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.
Automated Outfit Generator
Let the AI analyze your digitized wardrobe to instantly recommend weather-appropriate, stylish outfit combinations for any occasion.
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
Time-Consuming Outfit Selection
Before Doris, users spent excessive time every morning trying to physically pair items, leading to decision fatigue and delayed daily routines.
04
Lack of Personalized Style Recommendations
Shoppers were overwhelmed by generic fashion feeds that did not cater to their unique body measurements, skin tone, or existing wardrobe aesthetics.
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
Partner e-commerce sites saw a drastic reduction in product returns as users could accurately visualize fit before purchasing.
Increased Daily Engagement
Users adopted Doris as a daily utility, logging in every morning to utilize the automated outfit generation tools, resulting in high retention.