Most teams still treat AI answers as a traffic problem. They watch referral visits from ChatGPT or Perplexity trickle in, compare them to organic search, and file the whole channel under "not yet."
But the visits that do arrive convert unusually well. Similarweb measured AI-referral traffic converting to sales at 11.4% in June 2025, against 9.3% for paid search and 5.3% for general organic.
When a buyer asks an AI engine for the best tool in your category, the engine writes a shortlist, and if you're absent from it, more traffic to your site won't fix that, because the buyer never came looking.
LLM seeding is the work of getting into that answer. It means creating and placing content on the sources AI models draw from, both the training data that shapes a model's weights and the web pages an engine retrieves while it writes, so that ChatGPT, Perplexity, and Google AI Overviews mention your brand and cite your pages when someone asks for a recommendation.
It covers your own site plus the review sites, community threads, Wikipedia entries, and videos an engine pulls from on a buying question.
We run an AI-visibility index and publish into this exact machinery every week, so we've had a close look at what gets pulled and what gets ignored.
Here's how the mechanism works, where the placements are, and what a program costs.
What is LLM seeding?
Let's start with a baseline. LLM seeding is distribution work aimed at the corpus an AI engine reads when it composes an answer.
You're working toward one of two outcomes. A mention is your brand named in the answer text. A citation is your URL used as a source. Each arrives through a different path, on a different timeline.
The slower path is training data. Crawlers such as GPTBot and ClaudeBot collect pages that model providers fold into a model's weights during pre-training, and whatever they collect only surfaces when the next model ships. A model typically ships many months after its training cutoff, and no provider publishes a retraining cadence, so a Reddit thread you seed this quarter may become part of what a model "knows" a year from now, with no link attached.
The faster path is real-time retrieval. Separate bots (OAI-SearchBot, PerplexityBot, Google's ordinary crawl) feed a search index the engine queries at answer time. This is retrieval-augmented generation, or RAG, the technique of fetching documents and handing them to the model before it writes.
The engine extracts source passages and a separate citation step attributes them under the answer. Seeding aimed at this path can show up in days.
What separates seeding from a PR placement list is what you optimize each placement for. You want the passage that names your brand to stand on its own, because the engine usually extracts a passage rather than reading the whole page.
How LLMs find and choose what to cite
Cited sources mostly come through retrieval, while unlinked brand mentions can come from training. On the retrieval side, engines rewrite your prompt into several targeted searches, rerank the candidates, and hand the model a shortlist, and the three big engines do this differently.
Google AI Overviews and AI Mode ground on Google's core Search index. Google's own guidance says eligibility is identical to standard Search, indexed and eligible for a snippet, with no extra technical requirements. Google also runs a query fan-out, firing related sub-queries across subtopics before it generates, so pages that rank for those sub-queries get pulled in alongside pages that rank for the head term.
ChatGPT search mixes OpenAI's own crawl, third-party search providers, and live page fetches, and some of what it cites is what it cached from an earlier fetch rather than what it retrieved for you just now.
Perplexity runs everything on a first-party index. Its engineering team describes 200 billion-plus tracked URLs, hybrid lexical-and-semantic retrieval, cross-encoder reranking, and sub-document scoring that indexes snippets instead of whole pages. A machine-learning model decides when a URL needs re-indexing, and a prefilter drops stale content before ranking, so refreshing a dated page counts as seeding too.
Here's how the common tactics map to the two paths:
| Tactic | Path affected | Timing |
|---|---|---|
| Publish or update an owned page | Retrieval | Days to weeks once indexed, then volatile |
| Update a G2 or Capterra profile | Both | Days for retrieval, months for training |
| Seed a Reddit thread or propose a Wikipedia addition | Both | Days for retrieval, next model release for training |
| Unblock GPTBot or ClaudeBot | Training only | Visible only when the next model ships |
| Change robots.txt for OAI-SearchBot or PerplexityBot | Retrieval | About a day to propagate |
How LLM seeding differs from SEO and link building
A page-one ranking helps, but it doesn't guarantee a citation. Across 863,000 SERPs and 4 million AI Overview URLs, only 37% of cited URLs ranked in the organic top 10. Pages two and three are in play for citations in a way they never were for clicks, and so are pages you don't own.
That changes three things you're used to reporting on.
- The unit of success. SEO reports a position and a click. Seeding reports whether your brand was named and whether your URL was used.
- Stability. A rank tracker assumes the thing being ranked persists between checks. Citations churn week to week, so a single snapshot tells you very little.
