AI-ready data & vector search for product discovery.
How we made a vast tiles and finishing catalog instantly discoverable — cutting search time by 70% and lifting conversion by 15%.
01 —The Impact
The results, up front.
Customers could finally find what they wanted in seconds. Faster discovery converted more browsers into buyers and freed sales staff to focus on high-value advice.
02 — The Challenge
Where they started.
A leading tiles and finishing retailer had thousands of products customers simply could not find. Traditional keyword search failed against a catalog where customers shop visually and by attribute, not by SKU name.
Slow, frustrating discovery meant lost sales and a heavy load on sales staff answering "do you have something like this?"
03 —What We Did
The work.
AI-ready data
We restructured and enriched the product catalog into clean, attribute-rich, AI-ready data.
Vector search
We implemented semantic vector search so customers find products by meaning and attribute, not exact keywords.
Discovery UX
We redesigned the discovery experience around how customers actually browse and decide.
04 — Approach & Methodology
How we got there.
We started from the customer’s mental model rather than the catalog’s structure, researching how buyers actually describe and choose tiles and finishes. That insight shaped an attribute-rich data model and a semantic vector-search layer that matches intent, not just keywords.
We then redesigned the discovery experience around real browsing behavior, validating each change with live users before rollout so improvements were proven, not assumed.
"For the first time our catalog works the way our customers think. Discovery went from a frustration to a strength."
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