Falkonry, Inc. announced the availability of two new operational AI product offerings–Falkonry Workbench and Falkonry Analyzer. With a user subscription, Falkonry Workbench enables customers to analyze large amounts of data and cost-effectively build predictive models that solve production problems, according to its maker. Falkonry Analyzer features a “pay-as-you-deploy” subscription that enables customers to scale the deployment of these models either on-premises, at the edge or in the cloud.
“Scalability and lower total cost of ownership (TCO) are critical metrics by which company executives evaluate deployment success of their AI initiatives, yet today’s solutions fall short on that criteria,” said Dr. Nikunj Mehta, Falkonry founder and CEO. “With Falkonry’s new and simplified subscription model, customers now have an extremely cost-effective and risk mitigated path to start realizing significant ROI through predictive operations.”
Customers using Falkonry’s Operational AI products have built tens of thousands of models, including the customer who has deployed models into production at a rate of one per week. Falkonry incorporates key automation capabilities for data preparation and model evaluation into its newest release; Workbench substantially reduces the traditional data-manipulation efforts required prior to any AI development by directly importing data files, according to Falkonry, which promises simplified tasks such as organizing and renaming signals. The ability to auto-scale idle cloud resources on-demand has enabled customers to achieve 50% reduction in typical operating costs, according to Falkonry.
“Falkonry’s Operational AI software enables our manufacturing engineers to build predictive models themselves, which is critical to deploy these solutions at scale,” said Dr. Jochen Bönig, head of strategic digitalization at Siemens, which has recently adopted Falkonry tools. “Our engineers were very quickly able to predict critical failures with a high degree of accuracy two to three months in advance.”
