Skip to main contentAI systems still struggle with the messy realities of data.
The core challenges:
- Complex Data at Scale: Databases spanning hundreds of tables, documents in dozens of formats, knowledge scattered across systems
- Lack of Business Context: Without domain ontologies and relationships, even advanced LLMs produce hallucinations
- Stale Knowledge: Static RAG doesn’t evolve as your organization and data change
Cognee solves these problems by creating a unified memory layer, combining knowledge graphs with vector search to give AI systems true understanding of your data.
Example Use Cases
The memory layer that makes autonomous agents actually work. Agents without memory can’t learn, can’t understand organizational context, and can’t improve over time. Cognee provides the missing piece.
Key capabilities:
- Persistent memory across agent sessions
- Domain-specific reasoning context
- Continuous learning and improvement
Connect data silos without replacing your existing systems. When the answer requires CRM + support tickets + contracts + operational data, Cognee provides the unified view.
Key capabilities:
- 30+ data source connectors
- Entity resolution across systems
- Granular access control by user, team, or organization
Bring AI memory to resource-constrained devices with cognee-RS, our Rust-based SDK. Run the full memory pipeline directly on phones, smartwatches, glasses, and smart-home hubs—sub-100ms recall, data stays local.
Key capabilities:
- Fully offline operation with on-device LLMs
- Hybrid execution—local or cloud based on connectivity
- Privacy-first architecture for sensitive data
Common Patterns Across Use Cases
Memory Enrichment
All use cases benefit from Cognee’s ability to consolidate information over time, not just at ingestion, but continuously as new data arrives and patterns emerge.
Ontology Management
Whether it’s financial instrument definitions, research taxonomies, or codebase architecture, Cognee aligns your domain-specific terminology into a coherent knowledge structure.
Hybrid Search
Every query leverages both graph traversal (understanding relationships) and vector similarity (semantic matching) for complete, accurate results.
Modular Customization
Cognee provides building blocks such as chunkers, loaders, retrievers, ontology definitions that you can customize for your specific domain without building from scratch.
Dive Deeper in Use Cases:
