Finding the ‘next Max Verstappen’ with data and smart algorithms.
Van Amersfoort Racing
Develop AI solutions that add value
From predictive models to computer vision. We develop data science solutions that address your challenge.
Trusted by leading organisations
Data science helps organisations get more from their data. From predictions to image recognition and machine learning. The aim is not to develop a model, but to solve a specific problem.
That’s why we don’t start with the technology. We start with the problem. Then we develop a solution that fits your organisation and is ready for everyday use.
A simple solution that works
The power of data science doesn’t lie in the complexity of a model. It lies in solving a specific problem.
That’s why we always choose a solution that fits the problem. Sometimes a predictive model is enough. Sometimes a challenge calls for machine learning. And sometimes another AI technique is the best choice.
Technology is never our starting point. The problem is.
What do we use data science for?
We use data science to tackle challenges where the available data can help us make better predictions, identify patterns or analyse information automatically. The right approach depends on the challenge and the data available.
What you can expect
01
Predictive Analytics. Want to anticipate what’s coming? With predictive analytics, we use data and predictive models to map out future developments. That might mean forecasting demand, estimating the capacity you’ll need or identifying when maintenance is due. This helps you look ahead, plan better and respond to changes sooner.
02
Machine Learning. Machine learning enables models to learn to recognise patterns in large volumes of data. These models can be used for predictive analytics, but also to detect anomalies, classify situations or automate processes. This makes analyses possible that would be difficult to carry out using fixed rules or manual methods.
03
Computer Vision. When large volumes of images need to be reviewed manually, computer vision can automate part of that assessment. Models can be trained to recognise specific features, objects or anomalies in images. For example, computer vision can be used in inspections and quality checks where visual assessment plays an important role.
04
Natural Language Processing. A lot of valuable information within organisations is held in text and documents, making it difficult to process automatically. With natural language processing, we analyse text and identify relevant information. This makes it possible, for example, to classify information from documents automatically or make it available for further processing.
Our approach
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We understand the challenge.
We identify the problem to solve and what success looks like.
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We develop the model
We choose the technique best suited to the challenge and develop a solution that works with the available data.
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We put the solution into production
We make sure the model becomes part of the process in which it is used. We also focus on monitoring and management so the solution continues to work reliably.
Case studies
Curious about what data science could do for your organisation?
We’d be happy to explore the possibilities with you.
Stef Stoelwinder
Business DevelopmentE-mail info@datasciencelab.nl
Phone +31 (0)20 3636 724
Frequently asked questions
Data science uses data to identify patterns, make predictions and improve processes. It draws on techniques ranging from simple regression to machine learning to solve a specific problem.
Data science is useful when an organisation wants to identify patterns, make predictions or use data to support decisions.
Machine learning is a technique within data science. Data science is the broader field that uses data to gain insights and develop solutions.
Because the same technique is not the best solution for every problem. We first establish what an organisation wants to achieve, then choose the technique best suited to it.
A model only delivers results when it becomes part of a workflow. That is why we focus on monitoring, management and continuous improvement, so the solution continues to work reliably.
Ready to get more value from data and AI?
Talk to us. We’d be happy to explore the next step with you.
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