Key takeaways
- Voice AI is a multi-layer stack — no single company covers everything from PSTN to LLM
- Indian-language voice AI is a distinct and growing segment, led by Sarvam AI and others
- The telephony layer (SIP trunking, media processing, number provisioning) is as important as the AI layer for production call quality
- Enterprise buyers typically integrate two or three vendors across the stack rather than a single all-in-one solution
How the voice AI market is structured
AI frameworks
Retell AI
Platform for building and deploying voice AI agents. Provides conversation state management, interruption handling, and dashboard-based agent configuration. Supports bring-your-own LLM and TTS. Integrates with Vobiz, Twilio, and Telnyx for telephony.Vapi
Developer-first voice agent SDK. Maximum flexibility — choose any ASR, LLM, and TTS. Full WebSocket access to the audio stream. Strong webhook system for event-driven architectures. Best suited for engineering teams building custom pipelines.Pipecat
Open-source voice agent pipeline framework. Modular — swap any component. No vendor lock-in. Growing community of connectors and extensions. Requires more engineering effort to deploy than managed platforms.LiveKit
Real-time media framework with an agent SDK. Open-source core. Strong WebRTC and SIP support. Python and Node.js agents with LLM tool calling. Good for multimodal (voice + video) use cases.OpenAI Realtime API
Direct voice interface to GPT-4o — speech in, speech out. Collapses ASR + LLM + TTS into a single API call, reducing latency. Function calling supported. Requires a telephony layer for PSTN.TTS (Text-to-Speech) providers
ElevenLabs
Industry-leading voice quality and voice cloning. Most natural prosody in the market. Strong multilingual support. High-quality output comes at higher latency than some alternatives — appropriate for use cases where voice quality outweighs millisecond differences.Cartesia
Low-latency TTS with high quality. Specifically designed for real-time voice AI — streaming synthesis with very low time-to-first-audio. Good balance of quality and latency for production AI calling.Deepgram Aura
Deepgram’s TTS product, designed for the voice AI pipeline. Fast streaming synthesis. Integrates natively with Deepgram ASR for a single-vendor ASR + TTS solution.Microsoft Azure Cognitive Services TTS
Broad language coverage (90+ languages, 400+ voices). Azure ecosystem integration. Used in enterprise deployments that already run Azure infrastructure.ASR (Automatic Speech Recognition) providers
Deepgram
Leading ASR for voice AI — purpose-built for real-time, low-latency transcription. Nova-2 model is widely used in production AI calling. Strong support for Indian English.AssemblyAI
High accuracy ASR with streaming and batch options. Good language support. Used in production AI calling pipelines.OpenAI Whisper
Open-source ASR model with strong multilingual performance. Widely deployed in self-hosted pipelines. Higher latency than Deepgram for real-time use; better for batch transcription.Google Speech-to-Text
Strong Indian language support across Hindi, Tamil, Telugu, and other vernacular languages. Used in Indian AI calling deployments where local language accuracy is critical.Telephony infrastructure
Vobiz
Built natively for AI voice agent deployments. Provides elastic SIP trunking, number provisioning (India 140-series, US local/toll-free/mobile), bidirectional WebSocket audio streaming, AMD, and an AI-optimised media server (HD Voice G.722/Opus, noise cancellation, voice isolation, PLC, FEC, VAD). Mumbai PoP for low-latency India connectivity. TRAI-compliant infrastructure for Indian 140-series deployments.Twilio
Pioneer CPaaS with the broadest developer ecosystem. No India PoP (routes through US/EU). No AI-optimised media layer. Strong documentation and integration partners.Telnyx
Developer-focused CPaaS with global carrier network. Competitive pricing. No India PoP. Strong US/EU coverage. Good WebSocket support for AI agent integration.Plivo
Well-established India and US coverage. TRAI-registered for India. Limited AI-specific media processing. Used for traditional cloud telephony deployments.India-focused voice AI companies
Sarvam AI
Indian-language-first AI platform. ASR and TTS trained specifically on Hindi, Tamil, Telugu, Kannada, Bengali, Marathi, Odia, and other Indian languages. Best-in-class accuracy for vernacular language voice AI. The go-to platform for AI calling targeting Indian consumers in their native language.Bolna
Open-source voice agent framework with Indian telephony integrations. Good documentation for Indian developer teams. Active open-source community.Ringg AI
India-focused AI calling platform with built-in TRAI compliance tooling. 140-series number support. Targets sales and collections use cases for Indian businesses.Rapida AI
Outbound AI calling platform for Indian B2C use cases — collections, appointment reminders, lead qualification. Vernacular language support.Dograh
Multilingual AI calling for Indian markets. Focus on regional language support and Indian business workflows.Choosing across the stack
A production voice AI deployment for India might look like:- AI framework: Retell AI or Vapi
- LLM: GPT-4o or Claude 3.5 Sonnet
- TTS: ElevenLabs or Cartesia
- ASR: Deepgram Nova-2 or Sarvam ASR (for Indian languages)
- Telephony: Vobiz (140-series India + AI-optimised media server + Mumbai PoP)
- Full stack: Sarvam AI for ASR + TTS + Bolna or custom framework for conversation management + Vobiz for 140-series telephony and media
Key evaluation criteria
Audio quality and latency: The telephony and media layer determines how much noise reaches ASR. Noisy audio → higher word error rates → more misunderstandings → worse conversations. Indian language support: For India, test ASR on actual recordings of your target callers — accent, background noise, code-switching (Hindi-English mixing) all affect accuracy significantly. Compliance infrastructure: For India (140-series, NDNC, TRAI TCCCPR) and US (TCPA, STIR/SHAKEN, 10DLC) — confirm the telephony provider handles this, not just the AI platform. Scale and reliability: Can the platform handle 500 simultaneous calls without degradation? What is the SLA for media delivery? Integration effort: How much engineering is required to connect your AI pipeline to real PSTN calls? SIP integration is more complex than WebSocket but more reliable at scale.Related
- Best voice AI platforms — platform-by-platform comparison
- Best SIP trunk for voice AI — telephony infrastructure comparison
- What is a voice agent? — AI voice agent fundamentals
- What is agentic AI? — autonomous AI agent architecture
- What is conversational AI? — conversational AI overview
- India calling regulations — TRAI compliance for voice AI
- US calling regulations — TCPA for AI voice agents