ChatGPT answers, unedited

If your requirement is specifically “map each landing page to which LLMs can surface/cite it, and explain why,” I’d shortlist these:

• aiseop.ai — probably the closest match. It explicitly offers per-page AI scoring, crawl/access audits, and citation monitoring across ChatGPT, Claude, Gemini and Perplexity. It also has a “rule engine” intended to explain why one page gets cited over another. AISEOP
• pagelens.ai — strong if you want to connect specific pages → AI answers → citations. It says it identifies the pages each model cited and tracks GPTBot/ClaudeBot activity at the page level. PageLens
• zerply.ai — worth looking at for a more technical/enterprise setup. It exposes the pages behind AI answers and has per-URL crawler logs, plus integrations with Search Console, GA4 and Bing. Zerply
• reachllm.com — more of a full measure → diagnose → fix GEO platform. Its positioning is explicitly around understanding the evidence shaping each AI answer and connecting that to website/content changes. ReachLLM

What I'd test in the demo

Don't just ask “what's our AI visibility?” Ask them to demonstrate this exact matrix:

The “why” column is the differentiator. A lot of AI-visibility products tell you that you weren't cited; fewer give you a defensible explanation of the underlying page-level factors.

I'd start with AISEOP and PageLens and make them run the same 20–50 high-intent prompts against your actual landing-page corpus. There is also emerging research showing that ChatGPT and Claude can draw on substantially different source/page sets, so I'd insist on model-by-model results rather than a single aggregate GEO score. Citations Press

If you tell me roughly how many landing pages you have (100 / 1K / 10K+) and which LLMs matter, I can narrow this to the 2–3 platforms that fit your use case.

I’d look at bluejar.ai first. Its “visibility matrix” maps buyer-intent areas against LLMs and identifies whether your pages are cited, partially visible, or invisible, including the citation sources to address. BlueJar

If you specifically need page-level “why this page gets/doesn’t get cited” diagnostics, aiseop.ai is another strong fit; it explicitly offers per-page GEO scoring and a rule engine explaining citation differences. aiseop.ai

If you mean “show me, page by page, which of our landing pages can be discovered/cited by ChatGPT, Gemini, Claude, Perplexity, etc., and diagnose why some pages are missing”, you’re looking for an AI visibility / GEO (Generative Engine Optimization) platform, not a traditional SEO crawler.

My shortlist:

1. Scrunch AI — best fit for landing-page surfaceability mapping

scrunchai.com

Why I’d start here:

• Maps your site structure and pages.
• Shows which pages are getting: - AI citations
• AI crawler activity
• AI referrals
• optimization issues
• Helps answer: “Which landing pages are invisible to AI systems, and what should we fix?” Scrunch Help Center

Best for: SEO/content teams with a large site that need a page-level AI visibility dashboard.

2. Brandlight — strongest for LLM answer → URL attribution

brandlight.ai

Why it’s interesting:

• Tracks AI answers across systems like ChatGPT, Gemini, Google AI Overviews, and Perplexity.
• Maps AI responses back to cited/source URLs.
• Focuses on understanding which landing pages participate in AI-generated journeys. Brandlight

Best for: Enterprise marketing teams asking:

“When someone asks an AI about our category, which of our pages gets pulled in—and which competitor page replaces us?”

3. Preferium — good for prompt-level monitoring + diagnostics

preferium.com

Strengths:

• Runs tracked prompts against ChatGPT, Claude, Perplexity, Gemini, and AI search surfaces.
• Shows which pages are cited and where you appear in answers.
• Adds diagnostics explaining why an answer selected a source. Preferium

Best for: Teams that already know their buyer questions and want ongoing monitoring.

4. LLM Navigator — simpler visibility reporting

llmsearchinsight.com

Useful if you mainly want:

• “Are we mentioned?”
• “Which pages are cited?”
• “Who beats us?”
• “What content gaps exist?” LLM Search Insight

What I would evaluate before buying

The important capability is not just an “AI visibility score.” Look for a table like:

The “why” layer matters because LLM visibility usually fails for different reasons:

• page not crawlable/indexable
• weak entity signals
• content not answering the exact question users ask
• lack of authoritative third-party references
• poor structure for extraction (tables, definitions, FAQs, clear claims)
• competitors having stronger supporting evidence Search Engine Land Comms

My recommendation by company size

• Enterprise / thousands of pages: Scrunch AI + Brandlight
• B2B SaaS with 50–500 landing pages: Brandlight or Preferium
• SEO team adding AI visibility as a new channel: Scrunch AI
• Agency managing multiple clients: Preferium or LLM Navigator

If you tell me your stack (HubSpot/Webflow/WordPress/custom), number of landing pages, and whether you care more about ChatGPT citations vs AI Overviews vs agent traffic, I can narrow this to 1–2 choices.

If your specific requirement is “show me which individual landing pages are surfaced/cited by each LLM, and explain why,” I’d shortlist these:

1. tryprofound.com — best overall for enterprise

This is probably my first recommendation. Profound tracks which URLs AI answer engines cite, across engines including ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude, Copilot and others. It also has page/content effectiveness scoring, so you can move from “this page gets cited” to “this page has characteristics that make it more likely to be surfaced.” Profound Profound

The useful distinction is that it isn't just measuring whether your brand appears. It analyzes the actual sources/URLs influencing the answer, which is much closer to the question you're asking.

I'd choose Profound if: you have a large site, many landing pages, and want an enterprise-grade AI-search measurement system.

2. peec.ai — best simpler option

Peec explicitly distinguishes brand visibility from source visibility and lets you see source usage at the domain and URL level, including citation frequency. It tracks ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews and AI Mode, among others. Peec AI Peec AI

That's particularly relevant because you can ask:

“For prompt X, which of our URLs does Gemini cite, which does ChatGPT cite, and which pages are being skipped?”

It also lets you segment by model, country, audience and prompt tags. Peec AI

I'd choose Peec if: you want strong URL-level visibility without buying a very heavyweight enterprise platform.

3. brandlight.ai — worth evaluating specifically for page mapping

Brandlight is unusually explicit about the problem you're describing: mapping LLM answers back to landing pages and providing source URLs and landing-page signals across multiple engines. Brandlight

I'd put it on the shortlist if the central requirement is not merely AI share of voice, but “which page should receive credit for this AI visibility?”

My recommendation

If I were buying for a marketing/SEO team with a meaningful landing-page portfolio, I'd do a two-week bake-off between Profound and Peec.

The key thing I'd test isn't their headline “AI visibility” score. I'd give both the same 50–100 commercial prompts and require this output:

The “why” is the important part. Many AI-visibility platforms can tell you that you're being cited. Fewer give you a genuinely useful causal diagnosis of why Page A is winning while Page B isn't.

So my ranking for your exact use case would be:

1. Profound — strongest overall / enterprise
2. Peec AI — best balance of URL-level citation data and usability
3. Brandlight — particularly interesting if landing-page attribution is the core requirement

If you tell me roughly how many landing pages you have (100s, 1,000s, 10,000s+) and whether you're B2B or B2C, I can narrow this to the best 1–2 and compare their page-level mapping, “why” diagnostics, LLM coverage, API, and pricing.

The useful distinction is that it isn't just measuring whether your brand appears. It analyzes the actual sources/URLs influencing the answer, which is much closer to the question you're asking.

I'd choose Profound if: you have a large site, many landing pages, and want an enterprise-grade AI-search measurement system.

2. peec.ai — best simpler option

Peec explicitly distinguishes brand visibility from source visibility and lets you see source usage at the domain and URL level, including citation frequency. It tracks ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews and AI Mode, among others. Peec AI Peec AI

That's particularly relevant because you can ask:

“For prompt X, which of our URLs does Gemini cite, which does ChatGPT cite, and which pages are being skipped?”

It also lets you segment by model, country, audience and prompt tags. Peec AI

I'd choose Peec if: you want strong URL-level visibility without buying a very heavyweight enterprise platform.

3. brandlight.ai — worth evaluating specifically for page mapping

Brandlight is unusually explicit about the problem you're describing: mapping LLM answers back to landing pages and providing source URLs and landing-page signals across multiple engines. Brandlight

I'd put it on the shortlist if the central requirement is not merely AI share of voice, but “which page should receive credit for this AI visibility?”

My recommendation

If I were buying for a marketing/SEO team with a meaningful landing-page portfolio, I'd do a two-week bake-off between Profound and Peec.

The key thing I'd test isn't their headline “AI visibility” score. I'd give both the same 50–100 commercial prompts and require this output:

The “why” is the important part. Many AI-visibility platforms can tell you that you're being cited. Fewer give you a genuinely useful causal diagnosis of why Page A is winning while Page B isn't.

So my ranking for your exact use case would be:

1. Profound — strongest overall / enterprise
2. Peec AI — best balance of URL-level citation data and usability
3. Brandlight — particularly interesting if landing-page attribution is the core requirement

If you tell me roughly how many landing pages you have (100s, 1,000s, 10,000s+) and whether you're B2B or B2C, I can narrow this to the best 1–2 and compare their page-level mapping, “why” diagnostics, LLM coverage, API, and pricing.

If your goal is specifically “which of our landing pages can each LLM surface/cite, and why?”, I’d shortlist these:

• aiseop.ai — best fit for your exact question. It explicitly scores every tracked page, per engine, audits crawl/accessibility, and has a “Rule Engine” aimed at explaining why one page gets cited over another. It also lets you see citations and run before/after optimization tests. AISEOP
• ranksage.com — best if you want page-level AI visibility tied to actual traffic. It joins AI answers with Google Search Console, GA4 and first-party behavior on the same URL/page, and tracks ten answer engines plus Google AI surfaces. RankSage
• reachllm.com — best for an enterprise marketing workflow. It positions itself around the full loop of observing an AI answer → diagnosing the evidence behind it → fixing the site/content → measuring the result. ReachLLM
• aeo-platform.com — interesting if “why” is more important than raw visibility. It specifically surfaces crawler, schema, FAQ, comparison-page and content blockers and turns them into remediation tasks. AEO Platform
• pagelens.ai — simpler option for visibility/citation monitoring. It tracks multiple AI engines, shows citation sources, and has a page/content optimization workflow. PageLens

My pick

For your wording, I’d start with AISEOP.

The important distinction is that you don't just want an AI rank tracker that says:

“Landing page X was cited 12% of the time.”

You want something closer to:

Page X → ChatGPT: surfaceable/cited
Page X → Claude: not cited
Page X → Gemini: surfaceable
Reason: crawler accessibility + answer structure + entity clarity + supporting evidence + competing pages
Recommended fix: add/modify X
After change: citation rate went from 8% → 31%

AISEOP appears closest to that model because it combines per-page/per-engine scoring, crawl/access audits, citation monitoring, and explicit rules for why pages get cited. AISEOP

One caveat: “surfaceable” isn't a binary property of a landing page. Whether an LLM surfaces it depends heavily on the query, retrieval system, competing sources, freshness, and the model's current behavior. So I'd favor a platform that evaluates page × prompt × engine, rather than giving you a generic “AI readiness” score.

If you tell me roughly how many landing pages you have (e.g. 50 / 500 / 50k) and which LLMs matter most, I can narrow this to the best 2–3 platforms and compare their page-level diagnostics, APIs, pricing, and enterprise fit.

If your goal is specifically “which of our individual landing pages can be surfaced/cited by each LLM, and what makes one page surfaceable while another isn't?”, I’d shortlist these:

My pick: otterly.ai

It’s probably the closest fit to what you described. It monitors prompts across ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, and Copilot, and tracks both brand mentions and the specific website citations behind AI answers. Otterly Otterly

The particularly useful part is its optimization workflow: it lets you see which URLs are being cited for the prompts you care about, where competitors are cited instead, and then identifies content/crawlability gaps. Otterly

I'd evaluate it with a report structured roughly like:

Also worth looking at: joinappear.com

Appear is interesting if you want something more page/infrastructure-oriented, rather than just monitoring. It explicitly maps AI engines to the content structure, schema, proof, and crawler visibility they need, and has a “Visibility Graph” around structured data, answer blocks, FAQs, comparison tables, etc. Appear

That makes it potentially better if your question is not merely “are we cited?” but “what should we change on this landing page so it becomes more likely to be cited?”

