We Ran the Numbers on Flower Delivery Brands in AI Search. Here's Who's Winning.

Ask ChatGPT "what's a good flower delivery service" and it gives you an answer instantly. Somebody has to get named in that answer, and somebody doesn't. That's the whole game now.

An AI visibility index is a snapshot of who wins that game inside a specific market. We built one for flower delivery because it's a market with a clean mix of national chains, regional players, and local shops, which makes it a good test case for a bigger question: what actually gets a business named by AI, and what gets it skipped.

We asked a batch of real buyer-style prompts across ChatGPT, Gemini, and Perplexity. Same-day delivery near me, best flowers for a funeral, cheapest flower delivery, most reliable flower delivery service. Then we logged every brand that got mentioned, how often, and in what context.

A few names showed up constantly across all three models. No surprise there, they've spent years building the kind of content and review footprint AI models pull from.

What was surprising is how many well-known, heavily-advertised flower delivery brands barely cracked the list. Big ad budgets and TV commercials don't move the needle here. AI isn't watching commercials. It's reading structured web content, review sites, and third-party mentions, and building its answer from whatever it can parse cleanly.

We also saw a handful of regional and even local flower shops show up in specific prompts, especially ones tied to a city or same-day delivery in a particular area. Those businesses had smaller footprints overall but nailed the local-intent prompts because their content was specific instead of generic.

Here's the part that should worry every marketing team that's been coasting on brand equity: AI doesn't care how long you've been around or how much you spent on your Super Bowl ad. It cares whether it can find a clear, current, well-structured answer to the question being asked.

We found brands with decades of market dominance getting skipped in favor of newer competitors simply because the newer competitor had a page that directly answered "same day flower delivery in [city]" while the legacy brand's site made you dig through five clicks to find the same information.

This is the uncomfortable truth about GEO that a lot of established brands haven't caught up to yet. Your reputation with humans and your reputation with AI models are graded on completely different curves.

Across every market we've run this kind of analysis on, not just flowers, three signals keep separating the brands AI names from the ones it skips.

First, structured content. Pages that clearly answer a specific question in the first few sentences get pulled into AI answers far more often than pages that bury the answer under paragraphs of brand story. AI models are extracting information, not reading marketing copy.

Second, consistent third-party mentions. Review sites, local directories, comparison articles, and press mentions all feed the models. A brand that only talks about itself on its own website is invisible compared to one that gets mentioned consistently across independent sources.

Third, freshness. Content that gets revisited and updated signals to AI models that the information is current and trustworthy. A page that hasn't changed in three years reads as stale, even if the business behind it is thriving.

One of the more useful things about running this across multiple models instead of just one is seeing where they disagree. ChatGPT leaned toward brands with strong review volume and structured FAQ content. Gemini pulled more heavily from Google-indexed local listings, which gave local flower shops a real edge. Perplexity favored sources with clear citations and recent publish dates.

If you only check your visibility on one model, you're getting a third of the picture at best. A brand that looks strong in ChatGPT might be completely absent in Gemini, and most businesses have no idea because they've never checked more than one engine.

This is exactly why scoring your AI visibility off a single blended number is misleading. A union score that averages three models together can look healthy while hiding that you're getting zero mentions on the model your actual customers use most.

The local and regional flower shops that showed up well in our index weren't outspending anyone. They were doing a handful of specific things: clear service-area pages, FAQ sections that matched real buyer questions, active review profiles, and basic schema markup so AI models could parse their business details without guessing.

None of that requires a massive budget. It requires knowing what AI models are actually looking for and building toward it deliberately instead of hoping general SEO effort translates over. It doesn't, not automatically.

This is the gap between a business that happens to get mentioned occasionally and one that's built specifically to be found. The second one wins consistently, the first one wins by accident.

Flower delivery was our test case, but the same exercise works for any market. Restoration companies, law firms, HVAC contractors, SaaS platforms, it doesn't matter. The question is always the same: when someone asks AI for a recommendation in your category, does your business show up, and if not, why not.

At OptimizeMyGEO we run this kind of market visibility snapshot for clients as a starting point, not a one-time report. We score visibility per model instead of blending everything into one inflated number, because a client needs to know exactly where they're weak, not a rounded-up average that hides the problem. Our Starter and Pro plans include panels across two to three engines depending on tier, and Enterprise clients get the full five-engine breakdown.

Start by asking the actual questions your customers would ask, across ChatGPT, Gemini, and Perplexity, and see if you show up at all. If you don't, look at whether your site actually answers those questions directly or makes people hunt for the information.

Check whether you have any recent third-party mentions, reviews, or citations. If your only mentions are on your own website, that's your biggest gap. Then look at your content's freshness. If your service pages haven't been touched in over a year, that's likely reading as stale to the models pulling from it.

If you'd rather not do this manually every month, that's the exact gap our Done For You plan closes. It auto-publishes the technical basics like llms.txt and robots.txt, and queues blog and FAQ drafts built around the questions AI is actually being asked in your market, so you approve instead of write from scratch.

Being a known brand and being an AI-recommended brand are not the same thing anymore, and the gap between the two is only going to widen. The businesses that treat this as a real discipline, checking per-model performance and closing structural gaps deliberately, are the ones that will own their category in AI answers a year from now. Everyone else will keep wondering why a smaller competitor keeps getting named instead of them.