Retail Giant Transforms Customer Experience at Scale

WebbyButter engineered a pioneering Retrieval-Augmented Generation (RAG) architecture tailored for retail scale. We built a real-time data orchestration layer that unifies customer interactions from mobile, web, and point-of-sale systems into a high-performance vector database. The AI engine uses this data to drive a natural language search experience—allowing customers to search for 'outfits for a summer wedding in Tuscany' and receive a curated, inventory-aware selection immediately. This isn't just a search engine; it's a digital personal shopper that learns and adapts with every click. The solution also includes an automated content generation pipeline that creates personalized product descriptions and marketing copy in real-time, ensuring that every touchpoint feels unique to the individual user. Our team also implemented a sophisticated feedback loop that allows the model to learn from customer interactions, constantly refining its recommendations to improve relevance and performance.

The impact was felt across every KPI. Within the first quarter of deployment, the client saw a 45% increase in conversion rates for users interacting with the AI personal shopper. By automating the merchandising process, the team saved over 60% of the time previously spent on manual product tagging and curation. More importantly, the system provided the client with unprecedented insights into customer intent, enabling them to optimize inventory based on real-time demand signals rather than historical averages. The digital storefront is no longer a static catalog—it's a dynamic, revenue-generating engine. Additionally, the reduction in false-start searches led to a 30% decrease in customer support inquiries related to product discovery. The project has laid the foundation for an enterprise-wide AI strategy, with plans to expand the personal shopper capabilities to in-store digital kiosks, further unifying the omnichannel experience.