Small and mid‑size companies generate a torrent of files—spreadsheets, design files, slide decks, Word docs, PDFs, audio notes and email threads—scattered across local drives, cloud services and messaging apps. Inconsistent naming and disorganized folders turn search into a guessing game, while version confusion stalls decisions, duplicates work and erodes team trust. Critical annotations get buried, contractual documents go missing and compliance risks rise, all dragging productivity and morale down. The AI‑Powered Document Librarian remedies this by ingesting every file type, using AI to extract context and metadata, and linking related resources so that natural‑language queries deliver precise results, instantly restoring teams’ focus on their core work
Feature Breakdown & MVP Boundaries
The MVP delivers a streamlined upload experience, powerful retrieval via an AI chatbot, flexible search, relationship visualization, and standard document viewing and sorting. Advanced analytics and external integrations are deferred to later releases.
Unified Upload & Metadata Extraction
Users drag‑and‑drop any file type—PDF, CAD, PPT, DOCX, JPEG, MP3, email thread, etc.—and the system immediately performs OCR, semantic parsing, and schema‑driven tagging (project, client, department, date, keywords) as part of the same workflow. Progress indicators show real‑time status and any auto‑tagging suggestions can be reviewed before finalizing.AI Chatbot Retrieval (RAG‑Powered)
Leveraging a Retrieval‑Augmented Generation framework, the chatbot is the primary interface for file retrieval. Users simply ask in natural language (“Show me the latest vendor invoices for Project Orion” or “Find design mockups that reference our new color palette”) and the system fetches, synthesizes, and ranks relevant documents, even suggesting related files based on semantic context.Faceted & Full‑Text Search
For users who prefer traditional querying, the library supports combined filters—file type, upload date, tags—and full‑text search across all ingested content. Results appear with inline previews and can be sorted by relevance, date, or custom fields.Relationship Graph Visualization
An interactive node‑and‑edge map reveals connections among files, projects, versions, and collaborators. Clicking any node brings up the document viewer, allowing users to navigate complex document networks with ease.Document Viewing & Sorting
Within the library, each document opens in a built‑in viewer supporting pagination, zoom, and annotations. Users can sort lists by name, date, file type, or metadata fields, ensuring quick access without leaving the interface.
Homepage branches into four sections:
Upload → Drag‑drop any file → “Edit & Confirm Metadata” → Document Info
Explore (Linking) → Multidimensional graph with filter controls → Click node → Document Info
Documents → Full‑text search + metadata filters → Pick result → Document Info
Retrieval / AI Chat → RAG‑powered chat with in‑chat filters and prompt lists → Select file → Document Info
Key User Flows
Upload Flow: Homepage → Upload → Confirm metadata → Document Info
Linking Flow: Homepage → Explore → Adjust filters → Click node → Document Info
Search Flow: Homepage → Documents → Search or filter → Document Info
Chat Flow: Homepage → AI Chat → Natural‑language query → Refine via filters/prompts → Document Info
Prototyping & Validation
Low-Fidelity: Greyscale sketches and wireframes tested with 5 users to validate user interactions and flow usability.
High-Fidelity: Clickable Figma and webpage prototype; weekly demos with engineers and PMs refined micro-interactions (hover states, loading indicators, error handling).
Interactivity: Hover states, transition animations on graph zoom
Accessibility: WCAG-compliant color contrast, keyboard navigation
Final Assets: Clickable Figma prototype link