Challenges
- Data Collection and User Profiling: Collecting accurate user data for personalized recommendations.
- Style Ambiguity: Interpreting user preferences accurately for suitable recommendations.
- Real-Time Fashion Trends: Staying up-to-date with changing fashion trends.
- Integrating User Feedback: Improving recommendations based on user feedback.
- User Adoption and Engagement: Encouraging users to try and regularly use the AI-based personal stylist
Solutions
- Integrating LLM and Langchain frameworks to create a local LLM for fashion e-commerce product queries.
- Generating personalized recommendations and outfit suggestions using Generative AI.
- Fine-tuning the model and collecting user data to set preferences.
- API for text input and providing desired output (product, image, textual query answer).
- Deployment on a cost-effective architecture.
Benefits
- Enhanced user satisfaction through personalized fashion recommendations
- Efficient shopping experience with time and effort saved through the AI-based personal stylist.
- Tailored recommendations can lead to higher conversion rates and increased customer loyalty, boosting the application’s revenue
- Cutting-edge AI-based personal stylist differentiates the application, attracting more users and establishing market leadership
- Offering insights into fashion trends and expanding users’ fashion knowledge, enabling them to discover new looks
Gen AI-Based Personal Stylist for clothing industry
In fashion e-commerce, the pressing need arises to implement an AI-based personal stylist solution adept at overcoming hurdles in user data collection, style interpretation, trend adaptability, feedback integration, and user engagement, thereby revolutionizing the clothing industry experience.
