Big Sister Swap has a brilliant concept: sending curated boxes of pre-owned fashion to individuals. Their goal was to make secondhand shopping feel as polished and personalized as buying brand-new. But as their inventory grew, a challenge emerged—how do you organize thousands of one-of-a-kind pieces, each with unique attributes?

Manual tagging wasn't just slow. It was impossible to scale. Big Sister Swap stylists spent hours searching for the right pieces to curate personalized looks. Generic visual search tools helped. But they couldn't filter by the specific details that matter in pre-owned fashion.

By partnering with YesPlz AI, Big Sister Swap received a complete fashion discovery stack tailored to its unique needs in just 2-4 weeks. No lengthy development cycles. No hiring an AI team. Just ready-made tools that understood pre-owned fashion from day one. Now, their stylists curate faster. Shoppers discover products more easily. The entire team focuses on their real mission: making sustainable fashion irresistible.

Challenges

• Pre-owned inventory doesn't come with clean product data: Unlike new fashion items with manufacturer specifications, secondhand pieces arrive with limited or one-of-a-kind attributes. Manually tagging each unique item with detailed attributes was eating up massive amounts of time.

• Stylists needed to find specific items fast, but the search wasn't precise enough: Big Sister Swap's in-house stylists manually curated personalized recommendations. They tried visual search tools that could find similar items from uploaded pictures. But these tools couldn't filter by the subtle details that matter, for example, an exact color variation or a particular style element.

• Building custom AI would take months they didn't have: The team knew AI could solve their discovery challenges. But building it from scratch meant hiring developers, training models, and months of testing before launch. For a startup focused on sustainable fashion, time and money should be invested in growth, rather than infrastructure. 

  I have such positive things to say about YesPlz AI. The integration process has been so amazing. Jiwon and her team are quick to understand the use-case and integration is pretty much seamless. Their models are really high quality and understand the nuances of fashion. The mannequin filter is such a clever way to search for clothing. I would highly recommend them to anyone looking to use AI in fashion.

  

  Zac Galibov

  CTO, Big Sister Swap

  Solutions: 

• Automated fashion tagging that understands pre-owned pieces: YesPlz AI automatically recognizes detailed fashion attributes from images and any available text - even when product information is incomplete or inconsistent. Each item gets accurately tagged the moment it's uploaded. Big Sister Swap's stylists can now find exactly what they're looking for without manual data entry slowing them down.

• 10 custom filters designed specifically for pre-owned fashion curation: Generic filters weren't cutting it for Big Sister Swap's unique needs. YesPlz AI configured ten tailored filters aligned with their business logic and integrated them into the Virtual Mannequin Filter. Now, stylists can quickly narrow down inventory by sleeve length, colors, subtle style variations, and other attributes that matter most for creating personalized pre-owned looks.

• A complete fashion AI stack, ready to go in 2-4 weeks: Instead of building from scratch, Big Sister Swap integrated YesPlz AI's full suite of discovery tools: Virtual Mannequin Filter, Complete the Look, Similar Look, and personalized shopping carts. The implementation was fast - discovery call, product data collection, and final integration completed in under a month. Big Sister Swap launched with enterprise-level AI capabilities without the enterprise-level development timeline.

AI Site Search: Before and After (e.g., "Crop tops")

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After

Fashion Filtering: Before and After AI Tagging

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Fashion Filtering: Before and After AI Tagging

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After

Fashion Filtering: Before and After AI Tagging

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Fashion Filtering: Before and After AI Tagging

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After

Lesson Learned

• Proactive planning makes all the difference: Every retailer manages their catalog differently, so we started with a dry run—prepping dummy data, fine-tuning models, and reviewing feature details upfront. This approach helped us spot challenges early and tailor our AI solutions to Looxloo’s unique navigation and migration needs.

• Frequent review sessions with clients are essential: By having check-ins, we made sure everyone was aligned on detailed needs like customizing the category management system for Looxloo. We learned that since Looxloo’s navigation starts with brands and then drills down to categories, our solution needed to fit that specific flow, not just a generic category structure.

Behind the Curtain

• AI-Powered Circular Fashion: How Big Sister Swap Leverages AI for Sustainable Fashion