제품 발굴을 위한 AI 지원 데이터 및 벡터 검색. UAE 타일 및 마감재 소매업체.
검색 시간을 70% 단축하고 전환율을 최대 15% 높이는 AI 지원 카탈로그 및 벡터 검색. Renascence는 한 전자상거래 타일 소매업체의 제품 카탈로그를 AI 지원 구조화된 데이터로 변환하여 벡터 검색 및 대화형 제품 추천을 가능하게 했습니다. 이전에는 고객들이 방대한 SKU 카탈로그를 수동으로 검색하는 데 어려움을 겪었습니다. 새로운 AI 경험은 검색 시간을 60~70% 단축하고 전환율을 10~15% 높였으며, 전년 대비 매출을 8~12% 향상시켰습니다.
01 —The Impact
The results, up front.
60–70% reduction in search time Conversion increased by 10–15% YoY revenue grew 8–12% Support workload reduced by 30–40% A significantly upgraded purchasing journey
02 — The Challenge
Where they started.
Large tile retailers rely heavily on SKU accuracy and searchability. Customers searching manually encountered friction that slowed decision-making and hurt conversion. Our objective was to turn the catalog into AI-ready data and build a conversational product discovery flow. We began by cleaning and structuring product data, then vectorizing it for similarity search. We implemented embedded vector search that allowed customers to find matching items via chat, natural language, or visual cues. The AI generated product cards with actionable CTAs, comparisons with similar SKUs, and dynamic item lists. This dramatically accelerated decision-making and created an intuitive “assistant-like” experience.
04 — Approach & Methodology
How we got there.
Customers previously spent far too long searching for tiles, often abandoning sessions. With vector search, the system instantly understood intent and surfaced the most relevant SKUs — reducing friction and enabling faster purchase decisions. AI cards and comparison tools addressed uncertainty by helping users evaluate alternatives quickly.
05 —In Practice
Project samples.
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