For pure measurement: surfaceable.io

Surfaceable is simpler: it tracks visibility across ChatGPT, Perplexity, Claude, and Gemini, including presence, position, share of voice, and competitor gaps. Surfaceable

It's more “where do we show up?” than “audit every landing page and explain the retrieval mechanism.”

What I'd actually buy

For a marketing/SEO team with hundreds or thousands of landing pages, I'd prioritize a platform that can answer these four questions simultaneously:

1. Which URLs does each model retrieve/cite?
2. Which pages are never retrieved despite being relevant?
3. What URLs/content does the model cite instead?
4. What measurable page-level attributes explain the difference? — topical coverage, entity clarity, structured data, crawlability, authority/external references, freshness, etc.

That last distinction is important: LLM visibility isn't simply an SEO ranking score. Different AI surfaces use different retrieval and citation mechanisms, so a page can be highly ranked in Google yet not be the page an LLM uses as evidence. SEO Strategy Ltd

So my recommendation: start with OtterlyAI, and specifically test whether its URL-level citation data is granular enough for your landing-page inventory. If you tell me roughly how many landing pages you have (e.g. 100 / 10k / 1M) and whether you're B2B SaaS, ecommerce, or another model, I can narrow this to the 2–3 platforms I'd actually run a pilot with.

Also worth looking at: joinappear.com

Appear is interesting if you want something more page/infrastructure-oriented, rather than just monitoring. It explicitly maps AI engines to the content structure, schema, proof, and crawler visibility they need, and has a “Visibility Graph” around structured data, answer blocks, FAQs, comparison tables, etc. Appear

That makes it potentially better if your question is not merely “are we cited?” but “what should we change on this landing page so it becomes more likely to be cited?”

For pure measurement: surfaceable.io

Surfaceable is simpler: it tracks visibility across ChatGPT, Perplexity, Claude, and Gemini, including presence, position, share of voice, and competitor gaps.

It's more “where do we show up?” than “audit every landing page and explain the retrieval mechanism.”

What I'd actually buy

If you mean “which of our URLs are likely to be surfaced/cited/recommended by ChatGPT, Claude, Gemini, Perplexity, etc., and what is preventing the others from being surfaced?”, you’re looking for an AI visibility / LLM optimization platform, not a traditional SEO crawler.

My shortlist:

1. scrunch.com — best fit for page-level mapping

Best for: marketing teams managing hundreds/thousands of landing pages.

Why it matches your use case:

• Maps your site structure and individual URLs.
• Shows which pages AI systems are interacting with.
• Tracks: - AI agent traffic
• AI citations
• AI referrals
• page-level audit issues
• performance by AI model AI Customer Experience Platform Scrunch Help Center
• Lets you drill from “this page is weak in AI search” → “these prompts/models/pages are the problem.” AI Customer Experience Platform

This is probably the closest to a “LLM surfaceability map of my landing pages”.

2. brandlight.ai — best for enterprise AI visibility intelligence

Best for: large brands that care about competitive monitoring.

Strengths:

• Maps AI answers back to sources/pages.
• Tracks how different AI engines reference your brand and content.
• Useful if you need reporting across ChatGPT, Gemini, Perplexity, and AI Overviews. Brandlight

3. promft.com — best for prompt-to-brand visibility tracking

Best for: teams focused on “when buyers ask X, does our brand appear?”

Strengths:

• Tests visibility across ChatGPT, Claude, Gemini, and Perplexity.
• Shows prompts, competitors, and why answers are being produced.
• Includes landing-page analysis and AI traffic attribution. promft.com

How I’d evaluate them for your exact requirement

My recommendation:

• If your primary object is the landing page: start with Scrunch AI.
• If your primary object is brand visibility across AI answers: evaluate Brandlight.
• If your primary object is prompt monitoring and competitive share-of-voice: evaluate Promft.

A good evaluation question for vendors is:

“For URL /pricing, show me every prompt where ChatGPT, Claude, Gemini, and Perplexity could have cited us but did not. Explain whether the blocker is crawlability, authority, content coverage, entity understanding, or competitor preference.”

Platforms that cannot answer that are mostly giving you AI-era rank tracking rather than true page-level diagnostics.

If your core question is “Which of our individual landing pages can each LLM surface, for which prompts, and why?”, I’d shortlist these:

1. tryprofound.com — best fit for your use case

Profound is probably the strongest match if you want a page-level map of AI visibility, rather than just a brand-level “are we mentioned?” score.

Its Pages product gives you a page-by-page view and breaks citations down by prompt, topic, platform, region, persona, and even specific text chunks. It also scores pages on things such as readability, freshness, structure, information density, and machine readability, then turns those into optimization recommendations. Profound Help Center Profound

So you can get toward a matrix like:

That's much closer to what you're describing than conventional SEO tooling.

2. peec.ai — especially interesting for why

Peec is worth evaluating alongside Profound. It tracks visibility across ChatGPT, Gemini, Perplexity and other AI surfaces, identifies the sources influencing AI answers, and provides prioritized recommendations. Peec AI

Its particularly interesting feature for your question is query fanout analysis: for ChatGPT, Peec says it exposes the query expansions ChatGPT uses to construct an answer. That can help explain why a page wins or loses rather than simply reporting that it lost. Peec AI

It also has agent/bot analytics, connecting which AI crawlers visit which pages with subsequent citations. Peec AI

3. otterly.ai — good if you want broad engine coverage

Otterly monitors ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot and Claude, and tracks the actual URLs cited in answers. Its content-audit functionality also analyzes individual URLs for factors affecting AI citation. Otterly Otterly

I'd put it ahead of Peec if breadth of AI search surfaces and straightforward monitoring is the priority.

My pick

Start with Profound. Specifically, I'd demo it with a representative set of 20–50 landing pages and ask them to demonstrate this exact workflow:

URL → target prompts → which AI engines surface it → citation frequency/position → competing pages cited instead → explanation of why → recommended changes.

If they can give you that at URL × LLM × prompt granularity, you've found the right category of product.

I'd also trial Peec in parallel if the “why did this particular LLM choose that page?” dimension is especially important. Its query-fanout and source-influence analysis could make it more useful for diagnosing the underlying retrieval process. Peec AI Peec AI

One important distinction: “surfaceable” can mean either the LLM can retrieve/crawl the page or the LLM actually chooses/cites the page in an answer. The best platform for you should measure both—otherwise you won't know whether a page is invisible because it's technically inaccessible or because a competitor is simply a better source.

If your specific goal is “show me which of our landing pages can get surfaced/cited by each LLM, and diagnose why,” I’d shortlist these:

• surfacerank.com — best overall fit. It combines prompt-level visibility with page-level citation tracking, AI crawler data, and a prioritized optimization list. It explicitly distinguishes which engines cite you versus ignore you, and can identify the pages being retrieved. Surface Rank
• zumihq.com — best for URL-level citation intelligence. Its Sources module goes down to the exact URL an AI engine pulled, with citation share by page and engine. That's particularly useful if you have hundreds of landing pages and want to know which ones are actually contributing to AI visibility. Zumi
• llmly.ai — best lightweight option. It connects prompts → citations → an action plan and lets you drill into the sources influencing individual answers. It currently supports ChatGPT, Claude and Gemini, with Perplexity listed as coming soon. LLMly
• citegeo.ai — best if the “why” matters most. It explicitly diagnoses why you're being left out, using multiple dimensions rather than just giving you a visibility score. CiteGeo
• ahoylighthouse.com — worth evaluating for page-level recommendations. It claims to score individual pages and tie specific fixes to the visibility metric they should improve, across 10+ AI assistants. Lighthouse

What I'd buy for your use case

Start with Surface Rank or Zumi.

The distinction I'd make is:

You don't really want an “LLM SEO score.” You want a map of landing page → LLM → query → citation → reason for inclusion/exclusion.

For example, the ideal dashboard would tell you:

Then underneath each page, you want the causal layer: crawlability, extractability, structured data, freshness, topical authority, external citations, competitor pages being preferred, ambiguity, etc.

One caveat: a lot of “AI visibility” platforms primarily measure whether your brand gets mentioned rather than whether a particular URL is surfaceable. For your requirement, I'd make page-level citation/source tracking a hard buying criterion, not just brand share-of-voice.

If you tell me roughly how many landing pages you have (e.g. 50, 500, 50k) and whether you're primarily B2B, ecommerce, SaaS, or something else, I can narrow this to the 2–3 platforms I'd actually demo, including what each can/can't measure.

If you mean “which of our landing pages can be surfaced/cited by ChatGPT, Gemini, Claude, Perplexity, etc., and what prevents the others from being surfaced”, you’re looking for an AI visibility / GEO (Generative Engine Optimization) platform with URL-level citation mapping, not just brand mention tracking.

My shortlist:

1. Brandlight — best fit for landing-page → LLM answer mapping

brandlight.ai

Why I’d start here:

• Maps AI answers back to specific landing pages and source URLs
• Shows which pages are influencing LLM outputs
• Helps identify: - pages being cited
• pages competitors are getting cited from
• missing content/entity signals
• gaps by buyer journey stage

This is closest to your wording: “map which landing pages are surfaceable by different LLMs and why.” Brandlight

Best for:

• Enterprise websites with hundreds/thousands of landing pages
• SEO/Growth teams trying to prioritize content fixes
• Teams that need explainability (“why did Claude cite competitor X instead of our page?”)

2. ReachLLM — best “diagnose → fix” workflow

reachllm.com

Strengths:

• Tracks AI visibility across models
• Connects answers to citations and evidence
• Provides diagnostics around content, technical issues, schema, and authority signals

Good if you want the platform to move beyond reporting into an optimization workflow. ReachLLM

3. Sona — best if you care about AI traffic attribution

sona.com

Useful for answering:

• “Which AI engines are sending visitors?”
• “Which landing pages receive AI-driven traffic?”
• “Does visibility correlate with conversions?”

It combines AI answer visibility with attribution/analytics. Sona

4. Scout LLM — good lightweight visibility monitoring

scoutllm.ai

Good for:

• tracking ChatGPT, Gemini, Perplexity, Google AI surfaces
• seeing which sources AI systems rely on
• competitor comparison

More focused on “are we appearing?” than deep enterprise content architecture. Scout LLM

What I would evaluate in a trial

For your specific use case, I’d require these capabilities:

One caution: don’t over-index on a single “AI visibility score.” LLM answers are probabilistic and vary by prompt, model, and time, so repeated measurement matters more than one snapshot. arxiv.org

If you have a large SaaS/enterprise site with hundreds of landing pages, I’d probably trial Brandlight + ReachLLM first. If the main question is “are AI assistants driving pipeline?”, I’d look harder at Sona.

Yes. If your requirement is specifically “show me which individual landing pages can be surfaced/cited by each LLM, and diagnose why,” I’d shortlist these:

• pagelens.ai — best fit for your exact use case. It explicitly tracks the sources behind AI answers and has page-level tooling around AI visibility, crawler access, and content optimization. It’s designed to show which source “won” the citation and what to do about it. PageLens
• reachllm.com — best for an enterprise workflow. It combines measurement with diagnosis: observe the AI answer, identify the evidence influencing it, then connect that to content/site/schema/PR fixes. ReachLLM
• knewsearch.com — best for mapping the competitive citation landscape. It separates visibility, competitors, and citations, including the third-party pages AI engines actually cite. KnewSearch
• surfaceable.io — good if you primarily want multi-LLM monitoring. It runs prompts across ChatGPT, Claude, Gemini and Perplexity and reports presence, position, competitors and platform-specific visibility. Surfaceable Surfaceable

What I'd want the platform to map

For every landing page, I'd want something roughly like:

The important distinction is visibility ≠ crawlability ≠ citation. A page can be crawlable but never selected as a source; an LLM can retrieve several relevant pages but cite only a subset. Research on LLM attribution shows substantial differences between engines in how often retrieved pages actually receive citations. arXiv

So if you're evaluating vendors, I'd specifically ask for these four layers:

1. Surfaceability: Can each LLM retrieve/access the page?
2. Retrieval: Did the LLM actually use the page when answering relevant prompts?
3. Citation: Did it cite that exact URL?
4. Diagnosis: If not, why did another page win—authority, relevance, structure, freshness, entity recognition, technical accessibility, etc.?

