> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Data Silos

## Enterprise Data Unification

Every enterprise has the same problem: valuable data locked in silos. Your CRM doesn't talk to your ERP. Your knowledge base doesn't connect to your support tickets. Your strategic documents live in SharePoint while operational data lives in Snowflake.

Cognee creates a unified memory layer that connects these silos without replacing them.

## The Siloed Data Problem

When someone asks "What's the full context on the Acme Corp relationship?", the answer requires piecing together:

* CRM opportunity and contact data
* Support ticket history and resolution patterns
* Contract terms and renewal dates
* Invoice and payment history
* Relevant Slack conversations and email threads

No single system has the complete picture. Neither does traditional RAG.

## Why Standard RAG Fails Here

Vector search treats each chunk independently. It might find a support ticket mentioning Acme Corp and a contract with their name, but it doesn't understand that:

* The support ticket was about a feature that the contract specifically excludes
* The escalation pattern matches a trend you're seeing with other enterprise customers
* The contract renewal is approaching and the recent ticket volume is a risk signal

Relationships matter as much as content. Cognee captures both.

## Implementation: Remember, Improve, Recall

The Cognee Memory Layer sits on top of your existing data infrastructure:

### Step 1: Remember Your Sources

Cognee supports 30+ data sources out of the box:

```python theme={null}
import cognee
from cognee import SearchType

# Connect structured data
await migrate_relational_database(graph, schema=schema)

# Connect unstructured documents
await cognee.remember("s3://bucket/product-docs/", dataset_name="enterprise_memory")
await cognee.remember("https://www.your-website.com", dataset_name="enterprise_memory")

# Connect semi-structured data
await cognee.remember("path-to-your-folders", dataset_name="enterprise_memory")
...

```

### Step 2: Improve the Shared Memory

Optionally run an explicit enrichment pass when you want to deepen the shared graph after ingestion:

```python theme={null}
# Enrich the unified memory layer
await cognee.improve(dataset="enterprise_memory")
```

Cognee automatically:

* Enriches the existing knowledge graph instead of re-ingesting the source data
* Builds additional retrieval structures on top of what `remember()` already stored
* Improves later `recall()` quality for the unified dataset
* Can add session-bridging and feedback-weight updates when you provide `session_ids`

### Step 3: Recall with Context

Now queries return connected knowledge, not isolated chunks:

```python theme={null}
results = await cognee.recall(
    query_text="Full context on Acme Corp",
    query_type=SearchType.GRAPH_COMPLETION,
    datasets=["enterprise_memory"],
)
```

Ready to unify your data silos? [Start with the open-source SDK](https://github.com/topoteretes/cognee) or [talk to our team](https://calendly.com/vasilije-topoteretes/) about enterprise deployment.