With its core expertise in product and software development, Persistent augments its approach to this market through its Flywheel framework, which covers core technology for data, monetization, and ecosystem integration combined with product engineering and lifecycle management.
Has expertise in key IoT-related partner’s platforms such as IBM, Azure, AWS and Google. As an IBM product engineering partner, it has more than 1000 engineers dedicated to IBM platforms such as IBM Watson, IBM Cloud, and IoT to create solutions for its customers.
Its primary focus is healthcare, financial services and industrial systems. Under industrial smart manufacturing, its solutions are focused on smart energy management, predictive maintenance, smart supply chain management and AI-enabled quality control.
M2M communication, video surveillance and real-time analytics are key building blocks of IIoT. Persistent has built applications around these technologies, including robot failure detection, smart meter analytics, oilfield data monitoring, instrument calibration, and remote shop floor monitoring.
Investments and Innovation
In the next 12 months, it will focus on end-to-end solutions in the areas of Smart Factory/Industry 4.0, robotic assistants and smart communities through partnerships with PLC, OPC and AI vendors. Will invest in opensource Robot Operating System that will increase choices for robot OEMs.
It is considering an inorganic approach to acquiring technologies such as edge machine learning and security capabilities critical to IoT smart manufacturing and industrial processes, as well as expanding its sales channel reach and its IoT software delivery capabilities to new geographies.
Persistent, with its focus on Industry 4.0, has become increasingly visible in various industry forums. Its leadership team has been taking a thought leadership position around the impact of Internet of things on manufacturing excellence from an efficiency and profitability perspective.
It has also begun to share its point-of-view through research papers and publications. The themes of these papers explore multiple critical facets of IoT-focused areas such as machine learning techniques applied to anomaly detection in IIoTdata from engine-based machines, among others.
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