Webinar: AI-Powered Graph Creation — Generate, Visualize, and Share Knowledge with HellixMap
Recorded on June 26, 2025.
This webinar marks the official debut of Bayesia’s newest product: HellixMap. Emerging from more than 25 years of Bayesia’s R&D in the field of Bayesian networks, HellixMap fully unlocks and expands the communication capabilities of the paradigm.
Webinar Recording
HellixMap Feature Overview
- Bayesian Network Graph Visualization and Sharing (directly from within BayesiaLab or by uploading an XBL file)
- Graph Generation with Hellixia
- Knowledge Graphs
- Causal Semantic Diagrams
- Semantic Flowcharts
- Causal Networks
- Risk-Centric Causal Networks
Background & Motivation for HellixMap
Hellixia: Harnessing Large Language Models
As the name implies, HellixMap is closely related to Hellixia, BayesiaLab’s subject-matter assistant powered by Large Language Models (LLMs). Hellixia’s core function is to extract knowledge from LLMs and either construct new Bayesian networks or enrich existing ones. At its heart, Hellixia is about knowledge ingestion and transformation.
HellixMap, by contrast, is all about knowledge communication. In fact, transparent communication has always been central to the Bayesian modeling framework. After all, the network graph is the model. In this respect, a Bayesian network stands in stark contrast to the black-box models that dominate much of modern AI.
The WebSimulator: A Gateway to Simulation
Before the advent of HellixMap, the BayesiaLab WebSimulator already offered a major leap in accessibility, enabling end-users to interact with Bayesian networks via a browser. It empowers users to set inputs and observe outcomes, without needing to engage with the full complexity of the BayesiaLab software environment. This made it especially useful for decision-makers and stakeholders who benefit from model-based insights without requiring technical expertise.
However, for those who wanted to explore the structure and mechanics of the underlying network, the WebSimulator had its limits. Full access to the model's structure still required a full installation of BayesiaLab.
HellixMap: A New Era of Network Exploration
HellixMap bridges this gap. It enables analysts to publish fully interactive Bayesian network graphs to the web, allowing any stakeholder to explore the model’s structure directly. Beyond mere access, HellixMap provides a comprehensive suite of visualization and layout tools to help users navigate and interpret complex networks.
Many Bayesian networks contain hundreds or even thousands of nodes and arcs. Effectively communicating such complexity demands more than just functionality, it requires intelligent design. That’s why HellixMap places a strong emphasis on visual clarity and aesthetic elegance, ensuring that the interface is both informative and visually engaging.
We invite you to preview HellixMap through two brief video clips. These will give you an early look at how the platform appears and performs from the end-user’s perspective. For a full introduction to HellixMap's capabilities, please join our webinar on June 26th.
Example 1: Causal Network for Renal Colic
Example 2: Visualizing Philsophy — Nietzsche's Thus Spoke Zarathustra
About the Presenters
Dr. Lionel Jouffe
Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks. After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.
Stefan Conrady
Stefan Conrady has over 20 years of experience in decision analysis, analytics, market research, and product strategy, having worked with Mercedes-Benz, BMW Group, Rolls-Royce Motor Cars, and Nissan across North America, Europe, and Asia. As Managing Partner of Bayesia USA and Bayesia Singapore, he is widely recognized as a thought leader in applying Bayesian networks to research, analytics, and decision-making. Together with his business partner, Dr. Lionel Jouffe, he co-authored Bayesian Networks & BayesiaLab — A Practical Introduction for Researchers, an influential resource now widely cited in academic literature. With their deep expertise in Bayesian networks for Key Driver Analysis and Optimization, Stefan and Lionel are highly sought-after consultants, advising global leaders such as Procter & Gamble, Coca-Cola, UnitedHealth Group, L’Oréal, the World Bank, and many of the world’s largest market research firms.