Managing a complex purchase journey at scale
Tire shopping involves multiple steps, from finding the right product and comparing options to making a purchase. For Tstation.com, which serves a large volume of online customers, the challenge was to simplify this journey through natural conversation while maintaining the accuracy and reliability required for a large-scale B2C service.
Building a reliable conversational service with a Multi-Agent Architecture
We implemented a conversational AI service based on a Multi-Agent Architecture, with specialized agents responsible for different parts of the customer journey. An orchestrator coordinates interactions across agents, while dedicated agents handle specific customer requests and enforce business policies and product eligibility rules.
Accuracy-critical information, including inventory and pricing, is retrieved directly from databases and APIs, while generative AI handles natural-language understanding and response generation. This hybrid architecture enables natural customer interactions without compromising the reliability of transactional information.
Connecting search, recommendation, and purchase in one conversation
The AI agent supports both keyword and natural-language search, narrows down products based on customer requirements, and recommends suitable options with relevant comparisons and trade-offs. Customers can then continue directly to nearby store discovery and appointment booking within the same conversational flow.
By connecting product search, recommendation, comparison, and purchase-related actions in a single interaction, the service reduces friction across the tire-buying journey and helps drive conversion.
