Queenly Builds New Formalwear Shopping Experience With Full Text Search Indexing

Steve Jurczak


Two years ago, we profiled Queenly, a promising startup that's ushering in big changes to the formalwear industry by making it more accessible for everyday people. The San Francisco-based company operates a marketplace and search engine for buying and selling formalwear such as wedding dresses, prom dresses, special occasion attire, and wedding guest dresses. Four years removed from its successful launch, Queenly is now rolling out new social commerce features that co-founders Trisha Bantigue and Kathy Zhou hope will give users a forum to discuss fashion tips, share recommendations, and develop a community of like-minded friends.

Ready to wear

Zhou, who is also CTO of Queenly, chose MongoDB because she'd previously used it as a student at the University of Pennsylvania doing hackathons. "It was super easy to set up when I was just that starry eyed, 19-year-old kid that, honestly, didn't know anything about databases," she says. That simplicity remains a selling point for Zhou. "It's been really great to train our engineering team on MongoDB," Zhou says. "Even if they're a client-side engineer and don't have a background in databases." That ease of use will continue to pay off as the company scales and grows its technical team.

Zhou's domain knowledge from working on search engines and recommendation systems at Pinterest led her to apply the advancements in algorithms and technology to the fashion industry. Full text search is a critical feature for building a truly personalized shopping experience that's tailored to the different life events that require formal wear. MongoDB Atlas Search is a fully integrated solution that makes it easy to add full text search with advanced functionality — fuzzy search, synonyms— to existing datastores. The simplicity of the out-of-the-box solution is huge for startups, Zhou says, because they're constantly growing and trying to structure their data along the way. "We have our own blended algorithms for ranking and delivering the most relevant search results to users, so plugging Atlas Search into our system helped fill in the user experience gaps when needed," Zhou says.

"MongoDB was the right choice at the right time," she says. "When it comes to being able to do more complex querying and searching, MongoDB felt pretty easy." She also likes using NoSQL schemas and NoSQL databases because of the flexibility. Startups see so many different curveballs, she says, and so many different things they want to test and try, and having the flexibility to do that has really helped, according to Zhou.

Data-driven differentiation

Both Zhou and CEO Bantigue have experience in the fashion world and use that experience to customize their service to their audience. As we mentioned in our earlier profile, both grew up in low-income, immigrant households and entered beauty pageants as a way to earn tuition money. So they know the experience of needing to find the dress of your dreams but with limited resources. It's that lived experience that enables them to create a great UI/UX that treats customers the way they want to be treated.

The co-founders, both 2022 Forbes 30 Under 30 honorees, combined their knowledge of the fashion industry with the ability to solve problems through data-driven methods to create differentiation in a crowded space. The search and indexing capabilities in MongoDB Atlas enable the Queenly application to curate a highly personalized visitor experience based on what you search for and spend time looking at.

Normally, building new shopping categories or recommendation features would entail building a new data pipeline or data science infrastructure. Zhou says the compound filtering and indexing capabilities in MongoDB enable them to get new categories off the ground quickly and iterate as needed.

“Communities on Queenly" has recently launched out of beta to all users, allowing them to ask each other questions like, "What kind of hairstyle should I wear for my wedding?" or "What kind of brands do you guys typically like?" Other interactive, social commerce type features that Queenly’s engineering team was able to quickly launch through the help of MongoDB’s indexing features include a Tiktok-style video feed and following feeds for user closets and brands.

Support for startups

Queenly is part of the MongoDB for Startups program, which helps startups build faster and scale further with free MongoDB Atlas credits, one-on-one technical advice, co-marketing opportunities, and access to a vast partner network. Zhou says the program has given them access to a level of specialized support that they wouldn't have had otherwise. "Clients our size might not get as much help as a really big company. I think it's really great that MongoDB for Startups exists so that us founders and small business owners can feel heard when it comes to just getting support," Zhou says.

If you want to learn more about Queenly, check out queenly.com. To apply to become part of a growing team, visit queenly.com/jobs.

Are you part of a startup and interested in joining the MongoDB for Startups program? Apply now.