Software stack

TypeScript is used for the front end because of the availability of graphics libraries such as Three.js, web-ifc.js and Google Blockly. For the back-end, Node.js, Go and Rust were considered. Node.js was selected because of high performance for I/O related tasks and the need for a JavaScript runtime engine for processing used defined scripts. Node.js also has the benefit of offering a single TypeScript language and environment for the front end and back end. Performance bottlenecks are avoided by using worker threads for CPU intensive tasks such as data import and data analysis.

Docker is used to package all front-end and back-end modules. Docker is also used for integration with third-party platforms. This allows the use of libraries written in various languages such as Python, Rust, or JavaScript while providing a consistent REST integration API. Kubernetes k3s is used for the deployment of microservices because it provides ease of installation and scalability and runs on low-end and high-end platforms.

Area	Component	Usage
Front-end	Material-UI	Framework for front-end components such as buttons, date pickers and menus.
	React	Framework for development of app and progressive web applications.
	Three.js	High performance 3D graphics framework based on WebGL.
	That open	Reading IFC files and accessing BIM metadata.
	Blockly	Visual programming for automation, compliance monitoring, and solid modeling.
	Websockets	Push state updates to browsers and mobile apps.
	TypeScript	Main development language for client and server.
	Ionic	Development of multi-platform apps with Bluetooth support.
	Vite	Frontend build tool, testing and module federation.
Back-end	TensorFlow	Machine learning framework for data analysis and forecasting.
	Node.js	Main development environment for client and server.
	VM2	Sandbox for running user defined JavaScript customizations.
	Express	Web application framework for implementing REST APIs and web sockets.
	Swagger	OpenAPI 3.0 compliant REST API documentation.
	MongoDB	Persistent storage of IoT data, application data and documents.
	Rust	High-performance and low footprint microservices for making custom connections to other systems.
	C++	Programming microcontrollers with sensors such as ESP32.
	Puppeteer	Data collection using web crawling.
	Mocha	Automated unit testing and integration testing.
DevOps	Claude code	AI assistant for coding and documentation.
	Puppeteer	Integration testing and generation of screenshots for documentation.
	Docker	Packing of microservices into containers for deployment on ARM64 and AMD64 platforms.
	Kubernetes	Deployment and scaling of docker containers.
	Helm	Installation and upgrading releases in Kubernetes.
	Gitea	Source code management and continuous integration.