GraphRAG & Graph Use Cases
Build Reliable, Scalable AI and Security Systems on FalkorDB's Graph Technology
Stop choosing between scale, latency, and accuracy. Use our high-performance, graph-native database enabling precise generative AI, fraud detection, access management, and advanced GraphRAG solutions.
- 10K+ Multi-Graph (Tenants)
- Horizontal Scaling
- Vector Capabilities
- Open-source
Generative AI
Precise Context for AI Models
FalkorDB connects datasets to deliver precise context, significantly improving the accuracy and relevance of generative AI outputs. Developers leverage seamless data retrieval to optimize model performance.
- Accurate data retrieval
- Enhanced output relevance
- Seamless dataset linking
Fraud Detection
Detect Fraud in Real-time
Identify fraudulent activities instantly through relationship analysis, reducing the response time and preventing potential damage. FalkorDB helps maintain data integrity by revealing hidden threats immediately.
- Instant threat detection
- Real-time analytics
- Identify hidden patterns
Access Management
Efficient Permission Controls
Simplify user and permission management through intuitive, relationship-based controls. FalkorDB reduces administrative complexity, ensuring security and compliance effortlessly.
- Simple permission setup
- Clear user management
- Reduced admin overhead
Pure Graph Database
Scalable Graph Data Storage
FalkorDB efficiently manages extensive graph datasets with predictable performance, supporting large-scale operations without sacrificing reliability or latency.
- x496 faster than Neo4j
- Reliable uptime performance
- Massive dataset handling
- Multi-tenancy (10K+ graphs)
- x6 more memory efficient, x11 higher throughput than Neo4j
Build fast and accurate GenAI apps with GraphRAG SDK at scale
FalkorDB offers an accurate, multi-tenant RAG solution based on our low-latency, scalable graph database technology. It’s ideal for highly technical teams that handle complex, interconnected data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.
