Engineered a high-throughput, fully concurrent voice AI streaming backend to solve the standard 1.5s+ Time-To-First-Audio (TTFA) latency bottleneck in conversational AI. By replacing sequential HTTP polling with a persistent full-duplex WebSocket architecture, the system achieves human-like reflexes with sub-300ms latency. Core Architectural Features: Concurrent Duplex Routing: Built a deterministic Go (Golang) backend utilizing Goroutines and Channels for thread-safe, non-blocking audio byte routing between the client and multiple AI inference endpoints. Semantic Chunking Buffer: Implemented a custom text-buffer that catches streaming LLM tokens and fires synthesis requests the exact millisecond it detects sentence-ending punctuation, eliminating robotic stuttering and maximizing throughput. Native Voice Activity Detection (VAD): Engineered zero-cost interruption handling using Go's context.Context cancellation. The system instantly detects user interruptions, kills mid-flight network requests to save compute, and flushes client-side audio queues for immediate responsiveness. Web Audio Sequencing: Developed a React/Next.js frontend leveraging the native browser AudioContext to queue and schedule incoming PCM WAV chunks with sub-millisecond precision.