Reinforcement Learning Architectures Drive Real-Time Conversion Lift
Static product recommendations act as a hidden bottleneck in e-commerce growth, failing to capture the real-time evolution of customer intent. Transitioning from rigid, pre-calculated lists to adaptive personalization allows recommendations to evolve as fast as the user's live session behavior.
Remediating Engagement Decay Without System Disruption
The retailer’s existing system had access to less than half of the common product information and lacked the context of a shopper’s cart or purchase history. The mandate was to optimize session-level decision-making across digital channels without disrupting existing infrastructure.
Architecture: Semantic Personalization
We designed a solution combining a leading LLM with Retrieval-Augmented Generation (RAG) to anchor the model to the retailer's proprietary data.
- Technical Components:
- Hybrid Search: Implemented a hybrid similarity-search endpoint to retrieve context from a vector database using numeric representations of scientific text.
- LLM-as-Judge: Evaluated RAG pipelines using other LLMs to score groundedness and faithfulness (achieving 4.5/5).
