As artificial intelligence and machine learning continue transforming industries, the demand for high-quality, diverse datasets to train and update these systems has never been greater. Yet real-world data is often scarce, poorly labeled, biased, or constrained by privacy concerns, making it difficult for developers to train reliable AI models. To solve this problem, data scientists are adopting software combining physics-based data engineering, OpenUSD, generative AI, and integrations with best-in-class simulators to build customized and scalable synthetic datasets.
Rendered.ai offers a comprehensive platform as a service (PaaS) that provides the critical infrastructure layer for synthetic data generation, encompassing everything from hosting diverse simulators and integrating 3D models to leveraging procedural world-building technology. Users can harness these capabilities through a simple no-code, graph-based framework, accelerating the creation of effective datasets and model development, testing, and validation. With features such as cloud-based compute orchestration, data analysis and QA, post-processing and domain adaptation, and computer vision model training, Rendered.ai delivers a streamlined and consistent user experience.
Powering Synthetic Data Generation with NVIDIA Technology
The capabilities of the Rendered.ai PaaS are amplified through deep integrations with cutting-edge technologies from NVIDIA that help to shorten and reduce the cost of AI and ML development cycles. NVIDIA technologies are used in the Rendered.ai platform for synthetic data generation in the following ways:
- A direct integration with NVIDIA Omniverse Replicator, a framework for building custom synthetic data generation pipelines, to allow for highly realistic simulation and scalable data generation.
- The use of NVIDIA’s OpenUSD-first approach for scene generation within the NVIDIA Omniverse platform means the seamless integration of data across applications, even outside of Omniverse.
- NVIDIA OptiX is used for advanced synthetic aperture radar (SAR) simulation in the Rendered.ai PaaS, enabling precise and efficient data generation for specialized use cases.
- An integration with NVIDIA TAO supports computer vision model training, helping customers accelerate the development of AI models with minimal overhead.
Rendered.ai’s adoption of NVIDIA technology enables highly detailed and realistic data generation for applications such as remote sensing and environmental monitoring.
An image generated from Rendered.ai’s SAR simulator built using NVIDIA OptiX technology.
Physically accurate synthetic imagery generated in the Rendered.ai platform with NVIDIA Omniverse Replicator and TAO.
Accelerating Synthetic Data Engineering with Generative AI
One of the most exciting advancements on the Rendered.ai platform is its integration of generative AI. While generative AI is already used to create procedural materials and explore text-to-3D capabilities, its most significant potential lies in developing agentic frameworks that drive the entire platform.
Agentic frameworks enable users to focus on the outcomes they want, such as specific experiments or customized datasets, without manually managing every step. By describing their goals, users can activate intelligent agents that handle tasks like:
- Content creation
- Tasking synthetic data applications
- Post-processing and quality assessment
- Dataset aggregation
- Model training
As generative AI technology advances, it will have a greater role in supporting sophisticated agentic frameworks that drive innovation and faster outcomes in predictive modeling.
Synthetic Data Generation with Generative AI: A Guide in Action
Rendered.ai has further enhanced its platform’s capabilities by incorporating NVIDIA’s automatic code generation tools as part of the workflows outlined in NVIDIA’s “Synthetic Data Generation with Generative AI Guide“. These tools significantly speed up the development of new synthetic data applications, helping the Rendered.ai team rapidly prototype and deploy solutions for its customers.
For example, when working with a manufacturing client, Rendered.ai used this workflow to create an accurate digital twin of an assembly line. This twin was used to simulate different failure scenarios, enabling the client to pre-train AI models for predictive maintenance. By automating key steps with NVIDIA technologies, the team delivered a fully functional synthetic data solution in record time.
The Future of AI Development with Synthetic Data
Rendered.ai’s use of NVIDIA technologies is helping drive technological growth, from enhancing model training with realistic simulations to automating workflows with generative AI. This work is paving the way for smarter, faster, and more accessible AI solutions for a broader set of use cases.
If you’re ready to explore how synthetic data and cutting-edge tools can transform your AI projects, request a free trial of the Rendered.ai PaaS or speak with an expert about your computer vision needs.
