Key Takeaways

  • Getting recommended by AI starts with a simple diagnostic: open ChatGPT, Claude, and Perplexity, ask what they know about your brand, and read the answer as a snapshot of what the models have been trained and retrieved to say about you.
  • AI systems recommend brands they can corroborate across multiple trusted sources, so the work is less about ranking a page and more about becoming a name the model keeps seeing in the places it already trusts.
  • Structured content, clear entity signals, original data, and third-party mentions matter more than clever prose, because retrieval systems reward specificity and citation-worthiness.
  • Reviews, forums, and expert-authored guest posts are undervalued because they feed the exact corpora that models pull from at answer time.
  • This is a durable brand play, not a growth hack. The teams winning here are treating AI visibility like PR and SEO combined, run on a quarterly cadence.

Seems like every brand now wants their brand to show up when a buyer asks Claude or ChatGPT for a recommendation? It's a fair question, and it's the right one, because the buying journey is quietly moving from a list of ten blue links to a single confident paragraph. If your name isn't in that paragraph, you aren't in the consideration set.

The good news is that this is knowable work. It isn't a black box, and it isn't a pay-to-play channel yet. My opinion, after spending most of this year testing prompts and watching what the models cite, is that AI recommendation is basically a corroboration game — models recommend the brands they can verify from multiple angles across sources they already trust, which means the whole game is figuring out which sources those are and getting your name inside them. Everything below is how to earn that.

One more framing note before we get into it. This is not SEO with a new coat of paint, and it is not a separate discipline called GEO or AEO that lives on an island. It's the same brand and content work you've always done, aimed at a new reader: a language model doing retrieval on behalf of your buyer.

Start with the diagnostic, because you can't fix what you haven't seen

Before you plan anything, do what Andy Crestodina suggests and just ask. Open your favorite model and prompt it with something like tell me everything you know about [your legal brand name], then follow up with the buyer-intent version: what are the best B2B marketing agencies for mid-market SaaS, or whatever your category actually is. Do this in ChatGPT, Claude, Perplexity, and Google's AI mode, and keep the outputs in a doc.

What you're looking for is threefold. First, does the model know you exist. Second, what does it think you do, and is that description the one you'd write yourself. Third, when it recommends competitors and not you, which sources is it citing. That third one is the map. Those cited sources are the corpora you need to be inside of.

Teams that run this exercise consistently see the same pattern. The models aren't confused, they're just under-informed. They know the categories, they know the well-covered players, and they know whatever a G2 or a Reddit thread or a widely syndicated article has told them. If your brand isn't in those places, you're not in the answer.

What actually moves AI recommendation rate

Once you've done the diagnostic, the work sorts itself into four buckets. I'd sequence them roughly in this order, though most teams end up running them in parallel once the machine is going.

1. Make your own site legible to retrieval

This is the table stakes layer, and it's the part most teams already half-do. Open your site to AI crawlers unless you have a real reason not to, because blocking them is the fastest way to be invisible.

Add proper schema markup: Organization, Product, FAQPage, Article, and Person schema for your executives and authors. Write in a way that answers questions directly in the first sentence of a section, then supports the answer with detail. Marketers on r/indiehackers make the same point from the practitioner side: skimmable, question-shaped content is what gets extracted, and the Search Engine Journal piece from July backs it up.

SEJ adds a wrinkle I think is underrated: cite your own data sources and research methods on the page. Models trust content that shows its work, and they're increasingly picking up primary-source language over restated summaries. If you ran a survey, publish the methodology. If you have benchmarks, put the sample size and dates on the page.

2. Get corroborated in the places models actually read

All of that on-site work only counts if the model can verify it somewhere else, which is what this bucket is about. Models are pulling from a knowable set of high-trust sources: category-defining publications, review sites like G2 and Capterra, Reddit threads, LinkedIn long-form, Wikipedia when applicable, and increasingly Substack and industry newsletters. If your category has an obvious top-ten roundup post on a trusted publication, being on that list is worth more than five pieces of your own thought leadership.

The Okoone piece has a good list of the earned tactics that still work here: guest posts written by a named expert on your team, media interviews, thoughtful commentary in forums where your expertise is genuine, and case-study posts that name the customer and the outcome. What's changed is the payoff. Every one of these placements is now feeding a retrieval index somewhere.

