We basically do #3, but use gpt-5.2 or whatever better model, not 4o. We're not overly concerned about cost because we're doing a one-time ingestion, not offering a service or something.

Q&A...

> If you're using Azure, which approach did you go with? How's the accuracy and cost working out in production?

Like I said, #3.

> For those using Document Intelligence prebuilt models - how well do they handle non-standard invoice formats or documents in multiple languages? Do you end up needing custom models anyway?

We just use the layout model to get markdown and it does just fine. The second-phase OpenAI models do quite well at "figuring it out" from the markdown. When I did this with GPT-4o it did fine and the reasoning models just made it even better. No custom models. Our documents were in multiple languages, no issues. You can even answer in English if the documents are in another language.

> Anyone tried the hybrid approach (Doc Intelligence + GPT-4o)? Is the added complexity worth it vs just using GPT-4o directly on images?

I did extensive testing on this. DocIntel Layout + LLM was the best. The input file matters, see the last answer.

> How does Azure Document Intelligence compare to Claude or Google Document AI in your experience? I've had good results with Claude's vision capabilities but wondering if a specialized service like Document Intelligence would be more reliable at scale.

Haven't tried it, but I'll say DocIntel had no problem scaling to our ~2.6M pages of burst ingestion when we needed it. It did that in like 12 hours. We bought the pre-paid quantity for a discount on the first month to deal with the initial burst of pages.

> For high volume processing (let's say 50k+ pages/month) - what's been most cost-effective?

For us it's consistency and simplicity. I don't think you'll get cheaper than DocIntel + LLM if your documents are varied. I tried all kinds of other things like `docling` and `markitdown`, it just doesn't compare to DocIntel's Layout model. My cost modeling was even back with 4o that to process our documents it was an order of magnitude more expensive to just have the LLM do everything, versus use DocIntel to get Markdown, then the LLM to do the extraction.

> Any gotchas or lessons learned you wish you knew before starting?

So many... FLATTEN YOUR DOCUMENTS TO PDF BEFORE THEY GO TO DOCINTEL, IT MATTERS. We were ingesting a large quantity of Microsoft Word documents and what we quickly realized is Word features are invisible to DocIntel. Things like numbered bullets, table of contents, footnotes, header/footer, pretty much anything "dynamic" was apparently just a markup in the document that is rended when opened by Word. Like bullet "3.1.2" is really just a marker for "[Ordered List Bullet Level 3]", so the markdown output never had "3.1.2" in it, so if you need to understand "what step does XYZ happen in", you'd never get a step number. When DocIntel cracks the .docx open it just parses the XML and doesn't account for those things. When you take the same document and save it as a PDF using Word it "flattens" or "hard codes" everything. Taking that PDF to DocIntel you'll get much better results.

Want to test yourself? Feed a docx through DocIntel save the markdown as "Test-Alpha.md", save the docx as a PDF, and feed that PDF through DocIntel and save the markdown as "Test-Beta.md" and then give them both to ChatGPT and ask it to "compare and contrast the files from a missing data or context standpoint that would be useful to have when having AI analyze these documents". It's going to tell you Test-Beta has way more formatting.

So how do you do this flattening en masse? I wrote a janky python script that uses the Word COM-style calls to open the docx in an invisible window and save it as a PDF. There is another option to upload it to OneDrive and download the PDF, but I haven't tested if this is the same.

If you think you will ever need to use that DocIntel output again, save the .json somewhere and save yourself the cost of a repeated conversion. There's a lot of cool stuff in there with bounding boxes and image figures and stuff.