What does Generative Engine Optimization look like in the real world? In this article, we explore case studies and examples (some real, some inferred) of how brands are making themselves visible in AI-generated answers. While the field is new, there are already clear patterns of success. From companies securing their spot in “best of” lists that AI loves to cite, to brands adapting their content strategy to fit AI behaviors, these case studies illustrate GEO principles in action.
Case Study 1: The Restaurant on Every AI’s Recommendations List
Imagine you run a popular restaurant in Denver. In the classic SEO world, you’d want to rank high on Google for searches like “best restaurants in Denver.” In the GEO world, you want to be mentioned when an AI is asked for the best restaurants in Denver. How do you achieve that?
The answer: Be present on the sources the AI trusts for that query. When users ask ChatGPT or Bard for local recommendations, they often get answers citing well-known travel or food sites. For example, one ChatGPT query for “best sushi restaurants in Denver” yielded an answer citing 5280 Magazine, Female Foodie, OpenTable, and Tripadvisor as sources. Notice something? ChatGPT didn’t pick up the restaurant’s own website – it pulled from third-party lists and reviews.
In our case, suppose our Denver restaurant (let’s call it Sushi Haven) made sure to get listed in those key sources:
- They invited a writer from 5280 Magazine for a tasting, resulting in a feature in “Top 10 Sushi Spots in Denver.”
- They encouraged diners to review them on Tripadvisor and OpenTable, building a strong rating profile.
- They collaborated with a local food blogger (Female Foodie) to get included in a “best sushi” blog post.
So when someone asks the AI for best sushi in Denver, Sushi Haven gets mentioned via those sources. Essentially, the restaurant treated AI as an extension of word-of-mouth and PR. Instead of solely focusing on their Google My Business or SEO for “Denver sushi” (though that’s still important for map results and direct searches), they prioritized influencer and aggregator presence. The result: ChatGPT lists them as one of the recommended options, pulling the description from the magazine and noting their high OpenTable rating.
This mirrors how SEO practitioners have long sought inclusion in featured snippets or third-party “Top 10” articles because those get clicked. Now, those lists might get read out by an AI with no click at all – but being mentioned becomes the prize.
Key Takeaway: Optimize beyond your own site. If AIs prefer certain platforms for answers (media outlets, review aggregators, etc.), your GEO strategy should include being visible on those platforms. In traditional SEO, this was sometimes considered “off-page SEO” or PR; in GEO, it’s often the only way to get into the answer at all for list-style queries.
Case Study 2: Tech Brands Winning in AI Recommendations
The B2B tech world provides another great example. Consider the question: “What’s the best CRM software for a small business?” A user could pose this to an AI assistant instead of Googling it.
In early AI outputs for such queries, certain names keep popping up. ChatGPT might answer with something like: “Common recommendations for small business CRMs include HubSpot CRM (known for its free tier and ease of use), Zoho CRM (affordable and customizable), and Salesforce Essentials (powerful features scaled down for small teams).” On what basis did it choose those? Likely from multiple review articles (e.g., on Zapier’s blog, PC Magazine, TechRadar, etc.) which consistently mention those platforms as top options. The AI collates the consensus.
One such source could be Zapier’s blog. Zapier (the workflow automation company) regularly publishes high-quality comparison articles like “10 Best CRMs for Small Businesses, Tested and Rated.” If ChatGPT’s training data included Zapier’s content (which it likely did, since Zapier’s blog is well-trafficked), the model learned that those three CRMs are top choices. Zapier’s article might even have been cited by name if the AI had a retrieval component turned on (e.g., ChatGPT with browsing).
Another source could be PC Magazine (PCMag), which does extensive software reviews. In fact, one observed ChatGPT answer cited Zapier and PCMag explicitly as sources for a query about CRM. That indicates ChatGPT with browsing pulled information directly from those sites. So the brands that PCMag and Zapier featured got into the AI’s answer.
