Synthetic data, start to finish.
Why real-world collection falls short, the methods we use instead, and how a dataset moves from scenario to trained model.
Vision models are only as good as the data behind them.
For teams working on models, data collection is the main driver of project cost and timeline. It is also the reason deadlines slip.
Real data is hard to collect
Every production line is dynamic, complex, and unique. Test slots are competitive. Unlike public datasets, industrial data usually has to be captured from scratch.
Labeling is expensive
Each data sample needs careful annotation. Costs and timelines stack up before training even starts.
Edge cases are missing
Models are trained on the data that was easiest to collect. Rare but critical scenarios remain underrepresented and systems less reliable in production.
Live testing is hard to schedule
Testing on commissioned production lines is shared across suppliers, integrators, and internal teams. Test time is limited, heavily coordinated, and costly to secure.
Datasets that match your production environment.
Simulate inspection setups
Lighting, cameras, materials, and angles are configured in software. The same scene renders in dozens of variations in parallel.
Generate labeled defect data at scale
Each rendered image comes with pixel-accurate labels. No manual annotation, no labeling backlog.
Cover rare and critical edge cases
The cases that fail in production are the cases simulation is best at producing. Set the conditions, render the data.
Augment from a few real samples
Generative image models extend the variation in a small real dataset, controlled by clear prompts.
Three steps from scenario to trained model.
- 01
Tell us the scenario
Share the inspection task, the defects you need to catch, and the production conditions you operate under.
- 02
We render the dataset
Photorealistic simulation and generative augmentation produce labeled samples across the full variation space.
- 03
You train and deploy
Datasets ship in standard formats. They drop into your existing ML pipeline alongside any real data you already have.
Built for pharma-grade vision systems.
Pharma production is where vision models face the strictest requirements. Defects are rare. Failure is expensive. Lighting and packaging vary across batches. This is the environment we design for.
- Reliable defect detection in production environments
- Faster delivery to end customers in regulated industries
- Coverage of rare defect classes that real data misses
“We replaced six weeks of data collection with a synthetic dataset rendered in two days. The defect detection model trained on it held up on the real line.”
Talk to us about your dataset.
Tell us the inspection task and the conditions. We will come back with what is feasible, the timeline, and the cost.