The Challenge
Users engaging with the AI shopping assistant often felt constrained by limited options, excessive follow-up questions, and a lack of personalization. These shortcomings led to user fatigue, misunderstandings, and a subpar shopping experience. Insights from user research (UXR) and transcripts revealed that users wanted more intuitive, human-like interactions that catered to their unique needs.

The Solution
A robust, adaptable framework was designed to transform AI conversations into sales-like consultations. By breaking user queries into three core components—use-case, constraints, and preferences—the framework enabled the bot to understand intent and deliver relevant, personalized results. Key enhancements included:

Allowing users to skip questions and navigate freely.
Providing contextual help for technical queries.
Transitioning to open-ended interactions after gathering essential details to prevent over-questioning.
Displaying diverse and curated results aligned with user preferences.