How to Get Cited in AI Search for “Best in Category” Queries

The L3 framework post broke down what category citation is and how the retrieval layer works. This one is the playbook. What to actually do, based on what type of company you are and where you’re starting from.

Quick recap if you’re jumping in: when someone asks AI “best CRM for small businesses,” or “top agencies in Denver,” or “best running shoes under $150,” the model retrieves editorial listicles, review sites, and comparison content… then synthesizes an answer from those sources. 0 out of 300 platform-query combinations in our testing cited a press release. Every single one was pulled from editorial content.

If you’re not in the sources AI retrieves for your category queries, you’re not in the answer.

This post is about how to get into those sources.

Step 1 for everyone: run the diagnostic

Before you do anything else, run your category queries across ChatGPT, Gemini, and Perplexity. Not once. Do 10-15 variations of how someone might ask for what you sell.

“Best [your category]” “Best [your category] for [your ICP]” “Top [your category] in [your city]” “[Your category] vs [competitor category]” “What [your category] should I use for [use case]”

For each query, on each platform, write down two things: which brands appear in the answer, and which sources get cited in the footnotes or inline links.

You’ll notice the same 5-10 sources keep showing up. Those are your L3 targets. Everything else in this post is about getting onto those sources.

You’ll also notice the three platforms often cite completely different sources for the same query. Only 12% of cited sources match across ChatGPT, Perplexity, and Google AI. This means you need presence across multiple source types, not just one.

Do this monthly. The sources shift. The order shifts. Brands rotate in and out. This isn’t a set-it-and-forget-it diagnostic. Soon you’ll be able to automate the whole process.

If you’re a SaaS or B2B company

Your L3 targets are probably G2, Capterra, Clutch, TrustRadius, and the editorial “best of” listicles in your vertical.

𝗥𝗲𝘃𝗶𝗲𝘄 𝘀𝗶𝘁𝗲 𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝘀. This is the lowest-hanging fruit, and most teams still don’t do it well. Your G2 profile needs to be complete… every field filled, recent screenshots, updated pricing, current integrations. Capterra and Clutch same thing. These aren’t vanity profiles. They’re the pages AI retrieves when someone asks for the best tool in your category.

Recent reviews matter more than total reviews. AI systems weigh freshness. A profile with 200 reviews from 2023 and nothing from 2026 looks abandoned. Get a cadence going… ask customers after successful outcomes, not at random. Build a system around it if you can.

𝗘𝗱𝗶𝘁𝗼𝗿𝗶𝗮𝗹 𝗹𝗶𝘀𝘁𝗶𝗰𝗹𝗲 𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁𝘀. Run your diagnostic and identify which “best X” articles AI is actually citing. Then work to get on those specific articles. Not all listicles. The ones that show up in AI answers.

Some of these are organic (the publication found you). Most require outreach. Reach out with a genuine case for inclusion… what’s different about your product, what gap you fill in their list, what data you can offer. Some accept sponsored placements. Some don’t. Know which is which. These can also be created and pitched fresh, with research data, quotes, or other info-rich context. DIY or find a provider to handle this for you, cough cough, Loganix… does this.

𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝘀𝗶𝘁𝗲. ChatGPT specifically cites first-party sites alongside editorial listicles. Gemini mostly ignores them. But ChatGPT is the largest platform, so it matters. Publish comparison pages on your own domain: “[Your product] vs [Competitor]” and “[Your category]: top options compared.” Structure them for extraction… front-loaded answers, entity-dense, declarative language. The specs from Anatomy of an AI-Citable Page apply directly here. HOWEVER, treat each listing objectively and equally. This can be very powerful, but you need to be honest and not biased.

𝗥𝗲𝗱𝗱𝗶𝘁. Brands with active Reddit and Quora discussion presence are 4x more likely to be cited by ChatGPT. For B2B, the subreddits where your buyers hang out are L3 gold. r/SaaS, r/startups, r/devops, r/marketing, whatever your vertical is. You need to be mentioned authentically in recommendation threads. Not by posting about yourself. By being useful enough that other people mention you when someone asks, “what tool do you use for X?”. Getting mentioned by others naturally is even better.

