Traditional Machine Learning, Tangible Business Impact
We solve real-world problems with proven Machine Learning techniques and a methodology refined across hundreds of projects. From demand forecasting to fraud detection, we turn your data into operational decisions — and we coach your teams so they can keep improving long after we leave.
Our Proven ML Methodology
Why such a structured approach? Because experience has shown that repeatable success comes from iteration. Our framework keeps business value in the driver’s seat, shortens feedback loops, and embeds governance from day one. Each step rolls naturally into the next — and then back again when new insights emerge — so your solution evolves with your organisation, not beside it.
Strategy
It's important to us to understand the context and objectives of your project. What business problem are we trying to solve, what has been tried in the past, etc? Additionally, we need to understand what the ambition is: what do we expect from the Machine Learning solution, who will use it and how frequently, and how will actionable results be implemented in the existing business processes? Through a series of workshops, we define and document this strategy as a project outline.
Data Gathering
We translate the business problem at hand into data and identify the right data sources and fields. We sit with the BI and data teams and jointly define the extraction and load methods we can leverage to get access to the data.
Data Discovery
Before building the model, we spend sufficient time on Data Discovery, running various discovery analyses to challenge or confirm our initial hypothesis. We get to know the data, iteratively provide feedback and thus crystalize the case at hand of what we want to predict and how we can best do it.
Model Building
In this phase the actual modelling takes place. Our Data Scientists do feature definitions based on our discovery analyses and, run and compare different predictive models, evaluate their performance, fine-tune and rerun. This is an iterative process where we iteratively communicate feedback and performance back to the client.
Business Validation
The model needs to be relevant and actionable. Therefore, we set up a fine-tuning phase where we go, with the business, through the results and where possible even set up a real-life test. Furthermore, we help you to embed the resulting model within your business processes so you can fully leverage the newly created model. Every model delivered by our team results in an actionable working process that can be leveraged for a set of weeks or months providing real added value.
From POC to Scalable Production
A Proof of Concept should never be a dead-end. Once the model proves its value, we industrialise it through our AI Factory playbook: pipeline orchestration, automated testing, MLOps monitoring and continuous retraining. This factory mindset lets you roll out new ML assets faster, keep them healthy in production, and re-use components across departments — lowering long-term cost of ownership.
Examples of ML Projects We Delivered
We'be delivering use cases across industries and company size. Looking for something specific? Browse our full Use-Case Library with dozens of detailed or visit our customer reference page with all our customer testimonials
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