How We Built a Telephony AI Framework That Eliminates 90% of Voice Infrastructure Complexity

Most developers underestimate how hard voice AI actually is. To build a production-ready calling agent, you need to integrate: – SIP signalling – Real-time audio streaming – Speech-to-text – LLM orchestration – Text-to-speech Each layer introduces latency, failure points, and vendor dependencies. That’s where Siphon comes in. What Siphon Does Siphon acts as a middleware layer between telephony systems and AI models, abstracting the entire pipeline into Python. You define: agent=Agent(...) And Siphon handles: – WebRTC streaming – SIP negotiation – Interrupt handling – Model orchestration Key Features 1. Sub-500ms latency Human-like conversations require near-instant responses — Siphon achieves this using WebRTC streaming. 2. Modular AI stack Swap LLMs, STT, and TTS providers with a single config change. 3. Zero-config scaling Spin up more workers → Siphon auto-load-balances calls across nodes. 4. Data sovereignty All data stays in your infrastructure — no third-party data leakage. Why I