Graphwise GraphDB 11 taps into enterprise knowledge to deliver reliable AI

Graphwise, a leading Graph AI provider, is releasing GraphDB 11, a database engine that enhances enterprise knowledge management and allows organizations to create a foundation for reliable AI.

Version 11 introduces updates that make the platform better suited to serve multiple applications and use cases, reduce infrastructure costs, and simplify operations, according to Graphwise.

The latest release makes it easier to integrate with multiple Large Language Models (LLM) and enable AI applications to deliver more accurate and contextually relevant results.

GraphQL access streamlines the integration of this knowledge for developers, even those without a deep background in graph technology.

With MCP protocol support, V11 offers swift integration of data in agentic AI ecosystems and enables AI platforms such as Microsoft Copilot Studio to tap directly into their enterprise knowledge.

"Enterprise organizations continue to struggle with AI project abandonment due to a lack of AI-ready data, a significant challenge reflected in Gartner's prediction that through 2026, 60% of AI projects will face this very fate," said Atanas Kiryakov, president of Graphwise. “GraphDB 11 directly addresses this by delivering the data infrastructure and governance that is essential for cutting-edge AI, including generative AI. We empower customers to build intelligent, scalable applications by making their complex, unstructured data accessible and actionable through precise domain knowledge and robust reasoning."

GraphDB 11 introduces new features designed to bridge the gap between LLMs and structured knowledge so enterprises can build more intelligent and context-aware AI applications, including:

• Broad LLM compatibility and GraphRAG: The new features expand support for a wide range of large language models, including Qwen, Llama, Gemini, DeepSeek, and Mistral—plus the ability to deploy local or custom models. The improved Talk to Your Graph feature empowers GraphRAG (Retrieval-Augmented Generation), enabling natural language access to enterprise knowledge graphs helps businesses reduce hallucinations, improve accuracy, and drive more reliable AI-driven decisions.
• MCP support for enterprise agentic AI integration: This grounds AI in domain data, turning it from a generic tool into a strategic asset. By leveraging GraphDB’s structured knowledge and GraphRAG capabilities, organizations benefit from AI that delivers accurate, context-aware insights—reducing risk, improving decision quality, and driving measurable efficiency across workflows.
• Precision entity linking for reliable insights: By connecting language to meaning. Its advanced entity linking accurately maps terms and phrases to the right concepts or entities in the knowledge graph—eliminating ambiguity and improving how information is retrieved and applied. This enhances GraphDB’s Graph RAG capabilities, ensuring outputs are not just fast, but precise, relevant, and grounded in an organization’s data.
• Native GraphQL support: Enhancements aimed at making life easier for developers to easily use GraphQL to query their rich graph data, making data access straightforward and speeding up the creation of AI-powered applications in a secure, scalable, and reliable environment.
• Performance at scale: Improvements boost database performance including high availability, strong security, and flexible multi-tenancy to simplify common operational tasks and development efforts.
• Optimized performance for AI-driven knowledge hubs: The advanced repository caching dramatically speeds up operations to ensure the scalability and responsiveness users demand from knowledge hubs that support multiple use cases and projects coming from one knowledge hub.

For more information about this news, visit https://graphwise.ai.