Building Private RAG: A Blueprint for SharePoint & n8n

Most organizations already have the ingredients for enterprise AI success. They have SharePoint. They have years of accumulated knowledge stored across documents, spreadsheets, policies, manuals, contracts, and project files. They may even have access to powerful AI models. Yet when employees ask questions, the answers are often incomplete, inaccurate, or missing entirely.The problem isn’t the AI model.The problem is retrieval.In this episode of the M365 FM Podcast, we take a deep dive into building a fully private Retrieval-Augmented Generation (RAG) platform using SharePoint, Microsoft Graph, n8n, Mistral OCR, Azure OpenAI, PostgreSQL, Supabase, and Open WebUI. Rather than focusing on theory, this episode walks through the complete architecture required to transform a traditional SharePoint environment into a secure, enterprise-grade AI knowledge system capable of answering questions based on your organization’s own content.

WHAT RAG REALLY IS

Retrieval-Augmented Generation is often described as giving AI access to your documents, but that explanation barely scratches the surface. The reality is that a RAG system introduces an entirely new layer between the user and the language model. This retrieval layer determines what information reaches the model and ultimately dictates the quality of every answer.We explore how vector embeddings work, why semantic search differs fundamentally from keyword search, and why organizations that focus solely on upgrading models often fail to improve answer quality. You’ll learn why retrieval accuracy is the true foundation of successful enterprise AI.

WHY SHAREPOINT SEARCH IS NO LONGER ENOUGH

Traditional SharePoint search was designed for finding documents. Modern knowledge workers need answers.Throughout the episode, we examine why keyword-based search struggles to understand intent, context, and meaning. Questions asked in natural language rarely match the exact vocabulary used inside documents, creating a gap between what users need and what traditional search engines can deliver.This discussion highlights how vector search solves the vocabulary problem by searching for meaning rather than words, allowing organizations to unlock knowledge that was previously hidden behind folders, file names, and inconsistent terminology.

BUILDING THE COMPLETE PRIVATE AI ARCHITECTURE

The heart of the episode focuses on the architecture itself. We walk through every layer of the solution, beginning with SharePoint as the primary source of truth and Microsoft Graph API as the bridge between SharePoint and the automation layer.From there, n8n acts as the orchestration engine, coordinating ingestion workflows, retrieval workflows, document processing, and AI interactions. Mistral OCR transforms complex documents into structured content, while Azure OpenAI generates embeddings and powers the language model experience. PostgreSQL and Supabase provide storage and vector search capabilities, while Open WebUI delivers a familiar ChatGPT-style interface for end users.The result is a completely private AI environment where organizations maintain full control over their data, infrastructure, and compliance obligations.

DOCUMENT INGESTION, OCR, AND AGENTIC CHUNKING

One of the biggest challenges in enterprise AI is document preparation. Most organizational knowledge doesn’t exist as clean text. Instead, it lives inside PDFs, scanned documents, spreadsheets, images, diagrams, contracts, and complex reports.This episode explores why OCR quality directly impacts retrieval quality and why Mistral OCR has become one of the most compelling options for enterprise document processing. We also dive into agentic chunking, a more advanced approach to document segmentation that uses AI to identify logical boundaries instead of relying on fixed character limits.By preserving context and meaning throughout the ingestion process, organizations can dramatically improve retrieval accuracy and overall answer quality.

FROM VECTOR SEARCH TO AGENTIC RAG

Basic RAG systems stop at vector retrieval.This architecture goes much further.Instead of relying on a single retrieval mechanism, the AI agent can dynamically choose between multiple tools depending on the question being asked. For semantic questions, it uses vector search. When additional context is required, it retrieves complete source documents. When calculations, aggregations, or structured data analysis are needed, it generates and executes SQL queries against relational data.This multi-tool approach creates a significantly more capable assistant that can handle both unstructured knowledge and structured business data within the same conversation.

GDPR, DATA SOVEREIGNTY, AND COMPLIANCE

Privacy and compliance are not afterthoughts in this architecture. They are foundational design principles.We discuss how to build a solution that remains entirely within European infrastructure, leveraging EU-hosted services, Azure Data Zone deployments, self-hosted components, and privacy-conscious design decisions. The episode covers data residency, vector database sovereignty, retention strategies, deletion workflows, and the practical realities of building enterprise AI systems that satisfy GDPR requirements.For organizations operating in regulated industries, this section provides valuable insights into balancing innovation with compliance.

SELF-HOSTING, SCALING, AND PRODUCTION DEPLOYMENTS

Building a proof of concept is easy. Running a production-grade AI platform is something entirely different.The conversation explores infrastructure decisions, Docker deployments, worker architectures, Redis queues, PostgreSQL scaling, and the trade-offs between self-hosting and managed services. We explain why certain advanced capabilities require self-hosted environments and how organizations can start small before scaling into more sophisticated architectures.Special attention is given to reliability, monitoring, and operational best practices that become critical once users begin relying on the system every day.

KEY TOPICS COVERED

• Private RAG architecture using SharePoint and n8n
• Microsoft Graph API integration
• Mistral OCR for document intelligence
• Azure OpenAI embeddings and language models
• Agentic chunking strategies
• Vector databases and semantic search
• SQL-powered retrieval for structured data
• Open WebUI deployment
• GDPR and data sovereignty considerations
• Enterprise AI infrastructure and scaling

FINAL THOUGHTS

This episode serves as a complete blueprint for anyone looking to build a private, enterprise-grade AI assistant powered by organizational knowledge. Whether you’re a Microsoft 365 architect, IT leader, consultant, AI engineer, or business decision-maker, you’ll gain practical guidance on designing systems that are accurate, scalable, secure, and compliant.If you’re serious about moving beyond AI demos and building something that delivers real business value, this episode provides the architectural foundations, implementation strategies, and lessons learned necessary to make it happen.If you enjoyed this episode, please subscribe to the M365 FM Podcast, leave a review on Apple Podcasts, and connect with Mirko Peters on LinkedIn to continue the conversation around Microsoft 365, SharePoint, n8n, enterprise AI, automation, and Retrieval-Augmented Generation.

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