This is a long game. There’s no shortcut. But 72% of tech decision-makers use Reddit for peer reviews, and AI treats Reddit consensus as a high-trust signal.

𝗬𝗼𝘂𝗧𝘂𝗯𝗲. YouTube now appears in 16% of LLM answers as a cited source, up from behind Reddit a year ago. Educational videos that explain how to solve the problem your product solves get cited. Product demos don’t. If you can publish “How to [solve the problem your ICP has]” videos with your product shown in context, those become retrievable L3 content.

If you’re ecommerce or DTC

Your L3 targets are Wirecutter, vertical review sites, Reddit buying subreddits, and product comparison pages.

𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗽𝗮𝗴𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. Your product pages are potential L3 sources… but only if they’re structured for extraction. Attribute-rich descriptions with specific specs, not marketing copy. Comparison tables. Clear category labeling. Schema markup with complete product attributes. AI needs to be able to match your product to a category query. Vague product pages don’t get retrieved.

𝗥𝗲𝘃𝗶𝗲𝘄 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺. For consumer products, Wirecutter is the dominant L3 source in many categories. But check your diagnostic… it might be CNET, Tom’s Guide, Reviewed, or a vertical-specific site. Get reviewed by the sources AI actually cites for your product category.

Amazon is an interesting note here. It’s still the most-cited ecommerce site in AI answers (~2% of all commercial citations) despite blocking nearly 50 AI crawlers. This means Amazon’s presence feeds the training layer even when crawlers can’t reach it in real time. Keep your Amazon listings optimized regardless.

𝗥𝗲𝗱𝗱𝗶𝘁 𝗯𝘂𝘆𝗶𝗻𝗴 𝘁𝗵𝗿𝗲𝗮𝗱𝘀. Subreddits like r/BuyItForLife, r/GoodValue, and category-specific subs (r/headphones, r/skincare, r/running) are massive L3 sources. Reddit shows up in 97.5% of product review queries on Google. When someone on Reddit recommends your product in a “what should I buy” thread, that thread becomes retrievable content for AI.

If you’re local or service-based

Your L3 targets are Google Business Profile, Yelp, vertical directories, and local Reddit threads.

𝗚𝗼𝗼𝗴𝗹𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗣𝗿𝗼𝗳𝗶𝗹𝗲. Still, the foundation for local L3. Complete every field. Post regularly. Get recent reviews. Google’s own AI systems pull from GBP data for local category queries.

𝗬𝗲𝗹𝗽 𝗮𝗻𝗱 𝘃𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗱𝗶𝗿𝗲𝗰𝘁𝗼𝗿𝗶𝗲𝘀. Healthgrades for healthcare. Avvo for legal. Houzz for home services. Angi for contractors. These are the vertical equivalents of G2 and Capterra. AI retrieves them for local category queries by vertical. Claim your profiles. Fill every field. Get reviews.

𝗟𝗼𝗰𝗮𝗹 𝗥𝗲𝗱𝗱𝗶𝘁 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗺𝗲𝗻𝘁𝗶𝗼𝗻𝘀. There’s a case study of a pub in Seattle that started appearing in AI recommendations purely because people kept mentioning it in Facebook groups and Reddit threads when asked “where to watch Premier League in Seattle?”. The pub did nothing on Reddit themselves. Community mentions alone drove AI visibility. Shout out @JoJofurnival

This is the local L3 play: be good enough that people mention you when asked. Then make sure that mention is findable… the community needs to be on a platform AI retrieves from, and the mention needs to use your actual business name (not “that place on 3rd street”).

If you’re an agency or consultancy

Your L3 targets are Clutch, directory profiles, “best agencies” listicles, and thought leadership distribution.

𝗗𝗶𝗿𝗲𝗰𝘁𝗼𝗿𝘆 𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝘀. Clutch is the dominant agency directory in AI answers. Complete profile, recent case studies, recent reviews. Also check Semrush Agency Partners, DesignRush, and whatever vertical-specific directories show up in your diagnostic.

