Analyzing large conversational datasets is often inefficient due to the linear nature of text, which hinders the tracking of interaction evolution over time. To address this, we present FlowDisco, an interactive platform for the automatic discovery and exploration of dialogue flows. The framework uses semantic embeddings and modular clustering to transform raw text into probabilistic dialogue flows. By providing a web interface with dynamic filtering and a suite of analytical metrics, FlowDisco simplifies the visual identification and validation of conversational behaviors at scale. The platform’s utility is demonstrated through real-world application scenarios, including customer support interactions and multi-party political debates, where it successfully uncovers complex patterns and sentiment shifts that traditional sequential analysis often overlooks.