The Problem

Traditional text-based or DTMF (touch-tone dial) FAQ systems are rigid, causing customer friction and high support overhead. Customers have to navigate complex menus or type exact keywords to get answers. Real-time voice assistants often suffer from high latencies, double-responding (due to short pauses or intermediate transcription events), and phonetic misalignments (e.g. transcribing “Blackcoffer” as “black copper”, “black coffin”, or “back offer”). Furthermore, developing custom event-driven loops to handle turn-taking, VAD, and interruptions manually is highly complex and error-prone, leading to unstable conversational states.

Our Solution

We developed a highly responsive, low-latency conversational Voice FAQ Assistant that leverages LiveKit Cloud and LiveKit Inference. Rather than writing brittle custom event listeners and strict keyword-matching logic, we configured a native AgentSession loop that automatically orchestrates voice activity detection (VAD), speech-to-text (STT), text-to-speech (TTS), and LLM generation. By injecting the FAQ knowledge base directly into the system instructions of a cloud-hosted Gemma 4 31B model, the agent can understand and answer questions semantically, handling phonetic mis-transcriptions flawlessly without double-triggering or manual string matching.

Solution Architecture

The architecture consists of a real-time, low-latency communication pipeline built on LiveKit:

  1. User Voice Input: Captures the user’s microphone stream in real time.
  2. VAD (Voice Activity Detection): Built-in Silero VAD segmenting speech and determining turn boundaries.
  3. STT (Speech-to-Text): Deepgram Nova-3 transcribing speech streams into text.
  4. Agent Orchestrator: The LiveKit Agents SDK AgentSession coordinates turn-taking, handles interruptions, and routes state.
  5. Semantic LLM: A cloud-hosted Google Gemma 4 31B model (via LiveKit Inference) receives the conversation history, maps the query semantically to the FAQ data, and generates a concise response.
  6. TTS (Text-to-Speech): Cartesia Sonic synthesizes synthesized audio from the LLM text output in real-time.
  7. Audio Output: Streams back the voice response directly to the user’s speakers with sub-second end-to-end latency.

Deliverables

  1. Codebase Entrypoint: A lightweight and robust src/agent.py implementing the AgentSession and FAQAgent setup.
  2. Environment Configuration: Set up .env for LiveKit Cloud WebSocket URL and token credentials.
  3. Dependency Manifest: pyproject.toml and uv.lock configured with the correct versions of livekit-agents and livekit-plugins.
  4. Git Repository Integration: Configured remote origin and pushed the complete, unshallowed commit history to the GitHub repository

Tech Stack

  • Framework: LiveKit Agents Python SDK (v1.6.4+)
  • LLM: Google Gemma 4 31B (via LiveKit Inference)
  • STT (Speech-to-Text): Deepgram Nova-3 (via LiveKit Inference)
  • TTS (Text-to-Speech): Cartesia Sonic-3-Latest (via LiveKit Inference)
  • Runtime & Tooling: Python 3.11, uv package manager, Git

Business Impact

  • Reduced Support Overhead: Automates up to 80% of common customer service inquiries, allowing human agents to focus on complex, high-value tasks.
  • Improved Customer Experience: Provides instantaneous, human-like voice responses (sub-second latency) 24/7, reducing customer wait times to zero.
  • Robust Semantic Matching: Unlike brittle keyword-matching bots, the system understands context and handles accents, background noise, and pronunciation variants, resulting in a higher resolution rate on first contact.
  • Consolidation and Cost Savings: By routing all model endpoints through LiveKit Inference, the business avoids managing separate subscriptions and keys for Deepgram, Cartesia, and LLM providers, consolidating costs into a single billing structure.

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