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November 2026 · Vobiz Team Voice AI is software that can hear, understand, and speak — enabling machines to hold real conversations over the phone or through any audio interface. It powers the AI agents that answer customer calls, the voice assistants that book appointments, and the call bots that run outbound campaigns at scale. Voice AI is not just speech recognition — it’s the full pipeline from hearing a caller’s words to generating a contextually intelligent spoken response in under a second.
Key takeaways
  • Voice AI = ASR (speech recognition) + LLM (reasoning) + TTS (speech synthesis), all running in real time
  • End-to-end latency should be under 1,000ms for natural-sounding conversations
  • Voice AI platforms (Retell AI, Vapi, ElevenLabs, LiveKit) handle the AI stack; Vobiz handles the telephony layer
  • Indian language support (Hindi, Tamil, Telugu, Marathi) is available via Sarvam AI and regional ASR models
  • Voice AI replaces rigid IVR menus with open-ended conversational routing

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The Three Layers of Voice AI

Every voice AI system — however simple or complex — runs on three layers working in sequence:
The speed of each layer determines how natural the conversation feels. A human conversational gap is 200–300ms. Voice AI targeting under 1,000ms total latency feels responsive; above 1,500ms starts to feel robotic.

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ASR: Speech Recognition for Voice AI

ASR is the entry point — it has to transcribe exactly what the caller said, in real time, in whatever language and accent they’re using. Quality varies significantly by provider: For India-deployed voice AI handling regional language calls, Sarvam AI or Google STT with Indian language models gives the best accuracy.

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LLM: The Brain of Voice AI

The LLM reads the transcribed text, maintains conversation context, decides what action to take, and generates a response. For voice AI specifically, the LLM needs to:
  • Generate short, conversational responses — not paragraphs, but 1–3 sentences that sound natural spoken aloud
  • Maintain context across multiple turns of conversation
  • Trigger actions — look up order status, book appointments, transfer calls
  • Handle interruptions — when a caller talks over the response, the system should stop and listen
Model selection for voice AI:

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TTS: Giving Voice AI Its Voice

TTS converts the LLM’s text response to speech. Voice quality here directly determines how human the interaction feels: For Indian-market voice AI, Sarvam AI TTS produces the most natural-sounding output for Hindi and regional languages — an Indian-accented English TTS sounds more trustworthy to Indian callers than an American-accented one.

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Voice AI vs Traditional IVR

Voice AI doesn’t eliminate IVR — for simple, high-volume interactions (OTP delivery, appointment reminders), a DTMF IVR is cheaper and faster. Voice AI is the right choice when the conversation needs to be flexible, personalised, or multi-step.

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Voice AI Use Cases

AI receptionist: Answers all inbound calls, greets callers, routes based on spoken intent, books appointments, handles FAQs. Runs 24/7 with no queue wait. See What is an AI Receptionist. Outbound sales calls: AI agent dials prospect lists, delivers a natural-sounding pitch, handles objections using LLM reasoning, qualifies leads, books demos, logs outcomes to CRM. See AI Cold Calling. Customer support: Answers common questions (order status, return policy, account balance) without connecting to a human agent. Escalates complex issues with full context summary. Post-call surveys: Calls customers after a service interaction, asks 2–3 satisfaction questions in natural speech, transcribes responses, and feeds NPS/CSAT data to analytics dashboards. Appointment reminders: Calls patients or customers before their appointment, confirms attendance, reschedules if needed — all without human involvement. Collections and payment reminders: Calls on overdue accounts, reads the outstanding balance, offers payment options, connects to payment support if requested.

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Voice AI Platforms

Voice AI platforms provide the managed ASR + LLM + TTS pipeline with pre-built developer tools. The telephony layer (connecting to real phone numbers) is a separate component, typically provided by Vobiz via SIP trunking. All of these platforms connect to Vobiz to access real phone numbers and PSTN routing. The AI platform handles the conversation; Vobiz handles the call.

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Building a Voice AI System on Vobiz

Architecture:
Step 1: Get a phone number on Vobiz — Indian 10-digit, 140-series, or international DID. Step 2: Choose a voice AI platform and create your agent (system prompt, voice, language). Step 3: Create a Vobiz SIP trunk and connect it to the platform. For inbound calls, create an inbound trunk pointing to the platform’s SIP URI. For outbound, create an outbound trunk with digest auth credentials. Step 4: Test by calling your Vobiz number. The AI agent should answer, understand your speech, and respond naturally. Detailed setup guides:

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Latency Optimisation for Voice AI

The goal is under 1,000ms end-to-end. Each layer contributes: The key optimisation is streaming: start TTS as soon as the LLM generates the first few words, rather than waiting for the full response. Vobiz’s Mumbai infrastructure reduces network latency for India-originated calls.

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India Considerations for Voice AI

Language: Hindi and 10 Indian regional languages are supported across different ASR/TTS combinations. See the Sarvam AI integration for the most comprehensive Indian language coverage. TRAI compliance: Outbound voice AI calls to Indian consumers must use 140-series numbers, be DLT-registered, and honour NDNC/DND preferences. See India calling compliance. Latency: Vobiz’s infrastructure is based in Mumbai, keeping round-trip latency for India calls under 100ms on the network layer — critical for sub-second total response time. Caller trust: Indian callers often disconnect if an AI agent sounds foreign. Using an Indian-accented voice (Sarvam TTS, Google TTS with Hindi voices) significantly improves engagement rates.

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Next Steps