IoT Services

A digitized factory which uses an interconnected network of machines, with specific communication mechanisms, and sufficient computing power to use AI and ML to help analyse data and drive process improvements through continuous learning (as the name implies) is called a smart factory. A good example of a smart factory solution in the automotive industry is Tesla’s Gigafactory located in Berlin, Germany.

  • Real-time monitoring of production processes and machine performance.
  • Automated alerts for equipment maintenance and downtime prediction.
  • Integration with IoT devices for data-driven insights and control.
  • Production scheduling optimization for efficient resource allocation.
  • Quality control dashboards with defect tracking and analysis


Industrial Internet-of-Things (IIoT)

Keeping your factory requirement in mind we provide IIoT solutions involving connecting devices, machines, and systems to the internet to enable communication and data exchange. This connectivity facilitates real-time monitoring, data collection, and analysis, forming the foundation for smart manufacturing. We also setup a robust communication infrastructure, including wired and wireless networks

Sensors, data analytics & AI

Sensors are deployed throughout the factory to collect real-time data from equipment, machines, and production processes. Advanced analytics and AI technologies process the vast amounts of data generated by sensors and other sources. These technologies analyze data to identify patterns, predict equipment failures, optimize production parameters, and provide insights for continuous improvement.

Build Human-Machine Interfaces (HMIs)

We build human-machine interfaces that provide a user-friendly interface for human operators to interact with and monitor the manufacturing processes. This includes touchscreens, dashboards, and other visualization tools that display real-time data and insights. Robust cybersecurity measures are also implemented at all HMIs to protect against potential threats and unauthorized access.

Edge and cloud computing

Depending on the nature of the requirement, we process data near the source (i.e. edge of the network, hence called edge computing) or centralized cloud servers (cloud computing). Edge computing reduces latency and allows for faster decision-making by processing critical data closer to where it is generated. Cloud computing offers scalability and accessibility, especially for advanced analytics and AI applications.