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The revolutionary neural network of a new generation

Omega Server

A ready-to-use No-Code/Low-code next-generation neural network applicable for solving a wide range of AI/ML tasks. The Omega Server network has been developed using the Progressive Artificial Neural Network (PANN) technology, based on a fundamentally new neural network architecture that is closer to biological networks than all other artificial neural networks existing today.

  1. The computational engine includes: a library with a set of functions for working with a PANN type neural network, patented neural network architecture, and training algorithms.
  2. An application to upload your data to the Omega Server neural network and use it to solve your practical problems.

Unprecedented training speed

Allows one to perform neural network computations, training, and retraining of the neural network hundreds and thousands of times faster than any existing neurocomputers. With the increase in data volume, the need for computing power and training time grows insignificantly and linearly, in contrast to the exponential growth in such needs in classical neural networks. This advantage increases with increasing complexity and data volume.

Easy access at any professional level

To train the neural network with the declared speed, less sophisticated equipment is needed compared to classical neural networks. For example, instead of a desktop – a laptop, instead of a workstation – a desktop, instead of a server – a workstation, instead of a supercomputer – a server. It is compatible with various hardware: from microchips, video cards, and smartphones to supercomputers, boosting their computing capabilities by a multiple.

Drastic reduction of costs

With remarkable training speed and ease of maintenance, infrastructure, energy consumption, and maintenance costs are dramatically reduced.

Quality of AI/ML applications

Possibility of up-training, scaling the network during the training process, and continuous product improvement. Almost zero probability of ‘freezing’ as tasks get more complicated, and the network size increases.

Security and data protection

Computations can be performed at the client’s own facilities to guarantee the safety and confidentiality of data. Inherent high security and reliability.

Confirmed features


times fewer epochs are required to train the network.


times faster is the training speed on a 7,000 image data set.


Online up-training of an already trained network and ‘forgetting’ unnecessary data.


Increasing network size during computation.

IBM SPSS Statistics 22
Competing classical neural networks (Google, IBM, Miscrosoft)

Join the pioneers in the AI revolution