[ RETAIL ]
Recommendation Engine
A hybrid recommendation engine combining collaborative filtering with content-based scoring, served at sub-50ms latency.
Domain
RETAIL
Status
Delivered across e-commerce engagements
Use when
E-commerce or omnichannel retailers want basket-size growth from contextual recommendations.
Reference
Available for new engagements
01. The problem
Static cross-sell logic fails to adapt. Static recommendations miss revenue. Recommendations slower than 200ms get ignored.
02. The approach
Hybrid model combining collaborative filtering on aggregated anonymised signals with content-based scoring for cold-start coverage. Per-segment weighting tunes recommendations to how each cohort actually shops.
03. The architecture
Serving on Elasticsearch for sub-50ms latency across millions of items. Online evaluation harness exposes A/B configurations to the merchandising team.
04. The outcome
Basket value grows. Cold-start items surface within their first sessions. The merchandising team can run experiments without an engineering ticket.
[ ATI SHAPE ]
Predictive intelligence
Collaborative filtering + content-based scoring
Agentic execution
A/B harness as a per-experiment workflow
Secure data
Aggregated, anonymised signals only
Actionable outcomes
Sub-50ms recommendations served per session
[ IMPACT ]
<50ms
Latency at the recommendation surface
Hybrid
Collaborative + content for cold-start coverage
A/B
Merchandising-controlled experiments
[ NEXT STEP ]
See Recommendation Engine in your data.
[ READY FOR YOUR STORY? ]