# How to Deploy Meetily to a Production Environment: Complete Tauri Build Guide

> Deploy Meetily to production! Learn to compile your Tauri app, bundle Whisper ggml, and sign your binary for successful distribution. A complete build guide.

- Repository: [Zackriya Solutions/meetily](https://github.com/Zackriya-Solutions/meetily)
- Tags: how-to-guide
- Published: 2026-07-27

---

**To deploy Meetily to a production environment, compile the Tauri desktop application for your target platform, bundle the Whisper ggml model into the OS-specific application-support directory, and optionally code-sign the resulting native binary before distribution.**

Meetily is a privacy-first AI meeting assistant developed by Zackriya-Solutions that runs entirely as a local desktop application. Built with Tauri, it combines a Next.js 14 frontend with a Rust core that handles audio capture, transcription, and LLM summarization without external backend dependencies. This guide walks you through how to deploy Meetily to a production environment using the exact commands, file paths, and build steps defined in the repository.

## Production Architecture Overview

Meetily's production bundle is a native desktop binary that embeds three layers. The **Tauri entry point** in [`frontend/src-tauri/src/lib.rs`](https://github.com/Zackriya-Solutions/meetily/blob/main/frontend/src-tauri/src/lib.rs) registers commands such as `start_recording` and configures the application event loop. The **Rust core** handles platform-specific audio capture in [`frontend/src-tauri/src/audio/stream.rs`](https://github.com/Zackriya-Solutions/meetily/blob/main/frontend/src-tauri/src/audio/stream.rs), mixes streams and applies voice-activity detection in [`frontend/src-tauri/src/audio/pipeline.rs`](https://github.com/Zackriya-Solutions/meetily/blob/main/frontend/src-tauri/src/audio/pipeline.rs), and loads the Whisper ggml model for transcription in [`frontend/src-tauri/src/whisper_engine/whisper_engine.rs`](https://github.com/Zackriya-Solutions/meetily/blob/main/frontend/src-tauri/src/whisper_engine/whisper_engine.rs). The **Next.js frontend** in [`frontend/src/app/page.tsx`](https://github.com/Zackriya-Solutions/meetily/blob/main/frontend/src/app/page.tsx) listens for Tauri events like `transcript-update` and `summary-ready` to render the meeting interface.

## Prerequisites and Build Toolchains

Before you run the production build, install the common dependencies and platform-specific SDKs documented in [`docs/BUILDING.md`](https://github.com/Zackriya-Solutions/meetily/blob/main/docs/BUILDING.md).

From the repository root, install the Node.js and Rust tooling:

```bash

# Install Node.js dependencies

pnpm install

# Install the Tauri CLI if it is not already bundled

cargo install tauri-cli

```

Platform-specific requirements include:

- **macOS** – Xcode command-line tools and macOS 13 or later for ScreenCaptureKit support.
- **Windows** – Visual Studio Build Tools with the "Desktop development with C++" workload.
- **Linux** – `cmake`, `llvm`, `libomp`, and PulseAudio or ALSA development packages.

## Build the Frontend and Tauri Application

The production build compiles the Next.js UI into static assets and then links them into the Rust binary via the Tauri CLI.

### Compile Next.js Static Assets

Run the frontend compiler from the repository root:

```bash
pnpm run build

```

This outputs the compiled frontend assets to `./frontend/.next`.

### Run the Tauri Production Build

With the frontend assets ready, invoke the production Tauri build:

```bash
pnpm run tauri:build

```

The Tauri CLI automatically bundles the static assets and compiled Rust code into a native installer. Final artifacts appear under `frontend/src-tauri/target/release/bundle/`, such as `frontend/src-tauri/target/release/bundle/macos/Meetily.app`.

You can also enable GPU acceleration features declared in the root [`Cargo.toml`](https://github.com/Zackriya-Solutions/meetily/blob/main/Cargo.toml) if your target hardware supports them.

## Distribute Whisper Model Files

Meetily requires a local Whisper ggml model at runtime. The application looks for these files in the user’s platform-specific application-support directory:

- **macOS** – `~/Library/Application Support/Meetily/models/`
- **Windows** – `%APPDATA%\Meetily\models\`
- **Linux** – `$HOME/.local/share/Meetily/models/`

You can ship the model alongside your installer or fetch it with the helper script [`backend/download-ggml-model.sh`](https://github.com/Zackriya-Solutions/meetily/blob/main/backend/download-ggml-model.sh). For example, on macOS:

```bash
mkdir -p ~/Library/Application\ Support/Meetily/models
curl -L https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.bin \
  -o ~/Library/Application\ Support/Meetily/models/ggml-base.bin

```

## Code-Sign Your Production Binaries

Signed binaries prevent security warnings and gatekeeper blocks on macOS and Windows.

