Franz Inc. expands graph, vector, and Neuro-Symbolic capabilities for enterprise-scale AI systems
Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Graph Database technology for Neuro-Symbolic AI Solutions, today announced AllegroGraph v8.5, with an Enhanced AI-powered Natural Language Query interface. The new release helps enterprises build Agentic AI solutions by enabling more intuitive, human-like interaction between users and intelligent systems—critical for agents that need to reason, plan, and act autonomously.
AllegroGraph v8.5 combines knowledge graphs, vector embeddings, and neuro-symbolic reasoning to provide the semantic layer needed for AI agents to interpret data meaningfully and deliver more accurate, explainable results.
“With AllegroGraph 8.5, we’re making it easier for enterprises to build AI agents that can understand intent, reason over complex data, and deliver more explainable results,” said Dr. Jans Aasman, CEO of Franz Inc. “By combining natural language access with Neuro-Symbolic AI and knowledge graphs, AllegroGraph provides a stronger semantic foundation for trusted enterprise AI.”
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New Capabilities in AllegroGraph v8.5 include:
• Optimized Natural Language Query (NLQ): Faster, more token-efficient translation of natural language questions into graph queries, reducing LLM usage while improving response times.
• Expanded MCP Support: Simplifies connecting models, tools, and enterprise knowledge graph workflows into agentic AI systems.
• Faster Vector Processing: Accelerates vector creation and supports configurable vector sizes to optimize performance and cost.
• Enhanced Observability: Enhanced integration with Prometheus and Grafana for improved monitoring and operational visibility.
• Production-ready AI Semantic Graph Infrastructure: Strengthens AllegroGraph’s role as a production-ready platform for AI applications that combine knowledge graphs, vector search, and LLM reasoning.
Franz Inc. was recently listed as a Neuro-Symbolic AI vendor in Gartner’s 2025 Hype Cycle for AI in recognition of AllegroGraph’s Neuro-Symbolic AI capabilities. According to Gartner, “Neurosymbolic AI addresses limitations in current AI systems, such as incorrect outputs, lack of generalization to a variety of tasks and an inability to explain the steps that led to an output. The neurosymbolic approach leads to more powerful, versatile and interpretable AI solutions and allows AI systems to reason through more complex tasks. Generative AI systems are starting to leverage neurosymbolic methods to overcome their reasoning shortcomings.” Source: Gartner, Hype Cycle for Artificial Intelligence, July 2025.
“AI requires structured knowledge,” said Charles Betz, VP Principal Analyst at Forrester. “GenAI and large language models (LLMs) require structured and contextualized data. Graphs provide a foundational knowledge model that enhances AI-driven automation, reasoning, and prediction. If unstructured data and the LLMs and vector databases that make sense of it are like flesh, graphs are the skeleton, the bones that give it structure. You need both.” Source: Forrester, The Graphic Future of IT Management, March 2025.
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