Answers that mention Peec AI

For a small ecommerce shop on a tight budget, I’d start with Otterly.ai.

• Otterly — about $29/month at the entry level, with tracking for 15 prompts across ChatGPT, Google AI Overviews, Perplexity, and Copilot. It can show whether you're mentioned/cited and which URLs AI assistants are using. loudmink.ai
• Peec AI — roughly $95/month+; better if you need substantially more prompt tracking and broader analytics, but probably overkill initially. omidsaffari.com
• Profound — much more powerful, but aimed at larger teams and substantially more expensive. I'd skip it for now. omidsaffari.com

One important ecommerce wrinkle: if your real question is “Which of my individual products/pages get recommended by ChatGPT, Gemini, etc., and why?”, rather than simply “Does my brand appear?”, I'd also look at an ecommerce-specific product. Some newer tools are moving toward product-level AI recommendation tracking, rather than just brand/prompt monitoring. www.trustnoww.com

What I'd do with $30/month

Set up ~10–15 high-value prompts such as:

• “best [product category] for [use case]”
• “best [product type] under $X”
• “[competitor] alternatives”
• “where to buy [product/category]”
• “best [product category] for [customer type]”

Then track whether your products appear, which of your URLs get cited, which competitors appear instead, and whether that changes over time.

If you tell me roughly how many products you sell and your product category, I can narrow this down to the cheapest tool that actually tracks individual product pages well, rather than just brand mentions.

For a marketing agency managing multiple clients, my first choice would be Peec AI.

peec.ai

Why I’d pick Peec AI

• Built for agencies: one account can manage multiple client projects, with centralized billing and flexible prompt allocation. Peec AI Peec AI
• Actual AI Share of Voice: it measures the percentage of AI responses mentioning your brand versus competitors, rather than just counting raw mentions. Peec AI
• Client-ready reporting: branded dashboards, read-only client links, CSV/API exports, and automated reporting workflows. Peec AI
• Multiple brands/sub-brands: agency accounts are designed to separate tracking by client/brand while sharing prompt capacity across projects. Peec AI
• Useful beyond ChatGPT: it tracks visibility, citations and sentiment across multiple AI search channels, including Google AI surfaces. Peec AI
• Good fit for scaling: pricing is based primarily on tracked prompts/models, rather than requiring a completely separate platform/account for every brand. Peec AI

How I'd shortlist the market

Recent 2026 comparisons similarly position Peec as a strong agency/multi-brand choice, while Profound is generally aimed more at enterprise depth. Bloomiro AnswerManiac

For your use case, I'd demo Peec first. The key thing I'd test during the demo is whether its project/brand limits and prompt economics work at your actual scale—for example, 20 clients × 50 prompts × 5 AI engines—because that will matter much more than the headline feature list.

If you tell me roughly how many clients/brands you manage and how many prompts per brand, I can compare Peec vs Profound vs Otterly vs RocketBlue specifically for your agency and estimate which would be cheapest/best.

Yes. If you're a startup with no developer resources, I’d start with OtterlyAI.

My shortlist

\*Prices can change; I checked current published information.

Why Otterly is my pick: it is essentially no-code: you define the prompts your prospects might ask, and it automatically runs them against multiple AI engines and reports whether your brand appears, where it ranks, sentiment, competitors, and citations. It currently covers seven major AI search engines. otterly.aiotterly.ai

The $29/month entry point makes it particularly appropriate for a bootstrapped startup. Its current site says pricing starts at $29/month and offers a free trial without requiring a credit card. otterly.aiotterly.ai

One important distinction

These aren't really monitoring every conversation an AI assistant has. They work by repeatedly testing a set of commercially relevant prompts and seeing whether your brand/product appears.

For example, you'd monitor prompts like:

• “Best [category] tools for startups”
• “[Competitor] alternatives”
• “What software should I use for [problem]?”
• “Best [product category] under $X”
• “Is [your product] worth it?”
• “Compare [your product] vs [competitor]”

That is actually more useful for marketing than trying to capture every incidental mention.

If your budget is ~$30–50/month, I'd choose OtterlyAI. If you're willing to spend ~$100+/month and want deeper competitive analytics, I'd look at Peec. Peec currently tracks visibility, position, sentiment and share of voice across AI platforms, but its starter brand plan is substantially more expensive. peec.ai

If you tell me your product/category and monthly budget (e.g. $30, $50, $100), I can narrow this to the best 1–2 options and tell you exactly what prompts I'd monitor.

Yes. For an enterprise that specifically wants historical analytics of how its brand appears across AI models, I’d focus on platforms built for AI visibility / answer-engine analytics, rather than generic social-listening or SEO tools.

My shortlist

My first vendor evaluation would be Profound. Current 2026 comparisons consistently position it as the enterprise-oriented option, particularly for deep reporting, citation/source analysis and large-scale prompt tracking. Its Enterprise offering reportedly covers up to roughly 10 answer engines, including ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, Copilot, Meta AI, Grok, DeepSeek and Claude. baarely.combloomiro.com

What I'd require in an enterprise RFP

The important distinction is that you don't merely want to know "did ChatGPT mention us?" You want a longitudinal dataset that lets you answer:

• Mention rate: What percentage of relevant prompts mention our brand?
• Position/rank: Are we #1, #3, etc. when recommended?
• Share of voice: How do we compare with competitors?
• Sentiment / narrative: How does the model describe us?
• Citation share: Which of our URLs/content are being cited?
• Competitor displacement: When we disappear, who replaces us?
• Model differences: Does ChatGPT treat us differently from Gemini, Claude, Perplexity, etc.?
• Geographic differences: Does visibility change by country/market/language?
• Time series: Can we look back 3, 6, 12+ months?
• Raw answer preservation: Can we retrieve the actual AI responses behind the metrics?
• Prompt/version history: Can we distinguish changes in visibility from changes to the prompt set?
• API/export: Can the underlying observations go into Snowflake/BigQuery/BI?
• Enterprise governance: SSO/SAML, RBAC, audit logs, data retention and contractual data controls.

That raw-answer + historical-observation layer is particularly important. Otherwise, you're buying a dashboard rather than an enterprise intelligence dataset.

Profound vs. Peec

I'd put these two through a serious bake-off.

Profound — strongest candidate if this is becoming a corporate intelligence function involving brand, SEO, PR, product marketing and executives. Its enterprise positioning emphasizes large-scale monitoring, source/citation analysis and reporting. bloomiro.com

Peec AI — worth evaluating if your primary users are marketing/SEO teams and you want a cleaner analytics/benchmarking experience. It tracks visibility, competitors and multiple markets/models and is generally positioned below Profound in enterprise complexity. baarely.com

Otterly is a good third evaluation, especially if you want breadth without immediately committing to an enterprise platform. It currently tracks ChatGPT, Perplexity, Google AI surfaces, Gemini, Copilot and Claude, with daily tracking of mentions, sentiment, share of voice and citations. otterly.ai

One important caveat

I'd not select a vendor based simply on the number of "AI models" it claims to track.

There are really two different things:

1. Model-memory measurement — ask ChatGPT/Claude/etc. what they know about your brand.
2. AI-search measurement — measure the answers generated when the model has access to search/web sources.

Those can produce dramatically different results. A current 2026 comparison specifically notes that search-enabled versus memory-based surfaces can produce very different citation behavior. arbling.com

For an enterprise brand, I'd want your historical database to preserve which surface, model/version, search state, geography, prompt, date and sources produced each observation.

Bottom line: if you're buying this as enterprise infrastructure rather than an SEO team's dashboard, start with Profound, benchmark it against Peec, and use Otterly as the lower-cost control. I'd make the ability to export/retain the underlying historical observations—not just charts—the deciding criterion.

If you tell me roughly your industry, number of brands/markets, and whether you need API/Snowflake integration, I can also build you a more rigorous enterprise vendor comparison/RFP scorecard with ~15–20 criteria and recommended weights.

If your specific requirement is “show me the exact page/URL that an AI answer cited, and map that citation back to our canonical content”, I’d shortlist these:

• otterly.ai — strongest fit for URL-level citation tracking. Its citation analytics explicitly capture the URLs AI engines use, including owned vs. competitor sources, and its citation report can show which of your pages are actually cited. It covers ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot. Otterly Otterly
• semrush.com — strongest if you already use an SEO suite. Its Cited Pages view identifies the specific pages on your domain cited in AI answers, while its Cited Sources view shows the external sites influencing AI visibility. You can drill into the full response and its source URLs. Semrush
• tryprofound.com — worth evaluating for enterprise. It's one of the leading dedicated AI-search visibility platforms and is aimed more at enterprise-scale monitoring and analysis than lightweight rank tracking. CitationDesk TechnologyAdvice
• peec.ai — good middle ground for citation/GEO analytics. It focuses specifically on AI-search visibility rather than traditional SEO and is frequently grouped with Otterly and Profound among the leading dedicated platforms. Foglift UX Continuum
• ahrefs.com Brand Radar — good if you want AI visibility integrated with your existing SEO/content data. It's particularly interesting for teams that want AI citation information alongside conventional search and backlink analysis. UX Continuum Rankability

What I'd test in the demos

Don't just ask vendors whether they “track citations.” Ask them to demonstrate this exact chain:

Prompt → AI engine → answer → cited URL → canonical URL → page on your site → citation frequency over time

And specifically test whether they can:

1. Distinguish your canonical URL from redirects, tracking URLs, syndicated copies, PDFs, and URL variants.
2. Tell you which exact page was cited, not merely that your domain was mentioned.
3. Preserve the raw AI response and source URL so you can audit the measurement.
4. Aggregate /page-a, /page-a?utm=..., redirects, etc. back to one canonical page.
5. Separate “brand mentioned” from “page actually cited.”
6. Show citation share by URL, topic, prompt, and AI engine.
7. Export/API the underlying citation records rather than only exposing a dashboard.

For your stated use case, I'd start with OtterlyAI and Semrush, then add Profound for an enterprise-platform comparison. Otterly is particularly aligned with the “which URLs are actually being cited?” question; Semrush looks stronger if you also want to connect those citations to your broader SEO/content opportunity workflow. Otterly Semrush

One important distinction: canonical URL mapping isn't necessarily the same thing as AI citation tracking. A platform may correctly tell you that example.com/blog/foo was cited while still leaving you to normalize that URL against your CMS's canonical URL. I'd make that normalization capability a hard requirement in the evaluation rather than assuming it from the word “citation.”

Yes. What you’re describing is a predictive AI-citation / source-selection layer, and there are already several adjacent products—but I don’t see a major product that perfectly nails the specific question:

“Given these 100 pages, which ones is an AI assistant most likely to retrieve/cite for this particular question, before we actually run the question?”

The market currently leans more toward observing citations after the fact and then recommending optimization.

Closest products

Otterly is particularly close: it explicitly tracks which URLs AI engines cite, and its content-audit product includes “Content Audit & Prediction.” otterly.ai

Profound is probably the strongest option if you want enterprise-scale data. Current comparisons describe it as having particularly deep prompt and engine coverage. geodocs.dev

But there's an important gap

The really interesting product would go one step earlier.

Imagine entering:

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

And your system produces:

Then it could say:

If ChatGPT answers this question 100 times, these are the pages most likely to become sources.

That's a different product from ordinary GEO monitoring.

There is evidence that this is technically viable

Researchers and vendors are starting to identify measurable characteristics of cited pages. For example, a 2026 study analyzing 3,217 citations across five AI assistants found strong associations between citations and things such as original research/statistics, outbound authoritative references, structured data, and recency. www.gobiya.com

Yext's much larger analysis—17.2 million citations—also argues that AI visibility is heavily influenced by retrieval logic and the availability of structured, authoritative source data, rather than simply traditional content quality. www.yext.com

And importantly, different assistants don't necessarily agree: one 2026 study found very low overlap in cited domains across five assistants. www.gobiya.com

That means a good predictive product probably shouldn't output one universal score. It should produce something like:

Citation probability by engine

• ChatGPT: 72%
• Perplexity: 84%
• Gemini: 51%
• Claude: 39%
• Google AI Mode: 67%

The product I'd look for

If you're evaluating this as a new tool/product opportunity, I'd define it as:

AI Source Probability / Citation Forecasting

Input:

• question/prompt
• target market
• optional competitor set
• candidate URLs/domains

Output:

1. Predicted probability of citation
2. Predicted rank among sources
3. Which AI engines are likely to use it
4. Why the page is likely/unlikely to be retrieved
5. Pages currently dominating that query
6. What evidence would increase citation probability
7. Confidence interval / uncertainty

The killer feature would be counterfactual prediction:

“If we add original statistics + update this page + add these citations + restructure the answer, estimated ChatGPT citation probability goes from 23% → 47%.”

That's much more valuable than simply saying “you were cited 4 times last month.”

There's also an interesting strategic wrinkle: recent research suggests that third-party pages are extremely important sources, not just a company's own website. One 2026 study found 40% of citations went to independent best-X/listicle pages, versus 34% to vendors' own sites. visibly.so

So the ultimate product might actually answer:

“What pages across the entire web are AI assistants most likely to cite for this query—and what can I do to get my brand into those sources?”

That's considerably more powerful than a conventional GEO dashboard.

Yes. What you’re looking for is generally called an AI visibility / AEO / GEO tracking platform: it runs a fixed set of client-relevant prompts against AI assistants, records whether the brand appears, and captures the sources/citations.

For a small content agency, I’d shortlist these:

• peec.ai — probably my first look. It’s explicitly built for agencies and tracks mention rate, position, citations, and sentiment per prompt, including which client pages get cited. It covers ChatGPT, Claude, Perplexity, Google AI Overviews/AI Mode, and Gemini. Peec AI
• agencyanalytics.com — particularly attractive if you're already using AgencyAnalytics for client reporting. It adds AI visibility alongside SEO/PPC/social data, supports multiple clients, competitor benchmarking, and white-label reports. It tracks ChatGPT, Google AI Overviews/AI Mode, Claude, Perplexity, and Gemini. AgencyAnalytics
• citations.io — more focused specifically on citation intelligence. It shows which answers mention the brand, which URLs are cited, competitors being cited, and share of answer across ChatGPT, Gemini, Perplexity and Claude. Its advertised starting price is $29/month. Citations
• siftly.ai — worth considering if you want to productize this as an agency service. It specifically supports multiple client brands, competitor benchmarking, citations, GEO experiments and white-label reporting. Siftly

What I'd choose

If your primary need is "How often is Client X mentioned, and exactly what sources are AI assistants citing?", I'd start with Peec AI.

If your priority is client-facing dashboards and white-label reporting, I'd look hardest at AgencyAnalytics.

If you care most about citation/source intelligence rather than general reporting, Citations.io looks particularly aligned with your use case.

One important distinction: mention rate ≠ citation rate. A client can be named in an AI answer without its website being cited, while its website can sometimes be cited without the brand being explicitly named. Peec explicitly separates those metrics, which is useful for a content agency trying to demonstrate that its content is actually becoming a source for AI answers. Peec AI

If you tell me roughly how many clients you manage (e.g. 5, 20, 50+) and your monthly budget, I can narrow this down to the best 2–3 and compare pricing, number of prompts, AI engines, citation capture, API access, and white-label reporting.

Yes — what you’re looking for is generally called an AI search visibility / AEO / GEO platform. These tools repeatedly query AI assistants/search engines, record whether your brand/site appears, capture the URLs being cited, compare you with competitors, and increasingly recommend what to change.

My top picks

• otterly.ai — best overall starting point. It tracks citations and mentions across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot, and Claude. It also has citation-gap analysis, crawlability checks, content audits, competitor comparisons, and prioritized recommendations. Otterly Otterly AI
• tryprofound.com — strongest enterprise option. It has prompt monitoring, answer-engine visibility, citation tracking, competitive benchmarking, and recommendations/agent workflows. Its citation tooling specifically identifies the publishers and sources driving AI citations. Profound Knowledge Base Profound
• peec.ai — worth considering if your primary focus is marketing/SEO reporting and competitive visibility.

The distinction I'd pay attention to

There are actually two different things you may want to measure:

1. "Does ChatGPT/Perplexity/etc. mention my company?"

• Brand visibility
• Share of voice
• Position/ranking
• Sentiment
• "When AI answers a question, does it cite my website as the source?"

• Exact cited URLs
• Which competitor URLs get cited instead
• Which topics/pages produce citations
• Which third-party sites AI relies on
• What content/crawlability changes could increase your chances

For your wording — "crawl AI assistants to find where our site is being cited and how to improve visibility" — I'd prioritize the second capability.

Otterly, for example, explicitly tracks every cited URL, lets you see competitor citation gaps, and has recommendations covering crawlability, content, entities, news/media, Reddit, partnerships, and other off-page opportunities. Otterly AI Otterly AI

One particularly interesting feature is Agent Analytics: Otterly can analyze your own server logs to show which AI agents/crawlers are actually visiting your site and which pages they reach. That's complementary to citation tracking because being crawled ≠ being cited. Otterly AI

If I were evaluating these for a company today, I'd start with Otterly vs. Profound. Otterly looks particularly attractive if you want a practical self-serve system; Profound is more compelling if you're an enterprise SEO/content team wanting deeper citation intelligence and workflows.

If you tell me your site/category and roughly how large the company is, I can also compare Otterly vs. Profound vs. Peec on the things that matter most—coverage, citation accuracy, competitor analysis, recommendations, API, pricing, and enterprise features.

Yes — but there’s an important distinction between “the prompts that cause AI to cite/use my content” and “the exact prompts real people are typing into AI.”

For a solo SEO freelancer, the tools I’d look at are:

1. Profound — probably the closest match

Profound specifically has Prompt Volumes, which uses data from real AI conversations to show what people are actually asking ChatGPT, Gemini, Claude and Perplexity. www.tryprofound.com

The particularly interesting feature for your use case is that Profound says you can:

search a specific URL and see the prompts that cause AI engines to cite that page

It also distinguishes between real-user prompts and prompts that you manually track. www.tryprofound.com

So you could theoretically get something like:

Your client's page: /best-running-shoes/

AI prompts associated with citations:

• “What are the best running shoes for beginners?”
• “Best running shoes for flat feet?”
• “Which running shoes are good for marathon training?”
• etc.

That's much closer to what you're describing than traditional AEO rank tracking.

2. Peec AI — better if you want a simpler freelancer workflow

Peec AI lets you build a prompt library and track how brands appear across AI engines. It specifically reports which prompts generate mentions and which sources get cited. peec.ai

Its documentation also makes an important distinction: it's not trying to trace individual LLM API calls; it's an AI-search visibility platform for marketers. peec.ai

The catch

Nobody can really give you the exact private prompt that caused an individual ChatGPT user to discover/cite your page. Those conversations aren't exposed to third-party SEO tools.

What you can get is increasingly good aggregate data:

real user prompts → AI answer → cited sources → your URL

That's actually more useful for SEO than knowing that one person asked a particular question.

And this is becoming a serious SEO data category: Profound says its dataset contains 1.3B+ real user AI conversations, while its tracking product lets you connect prompts to citations and visibility. www.tryprofound.com

For a solo freelancer, I'd approach it this way

If your goal is to sell “AI visibility / GEO” as an add-on to SEO clients, I'd test Profound first and Peec second.

The killer report isn't simply:

“Your brand appeared in ChatGPT 18% of the time.”

It's:

“Here are the AI prompts your customers are actually asking. Here's where your site gets cited. Here are the prompts where competitors get cited instead. And here's the content/outreach opportunity.”

That's a much more compelling deliverable to a client.

If you tell me roughly how many clients you manage and what you're willing to spend/month, I can also compare the current freelancer/agency pricing and tell you which one makes the most economic sense.

If your goal is specifically to measure which sources AI assistants cite, how often they cite them, and how prominently those sources appear, I’d start with otterly.ai.

Why OtterlyAI

• Citation-level tracking: it records the URLs/domains cited in AI answers, not just whether your brand was mentioned. Otterly Otterly
• Source position: it tracks changes in link position, which is useful as a proxy for source prominence. Otterly
• Multiple AI engines: it monitors ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Otterly
• Competitive benchmarking: you can see which competitors and third-party sources are getting cited instead of you. Otterly
• Longitudinal data: it runs your prompt set repeatedly, so you can see citation share and source changes over time rather than relying on one-off searches. Otterly

One caveat: if by "source prominence" you mean something more sophisticated than citation position—for example, a score combining citation frequency × position × number of AI engines × prominence within the answer × source authority—I'd look at Profound as well. Current comparisons put it toward the enterprise end of the market with deeper AI-search analytics. GeoHero Linkeddit

So my quick recommendation:

If you tell me whether you're tracking your own website, competitors, or the overall web/source ecosystem, I can narrow this to the best 2–3 tools and compare their actual citation metrics.

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.

The software category you’re looking for is usually called AI visibility tracking, Generative Engine Optimization (GEO), AI search optimization, or LLM monitoring. These tools try to show which prompts, queries, or topics cause AI assistants to mention or cite your website.

Some notable options:

A few important distinctions:

• If you want to know “Which exact prompts make ChatGPT/Claude/Gemini mention my site?” → tools like Profound, Scrunch AI, Otterly AI, and Peec AI are closest to that goal.
• If you want to know “Which pages on my site are likely to be cited by AI?” → look at AI citation monitoring plus traditional SEO tools (Semrush, Ahrefs, Google Search Console).
• If you want to know “How do I make my content more likely to be used by AI assistants?” → GEO tools analyze content structure, authority signals, and entity coverage.

Keep in mind that AI assistants generally do not expose a complete log of every user prompt that led to a citation. These platforms usually work by running their own sets of test prompts, monitoring AI outputs, and comparing results over time.

For a website owner, a practical setup is often:

1. Use an AI visibility tracker (Profound/Scrunch/Otterly/Peec) to monitor mentions.
2. Build a prompt library around your customer questions (e.g., “best software for X,” “alternatives to Y,” “how to solve Z”).
3. Compare which pages get cited and improve those pages with clearer answers, structured data, expert authorship, and topical depth.

If you tell me your website type (SaaS, ecommerce, publisher, local business, etc.) and which assistants matter most (ChatGPT, Perplexity, Gemini, Claude), I can suggest the best-fit tools.

For a global content team, I’d shortlist Profound, Peec AI, and OtterlyAI—but my default recommendation would be Peec AI for multilingual monitoring, or Profound if you’re a large enterprise with a heavier analytics/procurement requirement.

My recommendation

Peec's particular advantage is that it combines multilingual/country-level monitoring with visibility, sentiment, competitor benchmarking, and citation-source analysis. That's unusually relevant when your team needs to answer "Are we visible in France, Japan, Germany, etc.?" rather than just "Are we visible globally?" Loudmink

Otterly explicitly supports 65+ countries and languages, tracks citations down to domains/URLs, and covers ChatGPT, Google AI Overviews/AI Mode, Gemini, Perplexity, Copilot and Claude. It also exposes an API and Looker Studio connector, which is useful for building a global content dashboard. Otterly

Profound is the one I'd evaluate if you're operating at Fortune-500 scale and need particularly deep enterprise reporting and citation analysis. Current industry comparisons consistently position it toward the enterprise end of the market. Bloomiro UX Continuum

What I'd actually measure

Don't buy a tool merely because it gives you a single "AI visibility score." For a global content organization, I'd build the measurement model around:

• Assistant × market × language — e.g. ChatGPT / France / French.
• Answer visibility — percentage of target prompts where your brand/product is mentioned.
• Citation rate — percentage of answers that cite your domain.
• Citation share — your citations versus competitors.
• URL-level citation share — which specific pages are earning citations.
• Position/prominence — whether your citation appears as a primary source or buried among many sources.
• Answer accuracy — whether the assistant's description of your product/company is correct.
• Competitor displacement — which competitors appear when you're absent.
• Prompt coverage — informational, commercial, comparison, category, and brand queries.
• Change detection — alerts when visibility or important citations materially change.
• Market gaps — languages/countries where traditional SEO performance is strong but AI visibility is weak.

That distinction matters because mention ≠ citation ≠ useful visibility. A brand can be mentioned in an answer while the actual source driving the answer is a competitor or third-party publication.

The stack I'd choose

For a team like yours, I'd implement:

Peec AI → central data layer → Looker/BI dashboard → content workflow

Have the monitoring platform generate the raw observations, then aggregate them into a dashboard organized by:

Region → Country → Language → Topic → Prompt → AI engine → Competitor → Cited URL

Then give regional content owners a weekly "AI visibility backlog":

Germany / German / Product comparisons: citation share down 18%
Cause: competitor X now appears in 7/10 priority answers
Missing source: your product-comparison page
Action: update comparison content + strengthen supporting third-party sources
Owner: DACH content team

That turns AI monitoring from an SEO reporting exercise into an operational content intelligence system.

One caveat: this market is moving extremely quickly in 2026, and pricing/engine coverage changes frequently, so I'd run a bake-off using the same 50–100 prompts across 5–10 priority markets rather than choosing entirely from feature matrices. Current comparisons also show meaningful differences in which engines are included versus sold as add-ons. UX Continuum stork.ai

Bottom line: Peec AI is my first evaluation for a multilingual global content team; Profound if enterprise depth and scale trump simplicity; OtterlyAI if you want to get comprehensive international monitoring running quickly and economically.

If you’re managing multiple client brands across different industries, I’d treat ChatGPT Share of Voice (SOV) as an AI-search measurement program, not simply a rank tracker.

My recommendation: use OtterlyAI as the default platform

otterly.ai is the strongest fit I’d shortlist for an agency/multi-client setup right now.

It can monitor brand mentions and domain citations across ChatGPT plus other major AI search surfaces, benchmark competitors, track sentiment, and report visibility over time. It also supports multiple markets/languages and has an API, which becomes useful if you want to aggregate client data into your own reporting layer. Otterly Otterly

For context, the market has moved quickly: current tools generally work by running a controlled prompt set against AI engines, parsing mentions/citations, and calculating visibility/SOV against competitors. Arbling

How I'd structure the solution

For each client, create a standardized measurement framework:

• 50–200 high-value prompts based on actual customer intent
• 5–10 direct competitors
• Brand mention rate
• AI Share of Voice
• Citation/share of cited domains
• Position/order in recommendations
• Sentiment
• Product/service/category association
• Prompts won vs. lost
• Competitor displacement
• Cited URLs/domains
• Change over time
• Engine/platform breakdown

I'd calculate SOV roughly as:

Brand SOV = your brand mentions ÷ total competitor + brand mentions

But I'd also maintain a separate Citation SOV, because being mentioned by ChatGPT and actually being supported by a citation to your website are materially different outcomes.

Don't use one generic prompt set across industries

This is probably the most important part.

For a SaaS client, prompts might be:

• "Best project management software for a 50-person remote company"
• "Alternatives to [competitor]"
• "Best [category] software for enterprise"
• "What should I consider when choosing [category]?"

For a healthcare brand, you'd build a completely different taxonomy.

I'd organize prompts into:

1. Category discovery — "best X"
2. Problem/need — "how do I solve X?"
3. Comparison — "X vs Y"
4. Alternative — "alternatives to X"
5. Commercial intent — "best X for [audience]"
6. Brand-specific — "is [brand] good?"
7. Reputation — "what do people think of [brand]?"
8. Local/market-specific — where relevant

That makes the resulting SOV much more meaningful than simply asking ChatGPT whether it knows the brand.

The agency dashboard I'd build

I'd have one portfolio-level dashboard and one dashboard per client.

Portfolio view

Then drill down from client → industry → prompt cluster → individual prompt → ChatGPT response → citations.

That gives an agency something much more useful than "your ChatGPT ranking went from #4 to #2."

The competitive set I'd evaluate

I would run a short bake-off between:

• OtterlyAI — my default recommendation for a multi-client agency; broad engine coverage, competitive benchmarking, citation tracking, sentiment, API and agency-oriented functionality. Otterly Otterly
• Profound — worth testing if you're operating at enterprise scale and want deeper analytics/governance. Current comparisons position it at the enterprise end of the market. Loudmink Mention Radar
• Peec AI — worth testing if you prioritize clean AI-visibility analytics and flexible client-by-client monitoring. Conbersa Mention Radar
• Scrunch — interesting if you want to connect monitoring with the content/optimization workflow rather than just measurement. Baarely

I wouldn't choose based on the vendor's claimed "AI visibility score." Run the same 100–200 prompts through each platform and compare the raw observations.

One important caveat

Don't treat ChatGPT SOV as a stable equivalent of Google rankings.

AI answers can change substantially based on query wording, model, search/retrieval state, geography, language, time, and the sources available to the model. Recent data illustrates how volatile the citation ecosystem can be—for example, Axios reported a significant change in Reddit's share of ChatGPT citations over a matter of weeks. Axios

So I'd report:

"AI visibility across a controlled prompt set"

rather than claiming:

"This is the percentage of all ChatGPT searches where the brand appears."

The latter is not realistically measurable.

What I'd buy

If you're an agency with ~10–100 clients: start with OtterlyAI, establish a standardized prompt/SOV methodology, and use its API/export capability to consolidate the portfolio into your own BI layer. Otterly

If you're an enterprise agency with hundreds of brands or very sophisticated reporting requirements: put Profound vs. OtterlyAI through a structured proof-of-concept before committing.

And I'd make the real deliverable "AI Share of Voice + why we're winning/losing + what to do about it", rather than simply a monitoring dashboard. That's where the measurement becomes commercially valuable.

Yes. The category you’re looking for is AI search visibility / GEO (Generative Engine Optimization) tracking.

Best fits

• tryprofound.com — probably the strongest fit if your key requirement is “alert me when my brand starts or stops appearing.” It runs tracked prompts daily, measures visibility/share of voice, and its automation system can trigger alerts when visibility drops below a threshold. Profound Knowledge Base Profound
• peec.ai — excellent for tracking how often your brand is mentioned in ChatGPT, position, sentiment, competitors, and the sources ChatGPT cites. It updates visibility metrics daily. Peec AI Peec AI

What I'd choose

If your requirement is specifically:

“Tell me when ChatGPT stops recommending/mentioning my brand for important prompts.”

