Blog
GEO strategies, AI citation tactics, and product updates from the Lumis AI team.
Schema Markup for Generative Engine Optimization: The Technical Guide to Feeding LLMs Structured Data
Schema markup for AI search is the standardized vocabulary of structured data that translates unstructured website content into machine-readable entities, enabling Large Language Models (LLMs) to conf
Optimizing for RAG: How to Ensure Your Content is Discoverable by Real-Time AI Engines
A RAG optimization strategy is the systematic process of structuring digital content so that real-time AI engines can accurately retrieve, synthesize, and cite it in generative responses. By aligning
The Anatomy of an AI-Citable Paragraph: Formatting Information-Dense Content for LLM Extraction
AI-citable content is structurally optimized text designed specifically for Large Language Models (LLMs) to parse, extract, and reference in generative search responses. By prioritizing high informati
From Keywords to Entities: How to Anchor Your Brand in the Knowledge Graphs Powering AI Search
Entity optimization for AI search is the process of structuring digital content so that large language models (LLMs) recognize a brand as a distinct, authoritative concept within their underlying know
AI Crawler Management: How to Configure Robots.txt to Maximize LLM Visibility Without Sacrificing Data Privacy
AI crawler optimization requires a bifurcated robots.txt strategy that explicitly allows user agents like GPTBot and ClaudeBot to crawl public-facing marketing content while strictly disallowing acces
How to Optimize B2B Case Studies and Whitepapers for LLM Summarization and Generative Search
To optimize B2B case studies and whitepapers for LLM summarization and generative search, marketers must structure long-form content with clear entity relationships, explicit statistical claims, and s
Multimodal GEO: How to Optimize Video Transcripts and Visual Assets for AI Search
Multimodal GEO is the strategic optimization of non-text assets—such as images, video transcripts, and audio files—to ensure they are accurately interpreted, retrieved, and cited by generative AI sear
The Evolution of E-E-A-T in the AI Era: Building Trust Signals for Generative Engines
E-E-A-T for AI search is the framework generative engines use to evaluate the experience, expertise, authoritativeness, and trustworthiness of a brand’s content before citing it in AI overviews.
Measuring AI Share of Voice: The New KPIs for Tracking Generative Engine Optimization (GEO) Success
AI share of voice is the percentage of times a brand is cited, recommended, or referenced by generative AI engines compared to its competitors for specific industry queries. Tracking this metric allow