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Aurus Voice Intelligence - Architecture Documentation

A native desktop voice assistant built with Tauri v2 + Next.js 14

Table of Contents

  1. Overview
  2. System Architecture
  3. Audio Pipeline
  4. AI Agents
  5. Frontend Architecture
  6. State Management
  7. API Reference
  8. Security Model
  9. Technology Choices

Overview

Aurus Voice Intelligence transforms spoken thoughts into structured, actionable outputs using a combination of real-time transcription and specialized AI agents. The application runs as a native desktop app with a Spotlight-style interface, triggered by Cmd+Shift+V.

Key Capabilities

Feature Description
Real-time Transcription Live speech-to-text via Deepgram Nova-2
7 AI Agents Specialized processors for different use cases
Native TTS Read results aloud using system voices
Offline Support Local Whisper model for transcription
Cross-platform macOS, Windows, Linux support

System Architecture

graph TB
    subgraph "Desktop Application"
        subgraph "Frontend - Next.js 14"
            UI[React Components]
            Store[Zustand Store]
            Hooks[Custom Hooks]
        end

        subgraph "Backend - Rust/Tauri"
            Audio[Audio Capture<br/>CPAL]
            Trans[Transcription<br/>Manager]
            Agents[AI Agents]
            TTS[Text-to-Speech]
            Secrets[Secrets Manager]
        end
    end

    subgraph "External Services"
        DG[Deepgram API]
        OAI[OpenAI API]
        ANT[Anthropic API]
        QR[Q-Records API]
    end

    UI <--> Store
    Store <--> Hooks
    Hooks <-->|Tauri IPC| Audio
    Hooks <-->|Tauri IPC| Trans
    Hooks <-->|Tauri IPC| Agents
    Hooks <-->|Tauri IPC| TTS

    Audio -->|WebSocket| DG
    Trans -->|WebSocket| DG
    Agents -->|HTTPS| OAI
    Agents -->|HTTPS| ANT
    Agents -->|HTTPS| QR

    style Frontend fill:#3b82f6,color:#fff
    style Backend fill:#f97316,color:#fff
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Architecture Principles

  1. Separation of Concerns

    • Rust handles all system-level operations (audio, network, secrets)
    • Next.js handles UI rendering and user interaction
    • No audio processing in JavaScript (avoids data corruption)

  2. Security First

    • API keys never touch frontend code
    • Secrets stored in OS keychain (macOS Keychain / Windows Credential Manager)
    • All external API calls from Rust backend

  3. Real-time Performance

    • Direct WebSocket from Rust to Deepgram
    • Streaming responses for all AI agents
    • 100ms audio chunks for low latency

Audio Pipeline

flowchart LR
    subgraph Capture["1. Capture"]
        MIC[Microphone] --> CPAL[CPAL Driver]
        CPAL --> RAW[Raw Audio<br/>Native Rate]
    end

    subgraph Process["2. Process"]
        RAW --> MONO[Stereo → Mono]
        MONO --> VAD[VAD Detection]
        VAD --> RESAMPLE[Resample<br/>→ 16kHz]
    end

    subgraph Output["3. Output"]
        RESAMPLE --> PCM[i16 PCM]
        PCM --> BUFFER[Recording Buffer]
        PCM --> WS[WebSocket Send]
    end

    subgraph Transcribe["4. Transcribe"]
        WS --> DG[Deepgram Nova-2]
        DG --> TEXT[Transcript Events]
    end

    VAD -->|Energy Level| FE[Frontend<br/>Visualization]
    TEXT --> FE
    BUFFER --> WAV[Save as WAV]
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Audio Configuration

Parameter Value Reason
Target Sample Rate 16,000 Hz Deepgram optimal rate
Channels 1 (Mono) Speech recognition standard
Bit Depth 16-bit PCM Linear16 encoding
Chunk Size 100ms (~1,600 samples) Balance latency/overhead
VAD Threshold 0.02 RMS Filters silence

Why Direct Rust → Deepgram?