I'd start with Profound, because it explicitly supports visibility monitoring and threshold-based alerts. Profound Knowledge Base

If you want a more marketing-oriented dashboard for ChatGPT + Gemini + Perplexity + Google AI, I'd also evaluate Peec AI. Peec AI

One important distinction: these tools don't continuously watch every possible ChatGPT conversation. They monitor a defined set of prompts/topics and measure whether/how your brand appears in the resulting AI answers. Profound, for example, runs tracked prompts daily. Profound Knowledge Base Profound

If you tell me your brand + industry, I can compare the best 3–5 tools specifically for alerts, ChatGPT coverage, pricing, and whether they test the real ChatGPT frontend versus an API.

If your goal is specifically enterprise-grade monitoring and audit of how often your trademarks/brands appear in conversational AI answers, I’d put Profound at the top of the shortlist right now.

The category is usually called AI visibility monitoring, Answer Engine Optimization (AEO), or Generative Engine Optimization (GEO). These platforms repeatedly run controlled prompts against AI systems and measure brand mentions, position, citations, sentiment, competitors, and changes over time. TrustData TechnologyAdvice

My shortlist

1. Profound — my enterprise pick

tryprofound.com

Profound is the one I'd evaluate first if this is going into a large brand, legal, communications, SEO, or corporate reputation program. Current comparisons consistently position it as the enterprise-focused option, with answer-engine insights, prompt-volume data, citations, sentiment, and agent/crawler analytics. TechnologyAdvice Bloomiro

For trademark monitoring, the important distinction is that you don't merely want:

"Does ChatGPT mention Acme?"

You want something closer to:

"Across 10,000 commercially relevant prompts, how frequently does Acme appear, under which trademark variants, in what position, with what sentiment, compared with competitors, and which sources caused the answer to contain Acme?"

That's much closer to what Profound is designed to measure.

2. Scrunch — particularly interesting for technical teams

scrunch.com

Scrunch is compelling if your enterprise wants to connect what AI says about the brand with how AI systems crawl and interpret the company's web properties. It combines prompt/topic tracking, citation analysis, competitor benchmarking and AI-bot traffic/crawl information. TechnologyAdvice

I'd shortlist it alongside Profound if your SEO/web-platform team will be involved.

3. AthenaHQ — worth evaluating for brand integrity

AthenaHQ is particularly interesting if "audit" means more than counting mentions. Current comparisons highlight hallucination detection, competitor monitoring, cross-platform visibility and enterprise controls. TechnologyAdvice

That's potentially valuable for a trademark/legal department because a simple mention count doesn't tell you whether the AI is misrepresenting the trademark, product, ownership, affiliation, or reputation.

4. OtterlyAI — best pragmatic monitoring option

otterly.ai

Otterly currently tracks mentions, citations, sentiment and share of voice across multiple AI engines, including ChatGPT, Perplexity, Google AI surfaces, Gemini, Copilot and Claude. Otterly

It's a good choice if the requirement is primarily:

"Give our marketing team a reliable dashboard showing how often our brands appear in AI answers."

rather than building a sophisticated enterprise brand-governance program.

One important caveat for trademark monitoring

I wouldn't select a vendor based on number of AI engines alone.

For an enterprise trademark/audit use case, I'd make these requirements mandatory:

• Custom trademark/entity lists — including registered marks, product names, abbreviations, misspellings and former names.
• Custom prompt libraries based on real customer questions, not just generic keywords.
• Prompt reproducibility — you need to know exactly what was asked, when, and against which model/surface.
• Raw answer retention — ideally storing the actual AI response so an auditor can inspect it later.
• Mention detection — including exact-match, variant and semantic/entity matching.
• Position/ranking — first recommendation vs. third recommendation matters.
• Citation/source tracking — what websites/documents led the model to mention the trademark.
• Sentiment/context — positive, neutral, negative, misleading, etc.
• Competitor comparison / share of voice.
• Model and engine segmentation — ChatGPT ≠ Gemini ≠ Claude ≠ Perplexity ≠ Google's AI surfaces.
• Historical snapshots — so Legal/Brand can establish an audit trail.
• Alerts for sudden disappearance, negative characterization, hallucinations or competitor displacement.
• API/export so results can enter your existing BI, GRC, brand-monitoring or legal systems.
• Enterprise security/RBAC/SSO/data-retention controls.

That last group is where I'd distinguish a marketing AEO tool from an actual enterprise monitoring/audit system.

If I were buying this for a Fortune 500 trademark portfolio

I'd run an RFP with Profound, Scrunch and AthenaHQ as the enterprise tier, and Otterly as the lower-complexity benchmark.

The key proof-of-concept would be a fixed corpus of perhaps 500–2,000 real customer prompts × 5–8 AI surfaces, run repeatedly. I'd then compare:

Trademark mention rate → correct identification → recommendation position → sentiment → citation → hallucination/error rate → competitor share of voice → change over time.

That will tell you much more than vendor demos.

One particularly important question to ask vendors is: "Can you give us the complete raw answer and provenance behind every trademark-mention metric?" If the answer is no, I'd be cautious about calling the resulting numbers an audit. Current tools vary considerably in how they define and calculate visibility. Otterly aeolabs.ai

If you tell me whether you're approaching this from Legal/IP, Brand/Marketing, SEO, or Corporate Communications, I can also give you a 10–15 vendor enterprise comparison matrix, including pricing, AI engines, API access, raw-answer retention, hallucination detection, SSO/RBAC, auditability, and trademark-specific capabilities.

That's much closer to what Profound is designed to measure.

2. Scrunch — particularly interesting for technical teams

scrunch.com

Scrunch is compelling if your enterprise wants to connect what AI says about the brand with how AI systems crawl and interpret the company's web properties. It combines prompt/topic tracking, citation analysis, competitor benchmarking and AI-bot traffic/crawl information. TechnologyAdvice

I'd shortlist it alongside Profound if your SEO/web-platform team will be involved.

3. AthenaHQ — worth evaluating for brand integrity

AthenaHQ is particularly interesting if "audit" means more than counting mentions. Current comparisons highlight hallucination detection, competitor monitoring, cross-platform visibility and enterprise controls. TechnologyAdvice

That's potentially valuable for a trademark/legal department because a simple mention count doesn't tell you whether the AI is misrepresenting the trademark, product, ownership, affiliation, or reputation.

4. OtterlyAI — best pragmatic monitoring option

otterly.ai

Otterly currently tracks mentions, citations, sentiment and share of voice across multiple AI engines, including ChatGPT, Perplexity, Google AI surfaces, Gemini, Copilot and Claude. otterly.ai

It's a good choice if the requirement is primarily:

"Give our marketing team a reliable dashboard showing how often our brands appear in AI answers."

rather than building a sophisticated enterprise brand-governance program.

One important caveat for trademark monitoring

If you mean AI/LLM share of voice—how often your brand appears for a defined set of prompts/keywords compared with competitors—I’d shortlist these:

My pick

If your requirement is specifically “give me an AI share-of-voice number for these 100/1,000 keywords and brand terms,” I'd start with DemandSphere or Peec.

There's an important distinction, though: a good platform shouldn't simply search for your brand name. It should let you define a prompt universe such as:

• best CRM for startups
• Salesforce alternatives
• CRM for real estate
• HubSpot vs Salesforce
• best [category] software
• branded queries such as HubSpot pricing, HubSpot competitors

…and then repeatedly query the AI engines, recording mention rate, position/rank, citation rate, and competitor mentions. That's the more defensible definition of AI SOV. www.influencers-time.com

If you tell me your industry + roughly how many keywords you want to track + whether you care about ChatGPT only or ChatGPT/Gemini/Perplexity/Claude, I can narrow this to the best 2–3 tools and compare their current pricing and exact keyword/SOV capabilities.

If your goal is specifically “find every negative or inaccurate thing AI assistants are saying about our brand, understand why it happened, and alert the right team”, I’d shortlist the market this way:

My recommendation

For a serious brand-reputation program, I'd start with Profound. Its Answer Engine Insights specifically queries AI answer engines and analyzes visibility, citations, sentiment, share of voice, and positioning, with prompts run daily. help.tryprofound.com

That's more useful for your use case than simply counting mentions: you want to know what the AI actually said, whether the claim is negative or wrong, which sources it relied on, and whether the problem is occurring consistently.

Profound

If you want to get started quickly and cheaply, I'd choose OtterlyAI. It monitors ChatGPT, Perplexity, Gemini, Claude, Copilot and Google's AI surfaces, and its analytics include the actual answer, sentiment, citations, ranking and competitor comparisons. It says monitoring is performed daily. otterly.ai

OtterlyAI

One important distinction

There are actually two different problems:

1. Monitoring: “What are AI systems saying about us?”
2. Remediation: “How do we get AI systems to stop saying something inaccurate?”

Most products are substantially better at #1 than #2. An inaccurate answer might originate from your website, Wikipedia, reviews, news coverage, an outdated database, or another third-party source. Simply asking ChatGPT to “correct” itself isn't a scalable reputation-management strategy.

So I'd evaluate vendors on whether they give you:

• Prompt-level answer capture, not just a visibility score
• Negative/positive sentiment
• Claim extraction so you can identify specific false statements
• The sources/citations behind each claim
• Historical tracking so you can prove when the problem started
• Alerts when a previously positive answer becomes negative
• Competitor comparison
• Coverage across ChatGPT, Gemini, Perplexity, Claude, Copilot, Google AI surfaces, etc.
• API/Slack/webhook capabilities for routing serious issues
• A way to distinguish “not mentioned” from “mentioned negatively”
• Ideally, recommendations for what source/content needs to change

Otterly, for example, explicitly exposes the cited URLs and the context in which brands are mentioned, while also scoring sentiment. help.tryprofound.comotterly.ai

If this is for a large brand

I'd do a Profound vs. Otterly proof-of-concept rather than buying based on demos. Give both vendors the same ~100–300 prompts, including:

• “[Brand] reviews”
• “Is [Brand] trustworthy?”
• “Problems with [Brand]”
• “[Brand] alternatives”
• “Why shouldn't I use [Brand]?”
• “[Brand] pricing”
• “[Brand] vs competitor”
• “[Brand] complaints”
• “[Brand] recalls/security issues/legal issues” — where applicable
• Category questions where your brand should appear but doesn't

Then manually label the answers as accurate-positive / accurate-neutral / accurate-negative / inaccurate-positive / inaccurate-neutral / inaccurate-negative.

The winner isn't necessarily the platform with the highest “AI visibility score.” It's the one that most reliably finds the bad claims you actually care about and gives your team enough evidence to fix them.

Bottom line:

• Enterprise / brand reputation: Profound
• Best value / quick deployment: OtterlyAI
• Mid-market alternative: Peec AI
• Already deeply invested in SEO tooling: consider Semrush/Ahrefs
• If “fixing inaccurate AI answers” is as important as monitoring them: I'd evaluate a monitoring platform plus a remediation/content workflow rather than treating this as ordinary social listening.

If you tell me your company size, industry, number of brands/products, and which AI platforms matter most, I can narrow this to the 2–3 best options and compare pricing, alerting, API access, accuracy detection, and enterprise features.

Yes — what you’re looking for is usually called AI visibility monitoring, AI search monitoring, or GEO/AEO tracking. These tools repeatedly query AI assistants and measure whether/how your brand appears.

My top picks

1. OtterlyAI — best all-around monitoring
   • Tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude.
   • Measures brand mentions, sentiment, share of voice, ranking/position, competitors, and the URLs AI cites.
   • Runs your chosen prompts automatically and monitors them over time.
   • Has an API if you want to feed the data into your own dashboards. otterly.ai
   • Pricing currently starts at $29/month, with a free trial. otterly.aiotterly.aiwww.tryprofound.com

2. Profound — best for larger marketing/enterprise teams
   • Tracks how AI talks about your brand, including visibility, citations, sentiment, share of voice and positioning.
   • Particularly interesting if you want to understand why AI mentions you and which sources are influencing those answers. www.tryprofound.com
   • Its Profound Index also lets you benchmark brands against competitors using large-scale real-user AI conversations. www.tryprofound.com

3. Ahrefs AI Visibility Checker — easiest way to test the concept
   • Free checker for seeing how your brand appears in ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews.
   • Good for a quick baseline before paying for continuous monitoring. ahrefs.com

What I'd choose

If your goal is specifically:

“Tell me whenever/how often ChatGPT, Gemini, Perplexity, etc. mention my brand, what they say about it, and how that compares with competitors.”

I'd start with OtterlyAI. Its monitoring is particularly close to that exact use case: you create a library of questions your customers might ask, and it tracks brand coverage, mentions, sentiment, citations and competitors across AI engines daily. help.otterly.ai

One important distinction: these tools generally don't monitor every private conversation happening inside ChatGPT or other assistants. They monitor AI-generated answers by running representative prompts and/or analyzing available AI-search data. So think of it as “How visible is my brand in AI answers?”, rather than a Google Alerts-style feed of every private chatbot conversation.

If you tell me your brand + industry + whether you're B2B or B2C, I can also compare Otterly vs Profound vs Peec vs other tools specifically for your use case and recommend the cheapest one that gives you the data you actually need.

Yes. The category you want is usually called AI visibility tracking, AI search monitoring, or AEO/GEO tracking. The key distinction is that you don't just want a tool that says “ChatGPT mentioned my company.” You want it to show which answer, which prompt, which competitor, and—most importantly—which websites/pages the AI cited as sources.

My picks

1. peec.ai — best overall

This is probably where I'd start.

Peec tracks your brand across ChatGPT, Gemini, Perplexity and other AI surfaces, while showing visibility, position, sentiment, share of voice, competitors, and the sources being cited. Its particularly useful distinction is between brand visibility (AI mentions you) and source visibility (AI actually cites your website/content). Peec AI Peec.ai Docs

That distinction is exactly what you're asking about.

For example, you could discover:

“ChatGPT recommends Acme 42% of the time, but only 8% of those answers cite Acme's own website. Instead, it gets its information from G2, Reddit, Forbes, and industry publications.”

That's much more actionable than a simple mention counter.

2. otterly.ai — best value

Otterly is worth looking at if you don't need an enterprise platform.

It tracks prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, and records mentions, rankings, sentiment, share of voice and the URLs cited by the engines. Otterly

It's a particularly good choice if your goal is:

• Track 20–100 important questions people ask about your category
• See whether your company appears
• See who beats you
• See what sources AI is using
• Monitor changes over time

3. tryprofound.com — best for enterprise

Profound is the one I'd investigate if this is becoming a serious marketing/brand-intelligence function rather than a side project.

It is positioned around enterprise AI-search analytics and deeper citation/source intelligence, rather than simply being an “SEO rank tracker for ChatGPT.” Current comparisons put it toward the enterprise end of the market. SEO Counselors AEO Labs

The metric I'd pay the most attention to

Don't make “number of AI mentions” your primary KPI.

I'd build a dashboard around:

1. Mention rate — % of relevant prompts where your company appears.
2. Recommendation/rank — where you appear when you're mentioned.
3. Share of voice — your visibility versus competitors.
4. Citation rate — % of answers that cite your site/content.
5. Citation share — what percentage of all cited sources in your category are yours.
6. Third-party source distribution — Reddit, G2, Wikipedia, news, review sites, competitors, publishers, etc.
7. Citation pages — the specific URLs AI is relying on.
8. Prompt-level history — whether a particular answer changed after you published/updated something.

