Agentic Shopping AI Assistant for E-commerce Platforms

Digital retail catalogs have grown faster than the interfaces designed to navigate them. Shoppers face search results that optimize for popularity over relevance, recommendation engines that reflect platform metrics more than individual need, and specification-heavy product pages that require effort to compare. The result is high drop-off between browse and purchase, elevated return rates from mismatched buying decisions, and a customer experience that doesn't reflect the depth of knowledge available within the catalog itself.

NeenOpal's Agentic Shopping Assistant removes the navigation burden from the shopper. The assistant engages through natural conversation, builds contextual understanding of requirements across multiple turns, retrieves semantically relevant products from live catalog data, supports comparison and specification queries, and guides the customer from discovery to purchase initiation without requiring them to understand how the catalog is organized.

What We Deliver • Multi-turn conversational interface on Amazon Bedrock with persistent session context maintained across the shopping journey • Real-time catalog retrieval using Bedrock Embeddings and semantic search, matching shopper intent against live product data • Preference modeling that refines recommendations based on stated constraints, browsing signals, and purchase history where available • Comparison and specification assistance enabling shoppers to evaluate options across relevant attributes in conversation • Cart and checkout integration for direct purchase initiation from within the assistant session • Analytics covering conversation flows, drop-off patterns, and recommendation acceptance rates for continuous optimization

Where This Applies • E-commerce and digital retail platforms with large catalogs where search and filter UX creates discovery and conversion friction • Consumer electronics and technical product retailers where specification-heavy purchasing requires guided decision support • B2B procurement portals where buyers need to match product specifications to requirements across complex and frequently updated catalogs • Subscription and reorder workflows where conversational interfaces improve replenishment accuracy and average order value

Why NeenOpal • Agentic architecture maintains full conversation context across turns, enabling requirement refinement without the customer repeating themselves • Semantic retrieval using Bedrock Embeddings surfaces relevant products beyond keyword matching, reducing zero-result and low-relevance discovery experiences • Conversation-to-conversion analytics connect session behavior to purchase outcomes, enabling data-driven optimization of recommendation logic • Built on Amazon Bedrock with AWS-native scalability to handle peak traffic without degradation in response quality or latency