AI is just software that uses data instead of code. We provide the tools to produce physics-based synthetic datasets for AI training and validation.
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Generate tens of thousands of images in a managed HPC environment
Procedural world generation and digital twin scraping/imports
In our experience, lack of diversity in the dataset is the root cause of most synthetic data failures. To address this issue, we incorporate procedural models over static ones whenever possible:
We provide an easy-to-use graphical interface for dataset generation AND a complete set of APIs for access wherever you need it.
Data can be downloaded locally or used with cloud-based pipelines (including directly to your AWS S3 bucket) keeping data residency near a global set of analytics tools.
Our architecture is cloud native; meaning almost instantly scalable compute environments are at your fingertips for both dataset generation as well as training and AI deployment.
Node-based data programming for automated creation of a synthetic ATR
library
Our shared simulation domains allow us to produce cross-domain, integrated, multi-sensor simulations to support robust data-fusion models from a single simulation enivronment.
All of our rendering engines are GPU-accelerated (including our full-wave Synthetic Aperture Radar engine!) meaning you'll have access to massive compute available via GPUs.
If we don’t have something you need in our tools library, we can build it for you or give you API hooks into the simulation environment.
Catch Nathan Kundtz, Rendered.ai Founder and CEO, discussing synthetic data generation with Adam Simmons of Project Geospacial.
Get to know more about the needs, challenges and benefits of a data engineering approach to proveable AI.
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We’re happy to help in any way.
