Ascendum

Are traditional search filters limiting your customers?

For leaders in healthcare and retail, Ascendum developed a cutting-edge platform that uses Large Language Models (LLMs) to turn unstructured review data into a conversational, highly personalized search engine.

• The Brief: Our goal was to move beyond basic keyword matching. We needed a system that could interpret nuanced human queries—like finding a "doctor who is great with anxious children,” by analyzing thousands of real-world user reviews.

• Our Approach: We implemented a sophisticated Vectorized Search architecture to enable semantic understanding of natural language. By deploying an agent-based architecture with specialized model configurations, the platform can summarize context and maintain relevance across complex, multi-turn chat experiences.

• The Outcome: The platform has redefined the search journey, providing tailored recommendations that significantly boost user satisfaction. Whether searching for specialized medical care or the perfect pair of walking shoes, users now connect with exactly what they need through a smarter, more intuitive interface.

Read the full case study here: https://www.ascendum.com/case-studies/we-developed-a-cutting-edge-personalized-search-platform-that-leverages-llm-backed-chat-experiences