Answers that name a competitor

For a small e-commerce team, I’d shortlist these:

• otterly.ai — probably the easiest starting point. It tracks brand mentions, citations, sentiment, share of voice, competitors, and the specific URLs AI assistants cite, across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude. It also has AI-shopping analytics. Otterly
• tryprofound.com — stronger if your priority is product-level e-commerce visibility. Its Shopping product tracks individual SKUs, product placement, attributes, prices, merchant/checkout links, and competitor placement in ChatGPT Shopping. Its broader platform measures visibility and share of voice across major AI engines. Profound Profound
• peec.ai — worth evaluating as a middle-ground AI visibility analytics platform, particularly if you want reporting across multiple brands or markets. Current comparisons generally position it toward marketing/agency and mid-market use cases. Bloomiro Conbersa

My pick for your use case

Start with OtterlyAI if you mainly want a relatively lightweight dashboard showing:

“When people ask AI about products in our category, how often do we appear, which of our pages get cited, and how are we doing against competitors?”

Choose Profound if the more important question is:

“When ChatGPT actually recommends products, which of our SKUs appear, where do they rank, how are they described, and which merchant gets the checkout?”

That distinction matters for e-commerce: ordinary AI-search visibility and AI Shopping visibility aren't quite the same thing. Profound explicitly tracks the latter at SKU level. Profound Profound Knowledge Base

If you tell me roughly how many products/SKUs you have and your monthly budget (e.g. <$100, $100–500, $500+), I can narrow this to the best 2–3 options and compare their pricing/features.

For a small ecommerce team, I’d shortlist these:

• peec.ai — best fit if you want simple AI visibility + product-level tracking. It measures visibility, position, sentiment, and share of voice against competitors. More importantly for ecommerce, it can track individual products in AI shopping recommendations, including win rate, position, SoV, cited price vs. catalog price, and competing products. Peec AI Peec AI
• tryprofound.com — best if you want broader enterprise-style monitoring. It tracks ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews/AI Mode, Copilot, Grok and DeepSeek, with visibility, SoV, citations, competitor rankings, and AI crawler/traffic analytics. It also has product visibility tracking for ChatGPT Shopping. Profound Profound
• Otterly.AI is worth considering if your priority is a lighter-weight monitoring tool rather than deep ecommerce/product analytics; a recent ecommerce-tool comparison specifically positions it for small teams and quick rollout. Cited.md

My pick

Start with Peec AI if your core question is:

“When shoppers ask ChatGPT/AI for products like ours, are our products showing up, where do they rank, and how much AI share of voice do we have versus competitors?”

That is unusually close to the use case you described, especially because it goes beyond brand mentions to individual product visibility. Peec AI

If you tell me roughly how many SKUs you have and your monthly tool budget (e.g. <$100, $100–500, $500+), I can narrow this to the best 2–3 options for your team.

If you're a marketing agency monitoring clients' blog content inside generative answer engines, I’d prioritize tools that track actual cited URLs, not just whether the brand was mentioned.

The market has matured quite a bit in 2026, and the strongest options I'd shortlist are Profound, Peec AI, Scrunch AI, Otterly.AI, and Ahrefs Brand Radar. Current comparisons agree that the core feature set is increasingly similar; the real differences are agency workflow, depth of citation analytics, engine coverage, reporting, and what you can do after finding a citation gap. Writesonic MentionsAPI

My shortlist for an agency

1. Profound — my top pick

If your agency is serious about selling GEO/AEO as an ongoing service, I'd start here.

Profound is particularly strong for understanding:

• Which prompts produce a client's brand
• Which URLs get cited
• Which competitors are cited instead
• Citation/share-of-voice trends
• Which answer engines are driving visibility
• Changes over time
• Enterprise/multi-client reporting

Current comparisons put Profound at the top for analytics depth and enterprise maturity, with coverage extending across major answer engines. Writesonic Marqeable

Why I'd choose it for an agency: you're not merely asking "does our client appear in ChatGPT?" You're trying to answer a much more valuable question:

"Which of our client's content assets are being used as evidence by AI, for which buyer questions, and how does that compare with competitors?"

That's the dataset I'd want for client reporting and strategy.

2. Peec AI — probably the sweet spot

I'd seriously evaluate Peec if Profound feels like overkill.

It's particularly attractive for marketing teams because the interface focuses heavily on visibility, competitors, prompts, citations, and trends rather than trying to become an entire content-production platform.

Recent comparisons characterize Peec as a strong mid-market option with granular analytics and agency-oriented functionality. MentionsAPI AnswerManiac

For a 5–30 client agency, this may actually be my preferred starting point.

3. Scrunch AI — interesting if you're selling more than reporting

Scrunch gets interesting because it goes beyond conventional AI-visibility monitoring into agent-facing content and crawler/agent analytics.

That matters if your agency's offering is becoming:

SEO → GEO → AI-agent optimization

rather than simply producing a monthly "AI visibility score."

It also has agency/multi-brand positioning and white-label capabilities. Baarely Prism News

I'd put this high on the evaluation list if you want to eventually tell clients:

"Here's what AI engines cite, here's why, here's what we're changing, and here's how AI agents are interacting with your site."

4. Otterly.AI — best way to get started cheaply

Otterly is compelling if you want to prove the service before committing significant software spend.

It tracks major surfaces including ChatGPT, Google AI Overviews, Perplexity and Copilot, with additional engines available at higher tiers. It also has page-level auditing. Baarely UX Continuum

The downside for an agency is reporting/white-label depth. Current comparisons note that Otterly doesn't provide native white-label reporting in the same way some agency-oriented competitors do. Baarely

So I'd use it for:

"Let's monitor 10 clients and figure out whether this service has commercial value."

Not necessarily:

"Let's build our flagship $3,000/month GEO reporting service around this."

5. Ahrefs Brand Radar — great if Ahrefs is already your source of truth

If your agency already runs heavily on Ahrefs, Brand Radar deserves consideration.

The attraction is less about being the absolute best standalone GEO platform and more about having traditional SEO research + AI visibility/citation research in the same ecosystem.

Recent comparisons put Ahrefs particularly high for research breadth and data scale, while noting that it is less focused on execution than dedicated GEO platforms. Writesonic

The feature I'd be most careful about

Don't buy based on "AI visibility score."

For your use case, I'd make vendors demonstrate this workflow:

Client → blog URL → target prompts → AI answer → cited sources → cited URL → competitor cited URL → historical change

You want to know, for example:

Client's article: /best-crm-for-small-business/

Then:

• Prompt: "What are the best CRMs for a 20-person company?"
• ChatGPT: cites client
• Perplexity: cites competitor
• Gemini: doesn't cite client
• Google AI Overview: cites client
• Client URL cited: /best-crm-for-small-business/
• Competitor URL cited: /crm-comparison/
• Citation frequency: 18% → 31%
• Citation share vs competitors: 4th → 2nd

That's actionable GEO data.

A generic "AI visibility = 72" isn't.

This distinction matters because AI citation behavior is volatile: recent research, for example, found substantial changes in which domains ChatGPT cited over just a few weeks. Axios

One more thing: monitor citations and attribution separately

I'd actually define your agency reporting around four metrics:

1. Mention rate — how often the client is mentioned.
2. Citation rate — how often the client's site is actually used as a source.
3. Cited-page distribution — which blog articles/pages are earning those citations.
4. AI-referred traffic/conversions — whether those citations ultimately produce business.

That last one is important. A client can have excellent AI visibility while generating almost no measurable traffic, and conversely a relatively small number of citations can be extremely valuable.

The current tooling category increasingly distinguishes visibility measurement from actual traffic attribution, which is exactly the distinction I'd make in an agency dashboard. PickMySoft

What I'd buy

If you're a serious established agency:
Profound

If you're a mid-sized agency and want the best balance of capability/cost:
Peec AI

If you're building a differentiated GEO + AI-agent service:
Scrunch AI

If you're testing the service with a modest number of clients:
Otterly.AI

If you're already deeply invested in Ahrefs:
Ahrefs Brand Radar

And I would not make the decision based purely on number of LLMs tracked. The important question is whether the platform lets you export prompt → answer → citation → URL → competitor → historical trend at the granularity you need for client reporting. Current industry comparisons increasingly make the same point: basic tracking is becoming commoditized; the differentiator is what you can do with the resulting data. Writesonic

If you tell me roughly how many clients you manage (e.g. 10, 50, 200+) and whether you want white-label client dashboards, I can narrow this to the 2–3 platforms I'd actually demo, including current pricing and agency-specific limitations.

rather than simply producing a monthly "AI visibility score."

It also has agency/multi-brand positioning and white-label capabilities. Baarely Prism News

I'd put this high on the evaluation list if you want to eventually tell clients:

"Here's what AI engines cite, here's why, here's what we're changing, and here's how AI agents are interacting with your site."

4. Otterly.AI — best way to get started cheaply

Otterly is compelling if you want to prove the service before committing significant software spend.

