Building an NLP application involves an NLP pipeline, which includes data acquisition, cleaning, and model deployment. Using a support ticket system as a case study, the process entails tagging ticket priorities, cleaning data, tokenization, feature engineering, and model evaluation. Continuous monitoring and updates ensure optimal performance in production.