Building retail assistants customers can trust with Databricks and Neo4j

Retail AI systems often struggle to gain consumer trust due to a lack of context awareness, memory, and grounding in actual product documentation, which are crucial for accurate and trustworthy interactions. A study by Gartner highlighted consumer skepticism, with 53% of respondents expressing distrust in AI search and summary results. To address these challenges, the Databricks Retail Assistant integrates a governed knowledge layer that combines customer data, product relationships, inventory, pricing, and documentation. This system employs a dual-agent architecture, leveraging Databricks for transactional data and Neo4j for product intelligence, enabling a seamless shopping experience that remembers context, reasons through product relationships, and retrieves accurate information from source documents. The solution uses advanced technologies such as persistent agent memory and a retrieval-augmented generation framework, facilitating a more trusted and context-aware shopping journey that connects discovery to checkout. By grounding responses in verifiable sources and maintaining continuity across interactions, the assistant aims to provide more relevant recommendations and a guided shopping experience, reducing unsupported answers and enhancing consumer confidence in AI-driven retail solutions.