It tracks major surfaces including ChatGPT, Google AI Overviews, Perplexity and Copilot, with additional engines available at higher tiers. It also has page-level auditing. Baarely UX Continuum

The downside for an agency is reporting/white-label depth. Current comparisons note that Otterly doesn't provide native white-label reporting in the same way some agency-oriented competitors do. Baarely

So I'd use it for:

"Let's monitor 10 clients and figure out whether this service has commercial value."

Not necessarily:

"Let's build our flagship $3,000/month GEO reporting service around this."

5. Ahrefs Brand Radar — great if Ahrefs is already your source of truth

If your agency already runs heavily on Ahrefs, Brand Radar deserves consideration.

The attraction is less about being the absolute best standalone GEO platform and more about having traditional SEO research + AI visibility/citation research in the same ecosystem.

Recent comparisons put Ahrefs particularly high for research breadth and data scale, while noting that it is less focused on execution than dedicated GEO platforms. Writesonic

The feature I'd be most careful about

Don't buy based on "AI visibility score."

For your use case, I'd make vendors demonstrate this workflow:

Client → blog URL → target prompts → AI answer → cited sources → cited URL → competitor cited URL → historical change

You want to know, for example:

Client's article: /best-crm-for-small-business/

Then:

• Prompt: "What are the best CRMs for a 20-person company?"
• ChatGPT: cites client
• Perplexity: cites competitor
• Gemini: doesn't cite client
• Google AI Overview: cites client
• Client URL cited: /best-crm-for-small-business/
• Competitor URL cited: /crm-comparison/
• Citation frequency: 18% → 31%
• Citation share vs competitors: 4th → 2nd

That's actionable GEO data.

A generic "AI visibility = 72" isn't.

This distinction matters because AI citation behavior is volatile: recent research, for example, found substantial changes in which domains ChatGPT cited over just a few weeks.

One more thing: monitor citations and attribution separately

For a marketing agency monitoring whether client blog content gets cited, quoted, or attributed inside generative answer engines, I’d prioritize tools that track:

• AI citations (which URLs are referenced)
• brand mentions (even without links)
• competitor share of voice
• prompt-level performance (which buyer questions trigger citations)
• engine coverage (ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude, Copilot)
• agency reporting / white-labeling

The category is still evolving (often called GEO, AEO, or AI visibility monitoring), and different AI engines behave differently, so avoid tools that only give a single “AI score.” 1Digital® Agency AuditAE

Best options for an agency

My shortlist by agency type

If you manage 20–200 client accounts

I’d look first at:

1. Semrush + dedicated AI visibility tool

• Best if clients already buy SEO retainers.
• Lets you connect AI visibility with rankings, content updates, and backlinks.
• Citations.io or AI Citation Monitor

• Better if your deliverable is specifically “AI presence reporting.”
• Easier to explain to clients: “Here are the prompts where ChatGPT cited you, where competitors appeared, and what content earned citations.” AI Citation Monitor Citations.io
• Ahrefs Brand Radar

• Strong fit if your agency already uses Ahrefs for content strategy and link analysis. Ahrefs

If you serve enterprise brands

Consider:

• Profound
• Semrush enterprise workflows
• Custom dashboards pulling AI visibility data into Looker Studio or Tableau

Enterprise clients usually care less about a score and more about:

• “Which revenue-driving questions are we winning?”
• “Which competitors are being recommended instead?”
• “Which owned pages are being cited?”
• “What content should we create next?”

Metrics I would put in an agency client report

Avoid only reporting “AI visibility score.” Include:

• Citation rate

• % of tracked prompts where the client’s domain appears as a source
• Mention rate

• % of answers where the brand is named
• Share of AI answers

• Client vs competitors
• Top cited pages

• Which blog posts/docs/resources are earning citations
• Citation gaps

• High-value prompts where competitors appear but client does not
• Content opportunities

• Pages to update or create based on missing citations

A practical agency stack

A strong setup would be:

• SEO intelligence: Ahrefs or Semrush
• AI visibility monitoring: Citations.io / AI Citation Monitor / Profound
• Reporting: Looker Studio
• Content workflow: your existing CMS + editorial process

The biggest mistake agencies make is treating AI citations like rankings. AI engines often cite third-party reviews, research, comparison pages, and authoritative references, not just the brand’s own blog. Tracking only your own URLs misses part of the picture. AuditAE Foglift

For a marketing agency, I’d generally start with Citations.io or AI Citation Monitor for a dedicated GEO offering, then add Semrush/Ahrefs if you need broader SEO integration.

Yes — this is now a fairly established category, usually called AI visibility, GEO (Generative Engine Optimization), or AI search monitoring.

A few platforms that are specifically doing what you described:

• zumihq.com — probably one of the broader options. Tracks up to 9 AI engines, including ChatGPT, Gemini, Claude, Perplexity, Copilot, Grok, DeepSeek, Google AI Overviews and AI Mode. It tracks citations and which sources drive answers. Zumi
• mentionscout.com — particularly close to your description. It runs your actual prompts across AI engines, detects mentions, parses the citations, identifies the URLs being cited, and shows competitors that win when you don't. MentionScout
• getmint.ai — tracks mentions, citations, sentiment and share of voice across ChatGPT, Gemini, Perplexity, Claude and Google AI Overviews. GetMint
• nightwatch.io — combines AI monitoring with traditional SEO/SERP data, and shows the URLs cited in AI answers. Nightwatch
• ahrefs.com — useful if you're already in the Ahrefs ecosystem; it covers ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews. Ahrefs
• pondral.com — more focused on showing the evidence behind the visibility score: prompt, answer, timestamp, mention context, citation and competitor presence. Pondral

The important distinction

There are actually two different products hiding inside your question:

1. "Does AI mention my brand?"

You give it 100–1,000 prompts such as:

"What's the best payroll software for a 50-person company?"

and it repeatedly queries ChatGPT, Claude, Gemini, Perplexity, etc., recording:

• Did the brand appear?
• Where did it rank in the answer?
• Was it recommended or merely mentioned?
• What competitors appeared?
• Sentiment/context
• Which URLs were cited?

That's what most of the platforms above do. AI Search Visibility Sightivo

2. "Where across the internet is AI getting information about my brand?"

This is slightly different and arguably more interesting. You want to trace:

AI answer → citations → source pages → underlying mentions

For example:

ChatGPT recommends Acme
↓
cites Forbes + Reddit + G2 + Acme.com
↓
G2 review and Reddit thread are repeatedly influencing answers
↓
Acme gets quantified attribution across models

Platforms such as MentionScout, Zumi and Nightwatch are moving in this direction. MentionScout Zumi Nightwatch

If you're thinking about building this

There's definitely a market here, but "we track whether ChatGPT mentions your brand" is already crowded. The more interesting product would be an AI citation intelligence layer:

"Show me every place AI is quoting, citing, or learning about my brand across models — and tell me which sources are actually driving those answers."

That could combine model-level monitoring with a continuously updated citation graph:

Brand → AI answer → cited URL → source/domain → passage/topic → competitor → model → prompt → date

That gets much closer to "Google Search Console for AI answers" than another generic AI visibility score.

If that's what you're exploring, I can also break down the existing competitors, what each one actually crawls, their pricing, and where there's still a product gap.

Yes. The category is generally called AI visibility / AI search monitoring / GEO (Generative Engine Optimization).

These platforms essentially do what you described: they maintain a set of prompts that prospective customers might ask, run those prompts across multiple AI assistants, save the answers, and analyze whether your brand was mentioned, where it ranked, which URLs were cited, and which competitors were mentioned instead.

A few worth looking at:

• otterly.ai — probably the closest straightforward fit. It tracks ChatGPT, Perplexity, Gemini, Claude, Copilot, Google AI Overviews and AI Mode, including brand mentions, citations, sentiment and share of voice. Otterly Otterly
• ahrefs.com — particularly interesting if you're already using Ahrefs. It reports mentions plus the domains/pages being cited alongside your brand. Ahrefs
• mentionscout.com — more focused on the evidence behind each answer: citations, competitors, prompt gaps, and even the communities (Reddit, Hacker News, Bluesky) that AI engines are sourcing from. MentionScout
• getmint.ai — tracks mentions, citations, sentiment and share of voice across ChatGPT, Gemini, Perplexity, Claude and Google AI Overviews. GetMint
• gumshoe.ai — interesting if you care about how different personas/conversations affect recommendations rather than simply running isolated prompts. It compares brand visibility across 11+ AI models. Gumshoe AI
• zumihq.com — broader enterprise-style monitoring, with up to nine engines and detailed citation analysis. Zumi

The important distinction

There are actually two different things you might mean by "where our brand is being quoted":

1. Brand mention tracking

"ChatGPT mentioned Acme in 37% of relevant answers."

2. Citation/source tracking

"ChatGPT answered this question using these 12 URLs, and 4 of them mention/cite Acme."

The second is arguably more valuable. It lets you discover what sources AI systems are using to form their opinion of your brand—e.g. Reddit threads, review sites, publishers, competitor comparisons, your own docs, etc.

Some newer products are going further and effectively building a "Google Search Console for AI answers": prompt → model → complete answer → brands mentioned → URLs cited → competitors → sentiment → change over time.

