A world-leading company specializing in the analysis of aircraft flight data recorders (FDRs) wanted to leverage the power of machine learning to analyze the massive amounts of data from FDRs and identify safety-critical events. Zetane's team of industrial AI experts worked on the project from data preparation to model design and training to debugging and optimization. The client used the resulting project to apply for an important R&D grant.
A global aerospace and transportation client faced a tight deadline to demonstrate the results of an object detection project. With the critical deadline looming, Zetane worked with the client to understand and explain how their data was influenced the machine learning model. This identified a major flaw in the model that was rectified on time and provided a remarkable and successful demonstration to decision-makers.
Presagis, a world leader in commercial-off-the-shelf-software (COTS) for modeling and simulation developed a working prototype of a machine learning solution to label roof-tops from satellite images. Decision makers initially did not support the project and it received little interest. An important conference for the company was weeks away and using excel spreadsheets, graphs and powerpoints would be insufficient. Zetane worked with the team to display the project using the Zetane Engine, where the rich visuals brought their work to life. Now convinced, management decided to use the Zetane visual demo at the conference, which received accolades from conference attendees.
A global defense company that manufactures heavy transport vehicles wanted to demonstrate the use of machine learning to predict the future maintenance needs and the failure of critical systems for these vehicles. They made good progress but struggled to bring the project to a working MVP and had a difficult time explaining how the AI-predictive-maintenance system would function in an operational context. Zetane industrial AI experts assisted the clients' AI team to: understand and explain the inner workings of the model; pinpoint where improvements could be made; created real-time dashboards that operators could understand; and created a demo that impressed the Board of Directors. No doubt, the successful MVP received further funding to scale the project.
An up-and-coming company that uses machine learning to optimize the use of renewable sources of energy by cities, was struggling to convey the benefits of their groundbreaking work to potential clients. With an important global conference on renewable energy fast approaching, the company tasked Zetane to convert their complex data and AI technology into a powerful and convincing demonstration that showed cost-savings and environmental benefits of their solution. This intuitive demo helped secure new municipal customers.
Canada's National Research Council developed a simulator to train surgeons in complex brain surgery procedures and had amassed large quantities of training data in the process. Their research team developed machine learning models with the data to predict patient outcomes from the movements of surgeons in the simulator. They needed to validate their results and demonstrate their findings. Zetane stepped in to bring their research to life as detailed, intuitive visuals that generated 3D simulations of the operations linked to the ML models. In addition to greater transparency of their findings provided by our powerful demo, the visuals by Zetane also helped the researchers identify and fix notable errors in their model.
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