Build a Custom Reimbursement and Invoice Workflow

Many businesses start with tools like Expensify or Ramp. These platforms are effective for standard employee expense reports but struggle with the complexity of vendor invoice processing. Their optical character recognition (OCR) is generic; it can pull a total from a clean receipt but often fails to correctly parse line items from a multi-page PDF invoice or a hotel folio. This forces your accounting team to intervene manually.

Consider a 20-person services firm where consultants submit project-related expenses. An invoice might contain a flight, a hotel stay, and a client dinner. A standard tool captures the total but cannot automatically split the hotel cost between the billable room rate and the non-billable mini-bar charge. An accountant must open the PDF, find the specific lines, and create separate journal entries, one coded to a project's cost-of-goods-sold and another to overhead. This 15-minute manual task, repeated 50 times a month, consumes over 12 hours of skilled accounting time.

The structural problem is that these SaaS products are built on a fixed data model. You cannot create a custom rule that says, 'If an invoice is from vendor X for project Y, it requires approval from manager Z.' The workflow logic is predetermined. They are designed as closed systems to serve the most common use cases, which leaves businesses with unique project accounting or approval needs stuck with manual workarounds.

The first step is a discovery process to map your exact needs. We would review your chart of accounts, reimbursement policies, and a sample of 5-10 of your most common invoice formats. This audit clarifies the specific business logic that off-the-shelf tools cannot handle, resulting in a clear architecture document that outlines all data sources, approval rules, and integration points for your sign-off.

For a reimbursement system, we would build a Python service using FastAPI that accepts invoices via a dedicated email address. The Claude API extracts line-item data from attached PDFs or images, converting unstructured text into structured JSON. That data is then validated against your business rules using Pydantic schemas to ensure every field is correct before it's processed further. This approach provides the flexibility to handle any invoice format and enforce any approval logic.

The delivered system integrates directly into your workflow. Your team forwards an invoice, and within 60 seconds, a categorized transaction with a link to the original PDF is ready for approval or is posted directly to your ledger. You receive the full source code, which runs on AWS Lambda for minimal hosting costs (typically under $20/month). The package includes a complete runbook for maintenance and operation.