Medical doctors frequently rely on assistance tools during the decision-making process or when determining suitable chemotherapy options. These tools can take the form of recommendation systems, online test calculators, or web-based applications. They provide support not only in making recommendations but also in conducting thorough profile investigations of patients. Previous researchers have developed web-based survival analysis tools in the cancer survival field. However, many of these tools provide only basic functionality and rely on simplistic models, offering only a superficial understanding of the data. In this study, we undertake a comprehensive analysis of risk profiles using survival clustering techniques applied to a real-world dataset and developed an accessible online Shiny application to facilitate easier utilization of our findings. By leveraging survival clustering, we aim to uncover distinct subgroups based on survival patterns and identify unique risk profiles associated with breast cancer patients. Our online app provides a user-friendly interface for researchers and clinicians to explore the results, enabling them to gain valuable insights into the complex landscape of breast cancer risk profiles. This interactive tool offers a more accessible means of understanding and utilizing the implications of our research in personalized medicine and clinical decision-making.