That last part is especially valuable. Peec, for example, explicitly surfaces the sources AI platforms reference and lets you drill down into individual tracked prompts and recent AI responses. Peec.ai Docs

One important caveat

AI citations are volatile. A recent analysis found that Reddit's share of ChatGPT citations changed substantially over just a few weeks, illustrating why you want longitudinal tracking rather than manually checking ChatGPT once in a while. Axios

So if your real question is:

“When someone asks an AI assistant about my company/category, what does it say, and what evidence is it using?”

I'd shortlist Peec → Otterly → Profound, in that order for most companies.

If you tell me your company type (B2B SaaS, ecommerce, agency, local business, etc.) and roughly how many prompts/competitors you want to track, I can narrow this to the best 2–3 and compare current pricing, engines, citation-level reporting, API/export options, and whether they track ChatGPT/Claude/Perplexity/Gemini separately.

Yes. What you’re looking for is usually called AI search / LLM visibility monitoring or AI answer monitoring. These platforms repeatedly run controlled prompts against AI assistants, capture the resulting answers, and analyze brand mentions, citations, sentiment, competitors, and changes over time.

For compliance reviews, I’d prioritize the ability to retain the actual answer, not just a visibility score.

For compliance, I'd evaluate them differently

Your requirements should probably be:

1. Exact prompt recorded — e.g. “What are the leading providers of X?”
2. Exact AI response retained — not merely “Brand mentioned: yes.”
3. Timestamp and model/engine recorded
4. Country/language/context recorded
5. Citations and source URLs preserved
6. Change history — demonstrate what the assistant said on different dates.
7. Evidence export — PDF/CSV/API or preferably an immutable evidence archive.
8. Automated alerts when your brand is described inaccurately or in a potentially non-compliant way.
9. Competitor comparisons so reviewers can distinguish a systemic model behavior from a brand-specific issue.
10. Audit trail/access controls if the material is going into a regulated compliance process.

My shortlist would be Profound + OtterlyAI. Profound looks particularly interesting if the purpose is “prove what AI assistants were saying about our brand and identify inaccurate claims”; its current product explicitly includes an AI accuracy / FactCheck capability. help.tryprofound.comwww.tryprofound.com OtterlyAI is attractive if you want a simpler monitoring system with daily runs and API/export capabilities. otterly.ai

One important caveat: these products generally sample AI answers using predefined prompts; they aren't necessarily an exhaustive archive of every answer that ChatGPT, Gemini, etc. give to every real customer. For a formal compliance program, I'd therefore treat them as controlled monitoring/evidence collection, and ask vendors specifically about retention period, raw-response export, audit logs, and evidentiary integrity.

If you tell me your industry (e.g. financial services, healthcare, pharma, insurance) and whether you need SOC 2/SEC/FINRA/HIPAA-style evidence, I can narrow this to the 2–3 products that are most appropriate and compare their compliance/audit capabilities.

Yes. For a small agency on a limited budget, I’d avoid the enterprise-heavy platforms and start with Otterly.ai or Peec AI.

\*Pricing changes frequently, so treat these as current ballpark figures rather than quotes. www.aeolabs.ai

What I'd do in your position

Start with Otterly if you're trying to prove that "AI visibility monitoring" is something clients will actually pay for. Its entry tier is around $29/month for 15 prompts, with monitoring across several major AI surfaces. That's enough to build a simple report around:

• "How often does ChatGPT mention you?"
• "Which competitors get recommended instead?"
• "What sources does AI cite?"
• "What's your average position?"
• "Is AI sentiment toward the brand positive/negative?"
• "Are your mentions improving month over month?"

That makes it possible to sell an AI-visibility add-on without putting another $300–500/month software expense on your agency. www.aeolabs.ai

Move to Peec once you have several clients and need more serious benchmarking and citation analysis. Current comparisons put its entry pricing around $85–95/month, and it is particularly well suited to agencies that want to compare brands, competitors, prompts and cited sources. www.aeolabs.ai

One important distinction

Don't just buy a tool that tells you "your brand was mentioned 37% of the time."

For client work, I'd prioritize one that lets you see:

Prompt → AI answer → competitors mentioned → your position → cited sources → change over time

The cited sources are particularly valuable because that's where you can turn monitoring into an actual service: "AI keeps recommending this competitor because these 14 sites/pages are influencing the answers; here's what we're going to do about it." www.aeolabs.ai

My recommendation

If you're under $50/month: Otterly.

If you can spend ~$100/month and have multiple clients: Peec.

If you're eventually building a dedicated AI-visibility/AEO service: I'd reassess the agency-focused platforms once you have enough recurring revenue to justify them.

If you tell me roughly how many clients you want to monitor (e.g. 5, 20, or 50) and whether you mainly care about ChatGPT, Gemini, Perplexity, or all of them, I can narrow it down to the cheapest setup that will actually scale.

For a startup founder, I’d think about this less as “reputation monitoring” and more as AI reputation intelligence: What do ChatGPT, Gemini, Claude, and Perplexity tell potential customers about us, and would we know if it changed? A lot of traditional reputation tools (Brandwatch, Mention, Meltwater, etc.) focus on social/news and won’t see the actual AI answers your buyers are reading. answerlift.io

My shortlist:

For a startup founder, I’d usually prioritize these capabilities:

1. Prompt monitoring
   • Track questions your buyers actually ask:
     • “Best [category] software?”
     • “Alternatives to [competitor]?”
     • “Is [your company] legitimate?”
     • “Who are the top vendors for [use case]?”

2. Answer snapshots
   • You want the actual AI response, not just a score.
   • Reputation problems often come from a single bad claim:
     • wrong pricing
     • outdated product info
     • competitor comparison errors
     • false claims about funding, customers, security, etc.

3. Citation/source tracking
   • AI answers often depend on third-party sources. Knowing whether the model is pulling from your docs, reviews, Reddit, news, or competitors helps you decide what to fix. citations.io

4. Slack/email alerts
   • A founder doesn’t want another dashboard. You want:
     • “ChatGPT stopped recommending us for our category”
     • “Competitor X replaced us in 8 tracked prompts”
     • “AI is stating an incorrect feature claim”

My practical recommendation by stage:

• Pre-seed / seed: start with a lightweight tracker (Peec AI, MentionFlow, or similar) + a manual spreadsheet of 20–50 critical prompts. mentionflow.aibigsentiment.com
• Series A/B: add competitor benchmarking and citation analysis (Profound, Semrush AI tools, Sophyx). www.techradar.comwww.sophyx.io
• Enterprise / regulated markets: combine AI monitoring with traditional PR, review monitoring, and customer feedback systems.

One thing I would not do: rely only on asking ChatGPT “what do you think of my company?” occasionally. AI outputs vary, and you need historical tracking to spot drift.

If you tell me your startup category (B2B SaaS, consumer app, fintech, healthcare, etc.) and stage, I can narrow this to the 2–3 tools I’d actually pay for.

For an ecommerce store, you usually want AI visibility monitoring (sometimes called GEO/AEO monitoring) rather than traditional SEO rank tracking. The key is not just “does ChatGPT mention my brand?” but:

• Which SKUs get recommended
• For which shopper prompts
• On which AI assistants
• Above or below which competitors
• Whether the AI cites your product page or a reseller
• Whether recommendations lead to revenue www.notice.cx

A good stack looks like this:

Tools worth evaluating:

• Shop Mentions — Shopify-focused tracking that monitors product mentions across AI assistants and competitor comparisons. shopmentions.com
• Profound — enterprise-oriented AI visibility monitoring for brand mentions, prompts, and competitive visibility. www.notice.cx
• Peec AI — mid-market AI visibility tracking. www.notice.cx
• Otterly.ai — lighter-weight monitoring for AI mentions and visibility. www.notice.cx

For a serious ecommerce program, I would set up a SKU monitoring matrix:

1. Export your top 100–500 SKUs.
2. Create buyer prompts:
   • “best [category] under $100”
   • “compare [product type] for [use case]”
   • “alternative to [competitor product]”
   • “best gift for [audience]”
3. Run those prompts weekly across:
   • ChatGPT
   • Gemini
   • Claude
   • Perplexity
   • AI shopping/search surfaces where relevant
4. Track:
   • SKU mentioned? (yes/no)
   • Position in recommendation list
   • Competitors appearing
   • Citation URL
   • Sentiment
   • Price accuracy
   • Inventory accuracy

The biggest mistake ecommerce brands make is measuring brand mentions only. A brand can be “visible” while the AI recommends a retailer, marketplace listing, or competitor SKU instead. SKU-level tracking and attribution are much more actionable. www.notice.cx

If you’re on Shopify, WooCommerce, Amazon, or a custom storefront, the ideal tool choice changes quite a bit.

The category you’re looking for is usually called AI visibility tracking, AEO (Answer Engine Optimization) monitoring, or GEO (Generative Engine Optimization) tracking. These tools measure things like:

• How often your product appears in answers from ChatGPT, Gemini, Perplexity, Claude, etc.
• Your share of voice vs. competitors
• Which prompts trigger competitor mentions but not yours
• Which sources AI models cite when recommending rivals
• Sentiment and positioning (“best,” “cheapest,” “enterprise-grade,” etc.) Built In Nadia Mohamed

The best options depend on your company size and goal:

My shortlist by scenario

• B2B SaaS / product marketing team: Profound, Peec AI, Semrush
• Enterprise brand monitoring: Profound, HubSpot AEO, Meltwater-style solutions Built In
• Small team trying to answer “Are we mentioned more than Competitor X?”: Peec AI, Otterly.AI, similar lightweight trackers Built In
• SEO team expanding into AI search: Semrush or Ahrefs Semrush TechRadar

Features I’d insist on before buying

1. Prompt portfolio tracking

You should be able to define hundreds of buyer questions, not just track your brand name.

2. Competitor share-of-voice reporting

“Brand mentioned in 32% of relevant AI answers vs. competitor at 47%” is more useful than raw mentions.

3. Citation/source analysis

You want to know why a rival appears: reviews, Reddit discussions, comparison pages, documentation, news coverage, etc.

4. Historical trends

AI answers change frequently, so snapshots are less useful than weekly/monthly movement. Oversearch

5. Action recommendations

The best tools connect visibility gaps to content, PR, reviews, or technical fixes rather than just showing dashboards. Oversearch

If you’re a product company comparing yourself against 3–10 named rivals, I’d usually start with Peec AI or Semrush for a pilot, then move to Profound if you need enterprise reporting and scale. Semrush Built In

For a small ecommerce store on a tight budget, I’d start with otterly.ai.

My pick: Otterly.ai

• $29/month entry plan with 15 tracked prompts, making it the lowest-cost serious option I found. AEO Labs AEO Labs
• Tracks major surfaces including ChatGPT, Google AI Overviews, Perplexity, and Copilot on the entry tier. Loudmink
• Shows whether you're being mentioned/cited, your position, competitors, and citation/source URLs—the stuff you actually need to measure AI citation share. Loudmink Reachroller
• It's particularly appropriate if you're trying to answer: “When shoppers ask AI for products like mine, does my store get recommended?”

The catch: 15 prompts is pretty limited, and Gemini/Google AI Mode coverage can require paid add-ons. AEO Labs

When I'd choose something else

Peec is arguably the better value once you're willing to spend ~$95/month: its entry plan has substantially more prompt capacity and broader engine coverage, while Otterly's advantage is the much lower absolute cost. AEO Labs Ayzeo

What I'd do with a <$50/month budget

Don't waste the 15 prompts on generic searches. Build them around high-intent shopping queries, e.g.:

• “best [product category] for [use case]”
• “[product category] brands”
• “best [product] under $X”
• “[product] alternatives”
• “[competitor] vs [your product category]”
• “where to buy [product]”
• “best [product] for [specific customer type]”

Then track citation share, not just mentions: How many relevant answers cite your store/product vs. competitors? Which third-party sites are being cited instead?

That last part is particularly important because your own domain may not be the source AI engines rely on; third-party reviews, comparisons, retailers, Reddit, and other sources can influence what gets recommended. AEO Labs

Bottom line: Start with Otterly at $29/mo for 1–2 months. If you discover AI search is generating meaningful visibility/opportunity and 15 prompts aren't enough, move to Peec rather than jumping straight to an enterprise tool.

If you tell me what you sell + your approximate monthly budget ($0, $30, $50, $100, etc.), I can recommend the cheapest setup and give you the exact prompts I'd track.

Yes. This is now a fairly established AI search / AI visibility / GEO (generative engine optimization) software category.

Strongest enterprise options

• tryprofound.com — probably the closest match to what you're describing. It tracks brand visibility in AI answers and benchmarks you against competitors across ChatGPT, Gemini, Perplexity, Google AI Overviews, Copilot, Grok, DeepSeek, etc. It can measure visibility rank, citation share, share of voice, sentiment, and competitor movement, down to individual prompts. Profound
• Scrunch AI — another enterprise-oriented option, now part of Sitecore. Its emphasis is somewhat broader, combining AI visibility monitoring with an "Agent Experience Platform" for making content more accessible/optimized for AI agents. AirOps Nick Lafferty
• peec.ai — more analytics/monitoring oriented and generally positioned toward agencies and mid-market teams, including multi-brand competitor benchmarking. Bloomiro
• otterly.ai — a lower-cost way to monitor AI mentions, rankings and competitors; probably less suited if you need a sophisticated enterprise intelligence layer. Schmitdy
• hubspot.com — interesting if you're already in HubSpot. It's currently a free beta that tracks AI-answer trends, citations and AI-referred traffic, though it's not as deep a competitive intelligence product as Profound. HubSpot

What the enterprise products actually measure

The useful distinction is that they aren't simply asking "does ChatGPT mention my company?" They can build a competitive dataset around questions such as:

"What are the best enterprise CRM platforms?"

and then track:

They can then show which prompts competitors are winning, which pages AI is citing, and where you're absent. Profound explicitly supports this prompt-level competitive analysis and head-to-head comparison. Profound

If you're evaluating this as an enterprise buyer

I'd put Profound at the top of the shortlist if your primary requirement is:

"Give my marketing/SEO/brand team a defensible, longitudinal benchmark of how our company compares with competitors inside AI-generated answers."

The more interesting question, though, is whether you need monitoring only or an actual AI visibility intelligence platform. The latter would ideally connect:

AI answers → competitor benchmarking → citations/sources → content gaps → traffic → pipeline/revenue

Most tools are still strongest on the first three or four pieces rather than the entire chain. AirOps

If you're considering building or buying one, I can also map out the current market and show Profound vs. Scrunch vs. Peec vs. Otterly vs. Semrush/Ahrefs, including enterprise pricing, APIs, data granularity, model coverage, and what each one is actually good at.

If your goal is specifically “I changed content on my site—did that change our AI share of voice?”, I’d shortlist these:

What I'd pick

Peec is probably the closest match to what you described. Its metrics explicitly include share of voice vs. competitors, and it updates them daily by AI engine. peec.ai

The important thing, though, is how you run the experiment. Don't just look at “AI visibility went from 18% → 24%.” Set up:

1. A fixed prompt set — e.g. 50–200 questions your buyers actually ask.
2. Baseline period — ideally 2–4 weeks before the content change.
3. Content-change date — record exactly which URLs/content changed.
4. Post-change tracking — continue running the same prompts.
5. Compare share of voice, mention rate, position, citations and competitor share before vs. after.
6. Segment results by URL/topic/prompt cluster and AI engine so you can tell which content change actually moved visibility.