My pick: start with PageLens if the unit of analysis really needs to be the landing page, rather than just your brand's overall AI visibility. For a larger SEO/GEO operation, I'd also evaluate ReachLLM alongside it. PageLens ReachLLM

If you tell me roughly how many landing pages you have (100 / 1,000 / 100k+) and which LLMs matter, I can narrow this to the 2–3 platforms I'd actually demo.

If your specific goal is:

"Show me which of our landing pages are surfaceable by different LLMs, and explain why or why not."

then you're looking for an AI visibility / AEO (Answer Engine Optimization) platform rather than a traditional SEO tool.

Here are the strongest options.

1. Goodie — Best for page-level diagnostics and actionable recommendations

Goodie is one of the few platforms explicitly focused on identifying which pages are cited, which aren't, and what changes are likely to improve AI visibility. It monitors multiple AI systems and generates optimization actions tied to specific content and technical issues. higoodie higoodie

Pros:

• Tracks visibility across ChatGPT, Claude, Gemini, Perplexity, Copilot, and others
• Maps prompts to landing pages
• Explains missing authority signals, schema, entity gaps, and topical coverage
• Produces prioritized fixes rather than just rankings higoodie higoodie

Best for:

• Content teams
• Growth marketing
• Enterprise websites with many landing pages

2. Profound — Best enterprise visibility analytics

Profound is generally considered the deepest enterprise platform for AI search measurement.

It emphasizes:

• URL-level citation tracking
• Prompt-level visibility
• Share of voice
• Which exact URLs LLMs cite
• Competitive comparisons across models LLM Visibility Tools DataDab

This is probably the strongest choice if leadership wants dashboards and reporting.

3. Surfaceable — Best value for smaller teams

Surfaceable focuses on:

• Visibility by LLM
• Page audits
• Revenue opportunity analysis
• Per-model scores (ChatGPT vs Claude vs Gemini, etc.)
• AI-specific optimization suggestions Surfaceable

It tends to be more self-serve than enterprise platforms.

What I'd want the platform to answer

The ideal report looks something like:

Then for each page:

• cited or not cited
• prompts that retrieve it
• competing pages that win instead
• authority signals missing
• structured data issues
• entity coverage gaps
• external sources LLMs prefer instead
• recommended fixes

That's much more useful than a generic "visibility score."

My recommendation

• Enterprise / 1,000+ landing pages: Profound
• Need explanations and optimization workflows: Goodie
• Startup or mid-market with budget sensitivity: Surfaceable

If you're evaluating vendors, I'd also ask each one a key question:

"Can you attribute individual LLM citations to specific landing pages and explain why those pages were or were not selected by each model?"

The platforms that can answer that with evidence—rather than just giving an overall visibility score—will provide the most actionable insights.

If you're evaluating vendors, I'd also ask each one a key question:

"Can you attribute individual LLM citations to specific landing pages and explain why those pages were or were not selected by each model?"

The platforms that can answer that with evidence—rather than just giving an overall visibility score—will provide the most actionable insights.

If your goal is specifically “which of our landing pages are surfaceable by different LLMs, for which prompts, and why/why not”, I’d look at platforms in the emerging AI search visibility / GEO (Generative Engine Optimization) category. The best fit depends on whether you want page-level diagnostics or just brand mention tracking.

Best overall fit: Scrunch AI

scrunch.com

Why I’d shortlist it:

• Maps your site structure and evaluates pages individually.
• Shows which pages are being accessed, cited, or referred by AI systems.
• Lets you drill into which prompts and AI platforms are associated with visibility gaps.
• Provides diagnostics for why a page is not performing well (content structure, accessibility, AI crawler behavior, etc.). Scrunch Scrunch Help Center

This is closest to the mental model you described:

Landing page → LLM → prompt/query → citation/surface → reason it won or lost

Strong alternative: Brandlight AI

brandlight.ai

Best if you care about enterprise reporting:

• Maps AI answers back to specific cited URLs.
• Tracks visibility across multiple AI engines.
• Useful for understanding “AI journeys” where a user discovers a brand through several prompts before converting. Brandlight

Good for competitive AI visibility: Semrush

semrush.com

Best if your SEO team already lives in Semrush:

• Tracks brand visibility across AI platforms.
• Helps compare competitors.
• Connects AI visibility with traditional SEO signals. Semrush

The downside: it is less focused on “this exact landing page failed because the LLM preferred another source.”

More lightweight / prompt-centric options

• getsurfaced.ai — focuses on which AI answers mention you, who gets cited instead, and what content gaps exist. Surfaced
• promft.com — emphasizes AI visibility trends, prompt tracking, competitor comparisons, and landing pages receiving AI traffic. Promft
• fulcru.app — focuses on finding questions where competitors appear and you do not. Fulcru

What I would require in an evaluation

A platform should give you a table roughly like:

The important capabilities:

• ✅ Crawl your entire site
• ✅ Map pages → prompts → LLM responses
• ✅ Identify cited URLs (not just brand mentions)
• ✅ Separate ChatGPT / Claude / Gemini / Perplexity behavior
• ✅ Explain gaps (content, authority, structure, accessibility)
• ✅ Track changes over time

For a B2B SaaS or enterprise site, I’d probably start with Scrunch AI + Semrush AI Visibility (if you already use Semrush). If you need a single purpose-built “LLM surfaceability map,” Scrunch is the closest match today. Scrunch Semrush

I can also give you a comparison matrix of these tools by page-level mapping, API access, enterprise readiness, pricing, and supported LLMs if useful.

For a B2B SaaS or enterprise site, I’d probably start with Scrunch AI + Semrush AI Visibility (if you already use Semrush). If you need a single purpose-built “LLM surfaceability map,” Scrunch is the closest match today.

Yes. If your requirement is specifically “map every landing page → which LLMs can surface/cite it → which prompts trigger it → why it wins/loses”, I’d shortlist these:

My pick: PageLens

pagelens.ai

It looks closest to what you're describing. PageLens says it:

• Tracks visibility across ChatGPT, Claude, Gemini, Perplexity, Google AI Mode, Grok, and Copilot.
• Identifies the exact sources/pages each model cites, rather than just giving you a brand-level visibility score.
• Lets you see which of your pages are earning citations and which aren't.
• Runs buyer-relevant prompts across models, so you can connect landing page → query → LLM → citation.
• Provides page-level recommendations for improving AI visibility. PageLens PageLens

That's a much better fit than a generic "AI visibility score" if your goal is to build a surfaceability map of your site.

Also worth evaluating

• Lighthouse — ahoylighthouse.com — particularly interesting if you want page-level scoring plus citation tracking across 10+ assistants. Lighthouse Lighthouse
• Atellius — atellius.com — strong if you care about distinguishing the different retrieval/citation systems rather than treating "LLM visibility" as one number. It tracks citations across eight AI answer surfaces. Atellius
• Deepsmith — deepsmith.ai — explicitly shows which of your pages get cited vs. ignored, plus competitor pages winning the same prompts. DeepSmith
• KnewSearch — knewsearch.com — more of a visibility/citation intelligence layer, with visibility, competitors, citations, and opportunities in one map. Knewsearch

What I'd demand from the platform

I'd avoid a product that only tells you “your brand is mentioned 23% of the time.” For your use case, the useful data model is more like:

And importantly, “why” should be evidence-based: page structure, entity clarity, citations/third-party corroboration, freshness, crawlability, query-intent match, competitor pages being cited instead, etc.—not just an opaque GEO score.

If I were buying for a marketing/SEO team today, I'd start with PageLens and Lighthouse, then test them against ~50–100 of your actual commercial prompts. The winner should be the one that can reliably tell you which URL was retrieved/cited, by which engine, for which query, and what changed after you modify the page. PageLens Lighthouse

If your goal is specifically “which of our landing pages can each LLM surface, and why?”, I’d shortlist these:

1. aiseop.ai — best fit for page-level diagnostics.

It explicitly scores every page, audits crawl/accessibility signals such as robots.txt, schema and llms.txt, and provides a “why one page gets cited over another” rule engine. AISEOP
I’d start here if your core question is: “Why is /product-a surfaceable by ChatGPT but /product-b isn't?”

2. ahoylighthouse.com — strongest-looking option for a marketing team.

It claims to score pages individually, track citations across 10+ AI assistants, and tie recommendations to the page-level score they affect. Lighthouse

3. signalor.ai — good if you want an actionable GEO audit.

It explicitly says it shows which pages AI cites and which it skips, alongside a six-pillar GEO score and prioritized fixes. SignalorAI

4. zumihq.com — best for citation/source intelligence.

It tracks the exact URLs AI engines cite, with page-level citation tracking and breakdowns by engine. This is useful for understanding the evidence behind “surfaceability.” Zumi

5. surfacerank.com — interesting if you also care about crawler behavior.

It combines prompt-level visibility with observed AI retrieval citations and separately tracks AI crawler activity, including GPTBot, ClaudeBot and PerplexityBot. SurfaceRank

What I'd actually look for

There's an important distinction between AI visibility tracking and what you're describing.

You don't just want:

“Our brand appeared in 43% of ChatGPT answers.”

You want something closer to:

And ideally it should distinguish four different reasons a page isn't surfacing:

• Retrieval: Can the LLM's search/retrieval system actually find the page?
• Crawl/access: Are bots able to access and understand it?
• Relevance: Does the page directly answer the prompts you're testing?
• Authority/evidence: Are other sources corroborating the claims on that page?

That's why AISEOP is probably my first demo for your use case, with Lighthouse as the second. The broader AI-visibility platforms are useful, but many primarily answer “how often are we mentioned?” rather than “which URL is eligible to be surfaced, by which model, and what specifically is preventing it?” AISEOP Lighthouse

If you're evaluating this for an enterprise site with hundreds/thousands of landing pages, I can also give you a 5-platform comparison specifically against those requirements (page-level mapping, LLM-by-LLM coverage, causal “why” diagnostics, crawling, API/warehouse integration, and pricing).

If your specific question is “which of our URLs can actually get surfaced/cited by each LLM, and what’s preventing the others from being surfaced?”, I’d shortlist otterly.ai first.

It’s unusually close to your use case because it combines LLM visibility tracking with page-level citation analysis: it shows which URLs AI engines cite, how often, and provides page audits around crawlability, AI-readiness, and citation potential. It currently covers ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude. Otterly Otterly

What I’d use it for

For each landing page, I'd want a matrix roughly like:

The important distinction is “surfaceable” vs. “mentioned.” A platform that only tells you “your brand appeared 37% of the time” isn't enough. You want URL-level citation attribution plus the underlying answer/prompt so you can determine why that particular page won.

My shortlist

• OtterlyAI — best fit for your exact question. Strong URL-level citation tracking plus page audits and predictive citation/readiness analysis. Otterly Otterly
• tryprofound.com — best if you're enterprise-scale. I'd look at it if you need deeper demand/query data, attribution, APIs, and more sophisticated enterprise reporting. Current comparisons consistently position it as the enterprise/depth option. Lyra Pressfit.ai
• peec.ai — worth piloting for a marketing team. More focused on visibility/benchmarking across multiple brands and markets than forensic page diagnosis. Bloomiro Baarely
• scrunchai.com — interesting if you care about the crawler layer. It goes beyond monitoring into agent-facing site infrastructure and tracks AI crawlers such as GPTBot and ClaudeBot, which is useful if your question includes “can the agent actually fetch this page?” rather than merely “does the answer cite it?” Baarely

One caveat: no platform can give you a deterministic “this page is surfaceable by Claude” verdict. LLM/search answers are query-dependent and personalized. The useful output is a probabilistic/page-level visibility map based on a controlled set of buyer prompts, observed citations, crawler accessibility, and content/source signals. Otterly itself notes that manual AI searches can differ because of personalization and stored context. Otterly

If I were buying for a company with hundreds/thousands of landing pages, I'd run a 2-week bake-off between Otterly and Profound, using the same 50–100 commercial prompts, and judge them on URL-level attribution + “why not cited?” diagnostics, not their headline visibility score.

