Imagine effortlessly navigating the vast ocean of academic knowledge, instantly accessing precise information without sifting through endless search results. This is the promise of SOAY, a groundbreaking AI-powered research tool. Researchers constantly grapple with the challenge of finding specific academic information, often jumping between different databases and platforms. Traditional search engines, while helpful, can't understand the complex relationships between different pieces of information. For example, finding the citation count of a specific researcher's most influential paper requires multiple searches and manual aggregation. SOAY tackles this problem by using Large Language Models (LLMs) to understand complex research queries and automatically execute the necessary API calls to retrieve the desired information. The key innovation is the use of "solutions" – pre-defined sequences of API calls that address specific types of queries. This allows the LLM to efficiently navigate the complex web of academic APIs, significantly speeding up the research process. SOAY was tested on AMiner, a comprehensive academic search platform. The results were impressive, showing a substantial improvement in accuracy and efficiency compared to existing methods. The system can handle complex, multi-step queries, like finding the collaborators of a researcher's co-authors, with remarkable speed. While SOAY demonstrates the potential of AI to revolutionize academic search, challenges remain. Ensuring the generated queries perfectly align with user intent and adapting to the constant evolution of academic databases are key areas for future development. However, SOAY represents a significant leap forward, offering a glimpse into a future where researchers can focus on what matters most – generating new knowledge.