It is important to clearly know your limitations. Executive managers should be well aware of the current state of the art as even small changes in project task can cost months if not quarters of additional development.

To avoid this fallacies we maximize transparency:

• Highlight the risky parts of developing component
  
• Clearly state metrics or formulate limitations of the project
  
• Use clear checkpoints of development
  
• Perform our own management of the team to prevent miscommunication

Our team of skilled professionals and experts is advantageous for AI component development. We are continually training in developing new ML solutions without spending time on usual tasks the IT division has to work on.

We encapsulated our knowledge and experience in a set of off the shelf solutions that can be configured to solve particular business tasks. Our components are completely open-ended, easily customizable and can be integrated into any infrastructure.

CI/CD is not enough for model zoo handling, to work with ML and data models in production one needs to employ automated model quality testing, anomaly detection and so on

What we use in our systems and projects:

• Model serving
• Relearning and classification cycle
• Automated quality testing

Building a great stable backend is crucial for both b2b and b2c applications, including low latency high load systems.

There is no use for even the greatest backend if the user cannot experience the result. Building frontends components full cycle from design to implementation and testing.

CI/CD, testing process, development, testing and production stand organization for stability of your service.

Modern open source and proprietary frameworks radically changed process of developing web apps and component. A lot of off the shelf solutions are present on the market that increase speed of development of components for both production usage and quick hyphotesys testing.


Modern techniques requires less time and less effort for getting things done.

AI components are useless without wrapping and integrating to the rest of the system and without (eventually) delivering to the system. We know exactly how the AI component should be integrated with the rest of the system, what metrics it should produce, and what resources should be taken into account.