ChatGPT answers, unedited

Yes. What you’re describing is emerging as AI visibility / GEO (Generative Engine Optimization) intelligence—but there’s an important distinction between tools that merely count mentions and tools that actually map the source → answer → brand relationship.

What the better systems can do

For a set of buyer prompts such as:

“What are the best payroll platforms for a 200-person company?”

they run the prompt across ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews/AI Mode, etc., and capture:

• Whether your brand appears
• Whether your brand is recommended vs. merely mentioned
• Which URLs/domains are cited
• Exactly which pages are cited
• Which competitors appear instead
• How frequently each source appears across answers
• Where your brand/source appears in the answer
• Changes over time

Some platforms are already doing pieces of this. For example, Kitbase explicitly drills from cited domains down to the exact URLs used in answers, while DeepSmith tracks the answers, mentions, citations and cited pages. Kitbase DeepSmith

The interesting part: ranking the influence of each page

This is where your idea gets more sophisticated than ordinary AI-visibility tracking.

You could build a graph like:

Web page → AI engine → answer → brand mention

and calculate something like:

Influence Score = how much a particular page contributes to AI answers mentioning/recommending your brand

For example:

That would let you answer a much more useful question than “How visible are we in ChatGPT?”

It would answer:

“Which 20 pages on the internet are actually causing AI systems to talk about us?”

And then the next question:

“Which pages should we improve, earn links/mentions on, or get corrected because they're disproportionately influencing AI's perception of us?”

There is academic work starting to formalize exactly this distinction. A 2026 paper proposes separating citation selection (the system retrieved/cited a page) from citation absorption (the page actually contributed information to the generated answer), and found that citation count alone doesn't capture influence. arXiv

Existing products I'd look at

• Citations.io — particularly close to your use case: it shows answers mentioning your brand, the sources being cited, competitors/sources chosen instead, and prioritized actions. Citations.io
• Kitbase — particularly interesting for the domain → exact URL drill-down and identifying third-party sources that repeatedly shape answers. Kitbase
• OtterlyAI — broader AI-search monitoring across ChatGPT, Perplexity, Google AI surfaces, Gemini and Copilot. Otterly
• Yext Scout — more enterprise-oriented AI visibility measurement around mentions, citations, sentiment and share of voice. Yext
• DeepSmith — explicitly exposes mention rate, citation rate, cited pages and competitor sources. DeepSmith

One caveat: “citation” doesn't necessarily mean “influence.” A page can be linked as a source without materially contributing to the answer, and some engines mention a brand without citing it. That's why separating mention, citation, and actual contribution is important. arXiv Search Engine Land

If you're evaluating this as a product opportunity, I think the real whitespace is not another “AI visibility score.” It's an AI influence graph: brand → prompts → answers → sources → individual URLs → competitors, with an influence score and recommendations for where to intervene. That is substantially more actionable for an SEO/content/PR team.

and calculate something like:

Influence Score = how much a particular page contributes to AI answers mentioning/recommending your brand

For example:

That would let you answer a much more useful question than “How visible are we in ChatGPT?”

It would answer:

“Which 20 pages on the internet are actually causing AI systems to talk about us?”

And then the next question:

“Which pages should we improve, earn links/mentions on, or get corrected because they're disproportionately influencing AI's perception of us?”

There is academic work starting to formalize exactly this distinction. A 2026 paper proposes separating citation selection (the system retrieved/cited a page) from citation absorption (the page actually contributed information to the generated answer), and found that citation count alone doesn't capture influence.

Existing products I'd look at

Yes. What you’re describing is essentially AI-search source attribution + influence scoring—and there are now several products getting quite close to it.

Closest matches

• Ahrefs Brand Radar — probably the most established option. It tracks AI answers across multiple platforms and identifies the specific domains and pages cited, as well as pages that were retrieved but not cited. It also provides AI share-of-voice and citation/mention metrics. help.ahrefs.comwww.zumihq.comhelp.ahrefs.com

• GetMentions AI — particularly close to your description. It maps the websites, pages, communities and videos influencing AI answers, identifies competitor gaps, and ranks potential sources to target. www.getmentions.ai

• Zumi — interesting if your goal is PR/content strategy. It explicitly ranks third-party domains and individual URLs by how often they shape AI answers, then identifies high-impact sources where competitors are mentioned but you aren't. www.zumihq.com

• GetMentioned — has a particularly useful "source map": domains and URLs are ranked by their share of AI citations, letting you see which publications/pages are effectively kingmakers in a category. www.getmentioned.co

The important distinction

There are actually three levels of attribution, and I'd make sure whatever you buy distinguishes them:

1. Mention — AI says your brand.
2. Citation — AI explicitly links/cites a particular page.
3. Influence — a page was actually retrieved/used in constructing the answer, even if it wasn't ultimately cited.

Ahrefs explicitly tracks both cited and found but not cited pages. help.ahrefs.comwww.zumihq.comhelp.ahrefs.com Academic research is also starting to distinguish between merely selecting a citation and actually absorbing information from that page into the generated answer. arxiv.org

That third category is the really interesting one for what you're asking.

If I were designing the dashboard

I'd want something like:

Then drill into:

Source → URL → prompt → AI engine → answer → your brand's position → competitors → citation → change over time

And importantly, I'd calculate influence separately by engine. A page that is extremely influential to ChatGPT may not have the same influence on Perplexity or Google AI.

So yes—the category exists, but the market is still young. If what you're envisioning is specifically "show me the web graph behind every AI answer mentioning my brand, then rank those URLs by causal/inferred influence" rather than merely "AI visibility tracking," that's a more sophisticated product than the typical GEO dashboard.

If you're evaluating this for your company, I can also compare the leading platforms feature-by-feature and identify which one gets closest to this exact workflow.

Yes. A new category of tools has emerged around AI visibility, Generative Engine Optimization (GEO), and Answer Engine Optimization (AEO) that does something close to what you're describing.

The most advanced products typically answer three questions:

1. Which prompts mention my brand?

• "Best CRM for startups"
• "Alternatives to X"
• "What software helps with..."
• They measure your share of voice across ChatGPT, Gemini, Claude, Perplexity, and AI Overviews.
• Which web pages influenced the answer?

• They capture cited URLs when available.
• They identify recurring domains (Reddit, G2, Wikipedia, review sites, blogs, your own pages, etc.).
• Some go further by estimating which pages are influencing answers even when an AI doesn't explicitly provide citations. arXiv arXiv
• How influential is each source?

