Transform your industrial manufacturing with a software-defined, AI-driven, smart manufacturing solution. Powered by our unified MQTT messaging platform and industrial IoT connectivity.
In manufacturing industries, factories often utilize various systems and devices sourced from vendors with diverse data formats and protocols.
Applications are built independently, resulting in ineffective data sharing and communication among applications, creating data silos.
Deploying AI/ML models in real-time on the factory floor is difficult due to the lack of technologies such as Industrial IoT, Big Data, and Artificial Intelligence.
Connect disparate industrial devices with 80+ industrial protocols, including Modbus, OPC-UA, Ethernet/IP, IEC104, BACnet, Siemens, Mitsubishi, and more.
Built on open-source software, running on standard X86 and ARM servers, eliminating the hassle of maintaining hardware PLCs and gateways from different vendors.
Transfer manufacturing data to the cloud in real-time for AI/ML training. Deploy and apply the trained ML models to the factory floor from the cloud.
Design and implement real-time data flows between industrial equipment, MES, WMS, and ERP systems with the Unified Namespace (UNS) architecture.
Process and analyze raw streaming data at the edge. Reduces latency, saves bandwidth, and offloads computational tasks from the cloud platform.
Seamless integration with cloud platforms by transferring manufacturing data from the edge to cloud storage, machine learning, and analytics systems.
Achieve unified access to plant data, including real-time collection of all industrial equipment data, multi-source data access, and enterprise system integration.
Provide a deeper understanding of the various stages of the production process to identify potential bottlenecks. Optimize workflow and improve overall operational efficiency.
Effective predictive maintenance and optimized resource utilization help reduce unplanned downtime and waste, lowering production and operational costs.
Detect anomalies and quality issues in the production process early through real-time data collection and analysis. Improve product quality and reduce defect rates.
Enable predictive analytics and maintenance, automated decision-making, adaptive control, and anomaly detection by integrating IoT, AI/ML, big data, and other technologies.
Adjust the production plan to meet changes in market demand quickly. Reduce inventory backlogs, shorten production cycles, and accelerate new product development and time-to-market.