Customer Support and Automated Trouble-Shooting

Standard support bots are frustrating. They either give you a generic "Reboot your router" link or they wait for a human to intervene. Graph RAG makes support bots Empathetic and Investigative. Because the bot has access to the Full Customer Graph, it doesn't just see the current problem; it sees the "Logic" of the customer's history.

In this lesson, we will look at how to build a Support Graph. We will learn how to link "Symptom" nodes to "Solution" nodes and how to use the "Past Success" of other customers to suggest a resolution for a new one. We will see how this reduces ticket volume and increases "First Contact Resolution" (FCR).

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1. The Support Graph Model

• (:Customer) {plan_level, tenure}
• (:Issue) {symptom, status}
• (:Resolved_By) (The relationship linking an issue to a specific action)
• (:Product) {version, known_bugs}

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2. The Investigative Loop

When a customer says "The app is crashing on my iPhone":

1. Graph Search: Look for other :Issue nodes with the symptom "Crashing" and the platform "iPhone."
2. Path Traversal: Follow the [:RESOLVED_BY] edges.
3. Cross-Reference: Check if the customer's version of the app has a [:KNOWN_BUG] relationship to a "Memory Leak" issue.

The Result: The AI doesn't just say "Update the app." It says: "I see you are on version 2.4. We have a known memory leak with that version and iOS 17. The fix is to clear your cache or wait for version 2.5 tonight."

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3. Empathy through History

A user who has reported the same bug 3 times in 2 days is Angry.

• A standard RAG system doesn't know this (unless the chat history is huge).
• A Graph RAG system sees 3 [:REPORTED] edges between the Customer and the Problem.

The Sentiment Logic: If count(reported_edges) > 2, the system automatically increases the "Priority" and triggers a "Sympthetic Prompt" for the LLM synthesis.

graph TD
    C[Customer] -->|Reported| I1[Issue: Slow]
    C -->|Reported| I2[Issue: Crash]
    I1 -->|Resolved By| A1[Action: Cache Clear]
    I2 -->|Linked To| B1[Bug: SDK 4.0]
    
    subgraph "Support Graph"
    I1 & I2
    A1 & B1
    end
    
    style C fill:#4285F4,color:#fff
    style B1 fill:#f44336,color:#fff

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4. Implementation: Finding a Solution via Similar Issues

MATCH (current:Customer {id: $id})
MATCH (other:Customer)-[:REPORTED]->(old_issue:Issue)
WHERE old_issue.symptom = $current_symptom
MATCH (old_issue)-[:RESOLVED_BY]->(solution:Documentation)
RETURN solution.url, count(other) as success_rate
ORDER BY success_rate DESC
LIMIT 1;

// This query finds the most "Historically Successful" 
// solution for this specific symptom.

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5. Summary and Exercises

Support Graph RAG turns an "FAQ Bot" into a "Virtual Technician."

• Symptom-to-Solution links provide the "Expertise."
• Customer History provides the "Context."
• Sentiment Detection (via relationship count) provides the "Empathy."
• Technical Hierarchy (Version -> Bug -> Fix) provides the "Accuracy."

Exercises

1. Logic Task: A user's payment failed. List 3 "Hop-points" in a graph you would check to solve this (e.g., Credit Card Expiration, Bank Connectivity, API status).
2. The "Repeat Caller": How would you write a Cypher query to count how many times a user has mentioned the word "Timeout" in the last 48 hours?
3. Visualization: Draw a graph linking a "Mobile App" to three different "Known Bugs." Link one bug to a "Solution Article."

In the next lesson, we will look at a high-security use case: Fraud Detection and Forensic Intelligence.