Instead of just listing citations, the better platforms rank:

• frequency of citation
• which prompts each page affects
• which AI models rely on it
• competitors appearing on the same source
• opportunities where your brand is missing

That's essentially an "AI citation graph."

Products that are closest

Several vendors now market this capability:

• MentionGEO — tracks AI mentions, cited sources, competitors, and recommends improvements. MentionGeo
• Cliro — explicitly maps which domains influence AI answers and tracks citation sources. Cliro
• GetMentioned — builds a map of domains and URLs that AI trusts for your category and shows your footprint within it. GetMentioned
• BlueJar — focuses on "citation source mapping" and identifying the pages that determine whether your brand appears in AI answers. BlueJar
• AIAttention — tracks brand mentions, rankings, competitors, and cited URLs across multiple models. AIAttention

What doesn't really exist yet

The feature you described is a bit more ambitious:

"Map every webpage feeding AI answers about my brand and rank each page by influence."

Current products generally infer influence using:

• citation frequency
• appearance across prompts
• appearance across AI engines
• prominence within answers

They cannot definitively measure every page that contributed to an answer because most foundation models don't expose their internal retrieval or weighting mechanisms. Influence is therefore an estimate, not a complete causal map. Recent research has started proposing ways to measure "citation absorption"—how much a cited page appears to shape the final answer—but this is still an emerging area. arXiv

If you were building a best-in-class platform

A compelling approach would produce an influence score for every URL, such as:

Then let users drill into a page to see:

• every prompt it influences
• every AI engine using it
• competing pages that outrank it
• estimated traffic or revenue impact if its influence increased

That would go beyond traditional SEO by surfacing a true "AI influence graph" rather than just rankings or mentions.

Then let users drill into a page to see:

• every prompt it influences
• every AI engine using it
• competing pages that outrank it
• estimated traffic or revenue impact if its influence increased

That would go beyond traditional SEO by surfacing a true "AI influence graph" rather than just rankings or mentions.

Yes. What you’re describing is now generally called AI visibility / GEO (Generative Engine Optimization) / AI citation tracking.

The important distinction is that you don't just want “does AI mention our brand?” You want a source-to-answer influence graph:

Web page → cited by AI answer → mentions/recommends your brand → influence score

Several products are getting close to this:

• Ahrefs Brand Radar / AI Visibility Checker — tracks AI mentions and, importantly, the top cited domains and specific cited pages associated with your brand. ahrefs.com
• Menra — probably closest to your exact description. It says it maps individual citations, categorizes sources (Reddit, reviews, news, your site, etc.), and scores source authority and impact based on citation frequency and other signals. www.menra.ai
• Slate — explicitly provides a URL-level citation table ranked by frequency and influence, plus the ability to trace which sites/articles/domains are driving citations. slatehq.com
• DeepSmith — tracks the actual answers across ChatGPT, Gemini, Perplexity, Claude and Google AI Mode and identifies which pages AI pulls into answers. deepsmith.ai
• visibility.so — has URL-level source rankings showing which pages AI trusts most for tracked topics. visibility.so

But there's an interesting gap

Most tools currently measure something like:

“This URL was cited 37 times.”

What you're describing is more sophisticated:

“This URL is responsible for 14% of the evidence associated with AI answers mentioning our brand, appears across 6 models, influences 23 tracked prompts, and is disproportionately associated with positive recommendations.”

I'd call that an AI Citation Influence Graph.

A useful scoring model could combine:

Then you could produce something like:

Top sources influencing AI's perception of Acme

1. example.com/review/acme — 91 influence
2. reddit.com/r/.../acme — 84
3. g2.com/products/acme/reviews — 79
4. forbes.com/... — 67
5. competitor.com/compare/acme — 61

And, critically, distinguish:

“AI cites this page” from “this page actually appears to influence what AI says about us.”

That latter attribution is much harder—and much more valuable.

If you're asking because you're evaluating this as a product/category to buy or build, I can also map the current landscape and tell you which existing solution comes closest to the exact source → answer → brand influence graph you're describing, including where each one falls short.

Yes. This is an emerging category often called AI visibility tracking, Answer Engine Optimization (AEO), or Generative Engine Optimization (GEO). The solutions are designed to answer a question that traditional SEO tools cannot: “When an AI gives an answer about our category, which web pages influenced that answer, and how important are those pages?” www.clearscope.io

Some tools that are close to what you’re describing:

The “influence ranking” part is where the market is still developing. The strongest systems generally calculate something like:

Source Influence Score =

• number of AI answers citing the page
• number of tracked prompts where it appears
• AI engine coverage (ChatGPT, Gemini, Perplexity, etc.)
• whether the page causes your brand to be mentioned
• competitor displacement (does it help you appear instead of a competitor?)
• recency/trend direction
• sentiment/context of the mention

For example, the output you’d want might look like:

A few caveats:

• No vendor can perfectly see the private training data behind models. They usually measure retrieval/citation behavior from repeated prompts, not the entire causal path. www.clearscope.ioserpapi.com
• AI answers vary by user, date, model version, and location, so influence scores are probabilistic rather than absolute. www.clearscope.io
• The most actionable insight is usually not “who cited us,” but “which pages are causing competitors to win AI recommendations and what evidence do we need to add?” www.zumihq.com

If you mean a PR/SEO team dashboard that shows “the 50 URLs shaping ChatGPT answers about our brand”, that is exactly the niche these newer GEO platforms are targeting. slatehq.comwww.zumihq.com

Yes. This is now a real category of AI visibility / LLM citation tracking, and several products are getting quite close to exactly what you describe.

The key distinction is between:

1. “Does the AI mention my brand?”
2. “What web pages caused/influenced that answer?”
3. “Which of those pages matter most, across thousands of answers?”

The third one is the interesting part.

The closest fits

• Scrunch — probably the closest match to your description. Its Influence Score ranks sources based on how broadly and consistently they are cited across prompts. It can show the exact URLs cited by ChatGPT, Gemini, Perplexity, etc. ai-cdn.scrunchai.com
• Ahrefs Brand Radar — maps top cited pages/domains and distinguishes between pages actually cited and pages AI retrieved but didn't cite. It also measures mentions, citations, impressions and AI share of voice. ai-cdn.scrunchai.comhelp.ahrefs.comceyo.aihelp.ahrefs.com
• Semrush AI Visibility — has Cited Sources and Cited Pages reports, plus source opportunities where competitors are being supported by sources that don't mention you. www.semrush.com
• GetMentioned — particularly focused on the source landscape, showing which domains/URLs are influencing AI answers in a category and where your brand is absent. www.getmentioned.co
• Ceyo — maps cited publishers, review sites, communities and owned pages, and tracks which sources are gaining/losing influence over time. ceyo.ai

What I'd want the product to actually show

Imagine you are Brand X and you track 2,000 buyer questions across ChatGPT, Gemini, Perplexity, Claude, Google AI Mode, etc.

