Compare Graphlytic vs. RelationalAI in 2025

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Description

Graphlytic is a web-based BI platform that allows knowledge graph visualization and analysis. Interactively explore the graph and look for patterns using the Cypher query language or query templates for non-technical users. Users can also use filters to find answers to any graph question. The graph visualization provides deep insights into industries such as scientific research and anti-fraud investigation. Even users with little knowledge of graph theory can quickly explore the data. Cytoscape.js allows graph rendering. It can render tens to thousands of nodes and hundreds upon thousands of relationships. The application is available in three formats: Desktop, Cloud, or Server. Graphlytic Desktop is a Neo4j Desktop app that can be installed in just a few mouse clicks. Cloud instances are great for small teams who don't want or need to worry about installing and need to be up and running quickly.

Description

RelationalAI represents a cutting-edge database system tailored for advanced data applications that leverage relational knowledge graphs. By focusing on data-centric application design, it effectively merges data with logic into modular models. These intelligent applications possess the capability to comprehend and utilize every relationship present within a model. The system employs a knowledge graph framework that allows for the articulation of knowledge in the form of executable models. These models offer the benefit of being easily expanded through declarative programs that are accessible and understandable to humans. With RelationalAI's versatile and expressive declarative language, developers can achieve a remarkable reduction in code size, ranging from 10 to 100 times less. This accelerates the development of applications and enhances their quality by involving non-technical users in the creation process while automating complex programming tasks. By leveraging the adaptable graph data model, users can build a robust data-centric architecture. Additionally, the integration of models paves the way for the exploration of new relationships, effectively dismantling barriers that exist between various applications. Ultimately, this innovative approach not only streamlines development but also fosters collaboration across different domains.