KI-bereite Daten & Vektorsuche für die Produktfindung. Fliesen- & Ausbauhändler in den VAE.
KI-fähiger Katalog + Vektorsuche, die die Suchzeit um 70 % reduziert und die Konversion um bis zu 15 % steigert. Wir haben den Produktkatalog eines E-Commerce-Fliesenhändlers in KI-fähige strukturierte Daten umgewandelt, was Vektorsuche und konversationelle Produktempfehlungen ermöglichte. Zuvor hatten Kunden Schwierigkeiten, große SKU-Kataloge manuell zu durchsuchen. Die neue KI-Erfahrung reduzierte die Suchzeit um 60–70 %, steigerte die Konversion um 10–15 % und verbesserte den Jahresumsatz um 8–12 %.
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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