MKB Datalab

Data science and AI in accountancy: greater efficiency at BGH | Rademaker

BGH | Rademaker, a leading accountancy firm with a strong focus on SMEs, strives to use innovative technologies to better serve its clients. when they wanted to professionalize their data management and analysis, they connected with student Luc Siecker through MKB datalab. together, they worked on three projects that helped Rademaker operate more efficiently and elevate their internal processes.

Automated reporting: Kortels' data integration with PowerBI

In collaboration with SME Datalab, Kortels implemented automated PowerBI dashboards to seamlessly integrate data from systems like Exact Online, SAP, and Microsoft Bookings. This innovation streamlined reporting processes, reduced manual effort, and provided real-time insights, enabling data-driven decision-making across the organization.

AI-powered risk analysis: SME Datalab's collaboration with Safety Region Brabant North

In collaboration with SME Datalab, Safety Region Brabant North implemented Retrieval Augmented Generation (RAG) to swiftly gather factual data from public documents. This initiative reduced the time-intensive nature of risk analysis and minimized the risk of using incorrect information or leaking sensitive data.

Simplifying server cabinet configurations for Minkels.

Minkels, a division of Legrand, took proactive steps to support their growth ambitions by gaining in-depth insights into the configurations of sold server cabinets.

Smarter screening for breast cancer

Stichting Yvya, together with EarlyWarningScan bv, is working with JADS to develop a new method of breast cancer screening that can also be used with young women, as opposed to mammography where screening is only possible for women after menopause.

"Dynamic pricing model for MTD: Using Web scraping to understand supply and demand better"

MTD's ambition is to develop a dynamic pricing model that calculates an optimal price based on event characteristics and MTD's expected available capacity on a given date. This will allow a more accurate response to predicted supply and demand during pricing discussions with clients.
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