IFS Expands AI Capabilities with Falkonry Acquisition

News Desk -


Dubai, United Arab Emirates – IFS, the global cloud enterprise software company, has announced a significant move to acquire Falkonry, Inc., a California-based Industrial AI software company. This strategic acquisition solidifies IFS’s position as the only vendor offering a comprehensive suite of Enterprise Resource Planning (ERP), Enterprise Asset Management (EAM), Field Service Management (FSM), and Enterprise Service Management (ESM) solutions, now enhanced with AI-generated anomaly detection capabilities. The acquisition is set to reshape the landscape of AI-driven automation and optimization in various industries.

Falkonry, Inc. specializes in high-speed data analysis for the manufacturing and defense sectors, leveraging advanced AI technology. Their self-learning solution is designed to continuously monitor vast amounts of data related to assets, machines, systems, and industrial processes. By identifying unusual behavior and root causes of failures, Falkonry’s AI empowers organizations to make timely decisions that prevent asset downtime, maintain product quality, and reduce emissions, all while streamlining processes and workflows.

In recent years, the proliferation of assets, machines, and fleets has generated an overwhelming volume of data, presenting operational challenges for real-time monitoring and improvements. Falkonry’s automated and self-learning AI technology democratizes intelligence, enabling operational personnel to take proactive actions to enhance maintenance, adjust processes, and automate workflows effectively.

The addition of Falkonry’s self-learning anomaly detection solution aligns with IFS’s overarching strategy of incorporating AI across its offerings, providing end-to-end intelligent insights in Enterprise Asset Management (EAM), ERP, Manufacturing Execution System (MES), Planning, Scheduling, Optimization (PSO), FSM, and ESM technologies. This integration aims to boost productivity for both personnel and assets.

Falkonry, headquartered in California, USA, with a regional presence in Mumbai, India, was founded in 2012 by CEO Nikunj Mehta. The company has served customers across North America, South America, and Europe, including the US Navy, Air Force, Ternium, North American Stainless, Harbour Energy, and SSAB, demonstrating its commitment to the industrial manufacturing and defense sectors.

Darren Roos, CEO of IFS, emphasized Falkonry’s unique advantages, stating, “Falkonry is unique in the market because its technology is agnostic and also it does not require data scientists. These are great differentiators for Falkonry that means the solution is both scalable and low-cost to implement – two fundamental attributes that very much align to our own values.” Roos added that Falkonry’s technology has broad applicability across industries, offering a significant market opportunity.

Nikunj Mehta, CEO of Falkonry, expressed his enthusiasm about the acquisition, stating, “The convergence of artificial intelligence and industrial processes has become increasingly crucial for organizations seeking to enhance productivity through data.” He highlighted the potential for innovation and value creation through this partnership with IFS.

Brian O’Rourke, IDC Research Manager for EAM and Smart Facilities, commented on the importance of AI-driven anomaly detection in today’s data-rich enterprises. He noted that organizations using AI and machine learning for self-learning asset performance anomaly detection can achieve critical insights faster, ultimately boosting productivity and business performance.

This acquisition follows IFS’s recent acquisition of Poka, a provider of connected worker technology. The combined capabilities of Falkonry and Poka, integrated into IFS Cloud, position IFS as the leading vendor for organizations looking to establish Smart Factories of the future.

The acquisition of Falkonry by IFS is expected to be completed in Q4 2023. Union Square Advisors LLC served as the exclusive financial advisor to Falkonry in connection with the transaction.

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