Explore our Services

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Methodology consulting

  • Data analysis and statistics
  • Machine learning
  • Artificial intelligence
  • Experiment design
  • Method development
  • Data and text mining
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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
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Data Science Platforms

  • Data analysis orchestration
  • Machine learning platform design
  • Data science infrastructure setup and hosting
  • Data science APIs as a service
  • Managed services for scientific data stores

Discover our Products

integrated development environment

Architect

  • 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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ShinyProxy

  • 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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RDepot

  • 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

Affordable Shiny app hosting for 500 concurrent users

on August 19, 2025

Cloud providers are no longer just offering traditional x86-based servers, ARM-based servers are now becoming a serious alternative. And for good reason: they’re often far more cost-effective and power-efficient than their x86 counterparts. For instance, we found a provider offering a server with 16 virtual cores and 32 GB of RAM for just €30 per month. To be realistic, most providers charge between €100 and €200 per month for such a server.

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ShinyProxy 3.2.0

on July 8, 2025

We’re happy to share that ShinyProxy 3.2.0 is now available. This release focuses on a range of small but meaningful improvements aimed at making ShinyProxy easier to use. Debugging issues is now easier than ever, thanks to clearer error messages and improved error handling. The documentation has been extended with more background information (allowing you to better understand how ShinyProxy works) and additional troubleshooting information. During the last months, we’ve noticed that the adoption of ShinyProxy has been rapidly growing.

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Hash-tag baby

on July 3, 2025

Introduction Naming a baby can be a difficult task. It can take - soon to be - parents quite some time to align on a name they both like. Afterwards it can also be challenging to keep the name a secret until birth. If you are lucky, there might be some close friends or colleagues who are simultaneously expecting a child. It can be an unique occasion where you can share experiences.

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checkglobals: an(other) R-package for static code analysis

on March 25, 2025

Introduction An important aspect of writing an R-script or an R-package is ensuring reproducibility and maintainability of the developed code, not only for others, but also for our future selves. The modern R ecosystem provides various tools and packages to help organize and validate written R code. Some widely used packages include roxygen2 (for function documentation), renv (for dependency management and environment isolation), and testthat, tinytest and Runit for unit testing[1].

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Technologies

  • R
  • Python
  • Julia
  • Spark
  • Rust
  • Go
  • Java
  • Docker
  • Kubernetes