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:
[email protected]
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: [email protected]
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
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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
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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: [email protected]
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