The defectyou don’thave yet.
Inspection models fail on flaws they have never seen. We generate them: labeled, physically grounded, by the thousand.
Book a pilot →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.
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.
Three stages, fully automated. The same pipeline you’ll find running in the Visynex console.
Upload one reference image
One photo of a defect-free “golden” sample part. The engine stages it and maps the surface into regions, so coverage can be balanced later.
- Golden sample staged
- Surface regions mapped
Defects are planned, then rendered into the part
The engine deconstructs geometry, materials and manufacturing process, plans a process-grounded defect taxonomy across every region, and the synthesis core renders photorealistic variants.
- Automated deconstruction
- Process-grounded defect planning
- Synthesis engine render
Every image is re-detected before it’s labeled
Each variant is independently re-analyzed to confirm the defect actually landed. Only then is it labeled: YOLO / COCO formats, absolute pixel values, ready to train.
- Independent verification
- Auto-labeled delivery · YOLO / COCO
Upload one reference image
One reference in. A verified defect out.
Drag to compare the golden reference against a Visynex-generated defect. Each defect is 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.
| Criterion | Manual collection | Generic synthetic tools |  Visynex |
|---|---|---|---|
| Defect realism | Real, if you can find them | Generic / not process-aware | Grounded in how the part is made |
| Labeling | Manual, slow, inconsistent | Not possible | Auto-labeled, independently verified |
| Surface coverage | Whatever occurs naturally | Unbalanced | Balanced across every region |
| Time to a dataset | Weeks to months | Weeks, low fidelity | Hours / days (one reference image in) |
Start 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.
Who we’re working with, described honestly.
We name a partner only with their written sign-off. Until we have it, the roster stays at sector level, which tells you exactly what we can back up today.
Day-one detection accuracy on a visual inspection model trained entirely on Visynex-generated synthetic defect data.
Without collecting a single real defect sample.
- Active
Automotive parts
Design partner. An industrial parts manufacturer training a production visual inspection model entirely on Visynex-generated defect data.
- In evaluation
Automotive OEM
Assessing coverage across a multi-part assembly line.
- In evaluation
Global food & beverage
Assessing fit for packaging and container surface inspection.
Named references available to serious pilot enquiries, under NDA.
A founder-led pilot, not a self-serve signup.
- 01
Share one reference image
One photo of your golden part is all we need to start.
- 02
We run the pipeline
We deliver a sample labeled dataset built for your part.
- 03
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 extends naturally to training data for robotics and other physical-AI applications: one reference image in, verified synthetic data out. 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, built for your part. See the fit before you commit to anything.
Prefer email? Write to humza@visynex-ai.com