Equal Foods operates in the supply chain across Europe. Invoices arrived from email, scans, and paper. The finance team had to read each file, type data into sheets, and cross-check records. 24vertex delivered a full invoice automation in two months that replaced this routine.

Our system pulled invoices from Gmail and from form uploads for scanned copies. AI and OCR read multiple languages and formats. The workflow saved clean data to Google Sheets, which finance used to reconcile against internal orders and government tax records. The outcome removed hours of manual entry and delivered a consistent process that saved about 18 hours per week.

Operational load before automation

Equal Foods processed 20 to 30 invoices a day. The team spent 3 to 4 hours each day to collect, read, and type invoice data. Files came in by email, printed pages, and handwritten notes. They needed 90 percent plus accuracy, then had to reconcile each invoice against internal order records and government-provided tax records. Suppliers sent documents in different languages and formats across Europe. The manual load made errors more likely.

• Intake from many channels: Gmail, printed pages, and handwritten notes.
• Target accuracy: 90 percent or higher on extracted data.
• Daily volume: 20 to 30 invoices.
• Time spent: 3 to 4 hours every day on typing and checks.
• Matching needed: internal order records and government-provided tax records.
• Vendors across Europe sent many languages and formats.

Automated architecture built in n8n

24vertex designed six connected automations using AI, n8n, OCR, Google Sheets, Gmail, and Slack. Each flow handled a single step and fed the next one. The setup focused on clear controls and outputs for finance.

• Email labeling in Gmail. The n8n flow read incoming messages, looked for PDF attachments and key phrases in the email body, and applied labels so the team could see invoice mail at a glance.
• Gmail channel extraction. The bot pulled invoice files from labeled emails, used OCR and AI to read the content, and wrote all required fields to a Google Sheet.
• Form channel extraction. A separate flow accepted scanned copies sent through forms, ran OCR and AI on those files, and added the same fields to the shared Google Sheet.
• Reconciliation against internal order records. The workflow compared invoice data to internal order data and flagged rows that did not match for fast review.
• Reconciliation against government tax records. The workflow checked tax details against government-provided records and flagged exceptions.
• Weekly reporting. The system built a weekly summary of what matched and what did not, then posted it to Slack and email for follow-up.

We configured AI to read multiple European languages and formats, so the same flows worked across suppliers without manual changes.

Measured gains

Equal Foods now saves about 18 hours per week in manual work. The team still handles 20 to 30 invoices a day, but they no longer type data into sheets or run checks by hand on every file.

The automations capture invoices from Gmail and forms, record the data in Google Sheets, reconcile with internal orders and government tax records, and send a weekly report in Slack. Finance sees clear status on matched and unmatched invoices and can focus only where attention is needed.

The build finished in two months and leaves room to handle more suppliers and higher volume in the future. These gains align with 24vertex's 300 percent ROI standard for automation projects.