Moonsift Case Study
UK-based startup Moonsift is a community of stylists, creators and people who love shopping online but are frustrated by the fragmented experienced. Moonsift offers an ecommerce browser extension for users to curate shoppable boards with products from across the internet. Stylists and curators use Moonsift to create collections, registries, and wish lists that can be shared and shopped with a simple link. While thousands of customers add products from tens of thousands of retailers per month to Moonsift, co-founders David Wood and Alex Reed have a bigger vision for improving product discoverability for online shoppers.
Moonsift is harnessing the power of AI—using machine learning models and Weaviate’s vector database and AWS—to help online shoppers discover the products they love.
$4 Billion in annual online sales, LTK is leading the online fashion charge.
Problem
Moonsift was seeking a solution that would take their customer data to the next level of discovery through keyword-based search system using semantic search. The problem here was that Moonsift needed a vector database that could index and search multimodal data across millions of objects.
Solution
After a thorough evaluation of a handful of open and closed-source vector databases and cloud platforms, Moonsift decided Weaviate and AWS as the best solution for their needs.
Moonsift cited the following reasons for choosing Weaviate and AWS:
- Open source, with an active community and managed cloud offering.
- Strong supports for popular LLMS and multi-modal models running on AWS Bedrock
- High query and throughput at scale
- Unique search features to drive performance and efficiency
Moonsift is now getting ready to launch their AI Copilot to the world. They’re seeing early results of the power of its ability to understand user intent and serve intelligent results. Some fun examples include “shirt that looks like a Caipirinha” or “skirt with a pattern inspired by ocean waves”.
As Moonsift prepares for the public launch of its shopping Copilot, the team is continuing to explore ways to optimize the cost, performance, and scale of their system. They are looking into Weaviate’s new feature, Product Quantization (PQ), which helps reduce the memory footprint of their system, by compressing vectors and performing rescoring, while retaining search relevance. Along with PQ they are also exploring multi-tenancy that will allow them to scale and perform personalized vector search for millions of customers.