Automating document processing: CVs, invoices and application forms
Re-typing information from PDFs is some of the least rewarding work in any office. It's also one of the easiest jobs to hand to AI safely, if you validate the output.
By the Po team · 9 September 2026 · 6 min read
Recruiters read CVs and copy names, skills and notice periods into a tracker. Finance teams re-type invoice totals and due dates. Admissions teams transfer details from scanned application forms. In each case a skilled person spends hours doing work a careful system can do in seconds.
How AI extraction works
A model reads the document, including scans and photos, and fills a fixed set of fields you define: for a CV that might be name, contact, current role, years of experience, key skills and notice period. Because the output follows a schema, it can be checked automatically before it goes anywhere.
Validation is the important part
- Required fields must be present, or the document goes to a person.
- Formats are checked: dates are dates, totals are numbers, emails look like emails.
- Business rules are applied: an invoice total must match its line items.
- Low-confidence fields are flagged rather than guessed.
With these checks in place, the system handles the clean majority on its own and sends only the genuinely messy documents to your team.
Where the data goes next
Extracted data is only useful when it lands in the right place: a candidate record in your ATS, a bill in Xero or QuickBooks, a row in the admissions sheet. Each destination receives only the fields it needs. Anything that triggers a payment waits for approval.
A sensible first project
Choose one document type with a steady volume, define the fields with the people who use them, and run the system alongside the manual process for two weeks. Compare the results field by field, fix the rules, then switch over.
Describe your document process in our workflow builder demo to see a proposed automation with its checks and approval points.