Privacy First

Your voice never leaves localhost.

No OpenAI. No Google Speech API. No AWS Transcribe. No cloud anything.

If data doesn’t leave the device, it cannot be intercepted, stored, or analyzed by third parties.

Implementation

All models run on-device using:

• ONNX Runtime (cross-platform inference)
• Quantized int8 models
• CoreML backend on macOS (Apple Neural Engine)

┌─────────────────────────────────────────┐
│              Your Device                │
│  ┌─────────┐    ┌─────────┐    ┌─────┐ │
│  │   Mic   │───▶│  Model  │───▶│ Text│ │
│  └─────────┘    └─────────┘    └─────┘ │
│                                         │
│         Everything stays here           │
└─────────────────────────────────────────┘
              │
              ╳  No network calls
              │

Trade-off

Users download ~500MB of model weights upfront. In exchange:

• No API bills
• No network round-trips (lower latency)
• No data exfiltration possible
• Works offline

Why Not Hybrid?

“What if we use local for drafts and cloud for final polish?”

No. This creates a false sense of privacy. Users think they’re protected, but their data still leaves the device. We reject half-measures.

The Models We Use

All models are open-source and can be audited.