Vai al contenuto


All the functionalities you can rely on if you choose Galileo.XAI


Define how to model your domain.
Easily import data from an any external source. Listen to external changes and import them


Discover insights, share snapshots with your team and create patterns you want to check periodically. Let Galileo.XAl notifies you once a pattern is discovered


Combine different graph algorithms to create your own recipes. Understand graph structures and network dynamics to underline critical points and communities


Use Graph-Al algorithms to learn directly from graph structures how to uncover new insights and solve your most urgent business problems


Harness the power of the graph to give context to your outcomes and avoid the black box effect. Increase trust on Al algorithms though explainability


Galileo.XAI connects with your informational assets, offering you advanced import, integration, modeling, cleaning, and quality features for structured and unstructured data.

Galileo.XAI acquires data from various sources such as relational databases, CSV, JSON and XML files, APIs, logs, sensors, social media, etc., in order to create a comprehensive and up-to-date view of the available data.

Galileo.XAI facilitates efficient data transfer between different components of the system, ensuring data integrity and security.

Galileo.XAI has built-in connectors for popular relational data sources.

Galileo.XAI supports data extraction in XLS and PNG formats (for graph investigations).

Galileo.XAI allows importing and exporting data in different formats to facilitate data exchange with other applications or systems.

Galileo.XAI supports the integration of data from multiple sources, allowing analysis and correlation of heterogeneous data.

By adding role-based access control (RBAC), Galileo.XAI ensures that all users have appropriate levels of access to the collected data and information. It creates multiple perspectives on the same data to meet the specific needs of different business user groups and restrict access that individuals with limited rights can have.


Galileo.XAI enables you to uncover new insights within your data through specific features for pattern identification, graph investigation, search, and discovery.

The free search feature with autocomplete allows you to manage billions of nodes and find the information you need when you need it.

Filters are provided to narrow down the data based on specific criteria, allowing you to focus your analysis only on relevant data.

Analyze data in its geographical location on a map. Geospatial analysis provides intuitive insights that cannot be acquired without spatial context.

Galileo.XAI supports the discovery of paths or connections between different data entities, helping to identify hidden relationships and patterns.

Galileo.XAI can apply clustering algorithms to group similar data together, providing a clearer perspective on hidden structures in the data.

Share your results and discoveries with colleagues by taking a snapshot of your analysis that your team can always refer back to.

Most networks are dynamic and change over time. Galileo.XAI helps you analyze and visualize your data from a temporal perspective using a dynamic widget.

Ensure that the story told by your data is perfect by using a wide range of styling options such as icons, colors, sizes, heatmaps, and other visual elements.


Galileo.XAI enables the understanding of graph structures and network dynamics by highlighting nodes or clusters through the definition of indices, rules, alerts, and generating dashboards to keep everything monitored.

Galileo.XAI facilitates collaboration among users, allowing them to share and work on data in a synchronized manner.

Galileo.XAI supports the analysis of data with multiple variables, enabling the identification of complex relationships among the data.

Automates workflows and data pipelines by executing recurring tasks periodically.

The platform provides reporting and statistics functionality to present analysis results clearly and concisely.

Define custom queries based on business rules to monitor crucial patterns in your data in real-time.

Administrators have full control over database indexes and constraints.

Configure the lifecycle of alerts on your data, including how they are processed, escalated, triggered, and dismissed. Receive notifications when significant events are detected in your data.

Galileo.XAI offers a fully customizable data visualization interface to keep track of analysis results.


Galileo.XAI leverages Graph-Al algorithms to acquire knowledge directly from graph configurations, enabling the discovery of novel perspectives and resolution of pressing business dilemmas.

Galileo.XAI includes a library of predefined and configurable network science algorithms that can be executed in a no-code/low-code mode, allowing the application of advanced analytical models to the data.

Galileo.XAI supports training machine learning models on available data to create custom predictive models.

Galileo.XAI enables predictive analytics by applying statistical models and machine learning algorithms to make predictions or generate insights from data.

Galileo.XAI allows the extraction of subgraphs from the dataset to perform targeted and more effective analysis using graph data science algorithms.


Galileo.XAI utilizes the potential of graphs to provide contextual understanding of your outcomes, mitigating the opaque nature of algorithms. Enhance trust in AI algorithms by incorporating explainability into the process.

Galileo.XAI provides predefined or customizable performance metrics to evaluate and monitor the performance of data and models. These metrics help assess the accuracy and effectiveness of the analyses and predictions.

Galileo.XAI offers visualizations to enhance the transparency of classification models. Users can visualize decision boundaries, feature importance rankings, and other relevant information, making it easier to understand and communicate the model’s behavior.

Galileo.XAI incorporates various explainability techniques to shed light on the inner workings of classification models. This includes generating explanations for individual predictions, identifying important features and their impact on the classification outcome, and visualizing decision boundaries.