Bill shock is still the top reason customers contact service centers, and a key trigger for churn. Moreover, for CSPs, billing errors translate into lost revenue, rework, and rising support costs. This Catalyst brings AI into real-time billing (RTB) to identify and resolve billing issues before they happen. Unlike traditional QA systems, that are based on predefined rules and end-of-cycle sample testing, this Catalyst introduces an innovative approach. It uses machine learning to identify issues by continuously analyzing vast amounts of data, including usage, CRM, customer interactions, financial records and others, to detect bill anomalies and errors. This approach enables predictive QA, detecting anomalies throughout the billing cycle, long before the customer sees the bill. It then suggests resolutions, powered by generative AI. With a clear user interface and built-in AI agents, billing teams gain real-time insights, intelligent recommendations, and full visibility, turning quality assurance into a proactive, intelligent process. The result: fewer errors, lower costs, faster time to revenue and increased customer satisfaction. As billing becomes more complex and this Catalyst helps CSPs scale accuracy without scaling overhead, empowering them to protect revenue and deliver trust with every bill.