Implement AI with Our Help

We combine machine learning tools and algorithms to help companies develop AI-driven products and solutions. Our team has extensive knowledge and experience in designing, implementing and integrating artificial intelligence solutions within each client’s business environment.

We’ve learned that although the company structures may be totally different, both startups and enterprise clients face the same challenges planning new data initiatives. With experience in machine learning, natural language processing, computer vision and predictive modeling we are able to offer artificial intelligence consulting to clients of all sizes.

Our clients either don’t have their own data science teams, or their team is too small to cope with all the tasks in the environment of a fast-growing company. We can save you time on hiring top-notch specialists. Our team of world-class data scientists and machine learning engineers will bring know-how to your project from day one.


  • Data Science Teams: In cases when a company already has a data science team, we become a valuable asset. We bring profound expertise in certain areas of artificial intelligence, such as computer vision, natural language processing and predictive analytics.

  • Data Engineering Teams: Machine learning may not be the key expertise of your company. In this case, we are working with an existing engineering team, providing the API of a machine learning system that fully corresponds to your needs and requirements. This way we allow your team to focus on their primary tasks, rather than try to learn an entirely new discipline.

  • Business Units: Typically, our cooperation starts with developing a proof of concept for a business unit. Often, the stakeholders are non-technical people who have very “high level” goals. We work closely with them to break down these goals into logical steps, defining and prioritizing use cases and providing the best solution for each use case in order to achieve complex results.

How We Solve Challenges


We review your current capabilities and define future goals to make recommendations for tools, technology and architecture.


We test a small-scale system, proving the viability of the machine learning models for your problem.


We insert the machine learning system into production, while considering costs of implementation and maintenance during deployment.


We improve upon previously- built models to continuously raise the quality of insights and keep up with the changing environment.