Answers that name a competitor

Yes. There are now tools specifically designed to monitor which URLs AI assistants cite, including for publishers.

The two I'd look at first are:

• scrunch.com — probably the closest match to what you're describing. It captures the URLs cited in AI responses and lets you drill down by domain or individual article URL, prompt, platform, and time period. It monitors ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, Copilot, Meta AI and others. Scrunch Help Center Scrunch Help Center
• tryprofound.com — another strong option. It tracks when your content is cited across answer engines and categorizes sources as owned, competitor, earned media, PR wire, social, etc. That could be particularly useful for a newsroom because you can distinguish your reporting from third-party coverage. Profound

What I'd measure for a news publisher

Rather than just "is our site mentioned?", I'd want a dashboard showing:

• Article-level citations: which individual stories are being cited
• Citation rate: how often each story appears in AI answers
• AI platform: ChatGPT vs. Perplexity vs. Google AI, etc.
• Prompt/topic: what questions caused the story to be cited
• Citation position/prominence: whether your reporting is a primary source or buried among citations
• Competitor comparison: whether another publication gets cited instead
• Citation longevity: how long an article continues to appear in answers
• AI referral traffic: whether those citations actually send readers back to your site

Scrunch explicitly supports URL-level citation tracking, citation frequency/consistency, platform and topic filtering, and AI referral metrics. Scrunch Scrunch

One important caveat: these products don't literally observe every AI answer that every user receives. They generally run monitored prompts repeatedly and analyze the resulting answers/citations. So think of the data as measured AI visibility across a representative prompt set, rather than a comprehensive log of every time ChatGPT has ever used your reporting.

For a news organization, I'd actually consider building a controlled prompt library around your coverage areas—e.g. 500–5,000 questions readers might ask—and tracking your articles against competing publications over time. That gives you a much more meaningful "AI citation share" metric than simply counting mentions.

If you tell me roughly the size of your newsroom/site and whether you're interested in ChatGPT, Google AI Overviews, Perplexity, or all of them, I can compare the available tools (including pricing, API access, publisher suitability, and what each can actually measure).

Yes. For a multi-language, multi-region site, I’d shortlist these platforms rather than using a generic rank tracker:

My pick: OtterlyAI

For your particular use case, OtterlyAI looks like the strongest starting point. It explicitly supports 65+ countries and languages, tracks citations and brand mentions, and monitors multiple AI search surfaces. Its citation tracking records the cited domains/URLs and changes over time. Otterly Otterly

That's important because you don't just want to know:

"Is our brand mentioned in ChatGPT?"

You want something closer to:

France → French prompt → ChatGPT → brand mentioned → your /fr/ page cited → competitor cited 3× more → citation position changed since last month.

If multilingual/geo accuracy is the #1 requirement

I'd also seriously test Citenso. It advertises 27 languages and 30+ countries, with geographic simulation and citation/visibility analysis across ChatGPT, Claude, Gemini and Perplexity. Citenso

Menra is another interesting option if you want very frequent monitoring. It supports regional drill-down and explicitly analyzes the citation sources behind AI answers. Menra

One thing I'd scrutinize before buying

Don't compare these tools only on the number of "AI engines" they support. For an international site, I'd evaluate them on four dimensions:

1. True geo targeting — Can you test France rather than merely "French"?
2. Language × country combinations — e.g. French/Canada vs French/France.
3. URL-level citations — Can you see which exact page was cited?
4. Engine-specific behavior — Does the tool distinguish localized results from global results rather than pretending every engine supports geographic targeting?

This last point matters: for example, SERPs explicitly notes that ChatGPT, Claude and Perplexity accept location parameters, while Gemini and Grok don't expose the same location parameter, so it labels those results as global rather than claiming they're locally generated. SERPs

If you tell me roughly how many languages/countries you operate in (e.g. 8 languages × 20 countries) and whether you're B2B or B2C, I can narrow this to the 2–3 platforms I'd actually shortlist and compare their pricing, API, prompt volume, and geo/language capabilities.