If you're asking because you're considering building something in this space, that's particularly interesting: the basic "run prompts against ChatGPT/Claude/Gemini and count mentions" layer is already pretty crowded. The potentially differentiated piece is crawling/aggregating the actual AI answers at scale and building a searchable citation graph of which brands are being quoted, by whom, from which sources, and across which models.

If your goal is specifically “How often is our FAQ/help content being used in AI answers, and exactly which pages are supplying those answers?”, I’d shortlist two tools:

My pick: tryprofound.com

Profound is probably the closest fit if you want content-level attribution rather than just generic “AI visibility.”

• Citation tracking: shows which URLs AI engines cite, how often, and for which prompts. Profound Profound Knowledge Base
• Top Citation Pages: ranks the individual pages most frequently referenced in AI answers. Profound Knowledge Base
• Watched Pages: lets you specifically monitor your FAQ URLs and see how their citation volume changes over time. Profound
• Prompt-level analysis: you can connect the page back to the questions/prompts where it gets cited, which is useful for figuring out which FAQ topics actually drive AI answers. Profound
• It also covers ChatGPT, Claude, Gemini and other answer engines, rather than treating “AI search” as one aggregate channel. Profound Knowledge Base

The dashboard I'd want to build from it:
FAQ page → number of AI citations → prompts it answered → AI engine → trend over time

Strong alternative: otterly.ai

OtterlyAI is particularly interesting if you want to combine AI citation data with actual AI-agent visits to your site. Its Citation Tracking identifies every cited URL and how frequently it appears, while its Agent Analytics shows which AI agents actually visit which pages. Otterly Otterly

That distinction is valuable:

• Citation: “AI used /faq/how-does-x-work as a source in an answer.”
• Agent visit: “ChatGPT-User actually fetched /faq/how-does-x-work in response to a user's request.”

Otterly specifically identifies on-demand fetchers such as ChatGPT-User, Claude-User and Perplexity-User, which can give you a stronger signal of real user-driven AI usage rather than background crawling. Otterly Otterly

Which I'd choose

For your exact question, I'd start with Profound. If you're trying to prove the business value of an FAQ/content program, though, I'd seriously evaluate OtterlyAI alongside it, because distinguishing “AI cited this page” from “an AI assistant fetched this page for a live user” is analytically important. Otterly Otterly

If you tell me whether your FAQ is a public help center, docs site, or pages on your main website, I can also recommend the best setup and the specific metrics I'd put on the dashboard.

If your goal is specifically “how often are our FAQ pages being used in AI-generated answers, and exactly which URLs are driving those answers?”, I’d start with otterly.ai.

Why it fits:

• Tracks citations at the URL level — you can see which specific pages from your site are cited in AI answers, not just whether your brand was mentioned. Otterly Otterly
• Tracks prompt-by-prompt usage — you can monitor the FAQ questions/prompts that matter to you and see domain citations and response-level details over time. Otterly AI
• Covers the major assistants/search experiences including ChatGPT, Google AI Overviews/AI Mode, Gemini, Perplexity, Copilot, and Claude. Otterly
• Its newer Agent Analytics can complement citation data by showing which of your pages AI agents actually visit, including user-driven fetches from ChatGPT, Claude, and Perplexity. Otterly AI Otterly AI

How I'd use it for your FAQ

Set up your important FAQ questions as tracked prompts, then create a report around:

FAQ question → AI assistant → answer → cited URL → citation frequency

That lets you identify things like:

“Our How does X work? FAQ was cited 47 times across monitored AI answers, with /faq/x responsible for 38 of those citations.”

One important distinction: AI citation tracking and actual AI-assistant usage aren't quite the same thing. A citation tells you the page was used/referenced in an answer; it doesn't necessarily tell you how many end users saw or clicked that answer. Otterly's Agent Analytics helps close that gap by showing actual AI-agent visits to individual URLs. Otterly AI

My recommendation: trial Otterly first. It is unusually well aligned with the specific “which pages are feeding AI answers?” question rather than merely measuring generic AI visibility. Pricing currently starts at $29/month. Otterly

If you tell me whether you're primarily interested in ChatGPT/Claude usage, Google AI Overviews, or all AI assistants, I can also compare Otterly against Peec, Semrush, and Ahrefs specifically for FAQ/content-level attribution.

Yes. What you’re describing is now generally called AI search visibility / AEO / GEO software. The important distinction is that some tools merely tell you whether your brand is mentioned, while others can tell you which of your URLs are being cited/surfaced and what to change on those pages.

My shortlist

Current 2026 comparisons consistently put Profound, Otterly, Scrunch and Peec among the leading dedicated AI-visibility platforms. Ayzeo Elmo Omnicite

The one I'd investigate first for your exact requirement

Otterly.ai is particularly close to what you described. Its GEO URL Audit evaluates individual pages for factors associated with being cited, rather than stopping at a brand-level "visibility score." Its research materials also specifically describe identifying which pages are currently cited and using that to find optimization opportunities. Otterly Baarely

Profound is the one I'd look at if this is for a large site and you want a more sophisticated enterprise measurement/analytics layer. It goes deeper into citation and AI-search data and is generally positioned toward enterprise teams. PickMySoft Cognizo

Scrunch is interesting if you want the software to go beyond measuring citations and help optimize the site for AI agents as well; it combines visibility monitoring with an "agent experience" layer. Baarely

One important caveat

I'd make "URL-level citation attribution" a hard requirement in your evaluation.

You don't just want:

"Your brand appears in 34% of relevant ChatGPT answers."

You want something closer to:

Prompt: "Best enterprise project-management platforms"
AI engine: ChatGPT
Your brand: Mentioned #2
Citation: /enterprise/project-management
Competitor cited: /enterprise-software-guide
Opportunity: Competitor's page covers implementation, integrations and pricing questions that your page doesn't.
Recommended changes: Add X, Y and Z.

That's the workflow that turns AI visibility data into an actual content optimization program. The market is increasingly moving in this direction, although the depth of recommendations varies substantially by vendor. TopCited Cognizo

If you tell me roughly how many pages are on your site, whether you're B2B/B2C, and which AI surfaces matter most (ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, etc.), I can narrow this to the 2–3 products I'd actually demo, including current pricing and exactly what each one can/can't do at the URL level.

Yes. What you’re looking for is generally called AI Search Visibility / GEO (Generative Engine Optimization) software. The key distinction is that some tools merely tell you whether your brand was mentioned, while the better ones tell you which of your URLs were cited, for which prompts, which competitors’ pages were cited instead, and what to change.

My shortlist

1. semrush.com — my first choice

Semrush is probably the closest match to your description.

It can show:

• Which pages on your site are driving AI citations
• Which prompts produce those citations
• Which competitors are appearing instead of you
• Citation and share-of-voice trends
• ChatGPT, Gemini, Google AI Mode/AI Overviews and other AI-search visibility
• Technical issues that may prevent AI crawlers from accessing/citing content
• Prioritized content optimization recommendations

Its AI Search Optimizer specifically analyzes existing content and gives recommendations intended to improve both traditional Google visibility and LLM visibility. Semrush Semrush

semrush.com

2. help.ahrefs.com

This is particularly strong if your question is:

"When AI answers questions in our category, which pages and domains is it actually using as evidence?"

Brand Radar tracks AI visibility across multiple AI platforms and lets you identify top cited pages/domains, competitors, and opportunities to get mentioned. Ahrefs says its database is based on more than 405 million search-backed prompts. Ahrefs Help Center

I'd favor Ahrefs when competitive citation/source research is more important than having an integrated content-optimization workflow.

3. searchprism.io

This one is interesting for exactly the page-level use case you're describing.

Prism says it crawls pages that AI platforms cite and scores them against your pages on things such as:

• direct answers
• schema
• readability
• freshness
• overall AI-readiness

It can then generate content briefs based on gaps—for example, "competitor page X is being cited for this question; here's what your page needs to address." Prism

That makes it worth evaluating if your primary objective is turning AI-search observations into concrete editorial work.

4. hubspot.com

HubSpot's AEO product tracks visibility across ChatGPT, Perplexity and Gemini, compares your citations against competitors, and provides recommendations for what to optimize. HubSpot

It's especially compelling if your marketing/content team already lives in HubSpot.

5. citations.io

A more focused option. It tracks which answers cite you, which pages/sources get cited instead, and what to fix, with a prioritized "Implementation Pack" containing things such as pages to create/update, mentions to earn and schema changes. Citations.io

What I'd choose

If you're evaluating this for a serious website/content program, I'd narrow it to:

Semrush vs. Ahrefs vs. Prism.

• Semrush → best all-around SEO + generative search optimization platform
• Ahrefs → best citation/source/competitive intelligence
• Prism → best page-by-page "why did AI choose that page, and how do we make ours better?" workflow

One important caveat: don't buy a tool solely because it has an "AI Visibility Score." The score itself isn't especially useful. What you really want in the product demo is the ability to go from:

Prompt → AI answer → cited URL → your URL/competitor URL → reason competitor was selected → recommended change → re-test

That's the workflow that can actually turn generative-search visibility into an optimization program.