You'd want a graph something like:

Then you could drill into:

G2 category page → 418 AI answers → 73 prompts → ChatGPT/Gemini/Perplexity → competitors mentioned 61% of the time → your brand 0%.

That's much more actionable than an ordinary "AI visibility score."

One important caveat

No tool can perfectly say “this page caused the AI to say X.” AI systems often retrieve multiple sources, don't expose their entire retrieval process, and may use pages without citing them. Ahrefs explicitly distinguishes “cited” sources from pages that were found/retrieved but not cited. ai-cdn.scrunchai.comhelp.ahrefs.comceyo.aihelp.ahrefs.com

So the best systems are really building a probabilistic influence/citation graph, not proving causal attribution.

There's also emerging research suggesting this is a particularly valuable layer to measure: one 2026 study of 167,551 URL-grounded citations found that 85.7% of citations to brands came from third-party sites, rather than the brands' own properties. arxiv.org

If you're evaluating this as a product opportunity rather than just looking for a tool, I think there's still an interesting gap: most platforms report source counts or citation rates, whereas a genuinely useful system would build an “AI answer influence graph” and rank individual URLs by incremental influence on brand recommendations. That could become a much more strategic equivalent of PageRank for AI search.

Yes. What you’re describing is basically AI-search source attribution / citation intelligence: not just “does ChatGPT mention my brand?”, but “which pages caused or influenced that mention, and how much influence does each page have?”

A few products are getting quite close:

• GetMentioned — probably the closest match. It traces AI brand mentions back to specific URLs and domains, and explicitly ranks/highlights the sources driving mentions. It also shows changes in source influence over time. www.getmentioned.co
• Ahrefs AI Visibility Checker — tracks brand mentions across ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews, including the top cited domains and specific pages associated with your mentions. ahrefs.com
• Pi Datametrics AI Search Visibility — particularly interesting for enterprise SEO. It lets you drill into cited domains, URLs and categories and compare your source footprint with competitors. pi-datametrics.com
• GetMentions AI — goes a step further by mapping sources such as editorial sites, Reddit, YouTube, etc., and identifying citation gaps where competitors appear but you don't. www.getmentions.ai

The important distinction

There are really three different levels of measurement:

Most platforms are good at the first two. The third is much harder.

For example, imagine you run 500 relevant prompts across ChatGPT, Gemini and Perplexity and get:

“For enterprise analytics, consider Brand X…”

with sources:

• Gartner page — 84 appearances
• G2 comparison — 61
• Reddit thread — 43
• Forbes article — 31
• Your website — 18

A genuinely useful system would calculate something like:

Source Influence Score

influence = citation frequency × answer impact × prompt importance × consistency

So you could discover that one obscure Reddit thread is actually more influential on AI recommendations than 20 pages of your own content.

That's the really interesting product category.

GetMentioned appears closest to what you're asking for today, because it explicitly describes mapping each AI mention to the URLs/domains behind it and identifying the content that influenced the recommendation. www.getmentioned.co

If you're asking because you're considering building this, rather than buying it, there's an interesting gap here: most current tools measure citation frequency, but a robust causal/influence graph of “page → AI retrieval → answer → brand mention → recommendation” would be considerably more valuable.

Yes. What you’re describing is essentially an AI-answer source attribution / influence map: not just “does ChatGPT mention our brand?”, but which web pages appear to be feeding those answers, how often, and how much each page matters.

There are already products getting fairly close:

• DeepSmith — tracks AI responses, the sources cited, which pages drive your citations, and competitor sources. deepsmith.ai
• HubSpot AEO — tracks mentions, citations, prompts, competitors and the URLs being retrieved/cited. blog.hubspot.com
• Ahrefs Brand Radar — measures mentions, citations, estimated impressions and AI share of voice. ahrefs.com
• DemandSphere AI Visibility — monitors multiple AI engines and explicitly exposes cited sources and citation types. www.demandsphere.com
• Meltwater AI Visibility API — interesting if you want to build this into your own analytics stack; its API exposes responses, citations, mentions, sentiment and share of voice. developer.meltwater.com

But your "ranks their influence" requirement is the interesting part

Most tools stop at something like:

AI answer → cited URL

A stronger system would build a graph like:

                         ┌─ nytimes.com/article-X ──────┐
                         │                              │
                         ├─ g2.com/product-review ──────┤
User prompt ──► AI retrieval ─► AI answer ─► Brand mention
                         │                              │
                         ├─ reddit.com/r/... ───────────┤
                         │                              │
                         └─ yourbrand.com/guide ─────────┘

Then assign every source an Influence Score based on things such as:

1. Citation frequency — how often the page is cited.
2. Answer penetration — how frequently the page appears when your brand is mentioned, even when it isn't explicitly cited.
3. Cross-model influence — ChatGPT vs. Gemini vs. Perplexity vs. Google AI Overviews, etc.
4. Prompt breadth — whether the page influences one narrow query or dozens of buyer questions.
5. Position in the answer — a source supporting the core recommendation should score higher than a peripheral source.
6. Citation persistence — does it keep appearing across repeated runs?
7. Competitive influence — does the same page also influence answers about competitors?
8. Content absorption — whether the AI appears to actually use information from the page rather than merely linking to it.

That last distinction is particularly important. Recent research proposes separating citation selection from citation absorption—i.e., a page can be retrieved/cited without necessarily contributing much of the actual answer. arxiv.org

So yes—you could build a genuinely useful "AI influence graph"

The output I'd want as a brand would look something like:

And then let you drill into:

“Why did this page score 92?”

→ cited in 47 sampled answers
→ directly supported 29% of recommendation statements
→ appeared across 14 prompt clusters
→ used by 3 AI engines
→ consistently associated with your brand
→ also influences 2 competitors

That would be considerably more actionable than a conventional AI visibility score.

