Protect your assets more effectively

APE-AI – Anomaly Prediction Engine

With the help of science specialists, our team undertook the project titled: “Smart anomaly prediction system in software environments”. Professor Lukasz Apiecionek supervises the scientific team’s work. He has carried out much scientific research in the field of anomaly detection in the form of DDoS attacks on IT systems.


Fuzzy Neural Network with OFN

Ready to create any kind of Deep Neural Network structures

About Project

The project aims to build an innovative solution that is fast, reliable and requires minimal hardware resources. Using ordered fuzzy numbers, the AI engine can process the problem faster than classic Deep Neural Networks, while also providing greater accuracy.

Advantages of

The major advantages of the APE-AI solution are that it is faster, more efficient, and requires less hardware compared to classic Deep Neural Networks. Additionally, it has the ability to recognize patterns more quickly and accurately than traditional machine learning methods, resulting in more accurate and reliable analysis, so it provides security. The fuzzy network offers a more cost-effective approach since it requires less hardware and can be implemented more quickly than classic Deep Neural Networks.

More Efficient

Ordered Fuzzy Numbers make the engine more efficient


Faster network thanks to a 50% reduction in network dimensions


Reduced error value up to 7 times

Deep Neural Network

Ordered Fuzzy Numbers (OFN)

Novel Fuzzy learning algorithm

Collaboration with University



AI Monitoring. Monitor the working status of devices and software with the AI engine.


React and prevent failure earlier thanks to the prediction of anomalies.


Detection of undetected anomalies – adapt the engine to predict anomalies in the future.


With the prediction of anomalies and reaction to them, you don’t need to worry about your reputation.


The AI engine with OFN could be used on small computers, has low energy usage, and is ideal for small businesses and IoT solutions.


This AI engine is perfect for SIEM systems, as it can quickly identify any anomalies that may have gone unnoticed. With its reliable performance, customers can be sure that their software environment is secure and well-maintained.

 AI engine can help the SIEM systems, delivering more effective and efficient results for users.

How does it work?

Cases of use the AI engine

Enterprise Software Environments

eg. To predict anomallies in Java Tomcat based Enterprise Software

OT Security Solutions

eg. To monitor all devices and software in Hospital or Manufacture

Smart Cities

eg. Intelligent trafic jam control with anomally detectoin and prediction

Stock market

eg. detect and predict fraud in the stock market

Healthcare Solutions

eg. Medical Image anomaly recognition

Internet of Things

eg. Malicious activities of equipment like electric scooters, bikers, cars etc. 

Finacial Sector

eg. detect and predict fraud in the stock market


eg. Fraudulent activities and patterns in accounts and smart contracts behaviour

Experience and passion

Customer satisfaction, creativity, and teamwork are key principles that lie at the heart of everything we do. Our aim is to define, shape, and influence the technological landscape and to create solutions that can make a real difference in the world.

We start every project with a deep dive into our client’s business concept to ensure the development of a highly effective and reliable eCommerce solution. To us, success for the client is success for us.

Prof. Łukasz Apiecionek with the scientific team

Professor at the Kazimierz Wielki University, at the Institute of Computer Science. Over 75 scientific publications are mainly about security and artificial intelligence. Over 10 years of experience in using fuzzy logic for security purposes.Coauthor of the book about Ordered Fuzzy Numbers.
Scientific project manager in research and development project co-funded by Polish National Research and Development Centre. The team of Data Scientists from Kazimierz Wielki University is working together on R&D projects on fuzzy logic and OFN.

Rafał Moś with the team of engineers

CEO of the KAELMO owner of the B4SPOT brand and the leading Business Architect of the solutions created by the company. Holds Bachelor’s degree in Computer Science improved with postgraduate studies in Banking. More than 20 years of experience in Software development in the financial, manufacturing and Public sector. Project manager of the research and development project co-funded by the Polish National Research and Development Centre. The team create all kind of IT solution, mainly for: eCommerce, document management, cybersecurity and dedicated systems.

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