Tutorials

SEO Tutorials Jun 6, 2026

Auditing Your Own Site for AI: robots.txt, llms.txt, JSON-LD, and the Four Gates of Citation

Most AI-visibility advice points outward — earn citations, get on Wikipedia, court the review platforms. All worthwhile. But there's a cheaper, faster lever sitting right under you: your own website. If a model can't retrieve your pages, can't rank them, can't extract clean claims from them, or can't attribute those claims back to you, no amount of off-site work fully compensates. This is a practitioner's walkthrough of the on-site AI audit — the files and signals that matter, organized around the four gates an answer has to pass through to cite you.

#GEO #Schema Markup #AI Discoverability

AI Visibility Tutorials Jun 6, 2026

Tracking the Queries That Matter: Keyword-Level Monitoring in the AI Era

A brand-level visibility score answers 'do AI models know us?' But buyers don't ask models about your brand — they ask about their problem. 'Best CRM for solo realtors.' 'Affordable accounting software Singapore.' 'Alternatives to [incumbent].' Whether you appear in those answers is a sharper, more commercial question than your headline score, and it deserves its own tracking. This post is about query-level monitoring: which queries to track, how to read the results per engine, and how to turn the data into work.

#GEO #LLM Monitoring #Playbook

SEO Tutorials Apr 20, 2026

The Wikipedia Lever: How a Well-Structured Entry Moves Your Knowledge Depth Score

Of every lever in Generative Engine Optimization, a well-formed Wikipedia entry has the most predictable payoff on how LLMs describe your brand. Wikipedia corpora are oversampled in nearly every major model's training data, cited heavily by search-augmented providers, and treated as a canonical fact source. Yet most brands either have no entry at all, a three-sentence stub, or an entry that was edited once in 2021 and left to rot. This is the playbook to fix that without getting your article deleted or your account blocked.

#For SEO Managers #Citations #Wikipedia

SEO Tutorials Apr 13, 2026

Schema Markup for LLMs: 7 Elements That Matter, 12 That Don't

Schema markup is the single most over-prescribed piece of tactical advice in GEO. Every checklist tells you to add it. Few tell you which parts actually affect how LLMs describe your brand, which parts only help Google's rich snippets, and which parts have become decorative. This post is the triage: the seven schema elements worth implementing properly in 2026 for AI visibility, the twelve you can safely deprioritize, and the one that matters more than all the rest combined.

#For SEO Managers #Schema Markup #AI Discoverability

SEO Tutorials Apr 6, 2026

Earning Citations on Sources LLMs Actually Trust in 2026

For twenty years, the SEO playbook said earn backlinks from high-authority domains. The GEO playbook is narrower and more specific. LLMs do not treat all links equally. Some sources are massively overweighted in training and retrieval — Wikipedia, a handful of major news outlets, a specific set of review platforms, and certain community sites. The rest contribute marginally or not at all. This post is the ranked list of sources that actually move AI visibility in 2026, with a practical path to earning placement on each.

#For SEO Managers #Citations #Digital PR

SEO Tutorials Mar 30, 2026

The Reddit Citation Ladder: From Zero Mentions to Default Source

Reddit is disproportionately cited in LLM answers. Search any BrandGEO audit's per-provider citation surface and Reddit threads appear alongside Wikipedia at the top of the retrieval list. Yet most brands approach Reddit in exactly the way that makes the platform hostile: promotional posts, shallow engagement, shadowbans within a week. This post lays out the ladder that works — the one that earns genuine citations over twelve months without tripping any of Reddit's defenses.

#For Founders #Citations #Reddit

SEO Tutorials Mar 23, 2026

G2, Capterra, Trustpilot: Which Review Platform Actually Affects Your AI Visibility?

Most B2B SaaS brands try to maintain presence on G2, Capterra, Trustpilot, and a scatter of smaller review sites simultaneously. That is a mistake. For AI visibility purposes, one of those platforms almost always dominates the others in your category — and the effort spent thinly across all of them produces weaker results than the same effort concentrated on the right one. This post is the framework for picking the primary platform, setting up the review-acquisition flow, and deciding what to do about the others.

#For Founders #Citations #Reviews

AI Visibility Tutorials Mar 22, 2026

Five Lenses for Reading an AI Visibility Report Your PM Will Miss

When a product manager reads an AI visibility report, they read it through the lens they have — the product lens. How does this relate to activation? Retention? Feature adoption? Funnel conversion? Those are reasonable questions. They are also the wrong first questions. An AI visibility report rewards a different set of lenses, most of which are standard in marketing thinking and unfamiliar to product. This post walks through the five lenses a marketing practitioner uses to read the same report, with notes on why each matters and where a PM's default reading falls short.

#For SEO Managers #AI Visibility #Framework

AI Visibility Tutorials Mar 18, 2026

The Six Dimensions of AI Brand Visibility: A Practitioner's Explainer

A single AI visibility score is a tempting shortcut. It is also a lossy one. "Your brand scores 63/100 on ChatGPT" does not tell you what to fix, or whether to fix anything at all. A useful audit breaks the score into dimensions — component questions, each with its own diagnostic and its own remedy. BrandGEO scores on six dimensions across a 150-point scale, normalized to 0–100. This post is a practitioner's explainer of each dimension: what it measures, why it matters, and what moves it.

#AI Visibility #Knowledge Depth #Recognition

SEO Tutorials Mar 16, 2026

Digital PR for LLMs: How to Get Quoted in AI Answers (Not Just Google News)

Digital PR was originally optimized for two audiences: human journalists looking for stories, and Google's news indexing system looking for fresh authoritative content. In 2026 a third audience has become the dominant one — language models building their summaries of your category. The craft of PR has to shift accordingly. This post lays out how the discipline is changing, what still matters from the old playbook, and what specifically you should write differently when the goal is to be quoted in AI answers.

#For CMOs #Citations #Digital PR

Tutorials Strategy & ROI Mar 12, 2026

"Why Not Just Ask ChatGPT Ourselves Every Week?" — The Real Cost of Manual Auditing

The most reasonable-sounding objection to AI visibility tooling is "we can just do this ourselves." A marketing coordinator opens ChatGPT on Monday morning, asks a few questions about the brand, pastes the responses into a shared document, and calls it measurement. It works for one person on one afternoon. It does not work as a repeatable process. This post walks through the true cost of manual auditing — in hours, in consistency, and in the specific things the human eye cannot reliably track — and compares it to the $79-a-month alternative that most marketing teams have not properly costed.

#LLM Monitoring #Playbook #Myth-Busting

SEO Tutorials Mar 10, 2026

The Entity-First Content Playbook: Structuring Pages for AI Retrieval

The content playbook that served SEO for a decade was keyword-first. Pick a target phrase, cluster supporting topics around it, match search intent, earn links. That playbook still works for Google — but it leaves a significant amount of AI visibility on the table. LLMs do not ingest pages as bags of keywords. They parse them as webs of entities and relationships. Restructuring content to match how the model actually parses is the difference between being retrieved in an answer and being skipped. This is the playbook.

#For SEO Managers #Schema Markup #Content Strategy