In the world of telecom support, where a high volume of customer complaints must be handled, intelligent automation is crucial for resolving issues and enhancing user satisfaction; however, manual classification is inefficient. In this regard, to address the customer support issue in the workflow, we propose an AI-based NLP and priority model that automatically analyses and ranks support tickets based on their urgency. Regardless of the development, current models have limitations because they provide a limited context and struggle to perform well at a real-time scale. The Real-Time Prediction API with Open Neural Network Exchange (Fast APION2X Runtime) helps address these challenges by leveraging FastAPI and ONNX, to provide a real-time urgency score or category to the user interface. Additionally, Based Text Augmentation (BERT), based on text enhancement, enhances data diversity and improves the robustness of training.Furthermore, Named Entity Recognition (NER) using SpaCy and the transformer model identifies important entities to improve feature representation. A Hierarchical Attention Network (HAN) infers semantics at both the word and sentence levels, enabling better prioritisation. Ultimately, an end-to-end AI workflow that significantly enhances the accuracy, scalability, and speed of telecom help desk request prioritisation is recommended.