Generative AI-Enabled Micro-Frontend Framework for Scalable and Intelligent Enterprise Retail Applications

Abstract

Generative artificial intelligence (AI) and micro-frontend architectures are two of the most disruptive technologies on this planet today, with enormous potential to change how we build large scale enterprise retail applications. We present a novel strategy to integrate generative AI capabilities into a micro-frontend architecture, for building extensible, cognitive modular retail systems. While traditional monolithic frontend architectures are not efficient enough to cater to modern retail environments which are highly dynamic, personalized & data-driven. This paradigm enables an end-to-end framework for frontend component design to decompose into independent deployable micro-units, encodable by generative AI models processed in real-time during model generation through user profiles driven context. We framed our solution around large language models (LLMs), retrieval-augmented generation (RAG) and the AI-driven decision pipelines are designed to integrate natively within their respective micro-frontend modules. In addition, it brings in federated deployment strategies along with event driven communication protocols and DDD based principles to ensure inter module consistency as well as resilience against operational failure. Clear experimental evidence demonstrates system scalability, user engagement and operational efficiency all outperform significantly on the parts of traditional enterprise retail architectures. The solution can be a template for any retailer looking to synthesize generative AI and micro-frontend engineering to deliver creative new intelligent shopping experiences.