The challenge
The product had to extract and process PDF content, support semantic matching, and present answers in a simple interface users could trust.
Case Study
AI PDF Chatbot
QPDF shows how I turn a document-heavy use case into a product people can actually use and understand.
The solution
I combined Next.js, FastAPI, PostgreSQL, and OpenAI to build a document question-answering flow that handles both frontend experience and backend retrieval logic.
What this delivered
- A strong example of AI chatbot and document workflow development
- A clear proof page for searchers comparing AI developer options
- Better internal SEO alignment with the AI development service page
Project details
Next.jsOpenAIFastAPIPostgreSQL
Key features
- PDF document processing and chunking
- Vector embeddings for semantic search
- Real-time chat interface
- Context-aware responses
Best fit services
- Next.js full-stack development
- FastAPI backend development
- Supabase and PostgreSQL architecture
Need a document AI product?
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