Previous architecture sent audio through the frontend:

Rust → JSON Base64 → JavaScript → WebSocket

Problems:

  • JSON serialization corrupted i16 samples
  • Base64 encoding added 33% overhead
  • JavaScript event loop introduced latency

Current architecture:

Rust → Binary WebSocket → Deepgram

Benefits:

  • Zero data corruption
  • Lower latency (~50ms improvement)
  • Reduced CPU usage

AI Agents

graph TB
    subgraph Input
        T[Transcript]
    end

    subgraph Agents["AI Agents"]
        AI[Action Items<br/>GPT-4o]
        TS[Tone Shifter<br/>Claude Sonnet]
        TR[Translator<br/>GPT-4o]
        DL[Dev-Log<br/>GPT-4o]
        BD[Brain Dump<br/>GPT-4o]
        MM[Mental Mirror<br/>GPT-4o]
        MU[Music Matcher<br/>GPT-4o + Q-Records]
    end

    subgraph Outputs
        TASKS[Tasks & Deadlines]
        REWRITE[Rewritten Text]
        TRANS[Translated Text]
        COMMIT[Commit + Ticket + Slack]
        MATRIX[Eisenhower Matrix]
        LETTER[Letter to Self]
        MUSIC[Music Recommendations]
    end

    T --> AI --> TASKS
    T --> TS --> REWRITE
    T --> TR --> TRANS
    T --> DL --> COMMIT
    T --> BD --> MATRIX
    T --> MM --> LETTER
    T --> MU --> MUSIC
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Agent Details

1. Action Items (GPT-4o)

Extracts actionable tasks from meeting notes or conversations.

{
  "items": [
    {
      "task": "Review PR #142",
      "assignee": "John",
      "due_date": "2025-01-30",
      "priority": "high",
      "context": "Blocking release"
    }
  ],
  "summary": "3 action items identified from standup"
}

2. Tone Shifter (Claude Sonnet 4)

Rewrites text in 8 different tones with intensity control (1-10).

Available Tones:

  • Professional, Casual, Friendly, Formal
  • Empathetic, Assertive, Diplomatic, Enthusiastic

Why Claude for Tone? Claude excels at nuanced writing style adaptation and maintaining context while shifting register.

3. Translator (GPT-4o)

Translates to 12 languages with automatic source detection.

Supported Languages: English, German, Spanish, French, Italian, Portuguese, Dutch, Russian, Japanese, Chinese, Korean, Arabic

4. Dev-Log (GPT-4o)

Transforms developer rambling into structured documentation.

Outputs:

  • Conventional Commit message
  • Jira/Linear ticket with acceptance criteria
  • Slack team update

5. Brain Dump (GPT-4o)

Categorizes unstructured thoughts into the Eisenhower Matrix.

quadrantChart
    title Eisenhower Matrix
    x-axis Low Urgency --> High Urgency
    y-axis Low Importance --> High Importance
    quadrant-1 Do First
    quadrant-2 Schedule
    quadrant-3 Delegate
    quadrant-4 Eliminate
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6. Mental Mirror (GPT-4o)

Transforms daily reflections into compassionate self-letters.

Sections:

  1. Reflection - What happened and how you felt
  2. Mental Check-in - Psychological state assessment
  3. The Release - Cognitive reframing of worries
  4. Message to Tomorrow - Encouragement for future self

7. Music Matcher (GPT-4o + Q-Records)

Analyzes mood from transcript and finds matching music.

Flow:

  1. GPT-4o extracts mood keywords
  2. Q-Records API returns matching tracks
  3. Results include mood tags, energy level, genre

Frontend Architecture

graph TB
    subgraph Layout["app/layout.tsx"]
        subgraph Page["app/page.tsx"]
            WC[WindowControls]
            VI[VoiceInput]
            TD[TranscriptDisplay]
            AS[AgentSelector]
            AR[AgentResults]
        end

        subgraph Settings["app/settings/page.tsx"]
            API[API Key Forms]
        end
    end

    subgraph Hooks
        UTE[useTauriEvents]
        UGS[useGlobalShortcut]
        UCB[useCopyToClipboard]
        UTTS[useTextToSpeech]
    end

    subgraph Store["Zustand Store"]
        RS[Recording State]
        TS[Transcript State]
        AGS[Agent Results]
        PS[Processing State]
    end

    VI --> UTE
    TD --> UTE
    AS --> Store
    AR --> Store
    UTE --> Store
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Component Responsibilities

Component Purpose Key Props/State
VoiceInput Record button, waveform visualization recordingState, audioEnergy
TranscriptDisplay Live + final transcript transcript, interimTranscript
AgentSelector Agent buttons grid activeAgent, isProcessing
AgentResults Dynamic results per agent type All agent result states
ToneSelector Tone/intensity controls selectedTone, toneIntensity
WindowControls Frameless window chrome Tauri window API

State Management

stateDiagram-v2
    [*] --> Idle

    Idle --> Recording: Start Recording
    Recording --> Processing: Stop Recording
    Processing --> Idle: Agent Complete

    state Recording {
        [*] --> Capturing
        Capturing --> SpeechDetected: VAD Trigger
        SpeechDetected --> Capturing: Silence
    }

    state Processing {
        [*] --> Transcribing
        Transcribing --> AgentRunning: Transcript Ready
        AgentRunning --> [*]: Results Ready
    }
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Zustand Store Structure

interface VoiceState {
  // Recording
  recordingState: 'idle' | 'recording' | 'processing';
  hasRecording: boolean;
  recordingDuration: number;

  // Transcript
  transcript: string;
  interimTranscript: string;

  // VAD
  isSpeechDetected: boolean;
  audioEnergy: number;

  // Agent Selection
  activeAgent: AgentType | null;

  // Agent Results (per agent)
  actionItems: ActionItem[];
  toneShiftResult: ToneShiftResult | null;
  translationResult: TranslationResult | null;
  devLogResult: DevLogResult | null;
  brainDumpResult: BrainDumpResult | null;
  mentalMirrorResult: MentalMirrorResult | null;

  // Streaming buffers
  toneShiftStreaming: string;
  translationStreaming: string;
  devLogStreaming: string;
  brainDumpStreaming: string;
  mentalMirrorStreaming: string;

  // Processing
  isProcessing: boolean;
  processingMessage: string;
  error: string | null;
}

API Reference

Tauri Commands

Audio

invoke('start_recording')
invoke('stop_recording')
invoke('is_recording') → boolean
invoke('save_recording', { filepath: string })
invoke('get_recording_duration') → number

Transcription

invoke('start_deepgram_stream', { apiKey: string })
invoke('stop_deepgram_stream')
invoke('is_deepgram_streaming') → boolean

Agents

invoke('extract_action_items', { apiKey, transcript })
invoke('shift_tone_streaming', { apiKey, text, targetTone, intensity })
invoke('translate_text_streaming', { apiKey, text, sourceLanguage, targetLanguage })
invoke('generate_dev_log_streaming', { apiKey, transcript })
invoke('process_brain_dump_streaming', { apiKey, transcript })
invoke('generate_mental_mirror_streaming', { apiKey, transcript })

Tauri Events

// Audio
listen('vad-event', (e) => { is_speech, energy })
listen('audio-chunk', (e) => { samples: number[] })

// Transcription
listen('transcript', (e) => { text, is_final, confidence })

// Agent streaming
listen('tone-shift-chunk', (e) => { text, is_complete })
listen('translation-chunk', (e) => { text, is_complete })
listen('dev-log-chunk', (e) => { text, is_complete })
listen('brain-dump-chunk', (e) => { text, is_complete })
listen('mental-mirror-chunk', (e) => { text, is_complete })

// Agent complete
listen('action-items-extracted', (e) => ActionItemsResult)
listen('tone-shift-complete', (e) => ToneShiftResult)
listen('translation-complete', (e) => TranslationResult)

Security Model

flowchart TB
    subgraph Frontend["Frontend (Next.js)"]
        UI[User Interface]
        UI -->|No API Keys| Store
    end

    subgraph Backend["Backend (Rust)"]
        CMD[Tauri Commands]
        SEC[Secrets Manager]
        API[API Calls]
    end

    subgraph OS["Operating System"]
        KC[macOS Keychain /<br/>Windows Credential Manager]
    end

    subgraph External["External APIs"]
        DG[Deepgram]
        OAI[OpenAI]
        ANT[Anthropic]
    end

