Computer Vision

Invisible opportunities. In plain view.

During your rounds through the shop, you acquire the most valuable impressions.
You keep in touch with what is happening in the store on a daily basis.
Your years of experience match flawlessly with what you see, hear, smell and feel.
And yet... Your entrepreneurial instinct tells you there is more than meets the eye in your business.
How do you get those invisible opportunities, visible?

Unleash your sixth sense with Computer Vision.

Want to know how your operation works more efficiently with smart cameras monitoring 24/7?
Always and everywhere have up-to-date insight into what needs to be done now?
Fully automated and without the use of proverbial hands?

We'll make it happen.

Any time. Anywhere.
Up-to-date information.

Fully automated operations and scheduling

A reminder of upcoming crowds in the car park and checkout area

Underutilised space

Information on hesitants and no-sales

Optimal navigation and routing

Availability of spaces

What's the state of cleanliness

Security risks

Interaction between customer and staff

Identification of objects and individuals

Localisation of products and orders

Providing sales and marketing with information and actions

And a whole lot more

Frequently asked questions about Computer Vision

Computer vision is a form of artificial intelligence in which computers and systems extract and understand information from digital images and videos.

It involves teaching computers to see and observe. You gather information from pixels, so to speak. Ultimately, computers convert that visual information into making decisions and taking actions.

So with Computer Vision, you process, understand and act on digital visual content at lightning speed and in an automated way.

In a nutshell, Computer Vision requires a camera, software and data. A lot of data.

Computer Vision leans on data. Data you obtain with a camera with which you capture images or videos.

Using software, you analyse the images and extract relevant information.

Underlying the software is an algorithm model. An algorithm allows the model to be self-learning from a context of visual data.

In other words, if you feed the model enough images then the algorithm teaches the model to eventually distinguish and understand images entirely independently.

Simply put, there are cameras and sensors that see what the human eye can perceive, and more. For instance, firstly, you can easily detect objects, measure distances, perceive numbers, thicknesses and colours, as well as recognise fractures. Then there are cameras and vision sensors that contain components beyond human perception. Think invisible UV markers and infrared. So for Computer Vision, all kinds of vision sensors are possible to achieve the right imaging.


- Improved accuracy, efficiency and automation
- High processing speeds and quantities
- Rapid automated testing of required adjustments in changing conditions without increasing workload for employees
- Continuous learning capability


- Concerns about safeguarding privacy and complying with ethical considerations
- Changing and limited light conditions affecting image quality
- Volatile environments where constant vibrations or sudden collisions occur
- Creating relevant and qualitative data sets

Improving quality control processes through visual inspection Improving product assembly and packaging with automation Preventing accidents and ensuring worker safety on production lines

Increasing crop yields and productivity by monitoring plant health Automation of tasks such as weed detection and pest control Opportunities for precision farming and resource optimisation

Optimal use of Public Spaces Improvement of liveability in municipalities with respect to privacy and ethical considerations Sustainable tourism, traffic management and detection of congestion by visual analysis

(In)visible solutions that work.