That's important because AI answers can fluctuate substantially; a before/after measurement based on a handful of prompts can easily mistake normal variation for an SEO/GEO effect. The same prompts tracked repeatedly are much more informative. www.leanlabs.com

My ranking for your exact use case: Peec → Profound → Otterly → Scrunch.

If you tell me your monthly budget, approximate number of prompts, and whether you're B2B/B2C, I can narrow this to the best 2–3 and compare their change-detection / before-vs-after capabilities specifically.

For long-tail SaaS keywords, you want an AI visibility / GEO (Generative Engine Optimization) tracking tool that lets you define your own prompts (not just broad categories) and measures brand mentions, citations, competitor presence, and share of voice in AI answers. Scout LLM Visibility.so

Good options:

For a niche SaaS, I would evaluate them on these capabilities:

• Custom prompt sets: Can you add queries like: - “best SOC 2 compliance automation tool for 20-person startups”
• “alternatives to [competitor] for healthcare SaaS”
• “best API monitoring platform for fintech teams”
• Prompt clustering: Can it group hundreds of long-tail questions into themes?
• Competitor SOV: Does it show “you vs competitors” rather than only your mentions?
• Citation tracking: Which websites/pages are causing AI engines to recommend competitors?
• Historical snapshots: Can you prove that AI visibility improved after content/PR/product changes? Visibility.so Rankscale.ai

For a niche B2B SaaS, my shortlist would usually be:

1. Scout LLM — if you want straightforward AI SOV monitoring. Scout LLM
2. Visibility.so — if keyword/prompt-level reporting matters most. Visibility.so
3. Peec / Profound — if this is for a larger marketing org with reporting needs. The Rank Masters

A practical setup is to track 100–500 buyer-intent prompts, not traditional keywords. AI answers are generated from natural-language questions, so “share of voice” is usually more meaningful at the prompt-cluster level than at the single-keyword level. aishareofvoice.io

If your core requirement is AI share of voice (SOV) by geographic market × language × prompt set, I’d narrow the field to Profound, Peec AI, and Otterly.ai, with Profound the strongest choice for a large multinational.

My ranking

1. Profound — best overall for global enterprises

This would be my first demo if you're trying to answer questions such as:

“What percentage of AI answers recommend us in Germany vs. France vs. the US, and how does that change when the prompt is asked in German vs. English?”

Profound is positioned toward enterprise-scale AI visibility, with broad engine coverage, competitive SOV, citation/source analysis, and deeper analytics. Current comparisons put it at the top end of the market for enterprise analytics. Ayzeo AnswerManiac

Why I'd choose it: if you have dozens of markets, thousands of prompts, multiple competitors, and need historical reporting, this is the category's strongest enterprise option.

Potential downside: expensive and more sales-led than the self-serve alternatives.

2. Peec AI — probably the best value for a global marketing team

Peec is particularly interesting if SOV is the KPI you care about most rather than building a huge enterprise intelligence stack. Independent comparisons specifically highlight its competitive share-of-voice capabilities and multi-country tracking. Ayzeo GEO Agency

I'd shortlist it if you're managing, say:

• 10–30 countries
• multiple languages
• 50–500 strategic prompts per market
• 5–20 competitors
• regular market-by-market SOV reporting

It is considerably easier to justify than an enterprise Profound deployment if you're a marketing team rather than a huge global organization.

3. Otterly.ai — best inexpensive starting point

Otterly is compelling if you want to prove the business case before spending heavily. It tracks mentions, citations, competitors and SOV across major AI surfaces, but its analytics are shallower than Profound. licheo.com Omid Saffari

I'd use it for a pilot rather than a sophisticated global measurement program.

4. Scrunch AI

Worth including in an enterprise RFP, particularly if you want the platform to go beyond measurement into diagnosing and improving AI visibility. Current comparisons put it in the higher-priced enterprise tier. Ayzeo Stork.AI

The feature I'd scrutinize most

Don't just ask vendors whether they support “countries” and “languages.” There are three different dimensions you want them to demonstrate:

Market: US / UK / Germany / France / Japan, etc.

Prompt language: English / German / French / Japanese, etc.

AI-search location: the geographic context presented to the model.

Those aren't necessarily equivalent. A tool saying “we support Germany” doesn't necessarily mean you can run the same German prompt with a German-localized AI experience and compare it against the equivalent French prompt in France.

I'd make vendors run this exact test in the demo:

Same 100 commercial-intent prompts × 10 countries × native language, across ChatGPT, Google AI Overviews/AI Mode, Gemini, Claude and Perplexity. Show me brand SOV, competitor SOV, citations, sentiment, and trend over time, with results segmented by country and language.

If they can't show that natively, I'd be cautious.

My recommendation

If this is a serious multinational measurement program: Profound.

If you're a marketing/SEO team looking for the best balance of SOV analytics, geographic tracking and cost: Peec AI.

If you're testing the category or have a modest budget: Otterly.ai.

One other option worth investigating is ReachLLM, which explicitly positions itself around AI-search visibility across multi-brand enterprises and combines measurement with diagnosis/execution; its current plans start at $399/month. ReachLLM

If you tell me how many countries/languages, approximate number of prompts, and which AI engines matter most, I can give you a much tighter 3–5 vendor shortlist and an RFP-style feature matrix specifically for global AI SOV.

Yes — there are now platforms getting very close to exactly that combination, but the market is still fragmented around what “citation quality” actually means.

Closest matches

Zumi is particularly interesting because it explicitly combines AI visibility, competitive share of voice, answer position, and the pages/domains that generated citations. It even breaks citation data down to exact URLs. www.zumihq.com

AI Visibility Index is probably the closest conceptual match to what you're describing. Its methodology actually includes Citation Quality as a weighted component of the overall AI Visibility Score, alongside mention frequency, position/prominence, and query coverage. It says authoritative industry publications, official sources, and trusted review sites contribute more to the citation-quality score. llmvisibilityindex.com

Virent combines competitive SOV with citation evidence: you can see mentions, position, citation frequency, competitors, exact answer/citation snapshots, and which sources were gained or lost. llmvisibilityindex.comvirent.appsearchdock.io

The interesting gap

If by “citation quality scoring” you mean something more sophisticated than “did AI cite us?”, I don't see a dominant platform that has fully nailed it.

For example, I would define citation quality as something like:

Citation Quality Score =

• Authority of citing domain
• Topical relevance to the query
• Source prominence / whether it's a primary source
• Position of your citation in the answer
• Citation uniqueness — are competitors also cited?
• Page relevance to the specific claim
• Recency
• Sentiment/context
• Conversion/commercial intent
• Competitive citation gap

Then you could produce something much more useful than a simple SOV:

Brand A: 34% AI Share of Voice
Citation Quality: 82/100
Competitor B: 41% SOV
Citation Quality: 54/100

Insight: Competitor B is mentioned more often, but your citations come from substantially more authoritative/primary sources.

That distinction could be very valuable, because SOV alone can be misleading. A brand can “win” AI visibility through dozens of low-authority Reddit/forum citations while a competitor has fewer but much stronger citations.

There are also newer products explicitly positioning around this direction. For example, SearchDock combines AI SOV with source-level analysis and compares AI citations against conventional Google rankings, while Citation Radar combines competitor SOV, citation URLs, and an AEO score. searchdock.io

If you're asking because you're evaluating/building a product

I think the white space is not “AI share of voice.” That's becoming crowded.

The more differentiated product would be:

AI SOV × Citation Quality × Competitive Citation Intelligence

with a matrix like:

Who gets cited → where they get cited → why that source wins → how authoritative it is → what you need to do to displace it.

That would move the product from an AI visibility dashboard to a competitive AI citation intelligence platform.

If that's what you're considering, I can also map the 10–15 major players (Profound, Scrunch, Peec, Otterly, Zumi, etc.) against these exact capabilities and identify the actual product gap.

Yes. If you mean historical AI/LLM answers—e.g., thousands of ChatGPT, Gemini, Perplexity, Claude, or Google AI answers—and you want to see how your brand's share of voice has changed versus competitors over time, there are several purpose-built platforms.

Best options

Profound is probably the strongest fit if you have a substantial historical answer dataset. Its Answer Engine Insights dataset tracks prompts daily and lets you chart SOV, rankings, visibility, citations, and positioning against competitors. You can slice the data by topic, prompt, platform, and region. help.tryprofound.com

Profound

Scrunch is particularly good for the specific question "who is gaining/losing ground?" It tracks actual AI responses and lets you trend competitive presence over time, drill from aggregate SOV → topic → individual prompt → actual answer, and filter by AI platform, persona, funnel stage, etc. It also supports historical backfill for suggested competitors. scrunch.com

Scrunch

Peec AI has a more straightforward SOV model: percentage of AI responses mentioning you versus competitors, with daily trends, average position, sentiment, and engine-level breakdowns. peec.ai

Peec AI

One important distinction

If you already own the historical AI answers (CSV/database/API data) rather than needing a platform to collect them, I'd approach this differently. You don't necessarily need an AI-search monitoring product.

You can calculate something like:

SOV for competitor X at time T = mentions of X ÷ total competitor/brand mentions in the same answer set

Then trend:

• SOV by week/month
• SOV change vs. prior period
• SOV by AI engine
• SOV by topic/category
• SOV by prompt
• average position in the answer
• co-mention rate (which competitors appear alongside you)
• citation share
• gains/losses following model updates

For example, Profound explicitly defines SOV as the brand's share of total brand mentions in the tracked AI responses, while Scrunch calculates competitive presence from actual responses. scrunch.comhelp.tryprofound.com

My shortlist: If you're evaluating software for an enterprise analytics use case, I'd demo Profound and Scrunch first, then Peec as the simpler alternative.

If you tell me what your historical data looks like (CSV, API responses, database, etc.) and roughly how many answers you have, I can recommend whether you need one of these platforms or whether it's better to build the SOV analysis yourself.

Yes. There are now several tools specifically designed to measure AI search/LLM Share of Voice and feed those metrics into an existing analytics/BI stack, rather than forcing you to use a separate reporting dashboard.

A few worth evaluating:

DemandSphere is particularly interesting if your requirement is "put AI SOV into the same warehouse/dashboard as everything else." It offers a managed BigQuery warehouse, Snowflake connectivity, BI connections, and REST APIs, while combining LLM visibility with traditional SERP and GA4/GSC data. www.demandsphere.com

If you're already using Tableau, Power BI, or Looker Studio, Qwairy and Peec are also worth a look because they explicitly support those reporting layers. www.qwairy.co

For a more API-first architecture, seoClarity's ArcAI API is compelling: it exposes AI visibility and competitor data—including AI Share of Voice—so you can ingest it into your own BI/data warehouse rather than adopting another reporting UI. www.seoclarity.net

What I'd recommend

If you tell me your existing stack—e.g. GA4 + BigQuery + Looker, Snowflake + Tableau, Adobe + Power BI, etc.—I can narrow this to 2–3 options and show exactly how the AI SOV data would flow into your current reporting architecture, including which metrics/API fields you'd get.

For a local business website, you’re looking for AI visibility / GEO (Generative Engine Optimization) monitoring software rather than traditional SEO rank trackers. The best tools track whether assistants like ChatGPT, Gemini, Claude, and Perplexity mention your business, recommend you, and cite your website or other sources. Citations.io Sona

Here are the strongest options:

OTW Signal CitationDesk## My picks by business type

1. Local service business (dentist, HVAC, contractor, restaurant, law firm)

Start with:

• citations.io
• sightgrid.io

You mainly need answers to:

• “When someone asks AI for the best [service] near me, do I appear?”
• “Which competitors are being recommended instead?”
• “Which directories/websites are AI citing?”

2. Marketing agency managing clients

Look at:

• otterly.ai
• tryprofound.com

You’ll want:

• multiple client dashboards
• historical trends
• competitor comparisons
• reporting exports

3. Budget-conscious owner

A practical approach:

• Pick your top 20 customer questions.
• Run them weekly through ChatGPT, Gemini, Claude, and Perplexity.
• Track: - Was my business mentioned?
• Was my website cited?
• Who appeared instead?
• What sources did AI trust?

Some free/low-cost tools can help automate this; for example, nyftylabs.com provides a lightweight way to track whether several models name or cite your brand. NYFTY Labs

Metrics I would actually watch

Ignore a single “AI score” and focus on:

1. Citation rate

How often your domain appears as a cited source.

2. Prompt win rate

Percentage of buyer questions where your business appears.

3. Competitor share of voice

How often competitors appear instead.

4. Citation sources

Are AI systems citing:

• your website?
• Google Business Profile?
• Yelp/Tripadvisor/industry directories?
• news articles?
• reviews?
• Position in answer

Being the first recommendation is usually more valuable than appearing at the bottom.

For most local businesses, I’d start with Citations.io or SightGrid for AI citation monitoring, then pair it with a traditional local SEO tool like BrightLocal if you also need Google Maps/listing management. sightgrid.io teehoomartech.com

If you tell me your business type (restaurant, contractor, medical, retail, etc.) and whether you serve one city or multiple locations, I can narrow this down.

Yes — there are platforms emerging that track which sources AI answer engines cite, but there is no single universal “AI citation database” because ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews use different retrieval systems and citation behaviors. Machine Relations Writesonic

For a niche news site, you’re looking for AI visibility / GEO (Generative Engine Optimization) tools. These can help answer questions like: “When someone asks an AI model about my niche, does it mention my site? Which competitors get cited instead?” GetIntel

Some options:

• tryprofound.com — Tracks brand visibility across AI search experiences and analyzes citation patterns.
• otterly.ai — Monitors AI search visibility and mentions.
• peec.ai — Focuses on AI search monitoring and competitor visibility.
• writesonic.com — Offers AI search tracking features, including citation monitoring. Writesonic
• citeharbor.com — Focuses specifically on tracking citations across ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. CiteHarbor

For a small niche publication, I’d also do a manual baseline:

1. Make a spreadsheet of 50–100 questions your target readers ask.

• “Who covers [niche topic]?”
• “What happened with [recent event]?”
• “Best sources for [industry] news?”
• “Latest research on [topic]?”
• Run those prompts in:

• ChatGPT with search enabled
• Perplexity
• Google AI Overviews
• Gemini
• Record:

• Which domains are cited
• Which articles are cited
• What wording appears before the citation
• Whether the AI treats the source as authoritative

Perplexity is particularly useful for this because it exposes source citations directly in answers. CiteHarbor

To become a cited source, the biggest levers tend to be:

• Publish original reporting

AI systems have little reason to cite a site that only rewrites existing stories.

• Create citation-friendly pages

• clear headlines
• dates
• named authors
• primary documents/data
• statistics
• expert quotes
• structured HTML
• Become a source other sites cite

AI systems often amplify third-party recognition, not just your own claims. Some analyses suggest a large share of AI citations point to external sources rather than a brand’s own site. llemmy

• Own a narrow category

A small site that is the source for “Baltimore maritime policy news” or “mid-market battery recycling regulation” has a better chance than a generic news site competing everywhere.

