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| <h1 style="">Welcome to Brief.AI</h1> | |
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| Brief.AI is an innovative platform tailored to hedge funds and investment banks, revolutionizing insights into | |
| earnings calls by harnessing large language models. | |
|  | |
| <h1 style="">🤔 Who is Brief.AI?</h1> | |
| Our platform aims to be the voice for any executive or analyst on the buy side or sell-side trying to analyze earning call transcripts through two products: | |
| 💬 **[Javi - Question Answering over Earnings Call Transcript](https://github.com/brief-ai-uchicago/Javi-The-Earnings-Call-Expert)** | |
| * The intelligent chatbot can engage in real-time queries regarding specific details from earnings call transcripts. This elevates user experience, ensuring immediate access to critical information without manual data trawling. | |
| 📃 **[Long-Short - KPI Extractor](https://github.com/brief-ai-uchicago/LongShort)** | |
| * This model efficiently extracts crucial performance indicators and financial metrics | |
| from a comprehensive collection of earnings call transcripts. | |
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| <h1 style="">🚀 What can this help with?</h1> | |
| 💬 **[Chatbot for Earnings Calls:](https://github.com/brief-ai-uchicago/Javi-The-Earnings-Call-Expert)** | |
| Our chatbot amplifies the functionality of large language models, empowering users to engage in interactive conversations with earnings calls. This versatile tool serves multiple purposes, such as: | |
| - 🤖 *Comparison across multiple documents* | |
| - The chatbot uses an agent to compare queries that retrieves multiple documents and is able to create a chain of thought reasoning chain to answer queries. | |
| - 🧠 *Memory:* | |
| - Memory refers to persisting state between calls of a large language model. You can continue to ask follow-up questions from initial queries without restating the context. | |
| - ⚡ *Punctual Information:* | |
| - The chatbot provides quick and precise responses to specific questions, making it ideal for extracting timely information from earnings calls. | |
| - 🚨 *Sentiment Analysis:* | |
| - Users can gauge the sentiment and emotional tone of earnings calls, helping them make more informed investment decisions. | |
| 📃 **[Detailed Earnings Calls Analysis:](https://github.com/brief-ai-uchicago/LongShort)** | |
| - 📚 Concise answers | |
| - Utilizing cutting-edge language models, our system delivers succinct, structured answers extracted from verbose earnings call transcripts, streamlining the distillation of key performance indicators (KPIs) for analysts and executives. | |
| - 🧐 Effective KPI extraction from long transcripts | |
| - Extracting data from unstructured sources like PDFs has become crucial for businesses, researchers, and individuals. Traditional manual methods are slow and error-prone, necessitating more efficient alternatives. | |
| For more detailed information on these capabilities and concepts, please refer to our comprehensive product documentation. | |
| <h1 style="">📖 Documentation</h1> | |
| For a complete guide to the documentation, please follow the steps outlined below to navigate through the GitHub organization: | |
| * [Javi-The-Earnings-Call-Expert](https://github.com/brief-ai-uchicago/Javi-The-Earnings-Call-Expert): Chatbot powered by langchain. | |
| * [LongShort-Dataset](https://github.com/brief-ai-uchicago/LongShort-Dataset): This is the dataset utilized for fine-tuning. | |
| * [LongShort](https://github.com/brief-ai-uchicago/LongShort): Fine-tuned models designed for KPI (Key Performance Indicators) extraction from earnings call transcripts. | |
| * [Website](https://github.com/brief-ai-uchicago/Brief-AI): Platform's UI/UX. | |
| * [Branding](https://github.com/brief-ai-uchicago/Branding): Repository containing branding documentation and assets. | |
| ## <h1 style=""> 🚀 Your Next Stop</h1> | |
| * [Github](https://github.com/brief-ai-uchicago/About-Us): Github organization page. | |