Enterprise Search and Knowledge Management: The Internal Brain

Most companies suffer from "Information Silos." The documentation is in Confluence, the current status is in Jira, the discussion is in Slack, and the actual expertise is in the head of a developer who is out for lunch. Traditional search fails because it only finds "Documents." Graph RAG succeeds because it finds The Big Picture.

In this lesson, we will look at the most common professional use case for Graph RAG: Enterprise Search. We will learn how to build a graph that links people to projects, projects to tools, and tools to documentation. We will see how this allows an employee to ask: "Who should I talk to about the server upgrade?" and get an answer based on actual relationship density, not just keyword matches.

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1. The Enterprise Knowledge Schema

To build an "Internal Brain," you need specific node types:

• (:Person) {skills, department}
• (:Project) {status, deadline}
• (:Document) {source_url, summary}
• (:Software) {version, language}

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2. Linking the Silos

The magic happens when you connect the data:

• (Sudeep)-[:CONTRIBUTED_TO]->(Project_Alpha)
• (Project_Alpha)-[:DESCRIBED_IN]->(Doc_101)
• (Doc_101)-[:MENTIONS]->(Python_3.12)

The Retrieval Strategy: When a new developer asks: "Is anyone using Python 3.12 in a production project?", the Graph RAG system doesn't just find the "Python 3.12" node. It follows the path back to the Person and the Project.

• Answer: "Yes, Sudeep is using Python 3.12 on Project Alpha. He recently updated the documentation for it here."

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3. The "Expertise Discovery" Pattern

Instead of searching for "Keywords," the AI searches for Centrality (Module 11).

• "Who is the expert on X?"
• Graph Logic: Find nodes related to X, and then find the :Person node with the highest Betweenness in that local neighborhood.

This is much more accurate than a bio that says "I know everything," because it is based on Evidence (actual project contributions and Jira tickets).

graph TD
    S[Slack: "Let's use Python"] -->|Extract| P((Python))
    J[Jira: "Bug in Python script"] -->|Extract| P
    D[Doc: "Python Tutorial"] -->|Extract| P
    U[User: "Sudeep"] -->|Author| J
    U -->|Speaker| S
    
    subgraph "The Enterprise Graph"
    U --- P
    end
    
    style P fill:#4285F4,color:#fff
    style U fill:#34A853,color:#fff

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4. Implementation: A "Resource Discovery" Query

MATCH (target:Tool {name: 'Kubernetes'})-[:USED_IN]->(p:Project)
MATCH (p)<-[:MEMBER_OF]-(expert:Person)
WITH expert, count(p) as project_count
ORDER BY project_count DESC
RETURN expert.name, expert.email, project_count
LIMIT 1;

// This query finds the person who has been on the 
// most Kubernetes projects—the 'Logical Expert'.

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5. Summary and Exercises

Enterprise Graph RAG turns a company from a "Gathering of Files" into a "Single Organism."

• Cross-Silo Linking provides context that document search misses.
• Expertise Discovery is based on action, not just bios.
• Multi-hop Queries allow users to navigate complex corporate hierarchies.
• Summarization provides high-level views of project health and tool adoption.

Exercises

1. Schema Drafting: List 5 node types you would add to a graph for an "HR and Recruitment" department.
2. The "Silo" Bridge: If one team uses "AWS" and another uses "Amazon Web Services," how do you ensure the graph links them correctly? (Hint: Module 11, Entity Reconciliation).
3. Visualization: Draw a graph linking yourself to one project and two colleagues. Now, add a link between a colleague and a "Skill" node. Can the AI "Recommend" that skill to you?

In the next lesson, we will look at customer-facing AI: Customer Support and Automated Trouble-Shooting.