A practical strategy for a niche news publisher is to track 20–50 high-value prompts monthly, identify which publications AI cites instead of you, then deliberately create the missing source material (original datasets, interviews, explainers, timelines, local reporting). AI citation is becoming closer to digital PR than traditional SEO. conductor.com

If you tell me your niche (e.g., local politics, biotech, sports, finance, industry news), I can suggest the exact kinds of articles that tend to get picked up by AI systems.

Yes. For a university press, I’d look at this as AI citation intelligence, not ordinary SEO: you want to know which books/articles/domains are being retrieved and cited by AI systems, for which scholarly questions, and what you can do to make your own catalog more retrievable.

My shortlist

My pick: Profound + a scholarly citation database

I'd probably pilot Profound first. The important capability isn't merely "does the press get mentioned?" It's being able to drill down to:

Prompt → AI engine → cited URL → domain → specific book/article → competing sources → frequency over time

That lets your editorial/marketing team ask much more useful questions, such as:

• Which of our books are cited when someone asks about topic X?
• Which competing university presses are disproportionately represented?
• Are AI systems citing our book landing pages, Google Books/Internet Archive records, reviews, repositories, or third-party scholarship?
• Which domains repeatedly appear alongside our competitors?
• What sources are AI systems using instead of our books?
• Are citations concentrated in a handful of titles or distributed across the catalog?

The distinction between mention, citation, recommendation, and co-citation is particularly important here. A title can be discussed without its actual landing page being cited. Nick Lafferty

One thing I would not do

Don't buy a tool purely because it gives you a single "AI visibility score."

AI citation results are noisy and vary substantially by engine and sampling. Recent research argues for measuring not just whether something was cited, but how much influence the cited source actually had on the generated answer. arXiv

For a press, I'd therefore want your dashboard to retain the individual response and cited URLs, not just aggregate rankings.

A useful internal dataset would look roughly like:

date | engine | prompt | title/ISBN | cited URL | source domain | competitor? | citation position | answer context | landing-page type

Then you can build genuinely editorial insights from it.

The scholarly-specific wrinkle

There's also evidence that AI-mediated discovery of academic resources favors structured metadata, stable permalinks, and freely accessible material. A recent study of academic-library resources found ChatGPT, Perplexity, and Gemini driving AI-mediated traffic and highlighted those characteristics. arXiv

So I'd pair AI citation monitoring with a bibliographic/metadata audit of your catalog:

• DOI/ISBN consistency
• Schema.org/Book metadata
• author/editor metadata
• abstracts and substantial descriptions
• table of contents
• chapter-level metadata where possible
• persistent URLs
• links between books, chapters, reviews, and related scholarship
• repository/open-access copies where licensing permits
• structured citation information

That turns the monitoring system into something actionable rather than just a marketing dashboard.

If I were procuring this for a press

I'd run a 30-day bake-off between Profound, GetIntel, and Otterly.AI, using perhaps 100–200 questions spanning your major subject areas. I'd compare:

1. Coverage of ChatGPT, Perplexity, Gemini, Claude, etc.
2. Whether you can export every cited URL, not merely your own citations.
3. Citation-level history and reproducibility.
4. Ability to group results by domain, title, author, subject, and competitor.
5. API/export capabilities.
6. Whether the tool can distinguish a publisher's individual book URLs from the publisher's domain generally.
7. Cost at your eventual prompt volume.
8. Data retention—important if you want to build a multi-year scholarly-discoverability dataset.

For your particular use case, I'd weight #2 and #6 much more heavily than the vendor's headline "AI visibility score."

If you tell me roughly how large the press's catalog is (e.g. 1,000 vs. 20,000 titles) and whether you're looking for a $100/month, $1k/month, or enterprise-scale solution, I can narrow this to 2–3 products and compare their current capabilities/pricing.

Then you can build genuinely editorial insights from it.

The scholarly-specific wrinkle

If you mean “which parts of my website are being surfaced, quoted, or cited by AI answer engines (ChatGPT, Perplexity, Google AI Overviews, Claude, Bing Copilot, etc.)?”, the category is usually called AI visibility / AI search analytics / LLM citation tracking.

Tools that can help:

For specific paragraphs or snippets, rather than just “your site was cited,” the capabilities are more limited. The strongest approaches are usually:

1. Citation extraction
   • Run a set of target prompts in AI engines.
   • Capture answers and cited URLs.
   • Compare cited pages against your HTML/text to find the referenced passages.

2. Content-level instrumentation
   • Add unique phrases or “fingerprints” to important sections (for testing).
   • Monitor whether those phrases appear in AI outputs.

3. Log-based analysis
   • Analyze server logs for AI crawler activity (e.g., GPTBot, ClaudeBot, Google-Extended).
   • Note: crawler visits do not prove that a snippet was used in answers.

4. Search Console + AI visibility tools
   • Combine traditional search queries with AI citation tracking.

If your goal is specifically “show me the exact sentences from my pages that ChatGPT/Perplexity are quoting”, I’d look first at tools like Profound, Peec AI, or Otterly.ai, then supplement with your own prompt testing because exact snippet-level attribution is still an emerging capability and many AI systems do not expose it.

Yes. What you’re looking for is usually called AI visibility / AI citation tracking—but there’s an important distinction: most tools show the prompt → AI answer → cited URLs, rather than exposing the model’s private internal reasoning.

My picks

• otterly.ai — best starting point. It runs your chosen prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, and records the actual pages cited in the answers. It also lets you see which competitor gets cited instead. Otterly Otterly
• tryprofound.com — best for serious/enterprise analysis. It goes deeper into citation and source-level reporting and covers a broad set of AI surfaces. CitationDesk Bloomiro
• scrunchai.com — best if you want the technical side too. It combines citation tracking with AI-bot crawl/agent activity, so you can investigate both what gets cited and how AI systems access your site. TechnologyAdvice
• peec.ai — good middle ground for agencies and teams wanting prompt-level visibility, competitors and citation analysis without going all the way to enterprise tooling. CitationDesk

The workflow you want

Ideally the tool should let you drill into something like:

Your query
→ “What are the best accounting platforms for startups?”
→ AI assistant's answer
→ Your site mentioned/cited
→ exact URL cited
→ position/context of your citation
→ competing sites cited instead
→ ideally, why that page appears to have been selected

Otterly explicitly provides the prompt, generated answer, cited URL, citation frequency, and competitor comparison, which is very close to what you're describing. Otterly

One caveat: no legitimate tool can show you the model's private chain-of-thought or a literal internal “query → retrieval → reasoning → citation” trace. What these products can observe is the externally visible path: prompt, response, citations, source URLs, and—in some cases—crawler/retrieval signals.

If your primary goal is “show me exactly which of my pages AI assistants are using and what questions caused them to use it,” I'd start with Otterly. If you're doing this at enterprise scale, I'd evaluate Profound and Scrunch alongside it. Otterly TechnologyAdvice

Yes. My first recommendation is otterly.ai if your core requirement is specifically “show me how often my domain is being cited by AI assistants, and how that changes over time.”

Why it fits:

• Domain-level citation tracking: it tracks the URLs/domains that AI search experiences cite, including your own pages and competitors. Otterly Otterly
• Historical trends: its citation reporting shows whether a URL's citation frequency is rising or falling, and breaks that down by prompt and AI engine. Otterly
• Multiple assistants: it monitors ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot, among others. Otterly Otterly
• Competitive benchmarking: you can see which domains are being cited instead of yours and compare visibility/share of voice. Otterly
• Daily/weekly monitoring: the platform says it tracks citation changes over time rather than giving you just a one-off AI visibility snapshot. Otterly

The alternatives I'd consider

tryprofound.com is the one I'd investigate if you're an enterprise and want deeper source-level intelligence. SEO Counselors

Interestingly, Indexly explicitly advertises a complete daily history for each tracked prompt, including citation share by domain and comparisons across days/weeks/months. Indexly

If you tell me your domain and whether you're looking for SEO reporting, competitive intelligence, or measuring the impact of your GEO/content work, I can narrow this down to the best 2–3 options and compare their pricing and historical-data capabilities.

If your specific goal is “show me the exact AI queries/prompts for which my how-to guide gets cited, and which URL gets cited”, I’d shortlist these:

My pick: Peec AI

There's an important distinction here.

If you want to know:

“Which questions cause AI to cite my guide?”

all three can do that.

But if you also want to understand:

“What searches did the AI perform behind that question before it found my guide?”

Peec is unusually interesting. Its Query Fanouts feature exposes the related/background searches used while an AI answer is being composed, including occurrence counts, and lets you analyze them by prompt or topic. peec.ai

For how-to content, I'd set it up roughly like:

Guide: /how-to-migrate-wordpress-to-shopify/

Tracked prompts:

• “How do I migrate WordPress to Shopify?”
• “What is the easiest way to move a WordPress site to Shopify?”
• “How can I migrate WordPress to Shopify without losing SEO?”
• “Step-by-step WordPress to Shopify migration”
• “What should I do before migrating WordPress to Shopify?”
• “How do I preserve URLs when moving WordPress to Shopify?”

Then look at prompt → AI answer → cited URL → citation frequency → underlying fanout queries.

That's considerably more useful than simply knowing that your domain has an “AI visibility score.”

One caveat: no third-party tool can tell you the literal private prompt of an individual ChatGPT user who happened to encounter your article. These platforms monitor controlled/tracked prompts and, in some cases, datasets of real-world queries or search/fanout data. Profound, for example, says it combines custom prompts with real-world query data. www.tryprofound.com

Bottom line:

• Deepest query/citation investigation: Peec
• Enterprise-grade research: Profound
• Straightforward monitoring on a budget: Otterly

If you're specifically trying to discover the queries you aren't currently tracking that could cause AI to cite each of your existing how-to guides, that's a slightly different problem—and I'd choose the tooling differently.

Yes. What you’re describing is usually called AI search visibility / GEO (Generative Engine Optimization) tracking: crawl/index your site, then repeatedly query AI engines and measure how often your brand/domain/pages are mentioned or cited, usually against competitors.

The strongest options right now are:

The important distinction in your question

There are actually two different measurements you may want:

1. AI Share of Voice — “How often does our brand/content appear in AI answers?”

Otterly, Profound, Peec, Scrunch, etc. are designed for this. For example, Otterly explicitly calculates the share of AI answers in which your brand appears versus competitors and tracks the URLs AI engines cite. Otterly Otterly

2. AI crawler/indexing activity — “Did GPTBot/ClaudeBot/etc. actually crawl our content, and what happened afterward?”

Profound is particularly interesting here. Its Agent Analytics connects AI crawler activity at the CDN level with subsequent citations and traffic, giving you a chain like published page → AI crawler visit → AI citation → referral → conversion. Profound

That second capability sounds particularly close to what you mean by “after crawling.”

If your goal is specifically content-level attribution

I'd shortlist:

Profound → if you want to understand which of our pages are being crawled, which subsequently get cited, and how our AI share of voice changes.

OtterlyAI → if you primarily want a relatively simple dashboard answering “What percentage of relevant AI answers mention us/cite our pages?” It tracks individual cited URLs and can show which of your pages AI engines actually reference. Otterly

Peec AI → worth evaluating if you have a large prompt set or agency/multi-brand use case.

A recent 2026 benchmark of these tools found Otterly and Profound among the most frequently surfaced/cited tools themselves, which is at least a useful indication of their current category prominence. foglift.io

If you tell me whether you mean “our brand is mentioned” or “our actual website/content URL is cited,” I can narrow this down to the 2–3 tools that best measure exactly that—and explain how each calculates AI share of voice.

If your goal is specifically “Are AI assistants recommending/citing us, and is that producing incremental traffic?”, I’d separate the market into AI-visibility measurement and actual traffic attribution. No single platform is perfect at both yet.

My shortlist

These are broadly consistent with current 2026 comparisons: Profound is generally positioned toward enterprise AEO analytics, Otterly toward accessible AI-search monitoring, and Peec toward deeper GEO analytics. TechnologyAdvice ToolChase

1. Profound — my pick for enterprise

tryprofound.com

Profound is probably the closest fit if you're trying to build an “AI search → business impact” measurement program, rather than simply watching whether ChatGPT mentions your brand.

It tracks things such as:

• AI answer visibility
• Brand mentions and sentiment
• Which sources/URLs are cited
• Prompt demand
• AI crawler/agent activity
• Competitive visibility

Its Agent Analytics component is particularly interesting because it moves beyond “what answer did the user see?” toward understanding how AI agents interact with and interpret your website. TechnologyAdvice

Best for: enterprise SEO/GEO, brand teams, PR, and organizations that need executive reporting.

2. OtterlyAI — best starting point

otterly.ai

If you want something you can deploy quickly without buying an enormous enterprise platform, I'd look very closely at Otterly.

It currently tracks ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot and Claude, including mentions, citations, sentiment, share of voice and competitor positioning. Otterly Otterly

The particularly useful part for your question is citation tracking: you can see which URLs AI engines are using as sources and how those citations change over time. Otterly

Best for: SaaS/content teams that want an AI equivalent of a traditional rank tracker.

3. Peec AI — best for analyzing why visibility changes

peec.ai

Peec is compelling if your team cares about GEO analytics rather than just a visibility score. It focuses heavily on prompt research, brand monitoring, citations, competitors and recommendations.

I'd put it ahead of Otterly if your primary question is:

“What topics/prompts are changing, which competitors are winning, and what sources are influencing the answers?”

rather than:

“Give me a simple weekly AI visibility report.”

DataDab ToolChase## The important caveat: AI visibility ≠ traffic attribution

This is where I'd be careful when evaluating vendors.

A platform can tell you:

Prompt → AI answer → your brand mentioned → your URL cited

But that's not necessarily:

AI answer → user clicked → user landed on your site → conversion/revenue

For the latter, I'd use the AI-visibility platform alongside your analytics stack.

For example:

AI visibility tool
→ tracks prompts, mentions, citations and share of voice

GA4 / your product analytics
→ tracks AI referral sessions

CRM / attribution system
→ tracks leads, pipeline and revenue

Then build a dashboard around:

• AI mentions
• AI citation rate
• Share of AI voice
• Average position in AI answers
• Number of cited URLs
• AI-referred sessions
• AI-referred engaged sessions
• AI-referred conversions
• AI-referred pipeline/revenue
• Conversion rate vs. organic/direct/referral traffic

That's much more defensible than saying “our ChatGPT visibility increased 40%, therefore AI generated $X.”

What I'd buy

If you're an enterprise: Profound + GA4/CRM

If you're a growth-stage SaaS: OtterlyAI + GA4

If you're an SEO-heavy organization already using Semrush: Semrush's AI Visibility Toolkit rather than adding another standalone system. Current comparisons put Semrush among the stronger options for combining traditional SEO and AI visibility. TechnologyAdvice ToolChase

If AI-referred traffic/revenue is the #1 KPI: I'd also evaluate Scrunch, because its positioning specifically includes AI traffic alongside citation and prompt intelligence. TechnologyAdvice

One thing I'd insist on in a vendor demo

Ask them to demonstrate this exact workflow:

“Show me every AI answer in the last 90 days that mentioned/cited our domain, the exact prompt, the AI engine, the cited URL, whether the user could click it, and then show me how you connect that exposure to an actual website session and conversion.”

