AI Search Visibility
LLM Seeding:Get Cited in AI Answers & Overviews
People now ask ChatGPT, Gemini, Claude, and Perplexity what to buy and who to trust, and the AI answers with a shortlist. LLM seeding puts genuinely useful content on the surfaces those models read and cite, so the answer includes you.
Seed the Sources, Earn the Citation
Strategy by Anthony Will, Founder & CEO · 13+ years in ORM · 5,000+ clients- LLM seeding, defined. Publishing content in the formats and places AI models read, summarize, and cite, so they mention and recommend you. It optimizes for citations, not clicks. Full definition →
- Rank alone won't get you cited. Studies have found roughly 90% of ChatGPT citations come from pages outside the top 20 Google results. AI pulls from different surfaces than SEO targets. Why it matters →
- White-hat only. No fake accounts, no fake reviews, no undisclosed promotion. We create genuinely useful content and earn real placements, starting with a free audit. Get your free audit →
What is LLM seeding?
The short answer
LLM seeding is the practice of publishing content in the formats and places that AI models (ChatGPT, Gemini, Claude, Perplexity) are most likely to read, summarize, and cite, so the AI mentions and recommends you when people ask. Instead of optimizing for clicks and rankings, it optimizes for citations and mentions inside AI-generated answers.
Our LLM seeding strategy, in full
The problem seeding solves is quiet but expensive: a buyer asks ChatGPT or Perplexity for the best provider in your category, and the model confidently names three competitors and never mentions you, because you're absent from the sources it trusts. Those sources are rarely your own website; industry research has found that roughly 90% of ChatGPT citations come from pages ranking beyond the top 20 Google results, so models lean on Reddit threads, Q&A sites, comparison lists, review platforms, and niche publications that classic SEO rarely touches. Here is the entire playbook we run to earn you a credible presence on exactly those surfaces, phase by phase. We publish it openly because the edge is not the secret, it is the execution: doing every phase, in order, transparently, and measuring the result.
Baseline audit
We ask ChatGPT, Gemini, Claude, and Perplexity the questions your real buyers ask (best providers in your category, whether you are legitimate, how you compare to named competitors, what to know before hiring you) and capture every answer word for word, with web search on and off where the platform allows it. We log which sources each answer cites, how you are described, and where you are absent. Then we run the same panel for your top competitors, so we know exactly who the models currently trust in your category and why. This baseline is the scoreboard every later phase is measured against.
Citation-surface mapping
From the audit citations and category research, we build the map of surfaces that models actually draw from in your niche: which subreddits and Q&A threads, which review platforms, which industry publications, which comparison and best-of pages, which YouTube channels. Every niche has a different map, and seeding the wrong surfaces wastes months. The output is a prioritized target list with an owner and an approach for each surface: create, earn, participate, or strengthen.
Citation-ready assets
Models cite content that is structured for extraction, so we build assets designed to be quoted: direct answers stated up front, clear headers and semantic chunking, comparison tables with transparent criteria, first-person evidence with stated credentials, FAQ blocks with the question as the heading, and structured data underneath. Where a best-of or comparison page is the asset, the selection criteria are published and defensible, because models favor sources that show their reasoning.
Earned placement
We place expert commentary and contributed pieces in real industry publications, get you quoted by journalists and newsletter writers covering your space, and pursue inclusion in the roundups and listicles that models already cite. Placement is earned on merit or transparently disclosed, never bought from link farms or fake news sites, which models and platforms increasingly detect and discount.
Community presence, transparently
Where buyers ask questions in communities (Reddit, Quora, niche forums), we help you participate as yourself: disclosed affiliation, genuinely useful answers, no scripts. We also make it easy for real customers to tell their own stories in those spaces. What we never do is astroturf. No fake accounts, no sockpuppets, no undisclosed promotion, both because it violates platform rules and FTC guidance and because detection burns the brand harder than absence ever would.
Review-platform depth
Models read the review platforms your category lives on (G2, Capterra, Trustpilot, Google, and the vertical sites specific to your industry) and they weight detailed, recent, context-rich reviews. We build compliant review-generation flows that ask real customers specific questions at the right moment, so the record models read reflects your actual customer base rather than your angriest outlier.
Entity infrastructure
Models have to resolve who you are before they can recommend you. We tighten your entity signals: Organization and Person structured data, consistent names and profiles across the web, Wikidata and Knowledge Graph presence where warranted, and, where you genuinely meet its notability bar, an accurate and well-sourced Wikipedia presence (which cannot be bought and which we never edit deceptively on your behalf), plus real author bios with credentials on everything you publish. This is also what separates you from namesakes, so the model does not blend your record with someone else's.