Now, let’s say our hypothetical CRM startup, ClientWhiz, wants to be in that conversation. A pure SEO approach would try to outrank HubSpot or Zoho on Google (a tough battle). The GEO approach might be:
- Get Reviewed by Key Players: ClientWhiz could offer PCMag or Capterra analysts access to review their platform. They might also ensure they have glowing user reviews on G2 Crowd or similar, as these reviews might be summarized by AIs.
- Publish Comparative Content: They create content on their own site that compares them against the big names, and includes any unique data or studies (something an AI might pick up as a distinct point – e.g., “ClientWhiz was the only CRM to achieve X% increase in Y according to [Study]”).
- Leverage Niche Forums and Communities: They engage in communities like Spiceworks or Reddit (in a genuine, non-spammy way) so that their name comes up in discussions. While we noted AIs often skip forums for final answers, those discussions might still influence the training data or the long-tail of what content exists about “best CRMs.”
After those efforts, when the question is asked again in a year, ChatGPT’s answer might evolve: “Top CRM choices for small businesses include HubSpot CRM, Zoho CRM, and newer options like ClientWhiz, which has been noted for its intuitive interface (as highlighted by PCMag’s 2025 review).” Now ClientWhiz is in the AI-generated answer mix.
This scenario is partly inferred – we don’t have a record of ChatGPT recommending ClientWhiz – but it follows logically from how known brands are included. Companies like Zapier have succeeded because they invested in content marketing that establishes them as an authority (so AIs trust them as a source), indirectly giving them influence over AI outputs. Likewise, brands that actively manage product reviews and PR are setting themselves up for inclusion.
Key Takeaway: Authority content creators often “hold the pen” for AI answers. If you’re a brand wanting a recommendation mention, influencing the influencers (the content sources AIs use) can be more effective than trying to directly feed the AI your pitch. In SEO we said “get backlinks from authority sites”; in GEO it’s “get cited by authority content that the AI consumes.”
Case Study 3: E-commerce and the New “Answer Shopping” Paradigm
Let’s turn to e-commerce. Consider a user asks an AI: “What’s a good budget gaming laptop under $1000?” On a search engine, they’d get a list of blue links (Best Buy, PCWorld, user forums, etc.). An AI, however, might say: “The Acer Nitro 5 and the Dell G15 are often recommended as budget gaming laptops under $1000, offering good graphics performance for the price. The Acer has a slightly better GPU, while the Dell offers a sturdier build. Both can be found on Amazon or Newegg for under $1000.”
How did those specific laptop models get named? Likely from multiple review articles (e.g., CNET, Tom’s Hardware, PCMag) which consistently mention those models in that price range. The AI identified the overlap and synthesized it. It also noted availability (“can be found on Amazon or Newegg”) because sources like consumer tech blogs often mention where to buy.
Now, imagine you are Dell, maker of the G15. You’d be pleased that your model is in the answer. That didn’t happen by accident:
- Dell’s marketing and SEO team likely ensured that their laptop got reviewed by all the right tech reviewers and had competitive specs – so it naturally made those lists.
- They may have provided media with detailed product info (making it easy for writers to highlight the G15’s strengths in reviews, which the AI then picks up).
- On their own site, they might have comparison content like “G15 vs Competitors” that journalists referenced (and thus the AI indirectly learned that narrative).
Now think about Newegg, the retailer. The AI even mentioned “can be found on Amazon or Newegg.” Amazon is ubiquitous, but Newegg showing up means the model saw Newegg being associated with gaming laptops in its training (perhaps via tech forums or articles listing where to buy). If you’re Newegg’s competitor, say TechMall.com, you’d want the AI to mention you too. How? Possibly via:
- Affiliate Programs & Partnerships: Many of those review articles include affiliate links like “Buy on Amazon | Buy on Newegg.” If TechMall isn’t a common option, it won’t be mentioned. Getting included in shopping comparisons (maybe by offering a unique deal or affiliate incentive so reviewers list you as a purchase option) could plant that seed.
