How to achieve sub-800ms latency and 50% lower telephony costs in Voice AI pipelines (Architecture Breakdown)

Hey everyone, I’ve been building and optimizing enterprise Voice AI infrastructure recently, and I wanted to break down a specific architecture that solves the two biggest bottlenecks in production: high per-minute telephony costs and response latency. Most off-the-shelf setups rely heavily on layered API wrappers which add noticeable delay and stack up fees fast. Here is the direct stack breakdown I've been using to keep response times sub-500ms while cutting operational costs: Telephony & Carrier Layer (The Cost Saver) • Instead of standard high-markup Twilio/SIP resellers, I route directly through Wholesale Tier-1 Carriers with 1/1 billing pulses. • This alone cuts outbound calling overhead by ~40-60% for high-volume outbound/inbound operations. Speech & Voice Layer (Low Latency Core) • STT: Deepgram Nova-2 (WebSocket stream) for rapid transcript generation. • LLM Orchestration: Claude 3.5 Sonnet / GPT-4o-mini tuned with strict system prompts for concise, multi-turn conversational flow. • TTS: ElevenLabs / Cartesia for ultra-realistic human speech synthesis with real-time interruptibility support. Backend & CRM Sync (The Logic Engine) • Logic Orchestration: n8n Workflows + Supabase for real-time state management. • Webhook Pipeline: During an active call, real-time webhooks handle slot checking (Google Calendar/CRM) in under 200ms without breaking the AI's vocal flow. • Post-Call: Automated summary, transcript parsing, CRM update (HubSpot/GoHighLevel), and immediate SMS/Email trigger. The Key Takeaway: Separating your VoIP layer from your AI orchestration layer gives you full control over latency, call routing quality, and unit economics — making high-volume AI voice operations actually profitable. Happy to dive deeper into the n8n webhook setup or direct SIP routing logic if anyone is currently building something similar!