Attributes are inconsistent
The same property is recorded differently across suppliers and seasons.
Catalogue, shelf and customer understanding
AI Data for Retail and E-Commerce
Visual search, shelf monitoring and recommendation models all start with a catalogue somebody had to label properly.
Catalogues That Models Can Read
Retail AI fails on attribute quality long before it fails on architecture. Colour called three different things, missing sizes, categories that made sense to a merchandiser and to nobody else. Fixing that — at catalogue scale, consistently — is what makes visual search and recommendation work.
Where Retail & E-Commerce AI Programmes Struggle
Catalogues are enormous
Millions of SKUs make manual consistency a process problem, not an effort problem.
Shelves are cluttered
Facings overlap, packaging reflects, and stock rotates constantly.
Fashion has no fixed vocabulary
Style and trend attributes shift faster than a taxonomy usually does.
AI Use Cases We Support
Product attribution
Colour, material, pattern, fit and style tagged to a controlled vocabulary.
Visual search training
Similarity sets and hard negatives built deliberately.
Shelf monitoring
Facing detection, share-of-shelf and out-of-stock labelling.
Planogram compliance
Comparing observed shelves against the intended layout.
Review and sentiment
Aspect-level classification of customer feedback.
In-store behaviour
Anonymised movement and dwell analysis from store cameras.
Data Types We Handle
- Product imagery Studio and user-generated photography, attributed and segmented.
- Shelf and store video Fixed and mobile capture for availability and compliance.
- Catalogue text Titles and descriptions normalised and entity-tagged.
- Customer text Reviews and support conversations classified by aspect.
How We Run the Programme
- One controlled vocabulary Agreed with merchandising before labelling, and versioned when it changes.
- Sampled consistency audits Ongoing agreement checks catch vocabulary drift early.
- Hard negatives on purpose Similarity models are trained on the confusions they will actually meet.
- Seasonal refresh The taxonomy is revisited each season rather than left to rot.
Build Retail & E-Commerce AI on Data You Can Trust
Tell us what you are building and we will come back with a plan, a sample and a price.