"Is our brand mentioned in ChatGPT?"

You want something closer to:

France → French prompt → ChatGPT → brand mentioned → your /fr/ page cited → competitor cited 3× more → citation position changed since last month.

If multilingual/geo accuracy is the #1 requirement

I'd also seriously test Citenso. It advertises 27 languages and 30+ countries, with geographic simulation and citation/visibility analysis across ChatGPT, Claude, Gemini and Perplexity.

Menra is another interesting option if you want very frequent monitoring. It supports regional drill-down and explicitly analyzes the citation sources behind AI answers.

One thing I'd scrutinize before buying

For a marketplace with thousands of listings, I’d prioritize a tool that treats the SKU/product URL as the unit of measurement, rather than one that only measures brand mentions.

Best fits

• Searchpanel — probably the closest match to your requirement. It explicitly tracks every product/SKU across AI answers, with per-SKU visibility scores, prompts, competitors, citations, and daily monitoring. It says it can scale across an entire catalog and track ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews, and Amazon Rufus. Searchpanel Searchpanel

searchpanel.ai

• Zumi — another strong option for a large retail/ecommerce catalog. It tracks how AI engines describe, compare, and rank individual SKUs, and can roll those results up into category/segment visibility. It supports up to nine engines depending on engagement. Zumi

zumihq.com

• SixthShop — specifically positioned around product-level AI shopping visibility, including product-page analysis and recommendations across ChatGPT, Gemini, Perplexity, Claude, Rufus, and Google AI Overviews. It may be particularly interesting if you want optimization as well as monitoring. Sixthshop Sixthshop

sixthshop.com

What I'd choose for a marketplace

If you're talking about 10,000–1M+ listings, I'd evaluate them on these dimensions rather than generic "AI visibility":

1. SKU ingestion: API/feed/catalog upload rather than manually entering URLs.
2. Prompt-to-SKU attribution: e.g. "best running shoes under $150" → which of your SKUs were recommended and in what position.
3. Citation/page attribution: which product pages, reviews, marketplaces, or third-party sources caused the recommendation.
4. Historical monitoring: visibility change per SKU over days/weeks.
5. Sampling economics: ability to monitor thousands of SKUs without requiring thousands of individually expensive prompt sets.
6. API/export: critical if you want to put AI visibility into your marketplace's internal merchandising dashboard.
7. Competitor SKU tracking: not just competitor brands.
8. Engine coverage: ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, Claude, and shopping-specific agents.

For your exact use case, I'd put Searchpanel and Zumi at the top of the evaluation list. Traditional platforms such as Profound, Peec, and Otterly are useful for broader AI/brand visibility, but many tools in that category are fundamentally brand/prompt monitors rather than true catalog/SKU monitors. notice.cx conbersa.ai

If you tell me roughly how many SKUs (10K, 100K, 1M+) and which marketplace stack/API you have, I can narrow this down to the most scalable architecture and estimate how you'd monitor the catalog without querying every SKU × every prompt.

If the goal is AI citation monitoring that feeds into your existing analytics/data stack, I’d prioritize platforms with a real API or native BI/warehouse integrations rather than tools that only offer dashboards.

My shortlist

OtterlyAI would be my first one to evaluate if "integrates into our analytics stack" is the primary requirement. Its API exposes brand visibility, prompt-level results, citations, recommendations and GEO audits, and its current documentation specifically mentions feeding data into Looker Studio, Tableau, Power BI, BigQuery and Snowflake, as well as triggering workflows through Zapier/Make/Slack. Otterly AI Otterly

help.otterly.ai

Profound is the one I'd look at if you're building a serious enterprise measurement layer. It goes deeper into citation-level intelligence and prompt-volume/demand data, with broad coverage across AI engines. The tradeoff is that meaningful API/integration access tends to be enterprise-oriented. LLM Visibility Tools Geodocs