There is evidence that this direction is becoming important: Yext's large-scale analysis found substantial differences in how AI engines retrieve sources, while its newer research specifically emphasizes retrieval logic rather than simply judging whether content is "good." www.yext.com

If you're thinking about this as a product opportunity: I think the gap is less “another AI brand-monitoring dashboard” and more “causal-ish influence graph for AI answers.” The hard/valuable part is estimating how much each underlying page actually contributes to what the model says, rather than merely counting citations.

Yes. What you’re describing is essentially AI-search citation intelligence / GEO (Generative Engine Optimization) analytics: not just “does ChatGPT mention my brand?”, but which web pages are feeding those answers, how often, and which sources have the most influence.

A few products are getting quite close:

• GetMentioned — probably the closest match to your description. It maps the domains and individual URLs cited by LLMs, shows citation frequency, and identifies which sources influence AI answers in your category. It also tracks competitor gaps. www.getmentioned.co
• GetMentions AI — explicitly analyzes the sources behind AI answers and can show things like PCMag cited 34×, G2 28×, Reddit 19×, plus whether your brand or competitors appear on those sources. www.getmentions.ai
• OtterlyAI — strong for monitoring brand mentions and citations across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, etc. It also provides share-of-AI-voice and citation analysis. otterly.ai
• Ahrefs AI Visibility Checker — useful if you already use Ahrefs. It reports the domains and specific pages cited alongside your brand. ahrefs.com
• MentionScout — goes further toward ranking the source/answer relationship, including citation URLs, answer position, visibility, and “answer gaps.” mentionscout.com

The important distinction

If your goal is what I think you're asking for, I'd look for a system that produces something like:

Then you can answer the much more valuable question:

“Which 20 web pages should we get mentioned on or improve because they're disproportionately responsible for what AI says about our brand?”

That's a more sophisticated problem than ordinary AI visibility tracking.

There's also evidence that this is the right level to analyze: a recent study of 167,551 URL-grounded citations across 128 brands found that 85.7% of citations pointed to third-party sites, and that citations were highly concentrated among a relatively small group of domains. arxiv.org

So if you're evaluating vendors, I'd specifically ask whether they can calculate URL-level source influence, rather than just:

1. brand mention count,
2. citation count,
3. AI share of voice.

If you tell me your industry/brand and roughly what you're trying to accomplish, I can also map out the best 3–5 platforms for this exact use case, including which ones actually provide URL-level influence/gap analysis versus just AI rank tracking.

Yes. What you’re describing is emerging as AI source attribution / LLM visibility—and there are now products that get fairly close to it.

The key distinction is that you don't just want “does ChatGPT mention our brand?” You want:

AI answer → cited/source pages → which of those pages mention us → estimated contribution/influence of each page

A few current solutions:

• GetMentioned — probably the closest match to your description. It says it traces an AI mention back to the specific URLs and domains referenced by the model, then shows which sources influenced the recommendation. It also gives URL-level and domain-level attribution.
• GetMentions AI — similar concept, with analysis of websites, pages, communities and videos influencing AI answers, plus competitor gaps.
• Wellows — tracks direct citations plus “implicit” citations and produces a citation-based visibility score. Its methodology explicitly crawls cited pages to identify brand/competitor mentions.
• Citations.io — stronger on monitoring AI answers and citations across ChatGPT, Gemini, Perplexity and Claude, with competitor/source tracking.
• rank.ai's AI Visibility Tool — tracks prompts, mentions, citations, competitors and citation leaderboards across several AI surfaces.

The important caveat

“Influence” is still the hard part. Most products can tell you that a page was cited; they generally can't definitively tell you how much that page caused the answer to say what it said.

Research published in 2026 is starting to distinguish citation selection from citation absorption—i.e., whether a source was merely linked versus whether its language, facts, evidence or structure actually made it into the generated answer. arxiv.org

That's actually a pretty compelling product opportunity.

You could build an AI Brand Influence Graph something like:

                  AI answer
                     │
          ┌──────────┼──────────┐
          ▼          ▼          ▼
      Forbes.com   Reddit     YourSite.com
          │          │             │
       URL #1     Thread #2      Guide #3
          │          │             │
          └──────┬───┴─────────────┘
                 ▼
          "Brand X is best for..."
                 │
        Influence score: 82

And score each source on things like:

1. Citation frequency — how often the page is selected.
2. Mention influence — how often answers mentioning the page also mention your brand.
3. Answer absorption — how much factual/content overlap exists between the page and the generated answer.
4. Position influence — whether the source appears early/high in the answer's citation set.
5. Competitive influence — whether the page helps you or a competitor.
6. Cross-model influence — ChatGPT vs. Gemini vs. Perplexity, etc.
7. Persistence — whether the page keeps appearing over repeated prompt runs.
8. Opportunity score — high-influence pages that mention competitors but not you.

That would be substantially more useful than a conventional “AI visibility score.”

There's evidence that this distinction matters: a recent study found that citation counts and actual brand visibility can diverge substantially—e.g. sources can be cited without the brand being named in the answer. searchengineland.com

So, yes: the category exists, but I don't think the market has completely solved the exact “map the causal/influential web pages behind our AI brand mentions” problem yet. GetMentioned appears closest to the workflow you're describing.

If you're evaluating this as a product/MarTech opportunity, I can also map the current vendors against the exact feature set and identify what the obvious whitespace is.

Yes. What you’re describing is essentially AI citation/source intelligence or GEO (Generative Engine Optimization) source mapping.

A few products now get fairly close:

• Ahrefs AI Visibility Checker — shows which domains and specific pages are cited when AI platforms mention your brand. It covers ChatGPT, Gemini, Perplexity, Copilot, and Google AI Overviews.
• CitationLab AI Monitor — tracks mentions/citations and explicitly provides citation share and source analysis.
• GEOly — particularly close to your description: it tracks citations and identifies the domains/pages being used when AI recommends a brand.
• Writesonic GEO — maps AI answers to cited pages and distinguishes pages that mention your brand from your own pages being cited.
• Citations.io — provides a live citation feed across ChatGPT, Perplexity, Claude, etc.

The interesting part: ranking influence

Most tools are better at answering:

“Which pages does AI cite when it mentions us?”

than:

“Which pages actually cause AI to say what it says about us?”

Those are different.

