{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/89ce38fd569b441085c162e6161fbe36\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/89ce38fd569b441085c162e6161fbe36-32d35f31a350cbf9.gif","duration":204.019,"title":"Engineering AI Suite with gRPC and Graph","description":"This Loom demonstrates an engineering AI suite that coordinates retrieval and knowledge-graph queries to produce grounded answers. Mali shows three Python services that use gRPC internally and REST at the edge, with an orchestrator running a react loop where a planner selects tools, gathers evidence, and synthesizes responses. It covers a Neo4j knowledge graph with about 21 nodes and 25-ish relationships, plus retrieval via a Quadrant Cloud vector store. A demo asks which SILD requirements apply to the battery pack and which norms back them, showing traces and full audits with graph results, vector-store chunks, and the LLM-generated output."}