Outfitter - On trend outfit recommendations

Drive engagement with a virtual stylist that generates outfit suggestions in real time. Streamoid Outfitter analyzes trends, inventory, brand style guides and more to create multiple shoppable looks for each product.

Features

Style
OptimiZation

Brand styles are automatically extracted from the catalog with the ability to code additional style guides into the system. No extra effort required by the client.

Style
Titles

Nudge shoppers to visualise outfits within a context with interesting outfit titles. Retailers can customise these titles to their brand language.

Business
OptimiZation

Retailers can choose to boost product visibility, promote or remove a category, recommend outfits from selected brands, and customize more.

Client
Dashboard

A simple, intuitive dashboard controls what is finally published. Our self learning styling engine uses feedback from any edits made and improves over time.

Highly customizable

With granular control over the styling engine, adding layers of intelligence to optimise for geographies, aesthetics and seasons trends is easy.

Scalable and Easy to Integrate

Our solutions are platform agnostic with easy to use APIs on just a single line of code. They require minimal to no IT support.

Inspire customers to visualise themselves in the clothing. Demonstrate product versatility while educating them on how to style it.

TRY THE DEMO

Add on Pro features for amplified performance and deeper insight.

OUTFITTER | EDITS
Style each product for relevant occasions and activities. Bring context back into the shopping experience

OUTFITTER | Geo
Optimize your catalog for different geographies keeping local sensibilities in mind

CONTACT SALES

Create unique brand experiences with our enterprise solutions.

Deliver deeper engagement with Streamoid’s domain-specific personalization platform. Embed hyper personalisation across your retail landscape with our bespoke AI engine.

LET’S CONNECT

Client Stories

Forever 21 India uses Outfitter to demonstrate to its customers how its clothes can be styled. While nudging customers to purchase the primary product, the recommendation engine also makes over 1 million products discoverable for the client, every month.