The author is seeking feedback on a proposed voice-AI tool designed to extract deeper insights from all voice and video agent conversations, rather than tracking typical metrics like sentiment or response times. The tool aims to transform unstructured voice data into structured, usable knowledge, similar to how platforms like Snowflake and Databricks handle raw data. It intends to uncover hidden insights such as emerging user topics, agent assumptions, undocumented internal processes, and patterns visible only across numerous calls. The vision is to create a discovery engine that allows teams to query and learn from past conversations. The author poses questions about whether this concept addresses a genuine gap in the voice AI market, the limitations of current tools, and the practical value of having access to all agent interactions. Feedback is encouraged on the potential usability and value of this tool in daily operations.
Source link
