AI-Powered Multi-Agent Fashion Assistant for Personalized Retail Recommendations

Abstract

As fashion retail navigates a new era shaped by heightened consumer expectations and rapidly evolving digital interactions, the need for deeply personalized, stylistically coherent, and context-aware recommendation systems has become paramount. Traditional engines, reliant on static rules or collaborative filtering, often fall short in capturing the complexity of human taste and the visual-semantic richness inherent in fashion products. This paper introduces Boyner’s AI-powered Multi-Agent Fashion Assistant, an enterprise-grade personalization platform architected on Microsoft Azure AI Foundry. The system orchestrates multiple specialized agents to deliver real-time, occasion-aware, and visually grounded fashion recommendations across omnichannel touchpoints. Leveraging multimodal embeddings, behavioral clustering, semantic search, and real-time trend signals, each agent operates with a distinct cognitive function, from silhouette-based outfit pairing to brand–season compatibility evaluation. Our implementation demonstrates how agentic AI systems can bridge the gap between algorithmic precision and stylistic intuition in large-scale fashion environments. The assistant not only enhances conversion and engagement metrics but also redefines the digital shopping journey as an explainable, adaptive, and human-centric dialogue. By operationalizing multi-agent orchestration within a live retail environment, Boyner pioneers a new paradigm in AI-powered visual discovery, offering a scalable blueprint for next-generation personalization in the global fashion ecosystem.