COMPUTER VISION ACCELERATED WITH SYNTHETIC DATA

Real data is: 

1. Time-consuming and expensive to acquire and label 
2. Often limited to common scenarios 
3. Ineffective in modeling for edge cases and rare objects

Synthetic data generation empowers engineering teams to design the training data they actually need, including rare events, foundational cases for experimentation, and diverse variations—before deployment or to update AI systems quickly. 

Yes, when done correctly.

Low-fidelity synthetic data can actually hurt models. Well-labeled, physics-based synthetic data accelerates training, improves model performance, and fills data gaps left by real imagery.

Rendered.ai focuses on training-ready realism for complex systems, not marketing art. 

Sometimes—but typically it is used to augment real data. 

The winning formula for using synthetic imagery in computer vision (CV) engineering: 

1. Generate customized synthetic data to bootstrap models quickly.
2. Extend training data to cover rare events synthetically. 
3. Auto-label real data to effectively merge real and synthetic into robust training datasets. 
4. Train CV models and infer performance on real-world test scenarios to inform data improvements. 
5. Iterate synthetic data generation to optimize model performance with the right mix of real-to-synthetic training data. 

Synthetic data acts as a force multiplier, reducing engineering headaches, lost time, and dollars to insufficient training information for computer vision systems. 

Every synthetic image generated on the Rendered.a platform and by our team of experts on behalf of our customers is fully labeled at creation. 

That means: 

1. Consistent, custom annotations mapped to the desired format 
2. No tedious, time-consuming manual annotation 
3. Immediate ground truth for computer vision model training and evaluation 

Rendered.ai also offers auto-annotation services for real datasets using models trained on synthetic data on the Rendered.ai platform — enhancing the value of the existing datasets you’ve been waiting to use. 

Synthetic data generated with Rendered.ai shines when: 

1. Training AI for rare events is important. 
2. Sensors are complex (e.g., synthetic aperture radar, infrared, hyperspectral, multispectral, and x-ray). 
3. Cost, access, privacy constraints, or risk limits real data collection. 

This comes up most often when engineering vision-based AI for: 

• Autonomous systems 

• Physical AI and robotics  

• Drones and counter-UAS defense systems 

• Satellite and aerial imagery 

• Manufacturing inspection 

• Maritime, transportation, and logistics 

• Security and surveillance 

If you're having trouble training models for all the test scenarios and edge cases needed, working with complex sensor types, or filling a massive data gap, synthetic data probably belongs in your AI pipeline. 

Rendered.ai supports RGB cameras and all advanced CV sensor modalities, which can be difficult to simulate and acquire viable real-world training data for, including: 

1. Synthetic Aperture Radar (SAR) 
2. Infrared (IR) 
3. Thermal 
4. Multispectral & hyperspectral 
5. X-ray 
6. Custom and emerging sensors 

This is where more generic synthetic data vendors quietly tap out and Rendered.ai excels. 

There are 3 things most synthetic data vendors don’t do well that Rendered.ai does by default:

1. Physics-based accuracy 

Images generated automatically abide by the laws of physics with respect to the interaction of lighting, applied materials, sensor physics, and geometry in each scene.
 

2. Sensor-specific simulation, specializing in difficult-to-work-with sensor modalities 

Not just “an image,” — the accurate view of what your sensor would see. While this may seem trivial for RGB cameras, it requires rich domain expertise and advanced data generation technology to render physically accurate synthetic images for complex sensor types, such as radar (e.g., SAR), infrared/thermal, remote sensing (e.g., multispectral and hyperspectral), and x-ray.

3. A data generation platform with engineering-first workflows 

Synthetic data generation + model training + validation on one platform. The full synthetic data generation factory — instead of just a toolkit or engine that you still need to build infrastructure on top of. Rendered.ai’s PaaS provides open framework to plug in preferred tools, already integrated best-in-class simulators (e.g., DIRSIG™, NVIDIA Omniverse, QSIM RT x-ray simulator), configurable automated generation workflows, and direct access to customization tools, asset management, model training, and validation designed for streamlined computer vision engineering collaboration. 

Minutes to days—not months. 

