Search and discovery that understands intent, not keywords
Shoppers type full sentences describing what they want. Most storefront search reads that as a bag of keywords and ranks whatever product happens to contain one of them. The gap between those two things is where the revenue goes.
What breaks in retail search
Someone searches for a warm jacket for hiking in the rain. Keyword search matches "rain" and returns umbrellas. The shopper does not rephrase, they leave. That query looked like a miss in your analytics, if it was logged at all.
The second problem is that merchandising teams cannot fix it. Every ranking change is an engineering ticket, so the queue grows and the seasonal adjustment lands three weeks after the season started.
What we build for retail
Search that works on meaning rather than tokens, ranked against your actual catalog, stock levels and what shoppers do after they search. Products that are out of stock stop outranking things you can actually ship.
Recommendations trained on your own behavioral data instead of a generic model of shopper behaviour. What works for a fashion catalog with heavy repeat purchase is different from what works for high-consideration items bought once every few years.
Catalog enrichment, which is usually the unglamorous fix that moves the numbers most. Missing attributes, inconsistent naming across suppliers and untagged products are invisible to search no matter how good the ranking is. Automating that cleanup often does more than a smarter algorithm on top of bad data.
Controls for the merchandising team, so ranking rules and campaign boosts are something they change themselves the same day they need it.
What we measure
Conversion from search, search abandonment rate, and how far down the results page people click. That last one is the useful early signal. When it starts dropping, ranking is getting better before revenue shows it.
