Our sophisticated content management system enables you to do virtually everything with video content. It is built with Django – a high-level Python Web framework for rapid development and clean and ergonomic design (for more details on CMS, see the "Main system modules" section). The Python interpreted high-level programming language is very flexible and saves significant developer resource.

The entire platform services infrastructure is built upon Apache Kafka and complies with the Representational State Transfer (REST) architecture. Apache Kafka is a simple and effective message broker that is based on the log storage mechanism, and it enables you to roll back the system and perform all logged operations if something goes wrong.

For video encoding we use the FFMPEG-based proprietary solution. You do not need to manually choose the video encoding configuration. The system automatically makes a decision about the quality and format of the output video.

There are dozens microservices that make up the system core,
and each microservice performs its business function. Any microservice update does not affect other business functions – this allows you to adapt the platform and its business logic for your specific needs.

The other advantage of microservice architecture is out-of-the-box scalability. The hardware can be combined with AWS/Azure/GCP services and automatically synchronized, so all you need for scaling control is just to properly set the system configuration.

For business intelligence (BI) purposes we use the Microsoft SQL Server. The toolset is built around our BI, and it gives you full control to collect analytics and visualize management reports. It's pretty simple to integrate Microsoft SQL Server's reporting with many other representation engines, including Microsoft Excel. The other benefits of Microsoft SQL Server are low cost support and full compatibility with industry standard BI solutions.

Our distributed data storage solution is built around Ceph, and it enables fully automated storage scaling at no additional cost. It also allows you to balance content load and manage replication processes.

The system is integrated with different kinds of monitoring systems: application performance monitoring (APM) for infrastructure performance tracing, infrastructure fault monitoring, identifying and allocating errors, and others.

We use Apache Cassandra as a scalable and highly available and performance-focused storage solution. Apache Cassandra can help you scale from 10K to 10M users with no downtime. For jobs that require consistency, a classic SQL (PostgreSQL) approach is used.

Since it is critical to ensure that video data is entered into the system in time to respond users' requests, we use Ceph distributed storage for video storage. Ceph adds the required number of physical storage systems when needed, and it stores the necessary information in the required number of instances automatically – that's how we save you effort to keep your system healthy.

The problem of most recommendation systems is properly training the model. That is why our recommendation system is based on a DLNN (Dynamic Learning Neural Network) and deep machine learning, which are integrated into the core and are already trained on the data we carefully collect from many sources. Our platform can both integrate with external recommendation systems and use its own solution.

The system allows you to create lists of recommended movies and TV-shows, and it is able to dynamically generate home pages or showcases to display user preferences.

The A/B testing system is a mechanism that allows you to improve the final product, i.e., the client application, by simply testing how a user responds to option A and option B. Our solution can integrate with an external A/B testing system (e.g., Optimizely) or use internal testing mechanisms. A/B testing improves the user's experience and allows you to grow your audience.