On-Premise AI Document Processing
Extract insights from PDFs using local LLMs without sending sensitive documents to external APIs
Oct 9, 2025
Privacy-First Processing All AI inference runs locally on your infrastructure No data sent to external APIs Complete compliance and data sovereignty control Intelligent Document Understanding Extract structured data from PDFs (invoices, contracts, forms) Answer questions about document content Automatic classification and summarization Cost-Effective Zero per-document costs No API fees or usage-based pricing Process unlimited documents with fixed infrastructure Flexible & Customizable Visual workflow editor - no coding required Easy to modify extraction prompts Support for multiple AI models (fast vs. accurate) Enterprise-Ready S3-compatible storage with MinIO GPU acceleration support (10x faster) Real-time and batch processing Production-ready with monitoring and error handling Easy to Deploy Docker-based setup in under an hour Works on CPU or GPU Scales from proof-of-concept to production
Organizations need to process thousands of documents with AI but can't risk sending sensitive data to external APIs. Cloud-based document AI services create compliance nightmares, unpredictable costs, and vendor lock-in. Legal, healthcare, and financial teams need intelligent document processing that keeps data on their infrastructure.
This pattern provides a complete, self-hosted document processing pipeline that automatically transforms PDFs into structured insights using AI. When you upload a PDF to your MinIO storage bucket, a webhook instantly triggers an n8n workflow that orchestrates the entire processing chain. Docling extracts and structures the text from your PDF, preserving layout and formatting context. LangChain then coordinates with your locally-hosted Ollama LLM to process the content according to your prompts—by default, it generates comprehensive summaries, but you can easily customize it for information extraction, classification, question answering, or any other document intelligence task. The processed output is saved as clean markdown back to your MinIO bucket, creating a fully automated, privacy-preserving document processing system that runs entirely on your infrastructure without any external API calls.
You'll need Docker and Docker Compose installed on your system, with at least 16GB of RAM available for running the LLM models (32GB recommended for optimal performance). Basic familiarity with Docker commands and environment variable configuration will help with setup and customization.
If you plan to process large volumes of documents or want faster inference times, an NVIDIA GPU with 8GB+ VRAM is highly recommended but not required—the system works fine on CPU, just slower. You should have approximately 50-100GB of free disk space to accommodate the Docker images, LLM models, and your document storage.
No prior experience with n8n, LangChain, or LLMs is necessary, as the pattern comes with pre-configured workflows ready to use, though understanding basic workflow concepts will help when you want to customize the processing logic.
Legal teams automatically summarize hundreds of contracts, extracting key clauses, obligations, and potential risks without sending confidential client documents to external services
Healthcare organizations process medical records and discharge summaries to create structured clinical data while maintaining HIPAA compliance through on-premise deployment
Financial institutions analyze quarterly reports and regulatory filings to extract KPIs, identify trends, and flag compliance issues, all within their secure infrastructure
Research organizations process academic papers at scale, generating summaries, extracting methodologies, and building searchable knowledge bases from their entire literature collections
Corporate teams transform meeting notes, technical documentation, and internal reports into structured knowledge bases, automatically categorizing and tagging documents for easy retrieval
Government agencies handle classified or sensitive documents in completely air-gapped environments, using the same intelligent processing capabilities but with absolute certainty that no data crosses network boundaries
Workflow not triggering
Check MinIO bucket creation container worked correctly setting up buckets and webhook
Verify the webhook or polling configuration in n8n
Review n8n execution logs
Ollama model errors
Ensure sufficient RAM is available
Pull the model manually:
docker exec -it ollama ollama pull llama3.2Check model compatibility with your hardware
Docling extraction issues
Verify PDF is not password-protected
Check PDF is not corrupted
Review Docling container logs
Out of memory
Reduce model size (use smaller models)
Increase Docker memory allocation
Process documents sequentially rather than in parallel
Clone and Navigate
Start Services
⏱️ First run will take 5-10 minutes as Ollama downloads the LLM model in the background.
Create n8n User
Open n8n at http://localhost:5678/home/workflows
Create your default user account on first access
Upload Documents
Open MinIO Console at http://localhost:9001/browser/pdfUsername:
minioadminPassword:
minioadminUpload your PDF files
Monitor processing in n8n workflow executions
Access Results
Check the pdf-summarized bucket in MinIO for processed markdown files.
Service URLs
n8n Workflow UI: http://localhost:5678/home/workflows
MinIO Upload: http://localhost:9001/browser/pdf
MinIO Console: http://localhost:9001
Ollama API: http://localhost:11434
Customizing the Processing Prompt
The default prompt summarizes documents. To customize:
Open the n8n workflow
Navigate to the "AI Processing" node
Modify the prompt template
Example prompts:
Summarization:
"Provide a concise summary of the following document:\n\n{text}"Key extraction:
"Extract key points and action items from:\n\n{text}"Translation:
"Translate the following text to Spanish:\n\n{text}"Data extraction:
"Extract all dates, names, and monetary values from:\n\n{text}"Q&A:
"Based on this document, answer: {question}\n\nDocument:\n{text}"
