← All Work
Build a Brief
Sportswear · Creative technology

An AI content architecture, benchmarked against real e-commerce.

Castore is Britain’s fastest-growing sportswear brand. The question was not whether AI could make a nice picture. It was whether it could hold the standard a premium brand actually ships to.

A capability test. This work was a strategic AI capability test, not a commissioned brand campaign. The architecture was developed for Castore and benchmarked against their e-commerce standard.

Castore engineered-knit performance tee, front, generated to e-commerce standard
ClientCastore
SectorSportswear · E-commerce
DeliverablesAI content architecture · Multi-SKU pipeline · Model integration
StatusCapability test
/01The Brief

The bar is not a campaign. It is the product page.

Can AI-generated content compete with e-commerce photography on quality?

We were introduced through a mutual contact to run a strategic capability test. Most AI content is judged against a campaign image, where atmosphere forgives a lot. That is the easy test.

E-commerce is the hard one. A product page is unforgiving: the garment has to be legible, the fabric has to read, the colour has to be right, and the same product has to look like itself across every frame. A customer deciding whether to spend money looks closely.

So the benchmark was set at the e-commerce bar, not the campaign bar.

/02The Architecture

A pipeline, not a prompt.

One good image is a demonstration. A hundred consistent ones is a system.

We built a content architecture rather than a set of images: a pipeline able to take multiple SKUs and existing models and hold quality across the whole set, so the output is repeatable rather than lucky.

That is the part that decides whether any of this is useful to a business. A brand shipping a season does not need one hero frame. It needs the fiftieth product to look as considered as the first.

/03The Test

Three product families, one standard.

Castore engineered-knit tee, full length on a neutral ground
Engineered knit. The jacquard has to resolve as structure rather than noise, which is where most generated fabric falls apart
Macro detail of the Castore wing mark on engineered knit
Macro detail of the engineered-knit ventilation structure
Macro is the honest test. At this distance the mark has to sit on the weave, and the ventilation holes have to be knitted rather than printed on
Castore knit tee, side profile
Castore knit tee, back view with vertical wordmark
Front, side and back of one SKU on one model, which is the actual shot list a product page needs
Castore Hertha BSC home kit, full length
Licensed club kit is the hardest case in the set: the crest, the sponsor and the league patch are all fixed artwork that has to survive intact
Macro of the embroidered Hertha BSC crest and Castore wing mark
Sleeve detail showing the Bundesliga 2 patch and sponsor lettering
Embroidery reading as thread, and applied lettering reading as applied. Fixed artwork is where a pipeline either holds or gives itself away
Hertha BSC shirt back with player name and number
Hertha BSC kit worn, arm raised, showing drape through movement
Name and number set correctly on the back, and the shirt still drapes when the body moves
Castore x Paul Smith zip top in acid yellow
Shoulder detail of the Castore x Paul Smith top showing the panel gradient
The Paul Smith collaboration, where two marks share one garment and neither can be allowed to drift
Castore and Paul Smith co-brand lockup with the Paul Smith stripe
The co-brand lockup and the stripe, held at the size a customer actually sees them
/04Why it matters

A capability partner, not a design supplier.

The deliverable here was not a set of pictures. It was the architecture, and an honest answer about where it holds and where it does not.

That is a different kind of engagement. A brand at this scale does not need someone to make them fifty images. It needs to know whether a system can make five thousand, and what the standard will be when it does.

01AI Content Architecture
02Multi-SKU Pipeline
03Model Integration
04E-commerce Quality Benchmarking
WhatsApp