What it does
Automatically summarizes uploaded PDFs in Google Drive using Claude, creating concise one-page briefs that extract key points, action items, and important details without reading the full document.
Why I recommend it
Nobody has time to read 50-page reports, contracts, or whitepapers. AI summaries let your team quickly grasp the essentials and decide what deserves deeper reading, saving hours of review time.
Expected benefits
- 80% faster document review
- Consistent summary format across all docs
- Key insights surfaced automatically
- Better meeting prep with less reading time
How it works
PDF uploaded to designated Google Drive folder -> trigger automation -> extract text from PDF -> send to Claude API with summary prompt -> create Google Doc with summary and link to original -> notify relevant team members.
Quick start
Start with a manual process: when important docs arrive, copy the text and paste into Claude for summary. Once you know what format works, automate the upload-to-summary pipeline.
Level-up version
Add intelligent routing based on document type (contracts to legal, research to product team). Extract specific fields like contract terms, budget numbers, or deadlines. Create searchable summary database for future reference.
Tools you can use
Storage: Google Drive, Dropbox, SharePoint
AI: Claude API, ChatGPT API
Automation: Zapier, Make, n8n
OCR: Google Cloud Vision, Adobe PDF Services for scanned docs
Also works with
Document platforms: Notion, Confluence for summary storage
AI tools: GPT-4, specialized doc analysis tools
Notification: Slack, email, Teams
Technical implementation solution
- No-code: Google Drive new file trigger -> Zapier -> extract text via PDF.co or similar -> send to Claude via API -> create Google Doc with summary.
- API-based: Drive webhook -> download file -> extract text with pdf-parse or Tesseract -> Claude API with structured prompt -> create formatted doc via Google Docs API.
Where it gets tricky
Handling scanned PDFs that need OCR first, maintaining formatting for tables and charts, ensuring summaries don’t miss critical nuances, and managing API costs for large documents.