𝗧𝗵𝗼𝘂𝗴𝗵𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗮𝘀 𝗟𝟯 𝗰𝗼𝗻𝘁𝗲𝗻𝘁. Pay attention: The content you publish about your methodology can become a category citation source if it’s structured correctly. When someone asks “best SEO agency for ecommerce,” AI might retrieve a blog post where you’ve compared approaches to ecommerce SEO and positioned your methodology. But only if that content is structured for retrieval… comparison-based, category-labeled, entity-dense.

Don’t just claim expertise. Publish the methodology. Show the framework. Give the comparison. AI can’t retrieve “we’re really good at this.” It can retrieve “here’s how our approach compares to alternatives for [specific use case].”

𝗖𝗮𝘀𝗲 𝘀𝘁𝘂𝗱𝗶𝗲𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗳𝗼𝗿 𝗲𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻. Your case studies need specific numbers, named outcomes, and clear category labeling. “Helped a client grow revenue” gives AI nothing. “Increased ecommerce conversion rate from 1.2% to 3.8% for a mid-market DTC brand through technical SEO restructuring” gives it a specific, extractable result mapped to a category.

The correction play

Sometimes AI is already answering category queries about you… just wrong.

@wilreynolds at Seer Interactive discovered that a single 2018 negative review, duplicated across 5 review sites, was showing up in 38% of their branded prompts. “High account manager turnover” appeared 67 times over three months of tracking.

They published a blog post with actual retention data (79.2% retention rate). Perplexity cited it the same day. After just 2 citations, LLMs stopped referencing “high turnover.”

But it faded. They had to republish updated content to maintain the correction. The retrieval layer isn’t permanent – dudes have ADD. If you’ve corrected a misconception, check quarterly that the correction is still holding.

The lesson: if AI is saying the wrong thing about you in category queries, you can fix it. Publish authoritative correction content on your own domain. Make the accurate data specific, recent, and easy to extract. The retrieval layer will pick it up. But you have to maintain it.

The distinctiveness problem

One more thing that applies across every company type.

When brands in the same category describe themselves with identical language… “innovative solutions,” “best-in-class service,” “trusted partner”… AI produces what @wilreynolds calls “generic babble.” Research on proactive interference across 35 LLMs confirms this: when similar entities compete in the same retrieval context, the model’s ability to distinguish between them degrades.

Your editorial placements need to describe you differently from how your competitors describe themselves. Specific capabilities, specific outcomes, specific positioning. If you sound like everyone else in the category, you’re adding to the interference problem and making it harder for AI to recommend any of you confidently.

Distinctiveness is an L3 signal. Maybe the most underrated one.

Where this sits in the framework

L3 is where most brands want to start because it’s where buyers enter. But it depends on L1 and L2.

If the model doesn’t know you exist (L1), it can’t match your name on a listicle to a resolved entity. If the model doesn’t have confidence in describing you (L2), it might mention you but hedge. “Some users have reported…” instead of “one of the top options is…”

The brands winning L3 consistently have L1 and L2 already in place. The brands showing up sporadically usually have weak foundations.

Build the entity. Build the depth. Then win the category.

Next: Layer 4, Informational Citation.

Sources referenced: Direct platform testing, Mar 2026. 100 queries, 10 verticals, 3 platforms. 0/300 press release citations. @hq_passionfruit + @ahrefs, 15K queries. 12% cross-platform citation overlap. @Superlinesio, Mar 2026. 615x citation volume variance. @SERanking, 2025. Reddit in 97.5% of product review queries. 4x ChatGPT citation with Reddit presence. Bluefish / Adweek, Jan 2026. YouTube in 16% of LLM answers. Tinuiti + Profound Q1 2026. Reddit citation share 73%+ growth. @forrester, 2023/2025. 72% tech decision-makers use Reddit for peer reviews. @wilreynolds / Seer Interactive, 2026. Brand correction GEO experiment. Wang & Sun, NYU/UVA, Jul 2025. Proactive interference across 35 LLMs.