Revolutionizing Customer Service: How Intelligent Luis Chatbot Integration with ServiceNow is Streamlining Support Processes
Abstract: The objective of this project is to create an integration between Microsoft LUIS and Service Now. Microsoft LUIS is a AI natural language processing service that allows developers to create apps that can understand user input, and Service Now is a service desk application for managing IT operations. This integration will enable users to submit service requests through natural language, allowing for a more convenient, intuitive way for users to interact with Service Now. The integration will allow for natural language requests to be converted into Service Now requests, allowing for greater automation of the service desk operations.
Language Understanding (LUIS) is a cloud-based conversational AI service that applies custom machine-learning intelligence to a user’s conversational, natural language text to predict overall meaning, and pull out relevant, detailed information. LUIS provides access through its custom portal, APIs and SDK client libraries.
Luis-based applications can support generic questions by leveraging the power of natural language understanding (NLU) technology to interpret user input and map it to the appropriate action. Additionally, many applications can allow users to upload documents directly to the platform for further analysis.
To integrate ServiceNow with MSLUIS, you need to complete the following steps:
1. Sign into your Microsoft Azure portal, navigate to Cognitive Services and create a LUIS application.
2. Once your LUIS application is created, you’ll need to train it by adding intents, utterances, and entities.
3. Once the LUIS app is trained, you’ll need to generate an API key.
4. Create Instance in service now & incident management in service now and generate API’s.
5. Configure the MSLUIS ServiceNow Integration App by entering the API key generated in step 3.
6. write python code for user quarries solved by chatbot for.e.g.fetch & raise the incident etc.
7. deploy chatbot in Azure Service. e.g. Ubuntu server
8. Test the agent by adding some user input to the chatbot and verifying the results.
Results:
Next steps: Sentiment Analysis based chatbot capability to understand sentiment analysis. It can detect the sentiment of a given text and classify it into Positive, Negative and Neutral categories of user queries.
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