Готовые к ИИ данные и векторный поиск для подбора товаров. Ритейлер плитки и отделочных материалов из ОАЭ.
Каталог, готовый к работе с ИИ, + векторный поиск: сокращение времени поиска на 70% и рост конверсии до 15%. Мы преобразовали каталог товаров интернет-магазина плитки в структурированные данные, готовые для ИИ, что позволило внедрить векторный поиск и диалоговые товарные рекомендации. Раньше клиентам было трудно вручную искать товары в обширном каталоге SKU. Новый клиентский опыт на базе ИИ сократил время поиска на 60–70%, повысил конверсию на 10–15% и увеличил выручку в годовом исчислении (YoY) на 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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