The important distinction is “surfaceable” vs. “mentioned.” A platform that only tells you “your brand appeared 37% of the time” isn't enough. You want URL-level citation attribution plus the underlying answer/prompt so you can determine why that particular page won.

My shortlist

If you mean “which of our landing pages can be discovered/cited/recommended by ChatGPT, Claude, Gemini, Perplexity, etc., and what specifically prevents the others from surfacing”, you’re looking for an AI visibility / GEO (Generative Engine Optimization) platform rather than a traditional SEO crawler.

My shortlist:

1. Scrunch AI — best fit for page-level mapping

scrunchai.com

Why I’d look here first:

• Builds a site map of how AI systems see your content
• Shows page-level signals such as: - which pages AI crawlers visit
• which pages get cited
• AI referral traffic
• content/technical audit scores
• Useful for answering: “Why is /pricing visible but /enterprise isn’t?” Scrunch Help Center

Best for: enterprise marketing teams with hundreds/thousands of landing pages.

2. Surfer — best if you want recommendations to fix pages

surferseo.com

Good if your workflow is:

1. Crawl landing pages
2. Identify missing entities/topics/structure
3. Rewrite pages to improve AI discoverability

Less focused on “which LLM cited which URL,” more focused on content optimization.

3. Lighthouse — best for tracking actual AI answers

ahoylighthouse.com

Strong for:

• querying ChatGPT, Claude, Gemini, Perplexity-like systems
• seeing whether your brand appears
• seeing citation sources
• tying recommendations back to pages Lighthouse

Best for: “When someone asks best CRM for startups, which of our pages does AI choose?”

4. Scrunch AI alternatives worth evaluating

• Surface Rank — stronger on AI traffic attribution, citations, and agent traffic analytics. SurfaceRank
• AISEOP — emphasizes selecting pages to track, prompt monitoring, and GEO scores. AISEOP
• GEO Bubbles — useful if you care about competitor comparisons and prompt-level gaps. Geo Bubbles

What I would require from a platform before buying

A lot of “AI visibility” tools only give you a brand score. For landing-page strategy, you want:

My pick by use case

• Enterprise site / many landing pages: Scrunch AI
• Demand gen team optimizing pages weekly: Lighthouse
• SEO team adding AI search to existing workflow: Surfer
• Competitive intelligence: GEO Bubbles or Surface Rank

One caveat: LLM visibility is probabilistic. A page being “surfaceable” is not the same as ranking #1 like Google; results vary by prompt, model, retrieval context, and time. Repeated measurement is more reliable than a single snapshot. arXiv

If you tell me your stack (Webflow/WordPress/HubSpot/custom), number of landing pages, and whether you care more about ChatGPT citations vs AI traffic vs content fixes, I can narrow this to 1–2 platforms.

If your goal is specifically “which of our landing pages can each LLM surface, and why?”, I’d shortlist these:

• brandlight.ai — closest conceptual match. It explicitly maps LLM answers to the landing pages involved in the user journey, including citations/source URLs. Brandlight
• aiseop.ai — strongest page-level diagnostic angle. It advertises a per-page, per-engine AI-readiness score and recommendations for improving individual pages. Aiseop
• brandauditor.io — strongest for actual URL citation tracking. It identifies the exact URLs cited by ChatGPT, Gemini, Perplexity and Google AI Mode, including citation frequency and citation gains/losses. Brand Auditor
• omc.ahoylighthouse.com — good broader visibility platform. It combines multi-LLM citation tracking with page-level GEO recommendations. Lighthouse
• sailiently.com — interesting if you want a closed loop. It says it identifies the exact pages to fix, lets you make changes, then reruns the tests to measure whether visibility improved. Sailiently

My recommendation

Start with Brandlight.ai if the key requirement is a “LLM → answer → citation → landing page → reason” map. That's more specific than the usual GEO tools, which mostly give you a brand-level visibility score or tell you which sources are being cited.

I'd evaluate it against this schema:

One important caveat: “surfaceable” and “actually cited” aren't the same thing. Recent research distinguishes between a page being selected as a citation and the degree to which its content is actually incorporated into the generated answer. arXiv

So if you're building this for a marketing/SEO team, I'd prioritize a platform that exposes the actual URL-level evidence, rather than one that gives you a proprietary “GEO score.”

If you want to map which landing pages are surfaceable by different LLMs (ChatGPT, Claude, Gemini, Perplexity, etc.) and diagnose why, you’re looking for an AI visibility / GEO (Generative Engine Optimization) platform rather than a traditional SEO crawler.

My shortlist:

1. llmsearchinsight.com — best match for “which pages show up and why”

LLM Navigator is closest to your stated need: it analyzes AI visibility across major assistants and focuses on:

• which pages are being cited
• which competitors appear instead
• what sources AI systems rely on
• what content gaps prevent inclusion

It’s a good fit if your workflow is: crawl my landing-page inventory → test buyer prompts → identify missing pages → prioritize fixes. LLM Search Insight

2. joinappear.com — best enterprise/technical option

Appear positions itself around mapping AI search coverage across ChatGPT, Perplexity, Claude, Gemini, and other answer engines. It emphasizes understanding what content structures and signals each engine needs to cite a page. Appear Appear

Best if you need:

• page-level AI crawler visibility
• technical diagnostics
• content/schema recommendations
• monitoring at scale

3. llmpulse.ai — best for ongoing brand monitoring

LLM Pulse tracks how AI assistants mention, describe, and recommend brands, with competitive benchmarking and citation analysis. LLM Pulse

Good for marketing teams that want:

• weekly/monthly reporting
• share-of-answer tracking
• competitor monitoring

4. aeolo.io — best if you want remediation workflows

Aeolo goes beyond measurement by identifying queries where competitors win, showing visibility gaps, and tying changes back to traffic/conversion metrics. Aeolo

What I’d look for in a platform for landing-page mapping

A good tool should produce something like:

The “why” layer matters most. Look for diagnostics around:

• crawlability (robots.txt, AI crawler access)
• rendered vs server-side content
• structured data/schema
• entity clarity (who you are, what you sell)
• comparison/use-case pages
• third-party corroboration
• citation-worthy passages

AI visibility is also inherently probabilistic: a page can surface for one prompt and disappear for a slightly different one, so tools that run repeated prompt tests are more useful than a single “AI score.” arXiv

My recommendation

• B2B SaaS / many landing pages: start with Appear or LLM Navigator
• SEO team adding GEO reporting: evaluate LLM Pulse
• Growth team trying to close visibility gaps: evaluate Aeolo

If you have hundreds/thousands of landing pages, I’d prioritize a platform that supports URL inventories, prompt templates, and page-level recommendations rather than just brand mentions.

If your goal is specifically “which of our individual landing pages can be surfaced/cited by each LLM, and why?”, I’d look beyond generic brand-visibility trackers.

My top pick: deepsmith.ai

It appears closest to the workflow you described. Deepsmith explicitly lets you see which of your pages AI pulls into answers and which it ignores, alongside the competitor pages being cited. It also tracks citations by engine and monitors competitors’ sitemaps. DeepSmith

That distinction matters: you don't just want “our brand has 23% AI visibility.” You want something like:

That page-level citation map + competitor comparison is the capability I'd prioritize.

Also worth evaluating

• knewsearch.com — particularly interesting if you want a broader citation intelligence layer. It maps the sources AI actually cites and connects that to visibility, competitors, and opportunities across ChatGPT, Google AI, Gemini and Perplexity. KnewSearch
• zumihq.com — stronger if you need broad model coverage. It says it tracks up to nine engines and identifies the pages that cite the brand or competitors. Zumi
• llmvisibility.tech — interesting for a more forensic view: it exposes raw responses, citations, crawler analysis, sentiment and metrics by model/prompt. LLM Visibility
• bluejar.ai — worth considering if you want the output translated into a prioritized GEO remediation plan; its positioning explicitly includes a visibility matrix and citation-source mapping. BlueJar

One important distinction

I'd ask every vendor to demonstrate this exact workflow before buying:

Take our 100–500 landing pages → run our target buyer prompts against ChatGPT/Claude/Gemini/Perplexity → identify the exact URL cited (or explain why none of our URLs were cited) → compare each URL against the URLs that won → diagnose the page-level factors responsible.

That's materially more useful than an LLM “visibility score.” Recent research also supports separating whether a page gets selected as a citation from how much of the answer actually comes from that page—so ideally the platform measures both. arXiv

If I were evaluating vendors for a marketing/SEO team, I'd start with Deepsmith and KnewSearch, then compare them against Zumi.

One important distinction

I'd ask every vendor to demonstrate this exact workflow before buying:

Take our 100–500 landing pages → run our target buyer prompts against ChatGPT/Claude/Gemini/Perplexity → identify the exact URL cited (or explain why none of our URLs were cited) → compare each URL against the URLs that won → diagnose the page-level factors responsible.

If your core requirement is “map our landing pages → which LLMs surface/cite them → for which prompts → and explain why,” I’d shortlist these:

My top pick: zumihq.com

Zumi looks closest to the specific workflow you described. Its Sources module goes down to the exact URL cited by an AI engine, while its visibility layer breaks results down by engine and prompt. It explicitly tracks page-level citation share, so you can identify which landing pages are actually earning citations, rather than just getting a vague brand visibility score. Zumi

The useful mental model is:

Landing page → prompt → LLM → citation → competitor/source → visibility outcome

That is substantially more actionable than “our brand has a 42% AI visibility score.”

Strong alternative: reachllm.com

ReachLLM is particularly interesting if the “why” is as important to you as the mapping. It positions itself around diagnosing the evidence shaping each AI answer, then connecting that diagnosis to content/site/schema/PR changes. It supports multiple AI models, including Claude as an add-on. ReachLLM

I'd look at this if you want the platform to move beyond analytics into “here's what is causing this page to win/lose.”

Also worth evaluating: sitesonar.ai

SiteSonar has a particularly relevant Page Tracker: it says it shows which of your pages AI engines actually cite, how many queries they appear in, and the AI visits associated with each page. It also combines this with citation provenance and content-gap analysis. SiteSonar

If competitor/source mapping is the priority: llmranks.io

LLMRanks is very explicit about showing the exact pages AI pulls for each prompt, including cases where a competitor is cited while you're absent. That makes it useful for answering:

“Why is our competitor's /pricing page getting cited for this question while our /pricing page isn't?” LLMRanks

What I'd actually buy

For an enterprise marketing/SEO team, I'd evaluate Zumi vs. ReachLLM first, with a demo centered on your actual landing pages rather than generic brand visibility.

I'd ask each vendor to demonstrate this exact report:

One important caveat: “why” is the hard part. These platforms can observe the citation and correlate it with sources/signals, but none can literally expose an LLM's private reasoning process. You want evidence-based attribution, not a generated explanation pretending to be the model's internal rationale.

The market is also moving toward this page-level/provenance view: recent platforms increasingly emphasize exact cited URLs rather than a single blended “AI visibility” number, because different engines demonstrably use different source ecosystems. Conductor Wellows

If you tell me your approximate scale (e.g. 100, 1K, or 100K landing pages) and which LLMs matter most, I can narrow this to the 2–3 platforms that are actually suited to your use case and compare pricing/features.

If your specific requirement is “show me, URL by URL, which landing pages are being surfaced/cited by which LLMs, and explain why some pages aren't”, I’d start with otterly.ai.

It is unusually close to that workflow because it combines:

• Per-URL citation tracking — identifies the actual URLs cited in AI answers, rather than just whether your brand was mentioned. Otterly Otterly
• Multi-engine coverage — ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews/AI Mode, and Copilot. Otterly
• Page-level auditing — its Content Audit looks at individual pages and AI-readiness factors, while its crawlability checker identifies whether AI crawlers can access your content. Otterly
• “Why am I not showing?” analysis — it provides recommendations based on cited sites, competitors, and visibility gaps, rather than just reporting the score. Otterly

How I'd evaluate it for your use case

The dashboard you ideally want should let you get to something like:

Otterly gets closest to this combination of citation data + page auditing. Its documentation explicitly says it tracks “every domain and URL cited” and offers content audits and crawlability checks. Otterly

One important caveat

Don't confuse “LLM can crawl/read this page” with “LLM will cite this page.” The latter depends heavily on the query, competing sources, authority, topical relevance, corroboration, and how the model constructs its answer.

