SYNETIC.ai

What is synthetic training data?

Synthetic training data is computer-generated imagery created using physics-based rendering engines like Unreal Engine. Unlike traditional data collection that photographs real-world scenes, synthetic data is procedurally generated with perfect labels, systematic variation of conditions (lighting, angles, occlusion), and zero annotation errors. Our approach has been validated by the University of South Carolina to outperform real-world datasets by 34%.

Why is synthetic data better than real-world data?

Synthetic data outperforms real-world data for four key reasons: 1) Perfect labeling with zero human annotation errors, 2) Systematic coverage of all lighting conditions, camera angles, and edge cases rather than whatever was randomly captured, 3) Ability to amplify rare scenarios (backlit objects, heavy occlusion, damage) that represent less than 5% of real datasets but are critical for robust models, and 4) Physics-based rendering ensures photorealism without domain gap. Independent research at USC proved 34% better performance using 2K synthetic images versus 2K real images.

How long does it take to get my dataset or model?

Synthetic datasets are delivered in 1-2 weeks. Custom trained models (which include dataset generation plus full model training) are delivered in 2-3 weeks. This is 10-20x faster than traditional approaches that require 6+ months for data collection, manual labeling, and iterative model training.

What is your performance guarantee?

We offer a 100% money-back guarantee. If our synthetic-trained models don't meet or exceed the performance of models trained on real-world data for your specific use case, you get a full refund. No questions asked. This guarantee is backed by university-validated research showing consistent performance improvements across multiple computer vision architectures.

Do I need to provide my own data?

No. We generate all training data synthetically using 3D rendering. You simply describe your use case and requirements, and we procedurally generate comprehensive datasets covering all relevant conditions. If you have existing data you'd like to validate against, we can use it for comparison, but it's not required for training.