🔹 Inspiration
Online shopping has a $800B+ annual returns problem.
Why? Because customers can’t truly see how a product will look on them or in their homes.
We asked ourselves:
“What if AI could close the imagination gap — letting you **try on clothes* or place décor in your room before hitting buy?”*
That question sparked this project.
🔹 What We Built
Our solution is an AI-powered Try-On & Try-In system:
- Upload a selfie → preview sunglasses, shirts, or watches on yourself.
- Upload a room photo → visualize candle holders, jars, or furniture inside your space.
- All generated seamlessly & photorealistically using Google Gemini AI.
🔹 How We Built It
- Base Platform: Deployed Google’s Online Boutique microservices demo on GKE Autopilot.
- Custom Try-On Service: Flask backend + Gemini API to combine user images with product images.
- Integration: Extended Online Boutique’s frontend templates with a
Try Onbutton and upload form. - Infra & DevOps:
- GCP Cloud Build + Artifact Registry for CI/CD.
- Kubernetes manifests for scalable deployment.
- LoadBalancer for external access.
- GCP Cloud Build + Artifact Registry for CI/CD.
🔹 What We Learned
- How to orchestrate microservices on GKE Autopilot.
- Best practices for integrating AI into cloud-native apps.
- Prompt design for photorealistic image generation.
- Handling cross-service communication and secure API integration.
🔹 Challenges We Faced
- Image alignment & realism: Ensuring products looked natural (e.g., glasses on a face vs. candle holder on a table).
- Kubernetes integration: Debugging pod networking + service exposure.
- API limits & latency: Optimizing requests so the experience feels instant.
- Frontend integration: Embedding a new Flask service seamlessly into an existing Go/HTML microservice frontend.
✨ In short:
This project is more than a demo — it’s a proof-of-concept for the future of e-commerce, where AI + Cloud eliminate uncertainty and create confidence-driven shopping.


Log in or sign up for Devpost to join the conversation.