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.
We built a Live Sales AI Presenter to model what a real-time AI avatar system looks like when it operates inside a real workflow rather than a demo sandbox. It is a slide-aware AI sales presenter combining deck ingestion, presentation control, live Q&A, WebRTC media, Pipecat orchestration,
Porting your business phone number to add AI call handling is an operational risk most companies don’t need to take. Your number may be tied to other services. Your carrier contract may have obligations. And if something goes wrong mid-port, customer calls go nowhere. SIP forwarding sidesteps
Prompt engineering gets you a demo. Context engineering gets you a production Voice AI agent. Think of LLMs as the world’s most brilliant librarians: they’ve read almost everything ever written, but without your help, they have the short-term memory of a goldfish. For text-based chatbots, a forgetful