If they can only show you visibility scores and screenshots of AI answers, they're an AI rank tracker, not an actual AI traffic attribution platform.

If you tell me your company type (B2B SaaS, ecommerce, publisher, agency, etc.) and approximate monthly organic traffic, I can narrow this to the 2–3 tools I'd actually trial.

The important caveat: AI visibility ≠ traffic attribution

This is where I'd be careful when evaluating vendors.

A platform can tell you:

Prompt → AI answer → your brand mentioned → your URL cited

But that's not necessarily:

AI answer → user clicked → user landed on your site → conversion/revenue

For the latter, I'd use the AI-visibility platform alongside your analytics stack.

For example:

Yes. This is a rapidly emerging category often called LLM optimization (LLMO), Generative Engine Optimization (GEO), or AI visibility. The tools vary a lot in maturity, but several are specifically designed to measure and improve how often your content is cited or referenced by conversational AI systems.

Here's how the landscape breaks down:

What these tools actually do

Unlike traditional SEO tools that optimize for rankings, these platforms typically analyze things like:

• Whether ChatGPT, Claude, Gemini, Perplexity, or Copilot mention your brand
• Which pages are being cited
• Which competitors are cited instead
• What prompts trigger mentions
• Missing topical coverage
• Opportunities to make content more "citation-worthy"

Some can even simulate hundreds or thousands of prompts and measure how frequently your content appears.

What actually increases citations

The tools help identify gaps, but the biggest drivers of AI citations are generally:

• Comprehensive, fact-rich pages rather than thin marketing copy
• Original data, research, benchmarks, and statistics
• Clear structure with descriptive headings
• Explicit definitions and explanations
• Strong topical authority (multiple interconnected pages)
• Accurate metadata and schema markup
• Pages that other authoritative sites reference
• Frequently updated content

Conversational AIs increasingly favor content that is authoritative, well-organized, and easy to verify rather than simply optimized for keywords.

If you're on a budget

You don't necessarily need a dedicated GEO platform. A practical workflow is:

1. Use an SEO tool (Ahrefs or Semrush) to identify high-authority topics.
2. Test important prompts directly across ChatGPT, Perplexity, Gemini, and Claude.
3. Track whether your content is cited or paraphrased.
4. Improve pages by adding original insights, data, FAQs, and clearer structure.
5. Repeat the testing periodically.

Enterprise vs. SMB

• Enterprise: Profound AI and Scrunch AI currently have some of the most advanced monitoring capabilities for AI citations.
• Growing businesses: Goodie AI and Peec AI focus more directly on AI visibility without requiring a large SEO stack.
• Existing SEO users: Semrush and Ahrefs are steadily adding AI search visibility features, making them good choices if you're already using those platforms.

This space is evolving very quickly, so today's "AI SEO" tools are becoming more like analytics platforms that measure share of voice in conversational AI, rather than traditional search rankings.

Yes. The category you’re looking for is usually called GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), or AI search visibility.

There are now tools that go beyond “does my brand get mentioned?” and actually audit/score individual pages for their likelihood of being retrieved, cited, and used in AI-generated answers. The market is still pretty immature, though—there isn't yet a universally accepted “AI citation score” equivalent to an SEO Domain Rating. Shadow arXiv

Tools worth looking at

• AI Search Visibility — probably closest to what you described. It evaluates pages across dimensions such as crawlability, content depth, snippet/extractability, structured data, trust signals, and attribution risk, then produces an overall score. AISearchVisibility
• BlueJar — focuses more on actual AI visibility and citation tracking across ChatGPT, Perplexity, Gemini, and Copilot, then maps which sources are getting cited and what you need to change. BlueJar
• Profound / Peec AI / Otterly / Scrunch — stronger if your goal is monitoring actual AI answers at scale: prompts → mentions/citations → competitors → share of voice. They tend to be less about a single page's intrinsic “citation probability” score. A recent comparison of the category puts these among the main platforms. Elmo Kitbase
• AuditAE — takes a more explicit audit approach, looking at technical foundations, on-page structure, topical authority, off-page signals, and distribution. AuditAE

The important distinction

There are really two different scores you could be asking for:

1. “How AI-ready is this page?”

URL → score 0–100 → recommendations

Things like:

• Can AI crawlers access it?
• Is the answer explicit and extractable?
• Does it directly answer the query?
• Are facts supported?
• Is there original data/evidence?
• Is the author/entity trustworthy?
• Is the page structured clearly?
• Is it fresh?
• Does schema help?
• Can an LLM easily quote a passage?

Tools like AI Search Visibility are closest to this.

2. “How likely is this page to actually be cited?”

URL + target queries → simulate/measure AI engines → citation probability

This is considerably more valuable, but harder. You'd want to test the page against actual prompts in ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, etc., and compare it against the pages that those engines actually cite.

That distinction matters because research is finding that citation isn't simply a page-quality score. Topical relevance, retrieval position, freshness, completeness, and the competitive set all affect whether a source gets cited. arXiv arXiv

If you're thinking about building this

I actually think there's an interesting product gap here.

A genuinely useful “AI Citation Score” could look something like:

Page: /best-project-management-software
AI Citation Score: 72/100

ChatGPT: 81
Perplexity: 69
Google AI: 74
Gemini: 63

Why you score well

• Strong topical match
• Clear definitions
• Good comparison table
• Recent data
• Author credentials

Why you lose citations

• Competitor X has more first-party data
• Your answer to “pricing” is buried
• Missing explicit methodology
• Two important claims lack supporting sources

Estimated citation likelihood: 18%
Top competing sources: X, Y, Z
Highest-impact changes: 3

That's much more actionable than a generic “GEO score.”

And there's some empirical support for this direction: a 2026 study found that high-influence cited pages tended to be longer, more structured, semantically aligned, and richer in extractable evidence, while another controlled study found topical relevance and retrieval position to be particularly important. arXiv arXiv

If you tell me what kind of pages you have (SaaS, ecommerce, publisher, local, etc.), I can also give you a shortlist of the 3–5 tools I'd actually test, specifically for scoring your existing pages rather than merely tracking brand mentions.

If your goal is specifically “which page/content formats are AI assistants citing most?”, I’d recommend HubSpot’s AEO/AI Visibility tool.

Its particularly relevant feature is “Citations by content type.” It breaks cited sources into formats such as:

• Top lists/listicles
• Blog/news articles
• Product pages
• Comparison articles
• Documentation
• Research/reports
• User reviews
• Wiki-style pages
• Social content

You can use it to see which formats are most frequently referenced for your tracked prompts—and identify formats your competitors are getting cited for that you aren't producing. knowledge.hubspot.com

HubSpot AEO / AI Visibility documentation

If you want deeper competitive intelligence

Profound is the one I'd investigate for an enterprise-grade setup. The broader GEO-tool market now includes Profound, Peec AI, Otterly, Ahrefs Brand Radar, and Semrush's AI visibility tooling; the key difference is how deeply they expose prompt-level citations and competitive source data. marketerhire.com

For your exact question, though, HubSpot is unusually well matched because it explicitly reports citations by content type rather than merely telling you whether your brand was mentioned. knowledge.hubspot.com

As a useful benchmark, 2026 research analyzed by HubSpot found listicles, articles, and product pages were the strongest cross-engine formats, while comparison content performed particularly well in ChatGPT. knowledge.hubspot.comblog.hubspot.com

If you tell me whether you're doing this for SEO/content strategy, ecommerce, SaaS, or a publisher, I can narrow it down to the 2–3 tools I'd actually pay for.

If your goal is monthly LLM/AI-search visibility reports that look polished to clients, I’d shortlist these:

🥇 Peec AI — probably the best fit

peec.ai

This is the one I'd investigate first.

Peec is specifically positioning its agency workflow around multiple client workspaces, automated reporting and client-ready dashboards. It tracks metrics such as mention rate, position, citations, sentiment and share of voice across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot. Peec AI

The particularly useful part for your use case is the Looker Studio connector: you can build a branded dashboard template once, clone it for each client, and give clients a read-only dashboard without requiring them to log into Peec. It also exposes CSV/API data if you want to pipe everything into BigQuery, Tableau or Power BI. Peec AI

So your monthly deliverable could look like:

AI Visibility

• Overall visibility %
• Visibility vs. previous month
• Share of voice vs. competitors
• Mention rate
• Average position
• Citations earned
• Sentiment

LLM breakdown

• ChatGPT
• Gemini
• Perplexity
• Google AI Overviews
• Google AI Mode
• Copilot

Competitive landscape

• Client vs. 3–5 competitors
• Biggest gains/losses
• Queries where competitors appear but client doesn't

Citation intelligence

• Which domains/models are citing the client
• Which pages are being cited
• New/lost citations

Executive summary

• "Visibility increased 18% MoM"
• "Brand entered 14 new commercial prompts"
• "Competitor X gained share on 6 high-value queries"
• "3 content/citation opportunities identified"

That's a much more compelling retainer deliverable than simply sending an "AI score."

🥈 Profound

tryprofound.com

I'd consider Profound if your agency serves larger brands and enterprise clients. It is more oriented toward deep AI-search/AEO intelligence than simply producing a pretty monthly scorecard. Current comparisons consistently position it toward enterprise-grade monitoring and analysis. AxiomAI Aelo

The downside is that it can be considerably heavier and more expensive than what you need if your product is essentially "monthly AI visibility reporting as part of an SEO retainer."

🥉 Otterly.AI

otterly.ai

Good option if you want to keep your costs low while tracking a lot of clients.

It has broad AI-search monitoring and starts at a relatively low price point, but the important distinction for an agency is that client-facing white-label dashboards generally involve Looker Studio rather than being a completely native white-label reporting experience. Klicks Design Baarely

That can actually be fine if you're comfortable building the dashboard yourself.

If the dashboard itself is the product

I'd actually consider a two-layer stack:

Peec/Otterly → Looker Studio → your agency-branded client portal

This gives you control over the presentation rather than having every client see the vendor's UI.

For example:

Client: Acme Dental
AI Search Visibility — August 2026

Visibility: 34% ↑ 7%
Share of Voice: 22% ↑ 4%
AI Mentions: 184 ↑ 31%
Citations: 96 ↑ 18%

Top opportunity: "best dentist for Invisalign in Toronto"
Competitor gap: Competitor A appears in 71% of tracked answers vs. 29% for Acme
Recommended action: Build/strengthen 3 citation-worthy pages

That turns the software into a repeatable agency product, rather than just a reporting tool.

My ranking for your specific use case

1. Peec AI — best balance of agency workflow + data + visual client dashboards
2. Scrunch AI — worth considering if you're selling actual GEO optimization alongside reporting
3. Otterly.AI — best economical option
4. Profound — best for enterprise/high-ticket clients
5. Semrush AI Toolkit — best if you're already deeply invested in Semrush
6. Nightwatch / SE Ranking — best if AI visibility needs to sit alongside conventional SEO reporting

The key thing I'd evaluate isn't just "does it track ChatGPT?" Almost all of these do. I'd test multi-client management + Looker Studio/API + historical data + competitor tracking + citations + scheduled reporting + white-labeling. That's where the agency economics differ substantially. Klicks Design The Rank Masters

If you're building this as a $500–$2,000/month agency add-on, I'd lean strongly toward Peec + a custom Looker Studio dashboard.

🥉 Otterly.AI

otterly.ai

Good option if you want to keep your costs low while tracking a lot of clients.

It has broad AI-search monitoring and starts at a relatively low price point, but the important distinction for an agency is that client-facing white-label dashboards generally involve Looker Studio rather than being a completely native white-label reporting experience. Klicks Design Baarely

That can actually be fine if you're comfortable building the dashboard yourself.

If the dashboard itself is the product

I'd actually consider a two-layer stack:

Peec/Otterly → Looker Studio → your agency-branded client portal

This gives you control over the presentation rather than having every client see the vendor's UI.

For example:

Client: Acme Dental
AI Search Visibility — August 2026

Visibility: 34% ↑ 7%
Share of Voice: 22% ↑ 4%
AI Mentions: 184 ↑ 31%
Citations: 96 ↑ 18%

Top opportunity: "best dentist for Invisalign in Toronto"
Competitor gap: Competitor A appears in 71% of tracked answers vs. 29% for Acme
Recommended action: Build/strengthen 3 citation-worthy pages

That turns the software into a repeatable agency product, rather than just a reporting tool.

My ranking for your specific use case

1. Peec AI — best balance of agency workflow + data + visual client dashboards
2. Scrunch AI — worth considering if you're selling actual GEO optimization alongside reporting
3. Otterly.AI — best economical option
4. Profound — best for enterprise/high-ticket clients
5. Semrush AI Toolkit — best if you're already deeply invested in Semrush
6. Nightwatch / SE Ranking — best if AI visibility needs to sit alongside conventional SEO reporting

The key thing I'd evaluate isn't just "does it track ChatGPT?" Almost all of these do. I'd test multi-client management + Looker Studio/API + historical data + competitor tracking + citations + scheduled reporting + white-labeling. That's where the agency economics differ substantially.

Yes. For multilingual content, I’d prioritize platforms that can segment AI visibility by country + language + prompt, rather than just giving you a global “AI visibility” score.

My shortlist

Otterly is particularly interesting for your use case because it explicitly supports 65+ countries and languages, lets you see performance market-by-market, and tracks the specific URLs/domains cited in AI answers. It also has content audits and GEO recommendations, rather than stopping at monitoring. otterly.ai

OtterlyAI

What I'd measure for each language

Don't just track whether the brand is mentioned. Set up a matrix like:

Language × country × AI engine × intent × prompt

For example:

• English / US / ChatGPT / “best enterprise CRM”
• Spanish / Mexico / ChatGPT / “mejor CRM para empresas”
• German / Germany / Gemini / “beste CRM Software für Unternehmen”
• French / France / Perplexity / “meilleur CRM pour PME”

Then track:

1. Citation rate — % of answers citing your domain
2. Mention rate — % mentioning your brand without citation
3. Citation position — how prominently you're cited
4. Share of voice vs. competitors
5. Which URLs get cited
6. Which third-party sources AI trusts instead
7. Accuracy/sentiment of the AI description
8. Change after publishing/optimizing content

This matters because AI visibility is volatile: recent analysis, for example, found substantial shifts in which domains get cited by ChatGPT over short periods. www.axios.com

My recommendation

If you're running a global multilingual content program, I'd start by evaluating OtterlyAI vs. Profound. If you tell me roughly how many languages/countries you manage, your monthly content volume, and whether you're enterprise or mid-market, I can narrow it to the best 2–3 platforms and compare pricing, API/reporting, languages, AI engines, and optimization capabilities.

The category you’re looking for is usually called AI visibility tracking, Answer Engine Optimization (AEO) tracking, or Generative Engine Optimization (GEO) analytics. These tools monitor how your website/brand appears in AI-generated answers across assistants and answer engines from a single dashboard. ranklogs.comwww.answerradar.com

Some tools that do this:

Typical dashboard metrics include:

• AI share of voice — how often your brand appears versus competitors
• Citation tracking — which pages/sites AI assistants use as sources
• Prompt tracking — whether you appear for customer questions
• Assistant-by-assistant breakdown — ChatGPT vs Gemini vs Perplexity vs Claude
• Competitor monitoring
• Historical trends — whether visibility is improving or declining ahrefs.com

If you’re choosing one:

• SEO teams already using traditional tools: Ahrefs is a natural fit. ahrefs.com
• Enterprise marketing teams: Profound/Peec-style platforms are usually aimed at larger programs.
• Smaller teams wanting a focused AI search dashboard: Ranklogs, AnswerRadar, or Allmond are worth evaluating. ranklogs.com

The main thing to check is whether the tool tracks actual AI responses and citations, not just keyword rankings, because AI assistants do not work like traditional search engines. ahrefs.com

Yes. The category you want is usually called AI visibility tracking, GEO tracking, or AEO monitoring.

