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Design a Real-Time AI Voice System

DecagonSoftware Engineer · Technical Screen
MediumSoftware EngineerTechnical ScreenML System Design

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This problem evaluates your ability to design a low-latency, real-time conversational AI system, focusing on component orchestration and performance trade-offs in speech processing pipelines.

Problem Statement

Design a real-time AI voice assistant system capable of handling natural, bidirectional human-computer interaction with sub-500ms latency. The system must process live audio input, generate intelligent text responses, and synthesize natural-sounding speech output.

Functional Requirements

  • Voice Activity Detection (VAD): Accurately identify when the user starts and stops speaking.
  • Speech-to-Text (STT): Transcribe streaming audio into text.
  • Orchestration/LLM: Determine the intent and generate a response.
  • Text-to-Speech (TTS): Convert the LLM's text output back into audio for the user.
  • Interruption Handling: Immediately stop current audio playback if the user begins speaking while the agent is responding.

Non-Functional Requirements

  • Latency: End-to-end response time must be low enough to feel conversational.
  • Scalability: The system should handle thousands of concurrent users.
  • Statefulness: Maintain conversation context over multiple turns.

Constraints

  • Assume an existing LLM API with streaming support is available.
  • You are designing the infrastructure to connect these modular services.

Expected Output

  1. A high-level system architecture diagram (described in text).
  2. Explain the flow of data through the system (e.g., how the audio buffer is managed).
  3. Propose a strategy for minimizing latency in the TTS/STT pipeline.
  4. Address how you would handle "barge-in" (interruptions) at the architectural level.

Hints

Hint 1

Consider using WebSocket connections to maintain persistent streams for full-duplex communication between the client and server.

Hint 2

Think about how to pipeline the TTS generation so that audio playback begins as soon as the first few tokens of the LLM response are generated, rather than waiting for the entire sentence.

Hint 3

Decide whether to use a centralized state store or maintain session context locally within an orchestration microservice to minimize database lookups.

Follow-up questions

  • How would you optimize for cost if the TTS model is expensive to run at high concurrency?
  • How would you implement a feedback loop to detect if the LLM is providing factually incorrect or unsafe information in real-time?
  • What metrics would you monitor to identify performance bottlenecks in a multi-stage pipeline?