What we build
Algorithms, data science, and software development — three disciplines that work together when the problem is hard enough to matter.
Algorithms
The hard problems in industrial software are algorithmic problems. Optimal routing, motion planning, state estimation, resource scheduling. They require careful modelling of the problem domain and tailored solutions built on solid mathematical foundations.
We have a deep bench of algorithmics expertise, and we work closely with your domain experts to make sure the model matches reality.
Data science
Industrial systems generate more data than any team can inspect manually. We build the pipelines, models, and dashboards that turn raw sensor streams and operational logs into actionable insight — and we make sure those insights reach the people who need them.
From anomaly detection to demand forecasting to predictive maintenance, we work across the full data science lifecycle: exploration, modelling, validation, and production deployment.
Software development
Algorithms and models only create value when they run reliably in production. We develop the embedded systems, cloud backends, and operator interfaces that complete the picture — built with the engineering discipline that industrial environments demand.
We work across the stack: C++ for real-time edge code, Python and Rust for data services, and modern web frontends for the humans who need to see what the machines are doing.
Pre-studies
Sometimes the right first step isn’t a full project — it’s finding out what the problem actually is, what already exists, and which pieces can be reused rather than reinvented. That’s a pre-study.
In three to eight weeks we turn a hunch into a clear problem definition, a survey of the relevant research and methods, and a working prototype — with a concrete plan for what comes next.
