Recipe 2 of 5 · document intelligence
You can automate real estate contract clause extraction today using Claude or ChatGPT and a Google Sheet. Here's the exact build, and where it stops working at scale.
Document-heavy processes share a common failure mode: the information exists but isn't findable when needed. Appraisals sit in email attachments. Lease terms live in a PDF five people have saved in five different folders. The contract deadline everyone missed was recorded somewhere — a note field, a spreadsheet, an email chain — but not in a place where a system could fetch it automatically.
The automation opportunity here isn't primarily about AI reading documents. It's about getting documents into a structure where they can be acted on and shared across a team. Once that structure exists, AI accelerates the review and drafting layers on top of it.
In a Google Sheet, create a table with columns: Asset ID, Asset Name, File Name, Document Type, Document Date, Document Author, Closing Date, Deposit Amount, Payment Date, Suspensive Conditions, Suspensive Deadline, Penalties, Other Conditions.
For any contract or lease that needs reviewing, paste the relevant sections into Claude or ChatGPT with the prompt below.
Paste the output into the Google Sheet document database. This gives you a queryable register of contract terms rather than a folder of PDFs.
Review the AI-extracted clauses and confirm accuracy before populating the sheet. This step ensures the register reflects verified data, not raw AI output.
In n8n, build a daily workflow that reads your contract terms sheet and checks for closing dates or suspensive condition deadlines falling within the next 30 days. When found, it sends a summary to the responsible manager.
Use Make or Zapier to pull confirmed contract data from your register and populate draft document templates automatically, reducing manual data entry at the drafting stage.
Yes, for extraction of well-defined fields — closing dates, deposit amounts, suspensive conditions — when you paste the relevant sections directly and ask for a structured list. Accuracy drops if you paste an entire multi-document bundle at once; work contract-by-contract and always have a human confirm the extracted values before they enter your register.
The same clause-extraction prompt works per document, but doing it across dozens or hundreds of leases individually is where a manual DIY workflow becomes the bottleneck — not the AI's accuracy, but the lack of a queryable, permissioned register behind it.
Access control, not scale. A shared spreadsheet has no field-level permissions — anyone with edit access sees every contract term, deposit amount and buyer detail, which doesn't satisfy institutional audit or segregation requirements.