We spent the past two weeks going through each of the five major LLMs one by one: how ChatGPT decides which SaaS products to recommend, what makes Claude cite a brand, how Microsoft Copilot pulls from Bing, what Gemini looks for in its knowledge graph, and how Perplexity assembles its answer pages. Each post covered the mechanics of a single model.
This post is the synthesis. The combined checklist. The thing you actually run against your own product before the next quarter starts.
One pattern repeated across all five models: the brands that showed up consistently had done the same small set of things. Not expensive campaigns. Not viral content. Structured data, comparison coverage, and an llms.txt file. That is the whole checklist.
Why five LLMs instead of one
The instinct when founders discover AI search visibility is to pick the biggest model (usually ChatGPT) and optimize for that. That works until it does not. Perplexity now handles a meaningful share of research queries in B2B. Copilot is embedded in every Microsoft 365 seat. Gemini is the default on Android and Google's AI Overviews. Claude is the preferred research tool for a growing segment of technical buyers.
Your buyers are not loyal to one AI. They use whichever is open in their browser. A brand that appears in ChatGPT but not Perplexity loses the buyer who started their research in Perplexity. That is not a hypothetical loss anymore. It is the current state of B2B buying.
The good news: the fixes overlap heavily. Getting this right in one model usually improves your standing in the others. The checklist below is ordered from highest leverage to lowest, based on what we saw move the needle across all five simultaneously.
The five-point checklist
1. FAQPage structured data on every high-traffic page. This was the single most consistent differentiator across all five models. LLMs read structured data to understand what a page is about and what questions it answers. A product page with five buyer-intent question-and-answer pairs in JSON-LD format is treated as a more citable source than an identical page without it. This is not a theory. It is what the models do with the markup when they summarize a product category. If you have not added FAQPage schema to your product pages, pricing page, and top blog posts, that is the first thing on your list.
2. A published llms.txt file at the root of your domain. Several LLMs now actively read this file to understand what a site does and how it wants to be described. The format is simple: a short description of your product, a list of your key URLs, and the primary use case you serve. A missing llms.txt is not a crisis. A well-written one is a free signal boost that takes under an hour to publish.
3. Comparison content against named alternatives. Every LLM we tested cited comparison pages more often than standalone product pages when answering a category query. If a buyer asks "what is the best AI operations platform for B2B SaaS," a model is more likely to cite a page titled "OperatorIQ vs [alternative]" than a page titled "About OperatorIQ." This is because comparison content gives the model something to say: it can position your product, describe the tradeoff, and still feel like a useful answer to the buyer. Write two or three comparison posts. They compound.
4. Bing indexing, not just Google. Perplexity draws heavily from Bing. Copilot is Microsoft's model. Both are significant in the B2B research phase. If your SEO has been entirely Google-focused, you may be indexed poorly in Bing, which means you are invisible in two of the five major LLMs regardless of your Google ranking. Submit your sitemap to Bing Webmaster Tools. Use IndexNow to push new content immediately. This costs nothing and takes an afternoon.
5. Consistent category language across all pages. LLMs build their understanding of your product from the language you use to describe it. If your homepage calls it an "AI operations platform," your pricing page calls it an "autonomous workflow tool," and your blog calls it an "agent orchestration system," the model gets a confused signal and may describe you in whatever category language it defaults to, which may not match what your buyers are searching for. Pick one phrase. Use it consistently. The models will pick it up.
What each model weighs most heavily
The checklist above applies across all five. But each model has a distinct weighting that is worth knowing before you prioritize your time.
ChatGPT (OpenAI) is still the most training-data-dependent of the five. Content that has existed on the web for longer, been linked to more, and been discussed in forum threads ranks higher in ChatGPT's citations. New content takes longer to influence ChatGPT than it does to influence Perplexity. The fix is patience plus the comparison content strategy above.
Claude (Anthropic) weights factual accuracy and verifiability. A product with a clear, specific description of what it does, a published pricing page, and a technical blog that demonstrates depth is cited more often than a product with vague positioning. Claude is skeptical of marketing language. Write like an engineer explaining a system.
Microsoft Copilot pulls from Bing's index and gives extra weight to content that surfaces in Bing News and LinkedIn. If your company has a LinkedIn page with regular posts, and your content is indexed in Bing, Copilot finds you. This is the easiest of the five to influence with small actions.
Google Gemini uses the Google knowledge graph more heavily than any other model. Your Google Business Profile, structured data across your site, and any Wikipedia-adjacent coverage of your product or company all contribute. Gemini also uses Google's AI Overviews, which means appearing in an AI Overview for a category query is one of the strongest AI visibility signals available right now.
Perplexity is the most transparent of the five: it shows you its sources. When Perplexity answers a query about your product category, scroll to the citations. If your domain is not there, you are not being cited. The fix is the same as for Bing: submit your sitemap, use IndexNow, and write content that directly addresses the exact questions your buyers ask. Perplexity rewards question-answering content more than any other model.
The quarterly audit habit
AI search visibility is not a one-time fix. Models update their training data, adjust their citation preferences, and change how they respond to structured markup every few months. A ranking you earned in Q1 may not hold in Q3 if competitors improve their structured data and you do not.
The simplest habit is a ten-minute quarterly spot-check: open each of the five models, type in your three most important buyer queries, and note where you appear. If the answer is "nowhere," that is the signal to run through the checklist again. If the answer is "in three of five," that is a solid foundation. If the answer is "in all five, consistently," you have a real moat.
Most B2B SaaS products we audit are in the first category. They appear in one or two models and have significant gaps in the others. That gap is closable. The checklist above closes it.
One thing that does not work
Trying to game AI search by stuffing keywords into pages does not work. LLMs are not keyword matchers. They are pattern recognizers. A page that repeats "best AI operations platform" fifteen times reads as low-quality to a language model and gets ignored in favor of pages that use natural language to explain how the product works and what problems it solves.
Write for a smart reader who has never heard of your product. Explain the mechanism. Answer the questions they would actually ask. That is what gets cited.