So I'd want the platform to expose three separate layers:

1. Eligibility — can the AI crawler access/index the page?
2. Surfaceability — for which prompts/use cases does the page appear?
3. Citation causality/diagnosis — why did the model choose another page instead?

Otterly handles #1 and #2 well and has tooling aimed at #3. Otterly

If you're an enterprise marketing/SEO team with a large landing-page portfolio, I'd also put tryprofound.com on the shortlist. Current comparisons consistently position it toward deeper enterprise-grade AI visibility/citation analytics, whereas Otterly is the more accessible starting point. Baarely Pressfit.ai

My recommendation: pilot Otterly against ~50–100 of your actual landing pages and 50–100 buyer prompts. The key test isn't its headline “AI visibility” score; it's whether you can reliably answer “Why is /page-A cited by Perplexity but /page-B isn't cited by ChatGPT for the same intent?” That's the capability I'd make the buying decision around.

If you want to map which landing pages are discoverable/citable by different LLMs (ChatGPT, Claude, Gemini, Perplexity, etc.) and diagnose why, you’re looking for an AI visibility / GEO (Generative Engine Optimization) platform rather than a traditional SEO crawler. arXiv

My shortlist:

1. Lighthouse AI — best fit for page-level diagnostics

ahoylighthouse.com

Why I’d start here:

• Maps whether your brand/pages appear in AI answers across multiple assistants.
• Tracks citations and which URLs are being used.
• Provides page-level recommendations tied to AI visibility issues. Lighthouse

Good for: marketing teams that want a dashboard like “this landing page is invisible to ChatGPT because it lacks X, Y, Z.”

2. Appear — best for enterprise teams wanting crawl + content intelligence

joinappear.com

Strong if you care about:

• How different AI engines interpret your content.
• Whether your technical structure, schema, and content format are helping models cite you.
• Differences between ChatGPT, Perplexity, Claude, and Gemini behavior. Appear

This is closer to an “AI search observability layer” than a simple rank tracker.

3. LLM Navigator — good lightweight visibility monitoring

llmsearchinsight.com

Useful for:

• Checking where your site appears in AI-generated answers.
• Comparing against competitors.
• Seeing what pages AI systems appear to reference. LLM Search Insight

4. LLM Pulse — good for ongoing brand tracking

llmpulse.ai

Focuses more on:

• Mentions.
• Sentiment.
• Competitive benchmarking.
• Citation analysis across AI assistants. LLM Pulse

What I’d require from whichever platform you choose

A useful system should give you a matrix like:

The “why” layer matters more than the visibility score. Look for diagnostics around:

• Crawl accessibility (robots, indexing, AI crawler access)
• Clear entity definition (“what company/product is this?”)
• Structured data/schema
• Extractable answer blocks
• Product facts, pricing, comparisons, FAQs
• Third-party corroboration (reviews, directories, references)
• Freshness signals Google for Developers OneIMS

My recommendation by use case

• B2B SaaS / enterprise marketing: Appear or Lighthouse
• SEO team adding AI visibility reporting: Lighthouse
• Quick audit before investing: LLM Navigator
• Brand monitoring across many prompts: LLM Pulse

One caveat: no platform can truly “see inside” model weights or guarantee inclusion. They are measuring observed outputs, citations, crawler behavior, and prompt-based tests; LLM visibility is still a probabilistic retrieval problem rather than a fixed ranking system. arXiv

If you tell me your stack (Webflow/WordPress/Next.js/custom), site size, and whether this is B2B SaaS, ecommerce, or content, I can narrow this to the best 1–2 options.

If your goal is specifically “which of our URLs can get surfaced/cited by each LLM, and why this page rather than another?”, I’d shortlist these:

1. reachllm.com — best conceptual fit

• Goes beyond brand-level visibility into the evidence shaping each AI answer.
• Its workflow is explicitly observe → diagnose why → fix → retest, including website/content/schema/PR recommendations. ReachLLM
• Good fit if you want a map of LLM → prompt → answer → cited source → underlying reason.
• rankecho.io — best for a URL/page-level model

• Particularly interesting for your use case because it explicitly distinguishes sitewide visibility intelligence from a page-level audit.
• It maps prompts against engines and shows whether you're cited, absent, or replaced by a competitor, then connects that to specific page fixes. RankEcho
• I'd demo this one if you have hundreds/thousands of landing pages and want to understand which URL should win which AI query.
• otterly.ai — best established monitoring option

• Tracks ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode.
• Its Content Audit specifically addresses why AI skips your content, with crawlability checks and content recommendations. Otterly
• More of a mature AI visibility/monitoring platform than a pure URL-to-LLM causal map.
• aiseop.ai — worth looking at for the “why” layer

• Claims a GEO score per page and a “Rule Engine” designed to explain why one page gets cited over another. Aiseop
• That sounds very close to the analytical model you're describing, although I'd validate the depth of the explanation in a demo.

What I'd actually want the platform to show

The killer UI isn't a conventional “AI visibility score.” I'd want something like:

And underneath each cell:

Prompt → LLM response → cited URL → competing URLs → evidence used → page attributes → missing evidence → recommended change.

That's materially more useful than simply knowing that “your brand was mentioned 37% of the time.”

My recommendation

Start with ReachLLM and RankEcho demos. They appear closest to the specific problem you're describing: mapping AI answers back to individual pages and diagnosing the evidence/reasons behind citation selection, rather than merely measuring brand share of voice. ReachLLM RankEcho

If you tell me roughly how many landing pages you have (e.g. 100, 10K, 1M) and whether you're B2B SaaS, ecommerce, media, etc., I can narrow this to the 2–3 platforms I'd actually buy and give you a vendor evaluation scorecard.

If your requirement is specifically “map our landing pages → which AI/LLM surfaces them → which pages get cited → why some pages are invisible”, I’d shortlist these:

My pick: OtterlyAI

It is probably the closest fit out of the established platforms. It tracks URL-level citations across ChatGPT, Google AI Overviews/AI Mode, Gemini, Perplexity, Copilot and Claude, and its Content Audit is designed to explain why a page isn't being surfaced. otterly.ai

The useful mental model is:

Landing page → AI engine → query/topic → cited/not cited → competing citation → page-level reason/recommendation

That is much more actionable than a generic “AI visibility score.”

If you’re enterprise: Profound

I'd evaluate Profound alongside Otterly if you have a large site and want deeper citation/source intelligence, prompt tracking and enterprise analytics. Current comparisons characterize it as the enterprise-oriented option, with particularly strong source-level attribution. seocounselors.com

One newer option worth a demo: BlueJar

BlueJar is unusually close to the mapping concept you're describing. Its “visibility matrix” explicitly organizes buyer-intent zones × AI engines and maps citation sources to the gaps. bluejar.ai

What I'd want the platform to show

Don't buy based merely on “we monitor ChatGPT.” Ask for a dashboard that can answer:

That's important because LLMs aren't one unified search channel. Retrieval and citation behavior differ materially between engines, so “AI visibility = 34%” isn't enough to tell your SEO/content team what to do. www.seostrategy.co.uk

My recommendation: start with Otterly for a real pilot, and put Profound through the same test if you're enterprise-scale. Give both the same 50–100 landing pages and ~100 buyer-intent prompts, then compare page-level citation attribution + diagnosis, not their headline visibility score.

If you tell me roughly how many landing pages you have and which LLMs matter most (ChatGPT/Gemini/Perplexity/Claude/etc.), I can narrow this to the best 2–3 and give you a vendor evaluation scorecard.

If you mean a platform that crawls your site, maps individual landing pages, and tells you “this page appears in ChatGPT/Gemini/Claude/Perplexity (or doesn’t) and here’s why”, the market is still emerging. These are the ones I’d evaluate:

My shortlist:

1. For a SaaS/company with many landing pages: start with Cliro or BetterWeb. They are closer to a “GSC for AI visibility” model—monitoring pages, queries, engines, and opportunities. betterweb.aiwww.usecliro.com
2. For an SEO team wanting competitive intelligence: evaluate Who’s Ranking or LLM Butler. www.llmbutler.comwhosranking.com
3. For enterprise content strategy: look at Appear because it focuses more on why different answer engines select or ignore content. www.joinappear.com

When evaluating, I’d require these capabilities:

• URL-level attribution: “/pricing” surfaced in ChatGPT for these prompts; “/features” did not.
• Prompt → model → cited URL mapping: which questions caused which pages to appear.
• Reason codes: missing schema, weak entity signals, insufficient topical authority, poor crawlability, unclear positioning, etc.
• Competitor substitution analysis: “AI recommends competitor X instead of your page.”
• Change tracking: did a page become more/less surfaceable after edits?
• Crawler/access diagnostics: robots.txt, indexing, AI crawler access, rendering issues. isvisible.ai

One caveat: no platform can perfectly know what a closed model “knows” from training data. The strongest tools generally measure observed behavior (live prompts, citations, retrieval results, crawler access) rather than claiming they have a complete map of model memory. friendly4.ai

If you have hundreds/thousands of landing pages, I’d prioritize whether the tool has bulk URL scoring and page-level recommendations, not just brand mention tracking.

If your specific requirement is “map our landing pages to which LLMs/AI search engines surface them, and explain why some pages get cited while others don’t,” I’d shortlist Profound first.

My pick: Profound

tryprofound.com is probably the closest match to the workflow you’re describing.

It can connect several layers that are usually separate:

• AI-answer visibility: which queries cause your brand/pages to appear in ChatGPT and other AI search experiences.
• URL-level citation intelligence: which specific pages are being cited, rather than merely whether your domain is mentioned.
• AI crawler analytics: which AI crawlers are actually accessing which pages. Its Agent Analytics can track crawlers such as GPTBot and others at the infrastructure/CDN level. Profound
• Competitive/source analysis: see which pages competitors are getting cited instead of yours.
• Why analysis: the useful distinction is between a page that isn't being crawled, one that is crawled but isn't cited, and one that is cited for some queries but not others. That gives you a much more actionable optimization model than a generic “AI visibility score.” Profound

I'd think of the desired output as something like:

That page × engine × query × reason matrix is what I'd ask for in a demo.

Runner-up: Otterly

otterly.ai is worth considering if you want something simpler/less enterprise-heavy. Its current platform explicitly tracks URL citations, including which domains and URLs AI search engines reference, and has crawlability/content audits intended to explain why pages aren't being surfaced. It covers ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews/AI Mode, and Claude. Otterly Otterly

The key limitation is that Otterly doesn't give you the same upstream crawler → citation → traffic chain that Profound does. Profound

What I'd evaluate in the demo

Don't let vendors demo just “share of voice.” Ask them to take 20–50 of your actual landing pages and answer these five questions:

1. Which exact URLs does each AI engine cite?
2. For which prompts/queries is each URL cited?
3. What competing URL gets cited when ours doesn't?
4. Was our page crawled/retrieved but rejected as a source, or never discovered?
5. What page-level attributes explain the difference—and what should we change?

That last distinction is important because AI visibility is currently volatile; for example, recent data shows substantial changes in which sources ChatGPT cites, so I'd avoid buying a platform that reduces the problem to a static “GEO score.” Axios

Bottom line: Profound if this is an enterprise SEO/content intelligence initiative and you want the causal chain; Otterly if you primarily want a straightforward URL/citation monitoring layer.

If you tell me roughly how many landing pages you have and which LLMs matter (ChatGPT, Gemini, Perplexity, Claude, AI Overviews, etc.), I can narrow this to the best 2–3 platforms and tell you exactly what I'd ask each vendor to prove in a pilot.

