Visual Inspection
Automate visual inspection with computer vision trained on your own images — reviewed by a human, not accepted blindly.
Manual visual inspection is slow, subjective, and hard to standardize across shifts and sites. Most teams have neither labeled data nor an in-house ML group to build a custom inspection model.
Assembly-line frames, presence/absence checks, surface-finish inspection, and packaging integrity — labeled as pass/fail or by defect class.
- 1Collect representative pass/fail and defect imagery from your process.
- 2Annotate with boxes, polygons, or classification — with AI-assisted proposals you review, so nothing is accepted silently.
- 3Version your dataset and run quality/health checks to catch class imbalance and split leakage.
- 4Export a training-ready dataset and run detection on a private worker, including live-camera inference.
What you get
A repeatable, reviewable inspection model grounded in your own images, with a human-in-the-loop step that reduces silent errors.
Current readiness
Annotation, review, versioning, and export are live. Detection and live-camera inference run on a worker your organization controls; managed (Anastig-hosted) inference is planned.
What Anastig Studio brings to visual inspection.
Statuses are live from our capability catalog — anything marked “Planned” is on the roadmap, not shipped.
Bounding Box
Available nowDraw and edit rectangular detection boxes in original image coordinates.
Classification Labeling
Available nowAssign a single class label to the whole image.
YOLO Detection Export
Available nowNormalized YOLO .txt labels + dataset.yaml (YOLO format).
Start in the product
Scope it with us
Tell us about your visual inspection task — images, throughput, and where the model needs to run — and we'll come back with an honest read on what works today and what needs a pilot.
Contact us