AI Triage Bot Enhancement for IT Ticket Classification

zofiQ, Inc is seeking to enhance its existing AI triage bot to improve the classification accuracy of IT tickets. The current system struggles with accurately categorizing company names, boards, ticket types, and subtypes, leading to inefficiencies in ticket resolution. The goal of this project is to refine the AI model to ensure precise classification, thereby streamlining the IT support process. Learners will apply their knowledge of machine learning and natural language processing to analyze the current system, identify areas for improvement, and implement solutions. The project will involve tasks such as data analysis, model training, and testing to achieve a more reliable classification system. By the end of the project, the team will have contributed to a more efficient IT support workflow at zofiQ, Inc.

Automated API Integration using LLMs

zofiQ, Inc aims to streamline the process of integrating various APIs by leveraging the capabilities of Large Language Models (LLMs). The current manual process of API integration is time-consuming and prone to errors, which can hinder the efficiency of software development. The goal of this project is to develop an automated system that uses LLMs to understand API documentation and generate integration code. This will not only reduce the time required for integration but also minimize human errors. The project will involve researching existing LLM capabilities, designing an automation workflow, and implementing a prototype system that can handle basic API integration tasks. Key points: - Automate API integration using LLMs. - Reduce manual effort and errors in API integration. - Develop a prototype system for basic API integration tasks. - Apply classroom knowledge of machine learning, natural language processing, and software development.