GEO Content at Scale: How to Rank in AI Search Without Poisoning Your Brand

This post details the integration of Milvus with OpenClaw and LLMs to build a production pipeline for Generative Engine Optimization (GEO). It showcases how Milvus is used for knowledge storage, semantic deduplication, and RAG retrieval across two collections (knowledge base and article archive). The pipeline expands seed topics into long-tail queries, semantically deduplicates them using Milvus, and then generates articles using LLMs, grounding the output in verified source documents. The post also highlights the use of Milvus Lite for local prototyping and discusses the feedback loop mechanism where generated articles are written back to Milvus to enrich the deduplication and RAG pools.