What is the bigRing?

An innovative IT company specializing in AI-powered digital transformation, providing knowledge engineering and causes-effects relationships discovery software for decision making, cognition automation.


A unique framework, the consistent order of the general reality model of reduces the complexity of problems and enables a leap in optimization. And everyone can learn it, easily.


A cloud-based software system that integrates and understands data with advanced AI technology to support decision-making and accelerate research and development processes.

It’s about causal diagrams as a code-free knowledge transfer medium for creating an interdisciplinary approach to design and problem solving by depicting unique causes-effects mechanisms and a unified wisdom graph for understanding and sophisticated reasoning.

And advanced algorithms, including NLP extractions, GPT, RL, ML, and Graph, providing better control and management of AI operations and knowledge reproduction.


When the IQ of the collective consciousness reaches the population average, the group as a whole begins to benefit from the combined intellectual abilities of its members. When used correctly, it lead to better decision-making, innovation and efficiency. Maintaining a stable analytical continuum in this sense would mean something like self-organization of the social genetic code.
Karol MozsiExecutive, architect and owner of bigRing

As a finance-oriented manager, I prioritize investing in innovation, particularly in the areas of biomedicine and education. bigRing, our latest venture, acts as the third pillar, revolutionizing bioinformatics and cognitive development through advanced intelligence, thus paving the way for evolutionary advancements.

Roman MišúthExecutive, eduinformatics visionary, finance and owner of bigRing

I solved a lot of complicated tasks with an unconventional approach and innovations, which also proved true for me in bigRing, when we started designing bigBrains, they thought we were from another planet, but as you can see, tomorrow’s are finally today. I am happy that we bring intelligence and reason to our clients so that they can better face the challenges of the times.

Ivan NúdzikExecutive, architect and owner of bigRing

As an entrepreneurial physician, I believe in the potential of bigRing to bridge the gap between the analytical continuum and the concept of homeostasis in medicine. This holistic approach holds the promise of transforming healthcare and potentially bringing more smiles than tears to clinics, a challenging but noble journey.

Ivan JurášBioinformatics visionary, doctor and owner of bigRing

Bringing the right people together so that they could realize long-term visions was both a challenge and an adventure, but the courage and discipline of conscientious people and special partnerships bring fruit and seeds of new opportunities, and it is time for the next phase, when the caterpillar becomes a butterfly.

Božena KrausováNetworking, marketing and owner of bigRing

It is remarkable to observe the acceleration of the development of new technologies, when a person has experienced the evolution from hole-punch computers to AI, he knows better how to feel the impact of the ongoing revolution, amorphous teams are being built from large companies and the power to know is transferred directly to experts and scientists, which indicates a paradoxically positive outlook to the future as a threat from AI.

Milan HánInformatics visionary, evolutionist and owner of bigRing

I am a curious scientist and analyst, which is why searching for connections, uncovering causes and investigating their consequences is a fundamental instrument for me, and bigRing has this at its core – causality, which is the only truth for a scientist, in combination with generic algorithms, bigRing is a forest of possibilities for biomedicine as well.

Vladimír HegerHead of Biomed R&D

It is right to have respect for artificial intelligence, it is like with fire, it is a good servant but a dangerous master, this awareness brings respect and responsibility to my work, and I think that the way we transfer control over AI into the hands of experts, the only ones capable verifying its results and intervening is in accordance with this responsibility.

Yevhenii KnizhnytskyiHead of bigBrain R&D

It is a huge challenge for me to develop ergonomics for workflows that are very dynamic and for each user unique processes, specifically cognitive, software and AI, their harmonization creates an extensive but practical interface and the edge between man and machine gradually disappears, and the biggest challenge for me is the extension to virtual reality for causal diagrams.

Mykyta ShmatkoHead of bigRing R&D
Join usAre you an investor, developer or expert and are you looking for a challenge?


