Hello
I am looking to hire a skilled n8n developer to build a complex workflow that addresses a significant data processing challenge I am facing.
Project Goal:
The primary objective is to process a large volume of PDF documents (totaling approximately 500 GB) and create a system that allows users to ask questions about the content in multiple languages, receiving accurate answers based only on the provided documents.
Key Requirements & Scope:
Input Data: Multiple PDF files, ranging in size from 100 to 4,000 pages each. They contain a mix of text and images.
Processing: The workflow must be robust enough to handle the sheer volume and file sizes efficiently. It needs advanced OCR (Optical Character Recognition) capabilities to extract text accurately, including from images/scans within the PDFs.
AI/LLM Integration: Integration with a powerful Language Model (LLM) is required for semantic search and Q&A capabilities.
Multilingual Support:
Source PDFs are in English and German.
The user interface/interaction must support questions and answers in multiple languages (Croatian, English, German, etc.).
n8n Specifics: The solution should be built primarily within the n8n ecosystem, leveraging its capabilities for automation and integration.
About Me & My Offer:
I am an individual seeking a professional solution. I am prepared to pay for expertise and a functional, reliable workflow that solves this specific business problem.
If you have proven experience with large-scale data ingestion, n8n, OCR, and AI integrations (like OpenAI, LlamaIndex, LangChain within n8n), please reach out.
Next Steps:
Please comment here or send me a direct message with your relevant experience/portfolio. We can discuss scope, timelines, and compensation details privately.
Thank you,
Hi @Damir,
Thanks for sharing your project details — this is exactly the type of system I build.
I’m Muhammad Bin Zohaib, an AI Automation Specialist, Full-Stack Developer, and Certified n8n Developer (Level 1 & 2). I’ve delivered AI automation and RAG systems for clients in the UK, Canada, Germany, Greece, Singapore, Australia, India, Sudan, and Spain.
Your workflow (OCR → chunking → embeddings → search → multilingual Q&A) fits perfectly with my experience building large-scale PDF AI chat systems, including similar pipelines in n8n, LangChain, and Pinecone.
Here are my projects, demos, and case studies:
All Projects with Demo Videos:
https://muhammad-ai-automations.notion.site/Muhammad-Bin-Zohaib-AI-Automation-Projects-29da292a241380f889c2e337a134c010
Portfolio Website:
https://www.muhammadz.fun/
LinkedIn:
https://www.linkedin.com/in/mbz1415/
Email:
muhammad.specials@gmail.com
WhatsApp / Phone:
+92 336 0327970
If you’d like, I can walk you through the best architecture for your setup and the exact workflow to handle large PDFs (up to the sizes you’re working with).
Happy to jump on a quick call or continue here.
Looking forward to collaborating! 
Hello @Damir .
I’ve been just go through your post and I’m confident enough that I’m best fit for this project.
I’ve recently completed Alot of OCR related projects which are all about Pdf’s. You can review my profile on Community.
Otherwise I’m up for a one to one call to tell you about my experience.
Here is my email: muhammadmoosa.abc1@gmail.com
Here is my WhatsApp Number: +923013872642
Hi there. This is a classic RAG (Retrieval-Augmented Generation) challenge. Processing 500GB requires a specific architecture to handle OCR costs and Vector Database indexing efficiently.
I am an AI Research Engineer specializing in n8n and Python-based LLM pipelines. I have successfully built similar multilingual architectures and would love to share how I would handle the 4,000-page PDF limitations. Sent you a DM!"
Dear Damir,
I hope you are doing well. I am writing to express my interest in the n8n developer position you posted. With extensive experience in large-scale document processing, OCR pipelines, and AI/LLM integrations, I am confident I can deliver a robust, end-to-end workflow tailored to your requirements.
Your project—processing approximately 500 GB of multilingual PDF files and enabling AI-powered Q&A based exclusively on the document corpus—aligns strongly with my technical background. Below is a brief overview of how I can support your objectives.
Key Capabilities I Bring
-
Advanced OCR Expertise: Implementation of high-accuracy OCR pipelines using Tesseract, Google Vision, AWS Textract, and custom pre/post-processing techniques for mixed text-and-image PDFs.
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n8n Workflow Engineering: Development of scalable workflows involving chunking, batching, parallel execution, retry mechanisms, and structured data pipelines.
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AI/LLM Integration: Integrations with OpenAI, GPT-based models, LlamaIndex, LangChain, vector databases (Pinecone, Milvus, Chroma), and multilingual embeddings.
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Multilingual Q&A Systems: Building end-to-end systems that accept user queries in multiple languages and return context-validated answers extracted only from approved documents.
