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The fashion industry is embracing AI to enhance the shopping experience, making it more personalized and efficient. With AI-driven recommendations accounting for 35% of Amazon’s revenue (Forbes) and virtual try-ons reducing returns by 25-40%, brands are saving billions. Additionally, 73% of shoppers prefer brands that offer personalized experiences. Apps like Daydream are leading the way by curating hyper-personalized shopping experiences, revolutionizing how we shop, and making the process more tailored to individual preferences, body types, and lifestyles.
Other than Daydream, brands like Zalando and Gucci are also harnessing AI to enhance the shopping experience. Zalando’s “Fit Finder” AI predicts your perfect size with 95% accuracy, helping reduce returns and improve customer satisfaction.
Meanwhile, Gucci uses augmented reality to let users try on sneakers virtually, boosting conversions by 30% and allowing customers to experience products before making a purchase.
In this blog, we’ll explore the essential steps involved in developing an AI-powered fashion shopping app like Daydream. As the fashion industry quickly embraces AI, smarter shopping experiences are becoming the norm. We’ve partnered with numerous clients to create apps that utilize AI-driven recommendation engines, facial recognition for virtual try-ons, and dynamic styling suggestions tailored to each user’s wardrobe and preferences. With our deep expertise in AI and fashion app development, IdeaUsher can help you build an app that not only boosts user engagement but also drives sales and fosters long-term customer loyalty.
Key Market Takeaways for AI Fashion Shopping Apps
According to PrecedenceResearch, the AI-driven fashion shopping market is growing rapidly, with projections showing it could jump from USD 2.23 billion in 2024 to over USD 60 billion by 2034. This growth is fueled by the increasing demand for personalized shopping experiences and the rise of AI tools that help retailers enhance their digital presence. Consumers are seeking more convenient, engaging, and tailored ways to shop, prompting brands to adopt these technologies to meet their needs.
Source: PrecedenceResearch
AI fashion shopping apps are transforming the way we shop online. Features like virtual try-ons, personalized recommendations, and visual search make it easier for consumers to find clothes that match their style and fit preferences.
These technologies also help reduce return rates by giving shoppers more accurate sizing and style suggestions, leading to higher satisfaction and fewer returns. The ability to shop based on personal tastes is making the experience more enjoyable and efficient.
Several leading apps are setting new standards in the fashion industry. Glance AI, for example, acts like a personal stylist, offering outfit ideas based on individual body types and preferences.
The Yes, which was acquired by Pinterest, curates fashion choices based on user behavior and brand preferences. Meanwhile, Whering promotes sustainability by helping users create outfits from clothes they already own. In partnership with sizing tech companies like Bold Metrics and FitMatch.ai, these platforms are improving fit accuracy and reducing return rates, making online shopping more reliable and enjoyable for everyone.
Understanding AI in Fashion Shopping – What’s the Buzz?
Artificial Intelligence is at the heart of a major transformation in the fashion industry, revolutionizing the way consumers shop and engage with brands. By enabling smarter, faster, and more personalized experiences, AI is making it possible for retailers to deliver tailored shopping journeys that cater to individual tastes and needs.
Here’s a look at the key AI technologies driving this shift:
- Machine Learning: Analyzes user behavior and purchase history to predict preferences, optimize inventory, and suggest items based on customer likes.
- Computer Vision: Helps fashion apps recognize clothing, patterns, and styles, powering visual search and virtual try-ons, allowing customers to see how clothes will look on them before purchasing.
- Recommendation Algorithms: Suggest products based on browsing habits, past purchases, and social media activity, refining suggestions over time for more relevance.
- AI Chatbots: Offer real-time styling advice, answer queries, and guide users through the shopping process, enhancing discovery and engagement.
Together, these AI technologies enable fashion brands to provide smarter, more relevant experiences for customers, reduce return rates, boost sales, and foster stronger customer engagement. The days of one-size-fits-all retail models are over, replaced by highly personalized, dynamic shopping experiences.
