Without AI-ready data, most generative AI projects fail. Learn to standardize enterprise data with graph databases like FalkorDB for scalable AI success.
Learn about integrating Graph Neural Networks (GNNs) with LLMs for precise relational modeling and improved AI performance in fraud detection, healthcare, and more.
Learn what a graph database is and how to deploy FalkorDB graph databases on AWS/GCP, run Cypher queries, and benefit from the power of interconnected data.
At Nvidia GTC 2025, FalkorDB presents on real-time knowledge graphs for GenAI. Learn how graph databases enable LLM-enhanced reasoning and fraud detection, solving critical AI challenges.
FalkorDB unveils v4.8: up to 42% more memory efficient, outpacing Neo4j by 7x. The latest FalkorDB release focuses on optimizing resource utilization, reducing memory footprint, and accelerating query execution.
FalkorDB joins forces with Lightning.ai to simplify GraphRAG deployment. No local setup, cloud-ready, and optimized for scalable AI workflows.
TrustGraph revolutionizes knowledge extraction through autonomous agents and graph-based RAG, now integrated with FalkorDB for enhanced document analysis and insights.
Graph RAG emerges as a powerful solution to Gartner’s RAG system challenges, offering improved data representation, retrieval mechanisms, and context preservation compared to Vector RAG.
FalkorDB now integrates with AG2.ai (v0.5), enabling developers to build multi-agent systems using GraphRAG, real-time knowledge graphs, and scalable multi-tenancy.
Our work on knowledge graphs and GraphRAG has earned us a spot among the 2024 Tech Trailblazers Awards finalists. Cast your vote!
Ultra-fast, multi-tenant graph database using sparse matrix representations and linear algebra, ideal for highly technical teams that handle complex data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.
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SOLUTIONS
Simply ontology creation, knowledge graph creation, and agent orchestrator
Ultra-fast, multi-tenant graph database using sparse matrix representations and linear algebra, ideal for highly technical teams that handle complex data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.
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CTO at Intel Ignite Tel-Aviv
I enjoy using FalkorDB in the GraphRAG solution I'm working on.
As a developer, using graphs also gives me better visibility into what the algorithm does, when it fails, and how it could be improved. Doing that with similarity scoring is much less intuitive.
Dec 2, 2024
Ultra-fast, multi-tenant graph database using sparse matrix representations and linear algebra, ideal for highly technical teams that handle complex data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.
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