Cutting voice AI latency down to sub-600ms for real-time customer calls 🎙️⚡
Currently testing a conversational voice pipeline combining WebSockets, streaming speech-to-text, and fast LLM inference to handle real-time inbound booking.
The hardest part isn’t the generation—it’s natural turn-taking, background noise filtering, and smooth voice interruption (VAD) without awkward robotic pauses.
Sneak peek of the live latency graph and audio streaming architecture below! 👇
Built an n8n automation that recovers missed customer calls, starts an AI conversation, checks CRM context, routes urgent requests, and tracks callback SLAs until the customer is handled.
SupportFlow AI is a customer support management system built with Python, PySide6, FastAPI and PostgreSQL.
It helps teams manage customer requests, assign priorities and responsibilities, track conversations, and generate AI-assisted response suggestions while keeping a human in control of the final reply.
I built the application structure, interface, backend integration, database workflows, API layer and automated testing.
The conversation-revision idea sounds useful. I would also check that revision on the server when accepting the send, so a message arriving between review and click cannot slip through. Mark the draft stale while keeping the human edits visible. Two useful regression cases: a...