Semrush makes the most sense if your analytics stack already revolves around Semrush. Its AI Visibility Toolkit tracks citations across ChatGPT, Perplexity, Gemini and Google's AI experiences, while its existing API/SEO ecosystem can reduce the number of separate systems you have to wire together. Citation Hawk Otterly

Conductor is worth considering for a larger enterprise marketing organization. It combines AI visibility with the broader SEO/content workflow, and current comparisons list both API and MCP connectivity. Otterly

What I'd actually build

Rather than making the AI-visibility vendor your analytics system of record, I'd structure it like:

AI citation platform → API → warehouse → your BI/analytics layer

For example:

OtterlyAI → API → Snowflake/BigQuery → Looker/Tableau/Power BI

Then store dimensions such as:

• AI engine/model
• prompt
• brand mentioned/not mentioned
• cited/not cited
• cited URL
• citation position
• competitor mentions
• sentiment/recommendation
• geography
• timestamp
• prompt category/persona

That lets you correlate AI visibility → website traffic → conversions/revenue, rather than treating an AI visibility score as the KPI.

One particularly important distinction: mention monitoring isn't the same as citation monitoring. If your objective is "which pages/domains are AI systems actually citing?", make sure the vendor exposes the underlying source URL/domain and prompt-level response data, not just a proprietary visibility score. Current comparisons specifically highlight this as an important differentiator. Toolsolved Otterly

If you tell me what your existing stack is (e.g. GA4 + BigQuery + Looker, Snowflake + Tableau, Segment + Amplitude, etc.), I can narrow this to the 2–3 best integrations and show exactly how I'd wire the data in.

Semrush makes the most sense if your analytics stack already revolves around Semrush. Its AI Visibility Toolkit tracks citations across ChatGPT, Perplexity, Gemini and Google's AI experiences, while its existing API/SEO ecosystem can reduce the number of separate systems you have to wire together. Citation Hawk Otterly

Conductor is worth considering for a larger enterprise marketing organization. It combines AI visibility with the broader SEO/content workflow, and current comparisons list both API and MCP connectivity. Otterly

What I'd actually build

Rather than making the AI-visibility vendor your analytics system of record, I'd structure it like:

AI citation platform → API → warehouse → your BI/analytics layer

For example:

OtterlyAI → API → Snowflake/BigQuery → Looker/Tableau/Power BI

Then store dimensions such as:

• AI engine/model
• prompt
• brand mentioned/not mentioned
• cited/not cited
• cited URL
• citation position
• competitor mentions
• sentiment/recommendation
• geography
• timestamp
• prompt category/persona

That lets you correlate AI visibility → website traffic → conversions/revenue, rather than treating an AI visibility score as the KPI.

One particularly important distinction: mention monitoring isn't the same as citation monitoring. If your objective is "which pages/domains are AI systems actually citing?", make sure the vendor exposes the underlying source URL/domain and prompt-level response data, not just a proprietary visibility score. Current comparisons specifically highlight this as an important differentiator.

Yes. What you’re looking for is generally called AI visibility / GEO (generative engine optimization) / AI citation tracking.

For a B2B company with a substantial technical-docs site, I’d look at:

• clarity.microsoft.com — particularly interesting because its Citation dashboard reports which pages are cited, how often they’re cited, the queries associated with them, and share of authority. It also distinguishes citation activity from normal search rankings. Microsoft Learn Microsoft Learn
• ahrefs.com — its Brand Radar specifically tracks both pages that AI cites and pages that AI retrieves but doesn't cite, which is useful if you want to understand the difference between being part of the model's retrieval process and actually being shown as a source. Ahrefs Help Center
• citations.io — tracks sampled answers across ChatGPT, Gemini, Perplexity and Claude, including the sources cited in those answers. Its evidence view lets you inspect the actual answer behind a citation metric. Citations Citations
• aiclicks.io — explicitly connects AI citations back to their source pages and covers a broad range of AI assistants. AIclicks
• help.webflow.com — worth knowing about if your docs/content already live in Webflow. It runs defined prompts through ChatGPT, Claude, Gemini and Perplexity and reports which domains are cited. Webflow Help Center

For technical docs specifically

I'd prioritize Ahrefs + Microsoft Clarity if your question is:

“How many times did an AI assistant actually use/cite this particular documentation page?”

