Graphwise Unveils GraphDB 11: Transforming Enterprise Data for AI Applications
Graphwise, a prominent player in the Graph AI sector, has officially launched GraphDB 11, a cutting-edge database engine designed to empower organizations with the capabilities necessary for effective knowledge management in the realm of AI applications. This release not only cements GraphDB's status as the most versatile graph database currently available but also introduces enhancements that promise to reduce infrastructure costs and streamline operational processes significantly.
The improvements seen in GraphDB 11 include easier integration with various Large Language Models (LLMs), enhancing AI applications' ability to provide precise and contextually relevant results. With the integration of GraphQL access, even developers lacking extensive experience in graph technologies will find it straightforward to leverage knowledge effectively across platforms.
A notable addition with this version is the support for the MCP protocol, facilitating the seamless incorporation of data in AI environments. This will empower tools such as Microsoft Copilot Studio to interact directly with enterprise knowledge bases. The necessity for such advancements has become clear, as many enterprises face challenges with AI projects failing due to unprepared data. As highlighted by Atanas Kiryakov, President of Graphwise, Gartner notes that a staggering 60% of AI initiatives will encounter abandonment by 2026. In this light, GraphDB 11 addresses the pressing need for a robust data infrastructure that fosters both generative and progressive AI initiatives across a variety of applications.
Key Features of GraphDB 11 include:
1. Broad LLM Compatibility: This version greatly expands support for numerous large language models, including Qwen, Llama, Gemini, DeepSeek, and Mistral, alongside the capability to deploy custom models locally. The enhanced Talk to Your Graph feature, part of the new GraphRAG (Retrieval-Augmented Generation) functionalities, allows natural language queries to access enterprise knowledge graphs effectively, leading to reduced inaccuracies—commonly referred to as hallucinations—and fostering more reliable AI-driven insights.
2. MCP Support for Enterprise Agentic AI Integration: GraphDB 11 redefines AI from a mere tool to a strategic asset by grounding its functionality in domain-specific data. By utilizing GraphDB’s structured knowledge and the GraphRAG features, organizations can harness AI to provide accurate, context-driven insights, ultimately improving decision-making quality and operational efficiency.
3. Precision Entity Linking: The new version enhances clarity through its advanced entity linking capabilities, ensuring that terminologies are accurately connected to their respective concepts within knowledge graphs. This improvement means that information retrieval is not only quicker but also yields results that are more relevant and reliable.
GraphDB 11 also seeks to enhance core platform functionalities, positioning organizations to efficiently build and scale intelligent applications that utilize graph data across diverse use cases. This includes:
Graphwise aims to ensure that organizations unlock their AI investment return by providing them with the most reliable and comprehensive industry solutions within the knowledge graph and semantic AI arena. As the demand for effective data management heightens, Graphwise’s strategic direction—following the merger of Ontotext and Semantic Web Company—is set to create new industry standards, bolstered by their growing global workforce and geographical presence.
To explore the innovative features and capabilities of GraphDB 11, interested parties can learn more directly from Graphwise.
The improvements seen in GraphDB 11 include easier integration with various Large Language Models (LLMs), enhancing AI applications' ability to provide precise and contextually relevant results. With the integration of GraphQL access, even developers lacking extensive experience in graph technologies will find it straightforward to leverage knowledge effectively across platforms.
A notable addition with this version is the support for the MCP protocol, facilitating the seamless incorporation of data in AI environments. This will empower tools such as Microsoft Copilot Studio to interact directly with enterprise knowledge bases. The necessity for such advancements has become clear, as many enterprises face challenges with AI projects failing due to unprepared data. As highlighted by Atanas Kiryakov, President of Graphwise, Gartner notes that a staggering 60% of AI initiatives will encounter abandonment by 2026. In this light, GraphDB 11 addresses the pressing need for a robust data infrastructure that fosters both generative and progressive AI initiatives across a variety of applications.
Key Features of GraphDB 11 include:
1. Broad LLM Compatibility: This version greatly expands support for numerous large language models, including Qwen, Llama, Gemini, DeepSeek, and Mistral, alongside the capability to deploy custom models locally. The enhanced Talk to Your Graph feature, part of the new GraphRAG (Retrieval-Augmented Generation) functionalities, allows natural language queries to access enterprise knowledge graphs effectively, leading to reduced inaccuracies—commonly referred to as hallucinations—and fostering more reliable AI-driven insights.
2. MCP Support for Enterprise Agentic AI Integration: GraphDB 11 redefines AI from a mere tool to a strategic asset by grounding its functionality in domain-specific data. By utilizing GraphDB’s structured knowledge and the GraphRAG features, organizations can harness AI to provide accurate, context-driven insights, ultimately improving decision-making quality and operational efficiency.
3. Precision Entity Linking: The new version enhances clarity through its advanced entity linking capabilities, ensuring that terminologies are accurately connected to their respective concepts within knowledge graphs. This improvement means that information retrieval is not only quicker but also yields results that are more relevant and reliable.
GraphDB 11 also seeks to enhance core platform functionalities, positioning organizations to efficiently build and scale intelligent applications that utilize graph data across diverse use cases. This includes:
- - Native GraphQL Support Enhancements: Allows developers to easily query their substantial graph data, thus expediting AI application development in secure and scalable environments.
- - Performance at Scale: Systematic improvements ensure that performance remains optimal, with high availability, robust security measures, and flexible multi-tenancy designed to simplify typical operational and developmental efforts.
- - Optimized Performance for AI-Driven Knowledge Hubs: Advanced caching mechanisms boost operation speeds, guaranteeing that knowledge hubs can perform responsively to meet the demands of multiple projects derived from a single knowledge repository.
Graphwise aims to ensure that organizations unlock their AI investment return by providing them with the most reliable and comprehensive industry solutions within the knowledge graph and semantic AI arena. As the demand for effective data management heightens, Graphwise’s strategic direction—following the merger of Ontotext and Semantic Web Company—is set to create new industry standards, bolstered by their growing global workforce and geographical presence.
To explore the innovative features and capabilities of GraphDB 11, interested parties can learn more directly from Graphwise.
Topics Business Technology）
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