Proof
Case studies: the evidence, honestly labelled
A rule we won't break: no invented testimonials, no borrowed screenshots passed off as ours. Our client case studies will appear here as they're completed and approved. Until then, this page reviews independently verified results from the wider industry, clearly attributed, so you can judge the playbook on real evidence.
What results can this kind of work actually produce?
The strongest documented example: a Texas structural concrete company generated $115,000 in revenue from seven organic leads within 30 days of a GEO-first website build, earning recommendations across four major AI platforms (Heliux Digital, independently corroborated).
What makes that case worth citing isn't the headline number (one month, one company, one market); it's the mechanism: a site built machine-readable from day one, with deep service content and verifiable facts, got absorbed into AI recommendations fast, and the leads were exclusive rather than resold. That's the same architecture behind our website builds and AI search work. Treat any single case as an existence proof, not a promise; anyone quoting you someone else's results as your forecast is selling weather.
Does the "search everywhere" approach hold up for small local businesses?
The documented pattern says yes. A car-wash business went from invisible to roughly 700 branded Google searches a month and a top recommendation on four major AI assistants after a coordinated program: about 200 consistent local citations plus content the engines could verify (Arfadia case study).
Two honest footnotes: the business is in Indonesia, and "branded searches" measure people seeking that business by name: awareness, not just ranking. What transfers across markets is the input list, which is identical to the local SEO fundamentals we run: consistent citations, real reviews, verifiable facts, content that answers actual questions. The playbook is not exotic; the discipline is what's rare.
Is there real-world evidence for the machine-readability layer?
Yes, qualitative but instructive. An interior-design e-commerce brand generated its llms.txt from its production database so AI models would "rely on verified brand facts rather than probabilistic hallucinations"; models immediately began answering detailed questions from the file's FAQ instead of improvising (Netkodo case study).
That's the value proposition of the whole machine-readable layer in one sentence: you either feed the engines verified facts, or they guess. We deploy the same layer (schema, llms.txt, consistent entity data) as standard technical work, with the honest caveats about llms.txt adoption spelled out in our guide.
Where are Viziblty's own case studies?
In progress, and they'll be published here with the client's name, the timeline, the work done, and numbers pulled from their own analytics, the same standard our monthly reporting already meets. What we won't do is pad this page with anonymous "300% growth!" cards nobody can check.
Want to be the first one on this page? Early clients get exactly that leverage with us. Start with the free audit or get in touch.
| Element | Standard |
|---|---|
| Named client, with permission | No anonymous miracles |
| Starting baseline | From their historical Search Console / GA4 data |
| The actual work log | What was done, in what order |
| Results | Their own analytics, screenshotted and dated |
| Timeline & caveats | Including what didn't work |
The next case study on this page could be yours.
We're building our first published client studies now: early clients get outsized attention and a documented result they can reuse.