Overview of the Daydream App
One standout example of AI transforming the fashion shopping experience is Daydream, an app founded by e-commerce pioneer Julie Bornstein. Daydream is reshaping how consumers discover and shop for clothing, offering several innovative features that leverage AI to enhance the shopping experience:
Smart Outfit Suggestions
Users of the Daydream app can input specific queries such as, “I need a dress for a Paris summer wedding.” The app’s AI engine then generates personalized outfit recommendations based on the user’s style, budget, and occasion. Over time, the app’s “Style Passport” learns from the user’s choices, refining future recommendations to better match their evolving preferences.
Visual & Conversational Search
Daydream offers a unique search experience, enabling users to upload an image or describe an item in natural language (e.g., “floral midi dress with puff sleeves”). Unlike traditional search engines that rely on rigid keywords, Daydream’s AI understands nuanced requests, such as “show me outfits similar to this influencer’s post,” making it easier to find exactly what you’re looking for.
Virtual Try-Ons (AR Integration)
One of the biggest breakthroughs in fashion retail is the integration of AR for virtual try-ons. With Daydream’s AR feature, users can visualize how clothes will look and fit without physically trying them on. This not only enhances the shopping experience but also significantly reduces return rates, by as much as 35%, by giving consumers a clearer idea of how a product will look in real life.
Trend Prediction & Social Shopping
AI in Daydream also helps identify emerging fashion trends by analyzing real-time data from social media, runway shows, and sales patterns. The app even allows users to share curated collections with friends for feedback, making shopping a more social experience.
The Value of AI-Driven Personalization
The real power of AI in fashion shopping lies in its ability to create personalized experiences that go beyond the generic browsing models used by traditional retailers. Here’s how AI is improving fashion shopping for consumers and businesses alike:
Hyper-Personalized Recommendations
AI tailors its suggestions to an individual’s unique body type, past purchases, and even local weather conditions. For example, a shopper in London might receive a recommendation for a lightweight trench coat suited for rainy weather, while someone in a warmer climate might be shown a flowy summer dress. This level of personalization makes the shopping experience more relevant and enjoyable.
Brands like Zalando are seeing tangible results from AI-driven personalization. Zalando has successfully increased revenue by 40% through personalized emails and targeted offers, which speak directly to individual customer preferences.
Reduced Decision Fatigue
With so many options available online, decision fatigue can set in quickly. AI solves this problem by narrowing down choices to highly relevant options based on the shopper’s tastes. By presenting a more curated selection of products, AI makes shopping quicker and more efficient, improving conversion rates and customer satisfaction.
Sustainable Shopping
AI is also helping promote sustainability in fashion by enabling made-to-order models. Brands like Laws of Motion are using AI to minimize waste by producing only what’s demanded, reducing overproduction and excess inventory.
Seamless Omnichannel Experiences
AI enables seamless experiences across devices, meaning a cart saved on a mobile app will appear on a desktop, and digital wish lists are accessible by in-store stylists. This ensures that customers can shop the way they want, whether online, on mobile, or in-store, with their preferences always in sync.
The Future: Beyond Daydream
The future of AI in fashion retail is bright, with numerous exciting possibilities on the horizon:
- AI-Generated Fashion Collections: AI can be used to design entire fashion collections. Brands like Collina Strada are already experimenting with AI-designed outfits, exploring how algorithms can create unique, fashion-forward pieces based on trends and consumer preferences.
- Metaverse Integration: As the metaverse grows, fashion brands will begin to design virtual clothing for digital avatars. In this new digital realm, owning digital clothes will be a status symbol, and AI will help design and sell these pieces to consumers in virtual worlds.
- Voice-Activated Shopping: The integration of AI with voice assistants like Alexa will allow users to shop with just a command. “Alexa, find me a sustainable black blazer,” could soon become a regular part of the shopping experience, making it easier and more convenient than ever before.
Business Model of the Daydream App
Daydream’s business model revolves around an affiliate-based structure, rather than traditional retail or advertising models. Here’s a clear breakdown of how it generates revenue, its current financial standing, and its future potential:
Affiliate Commissions
Daydream doesn’t hold any inventory or process transactions. Instead, it serves as a recommendation platform. When users click on product links and make a purchase from a partner retailer’s site, Daydream earns a commission, which is generally around 20% of the sale.
