PaperLens AI

AI Research Paper Analyzer that uses Amazon Nova to turn complex research papers into simple summaries and key insights. Search for papers or upload a PDF to instantly get explanations and quizzes.

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Inspiration

Research papers are often difficult to understand because they are long and highly technical. We built PaperLens AI to make research easier to explore and understand by combining AI-powered paper search with automatic explanations.

What it does

PaperLens AI helps users understand research papers faster. Users can search for papers by topic or upload a PDF to get simple summaries, key insights, explanations, and quiz questions.

How we built it

Frontend: Next.js, TypeScript, Tailwind CSS
Backend: Python, FastAPI
AI: Amazon Nova via AWS Bedrock
PDF Processing: pdfplumber / PyPDF2

User → Next.js App → FastAPI Backend → AWS Bedrock → Amazon Nova → AI-generated insights.

Challenges we ran into

Handling long research papers and extracting clean text from PDFs was challenging. We also had to carefully structure prompts to fit AI token limits while still producing useful explanations.

Accomplishments that we're proud of

We built a working platform that can search, analyze, and explain research papers using AI, making academic knowledge more accessible to students.

What we learned

We learned how to integrate Amazon Nova with AWS Bedrock, design effective prompts for document analysis, and build a full-stack AI application.

What's next for PaperLens AI

We plan to add semantic search, interactive Q&A with papers, and integrations with academic databases like arXiv and Semantic Scholar.

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