LLM Seeding:Get Cited in AI Answers & Overviews

LLM seeding means publishing content in the formats and places that AI models like ChatGPT, Gemini, Claude, and Perplexity are most likely to read, summarize, and cite, so the AI mentions and recommends you when people ask. Where traditional SEO optimizes for rankings and clicks, LLM seeding optimizes for citations and mentions inside AI-generated answers. In practice it means earning credible presence on the third-party surfaces models trust: communities, publications, comparison lists, review platforms, and video.

GEO (generative engine optimization) is about your own website: structuring and writing your content so AI models can understand it, quote it, and cite it. LLM seeding is about everyone else's websites: placing and earning content across the third-party surfaces models trust most, like Reddit, Q&A sites, industry publications, and review platforms. They are complementary halves of an AI search strategy, and most engagements should include both.

AI citation optimization is the work of becoming a source, or being named by the sources, that AI assistants draw on when they answer. Citations are the links an engine shows beside its answer. Brand mentions are the times it names you in the answer itself, with or without a link. Both matter, and they are measured separately. Because models read text and not only links, an unlinked brand mention on a credible page can still inform what an AI says about you, which is why seeding tracks brand mentions in AI answers alongside citations.

Four things. A baseline audit showing how the major models answer your buyers' questions today, who they name, and which sources they cite. A citation-surface map that ranks the communities, review platforms, lists, and publications worth working on in your niche. A running log of everything done, with a link and a disclosure note for every asset, placement, and community answer. And a monthly report that re-runs the same questions so you can see which answers changed. The scope for each is agreed in writing before work starts.

No. Reddit matters because AI assistants draw on community discussion, and our community participation is transparent and genuinely helpful, but it is one channel among several: earned publication placements, comparison and list content, honest customer reviews on platforms like G2 and Trustpilot, YouTube with descriptive metadata, and entity signal work all feed AI answers too.

For the earned-media part of seeding, yes: we pitch expert commentary, contributed articles, and roundup inclusion to publications relevant to your category, and we log whether the sources an engine cites include that coverage. Our PR for AI search page covers the full program, including guaranteed featured articles that are paid for only after they are published.

When models search the web live (retrieval-augmented answers, like Perplexity or ChatGPT search), they read sources as they are today, so newly placed sources tend to show up first once they are indexed. Answers that come from a model's training data move slower, because they wait on the model being retrained on a newer web snapshot. We verify progress with monthly monitoring.

It complements SEO rather than replacing it. Traditional SEO earns rankings and clicks from search results; LLM seeding earns mentions and citations inside AI answers. The overlap is smaller than most people assume, because AI assistants can cite pages that do not rank on the first page of Google. Strong SEO still helps, especially for models that search the web live, but the surfaces that win AI citations (communities, Q&A, lists, reviews) need their own deliberate program.

Analysts, writers and PR specialists on our team. We create genuinely useful, citation-ready content, earn placements in real publications, participate in communities transparently, encourage real customers to leave honest reviews, and strengthen factual entity signals, and everything is logged with its link and disclosure.

With a standing monthly audit. We re-run a fixed set of the questions your buyers ask across ChatGPT, Gemini, Claude, and Perplexity, then track whether you are mentioned, whether you are cited, what the AI says about you, and how that compares to competitors over time. We pair that with referral data from AI assistants in your analytics, so you see movement in the answers themselves, not just activity reports. If an answer turns out to be inaccurate, not merely missing you, that is handled by our AI reputation monitoring service.

If you qualify, yes. We start with a free notability assessment. If you meet the bar and we take the case, creating a new page is results-based: you pay only if the page is published. If you don't yet qualify, we say so and put the seeding work on the surfaces that will move first. Wikidata, a structured database that search engines and AI systems can draw on for entity facts such as those shown in Knowledge Panels, has its own, generally lower, inclusion bar and is often the better near-term target.

It starts with a free AI visibility audit, so you see the baseline before spending anything. From there, scope depends on how many surfaces matter in your niche, how competitive the category is, and whether we're also running GEO work on your own site. It is typically a monthly engagement, and we quote it in writing before you commit.