4 min

Document Processing

Auto-Summarize & Extract

Documents Are Everywhere

Every organization runs on documents — expense reports, meeting notes, contracts, proposals, status reports. Most of this content sits unread in shared drives, buried in email attachments, or lost in chat threads.

Document processing automation reads these files for you, extracts the important parts, and puts the information where it's useful.

Summarization: The Quick Win

Document summarization is the fastest automation to deploy and the easiest to get value from. For any uploaded document, AI generates:

Executive summary — 2-3 sentences capturing the main point

Key findings — Bullet list of important facts, numbers, or decisions

Action items — Who needs to do what, by when

Open questions — Unresolved issues that need follow-up

A 10-page meeting notes document becomes a 6-line summary your team actually reads.

Information Extraction

Extraction goes deeper than summarization. Instead of a summary, you get structured data:

Document Type	Extracted Fields
Expense report	Total amount, vendor, category, date, approver
Meeting notes	Attendees, decisions, action items, next meeting date
Contract	Parties, effective date, term length, key clauses
Invoice	Vendor, line items, subtotal, tax, total, due date

Extracted fields go into structured formats (JSON, spreadsheet rows, database records) that other systems can consume. An expense report's total and vendor automatically populate your accounting system.

Document Classification

Before you can extract the right fields, you need to know what type of document you're looking at. Classification identifies:

Document type — Invoice, receipt, contract, report, memo

Department — Finance, HR, engineering, sales

Priority — Urgent review needed, routine, archival

Sensitivity — Public, internal, confidential, restricted

Classification runs first, then routes to the right extraction template. An invoice gets different extraction rules than a meeting notes document.

Batch Processing

Real value comes from processing documents in bulk. Instead of handling one document at a time:

TRIGGER: New files in /uploads folder
  → For each file:
    → Classify document type
    → Extract fields using type-specific template
    → Generate summary
    → Store extracted data in structured format
    → Move processed file to /processed folder
  → Generate batch report (X files processed, Y errors, Z flagged for review)

Batch processing handles the backlog — the hundreds of documents already sitting in shared drives that nobody has time to read.

Building Your Document Workflow

In this module, you'll process the pre-seeded documents in your project:

An expense report with line items and totals

Meeting notes with attendees, decisions, and action items

Your workflow will classify each document, extract the relevant fields, generate a summary, and output structured data. The goal: any document uploaded to your system gets processed automatically within seconds.

Quality Control

Document processing accuracy depends on document quality. Build in checks:

Confidence scores on extracted fields (flag low-confidence extractions for human review)

Cross-field validation (does the line item total match the stated total?)

Format validation (is the extracted date actually a valid date?)

Missing field alerts (expected fields not found in the document)

Automation that produces wrong data is worse than no automation. Always verify before trusting.

This is chapter 4 of AI Automation Without Code.

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Ch. 3: Email Automation

Ch. 5: Slack Alerts