We first perform hypothesis development to formulate the problem and identify learning objectives. We then prepare the data pipeline or a data lake as required by the application.
We use multiple AI techniques for Supervised Learning (Linear Regression, Neural Networks etc.); and Unsupervised Learning (Principal Components Analysis, Anomaly Detection etc.) to generate actionable insights from the data. We automate the process to refine the algorithm iteratively as and when data sources and quality changes.