Every organization has a shared drive or document management system where files go to die. Documents land in the wrong folder, use inconsistent naming, or sit in an inbox folder indefinitely because nobody wants to spend time filing them properly.

The Problem

Manual document filing requires reading each document, understanding its type and context, and deciding where it belongs in a folder hierarchy. For teams processing dozens of incoming documents daily - contracts, invoices, reports, correspondence - filing is a constant low-priority task that never gets done well.

The AI Approach

Document classification models can read a document and assign it a category with high accuracy. An LLM can go further: it can extract metadata (date, parties involved, project name) and generate a standardized filename, then file the document in the correct location.

Three-Step Build

Step 1 - Document intake. Set up a watched folder or email inbox where incoming documents land. Extract text using OCR if needed.

Step 2 - Classify and extract metadata. Send document text to an LLM with your folder taxonomy as context. The model returns document type, suggested folder path, and extracted metadata fields (date, author, subject, project).

Step 3 - File and tag. Move the document to the classified location with a standardized filename. Apply metadata tags in your document management system for searchability.

Where It Breaks

Documents that span multiple categories (a contract amendment that is also a change order) need a primary classification rule. Scanned documents with poor OCR quality produce unreliable classifications. New document types not in your taxonomy need a fallback to human review.

The Production Path

Start with a small set of well-defined document types. Use a confidence threshold to auto-file high-confidence classifications and queue ambiguous ones for human review. Expand the taxonomy as the system learns from corrections.