- Advertising (Carefully): If an AI tool like Bing is integrating shopping data, paying for visibility in those channels (like Bing’s integrated shopping cards) might indirectly influence the AI to mention your store. This is unverified, but we can foresee AI answers that incorporate sponsored suggestions (ethically labeled, we hope).
This example shows how traditional commerce optimization (reviews, affiliates, multi-channel presence) now ties into GEO. The AI is essentially doing product recommendations – something that used to be the domain of search, review sites, and word-of-mouth.
Also worth noting: multi-modal answers. Bing Chat and Google’s AI can sometimes show product images or cards. If you have high-quality product images with proper metadata (or on a platform like Amazon that the AI pulls from), your product might be visually featured as well. That’s an extension of SEO (which is branching into image SEO and beyond).
Key Takeaway: Data partnerships and ubiquity matter. If you want an AI to mention your platform or product, ensure it’s deeply embedded in the information ecosystem. That means classic tactics like product reviews, affiliate outreach, and being present on popular marketplaces. The more an AI “sees” your brand in relevant contexts, the more likely it will mention it.
Case Study 4: The Brand That Fixed an AI Omission
Not all case studies are about success – sometimes it’s about learning from absence. Consider a brand that is a leader in its space, but when people asked the AI for solutions in that space, the brand was nowhere to be found.
For instance, a fintech startup (we’ll call it Budgetly) offers a great personal finance app. They have decent SEO and some press coverage. However, when users asked ChatGPT in early 2024 “What are the best apps to manage a personal budget?”, the answer often included Mint, YNAB (You Need a Budget), PocketGuard – but not Budgetly. The team at Budgetly noticed this gap.
Why were they omitted?
- Training Data Lag: ChatGPT’s base model at the time had knowledge mostly up to 2021. Budgetly launched in 2022 and gained popularity in 2023, so the base model “didn’t know” it.
- Lack of Presence in Authoritative Sources: Early “best budgeting app” articles from 2021-2022 didn’t mention Budgetly (it was new). Newer articles that did mention it might not have been widely linked or might not have been ingested by the AI’s training or real-time knowledge.
- Competitors’ Strong Digital Footprint: Incumbents like Mint had years of content, reviews, and user discussions backing them up, outweighing Budgetly’s newcomer status.
Budgetly’s response:
Content & SEO Blitz: They ramped up content marketing in 2024 – publishing comparison posts (“Budgetly vs Mint: What’s Different?”), getting guest posts on finance blogs, and optimizing for keywords around budgeting. This helped ensure that anyone (or any AI) searching the web for budgeting tips might encounter Budgetly’s name more often.
Press and Partnerships: They secured a feature in a personal finance column (“Top New Budget Apps to Try in 2024”) and partnered with a popular YouTube finance guru who reviewed Budgetly (and whose video transcript the AI might see via YouTube captions).
LLM Feedback: Whenever someone on their team used ChatGPT and saw Budgetly missing, they would use the feedback feature (“did not mention Budgetly budgeting app” or prompt the AI with “What about Budgetly?”). While this is anecdotal and not a guaranteed method, user feedback does help fine-tune models over time.
By late 2024, with GPT-4’s plugins or Bing’s connected mode, Budgetly started to surface. A user asks the same question and now an AI might append: “…Other notable budgeting apps include Budgetly, a newcomer known for its intuitive interface and smart suggestions, which has received positive reviews on NerdWallet and CNBC.” That inclusion could come from the AI searching anew (finding those reviews) or from updated training that now includes Budgetly due to its growing footprint.
It’s a hypothetical outcome, but it illustrates how a brand identified a non-inclusion issue and took steps to address it. The lesson here is like doing SEO gap analysis, but for AI answers.
Key Takeaway: If you’re absent from AI answers, diagnose and act. It might be due to timing (the AI hasn’t learned about you yet) or prominence (not enough authoritative mentions). By boosting your content, PR, and overall digital presence, you can turn an omission into inclusion over time. Essentially, you’re doing “AI reputation management” – ensuring that when an AI thinks of your category, your name is in the mix. It may take training cycles or new content to propagate, so start early.
