Customer service increasingly relies on chatbots and Natural Language Processing (NLP) to support high volumes of common inquiries. The importance of this topic underlines the increased need for scalability, efficiency, and consistency in the delivery of customer service and the difficulties associated with sustaining the quality of service during peak periods. However, the ineffectiveness of standard chatbot assessment techniques represents the key issue as they cannot accurately capture chatbot performance. The present work proposes an applied two-stage approach, combining business description and predictive modeling on a transformer. Compared to the preceding analytical and previous works, the new solution involves several real-world datasets. This includes customer satisfaction studies, chat log files, business performance, and industry case studies. In the first phase of the proposed approach, several business descriptions and quantitative metrics, such as time to respond, resolution rate, level of customer satisfaction, and cost saving were implemented. In the second phase, pre-trained models, namely Robustly Optimized Bidirectional Encoder Representations from Transformers (BERT) Pretraining Approach (RoBERTa) and Cross-Lingual Language Model (XLM-R) were used to determine the intention of the users, recognize sarcasm, and measure satisfaction. The results suggested great operational benefits accrued by improvements in responsiveness, automation, cost savings, and more linguistic phenomena associated with the user emotions. Such observations clearly demonstrated the viability of achieving a more comprehensive and practical assessment of chatbots by relying on statistical analysis and advanced language models. The observations not only demonstrated practical uses of the proposed assessment, but also opened the door to hybrid human and Artificial Intelligence (AI) systems.