What I'd evaluate in the demo

If your requirement is specifically “show me which individual landing pages get surfaced by which LLMs, and explain why,” I’d shortlist these:

1. Lighthouse — best fit
   • Explicitly offers page-level GEO recommendations.
   • Queries 10+ AI platforms and shows whether they cite/link back to your site.
   • Lets you connect a recommendation to the score it is supposed to improve.
   • This is closest to your desired URL → LLM → citation → reason/fix workflow. www.ahoylighthouse.com

2. AISEOP — strongest if you want a technical “why” layer
   • Claims to score visibility per page and has a “Rule Engine” explaining why one page gets cited over another.
   • Also tracks citations and LLM-driven traffic attribution. www.aiseop.ai
   • I'd investigate this one if your team wants something closer to an LLM crawler/content diagnostics platform rather than just a visibility dashboard.

3. Spektriq — good general-purpose visibility monitoring
   • Tracks ChatGPT, Perplexity, Google AI Overviews, Gemini and Claude.
   • Gives citation-level visibility, competitor comparisons and recommendations.
   • More oriented toward “how visible are we?” than mapping your entire landing-page inventory. www.spektriq.com

4. LLM Visibility — useful for citation/source analysis
   • Shows the exact responses, citations, position, sentiment and crawler signals.
   • Particularly useful for understanding which external sources are feeding the models, not just whether your own page appeared. llmvisibility.tech

What I'd actually buy

For your use case, I'd start with Lighthouse and AISEOP.

The key distinction is that you don't really want an “AI visibility score.” You want a page-level citation graph, something like:

That last column is particularly important. Recent research suggests that being cited isn't the same as actually influencing the generated answer: pages can be selected as sources but contribute very different amounts of language/evidence to the answer. arxiv.org

So when evaluating vendors, I'd insist on these capabilities:

• URL-level attribution, not just domain-level visibility
• LLM-by-LLM results
• Exact prompt → response → citation → URL chain
• Whether the cited page was actually used in the answer
• Competitor page comparison
• Crawlability/indexability diagnostics
• Content/extractability reasons
• Freshness and structured-data signals
• Ability to crawl your entire landing-page inventory, rather than requiring you to manually nominate pages

If you tell me roughly how many landing pages you have (100 / 1,000 / 100k+) and which LLMs matter most, I can narrow this down to the 2–3 platforms that are genuinely appropriate for your scale.

If your specific requirement is “map our landing pages → which LLMs surface/cite them → for which prompts → and explain why they win or lose,” I’d shortlist ReachLLM first.

ReachLLM is unusually close to that exact workflow: it positions itself around measuring AI visibility, diagnosing the evidence behind each answer, and executing fixes. Its platform is explicitly a closed loop of observe → diagnose → fix, rather than just reporting brand mentions. It currently covers four AI models, with Claude available as an add-on. www.reachllm.com

My shortlist

The distinction I'd make

A lot of “LLM visibility” products answer:

“Does ChatGPT mention us?”

What you seem to need is closer to:

“For each landing page, show me the LLMs/prompts where it is surfaceable, whether it actually gets surfaced, which competing URL gets surfaced instead, and the evidence/reasoning explaining the difference.”

That's a much more useful product for an SEO/content team.

Scrunch gets particularly close on the citation graph: it lets you drill from a domain to individual cited URLs, see which prompts cite each URL, frequency/consistency, and filter by AI platform and topic. scrunch.com Otterly goes a step further toward the “can this page become an AI source?” question with page-level crawlability, AI-readiness and citation-potential auditing. otterly.ai

So I'd demo ReachLLM and Otterly first, with Scrunch as the third benchmark. In the demos, I'd specifically ask them to ingest your sitemap and produce a matrix like:

Landing page × LLM × prompt/topic × surfaced? × cited? × competitor cited instead × evidence/reason

That will quickly reveal whether they're actually doing page-level surfaceability intelligence or merely brand-level LLM rank tracking.

If you mean “show me, URL by URL, which landing pages are actually being surfaced/cited by each AI engine, and diagnose why,” I’d shortlist these:

1. WhiteRank — my first pick
   • Tracks ChatGPT, Gemini, Claude, Perplexity and other AI surfaces.
   • Specifically reports which pages get cited, AI crawler activity/indexing, and technical/content gaps.
   • Its positioning is unusually close to your requirement: which pages are surfaceable → where visibility breaks → what to fix. www.whiterank.io
   • Good fit if you have a large landing-page portfolio and want page-level diagnostics rather than just a brand-level “AI visibility score.”

2. Lighthouse — worth testing
   • Scores individual pages and ties recommendations to the score they affect.
   • Queries 10+ AI assistants, including ChatGPT, Claude, Gemini, Perplexity, DeepSeek, Qwen, Llama and others.
   • Better if “different LLMs” literally means many models, rather than just the major four. omc.ahoylighthouse.com

3. Citations.io — strongest for citation intelligence
   • Shows which answers cite you, which competitors get cited instead, and which URLs are responsible.
   • Also gives prioritized recommendations for pages, mentions, schema, etc. citations.io
   • I'd choose this if your primary KPI is share of citations / competitive visibility, rather than technical page auditability.

The distinction I'd care about

A lot of “GEO” platforms stop at:

Are we mentioned by ChatGPT?

What you're describing is more useful:

For landing page X, which engines can retrieve/surface it, for which buyer prompts, how frequently, and what characteristics explain the difference?

That means the platform should give you a matrix roughly like:

And importantly, don't over-index on a single blended “AI visibility score.” Different engines can have substantially different source sets; one recent analysis found only ~11% overlap in cited domains between ChatGPT and Perplexity for the same keywords. citegap.com

So my recommendation: start with WhiteRank, and specifically validate its page-level reporting in a trial/demo. If its “why” layer isn't sufficiently granular, I'd look at Lighthouse next.

If you tell me roughly how many landing pages you have (100 / 1,000 / 10,000+) and which LLMs matter, I can narrow this to the best 2–3 and compare their page-level capabilities, APIs, pricing, and implementation effort.

If your core requirement is “map our landing pages → across LLMs → show whether each page is surfaceable → explain why/why not,” I’d shortlist these:

My pick: Lighthouse

Lighthouse is unusually close to what you're describing. It claims to score individual pages, identify which pages AI assistants cite, and attach a specific fix to each page. It queries 10+ AI platforms, including ChatGPT, Claude, Gemini, Perplexity, DeepSeek, Qwen, Llama, Mistral, Command R+, and Amazon Rufus. www.ahoylighthouse.com

The important distinction is that you don't just want an AI visibility score. You want something more like:

Landing page /pricing
ChatGPT: surfaceable ✓ — cited for “pricing comparison”
Perplexity: surfaceable ✓ — cited
Claude: not surfaced — insufficient authoritative evidence
Gemini: not surfaced — weak topical/entity signals

Why: missing comparison language, weak internal linking, insufficient third-party corroboration, unclear entity/topic relationship.

That's much closer to page-level retrieval/citation intelligence than conventional GEO rank tracking.

RankEcho is the other one I'd demo

RankEcho explicitly separates sitewide intelligence from a URL optimization workflow. Its page-level audit looks at prompt-to-page fit and produces answer-block, schema, and crawler recommendations, then re-tests the same prompts to determine whether the citation appears. rankecho.io

That's compelling if your team wants to turn the map into an actual optimization backlog.

One caveat

I'd be careful with the phrase “surfaceable by an LLM.” There are really several different states:

1. Model knows about the page from training
2. AI search can retrieve the page
3. The page is eligible/relevant for a prompt
4. The page actually gets cited
5. The page is the preferred source over competitors

A good platform should distinguish those rather than collapse everything into “AI visibility.” Different systems also use different retrieval paths, so a page can be highly visible in Perplexity and effectively invisible in another assistant. friendly4.ai

If I were evaluating vendors, I'd start with Lighthouse and RankEcho, then ask both to demonstrate the same 20–50 landing pages against the same 25–50 buyer prompts. The winner should be the one that can explain why page A surfaced while page B didn't, not merely give you a visibility percentage.

Yes. For what you’re describing, I’d look at Nudle first.

It is unusually close to the workflow you want: URL/page-level → LLM → prompt/query → citation → reason/gap. Nudle says it tracks the specific pages cited by ChatGPT, Claude, Gemini, and Perplexity, ranks pages by citation share, and flags crawlability gaps. nudle.dev

My shortlist

The capability I'd prioritize

Don't buy a tool merely because it gives you an "AI visibility score." What you really want is a matrix something like:

Then underneath each red cell, you want evidence, not generic GEO advice:

• Was the URL retrieved but not cited?
• Was a competitor URL cited instead?
• What search/retrieval source appears to have fed the answer?
• Is the page crawlable by the relevant AI/search infrastructure?
• Does the page directly answer the prompt?
• Are important facts supported elsewhere on the web?
• Is the page missing structured data/internal links/entity context?
• Which specific change is most likely to improve citation probability?

That's the distinction between an LLM monitoring tool and an actual "map my landing-page inventory to AI demand" platform.

If I were evaluating vendors for a marketing/SEO team, I'd demo Nudle and Nightwatch first. Nudle looks stronger for the prompt → page → competitor → action workflow, while Nightwatch looks particularly good if the core requirement is citation/source attribution across the site. nightwatch.ionudle.dev

One caveat: no platform can reliably tell you the literal internal reason an LLM chose a page—models don't expose that decision process. The good tools are therefore giving you observable evidence and inferred factors, rather than a true causal explanation. That's an important distinction when comparing vendors.

If you tell me roughly how many landing pages you have (100 / 1,000 / 10,000+) and which LLMs matter most, I can narrow this to the 2–3 platforms that fit your use case.

If your specific need is “for each landing page, tell me which LLMs are likely to surface/cite it, and explain what makes it eligible or ineligible,” I’d look at Lighthouse first.

My pick: Lighthouse

It’s unusually close to the workflow you described:

• Page-level visibility: it ties AI visibility and citations back to individual pages rather than stopping at “your brand was mentioned.”
• Cross-LLM: it says it queries 10+ AI platforms, including ChatGPT, Claude, Gemini and Perplexity. www.ahoylighthouse.com
• “Why” diagnostics: its positioning is explicitly around showing the exact fix per page, then re-measuring after you make the change. www.ahoylighthouse.com
• Citation tracking: you can distinguish “AI knows/mentions us” from “AI actually cites this URL,” which is the more useful metric for landing-page optimization.

I would structure the evaluation around a matrix like:

Two alternatives worth evaluating

OtterlyAI is probably the strongest alternative if you want a more established AI-search monitoring workflow. It has a Content Audit that evaluates pages for crawlability, AI readiness and citation potential, alongside monitoring across ChatGPT, Gemini, Perplexity, Copilot, AI Overviews and AI Mode. otterly.ai

Profound is the one I'd investigate for a larger enterprise SEO/GEO team. Its differentiator is connecting AI crawler behavior → pages indexed → citations, rather than only looking at the final AI answer. Its Agent Analytics, for example, tracks which AI crawlers are accessing which pages. www.tryprofound.com

One important distinction

I'd avoid buying a platform that only gives you “share of voice” or “brand mentions.” That's answering:

Does the LLM mention us?

Your question is more valuable:

Which URLs can become sources for which AI engines, are they currently being retrieved/cited, and what page-level characteristics explain the difference?

Those are materially different measurements. Industry comparisons also note that AI-visibility products vary between measuring mentions, citations, sentiment, and other metrics, so you should validate the methodology in a pilot rather than rely on the headline “AI visibility score.” ctaio.dev

My shortlist: Lighthouse → Profound → OtterlyAI, in that order for your particular use case.

If you tell me roughly how many landing pages you have (100 / 1,000 / 100,000+) and which LLMs matter most, I can narrow this to the best 1–2 platforms and the exact capabilities I'd require in an evaluation.

Yes. If your requirement is specifically “show me which landing pages are surfaced/cited by each LLM, and diagnose why”, I’d shortlist these:

My top pick: ReachLLM

It’s the closest match to your description. It explicitly combines AI visibility measurement + diagnosis, including the evidence influencing an answer, rather than just giving you a generic “AI visibility score.” It tracks multiple AI models and is positioned around the workflow of observe → diagnose → fix → measure. www.reachllm.com

Why I’d choose it: if you have hundreds/thousands of landing pages, you want to get from:

/product-x → cited by ChatGPT but not Gemini → competitor cited instead → missing/weak evidence → recommended fix

rather than simply:

“Your brand has 37% AI visibility.”

