Sowilo Raises Pre-Seed Funding to Automate Fashion Product Content

A fashion retailer may photograph a new collection in one afternoon. Turning every image into accurate, searchable and multilingual product pages can take far longer.

Icelandic AI company Sowilo has raised an undisclosed pre-seed round to expand Catecut, its fashion product intelligence platform. The financing included Iceland’s Kría fund, TGC Capital and angel investors from Iceland and Australia.

Founded by Heiðrún Ósk Sigfúsdóttir, Sowilo will use the capital to develop Catecut, grow its international customer base and accelerate adoption of its newly launched Shopify app.

The photograph becomes the product record

Catecut begins with an asset every fashion retailer already has: the product image.

Its computer-vision system identifies the item, category and visible design attributes before producing titles, descriptions, metadata, tags and image alt text. The content can be adapted to a retailer’s brand voice and generated in multiple languages.

The platform can process new listings or refresh existing catalog pages, including the underlying data needed for conventional search and generative-AI discovery. Its Shopify app now makes those capabilities available directly through a merchant’s store dashboard.

This is not simply a writing assistant for product descriptions. Sowilo is attempting to create a structured intelligence layer around fashion inventory – one that understands what appears in an image and converts it into usable commercial data.

Fashion’s catalog problem grows with every channel

Product information becomes difficult to manage when a brand sells across its own website, marketplaces, social commerce and several countries.

Descriptions may be inconsistent. Attributes may be missing. Search filters fail when products are tagged differently, while international expansion creates another layer of translation and localization work.

Catecut’s proposition is that one visual analysis can supply the structured information required across several channels. That could shorten the gap between a product shoot and the moment an item becomes discoverable and ready for sale.

The company already works with apparel and jewelry retailers in the United States and has tested its system with international enterprise retailers. Earlier company information also points to usage across North America, Europe, the Nordics and Singapore.

Shopify provides the distribution engine

The global Shopify release gives Sowilo a way to reach emerging brands without selling and implementing every account manually.

Its enterprise work remains important, but the app creates a lower-friction route into smaller retail teams that face the same catalog challenges with fewer internal resources.

Sowilo plans to expand adoption in North America and Europe while establishing a stronger presence in Asia-Pacific. The company previously received an Icelandic technology-development grant to market Catecut in Singapore, Australia and New Zealand.

The difficult part is accuracy at scale

Automating product content is useful only when the system correctly understands subtle design details.

Fashion terminology can depend on material, construction, silhouette, fit and context—not everything is reliably visible in a photograph. Retailers will therefore judge Catecut on accuracy, consistency and the amount of human review still required before publication.

The commercial opportunity is clear: product content is becoming infrastructure for search, recommendations, marketplaces and AI shopping assistants.

Sowilo now has to prove that Catecut can turn fashion imagery into dependable product data across thousands of styles, languages and brand identities – without making every storefront sound the same.

Source : Tech.eu