Statistical rigour and ML-powered analysis that drives real decisions.
Data science bridges the gap between raw data and business insight. Our data scientists apply statistical modelling, machine learning, and exploratory analysis to answer your hardest questions — from customer churn prediction to demand forecasting and pricing optimisation.
Discuss Your ProjectModels that forecast outcomes before they happen — churn, demand, fraud, and more.
Statistical rigour that tells you what caused a change, not just what changed.
Findings translated into clear business language with actionable recommendations.
Translate business question into a measurable analytical problem.
EDA, feature engineering, and hypothesis generation from the data.
Algorithm selection, training, validation, and interpretability review.
Results presented as actionable recommendations and deployed models.
Analytics & Insights
Answer your most critical business questions with data you can act on.
Architectural BIM, scan-to-BIM, 3D visualisation, and automation — all under one roof.
Common questions about our Data Science service.
Analytics focuses on understanding what happened and why (descriptive and diagnostic). Data science builds predictive and prescriptive models that forecast what will happen and recommend actions. Both are valuable and often run in parallel.
It depends on the problem. Logistic regression for churn prediction can work with a few thousand records. Deep learning for image classification needs tens of thousands. We assess your data during discovery and tell you what is achievable before we start building.
Every engagement includes a business-facing findings document that translates statistical results into plain English with clear recommendations and quantified business impact. We never present a model performance metric as the headline.
Yes — churn prediction is one of our most common engagements. We identify at-risk customers 30–90 days before churn using behavioural features, train a gradient boosting model, and deliver actionable segment outputs directly to your CRM or BI tool.
We separate model accuracy from business value. A model can score 90% accuracy but still be useless in practice. We design evaluation frameworks tied to real business outcomes — revenue retained, conversions generated — and measure those alongside technical metrics.
Both. Initial model development is typically project-based with a fixed scope. Ongoing monitoring, retraining, and new analysis work is well-suited to a monthly retainer where we act as an extension of your analytics team.
Python is our primary language — pandas, scikit-learn, XGBoost, and Plotly for most projects. R for statistical work where specialist packages are relevant. MLflow for experiment tracking, and Jupyter for exploratory analysis with rigorous documentation.
Our team will scope your requirements and come back with a clear proposal within 48 hours.