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We developed a cutting-edge personalized search platform that leverages LLM-backed chat experiences.
The brief.
We developed a cutting-edge personalized product search platform that leverages LLM-backed chat experiences to transform consumer review data into a powerful search tool. By vectorizing customer reviews, the platform enables more personalized and meaningful search experiences, far surpassing traditional filtering methods. Two key use cases highlight the platform's versatility: a doctor search tool and a sneaker assist tool.
Our approach.
Our approach centered on enhancing search functionality by integrating advanced AI techniques that interpret user queries in a more natural and contextually aware manner. This allowed us to deliver personalized recommendations based on real customer reviews.
Vectorized search for personalization
Implemented vector indexes to enable semantic and natural language search across large, unstructured data sets, enhancing the relevance of search results.
LLM-backed interpretation
Utilized large language models to interpret user queries, understanding nuanced requests like finding doctors who are good with young children or identifying comfortable shoes for walking.
Context optimization
Optimized the platform for handling the context limitations of LLM applications, including summarizing previous context and large content blocks to maintain relevance in responses.
Agent-based architecture
Deployed an agent architecture that uses specialized model configurations for different tasks, ensuring the best performance for each specific query.
Our personalized product search platform uses AI to go beyond basic filtering, delivering tailored recommendations based on real user insights. It's a smarter, more intuitive way to connect users with exactly what they need.
The outcome.
The personalized product search platform has redefined how users interact with customer review data, providing them with tailored recommendations that better match their needs.
Enhanced search experience
Users receive more accurate and personalized recommendations based on the nuanced interpretation of their queries, whether searching for doctors or shoes.
Improved user satisfaction
The platform’s ability to match users with products or services that closely align with their preferences has led to higher user satisfaction compared to traditional search methods.
Versatile application
The platform’s adaptable architecture supports various use cases, from healthcare to retail, demonstrating its broad applicability across industries.
Scalable and efficient
The use of vector indexes and optimized LLM processing ensures that the platform can scale to handle large data sets while maintaining fast, accurate search results.