Ing. Peter Szalay, a biochemistry and clinical laboratory diagnostics expert, is crucial to bigRing’s platform, especially in metabolism and lifestyle disease research. As the creator of the “Metabolic Tuning” concept, which aligns with bigRing’s vision, he advocates for integrating various biomedical data for comprehensive health insights. His expertise in vitamins and trace elements enhances bigRing’s personalized medicine efforts, driving advancements in metabolic health research. Szalay’s role in the “Software Solution for the Metabolic Intervention Concept” project showcases his practical application of knowledge and his effective collaboration with bigRing, underlining bigRing’s focus on leveraging expert insights for complex metabolic process research and development.

Peter SzalayMetagraph Architect and Metabolic Health Expert

Based on his expertise in physical-chemical analysis and biocompatible materials, Mgr. Samuel Furka, Ph.D., plays a crucial role in bigRing’s biomedical research and development. His work in bioprinting and body fluid analysis aligns with bigRing’s focus on innovative health solutions. Furka’s international collaborations and advanced research projects significantly contribute to bigRing’s mission of integrating diverse biomedical data for groundbreaking discoveries.

Samuel FurkaCollaborative Investigator in Physical-Chemical Bioanalysis for bigRing
Join usWe are actively seeking experts across various disciplines to join the bigRing biomedical community, led by our Head of Biomed Community, Vladimír Heger. Our collaborative projects are geared towards high-level expertise, offering unique opportunities to work alongside diverse clients on advanced biomedical projects.


The bigRing strategy represents a progressive, problem-solving approach to intricate challenges, seeking to address interconnected, multifaceted variables tied to a unique mutation scenario.

The strategy integrates modern methodologies and technologies, pushing beyond current standards.

The bigRing strategy intends to progress research by shifting from the fragmented analysis of multifactorial data to the causal-based proprietary approach.

It focuses on the analysis of multifactorial data, aiming to enhance research related to the unique mutation scenario. This is achieved by developing structured graph datasets, creating graph algorithms, inferring causal relationships, and recommending more efficient problem-solving. Various tools and methods, including Meta Graphs, Knowledge Graphs, graph databases, and graph computing, are employed to realize these objectives. Also integral to the approach are causal reasoning, root cause analysis, visualizing causality, community detection, and assessment of similarities in causal graphs.

Beyond the state of the art

Meta Graph and Knowledge Graph

The strategy’s use of Meta Graphs and Knowledge Graphs propels data integration forward, structuring and coordinating multifactorial data using semantic relationships. A forward-thinking approach, it improves data organization and retrieval.

Counterfactual Reasoning

Counterfactual reasoning is a relatively novel method that surpasses traditional causal analysis. It allows for exploratory hypothetical scenarios, offering deeper insights into potential effects of altering specific variables.

Community Detection and Centrality

Identifying communities and assessing centrality in causal graphs goes beyond conventional causal analysis. This can help discover hidden patterns and key players in the intricate web constituting the unique mutation scenario.

Similarities in Causal Graphs

Analyzing similarities in causal graphs enables comprehensive understanding of recurring causal factors in different cases of the issue. This can lead to more generalized solutions and interventions.

Meta Graph

A Meta Graph represents relationships and connections between disparate graphs or data sources. bigRing uses this to integrate and organize diverse multifactorial datasets related to the problem domain, facilitating a bird’s eye view of the data landscape, and easier coordination and analysis across multiple domains.

Knowledge Graph

A Knowledge Graph is a structured representation of knowledge and information, using semantic relationships. In bigRing, it is used to structure and coordinate multifactorial data semantically, allowing integration of domain-specific ontologies and meaningful linkage between different data elements.

Directed cyclic graphs

Directed cyclic graphs allow for cycles or loops in their structure. Within causal analysis, they can model complex dependencies and feedback loops between variables, vital for understanding intricate systems’ causality.

Directed Acyclic Graph

Directed Acyclic Graph (DAG) is a graph without cycles or loops. Frequently used in causal inference to represent causal relationships between variables, it is a strong tool for identifying causality in data.

