Asim Khan@asim_khan_371

LUNA is an AI-powered chatbot specifically designed to tackle the lack of reliable menstrual health resources for women and girls across South Asia. Our core features offer period tracking, age-appropriate health information, access to hygiene products, and a safe space to ask the questions that cultural taboos silence. LUNA's multi-pronged approach is crucial: Accessibility: The chatbot format works even on low-bandwidth phones, reaching underserved communities. Sensitivity: Information is medically verified and tailored to combat cultural myths about menstruation. Scalability: LUNA aims to expand its reach through strategic partnerships with NGOs, schools, and healthcare providers.

Period Care is a holistic health platform designed to support individuals throughout their entire menstrual cycle, not just during their period. Our app offers personalized health insights, food recommendations, and emotional well-being tips tailored to each phase of the cycle. Users can track their periods, mood swings, and sleep patterns, helping them better understand their bodies. Additionally, the app provides easy access to nearby stores for purchasing pills or pads and allows users to book appointments with gynecologists. By focusing on both menstrual and non-menstrual phases, Period Care empowers users to take control of their health and well-being year-round.

Today’s litigators are expected to quickly make well-informed decisions and develop strong strategies and a big-picture perspective. To accomplish these goals in shorter time frames, we present Legal Buddy to streamline the document review and analysis process. Legal Buddy gives attorneys more time for strategic planning by providing a report with an overview of the case, a liability analysis, a case analysis, applicable laws and defenses, damages, and recommendations for the case. The attorney (user) can upload medical records or summaries, witness statements, deposition transcripts or summaries, pleadings, and other legal documents, notes, or summaries and the system will use Upstage's Document OCR to extract text from the documents. Then, the Llama model summarizes all the text and the text is given to the OpenAI's o1 LLM to generate a liability analysis and report. Additionally, we stored a data set of U.S. case law on MongoDB using LlamaIndex and enabled a vector search to find the relevant cases for the case analysis feature. The system matches information from our specific case from the uploaded documents with other cases from the data set of U.S. case law. OpenAI's o1 reasoning is used to generate case analysis and the case analysis is included in the report. There is currently a resource limit on the length of the documents that can be uploaded and the amount of cases that can be stored, so for now we are uploading summaries of legal documents instead of the actual documents and the data set we are storing is a sub-set (about 500 cases) of the entire U.S. case law data set. We would like to have more processing and storage functionality, but that just wasn't feasible for this hackathon. For the future, we would like the user to interact with live data from a data source (i.e. Westlaw, LexisNexis) instead of a data set. We would also like the user to query case information through a chat interface.