# Overview

AI 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.

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## Example Use Cases

### [Vertical AI Agents](./vertical-ai-agents)

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

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### [Enterprise Data Unification](./data-silos)

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

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### [Edge AI & On-Device Memory](./edge-ai)

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

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## 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.

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## Dive Deeper in Use Cases:

* [Vertical AI Agents](./vertical-ai-agents) - The memory layer that makes autonomous agents actually work
* [Enterprise Data Unification](./data-silos) - Connect data silos without replacing your existing systems
* [Edge AI & On-Device Memory](./edge-ai) - Rust-powered AI memory for phones, wearables, and IoT devices


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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