Topics We Cover

Generative Engine Optimization is the practice of optimizing content and digital presence so that large language models cite, quote, and recommend a brand in their generated responses. It is arguably the most consequential shift in search marketing since the rise of Google itself — and it is still in its earliest stages. That combination of enormous stakes and rapid evolution is exactly why GEO sits at the center of our editorial coverage.

We cover GEO because no other discipline in marketing is moving this fast with this little established consensus. The rules are being written in real time: what works on ChatGPT may not work on Perplexity; a strategy that earns citations on Gemini might be invisible to Claude. Our reporting cuts through the noise to explain what actually drives AI citation, which tactics are backed by evidence, and which are recycled SEO assumptions dressed up in new language.

Our GEO coverage spans platform-specific strategies, citation optimization methodologies, content formatting for LLM consumption, and competitive displacement analysis. We test claims, interview practitioners, and publish the kind of detailed tactical breakdowns that marketers can actually use — not surface-level trend pieces.

Whether you are a brand trying to understand why you are absent from AI-generated answers, an agency building a GEO practice, or a solo practitioner looking to stay ahead, our coverage is designed to give you the clearest possible picture of what works, what does not, and where the field is headed.

What large language models say about your brand is becoming as important as what Google ranks for your keywords. When a potential customer asks ChatGPT for a recommendation in your category, the response they receive shapes their perception and purchasing behavior — and you may have no idea what that response actually says. LLM monitoring is the emerging discipline of tracking, analyzing, and influencing how AI systems represent brands, products, and organizations.

We cover LLM monitoring because it is one of the fastest-growing and most misunderstood areas of the AI search ecosystem. A wave of new tools and platforms has entered the market, each claiming to offer visibility into AI-generated brand mentions, but the capabilities, methodologies, and reliability of these solutions vary enormously. Our editorial mission is to cut through the marketing claims and help readers understand what these tools actually do, how they work, and which ones deliver real value.

Our coverage includes hallucination detection — how brands can identify and respond when AI models make false claims about their products or capabilities. We analyze sentiment tracking methodologies, competitive share-of-voice measurement across AI platforms, and the enterprise monitoring strategies that large organizations are beginning to deploy. Tool and platform reviews are a core part of our LLM monitoring coverage, giving readers honest assessments of the solutions available in the market.

For any organization that cares about brand reputation, our LLM monitoring coverage provides the context and analysis needed to navigate a landscape where AI models are becoming primary information gatekeepers.