Transcription is commodity at this point. Otter, fireflies, fathom, read ai, whisper, assembly ai all produce accurate transcripts at reasonable prices. If ai call summary means turning audio into text, category solved, pick whatever. For business phone calls with customers the transcript is step one of a multi-step problem. Does the summary get structured for compliance requirements? Flow into your management system automatically? Generate tasks? Score the conversation against process standards? Most tools: no to all of those. They hand you text and the downstream workflow is yours to build. This matters differently by industry. A marketing agency getting meeting summaries probably just needs bullet points in slack. An insurance agency getting call documentation needs e&o compliance formatting pushed into their ams. A healthcare practice needs hipaa-formatted notes in their ehr. A law firm needs privileged conversation records in clio. The few tools that actually solve the downstream problem had to go deep into a single industry to do it. sonant built their post-call piece specifically around insurance compliance formatting and ams data flow. Nuance dax did something similar for healthcare clinical encounters though phone calls aren’t really their focus. General ai call summary tools don’t go there because you can’t build compliance aware documentation without understanding what compliance means in each vertical, and that knowledge doesn’t transfer across industries. If you’re evaluating ai call summary tools for any customer-facing operation, the question isn’t transcription quality. It’s what happens with the data after. submitted by /u/JosephPRO_
Originally posted by u/JosephPRO_ on r/ArtificialInteligence