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Large-Volume Data Handling: Experience designing ingestion workflows for data sets exceeding 1–3 TB with strict memory, performance, and accuracy constraints.
Proposed Technical Approach
-
Pre-Processing Layer: Automated ingestion of PDFs via n8n with file integrity checks, splitting large PDFs, and optimizing them for OCR.
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OCR Pipeline: High-fidelity OCR extraction with image preprocessing, layout detection, and confidence-scoring for multilingual documents.
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Embedding & Indexing: Chunking content and creating vector embeddings for semantic search using LlamaIndex or LangChain within n8n.
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Multilingual Q&A Engine: Integrating a robust LLM to support English, German, Croatian, and additional languages as needed.
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User Interaction Layer: Secure API or front-end interface to accept queries and return grounded, document-verified answers only.
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Scalability & Reliability: Use of asynchronous workers, scalable storage, and pipeline monitoring for long-running workflows.
Selected Case Studies
Case Study 1 – Enterprise OCR-to-LLM Workflow (270 GB Document Set)
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Designed a hybrid OCR + AI pipeline for a client processing thousands of scanned legal documents.
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Implemented segmentation, multilingual OCR, embeddings generation, and an LLM-based search/Q&A layer.
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Result: Reduced manual review time by 78% and achieved over 92% text extraction accuracy on mixed-quality scans.
Case Study 2 – n8n Automation for High-Volume Data Ingestion
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Built a fully automated n8n workflow ingesting 40,000+ PDFs weekly with parallelized parsing and storage validation.
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Implemented error recovery, checksum verification, and structured data extraction.
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Result: Zero pipeline downtime across 11 months and processing speeds improved by 4×.
Case Study 3 – Multilingual AI Knowledge System
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Developed a multilingual Q&A engine using OpenAI + vector embeddings for a European client.
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Supported 6 languages across 3 million text segments with real-time query validation.
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Result: Delivered 98% retrieval accuracy and cut customer support load by 60%.
Why I’m a Strong Fit
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Proven track record in OCR, n8n, AI pipelines, vector search, and multilingual LLM systems.
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Hands-on experience with massive data workloads and performance optimization.
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Strong focus on reliability, explainability, and efficient scaling.
I would be happy to discuss your project in detail, review your current environment, and propose a tailored architecture.
Hey @Damir
I can build a complete n8n-powered pipeline that ingests your 500GB PDF archive, performs high-accuracy OCR, and enables multilingual Q&A powered strictly by your documents. The outcome: a fast, reliable, secure system that answers user questions in any language using only your data.
Here’s the approach:
• Set up large-scale PDF ingestion with chunking, queuing, and advanced OCR (Tesseract/Google Document AI)
• Extract, normalize, and embed text using LlamaIndex/LangChain inside n8n
• Build a multilingual vector search layer + RAG pipeline (Croatian, German, English)
• Deploy a user-facing Q&A interface that queries only your document embeddings
Hi, n8n Level 2 certified. I’ve built AI workflows combining document processing, Claude API, and data extraction. Can help you set up OCR + AI parsing for your PDFs.
Available now — book a call: https://calendly.com/alessiobenincasa/30min
LinkedIn: https://www.linkedin.com/in/alessio-benincasa-salesforce/
Hey Damir
I got you, I have been building all forms of automations for the past 2 years and have built 100s of flows for my clients. Have worked with all sorts of companies and gotten them 10s of thousands in revenue or savings by strategic flows. When you decide to work with me, not only will I build this flow out, but also give you a free consultation like I have for all my clients that led to these revenue jumps.
I have built a similar workflow like this for one of my clients. I can not only share that but also how you can streamline processes in your company for faster operations. All this with no strings attached on our first call.
Here, have a look at my website and you can book a call with me there!
Talk soon!
Hi, I’m AK, an n8n automation specialist experienced in building large-scale OCR → chunking → embedding → vector search pipelines using Mistral OCR, OpenAI, Llama 3.1 via API, Supabase/ChromaDB, and fallback local models. I’ve designed end-to-end RAG systems inside n8n that ingest multi-GB PDF datasets, extract text from scanned documents, normalize multilingual content, generate structured metadata, and return accurate Q&A strictly grounded in the source files. I recently built a production-ready workflow for processing huge PDF sets via OneDrive → OCR → embeddings → Supabase, with multilingual querying (English, Spanish.) and optimized chunk indexing to avoid timeouts and reduce cost. Your requirement - ingesting 500GB+ of PDFs, running advanced OCR, and enabling multilingual Q&A inside n8n - is exactly the type of system I deliver. I can architect your workflow to scale, handle massive files reliably, maintain language fidelity, and integrate with high-performance vector search so users receive precise, source-bound answers. Happy to discuss scope and design a workflow blueprint before build-out.
