This initial stage involves a brief exploration of the topic with our data science team. We’ll explore the data, ask the big questions, and establish any goals for the project. We’ll also take the time to help you understand what opportunities exist and the pitfalls of machine learning implementation.


Once we’ve laid the framework, our data engineers will carefully examine the data sets you’ve provided to ensure they choose the right one. They’ll clean the data and engage in feature engineering to prepare a dataset for the future model. We combine classic Agile principles with the CRISP-DM model for data mining and analysis. A typical cycle focuses on one hypothesis to ensure the precision of tasks and results.


During this stage, the data science team will start to build and train models using prepared data to verify the hypothesis. The team will run several experiments to achieve a balance between accuracy and computer resource consumption. The goal of this stage is to get tangible results in the shortest period of time possible to prove the hypothesis.


After we’ve proved the hypothesis through raw modeling, our data engineers will continue to adjust and optimize the selected model. This stage will improve the overall accuracy and lower the amount of power and time it consumes.

What value do we provide at this stage?

We’ll analyze metric values and model performance to establish a better understanding of how we can make further improvements.


After verifying the model, we’ll deploy it on a test server where it can start to work with real data so we can monitor the results. If the model successfully achieves your business objectives in the test environment, we will deploy it in production.

What value do we provide at this stage?

You will receive a fully-validated model that you can use to create your software product, complete with AI-features.

What Makes Our Data Science Services Indispensable

  • Data preparation and refinement

    Our data scientists can detect the missing pieces and help you plug the holes in case the data exists in a scattered form and many of its components appear disjointed. As long as the pieces of information are not physically missing and are simply disjointed.

  • Domain expertise

    Data intelligence and processing are of no significance if they're not complemented with domain expertise because it's only with domain expertise that we can validate the efficacy of data analysis.

  • Exploratory approach

    Although we will use conventional statistical modelling and hypothetical assumptions, we also believe in the power of letting the patterns emerge on their own and then making sense of those patterns to find hidden nuggets of wisdom that are otherwise very hard to find.

  • Expertise across multiple technology stacks

    Our data scientists have vertical skills in a variety of technologies and tools including SAP PA, Google Machine Learning, Microsoft Azure Machine Learning, Amazon machine learning, Scala, hadoop, TensorFlow and much more.