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
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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

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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

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From our blog

tinytest2JUnit: report your tinytest results in the CI

on June 20, 2024

tinytest2JUnit: report your tinytest results in the CI Introduction “Lightweight is the right weight” is the motto of packages adhering to the tinyverse principle. One of the packages following this principle is the tinytest package, a zero-dependency unit testing framework. It is a nice alternative to the heavy-weight testthat testing framework, which (at the time of writing and including its recursive dependencies) has a dependency footprint of 30 packages!

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ShinyProxy 3.1.0

on May 7, 2024

ShinyProxy 3.1.0 Last year we released a major update of ShinyProxy, bringing High Availability, Scaling, App parameters, improved integration with Kubernetes and much more. Today’s release goes even further, bringing Pre-Initialization, Container sharing and even a new container backend. Along with these major features, the release is packed with numerous small improvements. ShinyProxy is a novel, open source platform to deploy Shiny apps for the enterprise or larger organizations. Nevertheless, ShinyProxy is also an excellent choice for deploying a handful of apps in smaller organizations.

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ShinyProxy 3.0.0

on March 2, 2023

ShinyProxy 3.0.0 Today we release version 3.0.0 of ShinyProxy, providing improvements on all aspects of using and deploying ShinyProxy. ShinyProxy is a novel, open source platform to deploy Shiny apps for the enterprise or larger organizations. Nevertheless, ShinyProxy is also an excellent choice for deploying a handful of apps in smaller organizations. While ShinyProxy is generally used to host Shiny applications, it has always been designed with the idea to host any application that can be packaged into a Docker container.

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Spreadsheets and robust backends: a love story?

on February 12, 2023

The source of every data science project is a dataset or even multiple. In general, scientists prefer to share data using a spreadsheet. This allows to quickly explore, enter and modify data. Software developers on the other hand, prefer to build around properly designed schemes and backends that ensure data integrity. Whoever wins this battle decides where the data is stored: in a type of spreadsheet file or in some kind of database.

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Technologies

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