BoardSpace aims to enhance its document management system by integrating an AI-powered feature that scans the content of uploaded documents to generate a list of suggested tags. This project involves developing a machine-learning model capable of understanding and extracting key themes and topics from various document types.

The goal is to streamline the tagging process, making it more efficient and accurate, thereby improving document retrieval and organization.

The project will require the team to apply their knowledge of natural language processing (NLP), machine learning algorithms, and software development to create a functional prototype. The learners will work on training the AI model, integrating it with the existing system, and testing its performance.

Key tasks include:

- Researching and selecting appropriate NLP and machine learning techniques.

- Developing and training the AI model using a dataset of tagged documents.

- Integrating the AI model with BoardSpace’s document management system.

- Testing and refining the system to ensure accuracy and efficiency.

The project deliverables will include a functional AI model integrated into BoardSpace’s document management system, capable of scanning and tagging documents accurately. Additionally, the team will provide a comprehensive report detailing the development process, the techniques used, and the performance metrics of the AI model. A user guide for the new feature will also be created to assist BoardSpace staff in utilizing the system effectively.

Specific deliverables:

- A trained and integrated AI model for document tagging.

- A detailed development and performance report.

- A user guide for the AI-powered tagging feature.

• Kickoff meeting with CEO and Developer
• Ongoing supervision and mentorship by Developer
• Weekly meetings
• Access to BoardSpace application
• Added to the company Slack channel