Natural language processing has made significant progress in recent years, but existing solutions are often task-specific and lack contextual understanding. Building models that can process spoken conversations is crucial for extracting valuable insights. Developers can leverage existing tools to create more intelligent conversation intelligence by assembling them strategically. To build a model that generates accurate conversation summaries, it's essential to identify topics, build timelines, and create summaries using a text summarization algorithm. By taking these steps, developers can overcome the limitations of task-specific solutions and create contextual understanding of everyday language.