AI Spotlight

Revolutionizing Customer Service Efficiency with AI

AI optimizes demand planning for fresh produce

This use case examines the application of large language models (LLMs) and Process AI in the customer service sector, specifically for automating routine inquiries and reducing response times. By leveraging AI's capabilities and Neural networks, this approach significantly enhances employee productivity and customer satisfaction in the vehicle logistics industry.

Key Benefits

  • Automate Routine Inquiries: Handles common questions, reducing workload through Decision Intelligence.
  • Reduce Response Times: Provides quick and accurate responses, enhancing customer satisfaction.
  • Enhance Employee Productivity: Allows human agents to focus on complex issues by automating routine tasks using Machine Learning.

Case Study


In vehicle logistics, LSPs (Logistics Service Providers) face challenges like handling numerous emails daily and long response times. The Customer Service AI Assistant automates the processing and replies to these emails, reducing the workload on human agents and improving response times through Digital Decision-Making.

How It's Done

  1. Data Collection: Gathering relevant data to train the model.
  2. Contextual Understanding: Ensuring the model processes context-specific queries using Process AI.
  3. Knowledge Grounding: Integrating real-time data through APIs, local code execution, SQL queries, or reading full files.

Despite prompt size limitations, techniques such as using a vector database help pull only relevant information, ensuring efficient and accurate responses. Neural networks play a critical role in understanding and generating appropriate responses.

Did You Know?

The vision for the Customer Service AI Assistant is to be part of "Ally," a centralized hub-framework for copilots within INFORM products, integrating:

  • Vector Database
  • Product API
  • Language Model
  • Product Frontend

This framework aims to revolutionize customer service operations, making them more efficient and effective through the use of Generative AI and Operations Research.

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