For years, SEO had a clear goal: get your page into the top results on Google. That goal has not disappeared, but it now shares the stage with something else.
People ask ChatGPT, Perplexity, and Claude to recommend software, compare prices, and fix problems. If your brand is not in the answer they get, you are not part of the decision.
That has created a new area of work called LLM seeding. Most advice on it is vague. Post on Reddit, pitch journalists, and hope.
Here, you will get to know the following approach laid out by Shai Belinsky, a senior SEO specialist at Similarweb: LLM seeding is a data problem.
You do not need more guesses about where to show up. You need a map of where AI models get their information, and a way to check whether your work is paying off.
What LLM Seeding Is
LLM seeding means placing your brand's content in the places that large language models trust and cite. Those places are mostly outside your own website: forums, review platforms, news outlets, comparison articles, and explainer guides.
You might say that is just off-site SEO, and there is overlap. Classic off-site SEO, such as backlinks, reviews, brand mentions, and articles on platforms like LinkedIn or Medium, is mainly aimed at raising your domain's authority.
LLM seeding has a different aim. It shapes your presence across the web so that AI models use it as a source of information about your category. It is also the practical engine behind Generative Engine Optimization, or GEO.
The result you are after is what the author calls brand memory. You want the model to connect your brand with a topic, for example, enterprise CRM software, so well that it names you even when the user never typed your brand name.
The Three Basic Tactics
Before getting into the data, it helps to know the common tactics. Most LLM seeding work falls into three groups, which you can think of as where, how, and who.
Where: Be Present on Platforms With Human Consensus
AI models give a lot of weight to what real people say. That is why user-generated content and third-party validation matter so much.
Forums such as Reddit, Quora, and Stack Exchange hold real answers from real people. Review sites such as G2, Capterra, and Trustpilot hold structured pros and cons, which models use when they build comparisons between products.
How: Make Your Content Easy to Read
If a model cannot parse your content, it will not use it. A few habits help. Writing in self-contained sections makes each part easy to pull out and reuse, although you should know that Google has publicly advised against creating content only to please language models, so write for human readers first.
Comparisons set out in HTML tables are easy for a model to read and repeat. Question-and-answer sections, paired with schema markup, help a model match a specific question with a specific answer.
Who: Earn Mentions From Recognized Publications
When respected news sites and industry publications cite your brand, it signals to a model that you are a known name in your space. This is the digital PR part of the work.
Why Tactics Alone are Guesswork
Those tactics make sense, but they only pay off when you apply them in the right places. Forums, tables, and reviews help when they sit where models actually learn, not where you happen to feel like posting.
Most companies handle this blindly. They type 50 prompts into ChatGPT, see which competitors get mentioned, and guess where to post.
The article points out two problems with that approach. First, if you ask an AI where it got its information, it can invent sources, so the answer may be a made-up citation. Second, you cannot check the millions of ways that people phrase their questions by hand.
The way out is to stop behaving like a user and start behaving like an analyst. The rest of this guide covers four steps that do that, using Zoho and the topic CRM software solutions as the worked example.
Step 1: Find The Sites AI Trusts
Suppose a competitor keeps getting recommended by ChatGPT. The reason is usually not magic. It is that the competitor appears on specific third-party sites that the model returns to again and again.
The author calls such a site a High-Weight Node: an outside website that AI models repeatedly trust on a given topic.
To find them, the author set up an AI brand visibility campaign for Zoho on the topic of CRM software and used Similarweb's citation analysis to see which domains and pages AI answers cite.
The results showed that visibility was driven mainly by third-party explainers and comparison pages, not by the brand's own pages. About 96% of the cited URLs appeared in answers that did not mention Zoho at all, and only 4% appeared in answers that did.
Zoho's domain influence score was 11.67, which means the models leaned mostly on outside sources. The most influential domains were educational and neutral, including Wikipedia, software explainers, and product guides.
The most cited pages were not vendor landing pages. They were articles on the benefits of CRM, CRM comparisons, and how-to guides from third-party sites. They had three things in common: they explained a category instead of selling one product, they compared several options side by side, and they answered basic questions that models reuse often.
The lesson is that language models do not trust brands by default. They trust explanations. So instead of spreading your effort over dozens of sites, focus on the small group of domains that carry influence on your topic. The author argues that seeding those sites pays off far more than broad, unfocused outreach.
Step 2: Let Real Prompts Decide What You Write
Once you know where to plant, you need to know what to plant. Keyword research tells you what people type into Google. For LLM seeding, you need to know what people ask AI systems, and those questions are conversational prompts.
