From Data Trails to Action Plans:
Fueling Personalized Self-service Experiences with FRAG^TM

In the modern support landscape, customer journeys have evolved into intricate webs, weaving through various channels and touchpoints, starting from initial discovery to final purchase. Beyond the point of sale, the journey persists, requiring enterprises to maintain a comprehensive grasp of their customers' behaviors. This is essential to deliver personalized support experiences and recommendations tailored to each individual's unique support path.

But, providing a consistent, unified, high-quality experience is easier said than done as customers have different personas, behaviors, preferences, intents, and emotions. Traditional search mechanisms hence fall short with large customer datasets spanning diverse data types, channels and formats.

This is where the role of a federated insights engine comes into the picture. With advanced Machine Learning (ML) at its helm, coupled with the integration of cutting-edge Large Language Models (LLMs), SearchUnify’s FRAG^TM (Federated Retrieval Augmented Generation) pipelines provide support agents a holistic view of customer journey at an ecosystem level. This facilitates:

Generation of hyper-personalized natural language responses to support queries in milliseconds.

Support Interface Customization to align with user preferences.

Real-time LLM-fueled visualizations of support knowledge gaps.

Actionable Insights and FRAG^TM Scores to enable proactive support decisions.

Ready to ride the support personalization wave? Join Taranjeet Singh, Principal Data Scientist, SearchUnify, at Big Data & AI World, London, for an enlightening exploration of the role of ML and LLMs in enabling more efficient, personalized, and satisfying customer experiences.