AI quality control on every part.
Defect detection, presence checks and OCR — running at the edge, next to the line, with retraining and rollback built in. Owned by the team that runs the line, not by an MLOps consultancy.
For
Vision / Line 3 / Cam 4
Inspector · ST-44 · End-of-line
Live frame
Decision
conf 0.94REJECT
Detections
Sample-based QA isn't enough anymore.
Customers expect 100% inspection. Hiring more inspectors doesn't scale — vision AI does, but only if it's deployable, retrainable and trusted.
Capabilities included.
Vision QA, scrap analysis and process interlocking — all wired together.
Computer Vision QA
Automate quality inspection with AI-powered cameras. Detect micro-defects and misalignments that human eyes miss.
Scrap Analysis
Identify root causes of scrap. Analyze defect patterns by shift or machine to implement corrective actions.
Process Interlocking
Prevent errors before they happen. Enforce sequential process steps to ensure specifications are met.
From sample to production AI.
Capture
Stream from any GenICam, GigE or USB camera.
Train
Label in ML Studio, train and evaluate without leaving the platform.
Deploy
Push to the edge with one click, near the camera.
Monitor
Watch drift, retrain on flagged samples, rollback if needed.
Every model lives — capture to retraining.
Vision in production isn't a model. It's a lifecycle. We ship the boring parts: labeling, evaluation, edge deploy, drift watch, and a one-click rollback you'll be glad exists.
Capture
Stream from any GenICam, GigE or USB camera, with frame metadata.
Label
ML Studio labeling with active learning — defects rise to the top.
Train
AutoML or your own PyTorch — same pipeline, full evaluation.
Deploy
One-click push to the edge box next to the camera, signed & versioned.
Monitor
Drift detection on inputs and outputs — auto-flag samples for retraining.
Quality outcomes that show up in numbers.
>99%
Defect detection rate
Catches defects human inspectors miss — every part, every shift.
−25%
Scrap reduction
Earlier detection means defective work doesn't continue down the line.
<200ms
Inspection time
Edge inference keeps cycle time intact.
Powered by the RockQ platform
This solution composes these platform capabilities. Each one is also available standalone.
An expert behind every solution.
Real engineers, real factory experience. Drop them a line — they'll respond, scope and propose a working architecture, not a sales deck.
See your defect on a working model.
Send us a few sample images — we'll come back with a baseline model and an architecture.