The biggest problems with note-taking are:
- Notes in physical books are hard to digitize without introducing significant friction.
- People rarely revisit their notes due to poor organization, leading to notes being dumped into files that quickly become gigantic, unusable, and eventually forgotten.
- Organizing notes requires a lot time and work.
We are solving this by creating a system for the entire note-taking process. We make digitization easy with OCR and UX for selecting the desired text. Post-digitization, we use LLMs to effectively organize the content into an organized database.
We categorize content by creating several layers of information that can be used at different times for different purposes:
- Title: A short, one-sentence description of the entire note. This is very useful when searching through many notes.
- Summary: A single paragraph that distills the information, acting as a thumbnail of the captured information.
- Tags: Allows grouping notes based on common topics, regardless of the source.
The goal is a system to make content as easy and efficient to navigate and use as possible, automating the process to save time and effort.
We differentiate ourselves from other platforms by not overly focusing on AI for its own sake. While many solutions concentrate primarily on summarizing information, we view AI as a tool that supports the overall UX. Our primary focus is on understanding the entire note-taking process holistically—how to capture, search, navigate, and utilize information most effectively.
To start with we’re targeting book readers, as this is the most straightforward application. However, we plan to expand this system to accommodate various types of content, including academic papers, articles, podcasts, and more.
We’re launching our MVP and gathering feedback from our first users. If you’re interested, you can try it here: https://ravenapp.ai
Any feedback is very welcome!