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.
An analysis of how signals from AI assistants (e.g. ChatGPT, Gemini) fill the visibility gap in the B2B purchase funnel. A description of practical methods: mention normalisation, tagging, event schema versioning, conservative lead scoring, and holdout experiments for impact verification.
A practical GEO checklist in five steps: define intent, complete metadata, verify facts, optimize format, and set KPI measurement. A short guide for beginner content managers to improve visibility in AI-based results.
Practical tips for quick GEO optimizations: organizing NAP, simplifying product descriptions, implementing JSON-LD and LLMs.txt, and fixing technical settings. Priority changes that reduce errors in generative responses and improve brand visibility.
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.
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.