AI chatbots are becoming increasingly central to enterprise customer service efforts. When combined with large language models (LLMs), AI chatbots can be trained on datasets from diverse service areas, enabling them to provide swift, precise, and personalized assistance. This not only enhances the customer experience but also offers to drive significant revenue growth. The telecom sector, in particular, is ripe for adoption of such technology, using it to effectively discern customer intent and tailor services accordingly. This Catalyst seeks to create an AI-based customer service system which can provide the best possible user experiences across common user scenarios. The aim of the project is to engineer advanced conversational AI chatbots that can understand and use comprehensive telco-specific data to execute defined and targeted tasks, and engage interactively on demand with end users. The core architecture will integrate enhanced LLMs that have been fine-tuned with specialized telco datasets. The system will incorporate a hybrid framework to coordinate a rule-based system, natural language understanding (NLU) modules, and LLM technologies to ensure high performance and adaptability in complex business scenarios. The Catalyst seeks to show how LLM-based chatbots can determine consumer intentions and proactively resolve current issues, such as recommending personalized data plans, explaining products or initiating direct subscriptions. The outcome will be to improve telco customer satisfaction and reduce long-term overheads.