forecasting with sparse or inconsistent data, automating decisions under regulatory constraints, quantifying downside risk, and prioritising interventions that affect outcomes. Orelian focuses on making models operationally useful beyond proofs of concept, including validation under real conditions and structured handover. The firm has delivered projects such as automated extraction of structured data from coroners’ reports, machine learning enhancements to charity classification systems, and clinical decision support tools for antidepressant switching. Its work is aimed at organisations for whom high-stakes decisions require transparent, defensible analysis rather than intuition alone.