製品発見のためのAI対応データとベクトル検索。UAEのタイル・仕上げ材小売業者。
AI対応カタログとベクトル検索により、検索時間を70%短縮し、コンバージョンを最大15%向上。eコマースのタイル小売業者の製品カタログをAI対応の構造化データに変換し、ベクトル検索と会話型製品レコメンデーションを可能にしました。以前は、顧客は大規模なSKUカタログを手動で検索するのに苦労していました。新しいAIエクスペリエンスにより、検索時間は60～70%短縮され、コンバージョンは10～15%増加し、前年比収益は8～12%改善されました。
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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