AI-Powered Mobile Shopping App with Visual Search & AR Try-On

HomeDecor Marketplace 2024
180KApp downloads in first year
67%Reduction in furniture returns via AR
8.2%Mobile app conversion rate
$18MMobile commerce revenue year one

How we got there

01

The Challenge

HomeDecor Marketplace offered 75,000+ furniture and home decor products online but faced high return rates (32%) due to customers being unable to visualize products in their spaces. Traditional product search required customers to know exact terms ('mid-century modern coffee table') which limited discovery, especially for visual shoppers who knew what they wanted when they saw it but couldn't articulate it in keywords.

The company needed a mobile-first solution that would enable visual product discovery, help customers confidently purchase furniture without seeing it in person, and create an engaging shopping experience. Key requirements included AI-powered visual search to find products from photos, AR furniture placement to preview items in actual spaces, personalized recommendations, seamless checkout, and real-time inventory updates. The app needed to handle large 3D model files efficiently and work on devices dating back 3 years.

02

Our Approach

We conducted user research with 90 home decor shoppers, analyzing their Pinterest boards, Instagram saves, and shopping behaviors. 82% reported using inspiration images but struggled to find similar products. We designed a camera-first experience where visual search was the primary discovery method, complemented by traditional category browsing and text search.

The app was built using React Native for cross-platform development with native modules for camera and AR functionality. We integrated Google Cloud Vision API for image recognition and trained custom machine learning models on 75,000 product images to achieve 91% visual search accuracy. The AR placement feature used ARKit (iOS) and ARCore (Android) to render 3D product models in real-world spaces, with realistic lighting and shadows.

Backend services built on Node.js with MongoDB handled product catalog, user profiles, and visual search indexing. We used Cloudinary for image optimization and CDN delivery. 3D models were optimized to average 2.5MB using mesh decimation and texture compression without visible quality loss. Lazy loading and progressive enhancement ensured smooth performance even on older devices.

03

The Results

The mobile app revolutionized how customers shop for home decor, with visual search enabling users to find products by snapping photos of inspiration images. The AR try-before-you-buy feature reduced furniture returns by 67%. Users spent an average of 12 minutes per session (3.5x web average) and the app achieved 52% monthly active user retention. Mobile conversion rate reached 8.2%, compared to 3.1% on desktop web.

AI Visual Search Technology

Users could snap a photo of any furniture or decor item—from a magazine, showroom, or friend's home—and the app would identify similar products from the catalog. The computer vision system analyzed colors, patterns, shapes, materials, and styles using convolutional neural networks. Results were ranked by visual similarity with 91% accuracy in finding matches. The feature drove 38% of product discoveries and had a 2.1x higher conversion rate than text search.

Augmented Reality Try-Before-You-Buy

The AR placement feature let customers virtually place furniture in their actual spaces before purchasing. Using device cameras and spatial mapping, the app accurately rendered life-size 3D models with realistic shadows and lighting that adapted to room conditions. Users could walk around items, view from multiple angles, and try different color/fabric options. Measurements were overlaid to ensure proper fit. This feature reduced furniture returns from 32% to 11% and increased purchase confidence—75% of users who tried AR went on to complete purchases.

Personalization & Discovery

The recommendation engine analyzed user behavior including viewed products, visual search queries, saved items, and AR try-ons to build style profiles. Machine learning models clustered users into style personas (Modern Minimalist, Rustic Farmhouse, etc.) and recommended products matching their aesthetic. The 'Complete the Room' feature suggested complementary items based on products already purchased or in cart. Personalized push notifications about price drops on saved items converted at 24%.

User Experience & Performance

The app launched in under 2 seconds on average devices. Product pages loaded instantly through aggressive caching and image optimization. The checkout flow was streamlined to 4 taps using saved payment methods and addresses. Real-time inventory sync prevented overselling, and delivery tracking provided live updates from warehouse to doorstep. Wishlist collaboration allowed users to share curated collections with family members for gift ideas. The app achieved 4.8/5 rating on both app stores from 12,000+ reviews.

Secure Telemedicine Platform for Regional Healthcare Network

MedConnect Regional Healthcare

Ready to discuss your project?

Let's explore how we can help bring your vision to life.

Start a Conversation