Every day, hundreds of order confirmations arrive in Van Marcke's mailbox. Usually, this information is embedded in a pdf file, from which information has to be copied into Van Marcke's mainframe. Until recently, this process was done entirely manually. After realizing this was an extremely time-consuming and non-value-adding task, Van Marcke called in our AI expertise combined with RoboRana's RPA bots. We automated this process with our Intelligent Document Processing tool, Klassif.ai.
Currently, radio broadcasters at VRT use a radiomanager to prepare their programs. The preparation includes all the different segments of the show, in which order and all the content. During the show the broadcaster will use this preparation but will not always stick to the planning. This makes it difficult to know which of the prepared items are actually aired, what is said during these segments and in what order they are broadcasted. To enable personalised radio broadcasting, VRT wants to build a service that automatically analyses the radio broadcasts and matches this transcripts to the preparation.
In Belgium, the majority of bridges was built over 60 years ago. The average lifespan of a bridge is around 70 years. This means that many bridges are reaching the end of their life. Bridge inspection is therefore crucial to detect signs of failure early on before a potential catastrophic failure. The process of inspecting a bridge is often difficult as they are hard to reach due to being over a motorway or train tracks. In this project, we validated the use of deep learning for automated crack detection using drones.
Some time ago we got word that KU Leuven ICTS was looking for an application to demo the AI capabilities of their new GPU supercomputer called Genius. And who better to build such an application than the newly minted Deep Learning division of an AI prototyping company? That’s right! Brainjar, aiming to bring the iterative and rapid development practices of Craftworkz to cutting edge deep learning projects, worked with the people at ICTS to create a demo that would demonstrate the power of their new GPU powerhouse.
VDAB has a database of over 11000 competencies, which describe an employee within his or her function. This database keeps on growing. One of the biggest challenges is avoiding duplicate competences and grouping competencies that resemble each other. Given the size of the database, it is no longer possible to perform this manually. The aim of this project was to build an AI-based solution that automatically links similar competences.