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10 Best Artificial Intelligence Companies to Watch 2020

Put your AI to work, the platform for managing your algorithms: Dutch Analytics


Users visualize and analyze the performance of production lines and machines leveraging the solution’s intuitive user interface. Predictive maintenance and quality alerts are provided in the context of the production process – through the digital representation of your manufacturing processes – enabling users to quickly pinpoint the root cause and determine the required maintenance action. Predictive maintenance provides manufacturers with a powerful solution for maintenance and service logistics optimization, helping to reduce costs, ensure uptime and maintain product quality. Unlike the high cost of preventative maintenance—where equipment is maintained on a specific schedule to avoid the risk of machine failure—predictive maintenance enables you to deploy service teams and equipment parts only when maintenance is really needed.Dutch Analytics specializes in the development of predictive maintenance solutions for major industrialplayers.

Dutch Analytics kick-started in December 2016 and has since then been able to develop products and services, which have attracted interest from a steeply inclining number of customers. An example of our applications is the prediction of railway switch failures in The Netherlands, which has one of the most dense railway networks in the world. Moreover, Dutch Analytics is actively expanding its predictive maintenance applications into various other industries, both in and outside of the Netherlands. Their fast-growing team consists of young, creative and open-minded people with various backgrounds who all share a fascination for solving relevant real-world problems with machine learning. Since the start, Dutch Analytics has quickly grown by building dedicated teams of young, highly specialized software developers and data scientists, sourcing the best talents from universities and leading companies in the industry.

Today, Dutch Analytics provides the software platform that the data scientists need to let their work have an impact. Dutch Analytics lets them focus on developing algorithms while they also take care of what comes next. Xenia is there to host, monitor and manage those algorithms throughout their lifecycle, with the flexibility, user-friendliness and scalability they need. Data scientists care about developing the best algorithms while software developers need to make it work. Dutch Analytics help by taking care of all software dependencies for the algorithm and add monitoring, scalability and security. This will transform data science models into reliable and controllable software operations.With Xenia, integration of algorithms into any business process becomes easy. End users can create value from their data and make fast and substantiated business decisions. By managing the algorithm lifecycle in one place, they can have better access and control over the insights they need, and never lose track of the bigger picture.

With Xenia you can deploy and manage your analytics within minutes. Save months of work on managing deployment pipelines and dependencies. Integrate efficiently with existing systems for data exchange. Monitor models and pipelines in a single environment. Scale automatically. Xenia’s User Interface will help you get your models deployed without requiring any DevOps knowledge. The Dutch Railway system is one of the most dense railway systems in the world. The Netherlands is among the top 3 best performing countries in the world regarding trains running on time. However, over 3000 malfunctions happen every year. Dutch Analytics accommodates multiple railway companies with a solution to predict switch malfunctions in the future. As a result, maintenance engineers are able to foresee future malfunctions of switches. This solution boosts machine up time of railway assets and reduces operational costs.

The exponential growth of data combined with new Analytics technologies enable industry players to establish new business models. Dutch Analytics helps industries in this transformation by integrating large streams of data, analyzing and monitoring critical assets. Industry players are now able to foresee future anomalies and reduce operational costs. Together with 9 other companies Dutch Analytics has been accepted by the Presiding Board to join UNIFE. UNIFE represents the European rail manufacturing industry in Brussels. The Association gathers over 100 of Europe’s leading large and SME rail supply companies’ active in the design, manufacture, maintenance and refurbishment of rail transport systems, subsystems and related equipment. UNIFE also brings together 14 national rail industry associations of European countries.By joining UNIFE’s platform, Dutch Analytics expects to create new future collaborations with UNIFE’s members to make the European rail world more AI-driven.

Yannick Maltha Co-founder and CEO of Dutch Analytics

At the Delft University of Technology he graduated cum laude in Systems Engineering, Policy Analysis and Management. During his studies, Yannick has been actively involved in the field of entrepreneurship. Starting with his own startup, Yannick became president of the student society of YES! Delft in 2013 (4th position in the European UBI Index 2015). In his final years of his study he supported the development of a startup incubator in South Africa, and the IRPdelft team offering business projects in emerging markets around the world.

Victor Pereboom Co-founder and CTO of Dutch Analytics

He did his bachelor in Aerospace Engineering with a minor in Econometrics and a master in Control & Simulation at Delft University of Technology. While in high school, he started his first company, which he sold at the age of 20. During his studies, he developed a large interest in Data Science and Machine Learning. In 2014, Victor spent half a year in the Silicon Valley startup ecosystem doing research for Healthcare Innovation Transfer on the use of data analytics in the healthcare sector.

“Our team consists of young and ambitious people with various backgrounds who are data science enthusiasts and enjoy tackling relevant real world problems.”