How do I merge products from multiple clothing manufacturers into one brand catalog?

NoLabel sources from more than ten blank-apparel manufacturers, including Stanley/Stella, Stillshirt, Nameson and Euroralex, each shipping data in its own format. Automated Commerce built an import preset that reads NoLabel's single source sheet the same way every time, regardless of which supplier's data sits behind it. The result is 2,305 products and 14,281 variants that read as one coherent own-brand line instead of ten separate suppliers.

How do you import product data from a spreadsheet with hundreds of columns and duplicate headers?

NoLabel's source of truth was a hand-maintained Google Sheet 265 columns wide, including three duplicate header columns. Automated Commerce built a deterministic import preset that parses that structure the same way on every run, duplicate headers included, instead of requiring manual cleanup each season. That preset proved reliable enough to become the template every other brand preset in the platform is now copied from.

Does an automated content ruleset actually catch bad product descriptions before they publish?

Yes. When NoLabel's format document was first codified, descriptions were rejected 43 times before the ruleset settled. Within about five versions of that document, the same checks (banned phrases, unit rules, echo detection) got the account to a clean audit, and now all 232 of NoLabel's SS26 descriptions are checked against the ruleset before they are applied, so nothing reaches the storefront unchecked.

NoLabel × Automated Commerce

Q: How do you keep product descriptions consistent across thousands of SKUs and several languages?

NoLabel's brand book was codified into a machine-checkable ruleset covering banned phrases, unit rules, echo detection and a separate voice for English, Dutch and German. Every generated description is checked against that ruleset before it is applied, which is how all 232 SS26 descriptions across 2,305 products were generated and applied without drifting off-brand. The catalog runs 313 collections in three languages on the same ruleset.

NoLabel is an Amsterdam menswear brand built on publishing the true cost behind every price tag.

Its whole catalog lived in a single Google Sheet, 265 columns wide. Automated Commerce turned that sheet into a repeatable system, and the import preset built for it became the template every other brand in the platform now starts from.

About NoLabel

NoLabel is an Amsterdam menswear brand founded in 2013. It sells minimalist essentials: shirts, knitwear, trousers, outerwear and footwear, styled as one coherent line. NoLabel sells in English, Dutch and German.

The challenge

NoLabel's source of truth was a hand-maintained Google Sheet, 265 columns wide, including three duplicate header columns. The catalog is sourced from more than ten blank-apparel manufacturers, among them Stanley/Stella, Stillshirt, Nameson and Euroralex, each shipping product data in its own format. Yet the storefront has to read as one coherent own-brand line, not a patchwork of ten different suppliers. Reconciling that sheet by hand, season after season, did not scale.

How Automated Commerce helps

Automated Commerce built a deterministic import preset that reads NoLabel's spreadsheet the same way every time, whichever supplier's data sits behind it, duplicate headers and all. That preset became the template every other brand preset in the platform is now copied from. NoLabel's brand book was codified into a machine-checkable content ruleset: banned phrases, unit rules, echo detection and a per-language voice for English, Dutch and German. Every generated description is checked against that ruleset before it is applied, so nothing reaches the storefront unchecked.

The result

The numbers behind the switch:

2,305 products and 14,281 variants run through one deterministic import preset.
More than 10 supplier brands consolidated into a single own-brand line.
313 collections structured across the catalog, sold in English, Dutch and German.
All 232 SS26 product descriptions generated and applied.
Content quality moved from 43 rejected runs to a clean audit within about five versions of the format document.