RuangKerja AI - Adaptive Workspace Intelligence

Project Overview

RuangKerja AI is a next-generation adaptive workspace intelligence platform designed to transform physical environments—such as offices, factories, and retail spaces—into self-optimizing ecosystems. By breaking away from traditional static mapping, RuangKerja AI builds Workspace Cognition Systems. These systems learn and understand how organizations in Indonesia truly utilize their physical spaces, and then dynamically optimize these environments in real-time to enhance productivity, well-being, and cultural alignment.

The core of our innovation is the "Cognitive Spatial Fabric" (CSF), a hybrid edge-cloud architecture that treats physical workspaces not as inert structures, but as living systems capable of learning, adapting, and self-optimizing based on occupant behavior, Indonesian work culture, and key business outcomes.

Table of Contents

• Project Overview
• Architecture
• Technology Stack
• Project Structure
• Getting Started
  • Prerequisites
  • Installation
  • Running the Development Server
• Building for Production

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Architecture

Our system is built on a robust, three-tiered stack designed for reliability, speed, and data privacy, tailored to the unique infrastructure realities of the Indonesian market.

graph TD
    subgraph Cloud Layer AWS Jakarta
        A1[Outcome Correlation Engine]
        A2[Multi-tenant Orchestrator]
        A3[API Gateway]
        A4[Knowledge Graph - Neptune]
        A1 & A2 & A3 --> A4
    end

    subgraph Edge Layer On-Premise
        B1[Spatial Graph Engine]
        B2[Cultural-Behavioral Layer]
        B3[Real-time Controller]
        B4[TensorFlow Lite / PyTorch Mobile]
        B1 & B2 & B3 --> B4
    end

    subgraph Sensor Layer Distributed
        C1["Cognitive Nodes (CNS-1)"]
        C2[Environmental Sensors]
        C3[Presence Tags]
        C4[IoT Integrations]
    end

    A4 -- "Encrypted Sync" --> B1
    B4 -- "Local Mesh" --> C1
    B4 -- "Local Mesh" --> C2
    B4 -- "Local Mesh" --> C3
    B4 -- "Local Mesh" --> C4

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• Sensor Layer: A distributed network of proprietary Cognitive Nodes, environmental sensors, and privacy-first presence tags gathers real-time behavioral and environmental data. This forms the nervous system of the workspace.
• Edge Layer: On-premise AI processing using lightweight models ensures instant decision-making and resilience, allowing the system to function even with intermittent connectivity.
• Cloud Layer: Hosted in Jakarta for data residency, our cloud infrastructure handles multi-tenant orchestration, long-term knowledge graph storage, and the powerful Outcome Correlation Engine that links spatial adjustments to business performance.

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Technology Stack

This project is built with a modern, robust, and scalable technology stack:

Technology	Description
	A React framework for production-grade applications with server-side rendering and static site generation.
	A JavaScript library for building user interfaces with a component-based architecture.
	A typed superset of JavaScript that enhances code quality and maintainability.
	A utility-first CSS framework for rapidly building custom, modern designs.
	A collection of re-usable UI components built with Radix UI and Tailwind CSS.
	Provides backend services, including authentication and a real-time NoSQL database (Firestore).

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Project Structure

The project follows a standard Next.js App Router structure, organized for clarity and scalability.

.
├── src
│   ├── app                 # Main application routes and pages
│   │   ├── globals.css     # Global styles and Tailwind directives
│   │   ├── layout.tsx      # Root layout for the application
│   │   └── page.tsx        # Main landing page component
│   ├── components          # Reusable UI components
│   │   ├── layout          # Layout components (Header, Footer)
│   │   ├── sections        # Page sections (Hero, About, etc.)
│   │   └── ui              # Base UI elements from ShadCN
│   └── lib                 # Utility functions and libraries
├── public                  # Static assets (images, fonts)
├── .env.local              # Local environment variables
├── next.config.ts          # Next.js configuration
├── package.json            # Project dependencies and scripts
└── tailwind.config.ts    # Tailwind CSS configuration

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Getting Started

Follow these instructions to set up and run the project on your local machine for development and testing purposes.

Prerequisites

• Node.js (v18.x or later recommended)
• npm or yarn

Installation

1. Clone the repository:

   git clone https://github.com/your-repository/ruangkerja-ai.git
   cd ruangkerja-ai

2. Install dependencies:

   npm install
   # or
   # yarn install

Running the Development Server

To start the development server, run the following command:

npm run dev
# or
# yarn dev

The application will be available at http://localhost:3000. The development server supports hot-reloading, so any changes you make to the code will be reflected in the browser instantly.

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Building for Production

To create a production-ready build of the application, run:

npm run build

This command will generate an optimized and minified version of the application in the .next directory. You can then start the production server with:

npm run start