문의하기 / 테스트 계정 신청
Capture and transfer knowledge
Automate repetitive tasks
pSeven Enterprise allows to capture even the most complex engineering processes, define its logic and collect, analyze and reuse engineering data thanks to powerful workflow engine and low-code approach.
pSeven Enterprise is a multi-user platform with simultaneous access and co-authoring of the workflows in the cloud. Shared workspaces are designed for departments or teams to share and edit workflows, results and files depending on the user roles - all in your browser in real-time.
pSeven Enterprise runs in a predefined and well-controlled IT environment on-premises or in a private cloud, and can be accessed from any browser or device. As a server-side web application, it allows running many resource-consuming studies simultaneously with a built-in resource manager, and running workflows offline without interruption. You can monitor, manage running process and analyze results anytime from anywhere. Execution of individual blocks is possible both on the Linux server and on remote Windows machines.
Workflows in pSeven Enterprise can be easily published and shared in a form of web apps with either automatically generated or custom developed GUI. These workflow-powered web apps serve as easy-to-use engineering calculators that hide unnecessary complexity and democratize the use of best practices for inexperienced users.
As a collaborative engineering platform, pSeven Enterprise is designed for automating complex simulation workflows and executing them in the cloud. All workflows are executed server-side in various predefined runtime environments with scalable resource management. All of that makes pSeven Enterprise a reliable backbone for PLM / SPDM systems in terms of handling simulation workflows.
pSeven Enterprise is equipped with a set of tools to efficiently explore model behavior using a wide range of Design of Experiments (DoE) techniques, solve multi-objective optimization problems and perform Uncertainty Quantification (UQ) studies with both fast-to-evaluate analytical models and computationally-expensive simulations.
By creating predictive models*, existing test, experimental, and/or simulation data can be used to predict response values for new designs, accelerate complex simulations by many orders of magnitude and capture essential knowledge.
* - Also often called machine learning models, response surface models (RSM), reduced order models (ROM), approximation models, surrogate models, metamodels etc.
Check out more diverse use cases using pSeven Desktop.
