1The Challenge
As LunaBeauty's catalog and user base grew, traditional search and discovery couldn't keep pace. The platform needed faster, more relevant search, intelligent recommendations, real-time personalization, scalable data pipelines, and infrastructure to support future AI initiatives — not just features, but a platform that continuously learns from user behavior.
2Our Solution
We built a comprehensive discovery ecosystem across four pillars. (1) Intelligent search architecture with relevance optimization, query understanding, and catalog transformation to improve findability. (2) A recommendation engine framework supporting similar-items, personalized discovery journeys, and behavior-driven suggestions, designed to evolve from rule-based to ML-driven personalization. (3) AI platform enablement with optimized data pipelines, feature engineering workflows, model-ready datasets, and event-driven architecture. (4) Cloud-native engineering with microservice optimization, infrastructure automation, and operational resilience to support long-term growth.
3The Outcome
Search relevance improved significantly, enabling users to discover a wider range of relevant products and increasing engagement across the full catalog. The recommendation framework reduced friction in the customer journey and improved conversion opportunities. Luna gained an AI-ready foundation for future capabilities including personalization, conversational experiences, and predictive analytics. Engineering overhead decreased through improved observability and automation.
Tech Stack
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