The answering service industry ($5B+ market) is getting eaten by AI, and the technical architecture is interesting.
The stack
Modern AI call answering typically chains:
- Telephony (Twilio/Telnyx/Vapi) — SIP trunking, call routing, DTMF
- STT (Deepgram/Whisper) — real-time transcription with <300ms latency
- LLM (GPT-4/Claude) — conversational logic, trained on business context
- TTS (ElevenLabs/Play.ht) — natural voice synthesis
- Integration layer — calendar APIs, CRM webhooks, SMS notifications
The hard part isn't any single component — it's making the full chain feel like a natural conversation with sub-second response times.
Latency is everything
Humans notice pauses >600ms in phone conversations. When your chain is STT→LLM→TTS, you're fighting:
- STT processing: 200-400ms
- LLM inference: 300-800ms
- TTS generation: 200-500ms
Total: 700ms-1.7s. The upper end feels robotic. Getting it under 800ms consistently is where the engineering challenge lives.
What's changed in 2026
- Streaming TTS that starts speaking before the full response is generated
- Speculative response generation (predicting likely responses while the caller is still talking)
- Fine-tuned smaller models that handle 80% of calls faster than GPT-4
Has anyone else built production voice AI systems? The latency optimization rabbit hole goes deep.
Full breakdown: voicefleet.ai/blog/ai-call-answering-service
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