3. Fix your entity graph

Corroboration only compounds if the model can tell that the mentions are about the same company, which is what the entity graph handles. Models understand the world as entities and relationships. Your brand is an entity. Your founders are entities.

Your product categories are entities. If those entities aren't clearly connected across the web, the model has a harder time deciding what you are. The SE Ranking piece calls this the source strategy, and the practical version is this: make sure the same clean description of what you do appears on your site, on your LinkedIn company page, on your executives' LinkedIn profiles, on review sites, in any directory that matters in your category, and in the bios of any guest posts you write. Consistency across sources is a corroboration signal.

4. Reviews and social proof at volume

The entity graph tells the model who you are; reviews tell it whether anyone likes you, in language it can quote directly. Trustmary's whole angle here is reviews, and I think they're right to press on it. Reviews are structured, dated, third-party language about your brand, which is basically the perfect input for a language model. If you have a review program, invest in it. If you don't, start one. And don't just chase G2 stars, chase written reviews with specifics in them, because the specifics are what get quoted.

When this is the wrong thing to focus on

I want to be honest about when this work doesn't pay off, because I've watched teams chase it into a wall. If your category is early enough that buyers aren't yet asking AI for recommendations, or if your deal flow is entirely outbound and relationship-driven, or if your ICP is small enough that ten named accounts is the whole market, then AI visibility is a distant priority. Do the diagnostic anyway, fix the schema, and move on. Direct outreach and one great case study will outperform a quarter of AI visibility work in that world.

Same goes for hiring an agency to do this. If you don't yet have a content engine producing anything worth citing, an AI visibility retainer is just going to formalize the void. Fix the content first. The retrieval work only compounds if there's something to retrieve.

How to run this as a quarterly loop

The cleanest way to package this is a quarterly loop. Every quarter, run the diagnostic across the four major models, log what they say about the brand and the category, identify the top ten cited sources in that category, and plan the quarter's earned media, review acquisition, and on-site content against the gaps. It looks a lot like the old PR-plus-SEO calendar, which is the point.

What's different is the measurement. Track mention rate: out of twenty buyer-intent prompts run across four models, how often does the brand appear, and in what position. That number moves slowly, but it moves, and it's the closest proxy we have to the thing the CMO actually cares about, which is whether the brand is in the room when the buyer asks.

If you want the longer version of how we're structuring this work for B2B tech clients, we wrote it up in our AI marketing blueprint, which walks through the diagnostic, the corpus map, and the quarterly cadence in more detail.

The window is open right now. The SERP for most of these queries is still scattered, the winners are inconsistent, and the models are still figuring out who to trust in most B2B categories. Run the diagnostic this week. The rest sorts itself out from there.

Frequently Asked Questions

Expect a two-to-three-quarter horizon before you see consistent mentions across models. Retrieval indexes update on their own cadence, and earned placements take time to compound. Schema and on-site fixes can show up in AI overviews within weeks, but real recommendation coverage across ChatGPT, Claude, and Perplexity is a longer game.

Is AI visibility the same as SEO?

No, but they overlap heavily. Traditional SEO targets a ranked list of links, while AI visibility targets being cited or named inside a generated answer. The underlying signals such as authoritative content, structured data, and third-party mentions are similar, but the tactics tilt more toward earned media, reviews, and entity consistency.

Do I need to pay for AI visibility tools?

Not to start. The diagnostic can be run manually with a spreadsheet and free-tier access to the major models. Paid tools become useful once you're tracking mention rate across dozens of prompts and multiple models on a weekly cadence, but I'd get the work moving before I bought software for it.

Which model matters most for B2B buyers?

ChatGPT is still the highest-volume consumer surface, but Perplexity and Claude both punch above their weight in B2B research because they cite sources more transparently. Google's AI mode matters because it's inside the surface buyers already use. Work on all four rather than picking one.

Can small brands compete against enterprise incumbents here?

Yes, and this is the most interesting part of the current moment. Because models weight corroboration and specificity, a smaller brand with a clear niche, strong reviews, and a handful of well-placed expert articles can outperform a bigger competitor with generic coverage. Specificity beats scale in retrieval.

Sources