R engineering, Shiny applications, SAS migration and data workflows. We help your teams build systems with explicit business rules, reproducible environments and a clear handover.
We intervene where analytical work becomes critical infrastructure: when reliability, reproducibility, security and maintainability directly affect delivery.
Architecture, refactoring, packages, performance, testing and production readiness.
↗Shiny applications engineered for real users.
↗Controlled migration and non-regression validation.
↗Reliable workflows with controls, logs and clear ownership.
↗Package governance, collaborative development and secure publishing.
↗Choose an intervention around the decision you need to make. Scope, schedule, responsibilities and pricing are defined in the proposal.
An R codebase that has grown, a fragile Shiny prototype or a migration to prepare. Start with a focused review before committing to a larger build.
R engineering, Shiny, packages, SAS migration or automation: define a useful scope, then build and validate it against agreed reference results.
Targeted training, code reviews and project mentoring connect technical practice with the tasks your team needs to perform.
A focused review can lead to a delivery or training proposal. Each phase has its own agreed scope; examples are not a fixed-price commitment.
Our expertise is particularly relevant in environments where analytical outputs must be explainable, traceable and dependable.
Engineering standards, automation, deployment and team enablement.
Reproducible workflows, validated analytics and sustainable research software.
Traceable analytical systems and robust reporting pipelines.
Automation, data products and decision support for operational teams.
Platforms, applications and reusable R components. Explore the scope of our work.
Posit suite installation, Docker, CI/CD, testing, training and Shiny application development.
Explore the Servier reference ↗A reference focused on R package development. Explore the capability and discuss your own package project.
Explore the INSEE reference ↗Functional finalisation, GitLab delivery, executable packaging and a structured test battery to support acceptance. The mission focused on reliability, reproducibility and operational handover.
Architecture
02Validation
03Handover
We make technical decisions explicit, reduce risk early and leave behind systems the internal team can understand and operate.
Business objective, constraints, users and definition of success.
Architecture, interfaces, data flows, validation and operating model.
Engineering, tests, observability, documentation and deployment.
Handover, training and the ability to evolve the solution internally.
R, Shiny, Posit, SAS-to-R migration, automation and applied AI. We design workshops and programmes from your real context, tooling and team maturity.
Prepare a useful R, Shiny or Posit project brief: objectives, users, existing systems, constraints and acceptance criteria.
Read the insight ↗Ten practical uses of generative AI that can improve the productivity of Data teams without lowering engineering or governance standards.
Read the insight ↗How to turn a useful Shiny prototype into a production-grade application that is secure, maintainable, performant and supportable.
Read the insight ↗Share the context, constraints and expected outcome. We will respond with a concrete, proportionate next step.
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