An automated tagger can label an image "blue dress." It can't tell your AI shopping assistant that it's a cottagecore, brunch-casual, A-line silhouette. That gap is where we work.
Building multimodal training data for AI stylists and visual search — the way Pinterest Lens or a great store buyer would see it.
E-commerce brands are racing to build internal AI shopping assistants and visual search engines. The models need to understand human style intent, not just detect objects — and that requires annotators with real fashion and merchandising judgment.
We staff and manage that annotation layer: labeling fit and silhouette, design aesthetic, occasion, and style compatibility — the vocabulary a stylist uses, structured into training data a model can learn from.
Every e-commerce brand with an app or a search bar is under pressure to ship an AI stylist or visual search feature — and every one of them hits the same wall: their product catalog wasn't tagged with style intent in mind. Retrofitting that taxonomy and re-tagging the catalog is unglamorous, detail-heavy work no engineering team wants to own internally. That's the opening.