Rezo.ai, India’s fastest evolving AI-powered contact center, is assisting one of India’s largest automobile companies with conversational AI. Rezo.ai is automating its contact centers and equipping contact center agents to instantly and effectively resolve customers’ queries related to car sales, service, surveys, etc. The AI-Powered Contact Center from Rezo.ai made a convincing argument to automate their human-led contact center and deliver a self-learning solution that is accurate, adaptive, and future-proof while reducing their operational costs by 20-30%.
Prior to automating contact centers with AI, the automobile manufacturer experienced low response and conversion rates, lack of customer interaction, overworked agents, and the limited capacity to reach a larger number of users rapidly in the outbound contact centers. The drawbacks being faced by the company’s outreach programs led to questions over their effectiveness and profit. Rezo’s AI-powered contact center created a healthy ecosystem for the automobile manufacturer, their customers and their respective dealers by automating customer agent interactions across multiple channels including voice and chat and enabling bi-directional interactions. Rezo.ai’s AI system can quickly categorize and subcategorize consumer queries using APIs, saving you and your team valuable time and effort.
Dr. Rashi Gupta, Chief Data Scientist, and Co-founder of Rezo.ai add, “When it comes to engaging with customers and creating a unique experience for them, Rezo.ai delivers strategic value to the automobile sector. It is critical in the current times to understand real-time analysis of interactions, identify both agent performance and customer experience to smoothen the conversational AI processes for any company. To train models from unstructured voice and text data, we use unique algorithms designed with next-generation technologies — AI, NLP, and NLU. These models are built to scale without a ramp-up phase, give a quick and consistent response to client inquiries, and save operational costs dramatically.”