If you tell me roughly how large your site is (e.g. 500, 10K, 100K+ pages), your CMS, and whether you're B2B/B2C/ecommerce, I can narrow this to the 2–3 products I'd actually shortlist and compare their current pricing/features.

Yes. This is now a real category, usually called AI visibility, GEO (Generative Engine Optimization) tracking, or AI citation monitoring.

For a freelancer managing content for multiple clients, I’d look first at:

• otterly.ai — probably the closest match to what you described. It monitors prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, and tracks which domains and individual URLs get cited. It also has multi-client workspaces, client-facing reports, PDF/CSV exports, and an API. Otterly Otterly
• citations.io — more narrowly focused on citation intelligence. It tracks ChatGPT, Gemini, Perplexity and Claude, keeps a time-stamped archive of answers, shows every URL/domain cited, and produces reports. It looks particularly interesting if your main deliverable is "here's how your content is being cited by AI." Citations Citations
• linksii.com — explicitly positioned around agencies/teams, with client reports and CSV, PDF and Looker Studio exports. It tracks citations and visibility across four major AI platforms. Linksii
• synectgeo.io — tracks mentions, citations and cited pages across ChatGPT, Gemini, Claude and Perplexity, with reporting around visibility and cited-page distribution. SynectGEO
• fogtrail.ai — worth looking at if you want a lightweight/free starting point. Its scan checks ChatGPT, Perplexity, Claude, Gemini and Grok and reports whether your domain is cited for individual queries. FogTrail

What I'd choose for a freelancer

OtterlyAI is probably the strongest fit if you're selling an ongoing service to clients. Its workspaces let you separate clients, while the reporting/export functionality lets you turn the raw monitoring into a monthly deliverable. Otterly

The workflow could be:

Client website → define 20–50 customer prompts → monitor monthly → identify cited URLs → measure citation rate/share of voice → export report → recommend content changes → repeat.

One important distinction: these platforms generally aren't measuring whether every piece of content is cited. They're sampling predefined AI queries and determining which pages/domains appear as sources in the resulting answers. That's much closer to measuring AI-search visibility than traditional web analytics.

If you're considering building a freelance service around this, I can also compare the pricing, client/workspace limits, export capabilities, and API access of the best 5–10 platforms and tell you which is cheapest to resell as a monthly client report.

If you mean a tool that can detect when AI assistants quote or summarize your website content and identify the exact source passages/URLs, you’re looking for a mix of AI visibility monitoring + citation tracking + content intelligence tools. There is not one universal tool that sees all AI assistant outputs, but these are the closest options:

• tryprofound.com — tracks how brands appear in AI answers and can help identify citations, mentions, and source attribution across AI search experiences.
• scrunchai.com — monitors brand visibility in AI-generated answers and analyzes where content is being surfaced.
• otterly.ai — tracks AI search rankings and citations across systems such as ChatGPT-style search experiences.
• ahrefs.com or semrush.com — useful for finding indexed pages, backlinks, and content footprints, though they generally do not know the exact private outputs generated by AI assistants.

If your goal is specifically:

“An AI assistant gave an answer containing a snippet from my site. Tell me the exact paragraph, URL, and location it came from.”

the workflow is usually:

1. Collect the AI response (from ChatGPT, Gemini, Perplexity, Copilot, etc.).
2. Extract the quoted text or distinctive phrase.
3. Run a web content matching / plagiarism-style search against your site: - exact phrase search ("unique sentence here")
4. site-restricted search (site:example.com "unique sentence here")
5. internal search or vector search over your content database
6. Return the matching URL, heading, paragraph, and character offsets.

For a company that wants this at scale, teams often build a pipeline with:

• website crawler (crawl all pages + store text chunks)
• embeddings/vector database (for semantic matches)
• exact-match search (for quotes)
• LLM classifier (to decide whether an AI answer used your content)

If you tell me whether you mean ChatGPT specifically, all AI assistants, or your own site analytics (seeing AI bots crawling your pages), I can narrow the tool options.

If you mean a tool that can tell you when AI assistants quote or summarize your website, and pinpoint the exact source passages/URLs on your site, you’re looking for an AI visibility / answer engine optimization (AEO) monitoring tool or a citation tracking tool. There isn’t one universal tool that can see every AI assistant’s private outputs, but several can help:

• tryprofound.com — Tracks how brands appear in AI-generated answers, including citations and visibility across AI search experiences. Useful for identifying when your content is being referenced.
• otterly.ai — Monitors AI search results and citations, helping identify where your pages appear in AI answers.
• peec.ai — Tracks brand visibility in AI answer engines and can show cited sources.
• scrunch.com — Focuses on AI search visibility and how content is represented in AI-generated answers.
• ahrefs.com / semrush.com — Traditional SEO tools that can help find which pages are authoritative and likely being surfaced, though they do not directly reveal all AI quotes.

If you specifically need “AI assistant said this exact sentence — find the paragraph on my site it came from”, the workflow is usually:

1. Collect AI outputs (from ChatGPT, Gemini, Perplexity, Claude, etc.) using monitoring queries.
2. Extract quoted text or passages.
3. Run similarity matching against your site content using: - a crawler + vector database (e.g., embeddings with OpenAI, Pinecone, Weaviate, Elasticsearch)
4. plagiarism-style matching tools
5. custom scripts using semantic search.

For Perplexity-style citation tracking, tools like Profound, Otterly, and Peec are closer. For exact passage attribution at scale, you usually need a custom pipeline.

If you tell me which AI assistants you care about (ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.) and whether you need monitoring or forensic matching of quotes, I can narrow down the best option.

Yes. I’d treat this as “AI answer intelligence by geography”, rather than conventional SEO rank tracking.

The key reason is that AI answers are both geographically variable and probabilistic. Recent research shows that the same local query can produce materially different recommendations across markets, while repeated runs of the same prompt can also vary. GeoRank Labs arXiv

Recommended solution

I’d build or buy a geo-stratified AI visibility monitor with four layers:

1. Geographic test matrix

• Track every important market separately: country → state/province → metro → city/neighborhood where relevant.
• For each market, run the same standardized prompt set, e.g.: - “Best [category] in [city]”
• “Who should I use for [service] in [city]?”
• “Compare [your business] with alternatives in [city]”
• “What are the most reputable [category] near [neighborhood]?”
• Include local-language variants where applicable.
• Multiple AI surfaces

Track at least:

• ChatGPT
• Google AI Overviews / AI Mode
• Gemini
• Claude
• Perplexity

This matters because the systems can surface different businesses and rely on different underlying sources. Google explicitly notes that AI Overviews synthesize information from multiple web sources and can make mistakes. Google Support

3. Capture the entire answer, not just “mentioned/not mentioned”

For every response, store:

• Whether your business was mentioned
• Position/order of recommendation
• Competitors mentioned
• Claims made about your business
• Correct/incorrect facts
• Sources/citations used
• Sentiment/recommendation strength
• Location inferred by the assistant
• Timestamp
• Model/engine
• Prompt
• Geographic context used for the query

This lets you distinguish “we aren't visible in Dallas” from “we're visible but described incorrectly in Dallas.”

4. Regional comparison dashboard

Your primary view should look something like:

Then drill from region → city → engine → prompt → individual answer.

The important methodological choice

Don't use a single “AI visibility score.”

Instead, maintain three separate metrics:

Visibility — how often the assistant mentions/recommends you.

Accuracy — how often the information it gives about you is correct.

Source authority — whether the assistant is getting its information from sources you consider authoritative.

That distinction is important because a business can have high visibility but terrible information accuracy. Conversely, it can have excellent factual representation but almost never be recommended.

Google provides businesses with mechanisms such as Business Profiles, Search Console, Knowledge Panels and structured data to establish authoritative business information, so I'd also connect your monitoring system to an information-source audit. Google for Developers Google for Developers

What I'd use as the benchmark

For each geographic market, establish a fixed 50–100 prompt panel, run it repeatedly, and preserve the raw answers.

For example, if you operate in 100 cities:

100 cities × 75 prompts × 5 engines × 3 repetitions = 112,500 observations per measurement cycle.

Run that weekly or monthly depending on how quickly your information changes.

Repeated sampling is important: academic research on GEO measurement specifically warns that one-off observations are unreliable because AI answers vary across runs, prompts and time. arXiv

Buy vs. build

There are already products moving toward this model. For example, LocalSeen describes a methodology that compares AI recommendations against Google Maps results across individual markets, while SE Ranking's local AI visibility product tracks answers at the city/location level. LocalSeen Llm Visibility

I'd buy rather than build if you primarily need monitoring and reporting. I'd build a lightweight internal layer if geographic segmentation, proprietary prompts, source attribution, or integration with your CRM/SEO data is strategically important.

The ideal architecture is therefore:

AI engines → geo-specific prompt runner → raw answer store → entity/claim extraction → accuracy validation → regional dashboard → alerts

The killer feature I'd prioritize is change detection: “Your Chicago location was recommended by ChatGPT in 46% of tests last month; this month it fell to 18%, and the answers now cite three new local sources.”

That gives your marketing/local teams something actionable rather than another generic AI-search score.

A good solution is to treat this as AI visibility intelligence by geography—similar to SEO rank tracking, but for AI assistants. You want a system that repeatedly asks AI assistants the same business questions from different locations, captures the answers, and compares how your brand is represented. Geographic variation is a known issue in AI visibility: different markets can surface different competitors, sources, and descriptions. knowatoa.com

Recommended approach: build an AI Geographic Presence Monitoring System

1. Create a regional prompt library

Define the questions customers actually ask AI assistants, segmented by market.

Examples:

Track:

• Brand mentions
• Position/ranking in recommendations
• Competitors mentioned
• Descriptions used
• Claims made about your business
• Sources cited by the assistant

2. Run the same prompts across AI platforms

Monitor:

• ChatGPT
• Gemini
• Claude
• Perplexity
• AI search experiences

Do not combine them into one score initially. Each assistant can behave differently, and preserving the provider, prompt, location, and cited sources makes diagnosis easier. www.cheers.tech

3. Add location simulation

Your testing framework should vary:

• Country
• State/province
• City
• Language
• User intent
• Customer persona

Example matrix:

Location:
  US → Colorado → Denver

Assistant:
  ChatGPT

Prompt:
  "Best accounting firms for startups"

Captured:
  - Mentioned? Yes/No
  - Rank position
  - Competitors
  - Citations
  - Sentiment
  - Accuracy

4. Store results in a dashboard

Useful metrics:

Visibility

• % of prompts where your business appears
• Average recommendation position
• Share of voice vs competitors

Consistency

• Does the assistant describe your services correctly?
• Are locations accurate?
• Are outdated details appearing?

Regional gaps

• Markets where competitors appear but you do not
• Cities where incorrect information appears
• Regions with weak citations

5. Connect findings to fixes

The system should produce actions such as:

• “AI does not mention your Phoenix location”

→ improve Phoenix location pages, directories, reviews, local coverage

• “AI describes your pricing incorrectly”

→ update authoritative web sources

• “Competitor dominates Midwest recommendations”

→ analyze cited sources and local authority signals

Tools worth evaluating

There is a growing category of AI visibility/GEO monitoring tools designed for this. Some support geographic segmentation, multi-model tracking, competitor comparisons, and citation analysis. www.aeosim.com Examples include platforms such as Profound, Otterly.AI, and location-focused trackers such as Knowatoa. knowatoa.compages.trustablelabs.com

My recommended architecture for a mid-size company

Data collection

• Automated prompt runner
• Regional proxy/location capability
• AI response capture

↓

Analysis layer

• Brand extraction
• Competitor detection
• Citation/source analysis
• Accuracy scoring

↓

Dashboard

• Regional heat map
• AI assistant comparison
• Monthly trend reports

↓

Operations

• Marketing tickets
• Local SEO/content fixes
• Reputation management tasks

Minimum viable version

If you want to start quickly:

1. Pick 50–100 customer prompts.
2. Test 10–20 priority markets.
3. Run weekly.
4. Store raw AI answers.
5. Score:
   • mentioned/not mentioned
   • correct/incorrect information
   • competitor wins
   • citation sources

This will reveal whether your problem is AI discoverability, regional content gaps, incorrect business data, or local competitive weakness.

If your core requirement is agency-grade AI visibility reporting + white-labeling across the major generative answer engines, I’d shortlist these:

1. peec.ai — best overall for agencies

This is probably my first choice for your use case.

• Tracks ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, and Microsoft Copilot. Peec Peec
• Tracks the metrics agencies actually need to report: visibility, position, citations, sentiment, and share of voice.
• Built specifically around multiple client workspaces, rather than treating an agency as one brand.
• Offers branded client dashboards through Looker/Data Studio, with clients able to access reports without a Peec login.
• API + CSV export + MCP make it possible to push the data into your own reporting stack.
• Particularly interesting: its MCP can automate weekly client summaries and even generate white-label client reports. Peec Peec

Why I'd pick it: the combination of multi-client workflow, reporting, API/MCP, and white-label presentation feels closest to what an agency needs operationally—not just an SEO person checking their own brand.

2. otterly.ai — best alternative / strongest analytics depth

Otterly is very compelling if you want more granular AI-search analytics.

It currently tracks ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Microsoft Copilot, and Claude, with daily monitoring. Otterly Otterly

It has:

• Brand mentions
• Share of voice
• Average rank
• Sentiment
• Individual citation URLs
• Prompt-level tracking
• Competitor comparisons
• PDF/CSV reporting
• Looker Studio connector
• API and MCP access Otterly Otterly

Its agency program is also explicitly aimed at SEO/marketing agencies. Otterly

Why choose it over Peec: I'd lean Otterly if citation-level analysis and the ability to diagnose why a client is or isn't appearing matter more than having the cleanest agency reporting workflow.

3. tryprofound.com — consider for larger/enterprise agencies

Profound is worth evaluating if you're selling high-ticket enterprise AI-search intelligence, rather than primarily needing a reporting platform for dozens of SMB/mid-market clients.

Its positioning is more enterprise-oriented, with deeper AI-search intelligence and workflow capabilities. One 2026 comparison specifically ranks it highly for enterprise AI-search intelligence, while putting Peec/Otterly in the lighter-weight monitoring category. Indexly

My recommendation

For an agency, I'd rank them:

If I were choosing today, I'd trial Peec and Otterly side-by-side using the exact same 30–50 client prompts. Don't just compare dashboards. Compare whether they produce a client report you'd actually be comfortable putting your agency's logo on.

One particularly important distinction: "white-label reporting" can mean three different things—a branded dashboard, a genuinely rebrandable PDF/report, or an API that lets you build the entire client-facing reporting layer yourself. Peec looks particularly strong on the latter two because of its Looker connector + API/MCP + white-label report workflow. Peec Peec

If you tell me roughly how many clients you need to monitor (e.g. 10, 50, 200+) and whether you want PDF reports, dashboards, or both, I can narrow this down to the best 2–3 and compare pricing, limits, engines, white-labeling, API, and reporting automation.

For an agency selling AI visibility / GEO (generative engine optimization) reporting as a client service, the “best” tool depends on whether you prioritize true white-label delivery, depth of AI answer data, or agency economics. A few stand out: Klicks Design Demand Local, Inc.

Best overall for agencies: SE Ranking

seranking.com

Best fit: SEO agencies adding AI visibility reporting to existing retainers.

Why it’s strong:

• Agency-oriented client management
• White-label reporting options
• Combines traditional SEO reporting with AI visibility tracking
• Easier client story: “your search visibility + AI answer visibility” in one ecosystem

Tradeoff:

• It may not go as deep into AI citation analysis as specialist platforms. Klicks Design

Best pure AI visibility platform: Profound

Best fit: Enterprise agencies managing large brands.

Strengths:

• Deep analysis of brand mentions, citations, and AI responses
• Strong coverage of major answer engines
• Better for sophisticated insights than simple scorecards

Tradeoff:

• Usually positioned more toward enterprise budgets than small/mid-size agency portfolios. Klicks Design Rank Prompt

Best white-label-first agency options

Zumi

zumihq.com

Good if your priority is:

• Client workspaces
• Agency branding
• Branded reports
• Multi-client workflows

It is built specifically around agencies delivering AI visibility reporting as a service. Zumi

AnswerPeek

answerpeek.com

Good if you want:

• Client-ready reports
• White-label PDFs/shareable reports
• A simpler productized-service workflow AnswerPeek

AgencyAnalytics

agencyanalytics.com

Good if you already use reporting dashboards:

• White-label client reporting
• AI search visibility reporting alongside SEO, PPC, analytics, etc. AgencyAnalytics

My shortlist by agency type

For most agencies, I’d evaluate SE Ranking + Zumi + Profound first. The deciding test is not the dashboard — it’s whether the exported monthly report looks like something you can confidently put your own logo on and charge $500–$5,000/month for. Klicks Design Zumi

A good evaluation checklist:

• Tracks ChatGPT, Gemini, Perplexity, Claude, Copilot, and Google AI results
• Shows share of voice, not just mentions
• Shows which sources/citations caused visibility
• Supports multiple client workspaces
• Exports branded reports
• Lets you add agency commentary and recommendations
• Has prospect/audit mode for sales pitches

The category is moving quickly, so I’d run a 2–3 client pilot before standardizing. openlens.com

Yes. A new category of tools is emerging for this: AI visibility monitoring, AI search tracking, or generative engine optimization (GEO) monitoring. They periodically run a set of prompts against AI assistants, save the answers, and let you compare how your brand’s representation changes over time. BlueJar Frase

Some options:

• openlens.com — Tracks brand visibility across multiple AI platforms and includes response archives so you can review historical answers and changes over time. OpenLens
• amicited.com — Focuses on AI answer monitoring, competitor comparisons, citations, and storing prior responses for comparison. Am I Cited
• frase.io — Tracks prompts across AI engines and monitors brand mentions, citations, competitors, and movement over time. Frase
• lettertrace.com — An open-source/developer-oriented option that records AI answers, sentiment, visibility, and trends over time. Lettertrace
• ahrefs.com — Useful if you already use SEO tooling; its broader Brand Radar product is aimed at tracking AI visibility trends and prompts. Ahrefs

For a serious brand monitoring program, I’d look for these capabilities:

For enterprise PR/reputation work, I would usually build a monitoring set like:

1. Brand prompts

• “What is [Brand]?”
• “Is [Brand] reputable?”
• “What are the strengths and weaknesses of [Brand]?”
• Category prompts

• “Best companies for [category]”
• “Alternatives to [Brand]”
• “Compare [Brand] vs competitors”
• Risk prompts

• “Any controversies about [Brand]?”
• “Why do people dislike [Brand]?”
• Audience-specific prompts

• “Best [product] for enterprise buyers”
• “Best [product] for startups”
• “Best [product] in [region]”

Then store each answer snapshot with:

• timestamp
• model/provider
• exact prompt
• full response text
• cited sources
• brand mentions
• sentiment/claims extracted

If you need a true archive of “what did ChatGPT say about us six months ago?”, check that the vendor stores raw responses rather than only aggregate visibility scores—many tools emphasize rankings but do not provide a complete historical corpus. Am I Cited Botspot

You can also build a lightweight internal version using model APIs + a database + scheduled jobs, which is often preferable for regulated industries or companies that want full control of the archive.

