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Real-Time Multimodal AI for Unified Omnichannel Retail Experiences

Author's Name: Udit Agarwal, Aditya Gupta

Author's Email: udit15@gmail.com, adityagupta8121@gmail.com

Abstract

The convergence of advanced artificial intelligence (AI) systems and high-throughput data architectures is fundamentally reshaping the retail sector, catalyzing unprecedented advancements in operational efficiency and customer engagement. This paper presents the complex infrastructure required to achieve unified, real-time omnichannel experiences. Specifically, the analysis details how multimodal deep learning, leveraging the simultaneous processing of visual, textual, and categorical data through transformer-based architectures, enhances product categorization and contextual intelligence far beyond single-input systems. We discuss performance Service Level Agreements (SLAs) relevant to real-time customer interaction, where read latency for AI bot queries may target p95 thresholds of ≤30msleq 30 ext{ms} ≤30ms. To meet these demands, the supporting architecture must integrate a high-speed Hybrid Transactional/Analytical Processing (HTAP) data store, Kafka Streams for sub-second event freshness, and low-latency caching. Furthermore, the strategic deployment of Edge-AI is essential for autonomous physical retail monitoring, integrating computer vision and sensor fusion to mitigate real-world variability. These integrated systems are positioned to address challenges in ensuring content and process consistency across diverse customer touchpoints, thereby reducing retailer uncertainty and positively impacting customer loyalty. This comprehensive framework underscores the pivotal role of real-time multimodal intelligence in propelling retailers toward greater agility and customer-centricity.

Keywords
Multimodal AI, Omnichannel Retail, Edge-AI, Low-Latency Architecture, Data Fusion, Real-Time Systems, Customer Journey Mapping, HTAP.