Case studies
How brands become clearer, more extractable, and more citation-ready for AI search.
LLMReach case studies show how GEO work is applied in practice: mapping buyer prompts, rebuilding answer-first content, improving technical AEO, strengthening entity signals, and connecting AI search visibility to qualified next steps.
These case studies focus on the operating system behind AI visibility, not unsupported performance claims. Each example explains the problem, the intervention, the pages or signals improved, and how the work made the brand easier for AI engines and buyers to understand.
Featured case studies
NexumAutomations
NexumAutomations needed a stronger foundation for AI search visibility. LLMReach rebuilt key parts of the site around buyer prompts, answer-first content, technical AEO, structured signals, and citation tracking so AI engines could better understand, extract, and evaluate the brand.
Read the case studyMore case studies coming soon.
We add new case studies when the work is ready to publish responsibly. If you want to understand what AI visibility gaps look like in your category before more examples are published, start with a free AI visibility audit.
Book a free AI visibility auditGET STARTED
Want to see where AI engines understand you, miss you, or recommend competitors instead?
The free AI visibility audit reviews how your brand appears across key buyer prompts, where competitors are being recommended instead, and which pages or technical signals should be improved first.
FREE AI VISIBILITY AUDIT
Find out which AI answers your competitors already own.
During a guided review meeting, LLMReach walks you through your priority buyer prompts, current AI visibility, competitor citations, source patterns, and the technical or content gaps that matter most. After the walkthrough, you receive a clear audit summary and prioritized action plan.
Prompt visibility · Competitor gaps · Citation opportunities
FAQ
Case Studies: Common Questions
What do LLMReach case studies show?
LLMReach case studies show how GEO and AEO work is applied in practice. They explain the starting problem, the prompt and citation gaps, the content or technical changes made, and how the brand’s AI search foundation became clearer, more structured, and more citation-ready.
What is an AI visibility case study?
An AI visibility case study documents how a brand improves the signals AI engines use to understand, mention, compare, and cite it. That can include prompt research, answer-first content, schema, internal linking, entity clarity, citation tracking, and conversion paths for AI-referred visitors.
How long does it take to see GEO results?
Timelines vary by category, baseline authority, technical quality, content depth, and competitive density. LLMReach starts by identifying the prompt gaps, page improvements, technical signals, and citation opportunities most likely to create movement first.
Do GEO case studies apply to my industry?
The core GEO methodology applies across industries, but the prompts, competitors, content gaps, and technical priorities differ by category. A B2B SaaS brand, e-commerce business, agency, and local service company may all need different prompt maps and page structures.
What metrics does LLMReach track for clients?
LLMReach can track AI visibility, share of voice, brand mentions, citations, average mention position, cited URLs, competitor presence, platform-level performance, and AI-referred traffic where analytics data is available. The exact measurement plan depends on the engagement and the buyer journey.
Can I see what AI visibility gaps look like for my brand?
Yes. The free AI visibility audit reviews where your brand appears, where competitors are being recommended instead, which prompts matter, and which pages or technical signals should be prioritized first.