- The asset. Link building buys a hyperlink pointing at your domain. Seeding buys your brand name inside the passage an engine extracts, often on a page you don't own and sometimes with no link at all. A G2 comparison that lists you third can get cited when your own comparison page doesn't.
None of this retires SEO. Google's eligibility rules for AI features are its Search rules, so crawlability and indexation are prerequisites, and strong organic rankings still feed retrieval on every engine. Treat seeding as the layer on top of a working SEO foundation, funded separately and measured differently.
Where to seed
For business software, review platforms outrank Reddit by a wide margin, which inverts what consumer-facing citation studies suggest. Fair warning, citation studies use incompatible denominators, so compare figures only within a single study.
- G2, Capterra, and Gartner Peer Insights. In an analysis of 22,729 AI Overviews on commercial keywords, 34.5% cited at least one review platform. Within those review-platform links, Gartner Peer Insights held 26%, G2 23%, and Capterra 18%. Each profile pairs structured attributes (rating, pricing, feature checklists) with first-hand reviewer text, roughly the mix a comparison answer needs.
- Reddit. Its weight depends heavily on the engine and the query. For niche B2B software prompts, Reddit took 1.4% of citations in a June 2026 sample of 1,263 prompts. It matters far more on consumer questions and on Perplexity, so check your own category before you staff a Reddit program.
- Wikipedia. Engines lean on it for entity definitions and category framing. A category article that names your product shapes how the model describes the whole space, which is why the conflict-of-interest rules below matter so much.
- Owned pages. Your site is the one placement you fully control, and in our experience it's still the biggest single lever. Comparison pages and documentation get retrieved constantly for vendor-selection prompts.
Content formats and structure LLMs quote most
Engines quote the top of the page and the text directly under a heading. In an analysis of 1.2 million ChatGPT responses, 44% of citations came from the first 30% of a page, and 78% traced back to a heading. Question-and-answer blocks were about twice as likely to be cited when the heading carried a question mark.
That pattern falls out of how parsers work, since engines index and extract snippets rather than pages. So chunk your page so that each heading plus its first paragraph answers one question on its own, and that chunk survives extraction without the surrounding context. The formats that fit that constraint are the ones that keep getting quoted.
- Listicles. Each numbered entry is a self-contained chunk with the brand name in the heading.
- Comparison tables. A row is a structured fact an engine can lift verbatim.
- Question-and-answer blocks. The question text matches prompt phrasing and the answer sits right under it.
- First-person reviews. They supply the authenticity signal engines balance against authority.
Schema markup is a different story, and the evidence is weaker than most vendors admit. Google's guidance says structured data isn't required for AI features and there's no special schema to add.
The most careful test we've seen tracked 1,885 pages that added JSON-LD against 4,000 matched controls. Google AI Mode moved 2.4% and ChatGPT 2.2%, both statistically indistinguishable from zero, while Google AI Overviews fell 4.6%. Keep schema for SEO, and put every fact you want cited in visible text, since that's where engines read it.
Making your site readable to AI crawlers
Blocking the wrong bot costs citations, and the bot names are easy to confuse. OpenAI's crawler documentation separates GPTBot (training), OAI-SearchBot (ChatGPT search indexing), and ChatGPT-User (user-triggered fetches). Anthropic publishes the same split as ClaudeBot, Claude-SearchBot, and Claude-User. Perplexity runs PerplexityBot for search indexing and Perplexity-User for user-triggered fetches.
A seeding-friendly robots.txt allows the search bots explicitly and makes a deliberate call on training:
User-agent: OAI-SearchBot
Allow: /
User-agent: PerplexityBot
Allow: /
User-agent: Claude-SearchBot
Allow: /
User-agent: GPTBot
Allow: /
User-agent: ClaudeBot
Allow: /OpenAI says a site that blocks OAI-SearchBot won't appear in ChatGPT search answers, and Perplexity says pages blocked from PerplexityBot won't have their text indexed. Use token-level directives rather than firewall rules, and expect about a day for a change to take effect. An llms.txt file is cheap to publish and mostly inert, so ship one, but we treat it as housekeeping rather than a lever.
How to measure LLM visibility and citations
Run every prompt more than once, or the numbers you report are noise. Identical prompts within a single model produce 10% to 34% variance from sampling alone. Over 17 weeks of tracking 82,619 prompts, Google AI Mode replaced 56% of its cited sources every week and ChatGPT replaced 74%.
Aggregate over a rolling window instead. The manual routine follows from those constraints.
