Explore our Services

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Statistical consulting

  • Data analysis and statistics
  • Experiment design
  • Method development
  • Machine learning
  • Data and text mining
  • Artificial intelligence
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Scientific programming

  • R, Python, Julia package development
  • Code review and optimization
  • Porting code to low-level languages
  • Parallel and distributed computing
  • In-database data science
  • GPU / FPGA programming
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Application Development and Integration

  • Desktop and web applications
  • Automation of analyses or predictive modeling
  • Data science APIs
  • Scientific data stores
  • Big data architecture
  • Data science tooling

Data Analysis Hardware and Hosting

  • Custom data analysis hardware
  • Data science compute infrastructure
  • Data analysis platforms hosting
  • Data science APIs as a service
  • Managed services for scientific data stores

Discover our Products

integrated development environment


  • IDE for data science, state of the art
  • Comfort and productivity for the R, Python and Julia developer
  • Support of low-level languages (C, C++, FORTRAN)
  • Server version for teams and HPC environments
  • Fully open source, including all enterprise features
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  • Shiny app deployment for companies and large organizations
  • Highly scalable design using Docker infrastructure
  • Authentication and authorization, single-sign on deployments
  • Usage statistics and administrator views
  • Fully open source, including all enterprise features
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R Service Bus

  • Middleware for automation of R-based jobs
  • Rich set of supported protocols (REST, SOAP, e-mail protocols, etc.)
  • Integrated management of multiple R pools for distributed computing
  • Synchronous and asynchronous APIs, admin API
  • Supports plain R scripts and packages out of the box
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  • Corporate management of R package repositories
  • RESTful APIs for package submission and repository generation
  • Authentication and authorization for actions on multiple repositories
  • Support of continuous integration infrastructure
  • Highly available repository set-up and full audit trails

Case studies

Learn more

From our blog

ShinyProxy 2.0.1 is out!

on August 7, 2018

ShinyProxy is a novel, open source platform to deploy Shiny apps for the enterprise or larger organizations. Embedding Shiny Apps Although Shiny apps are very popular for interactive data analysis purposes, many organizations communicated a need to more closely integrate these apps within larger applications and portals. In previous releases we broke down the walls to make this happen: hiding the navbar, single-sign on, theming the landing page and advanced networking support were only a few steps in that direction.

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Using R to Generate Live World Cup Notifications

on July 5, 2018

Here in Belgium, World Cup fever is at fever pitch, but with matches starting during work hours, how is a desk worker supposed to follow along? By leaving the R environment? Blasphemy. Today we show how to use R to generate live desktop notifications for The Beautiful Game. A notification system preview, free of local bias. Overview We break the process of producing a live score notification into the following steps:

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ShinyProxy 1.1.1 released!

on May 28, 2018

ShinyProxy is a novel, open source platform to deploy Shiny apps for the enterprise or larger organizations. Theming ShinyProxy 1.1.1 is in essence a maintenance release, but there is one new feature that has been on the wish list of our users for a long time: the possibility of theming the landing page of ShinyProxy which displays the overview of the Shiny apps. The standard display when using the ShinyProxy demo image from the Getting Started guide is a plain listing:

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ShinyProxy 1.1.0 released!

on March 25, 2018

ShinyProxy is a novel, open source platform to deploy Shiny apps for the enterprise or larger organizations. Scalability In our previous release (see this blog post) we announced our focus on scalability with support for Docker Swarm back-ends. With version 1.1.0 we moved to hyperscaling Shiny apps in the datacenter by adding support for Kubernetes. We have used it for customers that roll out internet-facing Shiny apps with high numbers of concurrent users and needs for automated deployment.

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