Generative · Ecommerce
Product imagery and copy generation for ecommerce
As a catalogue grows the bottleneck is consistency rather than production: every SKU needs different sizes, channels and languages. Zero-Shot builds systems that generate product imagery, descriptions and channel variants from rules, with output checked automatically against marketplace requirements.
What we build
- 01Automatic variant generation from a product photo to every channel format
- 02Background removal, shadows and scene placement, consistent across SKUs
- 03Multilingual product descriptions and attribute copy
- 04Marketplace rule checks for size, margins, text ratio and prohibited claims
- 05A bulk production queue that regenerates affected SKUs when a rule changes
How it runs
01
Extract the channel rules
Each marketplace's image and copy rules are written down — a list that usually lives scattered across the team.
02
Pilot on one category
One category is produced both ways and compared, so any difference in conversion shows up here.
03
Automate the checks
Output that breaks a channel rule is caught before publishing and reported with the reason.
04
Open to the catalogue
The bulk queue opens, and a rule change regenerates every affected SKU.
What we measure
- Time and cost to prepare content per SKU
- Marketplace rejection rate before and after automated checks
- Time to list, from product arrival to being live
- Conversion of generated imagery, A/B tested against manual work
- Share of output needing manual correction
When this is the wrong fit
If there is no product photograph, or its quality is very poor, the system cannot compensate — shooting comes first. We also do not build setups that show a product as something it is not; misleading imagery comes back as returns.
Common questions
Is there a risk of misrepresenting the product?
The system does not alter the product itself; it works on background, format and presentation. We do not generate output with altered colour, texture or proportions. That boundary is written into the rule set and enforced in the checks.
At what catalogue size does it make sense?
With a few hundred SKUs and a steady flow of new products the gain is clear. For a small, static catalogue manual production stays cheaper — and we measure that before recommending anything.
Won't the descriptions all sound the same?
They will if you use templates. That is why description generation is driven by each product's own attributes and similarity is measured: copy that reads too close to its neighbours is flagged before publishing. Search engines want the same thing.
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Last updated: 2026-09-05
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