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Creating a Smart Support AI Agent for Businesses

Customer Query

Introduction

In today's digital age, businesses need to provide quick and accurate responses to customer queries. A smart support agent can help achieve this. Here's a simple guide to creating a smart support agent, based on the flowchart provided.

Understanding the Smart Support Agent System
 

The Flowchart Overview

The flowchart shows a system where customer questions go through different steps to get the right answers. Here's what each part does.

  1. Customer Query: Where customers submit their questions.
  2. Query Classification: The system categorizes these questions into types like "Document," "Web," or "Database".
  3. Select Agent Based on Query Label: Based on the category, the system picks the right agent to handle the query.
  4. Document Agent: Handles questions related to documents.
  5. Website Agent: Deals with questions that need information from websites.
  6. Database Agent: Manages questions that require data from databases.
  7. Data from Sources: The collected information from documents, websites, and databases.
  8. LLM Model Summarization: Uses a Large Language Model (LLM) to summarize information and give natural responses.

Implement a Smart Support Agent

  1. Customer Query Handling
    • Technology: Chatbots or web forms.
    • Description: Create a simple interface where customers can ask their questions.
  2. Query Classification
    • Technology: Use Microsoft Copilot Studio, and LangChain for building chains of different components to classify queries accurately.
    • Description: Microsoft Copilot Studio flow, LangChain helps in chaining various machine learning models and logic to categorize questions correctly.
  3. Agent Selection
    • Technology: LangChain, Microsoft Copilot Studio Skill feature decision trees or logic-based routing.
    • Description: Automatically send the question to the right agent using predefined logic or AI-based decision-making.
  4. Document Agent
    • Technology: Vaana AI for document processing.
    • Description: Vaana AI can extract and process relevant information from documents efficiently.
  5. Website Agent
    • Technology: Web scraping tools like BeautifulSoup or Scrapy.
    • Description: Gather data from websites using these scraping tools.
  6. Database Agent
    • Technology: SQL or NoSQL databases (e.g., MySQL, MongoDB).
    • Description: Fetch data from internal databases securely.
  7. Data Integration
    • Technology: Data warehousing tools like Apache Hadoop or AWS Redshift.
    • Description: Combine all the collected data into one place for easy access and processing.
  8. LLM Model Summarization
    • Technology: Azure Open AI, Open AI, LLaMA 3 for summarization and natural responses.
    • Description: Use Azure Open AI, Open AI, and LLaMA 3 to create summaries and natural responses to customer queries.

Benefits of a Smart Support Agent

  • Improved Efficiency: Automates query handling, reducing response times and costs.
  • Better Customer Experience: Quick and accurate answers make customers happy.
  • Scalability: Handles more queries without needing more staff.
  • Data Insights: Provides valuable insights into customer behavior and issues.

Conclusion

Creating a smart support agent can greatly improve how your business handles customer queries. By using technologies like Microsoft Copilot Studio, LangChain, Vaana AI, and LLaMA 3, Azure Open AI, you can build a system that provides fast, accurate, and natural responses to your customers' questions.

Get started on building your smart support agent today and enhance your customer support!

If you have any questions or need help with implementation, feel free to reach out. We're here to help you every step of the way.


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