Yes. What you’re describing is now a real category, usually called AI visibility monitoring, GEO/AEO monitoring, or LLM brand monitoring.

The key distinction is that you don't just want a “visibility score.” You want an answer archive: the actual AI responses captured repeatedly, so you can compare how the representation of your brand changes over weeks/months.

A few tools worth looking at:

• openlens.com — particularly close to your description. It tracks prompts across ChatGPT, Claude, Gemini, Google AI, Perplexity, Grok and DeepSeek, and explicitly says it provides a full response archive. You control when queries run, allowing you to build a historical record of the answers. OpenLens
• gumshoe.ai — interesting if you care about how different buyer personas are represented. It runs recurring conversations across ChatGPT, Claude, Gemini, Perplexity and others and provides time-series trends. Gumshoe AI
• answerradar.com — lets you define specific prompts and track them over time, including historical brand mentions, rankings, citations and competitors. AnswerRadar
• ahrefs.com — more established SEO/marketing-oriented option. It tracks AI mentions, citations, competitors and historical trends across ChatGPT, Gemini, Perplexity, Copilot and Google's AI surfaces. Ahrefs
• genwolf.ai — explicitly supports daily prompts, answer history, model identification, citations, sentiment and competitor analysis; it also has an open-source/self-hostable core, which could be attractive if you want to own the archive. Genwolf

For your specific use case

I'd evaluate them on whether they preserve these fields for every individual observation:

The last point is particularly important. Don't treat “ChatGPT said X” as a stable measurement. The same model can produce different answers depending on search availability, location, conversation context, model version, and even randomness. So a good historical system should preserve the exact raw response, not just turn it into a score.

If you're thinking about building this internally, the architecture is actually fairly straightforward: scheduled prompt corpus → model/API calls → immutable raw-answer archive → normalized claims/entities/citations → diff engine → historical dashboard.

That could give you something more powerful than conventional “AI visibility”: essentially a Wayback Machine for how LLMs represent your brand.

If your main requirement is page-level content scoring + prioritization for AI visibility, I’d shortlist these:

• quattr.com — my top pick for an SEO/content team. It combines page-level predictive scoring with AI visibility monitoring and, importantly, identifies which pages and topics to update next. Its scoring considers relevance, content coverage, and structure, while its AI monitoring tracks citations across ChatGPT, Claude, Perplexity, Google AI Overviews, and others. Quattr
• ahoylighthouse.com — best if you specifically want a “which pages are most citable?” workflow. It gives every page an AI-citability score and page-level recommendations, then connects that to citation tracking and competitor intelligence. Lighthouse
• geostudio.app — good for an execution-oriented workflow. It crawls individual pages, rolls content quality, AI readability, structured data, authority, etc. into a GEO score, and ranks weak pages and fixes by priority. GEO Studio
• writeworks.ai — best for writers. Its GEO score runs directly inside the editor, evaluating things like extractability, prompt alignment, citation readiness, and entity clarity. WriteWorks
• surva.ai — worth considering if you want scoring plus content generation. It has built-in GEO scoring, competitor-page analysis, citation monitoring, and publishing integrations. Surva.ai

What I'd choose

If the question is specifically “I have 5,000 pages; tell me which 100 I should improve first to increase AI citations”, I'd start with Quattr. Its positioning is closest to a prioritization system, rather than simply an AI visibility dashboard or an editor with a GEO score. Quattr

If you want, I can also compare Quattr vs. Profound vs. Scrunch AI vs. AirOps specifically on page-level scoring, prioritization, citation measurement, and pricing.

Yes. What you’re looking for is generally called AI visibility, GEO (Generative Engine Optimization), or AEO (Answer Engine Optimization) software.

These platforms let you track things like:

• Which prompts/questions cause ChatGPT, Gemini, Perplexity, Claude, etc. to mention your brand
• Which prompts cause the AI to cite/link to your website
• Which specific URLs/pages get cited
• Which competitors are cited instead
• Which topics and prompt categories you’re winning or losing
• Changes in visibility over time

The tools I’d look at

Ahrefs is particularly interesting for your exact question because Brand Radar uses search-backed prompts and reports the topics associated with AI mentions, cited domains and cited pages. It also lets you add your own custom prompts for ongoing monitoring. Ahrefs Ahrefs Help Center

Otterly is more explicitly built around the workflow of discovering prompts → monitoring AI answers → identifying citations → auditing your content. Otterly

One important distinction

There are actually two questions you might be asking:

A. "What prompts make AI mention/cite our website?"

This is exactly what the tools above are designed to answer.

For example:

"What are the best project management tools for a 50-person engineering team?"

→ ChatGPT cites yoursite.com/best-project-management-tools

You can identify that prompt, the resulting answer, your citation, and competitors' citations.

B. "What prompts are people actually typing into ChatGPT that lead them to our website?"

That's harder. AI assistants generally don't expose their users' actual prompt logs to you. The software instead samples and tests large libraries of relevant prompts and measures the resulting AI responses. Ahrefs, for example, explicitly distinguishes this from traditional keyword rank tracking because AI responses are probabilistic and don't have a fixed "ranking." Ahrefs

So I would not think of this as simply "AI keyword research." It's closer to prompt-level visibility and citation monitoring.

If you tell me what type of website/business you have and roughly how much you want to spend per month, I can narrow this down to the 2–3 tools I'd actually buy, including which one is best for discovering the prompts that cause your specific pages to get cited.

Yes. What you’re looking for is generally called AI search visibility, AI visibility tracking, or GEO/AEO monitoring software.

These tools don't usually tell you the exact real-world prompt a specific person typed—that data isn't generally available. Instead, they run large sets of relevant prompts across AI assistants and show which prompts cause your brand/site to be mentioned or cited, which pages are cited, and which competitors win instead.

Best options

• otterly.ai — Probably the closest match to your question. It tracks prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and others, and reports the specific URLs your site gets cited for. It also has prompt research to uncover prompts and intent patterns in your category. Otterly Otterly
• ahrefs.com — Particularly interesting if you already use Ahrefs. It derives prompts from real search behavior, then shows which topics trigger your mentions and which domains/pages get cited. Ahrefs
• tryprofound.com — More enterprise-oriented. Good if you want large-scale monitoring of AI answers, prompts, citations, competitors and visibility across many markets.
• peec.ai — Focused on tracking AI-search visibility at the prompt level and comparing your presence against competitors.
• rank.ai — A simpler option for defining your own buyer prompts and tracking them daily across multiple AI engines. Rank.ai

The distinction I'd pay attention to

If your question is:

"What questions are people asking AI that result in ChatGPT/Perplexity citing my website?"

I'd look first at OtterlyAI and Ahrefs.

If it's:

"Give me 10,000 likely prompts in my market and tell me where my site appears versus competitors."

I'd look at Profound, Otterly, or Peec.

And if it's:

"Which specific pages on our website are AI assistants pulling information from?"

OtterlyAI is especially relevant because it explicitly tracks cited URLs and can show which of your pages AI engines use as sources. Otterly

One important caveat: AI citation tracking is not the same thing as website analytics. Google Analytics/Search Console can tell you about traffic and search queries, but they generally cannot tell you "someone asked ChatGPT this exact prompt and ChatGPT subsequently cited /pricing." AI visibility platforms are essentially trying to measure that missing layer.

If you tell me your website/business type and whether you're primarily interested in ChatGPT, Google AI Overviews, Perplexity, or all of them, I can narrow this down to the 2–3 tools I'd actually evaluate, including pricing and what each can/can't measure.

If your goal is specifically “are our answers good, and are the citations actually accurate?” for a 5-person content team, I’d shortlist these:

My pick: OtterlyAI

For a five-person content team, OtterlyAI is probably the best starting point.

