Turn one photo of your product into a
fully labeled defect-detection dataset.
One reference image in, a verified, auto-labeled synthetic defect dataset out — built for manufacturers training visual inspection models.
Day-one detection accuracy on an inspection model trained entirely on Visynex-generated synthetic data.
Visynex Studio — the console engineers run pilots in.
Ready-to-train output · verified before it's labeled
You can't train a defect model on defects you don't have yet.
Robust visual inspection needs large, labeled datasets that include real product defects. On a new line, those barely exist — and collecting them doesn't scale.
Defective units are rare
Real defects are expensive to collect and inconsistent — often unavailable at the volume a model needs before a line goes into production.
Manual labeling doesn't scale
Hand-labeling the few defect images you do have is slow, and label quality drifts across annotators and shifts.
Every new part restarts the clock
A new part type or defect class means collecting all over again — data becomes the bottleneck on every launch.
From one golden sample to a training-ready dataset.
Six steps, fully automated. Defects are grounded in how the part is actually made — and every image is verified before it's labeled.
One golden sample, staged and ready for the engine to analyze.
One reference in. A verified defect out.
Drag to compare the golden reference against a Visynex-generated defect — grounded in how the part is actually made, and independently verified before it's labeled.
Drag the divider · defect grounded in the part's casting process, planned across the full surface.
Manufacturers building their own visual inspection.
Visynex is built for manufacturers with in-house ML / QC teams standing up automated visual defect detection on production lines. That's our current, proven focus — not one segment among many.
Process-grounded realism, verified before it's labeled.
VisynexStart with a pilot on a single part type.
One-time proof of concept for new design partners.
For single-site ML / QC teams in production.
Annual contract for multi-site manufacturers.
Day-one detection accuracy on a visual inspection model trained entirely on Visynex-generated synthetic defect data.
— without collecting a single real defect sample.
"OMSA Automotive, an industrial parts manufacturer, is a design partner using Visynex-generated synthetic defect data to train a visual inspection model."
A founder-led pilot, not a self-serve signup.
Share one reference image
One photo of your golden part is all we need to start.
We run the pipeline
We deliver a sample labeled dataset built for your part.
Validate against your pipeline
Test fit in your own model training before a broader engagement.
Quality control is where we're proving this first.
The same engine — one reference image in, verified synthetic data out — extends naturally to training data for robotics and other physical-AI applications. QC today, broader physical-AI data infrastructure next.
Send us one photo of your product.
We'll turn it into a sample labeled defect dataset — see the fit before you commit.