Leveraging AI and LLMs for Customer Support Insights

Project Objective:

This project aimed to leverage data from ServiceNow to build a system that provides actionable insights into customer support tickets. Using Databricks, AI, and Large Language Models (LLMs), the project focuses on enabling a more customer-oriented approach to identifying and solving day-to-day customer challenges.

How It Started:

Customer support data often contains valuable insights but can be challenging to process and categorize at scale. This project set out to address this challenge by:

1. Extracting ticket data from ServiceNow into a scalable analytics platform.
2. Transforming and structuring the data for better usability and insights.
3. Leveraging LLMs to categorize tickets dynamically, based on recurring themes and user-defined inputs.

The goal was to enable a system that supports proactive decision-making and helps prioritize customer challenges efficiently.

What Was Built:

The project created an automated and intelligent pipeline comprising:

• Data Extraction & Transformation:

  • ServiceNow data was ingested into Databricks for processing.
  • AI-powered pipelines automated data cleaning and enrichment, preparing it for analysis.

• Dynamic Ticket Categorization:

  • LLMs categorized tickets into recurring themes, such as usability issues, performance bottlenecks, and feature requests.
  • Enabled dynamic categorization based on user input, ensuring the flexibility to address new and evolving challenges.

• Data Presentation:

  • Visual dashboards presented insights into ticket data, highlighting trends and problem areas.
  • Equipped support teams with actionable data to improve response times and focus on critical issues.

How It Works Today:

1. Data Flow Automation:

   • Customer support ticket data flows seamlessly from ServiceNow into Databricks pipelines.
   • AI models clean and structure the data, ensuring consistency and accuracy.

2. AI and LLM Integration:

   • AI pipelines automate data processing, while LLMs categorize tickets into actionable themes.
   • Enables quick identification of recurring issues for faster prioritization.

3. Visual Insights:

   • Dashboards built on the processed data provide a clear overview of support trends.
   • Support teams can identify high-impact issues and take action to address them.

Outcome:

While still evolving, the system is designed to:

• Enable Proactive Problem Solving: By categorizing tickets and highlighting recurring issues, the system helps teams focus on solving real customer problems.
• Provide Actionable Insights: Visual dashboards empower teams to make data-driven decisions, reducing the time needed to identify critical issues.
• Shift Toward Customer Orientation: The project establishes a foundation for better understanding customer needs and prioritizing solutions based on their day-to-day challenges.