Join us for the Community Call where Rida El Chall and Affan Abbas, AI Engineer and Data Scientist respectively from Orbis Holding will cover how MCP turns a brittle “LLM → Cypher → execute” chain into a reliable, extensible system with tool discovery, validation, and built-in clarification loops.

You’ll see how MCP reduces query failures on messy, real-world data (encoded fields, multilingual values), improves multi-hop reasoning, and adds governance/observability without slowing teams down. We’ll share concrete patterns that cut rework, boost answer quality, and make production rollouts safer and faster.

What we’ll cover / What you’ll learn:

Architectural trade-offs: Side-by-side of classic LLM→Cypher vs. MCP (failure modes, latency, reliability, extensibility, and ops/guardrails)

Live flow design: How MCP’s tool discovery, schema-aware validation, and clarification prompts drive higher accuracy on complex, multi-hop queries

Production patterns: Evaluation loops, fallback strategies, and governance/observability practices to ship and scale a GraphRAG app with confidence

Lessons Learned Building a RAG App on an MCP-Based Architecture