Building an AI Meeting Tool with Privacy First
Koundinya Lanka
January 10, 2026
Meeting data is among the most sensitive information a company produces. It contains strategic decisions, competitive insights, personnel discussions, and confidential negotiations. Yet most meeting intelligence tools send all of that data to the cloud for processing. We designed Karnyx differently.
The Cloud-First Privacy Problem
Most meeting transcription tools follow a simple architecture: record audio, send it to cloud servers, transcribe it there, store it in a database, and serve it back to the user. This model makes engineering simple but introduces significant privacy and security concerns:
- Audio files and transcripts pass through third-party infrastructure you do not control
- Data may cross geographic boundaries, violating data residency requirements
- Cloud providers have access to your most confidential conversations
- Network failures can delay or block access to critical meeting information
Our Local-First Architecture
Karnyx processes meeting audio locally on your Mac whenever possible. Here is how we architected the system for privacy without sacrificing intelligence:
On-Device Audio Capture
System audio is captured directly on macOS using CoreAudio. No external services, no bot joining your call, no third-party SDKs.
Local Transcription Engine
We use Whisper.cpp and optimized models that run entirely on Apple Silicon. Transcription happens on your Mac, not in the cloud.
Encrypted Storage
All meeting data is encrypted at rest with AES-256. Encryption keys are derived from your passphrase and never leave your device.
Optional Cloud Sync
Team features require cloud sync, but we only sync encrypted transcripts and metadata. Audio files never leave your Mac.
Privacy is not a feature we added later. It is the architectural foundation of the entire product. Every design decision starts with the question: can we do this without exposing user data?
How We Achieve On-Device AI
Running AI models on-device is technically challenging. Here is how we make it work:
Model quantization. We use 4-bit and 8-bit quantized versions of Whisper and LLaMA models optimized for Apple Neural Engine. This reduces memory footprint by 4-8x with minimal accuracy loss.
Hybrid inference. For features that require larger models (like semantic search embeddings), we use a hybrid approach: local model for privacy-sensitive data, cloud API for non-sensitive enrichment with user opt-in.
Background processing. Transcription and AI analysis happen in the background using macOS dispatch queues, so your Mac stays responsive even during intensive processing.
SOC 2 Compliance from Day One
Enterprise customers require certifications. We started our SOC 2 Type II audit in month one and have built compliance into our engineering practices:
- All code changes require security review and automated vulnerability scanning
- Production infrastructure is single-tenant per enterprise customer with isolated data planes
- Audit logs capture every access to meeting data with user attribution and timestamps
- Data retention policies allow customers to auto-delete data after configurable periods
Experience Privacy-First Meeting Intelligence
Karnyx brings enterprise-grade privacy to meeting intelligence. Get early access today and experience the difference of local-first AI.