AI Mini Scan
Enrich product data without inventing product facts
Descriptions, specifications and classifications are incomplete or inconsistent across systems. A scan identifies which gaps can be resolved from trusted information and which still require editorial or supplier input.
- Intended result
- More complete and consistent product records with an editorial approval step.
- Human approval
- Required
- Integration
- Likely
01
Situation
Descriptions, specifications and classifications are incomplete or inconsistent across systems. A scan identifies which gaps can be resolved from trusted information and which still require editorial or supplier input.
02
Current problem
Teams manually compare supplier files, catalogue records and descriptions to fill recurring gaps.
03
Desired change
The workflow proposes normalized attributes, classifications and descriptions with source evidence.
04
Possible workflow
-
Compare
Identify missing or inconsistent fields
Evaluate records against required attributes and category rules.
-
Propose
Generate structured changes
Use trusted supplier data, validation rules and controlled language generation.
-
Publish
Review before updating systems
Approve or reject each proposal before changing the catalogue or webshop.
05
Human control
Editors verify factual accuracy, tone, category fit and publication readiness.
06
Technology and data
- Product sources
- PIM, ERP, supplier feeds and approved product documentation.
- Validation
- Required fields, formats, vocabularies and category-specific rules.
- Editorial assistance
- Language generation constrained by verified facts and brand guidance.
07
Boundaries
- Every proposed fact needs an attributable source.
- Conflicting supplier data requires a resolution owner.
- Category and channel rules must be explicit.
- Editors approve changes before publication.
- Generated facts require a trusted source
- Category rules differ across catalogues
- Publication must remain controlled