The rise of artificial intelligence in recent years is grounded in the success of deep learning. Three major drivers caused the breakthrough: the availability of huge amounts of training data, powerful computational infrastructure, and advances in academia. We help our clients with all three parts of successful AI implementation going from data preparation to infrastructure set-up to applying the right algorithms for the specific business case. The process is based on our experience. Contact us to learn more.

Baseline questions for success

The main roadblock for companies launching AI projects is not the lack of data or data scientists, but the lack of a well-defined data-driven business cases. The questions everybody needs to answer before starting the project are: What do we want to predict and how do we measure the quality of the prediction? How will a prediction improve the KPI?

Rapid Analytics
and Model Prototyping

Once we have found the business problem and the KPI that AI solution will improve, we gather and transform the raw data into digestible formats & instances, define the steps of the workflow putting it into production, and organize the model maintenance and regular updates.

The Two-step Pre-training on Cheap Data-set

The basic idea of pre-training is that we first train a neural network (or another machine learning algorithm) on a large and noisy dataset in a related or same domain. It gives the network a rough idea of what the prediction problem looks like. In the second step, the parameters are further optimized on a much smaller and expensive data-set of the actual problem that we are trying to solve.