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

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Introduces a production pipeline for Generative Engine Optimization (GEO) that integrates OpenClaw, Milvus, and LLMs. Details the two-phase process: Phase 0 for ingesting source material into Milvus, and Phase 1 for expanding topics, semantically deduplicating queries using Milvus, performing dual-collection RAG from Milvus knowledge and article archives, generating articles with LLMs, and writing back to Milvus. Highlights Milvus's role in semantic deduplication, dual-collection RAG, and creating a feedback loop for improved content generation. Demonstrates using Milvus Lite for local development and outlines the skill structure within OpenClaw.