MACHINE VISION FOR PRODUCTION
See your production. Know its potential.
A filler stops for eight seconds. An operator clears a jam. The shift report only shows the total. Meijster uses camera footage to count output and show when, and how often, the line needed attention.
[Photo: camera above a production line]
LINE 01 / TEST SETUP
OUTPUT COUNT
Units detected this shift
DOWNTIME
Every stop made visible
HUMAN INTERVENTION
Understand operator involvement
SHIFT ACTIVITY / RUNNING · STOPPED · INTERVENTION
Example from a test setup. Illustrative data, not a customer result.
Most stops never make it into your OEE report.
[Camera frame with detection boxes]
PLACEHOLDER FOR YOUR PRODUCTION FOOTAGE
We build the models ourselves, and we install them ourselves.
We start at the line, not with a dashboard. We look at the camera angle, the lighting and the way products move. Then we train the model and install the system with your production team.
How it works
01 Understand your process
We look at your line, your goals and your existing cameras.
02 Develop the model
We train AI models in-house on footage from your production.
03 Implement & scale
We integrate the system on the floor and roll it out to more lines.
A short stop is still lost production.
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Who’s behind Meijster
[Name placeholder]. [Background in industrial machine vision: years, industries, types of systems built]. [Add a second or third sentence about the person or team who will visit your production line.]
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What about the people on camera?
[How Meijster handles privacy: what is recorded, whether processing is on-site, how individuals are or aren’t identified, GDPR, and how we involve the works council (ondernemingsraad)]
Which part of your line would you like to see properly?
Tell us where output is counted today, which stops go unrecorded and what cameras are already in place.
meijster.
AI VISION. PRACTICALLY IMPLEMENTED.
CONTACT
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COMPANY
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