Mental health care technology has advanced substantially, but technologies still exhibit multiple shortcomings. This includes a lack cultural adaptation, slow crisis response, and weak engagement. Though Wysa and Woebot provide users with text-based conversational and emotional support, their text-processing conversational agents cannot provide support in a personalized, and contextually specific and deep manner. The proposed methodology works with state-of-the-art neural network text processors to deliver high-quality text, and with a framework to provide therapeutic text, grounded in CBT, to provide personalized text-based mental health support. The system strives to provide contextually responsive support, nuanced emotional assessment, and multiple pathways in the processing of the conversational agent to provide personalized mental health support. In this paper, the system design, dataset preparation, ethics, and other factors including potential for success are discussed. While the system is not claimed to be clinically effective within a specific standard, the design is the most advanced in the community and in the world, and at a minimum, substantial drawing of dates is required to allow this system to be built upon this design, quantum is the quantity of this system.