No Ads or Pay-to-Play Listings
Unlike many platforms, Daydream doesn’t rely on ads, sponsored content, or paid placements. All product recommendations are purely based on AI personalization and user preferences, ensuring an authentic shopping experience that’s not influenced by paid promotions.
Brand & Retailer Partnerships
At its launch, Daydream boasts partnerships with over 200 retailers and brands, aggregating a vast catalog of nearly 2 million products from over 8,000 fashion brands. This extensive collection ensures a diverse offering, catering to various tastes and budgets.
Merchant Onboarding
Onboarding new brands is currently free, allowing new partners to easily join and expand their presence on the platform without any upfront costs. This inclusive model enables rapid expansion and growth of Daydream’s catalog.
Financial Performance & Metrics
- User & Partner Scale: Daydream’s platform has signed up over 2,000 multi-brand and mono-brand retailers, including renowned names like Jimmy Choo, Alo Yoga, and Net-A-Porter. It aggregates over 2 million products across 8,000 brands, ensuring a diverse catalog with something for everyone.
- Commission Rate: The affiliate commission Daydream earns is approximately 20% per sale, making it an efficient revenue model as it doesn’t need to handle product inventory or customer service directly.
- Consumer Access: The Daydream app is free for consumers to use. Revenue is generated solely from commissions earned when users make purchases through the app’s partner retailers, allowing Daydream to provide value to users without direct charges.
Funding Rounds & Investors
In 2024, Daydream raised a $50 million seed round, led by Forerunner Ventures and Index Ventures, with participation from Google Ventures (GV), True Ventures, and others. This large seed round highlights strong investor confidence in Daydream’s innovative approach and potential for growth.
Founding Team
The app was co-founded by Julie Bornstein, a seasoned leader with experience at major companies like Nordstrom, Stitch Fix, and THE YES. The team also includes veterans from Google, Amazon, Meta, Microsoft, and Farfetch, bringing a wealth of expertise in e-commerce and technology.
Features to Include in an AI Fashion Shopping App like Daydream
From our experience developing multiple fashion shopping apps, we’ve found that some features are universally popular among users. By analyzing user feedback and behavior, we’ve identified the tools that truly enhance the overall shopping experience. Here are the key features that consistently impress users in apps like these:
1. Natural Language Interaction
We’ve found that users love the ability to interact with the app using plain language. Whether they’re looking for an outfit for a job interview or searching for vintage sneakers, the ability to simply describe their needs makes the shopping process feel natural and effortless, without the hassle of complicated filters.
2. Personalized Style Advice
Through analyzing user preferences, browsing history, and even body type, the AI can offer personalized style advice that feels like a virtual personal shopper. This feature is incredibly popular, as users appreciate receiving clothing recommendations tailored just for them, based on their unique style.
3. “Say More” / Refinement Tools
A feature we’ve seen users frequently enjoy is the “Say More” refinement tool. If users aren’t happy with their initial search results, they can easily refine them. Whether they want longer dresses, different colors, or more formal styles, this feature empowers users to customize their search with just a few commands.
4. Realistic Digital Avatars
Creating a digital avatar based on a user’s body shape and measurements has been a game-changer. Users love seeing how clothes would look on their own body, which helps them make better decisions and feel more confident about what they’re buying.
5. Real-time Garment Overlay
Real-time garment overlay, especially with augmented reality, has become a fan-favorite. This feature allows users to see how clothes look on them in real-time, either on their avatar or through their live camera feed, offering a truly interactive and engaging experience.
6. Fit and Sizing Recommendations
Fit is a huge concern for shoppers, so we’ve integrated sizing recommendations based on the user’s measurements. This feature significantly reduces returns and builds trust, as users feel more confident in their purchases when they know they’re getting the right fit.
7. Pose and Movement Simulation
Users really appreciate when they can see how clothes will move and fit during everyday activities. The ability to simulate poses and movements makes the shopping experience dynamic and realistic, allowing users to get a true feel of how the clothing will perform in real life.
8. Image-Based Product Discovery
The image-based product discovery feature has become one of the most popular tools. Users can upload photos of garments they love, and the AI finds similar items across various retailers. It’s a quick and efficient way for users to shop for clothes they’ve spotted in magazines, social media, or even on the streets.