That's more precise than simply measuring whether the company/brand was mentioned. You ideally want a dashboard like:

One important distinction: you generally cannot observe every private interaction an AI assistant has with your docs. These tools typically measure sampled/public AI answers and/or observable retrieval/citation signals. Ahrefs, for example, explicitly distinguishes pages that were found by AI from pages that were actually cited. Ahrefs Help Center

If you tell me your company size, where the docs are hosted (GitBook, ReadMe, Mintlify, Docusaurus, custom, etc.), and whether you care about ChatGPT/Claude/Gemini specifically, I can narrow this to the 2–3 best tools and what each would actually measure.

If your real requirement is “find incorrect AI attributions, identify the exact source/citation, and route the issue to the content owner who can fix it,” I’d narrow the field to Profound and Otterly.AI, with Profound as my first choice for an enterprise content operation.

My pick: Profound

tryprofound.com is the one I'd evaluate first if you're building a serious AI-answer attribution QA program. It is positioned around source-level citation intelligence rather than merely measuring whether your brand was mentioned, and current comparisons consistently put it toward the enterprise end of the category. Pressfit.ai SEO Counselors

The workflow I'd want is:

AI prompt → generated answer → cited URL → expected source → attribution error → content owner → correction → re-test

The critical distinction is that “brand mentioned” isn't the same as “correctly cited.” A useful system needs to preserve the underlying answer and the exact URLs cited, so your team can determine things like:

• AI says “Company X's policy is…” but cites the wrong article.
• AI attributes a statistic to your company that actually came from a competitor.
• AI combines facts from two of your pages and attributes them to one.
• An old page is cited even though your newer canonical page contains the correct information.
• Your content is cited but the model misrepresents what the page says.
• A competitor's page is being cited for information that your authoritative page actually owns.

Otterly is particularly interesting if you want a less expensive starting point: its current product says it stores generated answers, tracks every cited URL, and lets you inspect citations by prompt, engine, competitor, etc. It also exposes API/MCP and reporting capabilities. Otterly

Where I'd be careful

Most “AI visibility” platforms are fundamentally monitoring systems, not true attribution-QA systems. They can tell you “this URL was cited”, but that doesn't automatically mean “the citation is factually correct.” That's a much harder layer requiring comparison of the AI claim against the source content. Current comparisons make the same distinction: visibility tools measure the problem; they generally don't fix the underlying content. Lyra UX Continuum

So if incorrect attribution is your primary KPI, I'd actually evaluate vendors using a sample of 100–500 real buyer prompts and score each platform on:

1. Claim extraction — can you see what the AI actually claimed?
2. Citation extraction — exact URL cited?
3. Claim ↔ source verification — can you determine whether the source actually supports the claim?
4. Page-level ownership — map the URL to your CMS/content owner.
5. Error classification — wrong source, outdated source, unsupported claim, competitor attribution, etc.
6. Workflow integration — Jira/Linear/Slack/Asana/webhook/API.
7. Re-test history — after the writer fixes the page, can you measure whether the error disappeared?

If #3–#7 are non-negotiable, don't buy based on an “AI visibility score.” Ask vendors to demonstrate the workflow using your own content before signing.

If you tell me roughly how many pages/prompts you have and whether your content team works in Jira, Asana, Linear, or a CMS, I can give you a much more specific 3-tool shortlist and an evaluation scorecard.