From a question to a meaningful comparison
We first establish which decision the analysis should support and which data are available. We then define a simple baseline and an appropriate evaluation. A more complex model has to justify its additional cost against that comparison. We examine data quality, missing values and generalisation to new cases alongside predictive performance.
Turning mathematics into working methods
Implementation may involve numerical methods, signal processing, optimisation, time-series models or learning algorithms. We connect the scientific assumptions to a verifiable implementation. Reproducible preprocessing and separate training and evaluation data help expose apparent gains caused by data leakage. For research projects, we can also investigate new methods in a bounded experiment.
Delivering results with their limitations
You receive the agreed software and an analysis that makes assumptions, comparison conditions and uncertainty explicit. If a method does not hold up under your conditions, that is a useful result too. Deployment in medical or other consequential decisions requires separate validation in the intended context.
What our work can deliver
- Reproducible analysis or research software
- Comparison against an appropriate baseline
- Documented assumptions and limits of use