82 lines
No EOL
3 KiB
Markdown
82 lines
No EOL
3 KiB
Markdown
# Overview
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AI systems still struggle with the messy realities of data.
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**The core challenges:**
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* **Complex Data at Scale**: Databases spanning hundreds of tables, documents in dozens of formats, knowledge scattered across systems
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* **Lack of Business Context**: Without domain ontologies and relationships, even advanced LLMs produce hallucinations
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* **Stale Knowledge**: Static RAG doesn't evolve as your organization and data change
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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.
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***
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## Example Use Cases
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### [Vertical AI Agents](./vertical-ai-agents)
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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.
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**Key capabilities:**
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* Persistent memory across agent sessions
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* Domain-specific reasoning context
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* Continuous learning and improvement
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***
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### [Enterprise Data Unification](./data-silos)
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Connect data silos without replacing your existing systems. When the answer requires CRM + support tickets + contracts + operational data, Cognee provides the unified view.
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**Key capabilities:**
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* 30+ data source connectors
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* Entity resolution across systems
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* Granular access control by user, team, or organization
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***
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### [Edge AI & On-Device Memory](./edge-ai)
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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.
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**Key capabilities:**
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* Fully offline operation with on-device LLMs
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* Hybrid execution—local or cloud based on connectivity
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* Privacy-first architecture for sensitive data
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***
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## Common Patterns Across Use Cases
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### Memory Enrichment
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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.
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### Ontology Management
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Whether it's financial instrument definitions, research taxonomies, or codebase architecture, Cognee aligns your domain-specific terminology into a coherent knowledge structure.
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### Hybrid Search
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Every query leverages both graph traversal (understanding relationships) and vector similarity (semantic matching) for complete, accurate results.
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### Modular Customization
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Cognee provides building blocks such as chunkers, loaders, retrievers, ontology definitions that you can customize for your specific domain without building from scratch.
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***
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## Dive Deeper in Use Cases:
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* [Vertical AI Agents](./vertical-ai-agents) - The memory layer that makes autonomous agents actually work
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* [Enterprise Data Unification](./data-silos) - Connect data silos without replacing your existing systems
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* [Edge AI & On-Device Memory](./edge-ai) - Rust-powered AI memory for phones, wearables, and IoT devices
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---
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> To find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt |