My AI Setup

Most people's AI stack looks like a messy browser tab graveyard — ChatGPT, Midjourney, ElevenLabs, Python scripts, and Notion docs trying to tie it all together. It works but barely scales.

An AI workspace fixes this by providing a unified environment where tools share context and communicate through standardized APIs. Agents can orchestrate tasks without manual copy-pasting between tabs.

What's an AI Workspace?

• Unified I/O: Files, models, and outputs live in shared dirs.
• Agent-accessible APIs: Each module exposes functions for orchestration.
• Composable pipelines: Workflows chain seamlessly.
• State persistence: Long-running tasks survive session restarts.

The Problem with Point Solutions

Using standalone tools adds hidden friction:

• Context re-entry
• Format conversion errors
• Rate limit juggling
• No automation path

The NEPA AI Workspace Stack

NEPA builds modular workspaces for specific mediums:

| Workspace | Function | | --- | --- | | Video | Transcribe, scene detect, extract clips | | Audio | Stem separation, effects, mixing | | Music | Text-to-music via MusicGen | | Image | Upscale, background removal, style transfer | | Code IDE | Multi-file generation, project analysis | | TTS | OpenAI, ElevenLabs, Google Cloud, Azure Speech | | Vector | SVG generation, vectorization | | 3D Viewport | Text-to-3D (Shap-E), mesh optimization | | Animation | Keyframe generation, RIFE interpolation | | Design Studio | UI mockups, logo gen, brand assets | | Game Scene | Asset gen, level layout | | AR/VR | Hand tracking, SLAM, spatial audio |

Every workspace is agent-controlled. An orchestrating agent can call functions like music_workspace.generate(prompt="lo-fi hip hop") and get back paths to outputs.

How a Real Workflow Uses Multiple Workspaces

async def content_pipeline(footage_path: str, topic: str):
    # 1. Video transcribe + extract clips
    transcript = await video_workspace.transcribe(footage_path)
    highlights = await video_workspace.extract_viral_clips(footage_path, transcript)
    
    # 2. Audio clean up
    cleaned_audio = await audio_workspace.denoise(highlights[0].audio)
    
    # 3. Music generate background track
    bg_music = await music_workspace.generate(
        prompt=f"upbeat background for {topic} video"
    )
    
    # 4. Video mix and export
    final = await video_workspace.compose(
        video=highlights[0].video,
        voiceover=cleaned_audio,
        background_music=bg_music
    )
    
    # 5. Image generate thumbnail
    thumbnail = await image_workspace.generate_thumbnail(
        frame=final.keyframe,
        style="youtube_thumbnail",
        text=topic
    )

This workflow runs headlessly once configured.

Setting Up Your First Workspace

Start with a bottleneck task. For most, video transcription and clip extraction is it. Steps:

# Install NEPA AI workspace CLI
npm install -g @nepa-ai/workspace-cli

# Initialize a workspace in your project folder
nepa-workspace init --type video

# Run a task
nepa-workspace run transcribe --input ./footage/raw.mp4 --output ./output/

The CLI handles model downloads and output dirs.

Why Workspaces Beat Standalone APIs

Workspaces handle auth, error handling, rate limits, local fallbacks transparently. Example:

result = await tts_workspace.speak(
    text="Hello world",
    voice="nova",
    # Falls back to local piper-tts if OpenAI API unavailable
    fallback_to_local=True
)

The Business Case

Save time — 2 hours/day pays off in weeks. For content creators, solo founders, and agencies.

→ Get the Full AI Workspace Suite at axon.nepa-ai.com