The challenge
Three staff spent most of each week keying supplier invoices into the ERP by hand — slow, error-prone and impossible to scale with shipment volume.
Our approach
We mapped the process, then built a document-AI pipeline: OCR and field extraction, validation rules against purchase orders, automatic ERP posting, and a review queue for the exceptions bots should not decide.
The outcome
Processing time fell by more than four fifths, the error rate dropped below one percent, and the team moved onto supplier negotiation instead of data entry.
Why this approach
Full autonomy was never the goal — trustworthy throughput was. Invoice data feeds payments, so a bot that guesses wrong quietly is worse than a human who types slowly. That is why the pipeline validates every extraction against purchase orders and routes anything ambiguous to a human review queue instead of deciding. The bots handle the volume; people keep the judgment calls. That split is also why the error rate could fall below one percent rather than merely moving errors around.
Payback came from picking the right process, not from clever engineering: high volume, clear rules, three systems involved and no API between them — the profile where automation pays for itself fastest.
If you are facing something similar
Count the hours your team spends re-keying data between systems in a normal week — that number is usually the whole business case. A process discovery audit ranks your candidates by return before anything is built, and the automation guide covers how to tell a process worth automating from one that is not.