9. “Complete the Look” / Outfit Generation
Another feature that always gets great feedback is the outfit generation tool. By using a single item the user likes, the app can suggest complementary pieces, creating a full outfit. This feature offers style inspiration and helps users build complete looks without extra effort.
10. Digital Wardrobe Organization
One feature that users have found incredibly useful is the digital wardrobe organizer. By uploading photos of their current clothes, users can have their wardrobe automatically categorized by type, color, season, and occasion. It’s an excellent way to help users keep track of what they already own and avoid duplicate purchases.
11. Gap Analysis and Sustainable Shopping
Our users have shown a strong interest in sustainability, and the gap analysis feature allows the app to suggest new purchases that complement their existing wardrobe. It helps users fill the gaps in their style, promoting more thoughtful and sustainable fashion choices.
12. Influencer-Style Content Generation
Finally, one feature that has really taken off is influencer-style content generation. Users love creating AI models wearing their selected outfits or generating short videos of themselves in various outfits. It’s a fun way to visualize how the clothes will look in real life, while also giving users a chance to share their style with others.
Building an AI-Powered Fashion Shopping App Like Daydream
Our expertise lies in crafting AI-powered fashion shopping apps, similar to Daydream, that deliver a personalized and efficient shopping journey. We combine cutting-edge technology with user-friendly designs to enhance the overall online shopping experience.
1. Market Research & Conceptualization
To kick off the development process, we begin by conducting thorough market research to understand the unique needs and challenges of your target audience. We analyze fashion consumer behavior, examine current shopping trends, and evaluate competitors in the AI fashion space. This allows us to identify the most promising opportunities for your app.
2. User Profiling & Personalization Engine
We create an advanced user profiling system that enables your app to deliver highly personalized experiences. During the onboarding process, users input preferences such as size, style, budget, and favorite brands, and our AI-powered personalization engine ensures that product recommendations are tailored specifically to them. Over time, the app adapts, improving its suggestions based on users’ interactions, making their shopping experience even more intuitive and enjoyable.
3. Frontend Development
Our frontend development focuses on delivering an aesthetically pleasing and easy-to-use interface for users. We prioritize a smooth, responsive design that works seamlessly across both iOS and Android devices, ensuring an optimal experience on all screen sizes. Using tools like React Native or Flutter, we create a visually appealing interface that guides users effortlessly through their shopping journey, with features that encourage interaction and engagement.
4. Backend Infrastructure & Cloud Integration
For the backend, we built a robust, scalable infrastructure that can handle large volumes of user data, transaction records, and AI processing. We use leading cloud services like AWS or Google Cloud to ensure that the app remains fast, reliable, and capable of scaling as your business grows. Security is also a top priority, ensuring that all user data is protected with the latest encryption standards and the app remains compliant with privacy regulations.
5. Natural Language Processing Integration
To enhance user interaction, we integrate NLP technology into your app. This allows users to converse with the app in a natural way, asking complex questions like “What are the best dresses for a beach wedding?” or “Show me blue jeans under $100.” Our NLP models can understand and respond to these queries with relevant recommendations, mimicking the experience of interacting with a personal shopper.
6. Image Recognition & Visual Search
Our app leverages advanced image recognition technology to enable visual search. This allows users to upload pictures of clothing or accessories they love and find similar products available in your app’s catalog. By incorporating tools like Google Vision or custom-trained deep learning algorithms, we ensure the app provides accurate and visually relevant suggestions that help users quickly find exactly what they’re looking for.
7. Product Discovery & Recommendation System
To further enhance product discovery, we build a sophisticated recommendation engine that suggests products based on users’ behavior, searches, and feedback. Whether it’s through collaborative filtering or content-based models, our AI continuously learns and refines its suggestions. This ensures users are always presented with highly relevant options that match their evolving tastes and shopping patterns, making the app more engaging and dynamic over time.
8. AI-Driven Search & Refinement Features
Our AI-driven search and refinement features make it easy for users to find exactly what they want. By interpreting detailed search queries, like “Show me red dresses under $150,” the app provides highly accurate results. Advanced filters let users refine their searches by price, style, and other attributes, enhancing the efficiency of the shopping process and ensuring a better user experience.
