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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.

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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.

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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.

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Implementation: Connect, Cognify, Query

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

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Step 1: Connect Your Sources

Cognee supports 30+ data sources out of the box: 
import cognee

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

# Connect unstructured documents
await cognee.add("s3://bucket/product-docs/")
await cognee.add("www.your-website.com")

# Connect semi-structured data
await cognee.add("path-to-your-folders")
...

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Step 2: Cognify

Build the knowledge graph that connects entities across sources: 
# Process all sources into a unified memory layer
await cognee.cognify()
Cognee automatically:
  • Extracts entities (customers, products, people, concepts)
  • Identifies relationships between entities
  • Creates semantic embeddings for content
  • Resolves entity references across sources (e.g., “Acme Corp” = “Acme Corporation” = “ACME”)

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Step 3: Query with Context

Now queries return connected knowledge, not isolated chunks: 
results = await cognee.search(
    query_text="Full context on Acme Corp",
    search_type=SearchType.GRAPH_COMPLETION
)
Ready to unify your data silos? Start with the open-source SDK or talk to our team about enterprise deployment.