Ten spreadsheets
per product.
Make it one record.

A PIM you set up yourself in days, not months. Every attribute, asset and channel value on one central record, with variants modelled the way you actually sell.

The Notes App for Power Users - Techbeta X Webflow Template
The Notes App for Power Users - Techbeta X Webflow Template
The Notes App for Power Users - Techbeta X Webflow Template
Screenshot of a Taxonomy Builder tool showing attributes for Power Drills and Angle Grinders, with teal highlighting reused attributes and grey for new attributes.
One
Record per product, everywhere
Any
Variant axis: size, colour, pack

<2

Minutes to build a schema

50+

Attributes suggested per category

Any

Product category, any industry
How it works

The many-versions problem. Finally solved.

Your products live in ERP exports, supplier files and channel spreadsheets, and no two agree. SKULaunch gives every product one central record that every team, supplier and channel works from. Standalone, or as the core of the full pipeline.

🗂️

Central product records

Every attribute, description, asset and channel value in one place. Change it once, it's changed everywhere.

🧬

Variant modelling

Parents and children done properly. Shared attributes inherited, differences captured at variant level, any axis.

🌳

Taxonomies & attributes

AI-suggested category trees and attribute schemas keep every record structured from day one.

SINGLE VERSION OF THE TRUTH

When everyone edits a copy, nobody has the answer.

A catalogue managed in spreadsheets forks every time someone hits Save As. SKULaunch holds one record per product and everything reads from it: your team edits it, suppliers feed it, channels publish from it. When a spec changes, it changes in one place and every output follows.

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One record per product: attributes, content, assets and channel values together
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Teams and suppliers work on the record itself, never on copies
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Every change tracked: who changed what, and when
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Channel outputs generated from the record, so they can't drift
Schema detail for Voltage attribute showing name as Voltage, data type Number, unit V (volts), accepted values 10.8, 12, 14.4, 18, 36, 54, required Yes, used in 14 categories.
VARIANT MODELLING

Sizes, colours, pack sizes. Modelled once, correctly.

Variants are where spreadsheet catalogues fall apart: fifty rows repeating the same brand and material, differing only in one column. SKULaunch models the parent once and lets each variant carry only what makes it different. Add a size without rebuilding the product.

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Any variant axis: size, colour, pack size, material, voltage
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Shared attributes inherited from the parent, overridden only where they differ
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Add or retire a variant without touching the rest of the product
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One parent, many variants: not fifty rows repeating the same data
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Parent/child formats mapped per channel at publish
Attribute library listing shared definitions for Voltage, Brand, IP Rating, and Weight with their data types, usage notes, and number of categories used.
TAXONOMIES & ATTRIBUTES

A category tree that fits your catalogue. Built in minutes.

A PIM is only as good as its structure, and structure is the part teams put off. SKULaunch's AI suggests the category tree and the attribute schema for each category: field names, types, units, accepted values. Edit what it suggests, and your records are validated against it from the first import.

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AI-suggested category trees for any industry
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Attribute schemas per category: types, units, accepted values
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Shared attribute library: define once, reuse across the tree
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Validation rules keep every record consistent as the catalogue grows
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Powered by the same AI behind Schema & Attributes
Review queue showing three tools: Makita DHP484Z pending review with options to approve, edit, or regenerate; Bosch GSB 18V-55 approved; DeWalt DCD796 pending review.
also in data structure

Explore the full platform

PIM sits at the core of Data Structure. Explore the rest.

DATA STRUCTURE

PIM

You're here.

DATA STRUCTURE

Mapping & Normalisation

Normalised data scores higher - fewer format errors, fewer missing values.

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DATA STRUCTURE

Product Data Quality

After normalisation, quality scoring tells you what's ready to publish.

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DATA STRUCTURE

Schema & Attributes

AI-built schemas and category trees: the structure your product records validate against.

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ENRICHMENT & CONTENT

Content Generation

Normalised attribute values produce more accurate, consistent product descriptions.

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See your catalogue as one version of the truth

Book 30 minutes. Bring your messiest category and we'll set it up as central records with variants modelled, live in the session.

© 2026 SKU Launch Ltd. All rights reserved.
Built for e-commerce teams who are done doing it by hand.