A useful system would build a graph like:

genui{"data_networks_databases_learning_block":{"type_id":"SQL_JOIN","locale_override":"en-US"}}

Brand → AI answer → cited URL → claim/statement → influence

For example:

The influence score shouldn't simply be citation count. I'd calculate it from things like:

1. Citation frequency — how often the URL appears.
2. Answer penetration — percentage of answers citing it that also mention your brand.
3. Position in the answer — whether the source is used for a key claim versus incidental background.
4. Claim absorption — whether facts/information from the page actually show up in the generated answer.
5. Cross-model influence — ChatGPT + Gemini + Perplexity + AI Overviews, etc.
6. Prompt importance — influence on high-value buyer questions should count more.
7. Competitor displacement — whether that source is associated with you versus competitors.
8. Recency/persistence — whether the source keeps influencing answers over time.

That last distinction is becoming particularly important. Recent research proposes separating citation selection from citation absorption—a page can be cited without actually contributing much to the answer. arxiv.org

If you're evaluating this as a product opportunity

I'd describe the product as:

“Google Search Console for AI answers—but instead of showing you which queries drive clicks, it shows you which web pages influence what AI says about your brand.”

The killer output isn't a visibility score. It's a Source Influence Map:

1,000 buyer prompts → 8,000 AI answers → 3,200 cited URLs → 147 URLs materially influencing your brand → top 25 sources to acquire/improve.

That would be substantially more actionable than simply telling a marketing team “your AI visibility is 37%.”

And there's evidence that the source universe is highly concentrated: one 2026 study of 167,551 brand citations found that 80% of citations came from roughly 18% of domains, while 85.7% pointed to third-party rather than owned sites. arxiv.org

So yes—the market is already moving toward this, but “rank the causal/informational influence of individual web pages on AI answers mentioning my brand” is a sharper product definition than most current AI-visibility tools.

Yes. This is becoming its own software category, usually called AI Visibility, Generative Engine Optimization (GEO), or Answer Engine Optimization (AEO).

The specific capability you're describing has two parts:

1. Discover which webpages influence AI answers about your brand.
2. Rank those pages by how much they influence those answers.

The first is commercially available today. The second exists, but most vendors approximate "influence" rather than measuring the model's true internal weighting.

Here's how the leading options compare:

The closest match to what you're asking

Ahrefs Brand Radar

It maps:

• prompts users ask
• AI responses
• brands mentioned
• which domains and URLs were cited
• which pages are cited most frequently
• competitors occupying those citations

It also connects those citations back to SEO, Reddit, YouTube, and other sources that influence AI visibility. help.ahrefs.com

OnCited

This is even more focused on citation intelligence.

For every tracked prompt it reports:

• the AI engines that mention you
• every webpage cited
• which competitors own those sources
• recurring citation sources (Reddit, Wikipedia, G2, blogs, etc.)
• changes over time

Their positioning is essentially "show me what AI is reading." www.oncited.com

What an ideal "influence score" would look like

If I were designing this product, I wouldn't simply count citations.

I'd score every URL using something like:

Influence Score =

• Citation frequency (40%)
• Citation position (first citation vs. fifth) (15%)
• Number of AI engines citing it (15%)
• Number of prompts it appears in (15%)
• Semantic overlap with the generated answer (10%)
• Freshness/recency (5%)

That produces a ranked list like:

Recent research suggests citation frequency alone isn't enough—pages vary in how much they actually shape the generated answer. Factors like topical relevance, structure, completeness, and explicit facts affect whether a source is selected and how heavily it's reflected in the final response. arxiv.orgarxiv.org

One gap in today's products

Most tools answer:

"Which pages were cited?"

Few answer:

"Which pages caused the model to say this?"

Those aren't identical. A page may be cited without contributing much to the wording, while another page may heavily influence the response. Measuring that "citation absorption" is an active research area rather than a mature commercial capability. arxiv.org

If you're thinking about building or buying a platform for enterprise brand monitoring, there's still room for a differentiated product that:

• runs thousands of prompts,
• reconstructs the retrieval graph,
• estimates per-page influence,
• identifies the specific claims each page contributed,
• and recommends the highest-ROI pages to improve or earn citations on.

That's a level of explainability that most current AI visibility platforms only partially provide.

Yes. This is becoming its own software category, usually called AI Visibility, Generative Engine Optimization (GEO), or Answer Engine Optimization (AEO).

The specific capability you're describing has two parts:

1. Discover which webpages influence AI answers about your brand.
2. Rank those pages by how much they influence those answers.

The first is commercially available today. The second exists, but most vendors approximate "influence" rather than measuring the model's true internal weighting.

Here's how the leading options compare:

The closest match to what you're asking

urlAhrefs Brand Radarturn0search1

It maps:

• prompts users ask
• AI responses
• brands mentioned
• which domains and URLs were cited
• which pages are cited most frequently
• competitors occupying those citations

It also connects those citations back to SEO, Reddit, YouTube, and other sources that influence AI visibility. help.ahrefs.com

urlOnCitedturn0search0

This is even more focused on citation intelligence.

For every tracked prompt it reports:

• the AI engines that mention you
• every webpage cited
• which competitors own those sources
• recurring citation sources (Reddit, Wikipedia, G2, blogs, etc.)
• changes over time

Their positioning is essentially "show me what AI is reading." www.oncited.com

What an ideal "influence score" would look like

If I were designing this product, I wouldn't simply count citations.

I'd score every URL using something like:

Influence Score =

• Citation frequency (40%)
• Citation position (first citation vs. fifth) (15%)
• Number of AI engines citing it (15%)
• Number of prompts it appears in (15%)
• Semantic overlap with the generated answer (10%)
• Freshness/recency (5%)

That produces a ranked list like:

Recent research suggests citation frequency alone isn't enough—pages vary in how much they actually shape the generated answer. Factors like topical relevance, structure, completeness, and explicit facts affect whether a source is selected and how heavily it's reflected in the final response. arxiv.orgarxiv.org

One gap in today's products

Most tools answer:

"Which pages were cited?"

Few answer:

"Which pages caused the model to say this?"

Those aren't identical. A page may be cited without contributing much to the wording, while another page may heavily influence the response. Measuring that "citation absorption" is an active research area rather than a mature commercial capability. arxiv.org

If you're thinking about building or buying a platform for enterprise brand monitoring, there's still room for a differentiated product that:

• runs thousands of prompts,
• reconstructs the retrieval graph,
• estimates per-page influence,
• identifies the specific claims each page contributed,
• and recommends the highest-ROI pages to improve or earn citations on.

That's a level of explainability that most current AI visibility platforms only partially provide.

Yes. This is becoming its own software category, usually called AI Visibility, Generative Engine Optimization (GEO), or Answer Engine Optimization (AEO).

