In today’s digital world, people and organizations often work across different platforms, such as Google Workspace, Microsoft Office, and Dropbox. Because the number of digital documents increases, it can make finding specific files difficult. According to McKinsey, employees spend around 19% of their workweek searching for information (2012). On the other hand, document search is often limited to keyword search. If you do not input the exact phrasing, you won’t find the file. These factors cause people to spend more time searching for information and organizations face increased information clutter. A recent study by Shahrzadi (2024) shows this overload can be bad for productivity and decrease decision quality. So, we saw an opportunity to create a technological solution to help users filter and manage knowledge more effectively, as it becomes more important. You can download and install our prototype from here. https://drive.google.com/drive/folders/1IaLMxqPjoi3t87T2_Y9kIJdRXZuYjxIH
Traditional keyword-based search tools such like Spotlight for mac systems, Windows Search, or Confluence depend mainly on text matching. They cannot fully understand the context or intent behind a query. When users remember only vague details, partial titles or general ideas, traditional systems often fail to give a useful result. As a result, users have to perform repetitive manual searches; they waste more time and effort and sometimes never find the intended document. In addition, traditional searches cannot generate summaries or insights from the results they find.
To solve this, our team designed LocalMind, which enables users to efficiently query the their files on meaning. You can type a query such as, “rental contract” and it will know what you mean and find the file, even if it is actually called “lease agreement”. It can even query and across languages. But the app does more than search. It can summarize documents and answer questions based on the content it finds, using Retrieval Augmented Generation (RAG) linked to an LLM. Instead of opening every file, you can retrieve specific information instantly with quotes and sourcing for verification. This saves time and helps users focus on actual work, rather than looking for files.
In terms of business value and market, LocalMind can be used by both individuals and companies. It can not only increase the productivity but also improve the way people leverage information. For businesses, this means better quality work and more value creation in a more efficient manner. It can improve performance. For individuals, it means users can spend less time to find documents and retrieve information. Moreover, unlike many cloud-based tools, LocalMind gives users control over their own data. Advanced Search queries can be stored and processed locally.
In the future, while ensuring privacy, we plan to expand LocalMind and integrate it with cloud synchronization to make it compatible with more platforms and meet the scalability and collaboration needs of different enterprises. We will also try to improve performance and optimize the user interface to enhance market competitiveness. In conclusion, LocalMind is not just a search tool. It is a smarter way of working.
Information Strategy: Team 28
References:
Groff, M., & Jones, T. (2024). Causes, consequences, and strategies to deal with information overload: A scoping review. Technology in Society. https://www.sciencedirect.com/science/article/pii/S2667096824000508
Manyika, J., Chui, M., & Sarrazin, H. (2012, August 21). Social media’s productivity payoff. McKinsey Global Institute. https://www.mckinsey.com/mgi/media-center/social-media-productivity-payoff
