Orclever Proceedings of Research and Development

Visual Discovery in Retail: Operationalizing AI-Powered Visual Search at Boyner

Mert Alacan

Boyner

https://orcid.org/0000-0003-3893-6309

Seza Dursun

Boyner

https://orcid.org/0000-0003-1389-072X

Bahar Önel

Boyner

https://orcid.org/0009-0007-4597-6591

Tülin Işıkkent

Boyner

https://orcid.org/0009-0005-5775-0093

Sedat Çelik

Boyner

https://orcid.org/0009-0003-0335-6440

DOI: https://doi.org/10.56038/oprd.v7i1.742

Keywords: Visual Search, Multimodal AI, GroundingDINO, SigLIP, Milvus, Retail Intelligence, Semantic Search, AI in E-Commerce, Omnichannel Retail, Customer Experience

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Abstract

In today's retail landscape, where millions of products and visual stimuli compete for customer attention, the integration of artificial intelligence into visual search has emerged as a crucial lever of operational efficiency. This paper presents Boyner Group's AI-powered visual discovery system, which enables customers to search using photos instead of keywords, making product discovery more intuitive and visually engaging. The architecture leverages a hybrid approach combining Large Language Models (LLMs), vision models such as GroundingDINO, and vector-based semantic similarity engines like SigLIP+Milvus to deliver scalable and high-accuracy image retrieval. The system, currently operational across the Boyner.com.tr ecosystem, supports enhanced filtering and storytelling capabilities, increasing customer satisfaction and conversion rates. The implementation process, system components, and operational results of this large-scale AI integration are explored, highlighting its transformative impact within omnichannel retail.

Keywords:   Visual Search, Multimodal AI, GroundingDINO, SigLIP, Milvus, Retail Intelligence, Semantic Search, AI in E-Commerce, Omnichannel Retail, Customer Experience

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