I’m exploring how AI-powered document extraction could be most useful in workflow automation and would love your honest input.
If you could automate the extraction of structured data from business documents (e.g. invoices, delivery notes, purchase orders, contracts, product/supplier docs, bank statements, handwriting etc.) and plug it directly into your n8n workflows:
Which use cases would create the biggest value for you?
Invoice/finance workflows
Delivery notes & logistics
Purchase orders/procurement
Product or supplier documentation
CRM/customer onboarding
ERP/operational workflows
Something else?
Also curious:
Where do current OCR / document parsing tools still fall short for you?
Really looking for honest feedback and real-world automation pain points.
Nice question, thanks a lot for bringing this up and asking for real-world feedback.
From what I’ve seen when helping teams automate with n8n, the biggest and clearest value usually comes from invoice/finance workflows and purchase orders/procurement first. These documents are high volume, relatively structured and directly tied to money, so every percent of accuracy and every minute saved in data entry or reconciliation is tangible ROI. CRM/onboarding and supplier documentation come right after that, especially when you need to create or update multiple systems (CRM, helpdesk, internal DB) from the same set of documents.
Where current OCR/document parsing tools still hurt us is less about “can it read text” and more about how automation-friendly the output is. Many tools give you raw text or very generic JSON, but they don’t understand business context (for example: telling invoice number vs PO number, mapping line items into a clean schema, handling slightly different templates from dozens of vendors). This means we still spend a lot of time building brittle post-processing logic before the data is usable inside n8n. On top of that, real-world docs are messy (scans, photos, multiple doc types in one file), and error handling or confidence scoring is often not exposed in a way that plays nicely with workflows.
If you’re exploring this space, I’d personally be excited about a “document extraction layer” that outputs opinionated, automation-ready schemas (invoice, PO, delivery note, KYC package, etc.), with clear confidence scores and validation hooks, so that in n8n we can just drop in one node, map fields and focus on business logic instead of custom parsing.
Thanks a lot for sharing this, and sorry for the delayed reply. I wanted to gather a bit more input before responding properly.
Your points resonate very strongly. From what we’re seeing as well, the biggest value is not simply in “reading” documents, but in turning them into reliable, workflow-ready data. Invoice and finance workflows are often the obvious starting point because the ROI is so tangible. But purchase orders, delivery notes, supplier documentation, and onboarding processes are just as interesting once they trigger actions across multiple systems.
I also fully agree with your point on current OCR and parsing tools. The gap is often not text recognition, but business context, validation, and usable structure. Raw text or generic JSON is rarely enough for real automation. Teams still need to build a lot of post-processing logic to make the output usable in n8n.
That’s exactly the direction I find most promising: a document extraction layer that provides clean, use-case-specific schemas, confidence scores, and validation options - so workflows can focus on the business process rather than parsing exceptions.
Really appreciate your thoughtful feedback. It is very helpful. I’ll keep you posted
Thanks a lot for sharing this, and sorry for the late reply.
This is very helpful and confirms exactly what we’re seeing as well: invoices, CRM onboarding and purchase orders seem to be among the clearest high-value entry points because they connect document extraction directly to operational workflows.
Your point on handwritten documents and inconsistent vendor layouts is especially interesting. That’s also where the gap seems to be moving from pure OCR toward more context-aware extraction, validation and workflow-ready outputs.
Really appreciate you sharing your stack as well - n8n + Claude + Sheets/HubSpot is a very practical setup.
Disclosure up front: I’m building a tool in exactly this space (Entity Enricher), so take my bias into account — but I’ve hit the same wall @nguyenthieutoan describes and I think his framing is the right one.
The gap really isn’t OCR anymore. Multimodal models read documents fine. The gap is that “raw text → generic JSON” leaves all the business logic to you: which JSON shape, which field is the PO number vs the invoice number, what to do when the model isn’t sure, and how to detect that it confidently made something up.
What’s worked for me, in order of impact:
Make the schema the contract, not the prompt. A typed schema with per-field descriptions (“this may also appear as X or Y”) outperforms prompt tweaks. Validation failures go back to the model for self-correction instead of failing silently.
Let the model say “I don’t know.” Most fabricated values come from required fields. Nullable fields + an explicit “prefer null over guessing” instruction kill most of it.
For documents tied to money: two models, not one. Run the same doc through e.g. Gemini and Claude, diff field-by-field. Agreement → auto-accept. Disagreement → that’s your confidence score, for free — route to human review. This is the “validation hooks” idea but it actually catches the errors a single model’s self-reported confidence misses.
I ended up building this pipeline as a product because rebuilding it per workflow was painful — it plugs into n8n as the extraction step (schema in, validated JSON + per-field arbitration trail out — it’s at entityenricher.ai). Happy to share a workflow example if useful, and equally happy to compare notes if you build it yourself — @B2Btech your “use-case-specific schemas + confidence + validation” wishlist is almost exactly the design doc.