9. Real-Time Analytics & Insights
With real-time analytics, we continuously monitor user behavior and product performance within the app. By tracking metrics like clicks, interactions, and purchases, we gain valuable insights that allow us to optimize the user experience and improve the app’s recommendations. This data-driven approach ensures that the app evolves in line with users’ needs, resulting in a more personalized and engaging shopping experience.
10. Monetization & Partner Integration
We help you implement a solid monetization strategy by integrating affiliate programs and establishing partnerships with fashion brands and retailers. This allows your app to earn commissions on purchases made through the platform. We also incorporate additional monetization features such as premium memberships, in-app purchases, and exclusive offers, ensuring your app is both profitable and user-friendly.
Cost of Developing an AI Fashion Shopping App Like Daydream
We believe in delivering exceptional AI-powered fashion shopping app that strike the perfect balance between functionality and cost-efficiency. Our goal is to offer high-quality products that meet your needs within a reasonable budget.
1. Discovery & Planning
| Activity | Description | Cost Range |
| Requirements Gathering | Initial consultations to define app scope | $1,000 – $3,000 |
| Market Research | Competitor analysis, technology selection | |
| Project Plan | Finalizing the plan for project execution |
2. Design (UI/UX)
| Activity | Description | Cost Range |
| User Flow Design | Creating user journeys and flow | $2,000 – $8,000 |
| Wireframes & Mockups | Designing initial app screens | |
| Interactive Prototypes | Building clickable prototypes for user feedback | |
| Branding & Visual Identity | Logo, fonts, colors, and visual elements |
3. Backend Development
| Activity | Description | Cost Range |
| Database Design | Structuring the app’s database schema | $5,000 – $18,000 |
| API Development | Building APIs for user authentication and product management | |
| AI Services Integration | Integrating AI components with backend | |
| Scalability & Security Setup | Preparing the app for growth and securing data |
4. AI/ML Model Development & Integration (MVP Level)
| Feature | Description | Cost Range |
| Personalized Recommendations | Simple algorithms for recommendations | $3,000 – $10,000 |
| 2D Virtual Try-On | Basic techniques for overlaying clothes on user images | $4,000 – $15,000 |
| Visual Search | Using pre-trained CNNs for product search | $3,000 – $10,000 |
| Size & Fit Prediction | Rule-based or statistical models for size prediction | $2,000 – $5,000 |
| Generative AI for Style Exploration | Third-party API integration (if applicable) | Beyond $100,000 |
5. Frontend Development (Mobile MVP – Single Platform)
| Activity | Description | Cost Range |
| UI Development | Building the user-facing application | $5,000 – $22,000 |
| API Integration | Connecting frontend with backend APIs | |
| AR/Computer Vision Features | Basic integration of AR and computer vision features |
6. Testing & Quality Assurance
| Activity | Description | Cost Range |
| Unit Testing | Testing individual app components | $2,000 – $7,000 |
| Integration Testing | Testing integration of different app modules | |
| System Testing | Overall testing of the system functionality | |
| User Acceptance Testing (UAT) | Testing with end-users for feedback | |
| Performance & Security Testing | Ensuring app speed and security |
7. Deployment & Launch
| Activity | Description | Cost Range |
| App Store Submission | Submitting app to Apple/Google stores | $500 – $2,000 |
| Web Hosting Setup | Setting up the app’s web hosting | |
| Initial Monitoring | Setting up analytics and monitoring systems |
8. Initial Post-Launch & Maintenance
| Activity | Description | Cost Range |
| Bug Fixing | Resolving post-launch issues | $1,000 – $5,000 |
| Minor Updates | Small app feature enhancements | |
| Model Retraining | Updating AI models based on user data | |
| Scalability Management | Handling early growth and traffic increases |
The figures mentioned are just rough estimates, and the total cost can range from $10,000 to $100,000 USD. To get a more accurate quote for your project, don’t hesitate to connect with us for a free consultation.
Factors Affecting the Cost of Developing a AI Fashion Shopping App like Daydream
The cost of developing an AI-powered fashion shopping app, like Daydream, is shaped by various factors that go beyond typical app development. While things like platform choice, team location, and app complexity certainly affect the budget, the unique demands of AI-driven features make a significant impact on overall cost.
