Quality Control
Build a reproducible, auditable quality-control workflow from your own inspection data.
Quality control needs consistent, auditable decisions from image data — but building a QC model usually means either an expensive platform or ad-hoc scripts nobody can reproduce.
Fill-level checks, label and print verification, dimensional and finish tolerances, and contamination screening.
- 1Turn your QC image archive into a labeled, versioned dataset in the browser.
- 2Use AI-assisted annotation and one-example Visual Similarity to find more of the same defect faster.
- 3Track dataset versions and validate every export before download — no silent bad data.
- 4Export to your training stack (YOLO, COCO, VOC, CSV) and keep an audit trail of what shipped.
What you get
A reproducible quality-control dataset-to-model workflow with versioning and export validation, so quality decisions stay consistent and auditable.
Current readiness
Dataset management, AI-assisted annotation, versioning, and validated export are live. Training and deployment connect to compute your organization controls and are maturing along the roadmap.
What Anastig Studio brings to quality control.
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.
Dataset Versioning
Available nowTrack changes to your dataset over time with named versions.
Start in the product
Scope it with us
Tell us about your quality control 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