Overview
This post covers the technical details behind data extraction with llms: architecture and implementation. We'll walk through the architecture, trade-offs, and real-world performance data from the Edesy platform.
The Challenge
Most voice AI implementations face trade-offs between quality, latency, cost, and language support. Getting all four right simultaneously — especially for Indian languages — requires careful architectural decisions.
How It Works
Architecture
The Edesy platform supports multiple approaches depending on the use case:
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Native audio-to-audio — Gemini Live 2.5 HD processes speech directly, generating speech output without intermediate text. Sub-500ms latency. 30 HD voices across 24 languages.
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Traditional pipeline — STT (Deepgram/Google Chirp/Azure) -> LLM (GPT-4o/Gemini) -> TTS (Google HD/ElevenLabs). More flexible but higher latency (800ms-2s).
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Hybrid approach — Use native audio for supported languages, fall back to pipeline for others.
Performance Data
From production deployments:
- Latency: Sub-500ms with Gemini Live 2.5 HD
- Success rate: 91% across 9,946+ conversations
- Languages: 73+ supported, 10+ Indian languages active in production
- Cost: INR 4-6 per minute for end-to-end voice AI
Implementation Details
The specific implementation depends on the use case. For Indian language deployments, we typically recommend:
- STT: Google Chirp for best Indian language accuracy
- LLM: Gemini Live 2.5 for native audio, GPT-4o for pipeline
- TTS: Google HD voices for Indian languages, ElevenLabs for premium quality
- Telephony: Exotel or Plivo for Indian numbers, Twilio for international
Real-World Results
Production data from 3,366+ agents across 436 workspaces shows consistent performance:
- 91% call success rate
- 71-second average call duration
- INR 4-6 per minute all-inclusive cost
- 24/7 availability with zero downtime
Getting Started
Built on production data from 3,366+ AI voice agents. Start your free trial.