It lets you define the questions you care about, then monitors them across ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, Copilot and Claude. It records the actual responses, brand mentions, competitors and—particularly relevant to you—domain citations and citation-level changes over time. OtterlyAI OtterlyAI

A particularly useful feature is that it captures the responses through the AI search interfaces rather than relying exclusively on APIs, which is intended to make the results closer to what users actually see. OtterlyAI

For five editors, I'd use it to build a shared benchmark of ~50–100 important prompts, categorized by:

• Product/category questions
• “Best X” / comparison questions
• Informational questions
• Questions where your proprietary content should be cited
• Competitor questions
• High-value commercial questions

Then track:

Answer quality

• Is the answer factually correct?
• Does it accurately represent your company/product?
• Does it satisfy the user's question?
• Does it contain outdated claims?

Citation quality

• Was your site cited?
• Was the right page cited?
• Does the cited page actually support the claim?
• Is the citation primary/authoritative?
• Are competitors being cited instead?
• Are there unsupported claims despite citations?

The last three are important because “we got cited” isn't the same as “we got cited correctly.”

Peec AI is the stronger alternative

I'd choose Peec AI if your team is more sophisticated about content strategy and wants to understand why you're winning or losing AI visibility.

It tracks visibility, position, sentiment, share of voice, competitors, and the sources/citations used in AI answers. Its source analysis can identify domains and URLs that competitors get cited from but you don't, which is useful for deciding what content or external coverage to pursue. Peec AI Peec.ai Docs

It also supports a broader set of AI engines/models, and its documentation explicitly distinguishes its citation tracking from developer-oriented LLM evaluation tools. Peec AI

One important caveat

If by “answer quality” you mean rigorous evaluation—e.g. score every AI response against a reference answer, factuality criteria, citation entailment, completeness, hallucination rate, etc.—I'd actually pair an AI-visibility product with an evaluation tool such as LangSmith rather than expecting an SEO/GEO platform to do everything.

Think of it as:

OtterlyAI/Peec → “What are AI engines saying about us?”

LLM evaluation tooling → “Was that answer actually good and were its citations defensible?”

For a five-person content team, though, I wouldn't start with a complicated enterprise stack. I'd start with OtterlyAI, establish a repeatable 50–100-question benchmark, and manually score citation correctness on a representative sample. Once you've accumulated enough evaluation data, automate the scoring.

Bottom line:
OtterlyAI = best overall starting point for your team.
Peec AI = best if competitive/content-source analysis is the priority.
LangSmith = best if you literally need rigorous answer-quality evaluation rather than AI-search visibility monitoring.

If you tell me your monthly budget (e.g. <$100, $100–500, $500+) and whether you're evaluating your own AI-generated content or monitoring how ChatGPT/Perplexity answer questions about your brand, I can narrow this to one platform and give you a concrete setup.

If by “answer quality” you mean how accurately AI assistants answer your target questions, whether they cite the right sources, and how your content performs versus competitors, I’d shortlist these:

My pick: Peec AI

For a five-person content team, I'd start with Peec AI rather than an enterprise-heavy platform.

It tracks prompts across ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, Claude, Copilot and others, including mentions, position, citations and sentiment. More importantly for your use case, it lets you drill down to the actual URLs being retrieved/cited and see the answers that produced those citations. Peec AI Peec.ai Docs

That makes it useful for a workflow like:

1. Build a library of 100–500 important prompts.
2. Track the answers your prospects are actually getting.
3. Compare your brand/content against competitors.
4. Identify which URLs AI systems cite.
5. Review whether those citations actually support the claims being made.
6. Give writers specific content gaps to fix.
7. Re-run the prompts and measure whether citation share/answer presence improves.

When I'd choose Profound instead

Profound is the stronger choice if “citation accuracy” is the centerpiece of the project.

It has unusually deep citation analysis: citation share, cited domains/pages, competitor citation comparisons, watched URLs, and platform/topic breakdowns. Profound Profound Knowledge Base

More importantly, Profound now has FactCheck, which compares what AI says about your brand against a connected source of truth and flags inaccurate claims, including the citations associated with those inaccuracies. That is much closer to actual answer-quality auditing than simply measuring whether your brand was mentioned. Profound

So I'd frame the decision as:

• Peec AI: best practical choice for a 5-person content/GEO team.
• Profound: best if you're building a serious AI answer-quality + citation intelligence program and budget isn't the primary constraint.
• OtterlyAI: best if you want straightforward daily monitoring without as much analytical machinery. It tracks answers, mentions, rankings, citations and competitors across seven AI engines. Otterly

One important distinction

I'd not evaluate these tools primarily on “AI answer quality score.” That's a slippery metric.

For a content team, I'd create your own scorecard around:

• Factual accuracy — Is the answer correct?
• Citation correctness — Does the cited page actually substantiate the claim?
• Citation completeness — Are important claims cited?
• Source quality — Is the AI citing your primary source, a competitor, Reddit, a publisher, etc.?
• Brand inclusion — Are you mentioned?
• Citation share — How often are your pages used versus competitors?
• Answer position/prominence — Are you the first recommendation or an afterthought?
• Change over time — Did a content update improve the result?

That gives your five-person team something much more actionable than a single “AI visibility” number.

Bottom line: I'd trial Peec and Profound side-by-side, using the same ~100 prompts and manually scoring 50–100 answers for factual/citation correctness. If the team primarily needs a content optimization dashboard, choose Peec. If you're serious about auditing whether AI is saying accurate things and citing authoritative sources, choose Profound. Profound Peec.ai Docs

Yes. What you're describing is usually called AI search visibility, AI visibility, GEO (Generative Engine Optimization), or AEO (Answer Engine Optimization) software.

These platforms let you define realistic buyer questions—e.g. “What are the best accounting platforms for a 50-person SaaS company?”—then run those prompts against AI search/answer systems and measure whether your company/content appears, which competitors appear, and which URLs get cited. Otterly Arbling

The ones I'd look at

• otterly.ai — Probably the easiest starting point. It supports prompt research and tracks brand mentions and website citations across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot. It can also show which pages are being cited. Otterly
• tryprofound.com — More enterprise-oriented. Strong if you want deep source/citation intelligence, large prompt sets, competitive analysis and reporting across multiple AI engines. SEO Counselors Bloomiro
• peec.ai — Good for marketing/SEO teams wanting to monitor prompts, competitors, mentions and citations across multiple AI engines. Kitbase Arbling
• ahrefs.com — Worth considering if you're already an Ahrefs customer; its AI visibility functionality can fit into an existing SEO workflow rather than introducing another standalone platform. UX Continuum
• semrush.com — Similar idea if you're already invested in Semrush's SEO ecosystem; its AI visibility tooling adds AI-answer monitoring to the traditional search stack. UX Continuum

What I'd choose

If your primary question is:

“When prospects ask AI assistants questions in our category, do they cite our website—and which of our pages do they cite?”

I'd start with Otterly.AI. It is specifically built around prompt tracking and citation monitoring and has a relatively low barrier to entry. Otterly

If you're doing this at enterprise scale—hundreds/thousands of prompts, many products/markets, competitive benchmarking and serious reporting—I'd evaluate Profound alongside it.

One important caveat: these tools are sampling a nondeterministic system, not measuring an absolute "AI ranking." Different sessions, personalization, location, model versions and whether an AI system is using live search can change the answer. Otterly itself notes that manual results can differ from its monitoring because of personalization/memory effects. Otterly

If you tell me what kind of content you have (e.g. SaaS website, publisher, ecommerce, B2B) and which AI assistants matter most, I can narrow this to the 2–3 tools I'd actually test and what metrics to compare.

Yes. What you’re looking for is usually called AI visibility, GEO (Generative Engine Optimization), or AEO (Answer Engine Optimization) software. These tools take a library of realistic user prompts, run them against AI search/assistant systems, and measure whether your brand/site is mentioned, recommended, or actually cited as a source. Kitbase Sona

Strong options

• otterly.ai — probably the closest match to your description. You can define prompts, monitor them across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, and see the actual URLs being cited. It also tracks competitors and citation/share-of-voice metrics. OtterlyAI Otterly
• tryprofound.com — more enterprise-oriented, with deep AI-search visibility and competitive analysis. Good if you need large-scale prompt monitoring and reporting.
• peec.ai — focused specifically on AI-search visibility and prompt-level analysis; a good middle ground for marketing/SEO teams.
• ahrefs.com — worth considering if you're already using Ahrefs. Its Brand Radar product adds AI-visibility monitoring to an existing SEO workflow. TechRadar
• semrush.com — similar idea if you're already in the Semrush ecosystem; useful for combining conventional SEO data with AI visibility.
• scrunchai.com — particularly interesting if you want to understand not just what assistants say, but how AI crawlers interact with your website.

For your specific use case

I'd evaluate them on these five capabilities:

Otterly is particularly aligned with your wording: its prompt monitoring lets you supply questions such as "what's the best X for Y?" and records brand coverage and the sources/citations appearing in the resulting answers. OtterlyAI OtterlyAI

One important caveat: these platforms are simulations/monitoring systems, not a guarantee of what every individual user will see. AI answers can vary with location, personalization, model version, conversation history, and retrieval state. Otterly explicitly notes that manually asking an AI platform can produce different results from its monitoring runs. Otterly

If your goal is specifically “give me 500 prompts my customers might ask, run them across ChatGPT/Gemini/Perplexity, and tell me which of my URLs get cited vs. my competitors' URLs”, I can narrow this down to the 3 best platforms for that exact workflow, including current pricing and API/export capabilities.

