Post by Sukumar M S

🚀 Week 2 – Building Enterprise Knowledge Agents with RAG + MCP at Fractional AI Cost.  🛠 One Engine • 🥅 Three Walls •  👷‍♂️Three Audiences • 🚫Zero Leakage After Week 1's AI Onboarding Automation, I wanted to explore a different challenge: Can a single AI engine securely serve customers, employees, and vendors without exposing data across boundaries? The result is a permission-aware RAG architecture built around token-based access and retrieval-level security. 🔐 Three Tokens. Three Knowledge Lanes. 👤 Consumer Token Access to pricing, products, FAQs and SLA information Knowledge sourced from PostgreSQL Delivered through Web Chat or Telegram 🏢 Internal Token Access to HR policies, SOPs, compliance and procurement documents Knowledge sourced from SharePoint Delivered through Microsoft Teams 🤝 Vendor Token Access to contracts, specifications and onboarding packs Knowledge sourced from Google Drive Delivered through partner experiences 🧠 The Key Differentiator Security is enforced at two independent layers. Layer 1 — Bearer Token Authentication Each audience receives its own token and endpoint. Layer 2 — Retrieval-Time Filtering Every chunk is tagged with metadata: audience = consumer | internal | vendor At query time, vector search is filtered by audience. This means: ✅ Consumers cannot retrieve employee data ✅ Employees cannot retrieve vendor content ✅ Vendors cannot access internal documentation The isolation is enforced by the retrieval engine itself—not by prompts. 💼 Business Use Cases ✔ Employee Knowledge Assistants ✔ Customer Self-Service Bots ✔ Vendor Portals ✔ Policy & Compliance Search ✔ Procurement Knowledge Hubs ✔ Service Desk Automation ✔ Product Documentation Assistants 💰 Why This Matters? Instead of deploying multiple AI assistants, organizations can operate: ✔ One Knowledge Engine ✔ One Vector Store ✔ Multiple Secure Audiences ✔ Shared Infrastructure ✔ Reusable MCP Services This significantly reduces infrastructure complexity while maintaining governance and data boundaries. ⚡ Performance & Scalability ⚡ Low-latency vector retrieval ⚡Context-aware responses with citations ⚡ Modular ingestion pipelines ⚡Easily extensible to additional repositories ⚡ Designed to expose capabilities through MCP for other AI agents to consume 🛠 Technology Stack n8n • Gemini 2.5 Flash • Gemini Embeddings • Qdrant • PostgreSQL • SharePoint • Google Drive • MCP • Docker 🎥 Demo walkthrough attached #AI #RAG #MCP #Gemini #Qdrant #n8n #EnterpriseAI #KnowledgeManagement #AgenticAI #VectorDatabase #Automation #GenerativeAI

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