Doris – AI Outfit & Style App
Doris creates personalized outfits, suggests smart combinations, and helps you get dressed with confidence using advanced AI virtual try on technology.
Industry
FashionTech & AI
Platform
Cross-Platform - iOS & Android
Location
Global
Outcomes
AI Virtual Try On
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Key Platform Features
Smart Browser
Visit any fashion website and see yourself wearing the clothes via AI virtual try on before completing a purchase.
Snap & Match
Take a photo of a physical item to instantly find where to buy it online or discover similar stylistic alternatives.
Digital Collections
Photograph your own clothes and mix them with new digital pieces to create shareable curated collections.
Personal Look Page
Utilize a dedicated digital canvas to seamlessly try full outfits on a personalized avatar reflecting your exact body type.
Our Services Provided
01
AI & Computer Vision Integration
Developed the Smart Browser virtual try on and image recognition engines using advanced machine learning models.
02
Mobile App Development
Built a seamless high performance interface for both iOS and Android platforms to ensure mass market accessibility.
03
UI/UX Design
Crafted a highly visual intuitive Look Page interface for effortless personal outfit curation.
04
Backend Architecture Setup
Engineered a robust scalable server infrastructure capable of processing heavy image segmentation tasks with zero lag.
05
Quality Assurance & Testing
Executed rigorous testing protocols across thousands of clothing items to ensure accurate reliable digital rendering.
Challenges Faced by Client
01
High Return Rates in Digital Commerce
Users struggled to visualize how online clothing would actually fit their specific body type and existing wardrobe, leading to massive return rates and lost revenue.
02
Wardrobe Disorganization
Users lacked a centralized digital ecosystem to mix and match the clothes they already owned, causing daily frustration and inefficient outfit curation.
03
Lack of Personalization
Generic product recommendations on traditional platforms failed to engage users who wanted styling advice tailored to their unique aesthetic preferences.
04
Physical Retail Limitations
Consumers faced significant friction when trying to safely and quickly test multiple complex outfits during physical shopping trips.
Tech Stack
Flutter
Node.js
Python
TensorFlow
Project Glimpse
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Delivered
Project Results
Here are the measurable outcomes and achievements we delivered for this project.
4
Key Outcomes
Boosted User Confidence
The platform delivered a highly accurate virtual try on experience that significantly boosted user confidence in online purchases.
Viral Sharing Loop
It created a viral sharing loop through the Share Collections feature, driving robust organic user acquisition across the fashion community.
Reduced Return Rates
Highly accurate virtual try ons enabled shoppers to verify fit and style instantly resulting in noticeably fewer sizing complaints.
Enhanced Daily Engagement
Users returned to the platform daily to digitally coordinate their existing physical wardrobes increasing overall retention metrics.