I work with the team behind Pandada. One problem we kept seeing in real analysis workflows was that the bottleneck often wasn’t the final chart or summary — it was the gap between mixed raw inputs and something decision-ready. In practice, those inputs rarely arrive in one clean table. They show up as spreadsheets, CSV exports, SQL results, PDFs, screenshots, and internal documents, each carrying a different part of the context. Our approach in Pandada has been to treat this as an analysis-structuring problem, not just a UI problem. Instead of assuming one schema upfront, we first infer candidate structures from different file types, then map overlapping entities and fields into a shared intermediate representation. On top of that, we generate an analysis plan from the user’s question in plain English, so the system is not only retrieving data but also deciding what operations are needed to answer the question. The output we care about is not a one-off chat response. We’ve been more focused on producing reusable summaries, charts, and reasoning steps that can be checked and shared with other people. One lesson we’ve learned is that users trust the system much more when they can see how a conclusion was formed, rather than just getting a polished answer. A limitation is that this still works best when the source material has enough structure to ground the analysis. Highly ambiguous screenshots or badly formatted documents still need human review. Demo: https://pandada.ai/ submitted by /u/Sharonlovehim
Originally posted by u/Sharonlovehim on r/ArtificialInteligence