A Multi-Model Artificial Intelligence System for Content Generation and Data Analysis in E-Commerce Operations

The rapid growth of product assortments and content diversity in digital retail platforms has made the scalability and consistency of content-oriented operations a critical challenge. Content workflows such as product image generation, description writing, advantage text creation, and customer review analysis are often manual or partially automated, leading to increased operational costs, quality inconsistencies, and delayed time-to-market. In this study, an end-to-end integrated multi-model generative Artificial Intelligence (AI) system designed to automate content processing in large-scale e-commerce environments has been developed. The proposed system is deployed in a live production setting processing hundreds of thousands of product images, tens of thousands of SKUs, and millions of customer reviews annually. It consists of five orchestrated AI modules: diffusion-based generation of mannequin-based and non-mannequin product scenes, category-specific Natural Language Processing (NLP) for Search Engine Optimization (SEO) oriented product description generation, automated advantage text creation, AI-driven image retouching and resizing, and localized review analysis for size recommendation generation. The system architecture combines diffusion-based visual generation, Transformer-based language understanding and generation, and an MLOps infrastructure supporting continual learning. Performance was evaluated in production through baseline comparisons between manual and AI-assisted workflows during pilot and progressive rollout phases. Evaluation employed module-level operational metrics, including physical consistency accuracy, deformation rate, textual coherence, SEO keyword alignment, and classification accuracy. Experimental results indicate approximately a 70% reduction in content production time, up to a 60% decrease in manual intervention requirements, and a 40–50% reduction in external content-related costs. In addition, the review-based size recommendation module demonstrated the potential to reduce size-related return rates by 5–8%. The findings provide empirical evidence that end-to-end, orchestrated multi-model generative AI systems can substantially enhance efficiency, standardization, and scalability in e-commerce content operations.