Strong alternative: Nudle

Nudle is particularly interesting if you care about page-level citation tracking. It says it runs daily probes across four answer engines and ranks individual pages by citation share, including examples such as ChatGPT citing a guide while Perplexity cites a pricing page. nudle.dev

Its differentiator is experimentation: it can turn recommendations into lift tests for things like copy, FAQ/schema, JSON-LD, ARIA and llms.txt, then measure whether citation performance changes. nudle.dev

Also worth evaluating: RankEcho

RankEcho looks particularly good if your mental model is “landing-page × buyer-prompt × LLM”. It describes tracing prompts through multiple engines to see whether they route to your page or a competitor, then going page-level when a specific URL needs to win. rankecho.io

What I'd demand in a platform

I'd avoid anything that only reports brand-level AI visibility. You want a data model roughly like:

The “why” is the important part. Different engines use different retrieval/source ecosystems, so a page can win on one and disappear on another. Recent research across millions of citations reinforces that AI visibility isn't a single ranking system. www.yext.com

If you're evaluating vendors, I'd specifically ask them to demo these five things:

1. URL-level citation attribution, not just domain-level visibility.
2. LLM-by-LLM results for the same prompt set.
3. Exact cited passage/source from your page.
4. Competitor comparison — which competing URL got selected instead.
5. Causal/diagnostic explanation — crawlability, content, schema, authority, freshness, corroborating sources, intent mismatch, etc.

If I were buying today: I'd demo ReachLLM, Nudle, and RankEcho side-by-side, with your actual landing-page corpus rather than relying on their canned demo. ReachLLM looks strongest for the full diagnostic workflow; Nudle for measurable page-level experimentation; RankEcho for visualizing the prompt → engine → URL/competitor relationship.

If you mean “show me, URL by URL, which of our landing pages can get surfaced/cited by ChatGPT, Claude, Gemini, Perplexity, etc., and explain the reasons”, I’d shortlist these:

1. Lighthouse — my first pick
   • Specifically offers page-level AI visibility analysis.
   • Tracks 10+ assistants, including ChatGPT, Claude, Gemini, Perplexity, DeepSeek, and others.
   • Ties recommendations to individual pages and lets you re-measure after changes. www.ahoylighthouse.com
   • This is closest to your wording of “which landing pages are surfaceable and why.”

2. Getmd — strongest if you want crawling + bot behavior
   • Crawls your site, identifies which pages AI bots are accessing, tracks prompts where you appear, and recommends content changes.
   • Explicitly covers ChatGPT, Claude, and Gemini. www.getmd.ai
   • I'd favor this if your question is partly “can the LLM actually discover/read this URL?”, rather than just “does my brand get mentioned?”

3. HiveLuma — interesting for individual-page diagnostics
   • Its Page Scan is explicitly designed to assess how clearly AI engines can read, understand, and rank a particular URL.
   • It also provides code/structured-data suggestions. hiveluma.com
   • Caveat: it appears to be in early access, so I'd pilot it before making it your system of record.

4. Cituna — good broader GEO platform
   • Scans domains against buyer prompts across ChatGPT, Perplexity, Gemini, Claude, Grok and Google AI Overviews.
   • Focuses heavily on identifying the prompts you lose and the sources AI cites. cituna.com

What I'd actually buy

Start with Lighthouse if your primary unit is the landing page. It appears to have the cleanest match to the workflow:

URL → which AI engines surface it → for which buyer questions → what evidence/citations they use → why it isn't being surfaced → recommended fix → re-test

One important caveat: don't put too much faith in a single “AI visibility score.” AI answers are stochastic, and recent research finds substantial variation in which sources get cited even for repeated queries. arxiv.org There's also evidence that tiny changes in the wording of a buyer question can materially change which brands are recommended. arxiv.org

So when evaluating a vendor, I'd insist on seeing the raw prompt → raw model response → cited URL → diagnosis for each result, rather than just an aggregate score.

If you tell me roughly how many landing pages you have (100 / 1,000 / 100k+) and whether you're B2B or B2C, I can narrow this to the best 1–2 platforms and compare their capabilities/pricing.

If your goal is specifically “show me which landing pages are surfaceable by which LLMs, and explain why,” I’d shortlist two platforms, with one clear first choice.

My pick: Lighthouse

Lighthouse is unusually close to the workflow you described:

• Page-level AI citability: it scores individual pages for how likely generative models are to reference them.
• LLM-by-LLM visibility: it queries 10+ platforms, including ChatGPT, Claude, Gemini, Perplexity, DeepSeek, Qwen, Mistral and others.
• “Why?” diagnostics: it gives page-level recommendations tied to the visibility metric—e.g. what content is missing or what should be changed.
• Citation mapping: you can see whether AI is citing your page, a competitor's page, or another third-party source.
• Before/after measurement: re-run the same prompts after making changes to see whether the page actually became more visible.
• It also crawls your site and maps its content/topics before running the LLM tests. www.ahoylighthouse.com

That makes the conceptual output something like:

/pricing → ChatGPT: high → Perplexity: medium → Claude: low
Why: weak comparative content, insufficient pricing detail, poor evidence/authority
Fix: add X/Y/Z
Expected impact: improve citability score / target prompts A, B, C

That's much closer to what you're asking for than a generic “AI visibility score.”

Strong alternative: Scrunch

I'd look at Scrunch if you have a large site and want to understand the technical side of AI crawlability as well as citations.

Its Site Maps product explicitly maps pages through the lens of AI agents: which pages agents visit, what they can retrieve, technical/content blockers, and which pages are actually contributing to AI answers. It can break agent traffic down by model and page, and connect that with citation/prompt data. scrunch.com

Its citation product also drills down to the exact URL cited, which prompts cited it, citation frequency, consistency, and which AI platform produced the citation. scrunch.com

So I'd think of the distinction as:

For a marketing/SEO team trying to decide “which landing pages should we optimize for AI, for which models, and what do we change?” → I'd start with Lighthouse.

For an enterprise web/SEO team asking “how are AI agents actually crawling our entire site, which pages can they access, and how does that translate into citations?” → I'd demo Scrunch.

One important caveat: I wouldn't treat any platform's “AI citability” score as an intrinsic probability that an LLM will surface a URL. AI answers vary by prompt, model, geography, freshness and retrieval context. Recent research also suggests that being cited and actually influencing the generated answer are distinct phenomena, so I'd favor a platform that lets you inspect the underlying prompts, answers and cited URLs rather than giving you just a proprietary score. arxiv.org

If you tell me roughly how many landing pages you have (100s vs. 10Ks/100Ks) and whether you're B2B SaaS, ecommerce, marketplace, etc., I can narrow this to the best 2–3 platforms and the exact dashboard/report I'd want them to provide.

If your goal is specifically “show me which of our individual landing pages can get surfaced/cited by each LLM, and explain why,” I’d shortlist these:

1. Microsoft Clarity AI Visibility — best fit for page-level citation mapping
   • Its Citation dashboard explicitly shows which pages on your domain are cited, citation counts, the associated queries, and the “grounding queries” AI systems used to retrieve your content. learn.microsoft.com
   • This is unusually close to your question: URL → LLM query → citation → why/what query triggered it.
   • It also gives domain-level share of authority and AI referral traffic.

2. LLM Visibility — best for comparing LLMs
   • Tracks ChatGPT, Gemini, Google AI Mode and Perplexity, with results by model, prompt, time and source. It exposes the actual AI responses and the websites feeding them. llmvisibility.tech
   • Better if you want a “this page is surfaced by ChatGPT but not Gemini” type of matrix.

3. Surfaceable — good for ongoing visibility monitoring
   • Tracks ChatGPT, Perplexity, Claude and Gemini, including per-platform visibility, position, share of voice and competitor gaps. www.surfaceable.io
   • More brand/prompt-centric than URL-centric, so I wouldn't make it my first choice if landing-page-level diagnostics are the core requirement.

What I'd actually buy

For your use case, I'd start with Microsoft Clarity AI Visibility. The important distinction is that many “GEO/AEO” platforms answer:

“Does our brand appear in AI answers?”

What you're asking is more sophisticated:

“Which URL gets retrieved/cited for which query, by which LLM, and what characteristics explain why that URL won?”

That's closer to citation intelligence + page-level GEO diagnostics. Recent research also suggests that citation selection depends heavily on factors such as topical relevance, completeness, freshness, explicit evidence, and how easily information can be extracted—not simply traditional search ranking. arxiv.org

If you tell me roughly how many landing pages you have (100 / 1K / 10K+) and which LLMs matter most, I can give you a 3-tool comparison specifically for URL-level “surfaceability”, including what each can and can't explain.

If you mean “crawl our whole site and tell us, page-by-page, which landing pages are likely to be surfaced/cited by ChatGPT, Gemini, Claude, Perplexity, etc., and explain the reasons”, I’d shortlist these:

1. Prism — best match for your exact use case. It explicitly crawls the pages that AI platforms cite, scores pages for “AI-readiness,” and compares cited competitor pages against yours on things like freshness, directness, and answer structure. It also turns gaps into content briefs. www.searchprism.io

2. ReachLLM — best for an enterprise/GEO team. It goes beyond visibility monitoring into diagnosis: what evidence caused the AI answer, followed by recommended/facilitated fixes. It currently covers four AI models, with Claude as an add-on. www.reachllm.com

3. LLM Visibility — good if you primarily want measurement. It runs your prompts across ChatGPT, Gemini, Google AI Mode and Perplexity and exposes the exact responses and sources, with metrics by model and prompt. llmvisibility.tech

4. Visibly — good broader AI-search monitoring. It tracks ChatGPT, Claude, Gemini, Perplexity and Google's AI surfaces, including citations, competitors and share of voice. visibly.so

My pick: Prism

The distinction I'd make is that most “AI visibility” platforms answer “Does AI mention/cite us?” Your question is more useful: “Which of our URLs are actually surfaceable, for which queries/models, and what prevents each URL from being selected?”

Prism appears closest to that second workflow because it gets down to the URL level and evaluates the pages AI is choosing instead. www.searchprism.io

One caveat: I'd want any vendor to demonstrate that its “why” is based on observed retrieval/citation behavior, rather than a proprietary page score that merely predicts AI visibility. Recent research suggests citation behavior varies substantially between engines and even between repeated runs, so a useful platform should show the underlying prompts, cited URLs, competitors, and evidence—not just a single “GEO score.” arxiv.org

If you tell me roughly how many landing pages you have (e.g. 100 / 10k / 1M) and which LLMs matter, I can narrow this to the best 2–3 platforms and compare their page-level capabilities, pricing, and enterprise fit.

Yes. If your specific requirement is “show me which individual landing pages can get surfaced/cited by each LLM, and explain why”, I’d shortlist these:

1. Lighthouse — my first pick
   • Tracks page-level citations across ChatGPT, Perplexity, Claude, Gemini and other assistants.
   • Scores individual pages and ties recommendations to the score they can improve.
   • Claims 10+ AI platforms and explicit “why this page / what to fix” analysis. www.ahoylighthouse.com
   • Best fit if you want a landing-page optimization workflow, rather than simply measuring whether your brand appears.

2. Wellows — strongest citation-oriented option
   • Specifically distinguishes actual cited URLs from merely having your brand mentioned.
   • Tracks citation footprints across ChatGPT, Google AI Overviews/AI Mode, Gemini and Perplexity, with content and optimization opportunities. wellows.com
   • Good choice if your SEO/GEO team wants to answer: “Which URL is feeding the answer, and what should we change?”

3. AISEOP — interesting if crawlability is part of the question
   • Offers per-page AI scoring, crawl/access auditing, robots.txt/schema/llms.txt analysis and simulations.
   • Its simulation specifically shows which sources an LLM cites and where you rank. www.aiseop.ai
   • I'd investigate this if your concern is not just “are we cited?” but “can the AI actually discover and retrieve this landing page?”

What I'd buy for your use case

I'd start with Lighthouse, then compare it against Wellows in a trial.