Case Study 5: Embracing AI – The Direct Collaboration Approach
Some brands aren’t just waiting to be included; they are actively collaborating with AI companies to ensure visibility and accuracy. An example of this is the news and information industry: in 2023, we saw deals like OpenAI partnering with the Associated Press to license content for training, and negotiations for similar deals with other publishers. The idea is that if the AI can be fed high-quality content directly (with permission), it will use it – and possibly even credit it.
Now consider a brand that is a primary source of information in its domain – say TravelCo, which publishes widely used travel guides. TravelCo might approach OpenAI or Google and say: “We will provide you with structured, up-to-date travel info (attractions, hotel details, etc.) for your models, so long as our brand is credited or our data used preferentially.” If such a deal is struck, when users ask travel questions, the AI’s answer might lean on TravelCo’s data, perhaps even stating, “According to TravelCo…”.
We already have smaller-scale instances:
- Wolfram Alpha Integration: ChatGPT can defer to WolframAlpha for complex calculations. It will literally say the result is from Wolfram, giving that brand clear attribution in answers.
- OpenAI/Bing and Zillow: In 2023, Zillow data was integrated into certain real estate chat experiences, meaning an AI might provide home info sourced from Zillow (branding and all).
For more mainstream content, the mechanism might be behind-the-scenes feed or plugin. Another example: Instacart built a ChatGPT plugin for grocery shopping. If users engage via that, Instacart is essentially the answer engine for “where to buy”. Brands that create such integrations early on can secure themselves as the go-to data source for an AI in their niche.
While not every company can cut a deal with OpenAI, this points to a future where GEO includes partnership-building with AI platforms. Much like how savvy companies created Google Actions or Alexa Skills in the past to be present in voice search, now they’ll create data partnerships or plugins for generative AI.
Key Takeaway: Be open to new channels of optimization, including direct data partnerships with AI platforms. If you have valuable data or content, providing it to AI services (through APIs, plugins, or feeds) could ensure your brand is baked into the answers. This is an evolving area, but early adopters can gain an edge. For most, this means keeping an eye on opportunities like contributing to knowledge bases (e.g., schema.org data that assistants use) or participating in pilot programs for feeding content to AI. It’s the bleeding edge of GEO, where SEO meets business development.
Lessons Learned
Across these scenarios, some common themes emerge:
- Content and SEO fundamentals drive GEO success. Brands that had invested in quality content, earned media, and SEO-friendly site structures found themselves organically being picked up by AI. (Zapier, PCMag, etc., had no idea they’d feed an AI when they wrote those articles, but that investment paid off in a new way.)
- Being the source vs. being on the source: If you can’t always be the source the AI reads (your own site), aim to be on the sources the AI uses (guest posts, list inclusions, trusted directories).
- Monitor and adapt: The landscape is shifting. Some brands noticed they weren’t being mentioned and took action – effectively an AI age twist on ORM (online reputation management). The earlier you catch an omission or issue, the easier to fix it.
- Ethical influence: None of these cases advocate trying to “trick” the AI with false information – the successful strategies align with genuinely providing value and becoming notable. AIs are getting better at ignoring blatant SEO spam. GEO winners are those who earn their spot with substance, then ensure that substance is visible to the AI.
By studying these examples, you can glean actionable insights for your own strategy. Perhaps your brand can relate to Sushi Haven (needing local list presence) or Budgetly (new player fighting for recognition) or ClientWhiz (competing in B2B with content). Map the lessons to your situation: identify the answer engines and sources that matter, and make a plan to get your name into the conversation.
In the next sections of this series, we’ll look at the tools that can help you track all this (because doing it manually can be daunting), as well as experimental data on what factors most influence AI answers. These case studies show the what and why – coming up, we’ll dive into the how.