field so I can see which rows are flagging as anomalous?”
Multi-supplier quote comparison audit
“I need to audit these 60 procurement records across group-a and group-b suppliers for the quoted_amount field — can you detect any big row-to-row changes and flag IQR outliers so I know which quotes need a second look?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-11-157?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 procurement records and need both adjacent-row delta analysis and IQR-based outlier detection in a single low-cost call ($0.01 USDC). Ideal for automated procurement auditing pipelines where you want statistical anomaly detection without live lookups, external APIs, or professional assessments. Prefer this over general-purpose analytics when your data specifically has row_id, company_group, date, quoted_amount, and quoted_units structure.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, company_group, date, quoted_amount, quoted_units) cause validation failure
- Invalid date format (must be YYYY-MM-DD) causes schema rejection
- Field selector not one of 'quoted_amount' or 'quoted_units' causes validation error
- Fewer than 1 row provided — minimum not met
- Numeric values outside ±1,000,000,000,000 range rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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