Use casesDigital commerce
Digital commerce
Shoppers ask in sentences. Your catalogue should answer.
Keyword search fails the moment someone describes what they actually want. We put semantic discovery and governed personalisation over the catalogue you already maintain, and we hold it to margin, not just clicks.
Measured
22%
Lift in average order value on assisted journeys
Connects to
ShopifySAP CommerceAlgoliaSnowflake
Where it hurts today
Long-tail queries returning nothing, then a bounce
Merchandising rules that need a developer for every season
Recommendations tuned for clicks while margin quietly slips
Product data spread across PIM, ERP and spreadsheets
What changes with Zitrino
Intent-level search that understands attributes, not just strings
Merchandisers change ranking logic themselves, with guardrails
Recommendations scored on contribution margin as well as conversion
One product view assembled from the systems of record you keep
Capabilities
What we actually deliver.
Four pieces of work. Each one ships on its own and earns its place before the next is started.
01Semantic discovery
Vector search over enriched product data, so "something warm for a coastal wedding" returns stock you can actually ship.
02Assisted selling
A conversational layer that qualifies, compares and closes — and hands off cleanly when a human should take over.
03Margin-aware ranking
Objectives are explicit. Conversion, basket size and margin are weighted by you, not inferred by a model.
04Commerce integration
Catalogue, pricing and inventory stay where they are. We read them, we do not fork them.
Built for this
The products we put in front of this problem.
Both are already in production elsewhere. Open one to see where it fits in this workflow.
The agent that sells, serves and reports
Explore the productSee it on your own data, in days.
Tell us how the work runs today. We will come back with a demo on a slice of your environment and an honest read on what it takes to put it live.