GEO Case Studies: AI Visibility Results for B2B Brands

Most GEO pitches you'll sit through this quarter lean on the same line: AI search is where your buyers are now. Fine. But you didn't get budget approved for a thesis, you got it approved for evidence. What follows are GEO case studies from our own client work, with the numbers exactly as they came out of the reporting tools, plus the measurement method you can copy whether you hire anyone or not.

One thing before the tables. Every figure below comes from a named tool, a defined window and a client who agreed to be anonymized. Nothing here is a projection dressed up as a result, and none of it means your site will produce the same curve. It means the curve exists and it's measurable.

What a GEO case study actually has to show

A credible GEO case study is defined as a documented before-and-after record of how often a brand's pages are retrieved and cited in AI-generated answers, measured in the AI platform's own search mode or in Search Console's AI search reporting, over a stated period, with the query set and the content shipped listed alongside the numbers. Anything less, a screenshot of one ChatGPT answer, a vendor's own dashboard, a vague claim about "increased visibility," isn't a case study. It's an anecdote with a logo attached.

Why insist on that definition? Because generative engine optimization moves two different things at once, and only one of them is easy to fake. Impressions tell you the engine is surfacing your domain. Citations tell you the engine is actually quoting your page as a source. A program can inflate the first without touching the second, and that's the failure mode buyers discover three months in.

If you want the mechanics of how retrieval and citation differ, we covered that separately in how brands get cited in ChatGPT search. For now, keep the distinction in your head. It shapes every number below.

GEO case studies with hard numbers

These four programs ran in different industries with different content volumes. All figures were read from the client's own Google Search Console AI search data and Semrush, as of 2026-08. We publish the growth multiple against each client's own starting period, not against zero, because that's the honest comparison.

Read the third column carefully, because it's the part most vendors hide. In every one of these programs, a handful of pages carried the citation load. For the industrial display maker it was a guide about pressure damage. For the laser equipment maker, cryolipolysis and HIFU articles. Not the homepage. Not the product category. The technical explainer that answered a question a buyer typed into an AI box at 11pm.

That pattern is not a coincidence, and it's why our own GEO work starts with query scope rather than with content volume. You can see how we structure that on the LLM SEO services page.

[free] Not sure whether ChatGPT cites you for these queries today? We check and reply within 24 hours. Get a Free AI Visibility Audit

Two GEO programs, side by side

Impressions are a leading indicator. Inquiries are the lagging one, and they're what your CFO asks about. Here's where a GEO program crossed from visibility into pipeline.

In two earlier GEO programs, the results below come from separate client engagements and are not part of the Google Search Console and Semrush dataset above.

In one RAGSEO client program (client anonymized), a lifting equipment manufacturer selling hoists, winches and cranes reached 186 AI-engine-driven inquiries, which was 35% of all inquiries. 62% of those came from Europe and North America, converting 28% better than the company's traditional channels, and the brand held a top 3 position in AI-generated answers for its core queries. After the Google AI algorithm update in March 2024, the citation rate stayed stable and rose 15% month-on-month. Before the project, the brand appeared in less than 1% of AI-generated results.

In another (client anonymized), a jewelry manufacturer ran from 2023-07-01 to 2024-09-30, a 15-month window. AI-driven inquiries reached 268, or 42% of total inquiries. 70% came from Europe, America and the Middle East, inquiry-to-order conversion ran 35% higher than traditional channels, and the brand sat consistently in the top 5 AI answers for core queries.

Notice what's absent from both: any claim that AI inquiries replaced the old channel. They didn't. They stacked on top of it, and they converted at a different rate. If you want to know what a program like this would cost before you read further, the GEO pricing page lays out the plans and what each one includes.

How to measure generative engine optimization

This is the section to screenshot. Measurement is where most in-house teams stall, because there's no single dashboard that covers every AI engine. There isn't one, and there won't be for a while. You measure in layers instead.

1. Fix your query set first. Write down 20 to 40 queries your buyers actually type, in their language, including the messy ones. A query set you change every month measures nothing.
2. Log citation presence manually, on a schedule. Open ChatGPT in search mode, not logged in, ask each query, and record whether your domain is cited and in what position. Screenshot it. Our working rule is a weekly pass, same day, same account state, because answer composition drifts.
3. Pull Search Console AI search reporting. Google's own documentation describes the AI search and AI Overviews reporting surfaces, and that's where impression and click data for AI surfaces lives. Export weekly rather than monthly so you can see a dip before it becomes a quarter.
4. Track citations per URL, not per domain. One page usually does the heavy lifting. If you only watch the domain, you'll miss which asset is earning the quotes and you'll under-invest in it.
5. Separate AI inquiries in your CRM. Add a source field. If your sales team can't tag an inquiry as AI-originated, you'll never connect the visibility work to revenue.
6. Report the starting period alongside the current one. Growth multiples without a baseline are marketing, not measurement.

There's a boundary you should hear from us rather than discover later. ChatGPT answers either from live web search, which GEO can influence, or from knowledge stored in the model without web access, which cannot currently be optimized by anyone. Optimizing for ChatGPT tends to help visibility in Gemini and Grok too, because they reference public web content, but each model runs its own mechanism and we evaluate only against ChatGPT search results. If a vendor promises you control over model memory, walk away.

When you want that measurement set up before committing to content production, an AI visibility audit is the cheaper first move.

Timelines: what moves when

Buyers ask this in the first call, so here's the honest shape of it. In our programs, significant improvements in rankings and organic traffic can usually be observed within 3 to 6 months. GEO citation movement tends to show earlier than that on long-tail technical queries, because there's less competition for the answer slot, and later on head terms where established domains already own the citations.

Our GEO result standard is written into the agreement: citations are monitored regularly with screenshots in ChatGPT search mode, not logged in; if the 3-month target is not met, a proportional refund applies; and monitoring continues after the target is reached. That last clause matters more than it sounds. Citation position decays if you stop publishing, and a program that ends at the target is a program that starts sliding the month after.

What slows a program down

Three things, in our experience. A thin knowledge base, where there's nothing factual for the model to quote. A site that loads slowly enough that retrieval crawlers deprioritize it, which is why we hold pages to an LCP of 2.5 seconds or under. And a query set built from keyword tools rather than from real buyer language. Fix those three before you blame the engine.

What these GEO case studies don't prove

They don't prove your industry behaves the same way. A flame-retardant fabric manufacturer and a PCB assembler produced very different curves, 4.0x against 7.5x, in the same reporting window, and neither number predicts yours. They don't prove AI citations convert better in every market. And they certainly don't prove that publishing more content is the answer, because in all four programs the citation concentration sat on a small number of deep, specific pages.

What they do prove is narrower and more useful: AI citation is measurable, it responds to content and technical work, and it can be tracked in the same reporting tools you already use for SEO. That's enough to justify a pilot. It isn't enough to justify a blank check, and any agency telling you otherwise is selling confidence rather than method.

If you want the full method behind these programs, including how the knowledge base is built and how distribution across authoritative platforms fits in, start with the GEO agency overview and then ask us the hard questions directly.