Case Studies: everything here comes from real projects I have personally executed.
Every article is based on a real-world implementation. I clearly document the context, approach, data outcomes, and reusable methods, so you can apply the ideas to your own projects and hopefully find them genuinely helpful.
These cases read like playbooks you can repeat, not slide-deck trophies. The through-line is simple: GEO only pays off when crawl health, extractable structure, and citation-ready paragraphs line up—before anyone chases “AI traffic,” which is noisy and easy to exaggerate. Assistants and classic retrieval still need the same dull prerequisite: pages that fetch reliably and one clear claim per block.
Specific project data may be masked for confidentiality when needed. Even with protected numbers, we still document the context, execution logic, and reusable methods so you can apply the approach in your own work.
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FAQ
Can you guarantee AI traffic or citation share?▼
No—we cannot guarantee it, and no one should make reckless guarantees. AI traffic and citation share are jointly shaped by platform behavior, query types, competitive context, and content quality, so they are not outcomes a service provider can promise unilaterally. What we focus on is making the controllable parts solid: crawl/index foundations, claim clarity, structured expression, citation-ready content design, and technical risk checks. What we can commit to is steady progress on these key links, with each optimization traceable and verifiable by data.
What artifacts do engagements usually produce?▼
Deliverables are scoped to the engagement and designed to be directly executable. For GEO / SEO diagnostics, this usually includes a diagnostic report, issue priorities, optimization recommendations, example pages/templates, and an execution roadmap. For project-based collaborations, deliverables may also include keyword planning, content and internal-link strategy, structured-data plans, publishing cadence, dashboards, and stage-by-stage reviews. We do not only give direction—we aim to give you assets you can actually implement and move forward with.
How do these cases relate to the free toolkit?▼
Case studies show how to solve real problems in real projects. The Toolkit is for diagnostics, and its results are reference inputs you can use to guide decisions and next steps.
After the case study: what to monitor next
Cases show how teams ship fixes. These pages show how to keep seeing brand movement inside AI answers.