Case study · AI Engineering
A fleet-management platform
Invoice intake was manual: PDFs uploaded, split and retyped by hand. We built AI-powered document processing with human review only for edge cases.
The mess we walked into
Invoice intake was a manual pipeline: PDFs arrived, someone split them, someone else retyped the data into the system. It was slow, error-prone, and scaled only by adding people. Errors surfaced downstream — where they were expensive to untangle.
What we did
- Built AI-powered document processing: automatic splitting of incoming PDFs
- OCR and structured data extraction tuned to the documents that actually arrive
- Human-in-the-loop review — the team only sees the edge cases the AI flags
- Integrated into the existing platform; no workflow retraining needed
Before / after
Processing per invoice minutes of manual work seconds, automated
Manual data entry the default for every document exception — flagged edge cases only
Errors discovered downstream caught at intake
Scaling more volume = more people more volume = same team
← All case studies"Invoice intake used to be a full-time job. Now the team reviews only the edge cases the AI flags — everything else just flows through."
Operations Manager, fleet-management platform, DACH
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