- Prompt set. Write 50 to 100 prompts a real buyer in your category would type, split between vendor-selection prompts ("best X for Y," "A vs B") and problem-first phrasing.
- Fields. For each engine, log whether your brand was named, whether a URL of yours was cited, which competitors appeared, and the sentiment of the sentence that named you.
- Repetition. Repeat daily with several runs per prompt, then report two-week rolling rates.
- Reporting. Presence rate is the share of responses naming you. Share of voice is your mentions divided by mentions of all tracked brands.
Monitoring tools automate that loop. For a category baseline rather than a brand-only view, we built CheckThat to measure AI visibility across nearly 200 categories, 5,800+ brands, and 2.6M+ AI responses, which lets you see where competitors sit before you decide which prompts to seed against.
Most teams lose the thread at this point, with a prompt baseline in one tool and a spreadsheet of Reddit threads and G2 placements in another. If keeping the two connected is your bottleneck, book a demo to see how the GrowthX platform runs seeding and visibility tracking in one workspace.
Platform rules and the risks of seeding badly
Wikipedia's conflict-of-interest guideline strongly discourages anyone with a stake from editing the affected article directly and asks them to propose changes on the talk page instead. Paid editors must disclose who they work for, and promotional accounts get blocked, usually indefinitely. The safe route is to earn independent coverage first, then propose a sourced addition on the talk page from a disclosed account.
Reddit's rules require authentic participation in communities you genuinely care about. Brand-affiliated subreddits must be labeled official, and Reddit's spam systems catch tens of thousands of inauthentic posts a day. A disclosed employee answering a real question with real detail is fine. A sockpuppet thread asking about your own product is exactly what those systems exist to catch.
Review sites carry the legal exposure. G2 requires reviews from real people with first-hand experience, caps the value of any incentive, forbids tying an incentive to the opinion expressed, and removes reviews written by an AI model. Above the platform sits the FTC's consumer review rule, in force since late 2024, which bans fake and AI-generated reviews and sentiment-conditioned incentives.
The risk you can't police away is what the model says about you unprompted. A hallucinated pricing tier or a stale negative review can propagate through the sources an engine trusts, so track sentiment alongside presence. A presence rate that rises while sentiment falls is a problem you caused by seeding without reading.
Cost, timeline and what to report
A seeding program costs about what a mid-tier digital PR retainer costs, because it uses the same labor. A June 2026 survey of digital PR practitioners put the average retainer at $5,458 a month and the average cost of an earned link at $597. No public benchmark exists for a program labeled LLM seeding, so budget from those inputs: writer time for owned pages, a compliant review-generation program, disclosed community participation, and a monitoring tool.
Timelines split by path. Retrieval-side placements can be cited within days to a few weeks. Training-side effects arrive with the next model release, which no provider schedules. Report the first path monthly and the second only when a new model ships.
Be honest about attribution up front. The universe of possible prompts is effectively infinite and every tool samples a static subset, so visibility scores tell you whether you're moving relative to competitors, not how many real buyers saw the answer.
The referral-traffic objection will come from finance, and the honest answer is that AI answers send very little traffic in absolute terms. Only about 1% of visits with an AI summary clicked a link inside it, and the click-through rate on the traditional results underneath fell from 15% to 8%. Seeding is shortlist work, measured by presence and share of voice, with referral traffic as a small secondary signal.
Report five things on a two-week rolling window:
- Presence rate, the share of responses that name you, split by engine
- Share of voice against three to five named competitors
- Cited domains, counted and split owned from third-party
- Sentiment of brand mentions, naming the sources behind negative framing
- Crawler access for OAI-SearchBot, PerplexityBot, Claude-SearchBot, and GPTBot, because one bad robots.txt edit zeroes everything else
How LLM seeding relates to AEO and GEO
Answer engine optimization, generative engine optimization, and AI visibility describe overlapping work from three vantage points. AEO started as a practitioner term for optimizing toward direct answers. GEO came out of a 2023 academic paper and covers tailoring a page's presentation, style, and content for visibility in generative engines, which is the content-format half of seeding. Google has said separate frameworks are unnecessary because AI Overviews and AI Mode run on the same Search infrastructure.
AI visibility is the outcome all of those tactics serve. We think about it across four dimensions: Presence (whether the brand appears), Reputation (how it's characterized), Perception (sentiment and framing), and Influence (how much the brand shapes the category narrative). LLM seeding is the input side of that model. The sensible sequence is to build the prompt baseline first, measure presence for a month, and only then decide which of the placements above is worth the retainer.