My email: pasnoor.ak@gmail.com
Portfolio: Notion
Hi,
This sounds like a really interesting project, 500GB of PDFs with multilingual RAG is definitely a complex challenge.
I have extensive experience with n8n workflows, OCR pipelines, and LLM integrations at scale.
Would love to discuss the architecture and approach in detail.
You can reach out to me on my email here
Colin
Hey, I can help with this — just a quick heads-up that n8n isn’t really suited for 500GB ingestion + OCR + embeddings. It’s great for orchestration, but the heavy lifting usually needs a proper backend + vector DB.
If you’re open to that approach (using n8n only where it makes sense), I’d be happy to chat and point you in the right direction — no spam or sales pitch.
I have built this using PDF API hub’s (verified node) - action - Analyze PDF
here is workflow
{
"nodes": [
{
"parameters": {
"resource": "ocrParsing",
"operation": "analyzeDocument",
"analyze_url": "``https://generated-image.s3.ap-south-1.amazonaws.com/pdfs/200826/8e78e0c5-fa04-4224-a811-c7aff3d0974d.pdf``",
"analyze_features": [
"TABLES"
]
},
"type": "n8n-nodes-pdf-api-hub.pdfSplitMerge",
"typeVersion": 1,
"position": [
976,
336
],
"id": "26a5ac9e-572b-40be-a47b-caf77664bdc0",
"name": "Analyze a PDF document",
"credentials": {
"pdfapihubApi": {
"id": "s46d70PNdmyuIKbF",
"name": "PDF Hub account 7"
}
}
}
],
"connections": {},
"pinData": {},
"meta": {
"templateCredsSetupCompleted": true,
"instanceId": "277842713620d9f5554de3b1518b865a152c8c4db680008bd8aec536fc18b4a8"
}
}
Hi Damir — the 500 GB scale changes the design quite a bit. I would not try to push each document through one long n8n execution. I’d use n8n as the orchestrator around object storage, a resumable job queue, and OCR/ingestion workers, with document hashes and page-level state so a failed 4,000-page file can resume instead of starting over.\n\nBefore committing the full corpus, I’d run a paid pilot across a representative sample: native English and German PDFs, scanned pages, mixed images, tables, and poor-quality OCR. The acceptance criteria should measure extraction quality, source citation accuracy, multilingual Q&A, and recovery after an interrupted job. The retrieval layer should always return page-level sources and refuse unsupported answers.\n\nMy document automation work includes parsing 84 bank statements into nine report types and hardening a nonprofit intake/API workflow with deterministic validation, duplicate recovery, audit evidence, and a 20-case edge suite. I also build the Python/Node services around n8n when the heavy processing should not live inside the workflow engine.\n\nIf the project is still open, I’d be glad to scope that representative pilot before anyone spends money indexing the full 500 GB.\n\nPortfolio: https://terry-benjamin-jr-production.up.railway.app
Hi Damir, if this is still open, TinyOps Studio LLC would treat the 500 GB corpus as a staged ingestion project. n8n should orchestrate uploads, queues, retries, and status changes, while OCR and indexing run in resumable workers outside one long execution.
I can take a fixed $1,000 pilot around a representative sample. It would deliver one working PDF-to-answer path, page-level source citations, English and German OCR checks, Croatian, English, and German question tests, failure-resume validation, and a measured cost and runtime model for the full corpus.
A public TinyOps n8n implementation example is github dot com slash tinyopsstudio slash n8n-stripe-failed-payment-recovery. It demonstrates idempotency, retries, alerts, fixtures, and handoff documentation. For this project, the first acceptance test would be grounded answers and source citations after an interrupted ingestion resumes cleanly.
One scope question determines the architecture: can the PDFs leave the current environment for managed OCR and embeddings, or must every document and index remain in a private cloud or on-premises?
I work asynchronously in writing. Details: tinyopsstudio dot com slash n8n-automation-specialist. Written contact: support at tinyopsstudio dot com.
Hey @Damir
I’ll build an n8n-driven pipeline that ingests your PDFs at scale, runs advanced OCR on text and scanned images, and powers accurate, source-grounded Q&A in Croatian, English, German, and more.
