Context

AI PDF Reader solves a practical problem: people want to ask questions about long PDFs without sending sensitive files to a remote service and without losing trust in the answers. The product had to feel simple for end users while still handling local model setup, document parsing, retrieval, study-guide generation, and desktop delivery.

Architecture decisions

• Built the application with Next.js and TypeScript, then extended it with Electron so the same product experience could run as a desktop app.
• Used PDF parsing plus chunking and embedding workflows so answers stay grounded in retrieved document sections instead of generic model output.
• Added local model status handling and desktop runtime directory management so offline usage is practical instead of fragile.
• Structured the product around clear flows: upload PDF, parse text, create chunks, embed locally, retrieve relevant sections, then answer with a local model.

Security analysis

• The primary trust boundary is the user device. Uploaded PDFs, generated embeddings, and local model assets all need clear handling because documents may contain sensitive information.
• Key threats include malicious PDFs, unsafe local file handling, prompt abuse, data leakage through logs, and desktop packaging risks when distributing bundled models.
• The design reduces exposure by preferring local-first execution, local storage paths, explicit security policy guidance, and tighter desktop boundaries through Electron preload isolation.

Delivery impact

• The project brings product thinking, AI integration, UX design, desktop packaging, and security awareness into one system.
• It combines architecture structure, retrieval design, runtime trade-offs, privacy posture, and practical implementation details.
• It spans interface design, backend workflow design, local AI integration, packaging, documentation, and release readiness.