ChatGPT answers, unedited

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 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.

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.