Complexity and Type of AI/ML Features
The sophistication of AI features like personalized recommendations, visual search, and virtual try-on capabilities determines costs. Simpler algorithms will be cheaper, while advanced solutions like real-time 3D AR with accurate fabric simulation require specialized expertise and more resources.
Data Acquisition and Preparation
AI models need vast amounts of high-quality data to function well. The time and resources spent collecting, cleaning, and labeling fashion-specific datasets (like images of clothing, body measurements, and user preferences) are significant, especially if data needs to be manually curated.
AI Model Training and Optimization
Training advanced AI models, particularly deep learning models, requires substantial computational power. This often means renting cloud services like AWS or Google Cloud, which can become costly. The number of training iterations and the need for fine-tuning can further drive up the price.
AI Infrastructure Scalability and Maintenance
Beyond development, the costs of hosting AI models and ensuring they run efficiently in production are considerable. Ongoing cloud fees, model performance monitoring, and retraining to adapt to new trends or behaviors all add to the budget.
3D Content Creation for AR (if applicable)
For apps with advanced virtual try-on features, creating high-quality 3D clothing models and integrating them with AR technology is a specialized task. The time and resources needed for realistic fabric simulation and smooth AR integration increase both design and development costs.
How the AI Works in a Fashion Shopping App like Daydream?
AI-powered fashion shopping app like Daydream personalizes the shopping experience by analyzing user data such as preferences, behavior, and trends. It uses machine learning to predict styles, computer vision for visual search, and recommendation algorithms to suggest tailored outfits. This creates a seamless, data-driven experience for each user.
1. Inputs: The Data That Fuels Personalization
For Daydream’s AI to offer the best recommendations, it first gathers a range of diverse data. This information is the foundation for all the predictions, styles, and suggestions that the user will receive.
A. User-Generated Inputs
| Category | Type | Description |
| User-Generated Inputs | Explicit Preferences | Inputs consciously shared by users, like style quizzes, size, color, and price filters, and saved favorites. |
| User-Generated Inputs | Implicit Behavioral Data | Tracks user behavior, including browsing habits, time spent on products, cart activity, and purchase/return history to refine recommendations. |
| User-Generated Inputs | Visual & Voice Inputs | Users can upload photos (e.g., from Instagram) for visual search or use voice search for specific requests (e.g., “Show me dresses under $100”). |
B. External & Trend Data
| Category | Type | Description |
| External & Trend Data | Social Media & Runway Trends | Daydream’s AI scrapes social media platforms like Instagram and Pinterest to track current and upcoming fashion trends. It also monitors fashion events like fashion weeks to incorporate global styles. |
| External & Trend Data | Inventory & Brand Data | The app aligns recommendations with real-time stock data, ensuring users are shown available items. It also integrates eco-friendly brand data for sustainability-conscious users. |
| External & Trend Data | Contextual Data | The AI personalizes recommendations based on factors like location, weather, and events (e.g., recommending winter coats in cold climates or outfits for specific occasions). |
2. The AI Engine: ML, Computer Vision & Algorithms
At the core of Daydream’s success is its powerful AI engine. This system combines several techniques, machine learning, computer vision, and recommendation algorithms—to curate each user’s unique shopping journey.
A. Machine Learning for Personalization
Collaborative Filtering: One of the primary ways Daydream personalizes its recommendations is through collaborative filtering. By analyzing the shopping behavior of similar users (those who have a comparable taste or shopping history), the system suggests products that those users have also liked or purchased. This method increases the chances of finding items the user may not have discovered otherwise.
Deep Learning for Style Profiling: Deep learning algorithms segment users into distinct style categories based on their preferences. For example, a user may be categorized as a “boho chic” enthusiast or a “minimalist.” These profiles then guide personalized outfit suggestions, ensuring that each user is exposed to styles that align with their personal taste.
Predictive Analytics: The AI tracks purchase patterns over time, allowing it to predict future needs. For example, if a user tends to buy a new dress every spring for vacations, the system may predict the user’s future behavior and suggest similar styles as the season approaches.
B. Computer Vision for Visual Discovery
Image Recognition: Users can upload photos of clothes they like, and Daydream’s computer vision algorithms identify key features such as fabric types, colors, and patterns. This visual data helps the system match items from the app’s inventory that closely resemble the uploaded image.
