Finding the ‘next Max Verstappen’ with data and smart algorithms.
Van Amersfoort Racing
Data & AI for financial and professional services.
In a sector built on trust, every decision must be justifiable.
The potential of data and AI is now clear. For financial and professional services organisations, the challenge is to use these technologies responsibly in processes where errors can have serious consequences. That calls for solutions that are not only smart, but also reliable, auditable and explainable.
Challenges in the sector
Many organisations are already experimenting with AI, for example to analyse documents, detect fraud and support employees. Moving to day-to-day use is proving more difficult. How do you prevent errors? When is human oversight needed? And how do you show how a result was reached? In a sector where trust and accountability are central, these questions need careful consideration from the outset.
How we support you
We help organisations integrate AI safely into their workflows. We look beyond a standalone application or pilot. We set up the entire process: from access to reliable information and clear controls to the role of employees and monitoring results. This creates a solution that works in practice and remains manageable as use grows.
Solutions
Poliskompas Ella, DSL Managed Dataplatform, MY Agent (internal chatbot), SmartlyInvoiced
One data platform for fragmented sourcesBring data from separate systems together in one place, where everyone sees the same figures.
Dashboard for day-to-day operationsSee how things stand on one screen, without having to build reports first.
Predicting when maintenance is neededPlan maintenance based on what the data says, not on a fixed schedule.
Case studies
Frequently asked questions
Tasks that require staff to search for, compare, check or summarise large amounts of information are particularly suitable. Examples include case file research, document processing, client acceptance and reporting. Together, we identify where support will save the most time and where human judgement remains essential.
Not everything has to be perfect before you can begin. You do need to know what data is available, how reliable it is and what it may be used for. An initial application can reveal where improvements to information management are needed.
This requires deliberate choices about architecture, access control and data use. We decide in advance what information an application may process, who can access it and how results are recorded. That makes privacy and information security part of the design, rather than something added afterwards.
AI can organise information, identify connections and make an initial proposal. But it does not automatically understand every business, legal or human nuance. For decisions with an impact, the professional remains responsible. The strength lies in the combination: technology provides speed and an overview, while people provide context and judgement.
An application only earns its place if it demonstrably helps people in their work. That is why users need to be involved early and success criteria need to be clear from the outset. Technical performance is not the only measure: time saved, ease of use, quality and trust matter too. These measures can then guide improvements and help scale the solution.