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November 2026 · Vobiz Team The voice AI platform landscape in 2026 has split into two distinct layers: the AI pipeline layer (ASR, LLM, TTS, conversation management) and the telephony infrastructure layer (SIP trunking, number provisioning, PSTN connectivity, media processing). The best voice AI deployments combine a capable AI framework with low-latency, AI-optimised telephony infrastructure. This comparison covers both layers and how they connect.
Key takeaways
  • Voice AI platforms handle the AI side: speech recognition, LLM reasoning, voice synthesis, and conversation flow
  • Telephony infrastructure providers handle the PSTN side: real phone numbers, call routing, media quality, compliance
  • End-to-end quality is determined by both layers — a great LLM on poor telephony infrastructure produces poor caller experience
  • Most AI platforms connect to telephony via SIP or WebSocket — your telephony choice is independent of your AI platform choice
  • Latency and audio quality at the media layer directly affect ASR accuracy and conversation naturalness

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The voice AI stack

Understanding voice AI platforms requires understanding the stack they operate in:
AI platforms are compared below. For telephony infrastructure comparison, see Best SIP Trunk for Voice AI.

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AI platform comparison

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Retell AI

Type: Managed voice agent platform (no-code + API) Strengths:
  • End-to-end platform: bring your LLM and TTS or use Retell’s defaults
  • Dashboard-driven agent configuration — usable by non-engineers
  • Built-in conversation state management, interruption handling, and backchannel
  • Native SIP integration with Vobiz, Twilio, and Telnyx
  • Good documentation and active developer community
Best for: Teams that want a managed platform rather than building the pipeline themselves; agencies building AI agents for multiple clients. Telephony: Integrates via SIP trunk. Use Vobiz US or India numbers for PSTN delivery with A-level STIR/SHAKEN and AI-optimised media processing. Pricing model: Per-minute usage; bring-your-own LLM reduces cost.

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Vapi

Type: Developer-first voice agent SDK and API Strengths:
  • Maximum flexibility — full control over ASR, LLM, TTS choice
  • Low-level WebSocket access to audio stream
  • Rich webhook system for call events
  • Good multi-language support
  • Strong developer documentation
Best for: Engineering teams building custom voice agent architectures; use cases requiring fine-grained control over every component. Telephony: SIP + WebSocket. Works with Vobiz, Twilio, Telnyx.

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ElevenLabs Conversational AI

Type: Voice-quality-first conversational AI Strengths:
  • Industry-leading voice quality — most natural-sounding TTS in the market
  • Conversational AI with built-in turn-taking and interruption handling
  • Strong multilingual voice synthesis
  • Voice cloning capabilities for brand voice
Best for: Use cases where voice quality is paramount — premium customer experience, brand voice consistency, multilingual deployments. Telephony: WebSocket audio streaming. Requires a separate telephony layer for PSTN calls.

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LiveKit

Type: Real-time media framework with agent SDK Strengths:
  • Purpose-built for real-time media — low-latency audio/video routing
  • Open-source core with cloud option
  • Strong WebRTC and SIP support
  • Python and Node.js agent SDKs with LLM tool calling
Best for: Teams that want open-source control; multimodal (voice + video) use cases; custom media routing requirements. Telephony: SIP integration via LiveKit SIP server. Can be bridged to Vobiz for PSTN connectivity.

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Pipecat

Type: Open-source voice agent pipeline framework Strengths:
  • Open-source; deploy anywhere
  • Modular pipeline design — swap any component (ASR, LLM, TTS)
  • No vendor lock-in
  • Growing ecosystem of connectors
Best for: Teams with strong engineering capability that want full control and no dependency on a managed platform. Telephony: SIP trunk + Daily.co or WebSocket transport. Works with Vobiz SIP.

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OpenAI Realtime API

Type: Direct voice LLM — speech-in, speech-out Strengths:
  • Low latency by collapsing ASR + LLM + TTS into a single model call
  • Native function calling over voice
  • Consistent voice quality (OpenAI TTS)
  • Simplest integration path for GPT-4o voice
Best for: Teams already on the OpenAI ecosystem; use cases where simplicity of integration outweighs flexibility. Telephony: WebSocket audio streaming. Requires telephony layer for PSTN.

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Indian voice AI platforms

For deployments targeting Indian consumers, several Indian-origin platforms offer localisation advantages: Sarvam AI: Indian-language-first voice AI — optimised for Hindi, Tamil, Telugu, Kannada, Bengali, and other Indian languages. ASR and TTS trained on Indian language data. Best choice for vernacular language AI calling in India. Bolna: Open-source voice agent framework with India telephony integrations. Good documentation for Indian developer teams. Ringg AI: India-focused AI calling platform with built-in TRAI compliance tooling. Rapida AI: Outbound AI calling platform focused on Indian B2C use cases — collections, reminders, lead qualification. Dograh: Multilingual AI for Indian markets with vernacular language support.

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Platform selection matrix

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What to look for in a voice AI platform

ASR accuracy for your use case: Test on real call recordings in your target language and accent. Word error rate differences across providers are significant for Indian English and vernacular languages. Latency: Measure end-to-end time from end of caller speech to first audio of AI response. >1.5 seconds feels sluggish. <800ms feels natural. The platform contributes ~300–600ms of this; telephony contributes the rest. Interruption handling: The AI should handle the caller speaking mid-response — stop its own speech, listen, and respond. Platforms differ significantly in how gracefully they manage this. Tool calling: For agentic use cases (booking, payment, CRM lookup), the LLM must support tool calling. Confirm latency impact — each tool call adds to response time. Language support: For India, confirm not just that a language is listed, but that ASR and TTS are trained on that language (not just translated). Indian language AI requires India-specific training data. Telephony integration method: SIP is more reliable for high-volume production; WebSocket is simpler for initial development. Confirm the platform supports your preferred integration method.

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The telephony layer matters as much as the AI layer

The AI platform gets most of the attention, but the telephony and media layer is equally important for production quality: Media processing: Does the SIP trunk provider run noise cancellation, voice isolation, and HD codec before audio reaches ASR? These steps can reduce word error rate by 20–40% on mobile calls. Latency: A Mumbai PoP vs routing Indian calls through the US adds 100–200ms of network latency — directly reducing conversation naturalness. Compliance: 140-series numbers for India, A-level STIR/SHAKEN for US, AMD for outbound — these are infrastructure features, not AI features. Scale: Can the telephony infrastructure handle 1,000 simultaneous calls without manual provisioning? Elastic SIP trunking is required for AI calling at scale. Vobiz provides the telephony layer — SIP trunking, number provisioning (India 140-series, US local/toll-free), bidirectional WebSocket audio streaming, AMD, and an AI-optimised media server (HD Voice, noise cancellation, voice isolation, PLC, FEC, VAD) — that all major voice AI platforms connect to.