R2.2 / Availability & Fulfillment Intelligence
Delivery & Fulfillment Coverage Dataset
Planned dataset for delivery promise, fulfillment options, slot availability, fees labels, and public evidence.
Retail & E-commerce
Availability & Fulfillment Intelligence
Direct file delivery / API access
Schema preview
Fulfillment Coverage
What it captures
- Retailer, marketplace, or seller
- Market, store, and channel
- SKU, category, and brand
- Price, promotion, availability, or content attribute
- First seen, last seen, and version status
- Evidence URL, screenshot, and QA status
Decision workflows supported
- Fulfillment coverage comparison
- Delivery promise monitoring
- Operations planning inputs
Buyer roles
- Category and merchandising leaders
- Revenue management teams
- Marketplace operators
- Brand and retail media teams
- Data and AI teams
Delivery models
- Direct file delivery
- API access
- Cloud push
Decision workflows supported
Pricing strategy
Monitor offer changes, bundle mechanics, channel variance, and competitor moves.
CVM / retention
Feed churn, retention, and portfolio-risk workflows with structured market signals.
Data & AI ingestion
Use stable IDs, schema fields, evidence references, and version states in AI pipelines.
Regulatory transparency
Review public terms, fair-use labels, evidence, and change history without overclaiming legal status.
Sample structure
Non-downloadable field preview
This preview shows the record structure without presenting field examples as live data.
Dataset scope
Planned; location and retailer dependent.
Future expansion area; public sources only.
Source URL, Capture timestamp, Screenshot archive, Page or document archive where available, Version hash, QA status
Proof fields travel with the record.
Records are designed to preserve the source, capture state, and QA context needed for downstream review.
Offer change trail.
Related datasets
Adjacent signals
Next step
Request a sample or map this dataset to your workflow.
DAISORO can discuss market scope, evidence depth, delivery model, and sample structure for your workflow.