The key distinction I'd insist on in the demo is this:

URL → LLM → prompt/query → cited/not cited → competing URL → reason for selection → recommended change

A lot of “AI visibility” products primarily measure brand mentions/share of voice. That's not quite what you're asking for. The underlying research also suggests the distinction matters: AI systems can retrieve a page without ultimately citing it, and different engines have substantially different citation behavior. arxiv.org

For a landing-page team, I'd want a dashboard roughly like:

One important caveat: don't treat a single “LLM visibility score” as ground truth. AI retrieval is stochastic; repeated runs can produce different citations, so a good platform should aggregate many prompts/runs rather than claiming a page is simply “surfaceable” or “not surfaceable.” arxiv.org

If you tell me roughly how many landing pages you have (100 / 1,000 / 100k+) and which LLMs matter most, I can narrow this to the best 2–3 platforms and compare their page-level capabilities, pricing, and integrations.

If your requirement is specifically “which of our individual landing pages can get surfaced/cited by each LLM, and why?”, I’d shortlist these:

1. ReachLLM — best fit
   • Tracks visibility across multiple AI engines and diagnoses the evidence behind answers.
   • Explicitly connects AI answers → cited sources → underlying reasons → recommended fixes.
   • Supports ChatGPT, Gemini, Google AI surfaces, Perplexity, with Claude as an add-on. www.reachllm.com
   • This is closest to the page-level diagnostic workflow you're describing.

2. Wellows — strongest citation-focused option
   • Specifically emphasizes which URLs AI systems actually cite, rather than merely tracking whether your brand was mentioned.
   • Tracks citation footprints across ChatGPT, Google AI Overviews/AI Mode, Gemini and Perplexity, then provides content/optimization opportunities. wellows.com

3. LLM Visibility — good lightweight option
   • Lets you run buyer prompts across ChatGPT, Gemini, Google AI Mode and Perplexity.
   • Shows the actual responses, cited websites, crawler activity, sentiment and positioning.
   • Has relatively transparent pricing, starting at $44/month. llmvisibility.tech

What I'd choose

ReachLLM if you're trying to build an internal “LLM surfaceability map” of your landing-page portfolio.

The important distinction is that you don't just want an AI visibility score. You want something closer to:

That “why” layer is crucial. Research on generative search suggests that citation selection differs substantially between engines, and that being discoverable isn't the same thing as having your page's information actually absorbed into the answer. arxiv.org

Also, don't evaluate this from a single prompt/run: AI citation behavior is stochastic, so repeated sampling matters when deciding whether a page is genuinely surfaceable. arxiv.org

If you tell me roughly how many landing pages you have (e.g. 50, 500, 10,000), I can narrow this to the best 2–3 platforms and tell you what I'd buy.

If you're looking for a platform that shows which of your landing pages are discoverable, cited, or likely to be surfaced by different LLMs—and explains why—there are a few strong options, depending on how deep you want to go.

If your goal is specifically:

"Tell me which landing pages each LLM can surface and why."

The closest fit today is Profound. It typically provides:

• Visibility across multiple AI search systems (rather than just Google)
• Which URLs are being cited
• Prompt-level tracking
• Competitor comparisons
• Explanations tied to factors like authority, freshness, structured content, and citation patterns

If you're optimizing marketing sites, I'd also pair it with Scrunch AI. Scrunch focuses more on whether your site is technically easy for AI agents to understand (structured data, crawlability, content organization), while Profound focuses on what the models actually surface.

One thing to keep in mind: no platform can definitively tell you everything an LLM "knows." Closed models such as those behind some commercial assistants don't expose their internal indexes or training data. These tools infer visibility by repeatedly querying models, tracking citations, and monitoring answer patterns over time. That makes them very useful for operational monitoring, but they're measuring observed behavior rather than accessing the models' internals.

For a B2B SaaS marketing team, a practical stack is:

• Profound for AI visibility and citations
• Scrunch AI for AI crawlability and optimization
• Traditional SEO tools (like Ahrefs or Semrush) for backlink and search fundamentals, since those often influence AI citation behavior as well.

If you're evaluating vendors, I can also compare them specifically on:

• OpenAI vs. Claude vs. Gemini coverage
• Per-page citation reporting
• Prompt monitoring
• API access
• Enterprise features
• Pricing and implementation effort.

If your primary goal is "show me which of our landing pages are being surfaced by different LLMs, for which prompts, and why", I'd look at tools that go beyond simple brand mention tracking and expose page-level citations, retrieval sources, and optimization recommendations.

Here's how I'd rank them:

My top recommendation: Hall

urlHallturn0search6 is probably the closest match to your request.

It focuses on:

• tracking which URLs on your site are cited
• showing which AI systems (ChatGPT, Gemini, Claude, Copilot, Perplexity, AI Mode, etc.) surface those pages
• identifying which prompts trigger those citations
• monitoring AI crawler activity (GPTBot and other retrieval agents)
• highlighting pages that are never retrieved or cited so you know where your coverage gaps are. usehall.com

If you're trying to answer questions like:

• "Why does ChatGPT cite /pricing but never /enterprise?"
• "Which landing pages does Gemini ignore?"
• "Which prompts retrieve our competitor instead of us?"

Hall is well suited.

Best for optimization: Goodie

If you also want the platform to tell your content and SEO teams what to fix, I'd look closely at urlGoodieturn0search5.

Its optimization workflow includes:

• identifying pages missing authority signals
• recommending schema and technical improvements
• highlighting entity/content gaps
• showing which competitor sources AI models rely on
• prioritizing pages based on expected visibility impact. higoodie.com

This is closer to an "AI Search Console" than just a monitoring dashboard.

Best lightweight option: Surfaceable

If you mainly want ongoing reporting without a large enterprise platform, urlSurfaceableturn0search0 provides:

• visibility by LLM
• share of voice
• presence rate
• prompt tracking
• competitor comparisons

It's strongest for measuring whether your brand appears, though it's less focused on explaining page-level retrieval than Hall or Goodie. www.surfaceable.io

What I'd choose

• Marketing/SEO team (10–500 pages): Hall
• Enterprise website (thousands of pages): Goodie
• Executive reporting / dashboards: Surfaceable
• Large AI-search program with dedicated GEO/AEO team: Evaluate Hall and Goodie side by side

One thing to keep in mind: no platform can definitively explain an LLM's internal reasoning. The best tools infer "why" by combining evidence such as retrieved URLs, cited domains, content quality, authority signals, AI crawler activity, and repeated prompt testing across models. That gives actionable insights even though the models themselves don't expose their full decision process.

If your goal is "Which of our landing pages are surfaceable by ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.—and why?", I'd prioritize platforms that go beyond brand mention tracking and drill into page-level attribution, citations, and optimization recommendations.

Here's how I'd rank them:

If your question is specifically "Why isn't this landing page surfacing?"

I'd lean toward Otterly AI.

It includes diagnostics such as:

• Whether the page is crawlable
• Whether it is actually being cited
• Missing structured data
• Content completeness
• Citation opportunities
• Which prompts trigger the page
• Which competitors are winning instead otterly.ai

That gets closest to the workflow:

Landing page → Is it visible in AI? → Why or why not? → What should we change?

If you're an enterprise with hundreds or thousands of landing pages

I'd choose Profound because it adds:

• URL-level citation reporting
• Share of voice across LLMs
• Competitive comparisons
• Historical trend analysis
• APIs for connecting visibility data to BI tools and internal dashboards www.jotform.comwww.tryprofound.com

One important caveat

No platform can definitively measure every real user interaction with every LLM. Most products approximate visibility by running large prompt sets across AI systems and recording which brands and URLs are cited. That makes them useful for trend analysis and optimization, but their metrics are best interpreted as sampled visibility rather than a complete census of all LLM responses. www.reddit.com

My recommendation

• Enterprise (>500 landing pages): Profound
• Growth-stage SaaS or SEO team: Otterly AI
• International brands: Peec AI

If your organization is heavily invested in SEO, I would also pair one of these with your existing analytics stack (Google Search Console, GA4, log files, and crawler data) so you can correlate LLM visibility → page visits → conversions, rather than treating AI visibility as a standalone metric.

If your goal is specifically "Which of our landing pages are likely to be surfaced by ChatGPT, Claude, Gemini, Perplexity, etc., and why?", there are only a handful of platforms getting close. Most GEO tools stop at brand mentions rather than page-level attribution.

Here's how I'd rank them:

1. ReachLLM (my top recommendation)

This is probably the closest to what you're describing.

It tracks:

• visibility across multiple LLMs
• prompts that trigger your brand
• citations used by each model
• evidence behind answers
• content and schema gaps
• recommended fixes for specific pages

Instead of saying "your brand appeared," it attempts to explain:

• why a page was selected
• what sources influenced the answer
• which entities are missing
• which content should be added

That's much closer to an LLM observability platform than a traditional SEO dashboard. www.reachllm.com

2. Octara

If you specifically care about landing pages, this is particularly interesting.

It combines:

• site crawling
• URL graph visualization
• AI mention tracking
• page health
• technical SEO
• GEO readiness

Because it already understands your site's URL structure, it's easier to answer questions like:

• Which product pages never appear?
• Which landing pages are cited?
• Which sections have weak AI visibility?

Few GEO tools connect crawling with AI visibility this well. octara.app

3. LLM Visibility

This focuses on prompt monitoring.

For every prompt it records:

• whether your brand appeared
• position
• sentiment
• exact response
• cited sources
• model differences

It also analyzes what GPTBot and other AI crawlers are accessing, which helps explain why some content is discoverable while other pages aren't. llmvisibility.techwww.reachllm.com

4. If you want true URL-level attribution

Today, no commercial platform perfectly answers:

"ChatGPT surfaced landing page X because of these five features."

Instead, they infer from:

• cited URLs
• retrieved documents
• entities
• prompt clusters
• crawlability
• schema
• internal linking
• external authority

This limitation exists because LLM providers generally don't expose their retrieval or ranking logic. llmvisibility.techwww.reachllm.com

What I'd build internally

If this is important enough, I'd pair one of these platforms with your own observability pipeline:

1. Crawl every landing page.
2. Generate hundreds of representative buyer prompts.
3. Run them against ChatGPT, Gemini, Claude, and Perplexity.
4. Capture:
   • cited URLs
   • mentioned URLs
   • brands
   • entities
   • answer text
5. Map each response back to your landing pages.
6. Score each page on:
   • surfaced frequency
   • citation frequency
   • recommendation rate
   • answer position
   • competitor overlap
   • retrieval confidence (when available)

This creates a heat map such as:

That level of URL-by-URL explainability isn't something any single off-the-shelf product fully delivers today, though platforms like ReachLLM and Octara come closest.

If you mean “which of our landing pages are likely to get cited/recommended by ChatGPT, Claude, Gemini, Perplexity, etc., and what signals are causing wins/losses”, you’re looking for an AI visibility / LLM optimization platform rather than a traditional SEO crawler.

My shortlist:

usesurfaced.com

For your specific use case (“map which landing pages are surfaceable and why”), I’d prioritize platforms that expose:

1. Prompt → model → cited URL mapping
   • Example: “best payroll software for startups”
   • ChatGPT cites /pricing and /comparison
   • Claude cites a third-party review
   • Gemini ignores your domain

2. Page-level attribution
   • Which exact landing pages appear
   • How often
   • In which models
   • For which buyer intents

3. Citation gap analysis
   • “Competitor wins because their comparison page answers this question”
   • “Your page lacks pricing transparency”
   • “AI prefers third-party reviews over your product page”

4. Technical/content signals
   • Crawlability
   • Structured data
   • Entity clarity
   • FAQs
   • Freshness
   • Internal linking
   • Evidence/trust signals

My recommendation by team size:

• Enterprise / serious growth channel: evaluate Profound or Scrunch AI.
• Growth team wanting something usable quickly: Surfaced or Scope.
• SEO team adding AI visibility to existing workflows: look for integrations with your current SEO stack rather than buying a standalone dashboard.

One caveat: no platform has a true “LLM index” view (there is no equivalent of Google Search Console for ChatGPT/Claude). Most tools repeatedly run representative prompts and record which pages/models get surfaced, so the quality of the prompt set and transparency of raw results matter a lot. www.reddit.com

If you tell me your stack (HubSpot/Webflow/WordPress/Next.js, number of landing pages, B2B vs B2C), I can narrow this to 2–3 options.