    UI -->|invoke| CMD
    CMD --> SEC
    SEC <-->|Secure Storage| KC
    CMD --> API
    API -->|HTTPS + API Key| DG
    API -->|HTTPS + API Key| OAI
    API -->|HTTPS + API Key| ANT
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Security Principles

  1. API Keys in Keychain - Never stored in files or environment variables
  2. No Frontend Secrets - All API calls originate from Rust
  3. HTTPS Only - All external communication encrypted
  4. Local Processing Option - Whisper model runs entirely offline

Technology Choices

Why Tauri + Next.js?

Alternative Why Not
Electron 100MB+ bundle, high memory usage
Pure Web No microphone access, no native TTS
Flutter Less mature desktop support
Qt/GTK Slower UI development

Tauri Benefits:

  • 5-10MB binary size
  • Native OS integration (keychain, TTS)
  • Rust performance for audio processing
  • Web technologies for rapid UI development

Why Rust for Audio?

Language Audio Latency Memory Safety Async Support
Rust Excellent Guaranteed Native
C++ Excellent Manual Complex
Node.js Poor N/A Good
Python Poor N/A Good

Why Zustand over Redux?

  • Simpler API, less boilerplate
  • No action creators or reducers
  • TypeScript-first design
  • Smaller bundle size

Why Deepgram Nova-2?

Service Latency Accuracy Streaming Price
Deepgram Nova-2 ~300ms 95%+ Yes $0.0043/min
Google STT ~500ms 93% Yes $0.006/min
AWS Transcribe ~1s 92% Yes $0.024/min
OpenAI Whisper Batch only 97% No $0.006/min

File Structure

ai-voiceagent/
├── app/                          # Next.js frontend
│   ├── components/               # React components
│   │   ├── AgentResults.tsx      # Dynamic agent output display
│   │   ├── AgentSelector.tsx     # Agent button grid
│   │   ├── TranscriptDisplay.tsx # Live transcript view
│   │   ├── VoiceInput.tsx        # Record button + waveform
│   │   └── WindowControls.tsx    # Frameless window chrome
│   ├── hooks/                    # Custom React hooks
│   │   ├── useTauriEvents.ts     # Tauri event listeners
│   │   └── useGlobalShortcut.ts  # Keyboard shortcuts
│   ├── store/
│   │   └── voiceStore.ts         # Zustand state management
│   ├── settings/
│   │   └── page.tsx              # API key configuration
│   ├── layout.tsx                # Root layout
│   └── page.tsx                  # Main application page
├── src-tauri/                    # Rust backend
│   ├── src/
│   │   ├── lib.rs                # Tauri setup + command registration
│   │   ├── audio.rs              # CPAL audio capture + VAD
│   │   ├── transcription.rs      # Deepgram/AssemblyAI/Whisper
│   │   ├── secrets.rs            # API key management
│   │   ├── tts.rs                # Native text-to-speech
│   │   └── agents/               # AI agent implementations
│   │       ├── action_items.rs
│   │       ├── tone_shifter.rs
│   │       ├── translator.rs
│   │       ├── dev_log.rs
│   │       ├── brain_dump.rs
│   │       ├── mental_mirror.rs
│   │       └── music_matcher.rs
│   ├── Cargo.toml                # Rust dependencies
│   └── tauri.conf.json           # Tauri configuration
├── docs/                         # Documentation
│   ├── ARCHITECTURE.md           # This file
│   ├── IDEAS.md                  # Future enhancements
│   └── TODO.md                   # Pending tasks
└── .memory/                      # Session checkpoints

Performance Metrics

Metric Target Actual
App Launch < 2s ~1.5s
Recording Start < 100ms ~50ms
Transcript Latency < 500ms ~300ms
Agent Response (streaming start) < 1s ~800ms
Memory Usage (idle) < 100MB ~80MB
Binary Size < 20MB ~15MB

Documentation generated for Aurus Voice Intelligence v0.2.1