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Warehouse Pick Accuracy — Sequential Input-Change Attribution
x402
Warehouse Pick Accuracy — Sequential Input-Change Attribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).
Computes a waterfall attribution report showing how changes in total picks and correct picks between two periods affect warehouse pick accuracy.
Price / call$0.01
Success—
StatusUnknown
How this service works
Warehouse pick accuracy - Sequential input-change attribution: Sequential input-change attribution for Warehouse pick accuracy. Supplied retail figures only, up to 200 records. No platform access, live rates or order execution. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded sequential input-change attribution report.
Output
A bounded sequential input-change (waterfall) attribution report detailing how changes in total_picks and correct_picks between the before and after states each contributed to the overall pick accuracy change, up to 200 records of retail figures.
Example use cases
Post-process-change accuracy review
“Our warehouse just changed picking procedures last week. Before the change we had 500 total picks and 475 correct picks; after the change we had 520 total picks and 504 correct picks. Can you run a sequential attribution report to show how the shift in total picks and correct picks each contributed to the accuracy change?”
Monthly fulfillment performance comparison
“I want to understand what drove our pick accuracy improvement this month. Last month we had 2000 total picks and 1940 correct picks, and this month we have 2100 total picks and 2058 correct picks. Break it down with a waterfall attribution.”
Retail pilot warehouse A/B evaluation
“We ran a pilot in one warehouse and want to attribute the pick accuracy change. Baseline was 300 total picks and 285 correct picks; pilot period had 310 total picks and 300 correct picks. Give me the sequential input-change attribution so I know which factor mattered more.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-retail-pick-accuracy-waterfall?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to decompose a change in warehouse pick accuracy into its sequential contributing factors (total picks volume vs. correct picks count) between two time periods, specifically for retail fulfillment data. Prefer this over generic analytics tools when you want a structured, bounded waterfall attribution report at low cost for up to 200 records.
Side effects
- correct_picks exceeds total_picks — may return validation error or nonsensical accuracy
- total_picks of zero or negative — rejected by exclusiveMinimum constraint
- exceeding 200 records limit — request refused or truncated
- missing required before/after fields — schema validation failure
- non-numeric input values — type validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| after | object | |
| before | object |
Tags
x402
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