JoWUA

Abstract

Natural Language Processing (NLP) technologies are being increasingly incorporated into business and pervasive systems to better user engagement, minimize input friction, and make applications more engaging and easy to talk with. This research provides a results-driven framework for adapting NLP into Oracle APEX—Oracle's low-code development platform—APEX aimed at improving the interface and responsiveness of web-based systems used in different contexts. The architecture proposed enables natural language query processing, automatic form creation, and contextual help by embedding workflows in APEX with lightweight NLP pipelines for intent detection, entity recognition, and adaptive dialogues. The experiments were along the lines of task automation, data retrieval, and user profile management among other domains. The findings show measurable enhancements in the completion time of tasks, satisfaction levels of users, flexibility in input, alongside a drastic reduction in errors during interactions. Latency measurement, system throughput, and other user interface metrics confirm the integration approach’s scalability and realistic performance bound with operational user loads. Within the APEX framework, the study also outlines methods for capturing user input design, collecting feedback, and updating the NLP models iteratively devising fall-back strategies during haptic interactions. This research presents a paradigm that is easy to scale, maintain, and manage as well as responsive towards versatile user needs through conversational intelligence in ubiquitous systems using Oracle APEX.