Case study
Retail personalization at scale
We helped a large omnichannel retailer deliver highly relevant search and recommendations across web, app and stores—driving measurable lift while reducing infrastructure spend.
+18%
Conversion
+12%
AOV
-22%
Infra cost
-30%
Time-to-test
Solution highlights
- RAG search over product, reviews & UGC
- Personalized ranking with session signals
- Real-time features via streaming pipelines
- A/B tests with guardrail metrics
- Edge delivery & CDN caching
- FinOps: rightsizing & autoscaling
Reference architecture
Data is ingested from catalog, inventory, and clickstream into a unified feature store. RAG search augments queries with semantic retrieval over product content, reviews and UGC. Real-time ranking models score candidates per session. Experiments run behind feature flags with guardrail metrics.
Feature store (online/offline)
Vector DB for semantic search
Streaming ETL & CDC
Model registry & rollout
Edge cache & CDN
Observability & SLOs
Results & operations
Personalization lift
+18% conversion, +12% AOV across key categories.
Experiment velocity
-30% time-to-test via flags, templates and auto-analysis.
Cost efficiency
-22% infra via autoscaling, right-sizing and cache hit-rate.
Want similar results?
We’ll assess your data, traffic patterns and constraints, then propose a rapid path to impact.