As Voice AI becomes more prevalent, applications need a way to store conversation data that supports observability, auditing, and long-term retention. The audio recording or a text transcript alone leaves out critical context. Additional details matter too, such as who was in the conversation, what devices were
Voice AI systems generate more than recordings and transcripts. Every production interaction produces a web of artifacts across multiple systems: call-setup metadata, ASR output, LLM responses, tool calls, CRM updates, escalation events, and compliance-relevant signals like caller identity verification. Most Voice AI architectures store some of these.
