Retail AI Commerce Engine
Personalization that lifted revenue per visit
AI search, recommendations, and dynamic pricing unified into one commerce brain.
Where the old way was failing
Site search failed on natural language — customers searching 'warm jacket for hiking' got keyword noise, and 38% of searches ended without a click.
Recommendations were generic bestseller carousels, identical for a first-time visitor and a decade-long loyalty member.
Pricing and promotions were set manually in spreadsheets, reacting to competitors weeks late and eroding margin on lines that didn't need discounting.
What we built
Semantic search & discovery
We replaced keyword search with a semantic engine that understands intent, attributes, and context — including typo tolerance and 'shop the look' style queries — across all 40K SKUs.
Real-time personalization
A unified customer profile drives recommendations across web, app, and email. Models re-rank in real time on the current session's behavior, not last month's segment.
Dynamic pricing engine
Demand forecasting and elasticity models suggest price and promotion moves per SKU, with guardrails the merchandising team controls. Humans approve; AI does the math.
What changed for the business
Average order value rose 27% within one quarter of launch.
Search-to-purchase conversion improved 34%; null-result searches fell by two-thirds.
Returns dropped 19% as customers found products that actually matched intent.
Margin improved on 600+ SKUs where automated pricing removed unnecessary blanket discounts.
“It's the first personalization project that showed up in the P&L instead of just the slide deck.”
Want results like these?
Book a strategy session and we'll map the same AI Engineering Framework onto your product, your data, and your industry.