The idea for FalkorDB arose from our observation that enterprises struggle to deploy LLM-based applications due to trust and reliability issues. We discovered that even the best vector/search database solutions face challenges in achieving high accuracy.
This insight resonated with us immediately, as we already offer the market’s best low-latency, high-accuracy Graph Database.
CEO & Co-Founder
CTO & Co-Founder
Chief Architect & Co-Founder
FalkorDB was founded in 2023 by former Redis veterans Guy Korland (CEO), Roy Lipman (CTO), and Avi Avni (Chief Architect).
What sets FalkorDB apart in the market is our low-latency Graph Database, which utilizes sparse matrices and algebraic expressions for data processing. This advancement enables quick and efficient graph queries, providing a unique blend of graph data management and LLM enhancement.
A significant issue for LLMs in large organizations is their inability to utilize internal organizational data. These models, trained on internet data, often produce unreliable outputs or “hallucinations.” By implementing RAG (Retrieval-Augmented Generation), FalkorDB ensures that LLMs can access and leverage current, relevant organizational information, thereby enhancing reliability and fostering greater adoption of the technology.
FalkorDB’s GraphRAG technology thus stands at the intersection of graph databases and LLMs, offering a solution that is both innovative and necessary for organizations looking to make informed, data-driven decisions.
Sr. Director, AI Infrastructure @ Microsoft
Professor at Texas A&M University
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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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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