Measure and iterate
Every month we re-run the prompt panel from Phase 1 across the major models and log four things per prompt: are you mentioned, are you cited, how are you characterized, and who else appears. Retrieval-augmented answers usually move first, in weeks, as the sources models search begin to change; training-data answers move on model refresh cycles, in months. The panel tells us which surfaces are producing citations, and the next cycle doubles down on what is working and reworks what is not.
The relationship to GEO, plainly: Generative Engine Optimization tunes your own site so models understand and cite it; LLM seeding builds your presence across the third-party surfaces models trust. They are the two halves of one AI search strategy, and we run them together. And the honest caveat that governs all of it: no one can guarantee that a specific model will cite you on a specific day. What we control are the inputs, measured monthly, compounded over quarters.
Why LLM Seeding Matters
AI answers are the new front page
AI assistants answer directly
When someone asks ChatGPT or Perplexity for the best option in your category, they get a short answer with a few names in it, not a results page. If the model never mentions you, you were never in the running. Being one of the sources it reads and cites is the new visibility.
Ranking well isn't the same as getting cited
Industry studies have found roughly 90% of ChatGPT citations come from pages ranking beyond the top 20 Google results. Models weight community discussion, Q&A answers, lists, and niche publications heavily, so a #1 ranking alone often earns zero AI mentions.
LLM seeding vs GEO: two halves, one strategy
GEO (generative engine optimization) optimizes your own site's content so AI models can understand and cite it. LLM seeding places and earns content across the third-party surfaces models trust most. They are complementary: GEO makes you citable, seeding makes you present where models actually look.
White-hat or it backfires
Communities ban undisclosed promotion, review platforms purge fake reviews, and the FTC penalizes deceptive endorsements. We never use fake accounts or sockpuppets. Our seeding is genuinely useful content, earned placements, transparent participation, and real customer reviews, which is also what holds up over time.
What We Seed
The surfaces AI models actually cite
Reddit and Q&A communities
Reddit is among the most-cited sources by LLMs, and Quora is heavily cited in Google's AI answers, per industry studies. We participate transparently and only where we add real value; undisclosed promotion violates community rules and we don't do it.
Industry publications and expert quotes
Earned articles, contributed expertise, and quotes in the trusted publications models treat as authoritative in your niche. Real bylines in real outlets, not paid link spam.
"Best of" lists and comparison pages
Structured roundups and comparison content are the exact format AI assistants lift recommendation answers from. We create and earn placements in credible, honest versions of them.
Review platforms
G2, Capterra, Trustpilot, and the category review sites models read when judging who to recommend. We help you systematically encourage real customers to leave honest reviews. Never fake ones.
YouTube with descriptive metadata
Models parse video titles, descriptions, and transcripts. Useful video content with clear, descriptive metadata becomes another citable asset in your footprint.
Wikipedia and Wikidata
Both rank among the most-used sources in AI training and grounding, and Wikidata feeds the knowledge graphs models lean on. But Wikipedia has a strict notability bar and conflict-of-interest rules: you cannot buy your way on, and editing your own page to promote yourself is against the rules. Where you genuinely qualify, we help keep the public record accurate and well-sourced. Where you don't yet, we say so and target surfaces that will move sooner.
Entity signals
Structured data, consistent profiles, and aligned identity information across the web, so models recognize exactly who you are and attribute the right facts to you.
The Process
How to do LLM seeding
- 01
Audit how AI answers today
See the baseline.We ask ChatGPT, Gemini, Claude, and Perplexity the real questions your buyers ask, capture the answers word for word, and record who gets mentioned and which sources get cited.
- 02
Map the surfaces models cite in your niche
Find the inputs.Every citation points somewhere. We identify the specific communities, publications, lists, and review platforms feeding AI answers in your category.
- 03
Create and place citation-ready assets
Seed the sources.Genuinely useful, quotable content published or earned on those surfaces: expert commentary, comparison content, transparent community answers, honest customer reviews, video.
- 04
Strengthen entity signals
Be unambiguous.Structured data, consistent profiles, and clear identity information so models attribute the new coverage to the right entity, you.
- 05
Monitor AI mentions monthly and iterate
Compound the wins.We re-run the audit prompts every month, track your mentions and citations against competitors, and double down on the surfaces that move answers.
Free to find out. No obligation, no pressure.
Get a free, honest assessment of what we can actually do, with no upfront cost and no obligation.
