Despite recent advances in automation, customer support still requires a substantial amount of human intervention through voice channels. With the aim of improving the work of human assistants, we developed a collaborative bot (cobot) to help them in the process of handling customer voice interactions. The cobot is a reasoning agent that starts from loading background customer data into a dynamic knowledge graph. Then it captures the audio stream of the conversation, converts it to text in real time, analyzes the blocks of conversation with neural technologies and “thinks” about the results. Assistants can also supply data to the cobot, based on the information they gather from the ongoing conversation. The reasoning agent provides information and action suggestions to the human assistant by applying heuristics on data collected from both automatic and human sources, based on a task and domain-specific conceptual models (ontologies). While designing a prototypical solution for utility services in Italy, we are faced with many problems, including spontaneous speech understanding, factual and linguistic knowledge representation, and efficient heuristic reasoning. We adopted a standards-based approach and experimented with open source reasoners and publicly available language models. The paper presents preliminary findings and outlines the system design, with focus on the interplay of neural language processing and logic reasoning.