Success Story
About the Client
The client is a leading US-based apparel eCommerce platform serving millennial and Gen Z shoppers. It offers a seamless, customised, and immersive online shopping experience designed to meet the expectations of modern digital consumers.
Business Situation & Requirements
The client wanted to offer Gen Z users a more engaging and easier way to find outfits that matched their style or recreate looks from social media. Existing visual search and styling features had room for improvement to make product discovery smoother and boost engagement.
To solve this, the client wanted an AI-enabled Shopping Assistant with visual search, smart outfit recommendations, and virtual try-on. The goal was to simplify product discovery and create a seamless shopping experience, guiding users smoothly from inspiration to purchase.
Key requirements were:
Define a scalable AI-first architecture and choose the right technology stack to support real-time personalisation, visual processing, and smooth integrations.
Design an intuitive interface that lets users upload wardrobe details manually or via photos with ease.
Build machine learning models to analyse preferences, browsing behaviour, and purchase history for relevant outfit recommendations.
Implement visual search so users can upload images or select inspiration to quickly find matching products.
Use augmented reality and 3D imaging to let users virtually try outfits and make confident purchases.
Develop a smart styling engine that combines clothing, footwear, and accessories, enhancing overall product discovery.
Solution
Our team collaborated with stakeholders to understand business goals and user needs. We identified key pain points, defined user personas, and mapped workflows to support the AI-enabled Shopping Assistant.
Next, our team upgraded the backend to enable visual search and real-time recommendations. We focused on secure processing, accurate results, and reliable performance, ensuring the AI-enabled Shopping Assistant delivers a smooth and enjoyable shopping experience.
The key features developed were:
AI-Powered Outfit Finder
- Users can easily recreate outfits from social media images or by uploading their own pictures.
- Next, the AI scans the image, identifies clothing items, and suggests similar or matching products.
- As a result, users can quickly put together complete outfits based on their style.
- Consequently, the shopping experience becomes more fun and personalised.
- In addition, it makes decisions easier by showing styles users are likely to love.
- Overall, it guides users smoothly from inspiration to purchase, making shopping faster and simpler.
AI-Powered Outfit Try-On
- We added virtual try-ons to bring the online shopping experience closer to in-store shopping.
- Additionally, the platform uses AI and augmented reality to show how outfits will look and fit based on user measurements or uploaded images.
- Consequently, this feature reduces uncertainty and helps users make better choices.
Furthermore, it minimises product returns and increases buyer confidence. - Ultimately, virtual try-ons make shopping more interactive, reliable, and enjoyable.
Smart Wardrobe Organiser
- To begin with, users can quickly find what they need by sorting items into categories like casual, formal, ethnic, party, and sportswear.
- Therefore, they can make better use of their wardrobe and plan outfits for any occasion.
- Besides that, it helps identify gaps in their collection and guides smarter shopping decisions.
- In the end, the digital wardrobe makes managing clothes simple, efficient, and more personalised.
Personalised Style Recommendations
- First, the recommendation feature was added to simplify decision-making and maximise the value of users’ wardrobes.
- Then, by leveraging LLM models, the platform analyses purchase history, wardrobe data, and personal preferences to suggest cohesive outfits for different occasions.
- As a result, users can save time and reduce decision fatigue.
In addition, it helps prevent impulsive or unnecessary purchases. - Finally, the feature makes outfit planning easier, smarter, and more personalised.
The Impact
The introduction of the AI-enabled Shopping Assistant greatly enhanced the client’s e-commerce platform, increasing both user engagement and sales. Features like virtual try-ons and personalised outfit suggestions made it easier for users to find what they wanted, which in turn reduced cart abandonment.
As a result, shopping became quicker and more enjoyable, encouraging customers to return. Additionally, these innovative features helped build strong brand loyalty, as shoppers began to view the client as a top choice for online shopping. Overall, the AI-enabled Shopping Assistant helped the client stay ahead in the competitive e-commerce market.
15,000+
Products Sold Through Image-Based Searches
40,500+
Virtual Try-ons
40%
Increase In Engagement Rates
