MODAMIA – Transforming Fashion Retail with AI-Driven Personalization

Transforming Fashion Retail with AI-Driven Personalization

Business Problem

MODAMIA faced two primary challenges:

Customization Overload: While allowing customers to design their own outfits was a unique selling point, many found it overwhelming and struggled to visualize how different pieces would come together.
Lack of Personalized Recommendations: Traditional recommendation systems failed to provide personalized suggestions that catered to individual style preferences and body types, reducing customer satisfaction.

About MODAMIA

MODAMIA is revolutionizing the fashion retail space with AI-driven personalized shopping experiences, allowing customers to design customized outfits tailored to their style preferences, body types, and occasion needs.

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Solution

Modamia partnered with GoML to build an AI-powered solution that enhances the shopping experience through:

1.

Photo-Based Outfit Recommendations:
Using CNN models, customers could upload their photos and receive personalized outfit recommendations based on specific prompts like “I want a golden gown for a party.” The AI would then generate a visual representation of the outfit on the user’s photo, leveraging MongoDB for real-time data processing.

2.

Dynamic Adjustments to Preferences:
The solution also allowed users to refine their choices by inputting specific preferences (e.g., “sleeveless”). The AI, powered by an MLP model, dynamically adjusted the outfit to meet these specifications, providing high-accuracy visual updates.

3.

Enhanced Personalization & Visual Realism:
By utilizing advanced CNN models, the AI system delivered realistic visual representations of how different outfits would look on each customer’s unique body type. The front-end, built on React.js, ensured seamless user interactions.

Architecture

Frontend (AWS Amplify)
– Serves web and mobile applications for user interaction.
Backend (AWS Lambda)
– Handles backend logic, querying databases, and invoking services like SageMaker for machine learning.
Amazon SageMaker
– Executes machine learning models for personalized recommendations.
MongoDB Atlas
– Manages data storage and real-time CRUD operations.

Outcomes

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Increase in Customer Engagement: The AI-driven recommendations and realistic visualizations led to a significant increase in customer interaction with the platform.

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Improvement in Conversion Rates: Personalized recommendations and dynamic adjustments resulted in a substantial increase in purchase conversions.

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Increase in Customer Satisfaction: The ability to visualize outfits on their own photos significantly improved customer satisfaction, fostering brand loyalty and retention.

Technology Stack