Machine Learning Engineer
Voice Analytics with AI
Call centers handle an immense volume of customer interactions every day, and it's crucial for businesses to evaluate the quality of these interactions to maintain high customer satisfaction rate. Traditionally, quality and assurance auditing has been a time-consuming and manual process, where human auditors listen to and evaluate customer calls. This approach is prone to human error, inconsistency, and scalability challenges. The Voice Analytics with AI aims to revolutionize the quality & assurance auditing process of call centers by transcribing and analyzing audio recordings using GPT-3 and Whisper models. The proposed solution leverages Automated Speech Recognition (ASR) and Large Language Models (LLM) to automate and streamline the quality and assurance auditing process. First, the system summarizes key information in call recordings, such as operator's name, issues, and solutions, and other relevant data points. After that, the solution conducts sentiment analysis to evaluate the tone and mood of the conversation using NLP and LLM. In addition, LLM evaluates customer experience and satisfaction levels and provides scores for each. Last but not least, the model ends the report of each call with feedback and insights about the performance of operator and suggests areas for improvement. Overall, the proposed solution has the potential to transform the call center industry, providing businesses with valuable accurate insights into their customer interactions and enabling them to take proactive steps to train their operators and improve their overall customer experience.
