Latest versions of the leading database engine power enterprise AI with knowledge-grounded data, LLM integration, and scalable graph performance
Graphwise is pleased to announce the availability of versions 11 and 11.1 of GraphDB — the leading database engine that enhances enterprise knowledge management and allows organizations to create a foundation for reliable AI.
AI-ready knowledge access
“Enterprises struggle with AI project failures 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, the most versatile graph database on the market, is accelerating enterprises worldwide to embrace semantic layer that unifies meaning across fragmented data, metadata, and knowledge silos. The latest enhancements augments GraphDB’s knowledge graph technology with GraphRAG — a graph based retrieval-augmented generation approach – that allows organizations to build agentic AI applications leveraging LLMs provider to synergize and reason with enriched connected enterprise knowledge.
What’s new in these latest versions
Versions 11 and 11.1 introduce powerful 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 & GraphRAG (Retrieval-Augmented Generation): The new features expand support for a wide range of LLMs, 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, 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.
The new versions also deliver core platform capabilities that make it easier and more cost-effective for organizations to build and scale intelligent applications that fully leverage graph data across multiple use cases. These include:
- Native GraphQL support: Enhancements help developers 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.
“GraphDB Versions 11 and 11.1 directly address 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,” adds Atanas Kiryakov
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