The specific capability you're describing has two parts:

1. Discover which webpages influence AI answers about your brand.
2. Rank those pages by how much they influence those answers.

The first is commercially available today. The second exists, but most vendors approximate "influence" rather than measuring the model's true internal weighting.

Here's how the leading options compare:

The closest match to what you're asking

urlAhrefs Brand Radarturn0search1

It maps:

• prompts users ask
• AI responses
• brands mentioned
• which domains and URLs were cited
• which pages are cited most frequently
• competitors occupying those citations

It also connects those citations back to SEO, Reddit, YouTube, and other sources that influence AI visibility. help.ahrefs.com

urlOnCitedturn0search0

This is even more focused on citation intelligence.

For every tracked prompt it reports:

• the AI engines that mention you
• every webpage cited
• which competitors own those sources
• recurring citation sources (Reddit, Wikipedia, G2, blogs, etc.)
• changes over time

Their positioning is essentially "show me what AI is reading." www.oncited.com

What an ideal "influence score" would look like

If I were designing this product, I wouldn't simply count citations.

I'd score every URL using something like:

Influence Score =

• Citation frequency (40%)
• Citation position (first citation vs. fifth) (15%)
• Number of AI engines citing it (15%)
• Number of prompts it appears in (15%)
• Semantic overlap with the generated answer (10%)
• Freshness/recency (5%)

That produces a ranked list like:

Recent research suggests citation frequency alone isn't enough—pages vary in how much they actually shape the generated answer. Factors like topical relevance, structure, completeness, and explicit facts affect whether a source is selected and how heavily it's reflected in the final response. arxiv.orgarxiv.org

One gap in today's products

Most tools answer:

"Which pages were cited?"

Few answer:

"Which pages caused the model to say this?"

Those aren't identical. A page may be cited without contributing much to the wording, while another page may heavily influence the response. Measuring that "citation absorption" is an active research area rather than a mature commercial capability. arxiv.org

If you're thinking about building or buying a platform for enterprise brand monitoring, there's still room for a differentiated product that:

• runs thousands of prompts,
• reconstructs the retrieval graph,
• estimates per-page influence,
• identifies the specific claims each page contributed,
• and recommends the highest-ROI pages to improve or earn citations on.

That's a level of explainability that most current AI visibility platforms only partially provide.

Yes. This is becoming its own software category, usually called AI Visibility, Generative Engine Optimization (GEO), or Answer Engine Optimization (AEO).

The specific capability you're describing has two parts:

1. Discover which webpages influence AI answers about your brand.
2. Rank those pages by how much they influence those answers.

The first is commercially available today. The second exists, but most vendors approximate "influence" rather than measuring the model's true internal weighting.

Here's how the leading options compare:

The closest match to what you're asking

urlAhrefs Brand Radarturn0search1

It maps:

• prompts users ask
• AI responses
• brands mentioned
• which domains and URLs were cited
• which pages are cited most frequently
• competitors occupying those citations

It also connects those citations back to SEO, Reddit, YouTube, and other sources that influence AI visibility. help.ahrefs.com

urlOnCitedturn0search0

This is even more focused on citation intelligence.

For every tracked prompt it reports:

• the AI engines that mention you
• every webpage cited
• which competitors own those sources
• recurring citation sources (Reddit, Wikipedia, G2, blogs, etc.)
• changes over time

Their positioning is essentially "show me what AI is reading." www.oncited.com

What an ideal "influence score" would look like

If I were designing this product, I wouldn't simply count citations.

I'd score every URL using something like:

Influence Score =

• Citation frequency (40%)
• Citation position (first citation vs. fifth) (15%)
• Number of AI engines citing it (15%)
• Number of prompts it appears in (15%)
• Semantic overlap with the generated answer (10%)
• Freshness/recency (5%)

That produces a ranked list like:

Recent research suggests citation frequency alone isn't enough—pages vary in how much they actually shape the generated answer. Factors like topical relevance, structure, completeness, and explicit facts affect whether a source is selected and how heavily it's reflected in the final response. arxiv.orgarxiv.org

One gap in today's products

Most tools answer:

"Which pages were cited?"

Few answer:

"Which pages caused the model to say this?"

Those aren't identical. A page may be cited without contributing much to the wording, while another page may heavily influence the response. Measuring that "citation absorption" is an active research area rather than a mature commercial capability. arxiv.org

If you're thinking about building or buying a platform for enterprise brand monitoring, there's still room for a differentiated product that:

• runs thousands of prompts,
• reconstructs the retrieval graph,
• estimates per-page influence,
• identifies the specific claims each page contributed,
• and recommends the highest-ROI pages to improve or earn citations on.

That's a level of explainability that most current AI visibility platforms only partially provide.

Yes. This is becoming its own software category, usually called AI Visibility, Generative Engine Optimization (GEO), or Answer Engine Optimization (AEO).

The specific capability you're describing has two parts:

1. Discover which webpages influence AI answers about your brand.
2. Rank those pages by how much they influence those answers.

The first is commercially available today. The second exists, but most vendors approximate "influence" rather than measuring the model's true internal weighting.

Here's how the leading options compare:

The closest match to what you're asking

urlAhrefs Brand Radarturn0search1

It maps:

• prompts users ask
• AI responses
• brands mentioned
• which domains and URLs were cited
• which pages are cited most frequently
• competitors occupying those citations

It also connects those citations back to SEO, Reddit, YouTube, and other sources that influence AI visibility. help.ahrefs.com

urlOnCitedturn0search0

This is even more focused on citation intelligence.

For every tracked prompt it reports:

• the AI engines that mention you
• every webpage cited
• which competitors own those sources
• recurring citation sources (Reddit, Wikipedia, G2, blogs, etc.)
• changes over time

Their positioning is essentially "show me what AI is reading." www.oncited.com

What an ideal "influence score" would look like

If I were designing this product, I wouldn't simply count citations.

I'd score every URL using something like:

Influence Score =

• Citation frequency (40%)
• Citation position (first citation vs. fifth) (15%)
• Number of AI engines citing it (15%)
• Number of prompts it appears in (15%)
• Semantic overlap with the generated answer (10%)
• Freshness/recency (5%)

That produces a ranked list like:

Recent research suggests citation frequency alone isn't enough—pages vary in how much they actually shape the generated answer. Factors like topical relevance, structure, completeness, and explicit facts affect whether a source is selected and how heavily it's reflected in the final response. arxiv.orgarxiv.org

One gap in today's products

Most tools answer:

"Which pages were cited?"

