An AI tool that pulls text from uploaded PDFs and lets you ask questions about what's in the paper. Instead of just reading, you can have a conversation with it.
Researchers and students at the institution had a hard time going from reading papers to actually understanding them.
Faculty and researchers at the institution deal with dense academic literature: papers full of complex methodology descriptions and statistical findings that require close, repeated reading. But the traditional workflow had no way to interrogate a paper beyond re-reading and taking notes by hand.
Students writing literature reviews faced even steeper barriers. They needed to synthesize dozens of papers into coherent arguments, but had no tools to quickly pull out a paper's core claims or compare findings across multiple works. The result was weeks of effort that still left gaps in understanding.
The institution needed a tool that could turn reading from a passive experience into an active one, where researchers could ask questions about methodology and get answers grounded in the paper's actual text.
Researchers skimmed papers without real engagement and often missed important nuances in methodology and argument structure.
Literature reviews took weeks of careful reading with no way to quickly extract and compare the main arguments across papers.
No way to quickly interrogate a paper's methodology, researchers had to manually decode statistical approaches and study designs.
Students struggled to pull arguments from complex papers, and synthesizing across multiple sources was slow and frustrating.
We built a pipeline that turns static PDFs into something you can actually ask questions about, powered by AI conversation.
Researchers upload academic papers in PDF format. The system extracts full text using smalot/pdfparser, preserving structure and section boundaries.
Extracted text is split into meaningful sections: abstract, methodology, findings, and references. This lets the AI give contextually aware responses.
The PaperConvo interface lets users ask natural language questions about the paper. The AI grounds every response in the paper's actual content.
Findings, methodology details, and citation contexts are surfaced on demand, so researchers can quickly understand a paper and compare it with others.
I can build an AI-powered tool tailored to your team's workflow, whether that's PDF processing, conversational analysis, or something else entirely.
Start a Conversation View All ProjectsSix features that make reading papers feel less like a chore and more like a conversation.
Upload any academic paper in PDF format. The system extracts full text instantly and preserves paragraph and section structure for accurate analysis.
The conversational interface lets users ask plain-language questions about any part of the paper, whether you want a high-level summary or a detailed methodology breakdown.
Identifies and explains a paper's research methodology and study design, so even non-specialists can follow along.
Pulls out the paper's core arguments and findings automatically, so researchers get a structured overview before diving into the details.
Explore how cited works are referenced within the paper. Understand the relationship between the current paper's claims and the literature it builds on.
Upload and analyze multiple papers in parallel. All conversations are saved, so researchers can revisit prior discussions and build on what they've already learned.
Three views of how the tool works in practice.
Papers are uploaded via drag-and-drop. The parser extracts clean text in seconds, ready for AI-driven analysis and conversation.
Researchers ask follow-up questions in natural language. The AI answers by pointing to specific passages from the paper's text.
Load multiple papers side by side, ask comparative questions, and surface methodological differences across related studies.
The Academic Paper Analyzer made a real difference for both faculty and students.
A few things I picked up while building this tool.
Researchers retained more when they could ask questions about a paper in natural language than when they relied on traditional highlighting and annotation. Active dialogue with the text builds stronger mental models of the paper's arguments.
Academic PDFs are notoriously inconsistent. Multi-column layouts, embedded figures, footnotes, and citation formats all needed careful handling. Spending extra time on solid parsing with smalot/pdfparser paid off in better answer quality downstream.
Researchers only trusted AI answers when they could verify the source. By tying every response to specific passages from the uploaded paper, the tool earned credibility that generic AI chatbots couldn't match.
If you need AI-powered document analysis, conversational tools, or a custom research platform, reach out. We'd like to help you build it.