Graph computing

Graph computing performs computations on graph data structures. In this context, it is used to develop and apply algorithms for analyzing and extracting insights from structured graph datasets, especially in identifying causal relationships and hypothesis generation.

Graph databases

A graph database system is designed to store and manage graph data efficiently. In this context, it acts as a mechanism for storing and retrieving the graph dataset, ensuring consistency and easy access.


Metamodeling creates models that describe other models, enabling higher-level abstractions and understanding. Here, metamodeling captures the relationships between different multifactorial data models, offering a higher-level representation for analysis.


Data extraction and transformation are processes for collecting and preparing data for analysis. These methods are key for extracting relevant multifactorial data and transforming it into a structured format suitable for graph-based causal analysis.

Our strategy represents an innovative and holistic approach to problem-solving, integrating cutting-edge techniques in data integration, causal analysis, and advanced graph-based methodologies.

It pushes the boundaries by introducing new concepts such as counterfactual reasoning and large-scale analysis of causal graphs that significantly impact diagnosis and interventions and herald a new era of precision in decision-making.

The relationship between cause and effect. It identifies the principles, elements, or events that directly influence the observed changes, impacts, status or outcomes in various phenomena.

In the context of graph-based analysis and bigRing, causal analysis is a significant component for decoding complex systems.

Directed cyclic and acyclic graphs, particularly, play a major role in portraying these causal relationships, capturing intricate dependencies and feedback loops involving multiple variables. These graph properties allow a more profound understanding of causality within different systems. Directed Acyclic Graphs (DAGs), for example, are extensively employed in causal inference due to their ability to outline clear causal associations among variables, providing a powerful tool for identifying causality within datasets.

On the other hand, directed cyclic graphs offer a platform to represent systems with cyclic events or variables, thus capturing complex dependencies and feedback loops that may exist. Meanwhile, graph computing aids in enhancing the application and understanding of these causal relationships. By performing computations on graph data structures, it becomes feasible to develop and apply algorithms that facilitate analysis of these structures and extraction of key insights and patterns, particularly in identifying causal relationships and prompting hypothesis generation.

Hence, the function and value of causality in bigRing not only lies in delineating the cause-effect dynamics but also in shaping the root of hypothesis generation and advancing knowledge discovery in multifactorial datasets.


Our Experience

At bigRing, we’re not just an IT company. Our extensive expertise spans AI-powered digital transformation services, knowledge engineering, and causation effect diagramming software, designed to make decision-making more analytical. We believe in developing cloud-based systems that bring together data in a whole new way, utilizing AI technology that propels forward research and development processes.

Our unique framework consistently orders the general reality model simplifying the complexities of problems, which further enables leaps in optimization.

Our products and services use causal diagrams designed to improve the interdisciplinary approach to problem-solving, underpinned by advanced algorithms that ensure control and management in AI operations.

When all these elements come together, we call it a collective consciousness. And when its intelligence level reaches or surpasses the population average, it leads to better decisions and efficiency.

Our Vision

In an era where self-teaching is becoming increasingly valued, our goal is to stabilise the analytical continuum. To achieve this, we plan to integrate it into the loop of self-learning and aim to automate this process. As a result, the idea of bigRing is to provide an instrument that not only uncovers causes but investigates their consequences, with causality being our core principle. We aim to not only facilitate better decision-making but also to inspire innovation. This is where you come in – we’re looking for those who feel a connection to this challenge and mission, who are passionate about innovation involving the analytical continuum, and who can see themselves being a part of our journey. As a part of our team, you will contribute to the revolution in bioinformatics and cognitive development and make evolutionary advancements a reality.

At the end of the day, we’re all about making AI a trusted tool, an integral part of cognition, and a gateway to unknown knowledge. Join us in our mission to change the way we understand and interact with technology.









Challenge: Uncovering unknown knowledge

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bigRing Platform, s.r.o.

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Bratislava – mestská časť Petržalka 851 01