Invoice Processing
Monitors Google Drive for new PDF invoices, uses AI to classify expenses and generate XML.
What it does
You save time on bookkeeping by letting this agent automatically find your PDF invoices in Google Drive, classify their expenses, and convert the data into XML for accounting systems. It routes high-value invoices to you via email for approval while sending routine notifications directly to Slack.
Step by step
- Parse the PDF binary stream using a library like PyPDF2 or pdfplumber to extract raw text strings.
- Construct an XML schema that maps extracted fields (vendor, date, amount) to standard accounting tags.
- Implement conditional logic branches that check if the invoice total exceeds a defined threshold for human review.
Where the LLM does the work
- Prompt the OpenAI model with few-shot examples to accurately classify ambiguous expense categories into GL codes.
- Instruct the LLM to extract specific line-item details from messy or non-standardized PDF layouts.
- Generate natural language summaries for Slack notifications that highlight key invoice details for quick team awareness.
Watch out for
PDF text extraction often fails on scanned images, so add an OCR step like Tesseract before sending data to the LLM.
XML generation might fail due to invalid characters from extracted text, so sanitize strings with regex before serialization.
Tools that fit
OpenAI Chat Model LLM Classify expenses and detect anomalies
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Invoice Processing: Monitors Google Drive for new PDF invoices, uses AI to classify expenses and generate XML. ## Specification - What it does: You save time on bookkeeping by letting this agent automatically find your PDF invoices in Google Drive, classify their expenses, and convert the data into XML for accounting systems. It routes high-value invoices to you via email for approval while sending routine notifications directly to Slack. - Trigger: Triggered by an event (Event · on new PDF in Google Drive or manual webhook) - Autonomy: You approve along the way - Expected setup effort: a few focused days - Tools/services involved: - Google Drive: Monitor and download invoice PDFs - OpenAI Chat Model: Classify expenses and detect anomalies - Google Sheets: Archive processed invoices - Slack: Notify finance team of updates - Gmail: Send approval requests for high value invoices ## Known pitfalls, handle each one explicitly in your implementation 1. PDF text extraction often fails on scanned images, so add an OCR step like Tesseract before sending data to the LLM. 2. XML generation might fail due to invalid characters from extracted text, so sanitize strings with regex before serialization. ## Reference implementation https://n8n.io/workflows/11911 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/automated-invoice-processing-with-ai/ via usecasesforagents.com
Frequently asked questions
Can I use a different Cloud Storage than Google Drive?
Yes. Google Drive is only the example cloud storage in this recipe. The same flow works with Dropbox, Google Cloud Storage, Microsoft OneDrive and Nextcloud. Swap the cloud storage connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for finance and accounting teams who'd rather review a draft than build one from scratch.