Inspiration
In today's fast-paced work environment, inefficient meetings consume valuable time and resources, leading to reduced productivity and increased costs. This solution aims to streamline the meeting process by leveraging AI to reduce unnecessary discussions, ensuring better-prepared sessions and more effective collaboration.
What it does
Request Team Agent: Users can request a meeting with a team agent through the platform.
Booking Appointments: The system facilitates scheduling appointments with the requested team agent.
Pre-Meeting Preparation: The requested team receives the meeting questions in advance, allowing them to prepare effectively.
They can upload relevant documents, which the AI agent uses for training. During the meeting, the AI agent can assist by providing answers and insights based on the uploaded notes, ensuring a productive session.
How we built it
Gemini AI: Used for natural language processing and document comprehension, allowing the AI assistant to analyze and respond to meeting-related queries efficiently.
Streamlit: Built the user interface, providing an interactive and user-friendly web experience where users can request meetings, upload documents, and interact with the AI.
Flask: Served as the backend framework, managing meeting requests, booking logic, and integrating the AI training pipeline to process and learn from uploaded documents.
Firebase: Utilized for real-time data storage, authentication, and seamless synchronization of meeting schedules and document uploads across users, ensuring a smooth experience.
Challenges we ran into
Complex Integration: Merging the AI agent, booking system, and document processing pipeline posed a challenge, requiring efficient data flow and synchronization.
Real-Time AI Training: Training the AI on uploaded documents dynamically before the meeting required efficient data handling and processing.
Accomplishments that we're proud of
We successfully completed the project, delivering a fully functional meeting assistant that streamlines the entire meeting process. One of the major achievements was training a new AI model on uploaded documents, enabling it to provide accurate and relevant answers during meetings. This significantly enhances the meeting’s efficiency by reducing the need for lengthy discussions.
What we learned
Throughout the project, we improved our efficiency in integrating multiple components, making the system more streamlined and responsive.
What's next for Team Representative Assistant
Looking ahead, we plan to expand the platform by introducing multi-agent collaboration. This will allow multiple AI agents to work together, providing broader expertise and improving meeting coverage. Additionally, we aim to enhance the AI’s document analysis capabilities, enabling it to generate insights and summaries from uploaded files, and making meetings even more productive.
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