Omnis

Inspiration

As students ourselves who have taken a breadth of classes, we realize that each class uses its own organization system and platforms. Some classes upload materials in three different places with lecture videos and supplemental videos in another place, whereas other classes keep everything in one large folder. Being able to track how each class operates is difficult, and we dont want that to impede a student's learning process.

We wanted to create a smarter way to study—one that goes beyond static notes and videos. Keeping track of lecture materials and truly understanding concepts can be overwhelming, so we built Omnis to act as an interactive AI tutor, making learning more intuitive, organized, and personalized.

What it does

Our tool allows you to keep everything in one place and also uses LLMs to parse this information so you can actually interact with it! Omnis allows students to upload lecture slides, notes, videos, and homework into a single system where they can chat with an AI assistant trained on their own materials. Users can ask questions, get clarifications, and explore concepts with references directly from their coursework, creating a personalized and centralized learning experience.

How we built it

We used InterSystems' IRIS Vector Search to embed the documents and information so that users can search and interact with it. We used ReactJS for our frontend, we run our own server with Python Flask, and we use Firebase for Cloud Storage and Authentication. We use OpenAI's language models for reasoning and generation. We built a pipeline to extract information from PDFs, videos, and notes into text embeddings, thus allowing the AI to generate responses grounded in user-provided content. A chat interface lets students interact seamlessly with the AI for real-time assistance. We also provide references back to the original documents that the user uploaded, specific to the timestamp and sentence so that the user can see the contextual grounding.

Challenges we ran into

Chunking and Context limits. Ensuring AI-generated responses remain grounded in provided materials and don’t hallucinate.

Accomplishments that we're proud of

Increasing generality of our tool with different file types and platform integrations. Implementing a robust retrieval system that provides accurate, source-backed answers. Synthesizing all sources into a knowledge base and a clean and easy-to-use AI!