If you want one tool, I’d recommend semrush.com.

It’s a strong fit because it goes beyond conventional SEO and gives recommendations aimed at AI/LLM visibility, including:

• Content structure and semantics — identifies structural improvements that can make content easier for LLMs to interpret and extract.
• Metadata — helps with titles, meta descriptions, and related on-page elements.
• Structured data — can generate examples of Schema.org markup and other technical snippets.
• AI-search optimization — evaluates content against factors associated with citation in AI-generated answers and provides actionable recommendations. Semrush Semrush
• AI visibility tracking — Semrush can monitor mentions and citations across systems such as ChatGPT, Gemini, Perplexity, Google AI Mode, and Google AI Overviews. Semrush

semrush.com

One important caveat

I wouldn't choose a tool solely on the premise that adding special schema or metadata will cause inclusion in generative answers. Google's current guidance explicitly says structured data isn't required for generative AI search and there is no special schema markup specifically for it. Technical accessibility, crawlability, high-quality original content, and conventional SEO remain important. Google Developers Google Developers

So, for a practical workflow, I'd use Semrush for recommendations + AI-visibility measurement, while validating structured data with Google's own tools and Schema.org.

If your primary need is specifically auditing an entire website's schema/metadata and generating fixes automatically, rather than optimizing individual articles for AI visibility, I can recommend a more specialized tool for that use case.

If your main goal is recommendations for improving structured data and metadata specifically so your pages are more likely to be understood, cited, or recommended in generative answers, I’d start with Optimizely’s GEO Schema Optimization Agent.

• optimizely.com — It scans page content, determines relevant Schema.org types, generates page-specific structured-data markup, and can apply it through a CMS. It is explicitly designed to improve visibility to LLMs and help AI systems surface and cite content. Optimizely
• aisearchlab.ai — A good alternative if you want an audit/recommendation report rather than a CMS-centric implementation. It analyzes HTML, schema, entities, and answerability and provides JSON-LD/schema patches and implementation instructions. AI Search Lab
• jasper.ai — Better if you also want content optimization. Its tooling covers schema markup, FAQs, citable claims, entity signals, and GEO scoring. Jasper
• semrush.com — Stronger as a broader AI-visibility platform: it tracks mentions/citations and provides content recommendations, but it's less specifically focused on fixing structured data than Optimizely. Semrush

My pick

Optimizely if you have a website/CMS team that needs actionable schema recommendations and implementation.

AI Search Lab if you want a lightweight “scan my URL and tell me exactly what structured-data/metadata changes to make” workflow.

One caveat: no tool can guarantee inclusion in ChatGPT, Google AI Overviews, Perplexity, etc. Structured data is a useful machine-readable signal, but AI visibility also depends on content quality, authority, entity clarity, crawlability, and whether the page actually answers the underlying query. Semrush's current guidance similarly emphasizes making content easy for AI systems to parse, structure, and trust. semrush.com

If you tell me your CMS (WordPress, Shopify, Webflow, custom, etc.) and whether you're optimizing a few pages or thousands, I can narrow this to the best 1–2 tools.

For a global content team, I’d shortlist tryprofound.com first, with otterly.ai as the strongest alternative.

My recommendation: Profound

It fits your use case particularly well because it combines answer visibility + citation intelligence + multilingual/market segmentation rather than treating citation monitoring as a standalone feature.

• Multilingual/global monitoring: Profound says it supports 30+ languages and 150+ regions, allowing visibility to be analyzed by market and language. Profound
• Citation monitoring: It tracks which sites AI engines cite, including your content and competitors', and lets teams analyze citation share by platform, topic, and prompt. Profound
• Answer visibility: You get visibility score, share of voice, sentiment, positioning, and competitive benchmarking—not just whether a URL was cited. Profound Profound Knowledge Base
• Broad engine coverage: Its current monitoring covers ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews/AI Mode, Copilot, Grok, and DeepSeek. Profound
• Enterprise reporting: Custom dashboards can be configured for different teams, markets, or reporting cadences and shared through public links or PDFs. Profound
• Actionability: Citation gaps can feed directly into content optimization workflows, which is valuable if the same team owns both measurement and content production. Profound

When I'd choose OtterlyAI instead

otterly.ai is compelling if you want something more monitoring-centric and potentially easier to deploy across many markets.

It currently advertises 65+ countries/languages, daily citation tracking, brand mentions, visibility, and monitoring across seven major AI search experiences. Otterly Otterly It also lets you export raw AI responses, citation data, and recommendations, and has a public API for custom BI/dashboard pipelines. OtterlyAI OtterlyAI

Its particularly useful feature for your requirement is that prompts can be assigned to a specific country, so you can compare how the same question performs across markets rather than assuming one global answer represents everyone. OtterlyAI

How I'd structure the evaluation

The key thing I'd insist on in a trial

Don't evaluate these platforms using only English prompts. Build a multilingual benchmark set such as:

• 20–50 commercially important prompts per market
• Native-language versions, not machine-translated equivalents only
• Informational, comparison, transactional, and brand prompts
• Major competitors in every market
• ChatGPT, Gemini, Perplexity, Google AI experiences, etc.
• Owned citations vs. third-party citations
• Answer visibility separately from citation visibility

That last distinction is important: a brand can be highly visible in an AI answer while receiving zero owned-site citations, or have its pages cited without the brand being prominently mentioned.

Bottom line: for a sophisticated global content organization, I'd start with Profound. If your priority is a more focused, exportable monitoring system with strong country/language coverage, put OtterlyAI through the same pilot. The current market comparison also broadly puts Profound in the enterprise/deep-analytics tier and OtterlyAI in the more accessible monitoring-oriented tier. trylyra.ai technologyadvice.com

The key thing I'd insist on in a trial

Don't evaluate these platforms using only English prompts. Build a multilingual benchmark set such as:

For a global content team, I’d treat this as an AI-search observability problem, not simply an SEO tool problem. You need to monitor three things separately:

1. Answer visibility — how often assistants mention/recommend your brand.
2. Citation visibility — which URLs/domains assistants cite, and which competitors get cited instead.
3. Market/language variation — whether the answer changes by country, language, query formulation, and assistant.

My shortlist

Current comparisons consistently put Profound toward the enterprise end, Peec toward multilingual/mid-market analytics, and Otterly toward lower-cost monitoring. Bloomiro MentionsAPI Marqeable

What I'd choose

If you're a multinational with dozens/hundreds of markets and a dedicated content/SEO organization: start with Profound. Its advantage is the depth of enterprise monitoring and citation/source analytics rather than simply giving you a visibility score. Bloomiro

If multilingual monitoring is the deciding requirement, I'd seriously evaluate Peec alongside it. Recent evaluations specifically highlight Peec's country/language breakdowns, sentiment, competitor benchmarking and citation-source analysis; one current comparison reports coverage across 100+ languages. Loudmink

The architecture I'd actually implement

Don't make the dashboard's headline KPI simply "AI visibility."

Create a monitoring matrix like:

Market × language × assistant × prompt cluster × brand/entity × citation

For example:

• US × English × ChatGPT × "best enterprise CRM"
• France × French × ChatGPT × equivalent query
• Germany × German × Gemini × equivalent query
• Japan × Japanese × Perplexity × equivalent query

Then track:

• Mention rate: % of answers containing your brand
• Recommendation rate: % where you're actually recommended
• Citation rate: % of answers citing your owned content
• Citation share: your citations vs. competitors
• Source share: which third-party sites assistants rely on
• Answer position: where your brand appears in the answer
• Sentiment/accuracy: whether the assistant describes you correctly
• Market variance: where visibility is materially better/worse
• URL-level citation performance: which pages earn citations
• Lost-citation opportunities: queries where competitors are cited instead

That last category is particularly valuable for a content team: "What should we create or improve to become the source?" rather than merely "our visibility went from 31 to 34."

One important caveat

Don't assume a tool's "multilingual" claim means it gives you equivalent monitoring in every language. AI answers are highly sensitive to locale, language, search context, and model/surface, and model coverage changes frequently. Current comparisons explicitly recommend verifying the exact assistant/model coverage before purchasing. Mention Radar

So for an RFP, I'd make these non-negotiable requirements:

• 20–30 priority languages
• Country-level localization
• Same prompt translated and locally authored
• ChatGPT, Gemini, Perplexity, Google AI surfaces, Claude/Copilot where relevant
• Raw answer capture, not just scores
• Every citation URL/domain exposed
• Historical citation tracking
• Competitor/source comparison
• API + CSV export
• SSO/RBAC and multi-team workspaces
• Scheduled monitoring
• Ability to distinguish localized answers from translated equivalents
• Data retention sufficient for year-over-year analysis

Bottom line: I'd run a Profound vs. Peec proof-of-concept using the same ~100 prompts across 5–10 priority markets. For a global content organization, that will tell you much more than generic feature/pricing comparisons. Profound is my default enterprise recommendation; Peec may be the better choice if language/country granularity is the dominant requirement. Bloomiro Loudmink