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 AI-Native Application Development

AI Voice Bot Orchestration

We build production-grade voice agent pipelines with end-to-end latency under 300ms — STT, LLM reasoning, TTS, and escalation flows — with full telephony integration and on-premise deployment for regulated industries.

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Pipeline Capabilities
  • Sub-300ms voice-to-voice response latency
  • Supports Pipecat, Ultravox, Moshi, LiveKit Agents
  • Configurable LLM backbone (GPT, Claude, Llama3)
  • Barge-in interruption & turn detection
  • RAG-based knowledge grounding for factual accuracy
  • Human escalation with full conversation context handoff
  • Fully on-premise deployable (HIPAA / GDPR ready)

How It Works

Voice Activity Detection & STT

User speech is detected via VAD, streamed to Deepgram or on-prem Whisper for sub-200ms transcription. Barge-in detection pauses the bot mid-utterance when the user interrupts — eliminating the robotic ‘please wait’ experience.

LLM Reasoning with RAG Grounding

The transcript feeds an LLM (GPT-4o, Claude, or on-prem Llama3) with a RAG-grounded system prompt. The model reasons over the conversation state, queries relevant knowledge, and produces a response — with guardrails preventing off-topic or hallucinated answers.

TTS Output & Escalation Logic

The LLM response is synthesised to speech via Coqui TTS or ElevenLabs in under 80ms. Escalation triggers — sentiment drop, repeated failures, explicit request — route the call to a human agent with full conversation context and a pre-loaded summary.

What We Build

Modular Pipeline Orchestration

Pipecat-style pipeline: VAD → STT → LLM → TTS, with each component independently swappable. Benchmark different STT models without rebuilding the pipeline.

Telephony Integration

SIP/PSTN via Twilio, Exotel, or your existing PBX. WebRTC browser-based calling also supported. IVR replacement and new inbound number provisioning included.

Domain-Specific Intelligence

Fine-tuned prompts, RAG grounding, and compliance guardrails per industry. BFSI bots stay within RBI-approved boundaries; Healthcare bots never give medical diagnoses.

Multi-Turn Conversation Memory

Session-scoped context window with a structured state machine for goal-directed conversations — appointment booking, lead qualification, account queries.

Analytics & Containment Dashboard

Per-call latency breakdown, containment rate, CSAT estimation, escalation pattern analysis — all in a real-time management dashboard.

Full On-Premise Deployment

Whisper / Deepgram, Llama3 / Mistral, Coqui TTS — all containerised and deployable on bare-metal. Zero audio data leaves the enterprise perimeter.

CentEdge vs The Alternative

Cloud-only Voice AI Vendors (Bland.ai, Vapi, Retell)
  • All call audio sent to vendor's cloud servers
  • Vendor controls LLM — you can't change the model
  • Per-minute pricing escalates with call volume
  • No on-premise option for regulated industries
  • Escalation to human requires separate contact centre
CentEdge Custom Voice Bot
  • Full on-prem option — audio never leaves your network
  • Choose and swap any LLM — GPT, Claude, Llama3, Mistral
  • One-time build cost, zero per-minute call charges
  • HIPAA and GDPR compliant on-premise deployments
  • Escalation routing built into the same platform

Who This Is For

  • BFSI: Account Queries & vKYC
  • Healthcare: Appointment Scheduling
  • Automotive: Service Booking
  • Ecommerce: Order Support
  • HR: Interview Pre-Screening
  • Government: Citizen Services

Technology Stack

Pipecat

Ultravox

Moshi

Deepgram / Whisper

GPT-4o / Llama3

Coqui / ElevenLabs TTS

WebRTC / SIP

Exotel / Twilio

Docker / K8s
FastAPI

Frequently Asked Questions

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What does sub-300ms end-to-end latency actually mean?

It means the time from when a user stops speaking to when they hear the bot's first audio response is under 300 milliseconds. This is achieved by running STT streaming (partial transcripts delivered continuously), using a fast-inference LLM endpoint, and starting TTS synthesis on the first token of the LLM response rather than waiting for the complete response. At 300ms, the conversation feels natural — comparable to a human replying to a simple question.

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Can the voice bot handle interruptions mid-sentence?

Yes. Barge-in detection is a core feature of every CentEdge voice bot. When the user starts speaking while the bot is talking, the bot's audio output stops within 150ms and the pipeline re-enters listening mode with the conversation context preserved. This eliminates the robotic 'I didn't understand, let me finish' experience common in older IVR-style bots.

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What telephony infrastructure is required to deploy a voice bot?

For PSTN/IVR replacement, CentEdge integrates with Twilio, Exotel, or your existing SIP-compatible PBX via SIP trunking. No new hardware is required. For web-based deployments, the bot is accessible via WebRTC directly in the browser. CentEdge handles number provisioning, SIP configuration, and failover routing as part of the build.

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Can the voice bot handle multiple languages in the same call?

Yes. Language detection can be configured to switch the STT and TTS models mid-call based on the detected language. For Indian deployments, Hindi-English code-switching (Hinglish) is supported natively by Deepgram's multilingual models. Separate LLM prompts and RAG knowledge bases can be configured per language.

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How does handoff to a human agent work?

When an escalation trigger fires, the bot plays a hold message, transfers the call audio to a human agent via SIP or WebRTC, and simultaneously pushes a pre-generated conversation summary to the agent's screen. The agent sees the full transcript, the bot's last response, and the reason for escalation before the customer is connected.

GET IN TOUCH

Let’s Build This
Together

Tell us about your project and we’ll return with an architecture overview and engagement proposal within 48 hours.

  • hello@centedge.io
  • +91 6362 814071
  • T-Hub, Hyderabad, India
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