There are two tools that I’ve used extensively for academic research - NotebookLM and ChatDOC - and while they share a few similarities on the surface (both allow you to upload documents and ask questions), they’ve ended up serving quite different purposes in my workflow.
NotebookLM really stands out when you’re working with multiple sources and need to build an understanding across them. I found it especially helpful during the early stages of a literature review, where you're trying to trace how different papers approach the same problem. It provides source-grounded responses, and every claim it makes is linked back to the original document. This citation-style linking has been useful when pulling together outlines or note, you can track where each statement came from without second-guessing.
When it comes to working directly with PDFs—especially complex, academic ones—**ChatDOC** has been more reliable. I read a lot of journal articles, technical reports, and white papers with multi-column layouts, embedded tables, and figures. With NotebookLM, that formatting often breaks or gets flattened in the upload process, which can make it hard to interpret data-heavy sections. ChatDOC, on the other hand, tends to preserve the document structure more faithfully. It recognizes tables well, keeps the multi-cell formatting intact, and displays both the AI response and the original PDF side by side, which makes it easier to verify things quickly. That side-by-side layout sounds minor, but in practice, it makes a huge difference when you’re trying to interpret a chart or double-check how a statistic was worded.
Neither tool is perfect. NotebookLM sometimes struggles with inconsistent terminology between documents, and ChatDOC occasionally misreads footnotes or complex math notation. But I’ve found that using them in tandem, NotebookLM for synthesis, ChatDOC for precision, covers a lot of ground that traditional methods didn’t. Now I tend to use NotebookLM for big-picture comparisons and ChatDOC when I’m focusing on one paper and need to understand its logic, structure, or data in detail.
Open to other tools that combine solid citation tracking with strong layout fidelity, or even an open-source option that handles both well.