AI Visibility Intelligence · LLM Citations

AI does not invent its answer. It reads a few sources.

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.

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Community threads Review sites Comparison pages Blogs Docs AI answer

GrackerAI dashboard showing AEO and GEO citation tracking across ChatGPT, Perplexity, Claude, and Gemini

You can see you are not cited. You cannot see why.

When a rival shows up and you do not, the model usually is not the problem. The sources are.

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.

The black box

You see the result, not the cause

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.

The scattered effort

Guessing where to invest

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.

The upstream rival

Competitors are already embedded

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.

How the source map is built

Tracing every answer back to the pages that produced it

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.

Capture

Collect answers across engines and prompts in your category.

Extract

Find the sources, both the cited ones and the hidden ones.

Resolve

Group links into domains and source types.

Score

Rank domains by how much they shape the answers.

Map

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.

The hard part we solved

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.

The lever

You cannot edit an AI answer. You can change what it reads.

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

SRC

Trusted sources

Reddit G2 Blogs

AI reads them

Synthesizes a recommendation.

ANS

The answer

What the model tells the buyer.

YOU

Your buyer

Acts on the recommendation.

You act here, at the source. Everything downstream follows.

The influence map

Not just who gets cited, but who actually matters

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.

Domains ranked by real influence, not raw mentions
Sorted into types: community, reviews, comparison, blogs, docs
Your presence marked on every source, gaps highlighted

GrackerAI Citations table of cited pages and domains by answer engine

Seeing the hidden sources

Two ways in, so a quiet engine cannot hide a winner from you

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.

Cited sources read directly from engines that show them
Hidden sources inferred from the answer's own content
One complete map, not just the half an engine reveals

Diagram showing two paths into the source map: direct citation reading for transparent engines and content-based inference for engines that hide their sources

From map to moves

Every gap becomes a ranked place to earn a citation

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.

Influence weighed against your absence and the effort to place
Specific, named opportunities rather than generic advice
Feeds straight into the Recommendation Engine to prioritize

Illustration showing citation gaps converted into a ranked list of specific placement opportunities, ordered by influence and ease of winning

How it compares

Knowing you are not cited is a complaint. Knowing why is a plan.

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

In action

What the source map lets you do

Two moves that turn from guesswork into a clear plan once you can see upstream.

Find where to earn placements

Stop spreading effort across every channel and aim it where the models are actually reading

Open the influence map and sort by the sources you are missing from.
Take the ranked opportunities, highest influence and biggest gap first.
Earn the placement: get listed, answer the thread, pitch the blog.
Watch your citations rise as the models start reading you in those sources.

See why a rival wins

When a competitor keeps showing up, the source map explains it in a way no visibility score can

Filter the map to the sources that cite the competitor most.
Find the high-influence ones where they appear and you do not.
Match their footprint: the same lists, threads and comparisons.
Close the gap source by source until the answers start naming you too.

"GrackerAI helped us reach developers and decision-makers right when they were researching. The pipeline impact has been remarkable."

+78% AI visibility

+52% business impact

Nathan Sharma, VP of Growth, MojoAuth

What it means for your team

Stop guessing at the answer. Go work on what produces it.

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.

8 engines whose sources are traced for your category

Cited + inferred sources found even when an engine hides them

Ranked domains scored by how much they shape answers

Gaps to moves every absence turned into a placement to earn

Works with

Citations show where to act. Here is what acts on them.

Recommendation Engine

The placement opportunities from your source map land here, ranked against every other fix by impact.

See Recommendation Engine

Content Engine

Turn a target source into the article, comparison or answer that earns you a place in it.

See Content Engine

AI Visibility Monitoring

The answers your source map is built from come from monitoring across up to 10 AI engines.

See AI Visibility Monitoring

AI does not invent its answer. It reads a few sources.