Import Your Data
Bring existing geometries, simulations, test results, parameters, and operating data into one engineering workspace.
The Workflow
Leverage Foundation Models
Start from pretrained Physics AI models, then apply them directly or fine-tune them for your design space.
Optimize and Improve
Accelerate design cycles, build reusable workflows to be used across teams, and drastically increase simulation engineer efficiency.
Export Optimized Designs
Select and export the best designs, validated simulation outputs, and reusable datasets into downstream engineering workflows.
The Platform. Drive Discovery
Vertical Applications
Complete Physics AI workflows for your industry.
Simulation Suite
Run familiar simulation software with your own licenses and keys or UniversalAGI’s.
Batch Simulations
Run thousands of near-instant simulations with Physics AI models.
The Lab. Build and Specialize Models
Model Factory
Built into the platform: train or fine-tune pretrained models on your data.
Foundation Models
Platform-native pretrained physics models, ready to apply or fine-tune to your use case.
Your Environment. Keep Your Data
Cloud Services
Run securely across cloud or on-prem infrastructure with governed APIs and compute.
Choose How You Deploy
Fully Managed UniversalAGI hosts and operates the complete platform, including compute, services, upgrades, and reliability. Data is encrypted in transit and at rest, isolated by deployment, and never used to train another customer’s model without explicit approval.
Bring Your Cloud Run UniversalAGI in infrastructure you control, including AWS, Azure, Google Cloud and on-premises.
Frequently Asked Questions
Yes. We start with the physics and decisions that matter to your team, then configure the model design, simulation pipeline, validation criteria, and optimization loop around them. Geometry, operating conditions, and program-specific governance can all be incorporated instead of forcing the work into a fixed template.
The platform is designed to extend existing CAD, CAE, simulation, and automation workflows rather than replace them. Programs can run licensed tools from Ansys, Siemens, and Dassault Systèmes, exchange data through standard engineering formats, and connect predictions or field outputs to internal systems through APIs. Other integrations can be planned around the tools your team depends on.
There is no universal minimum. The right starting set depends on the physics, operating range, and engineering decision the model needs to support. Existing solver runs, test measurements, and operational observations can all be useful. Pre-trained models can reduce the amount of application-specific data required, allowing teams to focus new simulation work on the cases that add the most information.
Our models output uncertainty quantification for individual predictions. Those signals help engineers decide when an answer is sufficient and when a case needs more simulation, data, or model refinement. Furthermore, each application is typically evaluated on simulation cases kept out of training and benchmarked against the engineering quantities relevant to that domain.
Choose a fully managed deployment hosted by UniversalAGI or bring your own cloud. Bring Your Cloud supports AWS, Azure, Google Cloud and on-premises.
Your data remains subject to the controls and infrastructure boundary chosen for your program. We use it to configure and operate your models; it is not added to training for another customer unless you explicitly approve that use. Bring Your Cloud keeps data, models, and model execution in infrastructure you control.