Case Study: Transforming SOP Documents into ML-Based Chatbot

Introduction Standard Operating Procedures (SOP) are vital documents for many organizations, providing guidance for various employee activities. However, these documents can be lengthy, complex, and difficult to search, leading to misinterpretation and noncompliance. To address this challenge, our team proposed an ML-based chatbot solution that can provide relevant responses to user questions, improving the accessibility and usability of SOP documents. Problem Statement Our client, a large organization with complex processes, faced the challenge of employees struggling to find relevant information within their SOP documents. This resulted in misinterpretation and noncompliance, posing potential risks for the organization. The client was looking for a solution that could make their SOP documents easily accessible and user-friendly. Solution Approach Our team proposed an ML-based chatbot builder that could convert structured documents into a chatbot. The chatbot would use natural language processing (NLP) algorithms to understand user questions and provide relevant responses from the SOP documents. We used a combination of open-source and proprietary ML tools to build the chatbot.The chatbot was designed to have re-training capability, allowing it to learn with time and user feedback. This feature ensured that the chatbot's responses became more accurate and relevant over time. The chatbot was also integrated with the client's internal communication platform to ensure that employees could access it easily. Results The ML-based chatbot solution provided significant benefits to the client. Firstly, it improved the accessibility and usability of the SOP documents, making it easier for employees to find relevant information. Secondly, it reduced the risk of errors and noncompliance by ensuring that employees had access to accurate and up-to-date information. Thirdly, it saved time and resources by eliminating the need for employees to search through lengthy documents manually. Lastly, the chatbot's re-training capability ensured that its responses became more accurate and relevant over time, providing continuous improvements to the user experience. Conclusion Our ML-based chatbot solution provided a user-friendly and efficient way for employees to access and interpret SOP documents. The chatbot's re-training capability ensured that it continuously improved its responses, making it a valuable asset for the client. The success of this solution highlights the potential of ML-based chatbots to improve the accessibility and usability of complex documents in various industries.