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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.
<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.
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