When an engine recommends a product, it is summarizing the handful of pages it trusts most: a Reddit thread, a review site, one widely-quoted blog. LLM Citations shows you exactly which sources shape the answers in your category, where you appear in them, and where you are missing — so you know precisely where to go earn your place.
No credit card required. Your source map in about a minute.
An AI answer is downstream of the pages it read. If a competitor appears and you do not, it is often because the engine leaned on a Reddit thread, a G2 list, and one popular blog — and the competitor is in all three while you are in none. Publishing more on your own site will not fix that, because your site is not what the model is reading.
You know you were left out of the answer. You have no idea which sources produced it, so you cannot trace the gap back to anything you can actually change.
PR, reviews, Reddit, a listicle, a guest post. Without knowing which sources the models actually use, your team spreads effort thin and most of it never moves a citation.
They win because they live in the sources AI trusts. Until you can see those sources, you cannot follow them there, and the gap quietly widens every quarter.
AI answers are downstream of a few trusted sources. See which ones shape your category, and you know exactly where to earn your place in the answer.
To map the sources behind your category, each answer is taken apart and followed upstream. The steps below turn a pile of replies into a ranked picture of which domains the engines actually rely on, and where you stand in each one.
Collect answers across engines and prompts in your category.
Find the sources, both the cited ones and the hidden ones.
Group links into domains and source types.
Rank domains by how much they shape the answers.
Check where you appear, and where you are missing.
Scattered answers in. A ranked map of who feeds them, and where your gaps are, out.
Some engines make this easy. Perplexity, Copilot and Google's AI Overviews show their sources, so we read the citations directly. Others, like ChatGPT and Claude, often answer without showing their work. For those, we infer the likely sources by matching the specific claims, numbers and phrasing in an answer against the candidate pages on the open web that could have produced them.
That inference is closer to detective work than to scraping a link, and it is what keeps the map complete even when an engine hides its references. Without it, you would only ever see half the picture, and the half you missed could be exactly where a competitor is winning.
This is the whole idea behind the page. The answer your buyer sees is the last step in a chain. You have no switch to flip on the model, but you have plenty of influence over the sources it pulls from. Get into the pages the models trust, and the answer follows.
You act here, at the source. Everything downstream follows.
One citation does not make a source influential. A blog quoted in a single reply matters far less than a review site the models return to again and again. So we rank each domain by how often it shows up across your category's answers, how prominently, and across how many engines.
Then we mark where you stand in each one. A high-influence source where you are not even listed is not bad news. It is the clearest instruction you will get all quarter about where to spend your effort.
If we only counted the citations engines choose to display, the map would have holes exactly where it matters. The engines that hide their sources are still pulling from somewhere, and that somewhere is often where the real influence sits.
So we work two ways at once. Where an engine shows its links, we read them straight. Where it does not, we trace the answer back to its likely origin by its content. Both paths feed one combined map, so nothing important slips through.
A map is only useful if it tells you what to do. So the influence scores and your presence gaps get weighed together, along with how hard each placement is to win, and handed to the Recommendation Engine as a ranked list of moves.
Instead of a vague plan to do more PR, you get an ordered set of specific bets: the review site to get listed on, the community thread worth answering, the blog worth pitching — each chosen because it is influential and you are absent.
| Mention or rank tracking | LLM Citations |
|---|---|
| Tells you whether you were cited | Shows the sources behind the answer |
| Only sees citations engines display | Infers the hidden sources too |
| Counts raw mentions | Ranks domains by real influence |
| Ignores where you stand in each source | Marks your presence and your gaps |
| Leaves you to guess what to do | Hands you ranked places to earn a citation |
Two moves that turn from guesswork into a clear plan once you can see upstream.
"GrackerAI helped us reach developers and decision-makers right when they were researching. The pipeline impact has been remarkable."
Once you can see the sources behind AI answers, earning citations stops being a mystery. You know which pages the models read, where you are missing, and which placement will move you most. It is the difference between hoping and aiming.
The placement opportunities from your source map land here, ranked against every other fix by impact.
Turn a target source into the article, comparison or answer that earns you a place in it.
The answers your source map is built from come from monitoring across all eight engines.
Get your free AI Visibility Score in about a minute. See exactly where you stand, where competitors are beating you, and the ranked fixes to get into the answer. No credit card, no commitment.
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