Invoice & Receipt Extraction
Reading, validating, and filing incoming invoices and receipts — from the email landing in your inbox to the data sitting in your accounting system.
In your team’s words
- “Invoices, receipts, and documents are processed by hand, with errors.”
- “We retype the same data between tools every single day.”
Systems involved
- Email (Gmail / Microsoft 365)
- Accounting (QuickBooks, Xero, or your ledger)
- AI extraction (OpenAI / Anthropic API)
- Orchestration (n8n or Make)
- Review queue (Slack / Teams / email)
- Audit log (spreadsheet or database)
See it work
Run the workflow yourself
A simulated run of this exact workflow pattern with sample data — built by us for this site. During an engagement, the real pipeline runs on your documents, your accounting system, and your validation rules.
Trigger: email arrives in billing@
Ready{
"from": "billing@acme-supplies.com",
"subject": "Invoice INV-2291",
"attachment": "INV-2291.pdf",
"received": "2026-09-21T08:14:02Z"
}The problem
Why this work is worth removing
Someone opens the billing inbox, reads each PDF, and retypes vendor, date, line items, and totals into the accounting system. Multiply that by every invoice you receive, and it is hours of data entry per week, with the failure mode you dread: a typo that surfaces during month-end reconciliation, weeks after it happened.
The work follows the same shape every time: read the document, check it makes sense, put the numbers where they belong, and flag the ones that don't. That shape is exactly what an extraction pipeline with validation rules handles, while a human keeps final say on anything unusual.
How it works
The workflow, step by step
The same shape as the build spec you approve, with acceptance criteria attached to every step.
Trigger
New email in the billing inbox (or a file dropped in a shared folder)
Capture & normalize
The attachment (PDF or image) is pulled from the email, sender and received time are logged, and the document is queued for extraction. Nothing is deleted — the original stays in the inbox.
Extract with AI
An LLM (OpenAI or Anthropic API) reads the document and returns structured JSON: vendor, invoice number, dates, currency, line items, totals — each field with a confidence score.
Validate against your rules
Checks you define as acceptance criteria: line items must sum to the stated total, the invoice number must not already exist, the vendor must be on the approved list, and currency/dates must be sane.
Post — or queue for a human
If every check passes at high confidence, a draft bill is created in your accounting system, coded to the category that vendor always uses. Anything else lands in a review queue (Slack, Teams, or email) with the source document and extracted data side by side.
Log & notify
Every document gets an audit-log row (input, extracted data, checks run, decision, time). Approvers are notified above the spend threshold you set.
Human control
Where people stay in charge
Any invoice that fails a validation rule or lands below the confidence threshold goes to a human before anything touches the books. Approvals above your spend threshold always stay human.
When it breaks
Failure modes, handled in the spec
Every integration fails eventually. These are the cases the build handles explicitly.
| If this happens | The workflow does this |
|---|---|
| Unreadable scan | Re-attempt with OCR once, then route to the review queue |
| Accounting API unavailable | Retry with backoff, then alert the owner — the document is never dropped |
| Duplicate invoice number from the same vendor | Blocked and flagged automatically |
| Every run logged, so any posting can be traced back to its source document | |
Outcomes
What this pattern is built to produce
Stated as capabilities, not promised numbers. Your baseline is measured in the audit and re-measured after handover, so the outcome is yours to verify.
- Invoices can be processed the day they arrive, instead of the day someone gets to them
- Data flows from document to books without being retyped — removing the typo class of errors entirely
- Rule failures (totals mismatch, unknown vendor, duplicates) surface before posting, not at reconciliation
- The person who did data entry now reviews exceptions — minutes of judgment work instead of hours of typing
- A complete audit trail of every document and every decision, available the moment an accountant asks
Delivered as
The service behind this workflow
Want this on your process instead of sample data?
The free audit ends with a working proof-of-concept of one automation built on your real tools and data. You evaluate evidence, not a demo site.
- 30 minutes, on your real processes
- Prioritized roadmap you keep either way
- Working proof-of-concept before any contract
- Reply within one business day