Few answer:

"Which pages caused the model to say this?"

Those aren't identical. A page may be cited without contributing much to the wording, while another page may heavily influence the response. Measuring that "citation absorption" is an active research area rather than a mature commercial capability. arxiv.org

If you're thinking about building or buying a platform for enterprise brand monitoring, there's still room for a differentiated product that:

• runs thousands of prompts,
• reconstructs the retrieval graph,
• estimates per-page influence,
• identifies the specific claims each page contributed,
• and recommends the highest-ROI pages to improve or earn citations on.

That's a level of explainability that most current AI visibility platforms only partially provide.

Yes. This is becoming its own software category, usually called AI Visibility, Generative Engine Optimization (GEO), or Answer Engine Optimization (AEO).

The specific capability you're describing has two parts:

1. Discover which webpages influence AI answers about your brand.
2. Rank those pages by how much they influence those answers.

The first is commercially available today. The second exists, but most vendors approximate "influence" rather than measuring the model's true internal weighting.

Here's how the leading options compare:

The closest match to what you're asking

urlAhrefs Brand Radarturn0search1

It maps:

• prompts users ask
• AI responses
• brands mentioned
• which domains and URLs were cited
• which pages are cited most frequently
• competitors occupying those citations

It also connects those citations back to SEO, Reddit, YouTube, and other sources that influence AI visibility. help.ahrefs.com

urlOnCitedturn0search0

This is even more focused on citation intelligence.

For every tracked prompt it reports:

• the AI engines that mention you
• every webpage cited
• which competitors own those sources
• recurring citation sources (Reddit, Wikipedia, G2, blogs, etc.)
• changes over time

Their positioning is essentially "show me what AI is reading." www.oncited.com

What an ideal "influence score" would look like

If I were designing this product, I wouldn't simply count citations.

I'd score every URL using something like:

Influence Score =

• Citation frequency (40%)
• Citation position (first citation vs. fifth) (15%)
• Number of AI engines citing it (15%)
• Number of prompts it appears in (15%)
• Semantic overlap with the generated answer (10%)
• Freshness/recency (5%)

That produces a ranked list like:

Recent research suggests citation frequency alone isn't enough—pages vary in how much they actually shape the generated answer. Factors like topical relevance, structure, completeness, and explicit facts affect whether a source is selected and how heavily it's reflected in the final response. arxiv.orgarxiv.org

One gap in today's products

Most tools answer:

"Which pages were cited?"

Few answer:

"Which pages caused the model to say this?"

Those aren't identical. A page may be cited without contributing much to the wording, while another page may heavily influence the response. Measuring that "citation absorption" is an active research area rather than a mature commercial capability. arxiv.org

If you're thinking about building or buying a platform for enterprise brand monitoring, there's still room for a differentiated product that:

• runs thousands of prompts,
• reconstructs the retrieval graph,
• estimates per-page influence,
• identifies the specific claims each page contributed,
• and recommends the highest-ROI pages to improve or earn citations on.

That's a level of explainability that most current AI visibility platforms only partially provide.

Yes. This has become a distinct category of software, often called AI visibility, Generative Engine Optimization (GEO), or Answer Engine Optimization (AEO).

The capability you're describing has two parts:

1. Source attribution: Identify which web pages, domains, Reddit threads, YouTube videos, documentation, etc. are being used or cited when AI systems answer questions about your brand.
2. Influence ranking: Estimate which sources have the greatest impact on whether your brand is mentioned or recommended.

The second part is harder because OpenAI, Google, Anthropic, and others do not publish a true "influence score." Most platforms infer influence by repeatedly testing prompts across models and measuring which sources consistently appear in citations or correlate with brand mentions. There is no official ranking from the AI providers themselves. arxiv.org

Some of the more relevant products include:

The workflow these platforms typically provide is:

• Run hundreds or thousands of prompts across ChatGPT, Gemini, Claude, Perplexity, and Google AI.
• Record every cited URL and domain.
• Count how often each source appears.
• Measure whether your brand is present on those sources.
• Compare against competitors.
• Rank opportunities such as "PCMag influences 14% of answers but doesn't mention your brand" or "This Reddit thread appears in 8% of AI responses." www.getmentions.ai

A more advanced implementation would build a graph like this:

Question
   ↓
AI models
   ↓
Referenced URLs
   ↓
Domains
   ↓
Brand entities
   ↓
Influence score

The influence score could combine factors such as:

• Citation frequency across AI models
• Consistency over time
• Presence across multiple query categories
• Whether the source contributes to positive recommendations versus factual mentions
• Competitor coverage
• Estimated traffic or authority

This produces an "AI Influence Map" showing which pages and domains most affect how your brand is described.

From a product perspective, there's still room for innovation. Most current tools answer:

• "Were we mentioned?"
• "Which URLs were cited?"

Far fewer answer:

• "If we improved or earned coverage on this specific page, how much would our AI visibility likely increase?"

That kind of causal influence ranking is still an emerging capability and isn't something AI providers expose directly. Most products estimate it from repeated observations rather than measuring it at the model level. www.getmentions.aiarxiv.org

If you're thinking about building such a platform rather than buying one, there's an opportunity to differentiate by treating the ecosystem as a network problem—ranking pages by their observed contribution to AI recommendations, similar to how PageRank modeled influence on the web.

Yes. This is an emerging category often called AI search optimization, LLM visibility, or AI brand monitoring. The short answer is:

• Several products attempt to identify which web pages are influencing AI-generated answers about your brand.
• None can definitively tell you exactly what every model is using, because most frontier models don't expose their retrieval or training provenance.
• The better platforms infer influence by testing AI systems repeatedly, tracking citations where available, analyzing retrieval behavior, and modeling which sources correlate with mentions.

The market currently includes tools such as:

Some well-known vendors in this space include:

• Profound
• Goodie
• AthenaHQ
• Peec AI
• Hall
• Scrunch AI
• Otterly.AI

Most of these focus on:

• prompting multiple AI assistants thousands of times
• recording responses
• extracting citations
• identifying recurring source domains
• measuring "share of voice"
• recommending content improvements

What you're describing is a bit more advanced

It sounds like you're asking for something like:

"Show every webpage that contributes to AI answers mentioning Brand X, then score each page by how much it influences those answers."

That would be closer to an AI influence graph than a citation tracker.

