First, we need to understand what problem you want to solve and see if it is a fit for machine learning.

We collect available data both structured and unstructured. If you don't have data, we'll collect it online if possible.

To make sense of data, it needs to be clean. We transform raw data and then it's ready for processing and analysis.

We discover patterns and relationships in data and extract relevant insights by using statistical methods

We apply machine learning algorithms to your dataset and run thousands of experiments to prove it works.

Finally, we deploy the model into production, iterate until it works properly, and develop features to access it.