Been struggling with extraction workflows that need to process invoices from PDFs, scanned contracts, and email attachments all in one system. Manual parsing eats too much time and different formats break our existing automations. How are others handling model selection for varied document types without building separate pipelines for each format?
Latenode’s model router automatically picks the best AI for each document type. Set up one workflow that handles PDFs with Claude for text analysis and OpenAI for structuring tables. Works with scanned docs too using OCR models.
I built a Python middleware that routes files based on MIME types to different libraries - PyPDF2 for PDFs, Tesseract for scans. But maintaining it became messy. Recently started testing AI services that promise automatic format handling, but still need better validation steps for financial docs.
Key is implementing a validation layer after extraction. I use a three-step process: 1) File type detection 2) Dedicated parser per format 3) Cross-checking extracted values against expected patterns. For legal docs, we added custom regex checks that flag mismatches in contract numbers or dates automatically.
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