Earnings events for public companies are major catalysts that significantly impact analysts' projections and, consequently, stock prices. While SEC filings provide a backward-looking historical perspective on a company's performance, earnings calls held by company executives and analysts offer valuable insights into the future performance of the company. Listening to these calls, however, can be a daunting task, often taking over an hour. Alternatively, reading the call transcript that can easily exceed 10,000 words is also time-consuming. I needed something fast, accurate, and to the point, highlighting what truly matters. That's why the startup was born. The solution was developed using natural language processing (NLP) techniques and large language models (LLMs). we summarize the important points from the call, collect and organize future positive and negative catalysts that could impact the company's future performance, gather the main topics that analysts were focused on, and conduct sentiment analysis on management's statements. All the information needs to be precise and free of hallucinations.