- **macOS** – Use `codesign` with an Apple Developer ID certificate.
- **Windows** – Use `signtool` with a standard code-signing certificate.
- **Linux** – Optionally sign the AppImage or `.deb` with GPG.

```bash

# macOS example

codesign --deep --force --options runtime \
  --sign "Developer ID Application: Your Name (TEAMID)" \
  Meetily.app

```

## Verify the Production Build

Launch the binary in release mode and confirm that audio devices are detected, transcription events fire, and offline LLM summarization works. On macOS:

```bash
open ./frontend/src-tauri/target/release/bundle/macos/Meetily.app

```

Set `RUST_LOG=info` to surface internal logs from the Rust core and verify that `transcript-update` events appear in the console output.

## Invoke Recording Commands in Production

The production binary exposes the same Tauri commands as the development build. Because the commands are compiled directly into the native binary in [`frontend/src-tauri/src/lib.rs`](https://github.com/Zackriya-Solutions/meetily/blob/main/frontend/src-tauri/src/lib.rs), you invoke them from the Next.js frontend exactly as you would during development:

```typescript
import { invoke } from '@tauri-apps/api/tauri';
import { listen } from '@tauri-apps/api/event';

// Start a recording session
await invoke('start_recording', {
  mic_device_name: 'Built-in Microphone',
  system_device_name: 'BlackHole 2ch',
  meeting_name: 'Quarter-Q Review'
});

// Listen for live transcript updates
await listen<{
  text: string;
  timestamp: string;
}>('transcript-update', (event) => {
  console.log(`[${event.payload.timestamp}] ${event.payload.text}`);
});

```

## Summary

- **Meetily is a Tauri desktop app**, so deploying to production means packaging a native binary for macOS, Windows, or Linux rather than provisioning a server.
- **Run `pnpm run build` followed by `pnpm run tauri:build`** to compile the Next.js frontend and Rust core into a single installer.
- **Place the Whisper ggml model** in the OS-specific application-support directory before launching the app.
- **Code-sign the binary** on macOS and Windows to prevent security warnings during installation.
- **Verify the build** by checking for `transcript-update` and `summary-ready` events with `RUST_LOG=info` enabled.

## Frequently Asked Questions

### Do I need a server to deploy Meetily to a production environment?

No. Meetily is designed as a privacy-first desktop application that runs entirely on the client. The Rust core in [`frontend/src-tauri/src/lib.rs`](https://github.com/Zackriya-Solutions/meetily/blob/main/frontend/src-tauri/src/lib.rs) and the audio pipeline in [`frontend/src-tauri/src/audio/pipeline.rs`](https://github.com/Zackriya-Solutions/meetily/blob/main/frontend/src-tauri/src/audio/pipeline.rs) handle all transcription and summarization locally, so you only need to distribute the compiled binary and Whisper model files.

### Which platforms support a production Meetily build?

The repository supports production builds for macOS, Windows, and Linux. The Rust audio layer includes platform-specific device discovery modules for Windows WASAPI, macOS ScreenCaptureKit, and Linux ALSA or PulseAudio under `frontend/src-tauri/src/audio/devices/`. Run `pnpm run tauri:build` on each target OS to generate the correct installer format.

### Where does Meetily look for the Whisper transcription model?

At runtime, Meetily expects the ggml model in the user’s application-support directory: `~/Library/Application Support/Meetily/models/` on macOS, `%APPDATA%\Meetily\models\` on Windows, and `$HOME/.local/share/Meetily/models/` on Linux. You can bundle these files with your installer or use the [`backend/download-ggml-model.sh`](https://github.com/Zackriya-Solutions/meetily/blob/main/backend/download-ggml-model.sh) script to fetch them.

### Can I enable GPU acceleration for the production build?

Yes. The root [`Cargo.toml`](https://github.com/Zackriya-Solutions/meetily/blob/main/Cargo.toml) declares optional features for GPU acceleration such as `cuda` and `vulkan`. If your target machines have compatible hardware, enable the appropriate feature during the Tauri build to speed up Whisper transcription.