Sarah LaValle: a Medusa.js fashion store with AI shopping and try-on
ScalaCode moved the Sarah LaValle women's fashion store from WooCommerce to Medusa.js for speed and easier integrations, then built AI into the shopping experience: search, recommendations, size advice, product descriptions, a styling assistant and Try Style virtual try-on.
A curated women's fashion boutique
Sarah LaValle sells curated women's fashion online, from tops, jackets and jeans to dresses, outerwear and seasonal sale edits. The store ran on WooCommerce, which had become slow and hard to integrate with the retailers and systems the business works with.
What the new store had to do
A slow store
WooCommerce pages had become slow, and speed matters when shoppers browse a fashion catalogue on their phones.
Integrations that took too much work
Connecting retailers, inventory feeds and EDI to WooCommerce was hard, so stock and catalogue updates needed manual effort.
Shoppers could not try clothes on
The brand wanted shoppers to find the right style and size, and see it on themselves, before they order.
A fast Medusa.js store with AI shopping built in
We replaced WooCommerce with Medusa.js, connected retailers and EDI, and added AI at each step of the shopping journey.
- 01
Medusa.js commerce backend
Medusa.js runs the catalogue, size and colour variants, bag and checkout, behind a fast Next.js storefront.
- 02
Retailer onboarding and one-click EDI
New retailers are onboarded through a simple flow, and EDI connections are set up in one click.
- 03
Inventory that updates itself
Stock levels update automatically from connected retailers, so the store shows what is available.
- 04
AI search and recommendations
Shoppers find styles with AI search, and each product page recommends similar styles.
- 05
AI size advice and styling assistant
Size advice helps shoppers pick the right fit, and a styling assistant suggests pieces that go together.
- 06
AI product descriptions
Product descriptions are drafted with AI, so new stock reaches the store faster.
- 07
Try Style virtual try-on
Shoppers upload a photo and see Sarah LaValle styles on themselves, using the try-on engine from our Try Style build.
A faster store that keeps its own stock up to date
Sarah LaValle now runs on a fast Medusa.js store. Inventory updates automatically, new retailers onboard easily, EDI connects in one click, and AI helps shoppers search, choose a size, style an outfit and try it on.
EDI connections
inventory updates
What Sarah LaValle runs on
Medusa.js
Next.js
EDI, retailer inventory feeds
AI search, recommendations, size advice, styling assistant, product descriptions, Try Style try-on
Plan a build like Sarah LaValle for your business
- 30 minutes with the ScalaCode team
- What is realistic, what it costs and how long it takes
- You decide afterwards whether to build with us