The author used prompt analysis on CRM-related prompts sent to ChatGPT, and found that Zoho's visibility was uneven. Some broad CRM prompts showed Zoho competing well.
Many practical prompts, about customization, integrations, analytics, and customer service workflows, showed no mention of Zoho at all. Competitors such as HubSpot, Salesforce, Microsoft Dynamics, and ClickUp showed up again and again in those spots.
Each of those gaps is an opportunity. A prompt where your brand is missing works like a content brief: write a strong answer to that exact question and place it on the high-weight sites you found in step one.
Step 3: Check That a Site is a Live Node
Not every third-party site plays the same role. Some actively take part in how people research with AI, and others are places where content just sits. Before you spend on PR or guest content, check which kind you are dealing with.
The author's method is to look at where a site sends its visitors next. If readers regularly go from a publisher's article to ChatGPT or Perplexity, that is a sign they are using AI to check, compare, or dig deeper into what they just read. In that case, the publisher is part of the AI research journey. The article calls this a Live Node.
The example is PCMag. Its outgoing traffic to ChatGPT has been growing, which suggests that readers treat it as a trusted source and then use AI to confirm recommendations, compare alternatives, and look further into purchase decisions.
For seeding, a placement on a site like that is more valuable than a placement on a site that is merely well known. It is more likely to reach people with strong intent, and more likely to be referenced again and again by models.
Step 4: Measure What is Working
Proving value is the hardest part of LLM seeding. The author tracks four measures.
Visibility Against Competitors, Over Time
A one-time score is not enough. You want to see how your visibility changes week by week compared with other brands on the same topic.
In the CRM example, HubSpot led, Salesforce followed closely, Zoho sat in the middle, and a long tail of smaller brands had single-digit visibility. This view answers one key question: are your efforts growing your share of visibility faster than your competitors' efforts are growing theirs?
Share of Model
Share of Model shows how often your brand appears in AI answers compared with competitors, split by AI platform.
In the example, ChatGPT produced the most visibility across all brands, AI Mode produced meaningful but lower visibility with a similar ranking, and Perplexity contributed a smaller share while still mattering for research-heavy prompts.
Zoho trailed HubSpot and Salesforce on all three, and the gap was widest in ChatGPT, the most influential platform in that category.
The practical conclusion is that gains on the dominant platform pay off disproportionately, while the other platforms reinforce your presence.
Brand Mention Volume
Here you look at individual prompts and ask a plain question: is my brand in the answers people are actually requesting?
In the CRM example, Zoho appeared in several high-visibility prompts alongside competitors, but was absent from a large set of common, high-intent prompts where competitors held the spots.
Counting how many prompts include your brand, which ones leave it out, and how that balance moves over time turns mentions from a vanity number into something you can act on.
As the count grows, it suggests the model is linking your brand to more questions and use cases, not only a narrow part of the category.
AI Traffic
Finally, check the visits that AI platforms send to your site. For zoho.com, traffic from AI was dominated by ChatGPT, with smaller amounts from Perplexity, Gemini, and Claude, and with natural ups and downs over time.
The author notes that these visits include a meaningful layer of high-intent clicks, particularly from people checking pricing, features, and trust signals.
Frequently Asked Questions
What is LLM Seeding in Simple Terms?
It is making sure your brand appears in the outside content that AI models rely on when they write answers. You optimize for where models learn, not only for where search engines rank.
How is it different from SEO?
SEO is about ranking your own pages. LLM seeding is about influence off your site, through citations, explainers, comparisons, and reviews that models reuse when they answer questions.
Is it the same as GEO?
No. GEO is the wider practice of optimizing for AI-generated answers. LLM seeding is a core part of it, focused on placing your brand in the sources models cite.
Which Types of Sites Matter Most?
The data in the article points to three groups: educational explainers and how-to guides, comparison and best-tools content, and trusted review platforms and forums.
How do I Know Which Sites My Category's AI Answers Trust?
Use citation analysis. It shows which domains and URLs show up in AI responses for your topic. High influence scores and repeated citations point to the sites worth prioritizing.
Does it Bring Traffic, or is it All Zero-click?
Both happen. AI answers include zero-click responses and also high-intent referral clicks. The volume is usually smaller than classic search, but the intent tends to be higher, especially for pricing, feature comparisons, and final checks.
How Long Until I See Results?
The effect builds over time. Early signs are more brand mentions and wider visibility across prompts. Stronger citation presence and AI referral traffic come after that.
Do I Need Special Content for AI Models?
No. The best content is clear, helpful, and written for people. Models benefit from clarity and structure, but usefulness comes first.
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