CV engineering teams use Rendered.ai to: 

1. Spin up fully labeled, training-ready datasets at lightning speed.
2. Iterate training imagery with trackable history for future reuse and rapid modification.
3. Regenerate data quickly when new requirements arise. 
4. Test models before hardware or sensors are deployed. 

The speed and quality that Rendered.ai provides is the competitive advantage you need to get your CV systems to market faster, minus extensive rounds of trial and error and the need to add expertise to your team. 

Exponentially so. 

Rapid synthetic data generation with Rendered.ai: 

• Overcomes the need for on-team domain expertise 

• Reduces reliance on hard-to-acquire real-world data collection 

• Cuts labeling costs and time 

• Shrinks the likelihood of late-stage model failures 

• Significantly shortens time-to-deployment 

Most teams don’t realize how much budget they’re burning on data until the end of the project and inevitably choose to stop doing it the hard way. 

100%! No computer vision model today performs perfectly across every use case. Without tailoring both the model and the data it’s trained on, specialized AI applications simply don’t work at scale.

Rendered.ai allows teams to customize: 

• Environments 

• Objects of interest 

• Sensor specifications 

• Distractors 

• Viewing geometry 

• Weather, lighting, occlusion 

• Edge cases and rare conditions 

• Annotation mapping 

You don’t adapt your model to the dataset—the dataset adapts to your model. 

Yes. Sometimes simulated data is the only option you have.  

Because data is generated—not collected from the real world—it avoids: 

1. Privacy regulation compliance issues (e.g., patients, children, consumers, etc.) 
2. Restrictions tied to moving real imagery between systems 
3. Risky or dangerous real-world capture (e.g., imagery from conflict zones, battlefronts, and other unsafe environments) 

When access to sensitive real-world test scenarios is challenging, synthetic data is often the only option in early-stage development of AI applications in fields like healthcare, defense, workplace safety, consumer retail, transportation, and more. 

It doesn't have to be. 

In fact, smaller teams benefit even more from using tools like Rendered.ai’s Synthetic Data Platform or Synthetic Data as a Service training data creation because they: 

• Have fewer resources available for viable data collection 

• Often do not have specialized synthetic data generation expertise on staff 

• Need faster iteration 

• Can’t afford long model retraining cycles 

Rendered.ai’s solutions are designed to be accessible and easy-to-use for small engineering teams with the ability to quickly scale for enterprise deployments without changing their tech stack or adding staff with specialized skillsets. 

Rendered.ai offers both subscription-based pricing for its Synthetic Data Platform as a Service and custom, project-based pricing for fully managed services like Synthetic Data as a Service, Model Development, and Auto-Data Labeling. This flexible structure allows teams to choose between hands-on platform use or expert-led delivery with minimal internal lift. 

Please refer to our Pricing page or contact us to get a quote based on your computer vision needs. 

Rendered.ai’s newest solution, the Agent Studio, enables the rapid creation of an AI agent built with personalized rule sets; secure access to preferred networks, tools, and datasets; and the LLM or IDE of your choice to quickly generate and iterate customized synthetic datasets and train CV models with simply human language prompts. Additionally, the Agent Studio offers readymade workspaces with agents pre-built to handle common complex, but repetitive, computer vision engineering tasks for you – including CycleGAN adaptation, text-to-3D model creation, SAR simulation, thermal solving, and more. 

If you are looking to exponentially accelerate the productivity of your engineering team and innovate faster, Rendered.ai’s Agent Studio is worth a look. Users can join our Beta Program now with a $250 credit towards use of the Agent Studio. Learn more.  

Absolutely.  

The Agent Studio is an easily accessible way to create AI agents that actually work to handle the complex, repetitive tasks that often draw out deep tech engineering, hardware, and design timelines. Using the Studio to build agents with layered rule sets that can be easily iterated ensures long-term Observability, Governance, and Persistence of your customized agentic workflows. 

Power users today are leveraging the Agent Studio to establish tailored agentic workflows to efficiently complete synthetic data generation, model training and inferencing, satellite antenna design, thermal analysis, professional PCB design, 3D CAD design, ray tracing lens design, and any other deep tech engineering job you can imagine. The Agent Studio is loaded with pre-built agent examples you can use now for common deep tech engineering, hardware, and design tasks or quickly modify based on your unique needs.  

See some of these examples in action.