Invoice and document OCR extraction into Procore and accounting
OCR reads incoming invoices and documents, extracts the fields you need, validates them against your systems and sends anything uncertain to a person.
The problem
Vendor invoices, subcontractor pay applications, lien waivers and certificates arrive as PDFs and scans in a shared inbox. Someone keys the vendor, amounts, retainage and cost codes into the accounting system by hand, and mismatches with the commitment are found late, if at all.
What we build
We build a pipeline that picks up documents from an agreed inbox or folder, reads them with OCR, extracts the fields you define and checks them against your vendor list, commitments and open invoices. Clean documents are prepared for your approval flow; anything low-confidence or mismatched goes to a review queue with the reason shown. Nothing posts to accounting without the approval step you set.
How it works
- 1
Define documents and fields
We agree which document types are in scope (vendor invoices, pay applications, lien waivers, certificates) and the fields to extract, such as vendor, invoice number, dates, amounts, retainage, job, cost code and commitment.
- 2
Capture and read
Documents are collected from a shared mailbox, SharePoint folder or upload screen and read with an OCR service. The original file is kept with the extracted data.
- 3
Validate against your systems
Extracted values are checked against the vendor master, job list, commitments and previous invoices in Procore and accounting, catching duplicates, unknown vendors and amounts over the remaining commitment.
- 4
Route and record
Documents that pass go into your approval workflow with the data pre-filled; exceptions go to a review queue with the failed check named. Every decision is logged against the document.
- Outlook or a shared mailbox
- SharePoint
- Procore
- Sage 100 Contractor
- QuickBooks Online
- OCR services such as Mistral OCR or Azure AI Document Intelligence
- Power Automate, n8n or Zapier
The value it creates
Time saved
Measured by timing manual entry for a sample of recent invoices against review time for the same documents after extraction.
Cost saved
Fewer keying errors and duplicate payments, measured by counting corrections and duplicates in a period before and after launch.
Early warning
Invoices that exceed the remaining commitment, duplicate a previous invoice or name an unknown vendor are flagged before approval, not after payment.
Standardization
Every document goes through the same checks and the same approval path, whoever opens the inbox that day.
Proof
Related use cases
Frequently asked questions
Have you built invoice extraction for a contractor?
Not as a published build yet. This is a pattern we design and build to your requirements, using pieces we have shipped elsewhere: OCR of project files in Connect, OCR into HubSpot in our Zapier walkthrough, and an OCR MCP server on our GitHub. We test field accuracy on a sample of your documents before go-live.
Will it post invoices to our accounting system automatically?
Only if you decide it should, and we recommend starting without it. By default the pipeline pre-fills your approval workflow and a person approves before anything posts.
How accurate is OCR on scanned or handwritten documents?
Typed PDFs extract well; poor scans and handwriting are less reliable. We measure accuracy per field on your own documents, and anything below the agreed confidence goes to the review queue instead of being trusted.
Can it handle pay applications and lien waivers, not just invoices?
Yes, each document type gets its own field list and checks. We usually start with the highest-volume type and add others once the first is running.
Next step
Which report or workflow would you like to improve?
Tell us what your team does today, which systems are involved, and what you want to change. We'll discuss whether there is a practical fit.
Prefer email? charley@buildflows.ai