Honest Timelines
How long until AI models cite you
No honest firm quotes one number for everything. The timeline depends on the type of work, so these are the real ranges we quote by scenario, and you get a case-specific estimate in writing before you commit to anything.
When a model searches the web live, as Perplexity, ChatGPT search, and Google's AI answers do, a well-placed new source can start being cited within weeks of getting indexed.
Helpful community answers and honest reviews accumulate votes, replies, and credibility before models weight them heavily. Genuine participation compounds; spam gets removed.
Answers a model gives from its training data shift only when the model is retrained on a newer snapshot of the web, on the AI company's schedule, not ours.
No one can promise that a particular model will cite you on a particular query. We improve the probability by controlling the inputs, then verify with monthly monitoring.
Why We're Different
White-hat LLM seeding vs. astroturfing
| Feature | Astroturf "Seeding" Vendors | Reputation Resolutions |
|---|---|---|
| Community posting | Undisclosed sockpuppet accounts | Transparent, genuinely useful participation |
| Reviews | Bought or fabricated reviews | Real customers encouraged to review honestly |
| Placements | Spam mentions on low-quality sites | Earned placements in publications models trust |
| Risk to you | Bans, FTC exposure, brand damage | Durable assets that keep earning citations |
| The promise | "Guaranteed AI mentions" | Honest: we raise the probability by controlling the inputs |
| Background | Pop-up growth-hack shops | 13+ years of reputation management since 2013 |
Who runs your case
Senior specialists, no junior handoffs
Reputation Resolutions is run and managed by a world-class team of online reputation management experts. Your case is handled by senior, multidisciplinary specialists: removal strategists who know each platform's rulebook, SEO and content experts who rebuild your search results, legal partners for the matters that need them, veteran PR professionals, and AI-search specialists who help you control what LLMs like ChatGPT say about you. There are no junior handoffs and no learning on your case, and every person here treats your name as if it were their own.
Get Started
See how AI answers your category today
A free audit: we'll run the questions your buyers actually ask across ChatGPT, Gemini, Claude, and Perplexity, show you who gets mentioned and cited, and map an honest plan to get you into the answer.
Free & Confidential
Get a Free AI Visibility Audit
No commitment. We'll show you who AI recommends in your category today and how we'd change that.
- A free audit to start, no cost and no obligation
- You pay only for results, never a retainer
- 5,000+ clients since 2013 across 40+ countries
- Confidential and senior-led from the first call
LLM Seeding FAQs
LLM Seeding, Answered Honestly.
Straight answers on the definition, GEO, Reddit, timelines, and pricing.
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. Our AI reputation management practice covers the full picture.
No, and honest vendors will tell you why. Reddit matters because it is among the most-cited sources by LLMs, but undisclosed promotional posting violates community rules, gets removed, and gets accounts banned. Our community participation is transparent and genuinely helpful, and 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.
Realistically, weeks to months. When models search the web live (retrieval-augmented answers, like Perplexity or ChatGPT search), newly placed sources can be cited within weeks of getting 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. And no one can guarantee a specific AI will cite you on a specific query; we improve the probability by controlling the inputs and 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: studies have found roughly 90% of ChatGPT citations come from pages ranking outside the top 20 Google results. 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.
Done the way we do it, yes. The line is deception: fake accounts, fake reviews, and undisclosed sockpuppet promotion are astroturfing, they violate platform rules and FTC guidance on deceptive endorsements, and we do none of it. Our seeding is creating genuinely useful, citation-ready content, earning placements in real publications, participating in communities transparently, encouraging real customers to leave honest reviews, and strengthening factual entity signals. That approach is not just cleaner, it is more durable, because platforms actively purge the fake stuff.
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 AI reputation monitoring for anything inaccurate that surfaces, and with referral data from AI assistants in your analytics, so you see movement in the answers themselves, not just activity reports.
Only if you already qualify, and only honestly. Wikipedia is one of the most-used sources in AI training and grounding, so it is a fair thing to ask about, but it is not a surface you can buy your way onto. It has a strict notability standard (meaningful, independent, third-party coverage) and clear conflict-of-interest rules that prohibit editing your own page to promote yourself. If you meet the bar, we help make sure the public record is accurate, neutral, and properly sourced, working within Wikipedia's own rules. If you don't yet, we tell you plainly and put the seeding work on the surfaces that will move first. Wikidata, the structured database behind many knowledge panels, has a lower 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 transparently before you commit.
Still not sure if your situation qualifies?
Get a straight answer from a senior specialist in one call: free, confidential, and you'll know exactly where you stand before you decide anything.
