Adapticon offers state-of-the-art data analysis, processing, and forecasting technology and services to help customers optimize their forecasting and decision making processes, and improve insight into their data. Examples include demand forecasting and inventory management, traffic forecasting and optimization, and analysis of customer and traffic behavior. The underlying technology that is used by Adapticon to support customer solutions is Dynamic Data Mining.

Dynamic Data Mining (DDM) is Adapticon's proprietary data-driven approach for processing and predicting data, based on automatic learning of patterns from historical data. It has its origins in modern artificial intelligence and machine learning research.

DDM consists of multiple components which are used to take various steps in a complete analysis or forecasting project. They are integrated in the complete AdaptiCast analysis and forecasting package.

Preprocessing analytics The Preprocessing Analytics component is used to obtain "cleaned" data suitable for further processing, and to construct new input features based on the data.	Imputation The Imputation component is used where necessary to "fill in" missing data resulting from sparse, incomplete data sources or faulty measurements.
Feature selection The Feature Selection component can be used for any forecasting task to automatically identify a near-optimal set of input variables, or features, for that particular forecasting task.	Dynamic forecasting The actual automatic building or "training" of the models is done with the Dynamic Forecasting System, based on the available historical data which is cleaned and imputed, and for which the Feature Selection component has provided suitable input features.
Performance analysis The Forecast Performance Analysis component analyzes the forecasting system, revealing which information has been used, how it is has been used, and where the strengths and weaknesses of the system lie.