Reimagining Invoice Data Extraction with Mistral Document AI

Alex Dang
Oct 3, 2025 9:29:39 AM

Before the rise of AI-driven document processing, extracting data from PDFs such as invoices or W-2 forms was typically handled through fixed coordinates. Even with advanced platforms like ABBYY Vantage, coordinates still need to be configured before machine learning can be applied. This approach introduces challenges: invoices vary widely in format, fields such as invoice numbers appear in different positions, and scanning errors can shift text placement. These inconsistencies often lead to false positives that may go unnoticed until much later in the process.

Mistral Document AI takes a different path. Instead of relying on coordinates, it applies large language model techniques similar to those behind ChatGPT, Gemini, and Copilot. With this approach, we were able to reliably extract invoice numbers, dates, and other fields without the need for manual coordinate mapping.

Our process involved:

  1. Uploading the PDF file to Mistral AI
  2. Retrieving the uploaded file’s URL
  3. Running OCR to convert the PDF into markdown text
  4. Using Mistral AI chat completions to extract structured data from the text
  5. Deleting the uploaded file once processing was complete

The results were impressive. Every data point on the invoice was successfully captured, and we could even request output in JSON schema format for direct use in development. The only limitation we encountered was the lack of built-in PDF splitting, which would require a third-party library such as AsposePdf.

Overall, the experiment demonstrated the potential of Mistral Document AI to transform document processing, and we look forward to seeing how it evolves alongside competing solutions.

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