We deployed for Be-Cause.shop an innovative recommendation system combining RAG and LLM, enabling personalized suggestions. This optimized cloud solution has significantly improved user experience while maintaining controlled operational costs.
ABOUT THE CLIENT
Be-Cause.shop stands as a pioneer in ethical and responsible fashion. The platform brings together a committed community of sustainable brands and conscious consumers, creating a unique ecosystem in the online fashion sector.
CHALLENGE
Be-Cause.shop faces three major challenges in its mission to offer a personalized shopping experience:
Database Connections
Integration and synchronization with multiple product data sources require a robust architecture capable of handling different data formats and structures while maintaining their consistency.
Relevant AI Matching
Developing AI matching algorithms capable of effectively analyzing user requests and providing truly relevant recommendations is a major technical challenge. The AI must understand users' real preferences and adapt to their evolving needs.
Processing Speed
Performance is crucial: users must instantly receive results matching their expectations. This requires advanced optimization of response times, from preference analysis to recommended product display.
SOLUTION
Our approach revolves around three main components:
RAG Recommendation System
Vector/embeddings calculation for each product
Use of MongoAtlas for embeddings storage
Intelligent product indexing for ultra-fast response time
Advanced semantic search
LLM Validation
Integration of GPT-4o Mini for cost optimization
Verification of recommendation consistency and adjustment
Generation of personalized explanations
Cloud Infrastructure
Cloud deployment for optimal scalability
Operating cost optimization
Real-time performance monitoring
METHODOLOGY
The project was carried out with a tight team consisting of:
1 AI expert
1 full-stack developer
1 product owner
Development was conducted in two-week iterations, with systematic testing phases and gradual deployment to staging then production.
CONCRETE EXAMPLE
User Request:
"I'm looking for casual office wear that's both elegant and eco-responsible. I prefer neutral tones and have a budget of around €200."
AI Analysis:
RAG identifies key criteria:
Use: casual office wear
Style: elegant but casual
Values: eco-responsible
Color preference: neutral tones
Budget: €200
LLM validates consistency and generates personalized response:
"Here's a selection that matches your eco-responsible business casual style:
Proposed Outfit:
Beige Tencel™ fluid shirt from EcoWear - €75 Made in France from biodegradable fibers
Taupe organic cotton straight pants from GreenStyle - €89 Fair trade certified production
Accessory: Natural cork belt from Cork Fashion - €35 Vegan leather alternative
Total: €199
These pieces are easily combinable with each other and your existing wardrobe. Tencel™ offers excellent comfort for the office while being environmentally friendly. Would you like to see other combinations in these tones?"
RESULTS
Our recommendation system combining RAG and LLM pushes the boundaries of e-commerce personalization. By combining the power of semantic search with language models, we offer users a truly personalized shopping experience:
Technical Performance
Fast response time thanks to cloud optimization
Scalable system according to query volume
Controlled operational costs thanks to GPT-4 Mini usage
User Experience
More relevant recommendations based on specific requests
Suggestions adapted to user requests
Personalized responses with clear explanations
Business Impact
Better product visibility
Effective highlighting of brand commitments
Facilitation of product discovery matching user criteria
PERSPECTIVES
The perspectives following the success of this first phase open the way to several promising developments: integration of predictive trend analysis, consideration of similar purchases, continuous improvement of the recommendation model, and enhanced personalization of explanations