Attribute fundamentals
An attribute describes a single piece of product information.
Examples:
Brand
Product Name
Life Stage
Voltage
Pack Quantity
Net Weight (g)
Wood Species
Attributes can apply across multiple families or only a single family, depending on your schema design.
Every attribute has three important parts:
Type (controls the format of the data)
LoVs (optional lists of allowed values)
Behaviour (how SKULaunch stores, validates and enriches the data)
Attribute types
SKULaunch supports a wide range of attribute types.
Each type has its own behaviour, rules and impact on AI enrichment and data validation.
Below is a description of each type.
1. Text
Free-form text for short strings.
Examples:
Brand, Colour Name, Model Name
Behaviour:
Accepts any string
No validation beyond basic formatting
AI extracts explicit text values
2. Textarea
Long-form text used for descriptions or multi-sentence content.
Examples:
Long Description, Ingredients, Care Instructions
Behaviour:
Supports paragraphs
Used for content generation
Not validated structurally
3. Identifier
Machine-focused string intended to uniquely identify a product.
Examples:
MPN, SKU code, GTIN (string)
Behaviour:
Not normalised
AI only fills when explicitly detected
4. Number
Numeric values without units.
Examples:
Pack Quantity, Protein Percentage, Wattage (if stored without units)
Behaviour:
Only accepts numeric values
AI normalises numbers
No unit interpretation
5. Dimension
Numbers with units.
Examples:
Length, Width, Height, Volume, Weight
Behaviour:
Supports unit detection and normalisation
AI can convert units
Ensures consistent measurement data
6. Boolean
Yes/No fields.
Examples:
Is Organic, Waterproof, Battery Included
Behaviour:
Accepts true or false
AI classifies explicit or strongly implied values
7. Date / Time / Timestamp
Structured date and time fields.
Examples:
Release Date, Expiry Date, Manufacturing Date
Behaviour:
Validates against date formats
AI extracts explicit dates only
8. Simpleselect
One value from a predefined List of Values (LoV).
Examples:
Life Stage, Food Format, Country of Origin
Behaviour:
Must match one LoV value
AI maps extracted text to the closest allowed value
9. Multiselect
Multiple values from a predefined LoV.
Examples:
Suitable For, Features, Compatible Models
Behaviour:
Allows multiple selections
AI may output several matches
10. Range
A numeric range.
Examples:
Voltage Range, Temperature Range, Age Range (minimum/maximum)
Behaviour:
Stores a lower and upper bound
Validates numeric structure
AI can extract ranges when explicitly stated
11. Price
Structured price information.
Examples:
MSRP, List Price, Wholesale Price
Behaviour:
Stores amount and currency
Validates number format
AI only extracts explicit prices
12. Rating
A scored rating.
Examples:
Energy Rating, Safety Rating, Efficiency Rating
Behaviour:
Stores a numeric score with optional scale
AI extracts explicit ratings only
13. Image
An image asset.
Examples:
Main Image, Additional Image
Behaviour:
Stores a single uploaded image
Allows validation and replacement
Used by AI Image Extractor to pull text from packaging
14. Video
Video asset storage.
Examples:
Demo Video, Installation Video
Behaviour:
Stored as uploaded video or URL
AI does not interpret video content
15. File
Any uploaded document.
Examples:
Datasheet PDF, User Manual, Warranty Document
Behaviour:
Stores the file in SKULaunch
Can be used by the AI File Extractor
16. URL
Single web link.
Examples:
Manufacturer Page, Product Link
Behaviour:
Must be a valid URL
AI populates only if explicitly present
17. Multiurls
Multiple web links.
Examples:
Download Links, Safety Certifications, Additional Resources
Behaviour:
Accepts multiple URLs
Validates formatting
18. Geolocation
Latitude/longitude coordinates.
Examples:
Manufacturing Location, Origin Coordinates
Behaviour:
Stores structured coordinate pairs
AI does not extract geolocation data
19. Multivalue
Multiple free-form text entries.
Examples:
Alternate Names, Synonyms, Internal Tags
Behaviour:
Allows an unstructured list of values
Unlike multiselect, values are not restricted by a LoV
20. Table
A structured multi-row, multi-column dataset.
Examples:
Technical Specifications Table, Size Chart, Nutritional Breakdown
Behaviour:
Stores tabular data
Rows and columns are defined per attribute
AI does not auto-populate tables in current versions