Here’s the approach:
- OCR + text extraction tuned for 100–4,000 page mixed-content PDFs
- Chunking and embedding into a vector store for semantic retrieval
- LLM Q&A layer answering strictly from your documents
- Multilingual query/response handling across source and user languages
- Batched ingestion built to handle the 500 GB volume reliably
Hi Damir, if this is still open: I’d start with a small paid pilot rather than the full 500GB scope - one representative batch (50-100 docs) through OCR plus parsing plus storage, to validate accuracy and cost per page before committing to the full pipeline. Before any code I ask for a small deposit up front (roughly 30% of the pilot price, refundable if I can’t deliver) - keeps a first project low-risk for both of us. Could you share what output format you need and where the pilot files could come from?
At 500 GB, the first deliverable should be a representative ingestion benchmark, not a promise that one large n8n canvas will scale. I build stateful AI applications and reproducible n8n acceptance workflows, but I do not claim a prior 500 GB production OCR deployment. I propose a paid $150 feasibility slice using three sanitized documents: one text PDF, one scanned/image PDF, and one long mixed PDF. The report would measure render/OCR time, extracted-page coverage, chunk provenance, duplicate handling, multilingual retrieval accuracy, and the storage/index boundary; n8n would orchestrate jobs rather than hold large binaries in execution history. Acceptance would include source-page citations and an explicit “not found in documents” case. If you can provide representative page counts, languages, hardware/cloud preference, and three safe samples, I can define the exact benchmark before discussing the full system.
500 GB of mixed text and image PDFs changes the shape of this project completely, so a few things I would fix before writing a single node.
n8n should orchestrate, not process. Pushing 4,000 page files through the workflow itself will exhaust memory. The pattern that survives is: n8n receives the file reference, dispatches the heavy work to a worker outside the workflow, and only handles state and retries. Otherwise a single large document takes the whole instance down.
Chunk at the page level, not the document level. Each page becomes its own unit with its own status, so a failure at page 3,200 does not force a reprocess of the previous 3,199. This is also what makes progress visible and the cost predictable.
Split the OCR path. Pages that already carry a text layer are extracted directly, which is fast and nearly free. Only the true image pages go to OCR. On mixed corpora this usually removes most of the cost people budget for.
For the answering side, store the page number and document with every chunk and return them with the answer. Without the citation the users cannot verify, and a confident wrong answer on legal or technical documents is worse than no system at all. Multi-language works fine on multilingual embeddings, but the question and the document language must be tested together, not assumed.
I build orchestration like this in production, with idempotency so a reprocess never duplicates, and a watchdog that alerts when a batch stops advancing.
One question that decides the architecture: is the 500 GB a fixed corpus you process once, or does it keep growing?
Hi Damir, I saw your 500 GB multilingual PDF OCR and Q&A project and the thread is active again. This is close to my AI and software work: n8n orchestration, API and LLM integrations, document pipelines, structured validation, retries and source-grounded outputs. I would not put 500 GB inside n8n executions. I would start with a paid feasibility slice using three representative PDFs, with a worker for OCR, page-level storage, embeddings, citations and a multilingual Q&A test. Then we can measure accuracy, throughput and cost before scaling. I can build the n8n orchestration and the supporting service. I am based in Berlin and can start after one week. Portfolio: https://victormaynard.com. I can do the feasibility slice for USD 250 fixed. If this is still open, what are the sample file types, preferred storage and budget?
Damir — at 500 GB the risk isn’t the OCR engine, it’s that nobody can tell you the per-page accuracy or cost until someone measures it on your actual files.
Two things worth separating: native-text PDFs need no OCR at all (much cheaper), and only the scanned pages go down the OCR path. Page-level chunking with independent status tracking too, so a 4,000-page file can partially fail and resume instead of restarting.
I work daily in Python + Claude agents. Public, tested work you can inspect: GitHub - koraynar/n8n-ai-workflows: n8n workflows built and CLI-executed locally: AI lead intake & qualification, Claude-powered email triage, and a deduping job-feed digest — each with a headless-executable twin and real run evidence · GitHub (three n8n workflows, CLI-executed, with run evidence in the repo) and GitHub - koraynar/doc-extract-mcp: MCP server that gives an AI assistant deterministic document tooling: PDF/text reading, chunking, JSON Schema validation, structured output — path-confined, fully tested · GitHub — the deterministic layer after OCR: page-marked extraction, chunking with overlap, JSON-Schema validation so citations stay accurate. To be clear, that repo does not do OCR itself; the engine is a separate pick, and picking it is part of the pilot.
Happy to run a measured pilot on 3 representative PDFs — accuracy, throughput, cost per page — before anyone commits to the full corpus. Fixed fee for the pilot, from $25/hr on ongoing work, white-label fine.