Authenticx has unleashed a new AI-powered quality management system, designed to shake up contact centre operations with a laser focus on healthcare.
Instead of relying on slow, subjective human evaluations, this system uses an MoE (Mixture of Experts) machine learning model, trained exclusively on healthcare conversations, to automate agent scoring, coaching, and trend analysis. It promises to eliminate inefficiencies, improve feedback loops, and boost training quality, all while making managers’ jobs a whole lot easier.
Why does this matter? Traditionally, quality assurance in contact centres has been a manual, inconsistent, and resource-heavy process. With AI stepping in, healthcare organisations can scale up audits without blowing budgets, provide more frequent coaching, and ensure agents have the insights they need to improve.
Amy Brown, founder and CEO of Authenticx, said: “Healthcare contact centres are drowning in patient interactions, facing mounting pressure to improve experiences and outcomes, while avoiding cost. Without automated quality management, organisations are forced to choose between comprehensive oversight or operational efficiency—often reviewing only a mere percentage of interactions. We want to eliminate this impossible trade-off.”
Early results are already making waves: One Fortune 500 player reported a 30% jump in evaluation accuracy and a 400% increase in QA audits within a year of implementing the solution.
This news follows Authenticx’s expansion into generative AI, cementing its place at the forefront of healthcare call centre tech. For agents, it means smarter coaching. For managers, it means better data. And for customers? Hopefully fewer frustrating calls.