A system like that could combine:

1. Prompt monitoring
   • Thousands of representative prompts

2. Citation extraction
   • Pages explicitly linked by AI

3. SERP analysis
   • Google/Bing rankings
   • News
   • Reddit
   • Wikipedia
   • Industry sites

4. Retrieval testing
   • Remove or modify pages and observe changes in AI outputs
   • Estimate causal influence

5. Knowledge graph
   • Brand → topic → webpage → AI model

6. Influence scoring
   • Example:

     docs.example.com/pricing      94
     wikipedia.org                 91
     gartner.com                   87
     reddit.com/r/...              83
     competitor blog               78

7. Opportunity analysis
   • "Updating this page is likely to increase AI mentions by X%."

The technical challenge

This is much easier for retrieval-augmented systems like Perplexity or AI search engines that provide citations than for models that don't expose their reasoning or retrieval sources. For models without citations, influence has to be estimated through experiments rather than observed directly.

Is there a clear market leader?

Not yet. Most platforms are still focused on:

• AI visibility dashboards
• share of voice
• prompt tracking
• citation monitoring

Few provide a convincing, page-level influence ranking across multiple AI models. An accurate, cross-model "influence map" remains an open problem, which is why there's substantial interest in this area.

If you're thinking about building this as a product, it would be a differentiated direction. A defensible approach would likely combine large-scale prompt testing, retrieval experiments, citation analysis, and graph-based influence modeling to estimate—not claim with certainty—which pages have the greatest impact on AI-generated brand mentions.

Yes. This has become a distinct category of software often called AI visibility, Generative Engine Optimization (GEO), or Answer Engine Optimization (AEO).

The capability you're describing has two parts:

1. Detect when your brand appears in ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, etc.
2. Attribute influence by identifying which web pages and domains those systems appear to rely on, then ranking them by how much they affect AI answers.

Several products now attempt this.

The closest match to what you're asking

The most advanced tools don't just say "your brand was mentioned."

They build something like this:

Prompt:
"Best CRM for startups"

↓
AI Answer

↓
Cited Sources

1. G2 review page
2. Reddit discussion
3. Vendor documentation
4. TechCrunch article
5. Wikipedia

↓

Influence Score

G2..................31%
Reddit..............24%
Vendor docs.........18%
Wikipedia...........12%
TechCrunch..........8%
Other...............7%

Some also aggregate hundreds of prompts and produce:

• source frequency
• citation share
• share of voice
• which competitor-owned pages are winning
• which third-party domains repeatedly influence answers
• how influence changes over time www.oncited.com

The important limitation

No one outside the AI providers can measure the true internal influence of a webpage.

Instead, these platforms estimate influence using signals such as:

• how often a page is cited
• how often information from that page appears in answers
• whether removing or replacing that page changes the answer
• consistency across many prompts and AI engines

Recent research suggests there's a difference between a page being cited and its content actually being absorbed into the answer—a page can shape the response even if it isn't explicitly listed as a citation. arxiv.org

If you wanted to build a better version

A next-generation platform could produce an AI Influence Graph:

Brand
   │
   ├── Prompt clusters
   │      │
   │      ├── AI engines
   │      │      │
   │      │      ├── cited URLs
   │      │      ├── uncited but semantically used URLs
   │      │      └── answer text
   │
   └── Influence Score

Example:

reddit.com/r/...
Influence: 92

g2.com/...
Influence: 88

yourcompany.com/docs
Influence: 81

Wikipedia
Influence: 74

Forbes article
Influence: 63

It could then answer questions like:

• "Which 20 pages most influence ChatGPT's opinion of our brand?"
• "Which competitor pages influence Gemini more than our own content?"
• "Which single article would most improve our visibility if we earned a mention there?"
• "Which sources are gaining or losing influence month over month?"

That kind of "citation graph" is where the market appears to be heading, but it's still early. Most existing products stop at citation tracking and don't yet provide a robust, model-agnostic influence ranking.

If you're thinking about building this as a product, I think the "page influence graph" is a stronger differentiation than simply offering another AI visibility dashboard. It addresses the question marketers increasingly care about: not just whether they're mentioned, but which specific pages are driving AI recommendations and how much each contributes.

Yes. This is an emerging category of software often called AI visibility, Answer Engine Optimization (AEO), or Generative Engine Optimization (GEO). Several platforms now attempt to answer exactly this question:

Which web pages influence AI answers about my brand, and how influential are they?

The capabilities vary quite a bit, though. Here's how they compare.

Some of the strongest products include:

• Profound – enterprise AI visibility with citation analysis and sentiment.
• Chatoptic – goes deeper by mapping citations to individual paragraphs and exposing which URLs influenced different parts of an answer. www.chatoptic.com
• AEOiQ – tracks AI visibility across multiple models and identifies the sources each engine trusts for your brand and your competitors. aeoiq.ai
• Cognizo – focuses on AI mentions, recurring narratives, and which external sources influence answers. www.cognizo.ai

What you're describing is slightly more advanced

A true "PageRank for AI influence" would look something like:

URL
↓
Referenced in 27 AI answers
↓
Influences 14 high-value buying questions
↓
Contributes 18% of brand mentions
↓
Influence score: 92/100

Most commercial tools don't yet calculate influence this rigorously. Instead they combine signals such as:

• citation frequency
• number of prompts where the page appears
• prominence within the answer
• AI engine coverage (ChatGPT, Gemini, Claude, Perplexity, etc.)
• authority/trust of the domain
• recency
• whether competitors are also cited

That produces an estimated influence score, not a definitive measure of causation.

The interesting opportunity

There's still a gap in the market for a product that builds a genuine AI citation graph, for example:

Buyer question
        ↓
AI answer
        ↓
Paragraph
        ↓
Supporting URLs
        ↓
Entities extracted
        ↓
Brand claims
        ↓
Influence weighting

From that graph you could answer questions like:

• Which 20 pages most influence AI's opinion of our brand?
• Which third-party sites matter more than our own website?
• Which competitor pages displace ours?
• Which pages influence purchase recommendations versus informational answers?
• How does influence change over time?

That would be considerably more actionable than today's "AI mention" dashboards.

One important caveat: this analysis is inherently incomplete because not every AI answer exposes its sources, and some models also rely on internal knowledge or retrieval that isn't fully visible. Even when citations are shown, they may not represent every source that influenced the answer. Research and industry testing suggest that visible citations are an imperfect window into the underlying retrieval process. arxiv.org

If you're thinking about building a product in this space, a page-level AI influence graph with competitive attribution would still be relatively differentiated from most existing AI visibility tools.