Our Technology

Having worked with products that serve billions of users and collect more than one billion requests per day, we have rich experience in large-scale ML pipelines and the ability to design and build production-grade ML infrastructure and AI algorithmic solutions employing state-of-the-art machine learning.

Machine Learning Infrastructure


As (Sculley et al., 2015) mentions, in real-world Machine Learning (ML) systems only a small fraction of the system is related to the ML model. There is a vast array of surrounding infrastructure and processes to support it, taking months for a large team of expert engineers (Dev Ops, ML and software engineers) to design and develop this surrounding infrastructure, compared to few weeks of a small team of data scientists to develop the ML algorithm.

Automating the end-to-end lifecycle of your Machine Learning applications.

Building on top of the state-of-the-art open source tools and frameworks we provide you with a ML infrastructure for fast, easy experimentation, model tracking, orchestration and compute resource allocation, CI/CD workflows, model deployment and performance monitoring.

Deep Learning

Deep learning (DL) is an area in machine learning which has been recently reinvented. It has already led to computational breakthroughs in fields, such as object recognition in computer vision and language modeling in NLP. With DL we can model high level data abstractions by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations. Nowadays, it is considered to be a cutting edge technology and is employed in a variety of applications, while producing state-of-the-art results, and either outperforming traditional, “shallow” approaches, or yielding similar results without any feature-engineering “hand-crafting” effort.

Solving the world’s toughest problems using the state-of-the-art deep learning technology.

In our team we have rich experience and expertise in deep learning. We develop solutions in classification, regression, detection, recognition and forecasting, while applying architectures such as deep neural networks (DNNs), convolutional deep neural networks (CNNs), deep belief networks (DBNs), recurrent neural networks (RNNs), in real-world industrial problems such as hyper personalization and recommendation systems, demand forecasting, fraud detection, stock forecasting, credit risk management to name but a few.