🧠 Building a Research Assistant with AI:
From Tool to Platform

🚩 The Challenge

I set out to solve a simple but common problem:

"How can I make research faster, smarter, and less painful when working with long academic or technical documents?"
The original idea was to build a lightweight tool for summarizing PDFs using GPT. But the project quickly evolved into something more—a modular, extensible AI-powered research assistant that supports multiple formats, automates citation handling, and exports professional summaries with just a few clicks.

🔧 The Solution

I built a full-featured AI-powered web app using Streamlit that integrates:

• 🧾 PDF and TXT file uploads
• 🌐 URL processing for online articles or PDFs
• 🤖 GPT-4 Turbo summarization (multiple styles)
• 📚 Citation detection and formatting (APA, MLA)
• 🧠 Topic detection
• 📤 Export to TXT, PDF, Markdown, HTML, BibTeX, CSV, and JSON
• 🔐 Local login system (with future encrypted database support)
• 🧪 Batch file processing
• 📁 Timestamped outputs and in-app previews

🔍 Feature Highlights

✨ Smart Summaries

Users can choose summary styles, reprocess specific sections, or ask the assistant to "Explain this further."

📑 Citation Wizard

The assistant detects in-text citations, fetches metadata via DOI/URL, and reformats them into APA or MLA automatically.

📂 Batch Processing

Upload multiple files at once (coming soon with progress bar support). Each document is summarized and cited separately.

📤 Flexible Exporting

Users can export results to multiple formats with a single click. File names are automatically timestamped for easy organization.

🧱 The Stack

Core Tools

• Streamlit for the interface
• OpenAI GPT-4 Turbo for summarization and explanations
• pdfplumber, PyPDF2, and BeautifulSoup4 for parsing
• fpdf for PDF export
• YAML + streamlit-authenticator for login and user config (Temporary, Soon to be replaced with SQLite)
• Custom Python utilities for citation parsing, formatting, and file management

💡 Technical Decisions That Paid Off

• Modularity: I kept the codebase clean by separating citation logic, export utilities, and AI interaction modules.
• Scalability-first: Even before SaaS considerations, I built with growth in mind—batch processing, export options, and file management all scale well.
• User-first UX: I prioritized ease of use: checkbox controls, file previews, and download buttons make this accessible to non-technical users.

📈 Impact & Use Cases

• While it started as a personal project, the app has real potential for:


• Students writing annotated bibliographies
• Researchers conducting literature reviews
• Consultants extracting insights from dense technical or industry reports
• Writers and journalists summarizing long-form research

“Finally, a research tool that doesn’t just summarize, but also tells me where the information came from.” — hypothetical future fan

🚀 What’s Next?

• I'm preparing the app for a potential SaaS offering. Future additions include:


• ✅ Encrypted database for user sessions & projects
• ✅ Project save/load features
• 🌍 Multilingual summarization
• 📊 Visual dashboards and infographics
• 🧩 Prompt customization
• 📋 End-to-end research project workflow support

💼 Work With Me

• This project is just one example of how AI tools—when applied thoughtfully—can solve real-world workflow problems.

If you're a:


• Research-heavy organization
• Educational platform
• Knowledge-based consultant or analyst
• Startup building custom AI workflows
• Experienced Python Coder

👋 I'd love to work with you.

💬 Let’s Talk About AI Solutions

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