TryLens

Project Concept

This is an intelligent virtual try-on and product recommendation system that combines Google’s advanced AI capabilities with real-time market product search to provide users with a complete shopping experience.
Business Value:
Reduces Return Rates: See before you buy
Increases Conversion: Visual confirmation builds confidence
Saves Time: Combined try-on + shopping in one platform
Personalized Experience: AI-driven recommendations based on user’s actual appearance

Entry

Status: Submitted

Last saved: October 11 at 7:14 PM CEST

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Team Roster

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Reetika Gautam Team Lead RSVP Approved

data scientist at publicis

AI Backend Development: Designed and implemented the complete FastAPI backend with Google Cloud integration, including Gemini 2.5 Flash model deployment, specialized virtual try-on endpoints, and comprehensive error handling with fallback mechanisms. Infrastructure & Testing: Set up Google Cloud Run deployment with GPU acceleration, implemented Google Cloud Storage integration for image processing, and created comprehensive testing frameworks to ensure production-ready reliability

Reetika Gautam is a Data Scientist and Assistant Research Engineer at SURGAR, specializing in AI and machine learning with over three years of experience. She holds a Master’s degree in Computer Vision from Université de Bourgogne and a Bachelor’s degree in Information Technology from Netaji Subhash Engineering College. Reetika is actively engaged in advanced projects such as video object segmentation and predictive modeling, leveraging her strong coding skills in Python, Java, and SQL. Passionate about technology and data, she applies computer vision techniques and builds scalable AI systems to enhance data insights and automation, continuously seeking to grow her expertise in the field.

video object segmentation, predictive modeling, machine learning, computer vision, data science, AI systems development, data analysis, cybersecurity application development.

Currently, Reetika Gautam is working on video object segmentation projects using advanced machine learning techniques, focusing on improving accuracy and efficiency. She is also involved in developing predictive models for data-driven decision-making, utilizing Python and SQL for data processing and analysis. Her work includes applying computer vision methods and building scalable AI systems to enhance data insights and automation within her role at SURGAR.

Sonny Mupfuni RSVP Approved

AI Researcher & Engineer at Sonny Mupfuni

Agent Architecture: Developed the intelligent agent system using Google SDK with advanced orchestration capabilities, enabling seamless integration between multiple AI models and handling complex multi-modal interactions. Frontend Development: Built the complete Next.js frontend interface with responsive design, real-time image upload functionality, and intuitive user experience for seamless virtual try-on interactions across devices.

Sonny Mupfuni is an AI Researcher & Engineer based in Paris, France. With a background in artificial intelligence and computer science from Université de Paris, he combines research expertise with hands‑on software engineering to build innovative AI solutions.

Artificial intelligence, Reinforcement learning, Decision intelligence, NLP, Machine learning, Data science, Algorithms, Multi-agent systems