Data & ML
Articles

Every AI initiative stands on a data foundation, and most of the expensive failures trace back to it. This category collects our writing on data engineering and classical machine learning: predictive analytics and forecasting, data models and why they decide what a company can build next, natural language processing, and the quiet work of making data consistent enough for a model to learn from.

Generative AI gets the attention, but the measurable money in most businesses still comes from the predictive stack: demand forecasts, churn models, scoring, anomaly detection. The articles here explain what those systems need in data volume and quality, which model families fit which problems, and how to keep a model accurate after the first quarter in production. For building the foundation itself, see the service page below.