Mr. Ravi Teja Pagidoju

Generative AI for Retail Space Optimisation

Abstract:

Space optimization in the retail industry is still one of the most time-consuming problems, like planogram design taking more than 30 hours of manual work for each complex layout. This session looks at how generative AI, especially diffusion models, can change how retail space is planned by making it possible to automatically create planograms for each store. Attendees will learn how to use diffusion models, which are the technology behind image generation tools like DALL-E, to solve problems with limited physical space. The presentation goes over the basics of diffusion models, how to add retail-specific limits to the generation process, and practical cloud-native architecture patterns for using AI on a large scale in businesses.  The session gives useful information that they can use to solve problems with managing physical retail space using modern AI techniques.

Profile:

Ravi Teja Pagidoju has 9 plus years of experience building scalable enterprise applications for retail, healthcare, and telecommunications. Currently working in retail technology, his expertise spans full-stack development (.NET Core, Java Spring Boot, React), cloud platforms ( Azure, GCP), and machine learning systems deployed at enterprise scale. He has architected and delivered applications that automate manual processes, integrate generative AI models, and serve a huge number of users with real-time performance. His technical focus includes building RESTful APIs, micro services architectures, and CI/CD pipelines using Kubernetes and containerisation. In addition to his industry work, Ravi conducts research in AI-driven retail optimisation with peer-reviewed publications in international journals on topics including automated planogram generation, hybrid AI-classical algorithms, and neural network optimisation. He holds a Master's degree in Computer Science and regularly bridges academic innovation with production deployment to deliver measurable business value in real-world retail environments.