Virtual Try-On (AR): Using augmented reality (AR), the app allows users to virtually try on items. By mapping a user’s body measurements to a 3D model of the clothing, the app provides a realistic preview of how an item might fit. This feature minimizes guesswork and helps users make confident purchase decisions.
Style Matching: Beyond showing individual items, the AI looks at what’s already in a user’s wardrobe (either by syncing with the app’s saved favorites or user uploads) and suggests complementary pieces to complete a look. For example, if a user has a classic black blazer, the AI might suggest trendy pants or a statement necklace to pair with it.
C. Recommendation Algorithms for Curated Outputs
Hybrid Filtering: Daydream employs a hybrid approach to filtering recommendations, combining both content-based filtering (focused on the attributes of individual items) and collaborative filtering (which relies on similar user behavior). This dual approach ensures that the app can offer a balanced mix of personalized and trend-conscious suggestions.
Context-Aware Suggestions: The app tailors its recommendations based on contextual elements, such as the time of day or season. For example, it may show workwear suggestions in the morning and casual evening attire in the evening. It also uses contextual awareness to suggest occasion-based outfits like “outfits for a wedding” or “vacation essentials.”
Reinforcement Learning: Daydream’s AI continually improves over time. As users interact with the app, it tracks the success of previous recommendations (i.e., which items were purchased) and refines future suggestions based on that data. The more a user engages with the app, the more personalized their experience becomes.
3. Outputs: The Personalized Shopping Experience
Once the AI has processed all of this data, it delivers a highly personalized shopping experience through several features that directly engage users.
| Category | Feature | Description |
| Smart Product Recommendations | “For You” Feed | A personalized feed tailored to the user’s style, size, and budget. It updates frequently, ensuring fresh and relevant options. |
| Occasion-Based Picks | Recommendations based on specific occasions, such as weddings, work attire, or casual weekend outfits. | |
| Virtual Styling Tools | Outfit Generator | Combines wardrobe items into full outfits. For example, pairing a user’s favorite jeans with complementary tops and shoes for a complete look. |
| AI Stylist Chatbot | A personal stylist chatbot that answers questions like, “What shoes go with this dress?” and offers tailored advice based on the user’s profile. | |
| Trend Forecasts & Alerts | “Trending Now” Section | Highlights popular items trending across social media, keeping users up-to-date on viral styles and fashion-forward pieces. |
| Restock Alerts | Sends notifications when a previously out-of-stock item is restocked, ensuring users don’t miss out on desired products. | |
| Hyper-Personalized Notifications | “You Might Like” Emails | Sends personalized email suggestions based on browsing and purchase history, offering reminders and new style recommendations. |
| Discount Personalization | Offers personalized discounts for categories the user frequently shops in, like “20% off shoes” or “15% off dresses,” making the shopping experience more cost-effective. |
Key Challenges in AI Fashion Shopping App Development
Through extensive experience working with numerous fashion tech projects, we’ve encountered several challenges in developing AI-powered fashion shopping apps. From privacy concerns to ensuring smooth integration with fashion ecosystems, here’s a look at the most common obstacles and how we address them to create seamless, user-centric fashion experiences.
1. Data Privacy & Ethical Considerations
AI fashion apps gather and use sensitive data such as browsing history, purchase behavior, and body measurements for personalization. Mishandling this information can lead to privacy breaches, putting the app at risk of regulatory penalties under laws like GDPR and CCPA.
Solutions
- We prioritize user privacy by implementing robust data encryption and anonymization techniques. Users are given clear and explicit consent options for data collection.
- Regular compliance audits ensure the app stays up-to-date with privacy regulations, especially as it expands to different markets.
For example, fashion apps like Zalando use differential privacy methods to analyze trends without exposing individual user data.
2. Bias in AI Algorithms
AI algorithms trained on limited or biased datasets can produce recommendations that overlook diverse body types, skin tones, or cultural fashion preferences, creating an exclusionary experience for users.
Solutions
- To prevent this, we ensure our training datasets are diverse and representative of various body shapes, sizes, ethnicities, and gender identities.
