AI · On-Device
Offline Voice Intelligence
Fully offline conversational AI for Apple Silicon. Local speech-to-text, on-device LLM inference, and multi-turn state management — zero cloud dependency.
The Challenge
Air-gapped
intelligence.
Cloud-based voice assistants transmit every spoken word to remote servers — a fundamental privacy compromise. Professionals working with sensitive information, or users in environments with unreliable connectivity, need conversational AI that operates entirely on-device with zero telemetry and zero network requests.
The challenge was to engineer a voice assistant that matches the conversational quality of cloud services while running completely air-gapped on Apple Silicon hardware.
The Architecture
On-device
pipeline.
Local Speech-to-Text
Continuous audio capture with voice activity detection. Speech segments processed through a local transcription model running natively on Apple Silicon — no audio data leaves the device.
On-Device LLM Inference
Transcribed text is processed by a locally hosted language model optimized for Apple Silicon. Metal GPU acceleration enables low-latency inference with quality comparable to cloud endpoints.
Multi-Turn Conversation State
Persistent conversation memory enables contextual follow-up queries. Previous exchanges are maintained in a sliding context window, enabling natural multi-turn dialogue without repetition.
The Outcome
Private
by architecture.
The resulting system delivers conversational AI capabilities without any network requirements. Every component — speech recognition, language understanding, and response generation — executes locally on Apple Silicon. The architecture ensures that no data, audio, or query content is transmitted externally, making it suitable for high-security and privacy-critical environments.