A guide to Share of Model Voice (SoMV): what the metric is, how to measure brand visibility in language model responses, which KPIs to implement, and how GEO is reshaping content priorities and reputation management.
Guides & Best Practices
Practical guides, proven strategies, and expert recommendations for building stronger brand visibility in AI. From step-by-step tutorials and implementation methods to real-world insights and optimization techniques, this section focuses on approaches that deliver measurable results in practice.
A guide comparing ChatGPT, Claude, Gemini, and Perplexity from a marketing perspective. You'll learn how to test brand visibility in AI, detect hallucinations, document results, and which platform is the best starting point for different priorities.
A guide showing how to use knowledge engineering and micro-categories to make language models choose your brand as the answer. Includes a gap audit, canonical signal formats, JSON-LD/Schema.org instructions, and an SBR measurement method.
A discussion of why placing a concise TL;DR block beneath the H1 heading increases data extraction accuracy by LLMs and RAG systems. Practical tips: what to include in a TL;DR, format examples, and how to measure effectiveness.
What is GEO (Generative Engine Optimization) and why is it becoming crucial in the AI era? Learn how optimization for language models works, how it differs from SEO, and how to increase your brand’s visibility in AI-generated answers.
Large language models are increasingly acting as intermediaries between users and brands, answering questions, comparing products, and recommending tools. The llms.txt file is a simple but strategically important way to provide AI with unambiguous, structured information about your brand, improving how it is understood, classified, and described in generated answers.