- We also use bias detection tools like Fairness Indicators and IBM’s AI Fairness 360 to continuously audit our algorithms. Additionally, combining AI with human stylist reviews ensures that all recommendations are inclusive.
Example: Pinterest’s skin tone filter allows users to search for fashion that better represents their skin color, promoting more inclusive results.
3. Seamless Integration with Fashion Ecosystems
Integrating AI fashion apps with e-commerce platforms, inventory systems, and payment gateways can be complex, especially when it comes to managing real-time stock updates, avoiding checkout errors, and ensuring smooth user interactions.
Solutions
- We adopt an API-first approach, using RESTful APIs to connect AI models with ERP, CRM, and POS systems, ensuring smooth data exchange.
- Real-time synchronization through webhooks keeps inventory and pricing data up-to-date, preventing stock mismatches. Collaborating directly with retailers ensures accurate product feeds and reduces reliance on third-party scrapers.
For example, ASOS integrates AI-powered search with its global warehouse system, ensuring live stock updates and preventing out-of-stock issues for users.
4. Overcoming Technical Limitations in AR & Virtual Fitting
Virtual try-ons and AI-based size recommendations often struggle with inaccurate body scanning, unrealistic fabric draping, and high latency in rendering, which can create a frustrating user experience.
Solutions
- We tackle these challenges by using 3D garment simulation tools like Clo3D, which provide realistic fabric movement and accurate try-on experiences. AI-powered size predictions are enhanced by combining user measurements with previous fit feedback.
- Edge computing is employed to process AR try-ons locally on the user’s device, eliminating lag and providing smooth, real-time results.
For instance, Gucci’s AR sneaker try-on uses lidar technology for precise fit visualization, ensuring users can see how shoes will fit in a realistic way.
Conclusion
AI has the power to revolutionize the fashion shopping experience by offering personalized recommendations, virtual try-ons, and dynamic outfit suggestions that cater to individual tastes and needs. Partnering with an experienced AI development company, like IdeaUsher, can help transform your vision for an AI-driven fashion app into a reality. If you’re ready to take the leap and create an innovative fashion app, we’d love to help you bring your ideas to life. Reach out to us at IdeaUsher to start the journey today.
Looking to Develop an AI Fashion Shopping App like Daydream?
At IdeaUsher, we specialize in turning your ideas into a reality. Our team of experienced developers, with a combined expertise of over 500,000 hours of coding, is dedicated to creating AI-powered fashion apps that provide personalized shopping experiences, from styling assistants to virtual try-ons. With our knowledge of machine learning and AR technologies, we deliver apps that are both seamless and engaging, ensuring users keep coming back for more.
Why Choose Us?
- AI/ML & Computer Vision Experts – We build smart algorithms to personalize fashion discovery for each user.
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FAQs
A1: Developing an AI fashion shopping app starts with identifying your core features, such as personalized recommendations, virtual try-ons, and user-friendly interfaces. You’ll need to assemble a skilled team of developers, designers, and AI experts who can build the app’s machine learning models, integrate computer vision for virtual fitting rooms, and ensure a seamless user experience. Once the foundation is set, you’ll iterate based on user feedback to refine the app and make it truly personalized for every shopper.
A2: AI fashion shopping apps come with features that enhance the user experience, like personalized product recommendations based on user preferences, virtual try-ons using augmented reality, and smart styling suggestions. The app might also offer size recommendations, a digital wardrobe to manage existing clothes, and a feature to find similar products through image search. The goal is to make shopping as seamless, engaging, and customized as possible for the user.
A3: The cost of developing an AI fashion shopping app varies depending on factors like the app’s complexity, the number of features, and the expertise required. Building an app with AI and machine learning components typically requires a team of skilled developers, designers, and data scientists. In addition to development, costs include testing, maintenance, and future updates. The overall price can be influenced by the app’s design, the number of integrations with third-party services, and the desired user experience.
A4: AI fashion shopping apps typically make money through affiliate marketing, where they earn a commission on sales made through links to partnered retailers. Other revenue models include sponsored listings from brands, where retailers pay to have their products featured prominently within the app, or subscription models for users who want access to premium features. Some apps might also offer data analytics services to retailers, helping them understand consumer behavior and trends in fashion.
