Computer vision and AI
Machine vision for manufacturing
Cameras and neural networks that inspect every item at line speed, guide robots and collect defect statistics for decisions on raw materials, recipes and equipment.
What it solves
Inspecting every item
No sampling — every item is checked regardless of line speed or time of day.
Rejection and sorting
Defective products are removed from the conveyor automatically.
Vision for robots
The robot sees the defect or part and acts precisely — e.g. removes the defect instead of rejecting the whole item.
Raw material and supplier grading
Objective batch grading instead of an operator's judgement, with quality history per supplier.
People safety
Video analytics that spots a dangerous situation and alerts the responsible staff at once.
Data for decisions
Defect statistics drive recipe changes, equipment tuning and supplier negotiations.
What we do
Inspection and rejection systems
For your product and line, in several versions for budget and throughput.
- Neural-network image analysis
- Installed on a running line without stopping it
- Pneumatic or mechanical rejection
- Defect reports and analytics
Robot vision
Computer vision that guides robot arms and adapts to seasonal changes in raw material.
- Finding and locating defects and parts
- Works with cobots and industrial robots
- Food-grade washdown design
Inspection and weighing stations
Stand-alone stations — no complex line integration.
- Industrial cameras and precision scales
- A batch processed in seconds
- Supplier analytics
Video analytics
Fixed systems with thermal, IR and regular cameras for 24/7 coverage.
- Danger detection and automatic alerts
- People and object counting
- Incident coordinates
Machine vision projects
Robotic Potato Post-Peeling Complex
Two-robot cobot system with computer vision for precision defect removal — the only market solution for automated spot post-peeling.
CobotsComputer VisionJAKA Pro 5
Learn More
Automated Pastry Quality Control & Rejection
Machine vision system with neural network analysis for pastry quality control on a conveyor. Three implementation options for different production volumes.
Machine VisionAIRejectionConveyor
Learn MoreAgricultureObjective assessment
AI-Based Potato Quality Control & Weighing
Automated weighing and visual quality inspection system for potato batches with precision scales and industrial camera.
AIMachine VisionIoT
Learn MoreAI-Based Water Safety Monitoring System
Fixed camera system with AI analytics for drowning detection, swimmer counting and automatic rescue service alerting.
AIVideo AnalyticsSafetyIP67
Learn MoreHow we work
- 01
Samples and goal
We collect product and defect samples and fix the quality criteria.
- 02
Data check
We capture, label and verify that the model tells the defects apart.
- 03
Installation design
Cameras, lighting, computing and rejection for your line.
- 04
Deployment
Installation on the line and model tuning on the real flow.
- 05
Support
Retraining for new products and seasonal raw material, service.
FAQ
How many defect samples are needed?
It depends on the product and defects. First we capture your product on site and verify the model reliably separates the defects — only then we design the installation.
Can it be installed on a running line?
Yes, inspection and rejection systems are designed for existing lines without stopping them.
Is it suitable for food production?
Yes, there are food-grade washdown solutions — for example, the potato trimming cell.
Send us the task
Describe the product and the defects to find — we will assess whether machine vision fits and offer a test on your samples.