FalkorDB is a low latency graph database that gives enterprise LLM applications the accuracy and reliability they need to reach production. By grounding retrieval in a knowledge graph instead of vector similarity alone, FalkorDB cuts hallucinations and returns answers that trace back to real, connected data.
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.
“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.”
CEO & Co-Founder
CTO & Co-Founder
Chief Architect & Co-Founder
Advisory Team
FalkorDB represents the first queryable property graph database using sparse matrices for adjacency matrix representation and linear algebra for graph queries. It leverages AVX acceleration for performance optimization and eliminates complex batch processing requirements.