Customer experience and customer support are undergoing a structural transformation. Traditional rule-based chatbots, static workflows, and ticket-driven support models can no longer meet the expectations of customers who demand instant, contextual, and human-like interactions across every channel. Large Language Models have introduced a new operating layer for CX and CS, enabling conversational systems that reason, retrieve knowledge, take actions, and continuously improve through feedback.
Deploying LLMs for Customer Experience & Support is a practical, systems-level guide for CX leaders, support architects, automation managers, and technical operators responsible for designing, deploying, and scaling AI-powered support systems in real organizations. This book goes far beyond prompt engineering and demos. It focuses on production-grade architectures, operational workflows, governance models, and performance strategies that turn LLMs into reliable support infrastructure.
The book walks through the complete lifecycle of LLM-driven CX systems: selecting and evaluating models, designing multi-agent support architectures, integrating enterprise knowledge and tools, orchestrating workflows across channels, ensuring safety and compliance, and operating systems at scale with clear KPIs and cost controls. It addresses real challenges faced by support organizations, including hallucinations, escalation handling, trust calibration, observability, human-in-the-loop review, and continuous optimization.
Whether the goal is to automate tier-one support, augment human agents, or build fully autonomous support workflows, this book provides a structured blueprint grounded in real-world deployment patterns. Readers will gain the clarity and technical depth needed to move from experimentation to dependable, revenue-protecting customer experience systems.
If the objective is to transform support operations with LLMs while maintaining control, reliability, and business alignment, this book serves as a definitive reference and implementation guide.
