The Synetic difference

No-gen synthetic image

Real-world generalized image

The old way: real-world data

Every vision system today has been trained the same way: collect real images, label them by hand, and hope there’s enough coverage to generalize.
But this approach is fundamentally broken.

Slow
Gathering real-world data can take months or years.

Expensive
Every dataset run costs money for labor, equipment, and annotation.

Incomplete
Edge cases — like glare, occlusion, rare weather, or fast motion — may never appear in your dataset.

Inaccurate
Human annotation introduces errors and inconsistencies.

Computer vision has been bottlenecked by these limitations for decades.

The generalization crisis

It’s not just that data is slow and expensive; most models simply don’t generalize.

AI:

87%

Don’t reach production

Often failing early when models collapse outside of their training set

80%

Fail to deploy

43% of Data scientists report failures due to deployment pipelines

90%

Fail Post Deployment

Frontier AI models due to performance degredation or integration issues

91%

Model Degredation

As models failt oadapt to new or changing data

Computer Vision accuracy loss examples:

59%

Defect Detection

A defect detection model at 99% test accuracy collapsed to 40% in production

95%

Pneumonia Detection

Pneumonia detection system scoring 98% F1 on training dropped to 3% on new scans

63%

Flower Classification

Flower recognition model hit 95% accuracy in testing but failed at 32% in real-world use

With Synetic rendered datasets, these failures don’t just get patched after the fact, they’re designed out from the start.

Every edge case can be simulated. Every annotation is exact. Every condition is under your control.

The result? Models that generalize to real-world environments better than those trained on real data itself.

Don’t let your project be a statistic

Schedule a demo

The shift: rendered data

Synetic AI turns data collection into data design. Instead of waiting for the real world to give you examples, you generate them, perfectly controlled, infinitely repeatable.

Photorealistic & physics accurate
Our 3D rendering pipeline ensures every frame reflects the real-world physics of light, materials, and motion.

Perfectly annotated
Change conditions at will: lighting, occlusion, weather, materials, motion blur, even sensor types.

Perfectly annotated
Bounding boxes, segmentation maps, depth, keypoints, generated automatically and aligned across every sensor.

landscape images stacked on top of one another

tank being detected in field using image recognition technology

The proof

This isn’t marketing hype, it’s peer-reviewed science. In collaboration with the University of South Carolina, Synetic AI datasets were tested head-to-head against real-world data on industry benchmarks. The result: 33% higher accuracy. That means models trained on Synetic data didn’t just match real data. They beat it.

The payoff

Rendered data doesn’t just replace real-world data — it outperforms it.

Better

Train models that generalize more effectively to real-world conditions.

Faster

Skip months of collection and labeling. Generate datasets on demand.

More efficient

Eliminate annotation costs and wasted collection cycles.

The future

The shift is inevitable. Computer vision won’t be built on the limitations of reality anymore.
It will be built on precision-engineered data, designed, tested, and deployed at scale.
Synetic isn’t just keeping up with that future. We’re creating it.

Stop annotating. Start generalizing

Your contact info

Machine-Learned Humor