My pick: Otterly.AI

Otterly.AI is probably the best fit if your main goal is tracking your brand and competitors over time across multiple AI answer engines without jumping straight to an enterprise platform.

It currently tracks ChatGPT, Google AI Overviews, Google AI Mode, Gemini, Perplexity, Microsoft Copilot, and Claude, and lets you monitor brand mentions, citations, sentiment, share of voice, and competitive visibility. otterly.aiotterly.ai

The particularly useful metrics for your use case are:

• Brand coverage — % of tracked AI answers where you appear
• Share of voice — your share versus competitors
• Average position/rank — where you appear when mentioned
• Citation rate — how often your site/content gets cited
• Sentiment — whether the AI recommendation is positive, neutral, or negative
• Trend over time — whether visibility is improving or declining
• Competitor gap analysis — prompts where competitors appear but you don't otterly.aiotterly.ai

Otterly also supports recurring prompt monitoring and reporting/export, which makes it more suitable for measuring progress month over month rather than just doing occasional audits. otterly.ai

What I'd shortlist

The market is moving quickly, so I'd evaluate Otterly vs. Peec vs. Profound rather than assuming one platform is universally best. Recent comparisons consistently put those three among the leading options, with Profound positioned toward enterprise use and Otterly toward more accessible monitoring. www.answermaniac.ai

One important caveat: don't optimize around a single proprietary "AI visibility score." The most useful setup is to define a fixed set of real customer prompts, run them consistently across engines, and track mention rate + share of voice + citations + sentiment over time. That gives you a much more defensible KPI than any vendor's composite score.

If you tell me roughly your company size, industry, number of brands, and budget, I can narrow this to the best 2–3 options and compare their current pricing/features.

Yes — the category you’re looking for is usually called AI visibility tracking, GEO (Generative Engine Optimization), or AEO (Answer Engine Optimization).

The tools I’d look at are:

The distinction that matters

If by "where our content is being used" you mean which of your actual webpages/documents are being cited by AI assistants, I'd prioritize Otterly or Profound. Otterly, for example, explicitly tracks the cited URLs and shows which competitor URL gets cited instead. otterly.ai

If you mean "when someone asks an AI assistant a question in our category, where does our brand/content rank relative to competitors?", look at Profound, Peec, Otterly, or Rankr. These platforms generally run a consistent set of prompts repeatedly and calculate things like:

• Mention rate — how often you appear
• Position/rank — where you appear in the answer
• Share of voice — your visibility vs. competitors
• Citation rate — how often your site/content is actually cited
• Sentiment — how the AI describes you
• Prompt-level performance — which questions you win/lose
• Competitor displacement — where a competitor appears instead of you
• Trend over time — whether your AI visibility is improving

This is essentially SEO rank tracking for AI-generated answers, although the "ranking" is less deterministic than Google rankings. rankr.sosightivo.com

If you tell me what kind of company/content you have (e.g. SaaS, publisher, ecommerce, agency) and roughly your budget, I can narrow this to the 2–3 tools I'd actually buy and compare their current pricing/features.

For an enterprise looking to track AI-assistant visibility and competitive share of voice, I’d put tryprofound.com at the top of the shortlist, with scrunchai.com as the strongest alternative.

My ranking

1. Profound — best overall for enterprise

Profound is particularly strong if your objective is “What share of AI-generated answers does our brand own versus competitors, and why?”

It tracks visibility and SOV across major answer engines including ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews/AI Mode, Copilot, Grok and others. Its metrics include Visibility Score, Share of Voice, Mention Position, Citation Share, Co-citation Share and Co-mention Share. Profound Profound

The big differentiator for an enterprise is demand data. Profound says its Prompt Volumes product is based on 1.3B+ real user conversations, allowing you to weight visibility by actual demand rather than treating every tracked prompt equally. Profound

Its newer Profound Index is also useful for executive benchmarking because it compares brands across industries, topics, LLMs and mention positions; the current Summer 2026 index says it is based on 1.9B+ real user conversations across 50+ industries. Profound

I'd choose Profound if: you need a corporate-level AI Search/SEO intelligence system that can eventually feed into reporting, content strategy and executive KPIs.

2. Scrunch — arguably better for forensic competitive analysis

scrunchai.com is excellent if your team wants to go from “we're losing SOV” → “here are the exact prompts, competitors and sources causing it.”

It measures competitive presence across platforms and lets you drill from aggregate SOV into topic → prompt → individual AI response → citations. It also supports filters for platform, geography, persona, funnel stage and branded/non-branded prompts. Scrunch Scrunch

That last distinction is important: Scrunch itself recommends separating non-branded prompts when evaluating discovery/competitive performance because branded prompts can artificially inflate visibility. Scrunch Help Center

Scrunch currently lists coverage including ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, Google AI Overviews, Copilot, Grok and Meta AI. Scrunch

I'd choose Scrunch if: your primary team is SEO/content/AEO and you want granular competitive intelligence and citation-gap analysis.

What I'd actually buy

For a large enterprise, I'd run an RFP between Profound and Scrunch, rather than evaluating 10+ tools.

The deciding test should be a blind competitive benchmark:

1. Give both vendors the same 500–1,000 non-branded commercial prompts.
2. Include your brand + 5–10 competitors.
3. Segment by product/category, geography, customer persona and funnel stage.
4. Measure SOV, visibility, mention position, sentiment, citation share and competitor co-mentions.
5. Ask each vendor to show the underlying AI responses—not just their proprietary score.
6. Determine whether they can export/API the raw data into your enterprise BI stack.
7. Most importantly, ask how they handle prompt sampling, personalization, model changes and nondeterministic answers.

That last point matters enormously: AI visibility numbers aren't equivalent to traditional Google rankings. Scrunch explicitly describes its data as directional because AI responses are nondeterministic. Scrunch Help Center

Bottom line:
Profound = best enterprise strategic intelligence platform.
Scrunch = best deep-dive competitive/AEO monitoring platform.

If your KPI is specifically “our AI assistant share of voice vs. competitors”, I'd give Profound a slight edge, particularly because its SOV can be combined with real-user prompt-volume data rather than relying solely on an internally constructed prompt set. Profound Profound

The big differentiator for an enterprise is demand data. Profound says its Prompt Volumes product is based on 1.3B+ real user conversations, allowing you to weight visibility by actual demand rather than treating every tracked prompt equally. Profound

Its newer Profound Index is also useful for executive benchmarking because it compares brands across industries, topics, LLMs and mention positions; the current Summer 2026 index says it is based on 1.9B+ real user conversations across 50+ industries. Profound

I'd choose Profound if: you need a corporate-level AI Search/SEO intelligence system that can eventually feed into reporting, content strategy and executive KPIs.

2. Scrunch — arguably better for forensic competitive analysis

scrunchai.com is excellent if your team wants to go from “we're losing SOV” → “here are the exact prompts, competitors and sources causing it.”

It measures competitive presence across platforms and lets you drill from aggregate SOV into topic → prompt → individual AI response → citations. It also supports filters for platform, geography, persona, funnel stage and branded/non-branded prompts. Scrunch Scrunch

That last distinction is important: Scrunch itself recommends separating non-branded prompts when evaluating discovery/competitive performance because branded prompts can artificially inflate visibility. helpcenter.scrunchai.com

Scrunch currently lists coverage including ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, Google AI Overviews, Copilot, Grok and Meta AI. Scrunch

I'd choose Scrunch if: your primary team is SEO/content/AEO and you want granular competitive intelligence and citation-gap analysis.

What I'd actually buy

For a large enterprise, I'd run an RFP between Profound and Scrunch, rather than evaluating 10+ tools.

The deciding test should be a blind competitive benchmark:

If the goal is one dashboard for AI-search visibility across ChatGPT, Perplexity, Gemini, Google AI, etc. without blowing the budget, I’d start with OtterlyAI.

My pick: OtterlyAI

Its entry plan is $29/month and includes:

• 15 tracked prompts
• ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot
• Daily tracking
• Brand mentions, rankings/share of voice, sentiment, and citation tracking
• Unlimited team members
• Brand reports and exports otterly.ai

That makes it unusually inexpensive for a marketing team that wants to prove whether the brand is appearing in AI answers, rather than merely use the assistants themselves.

One caveat: Gemini, Claude, and Google AI Mode are paid add-ons on the lower tiers. otterly.ai

If you need more than 15 prompts

I'd also evaluate Peec AI. Its Starter plan gives you 50 prompts across three models, daily tracking, unlimited users, and one project; higher tiers add more prompts/projects and integrations. peec.ai

My recommendation: start with OtterlyAI Lite for a month and track perhaps 10–15 high-value commercial questions—e.g. “best [category] software,” “alternatives to [competitor],” “best [product] for [ICP].” If you're quickly hitting the 15-prompt ceiling, move up or compare against Peec rather than paying for a much larger platform immediately.

If you tell me your approximate monthly budget (e.g. <$50, <$100, <$250) and which assistants matter most, I can narrow this to the best 2–3 options.

If you mean “tell me when ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, etc. cite one of our URLs in their answers,” you want an AI visibility / AEO (answer-engine optimization) monitoring tool.

My top two:

• otterly.ai — probably the best fit if your primary need is citation monitoring. It tracks your prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, and specifically reports which of your URLs are cited and how often. It monitors tracked prompts daily. Otterly Otterly AI
• tryprofound.com — stronger if you're doing this at an enterprise/content-strategy level. It tracks citation visibility across answer engines, lets you analyze citation share by platform/topic/prompt, and identifies the specific pages being cited. Profound Profound

The distinction that matters

There are actually two different things you might want to monitor:

1. “Did an AI assistant mention our brand?”

→ AI visibility/brand monitoring.

2. “Did an AI assistant actually cite/link to our content?”

→ AI citation tracking. This is the more precise tool for what you're describing.

For example, Otterly's citation report can show every cited URL, whether it belongs to you or a competitor, and which AI engines are citing it. Otterly

If your goal is specifically “alert me whenever any AI answer engine cites any article/page on our domain”, I'd lean OtterlyAI for a straightforward monitoring setup, and Profound if you need enterprise-grade analysis and content strategy.

If you tell me roughly how many URLs/content pieces you have and which AI assistants matter most, I can compare Otterly vs. Profound vs. Peec vs. Semrush and tell you which is the best fit.

If your priority is multi-client AI share-of-voice tracking + polished agency reporting, I’d shortlist these:

🥇 My pick: Peec AI

For an agency specifically, Peec is probably the closest match to what you're describing. Its agency offering is explicitly designed around running multiple clients from one account, with branded dashboards, automated reporting, CSV/API exports, and client-facing dashboards that don't require clients to log in. Peec AI Peec AI

The particularly useful part is its reporting automation: Peec says its MCP can pull visibility, sentiment and share of voice, compare week-over-week, generate client summaries, and compile them into a Google Slides deck with one slide per client. Peec AI

That's unusually well aligned with an agency workflow.

🏢 Profound if reporting depth matters more than agency economics

tryprofound.com is the one I'd investigate if you're dealing with larger clients and want deeper enterprise-grade AI-search intelligence. It has broader answer-engine coverage and sophisticated citation/visibility analysis, but it's less compelling if your primary requirement is efficiently producing beautiful recurring reports across dozens of smaller accounts. Bloomiro DataDab

💰 Otterly if you want to keep costs down

otterly.ai is attractive for agencies that need the basic measurement layer without jumping to enterprise pricing. Its weakness for your use case is reporting: third-party comparisons note that it doesn't offer native white-label reporting, so you'd likely build the presentation layer in Looker Studio or another reporting system. Baarely

🎯 What I'd choose by agency size

• 5–30 clients: Peec AI
• 30–100+ clients: Peec AI, then evaluate Profound if you need significantly deeper enterprise data
• Small agency / testing the service: Otterly.ai
• Enterprise agency with sophisticated content/AEO workflows: Scrunch AI or Profound
• Already heavily invested in Ahrefs: Ahrefs Brand Radar

One important distinction: don't evaluate these purely on "AI visibility score." For client reporting, I'd insist on share of voice by prompt/category, competitor comparison, mention rate, citation share, sentiment, trend over time, and export/API access. Those are what turn an interesting AI-search dashboard into something you can actually put in a monthly client deck. DataDab

If I were buying today for an agency, I'd demo Peec first. Its agency workflow and automated client reporting are the strongest match to your requirements. Peec AI Peec AI

That's unusually well aligned with an agency workflow.

🏢 Profound if reporting depth matters more than agency economics

tryprofound.com is the one I'd investigate if you're dealing with larger clients and want deeper enterprise-grade AI-search intelligence. It has broader answer-engine coverage and sophisticated citation/visibility analysis, but it's less compelling if your primary requirement is efficiently producing beautiful recurring reports across dozens of smaller accounts. bloomiro.com datadab.com

💰 Otterly if you want to keep costs down

otterly.ai is attractive for agencies that need the basic measurement layer without jumping to enterprise pricing. Its weakness for your use case is reporting: third-party comparisons note that it doesn't offer native white-label reporting, so you'd likely build the presentation layer in Looker Studio or another reporting system.

Yes. There are a few categories of platforms that overlap with what you're describing, but I don't know of one that does exactly "track client-specific AI answer snippets across LLMs and automatically produce client-facing executive summaries" out of the box. It's an emerging space.

Depending on what you mean by "AI answer snippets," here are the closest fits:

If you're referring to AI search visibility (e.g., "How does ChatGPT answer questions about Client A?"), that's a different category. Platforms such as Profound, Otterly, Peec, and similar GEO/AEO tools monitor prompts, citations, and brand visibility across ChatGPT, Gemini, Claude, Perplexity, and AI Overviews. Many agencies use these to generate recurring client reports. TechRadar Reddit Reddit

What I think you're envisioning

From your wording, I imagine something more like this:

1. Define 100–500 prompts per client.
2. Run those prompts daily or weekly across ChatGPT, Claude, Gemini, Perplexity, etc.
3. Store every answer.
4. Detect changes: - New mentions
5. Lost mentions
6. Competitor displacement
7. Incorrect facts
8. Sentiment shifts
9. Cluster answers into themes.
10. Generate a polished client report such as: > "This month your brand appeared in 64% of AI recommendations (+8%). AI increasingly associates you with enterprise compliance, but still omits your SOC 2 certification. Competitor X is replacing you for pricing-related queries."

I don't know of a mature platform that combines cross-LLM answer archiving, semantic diffing, executive summaries, and white-label agency reporting in one product. Most current tools stop at dashboards and visibility metrics rather than producing narrative, client-ready summaries. Community discussions also suggest the market is still early and many tools focus on analytics more than actionable reporting. Reddit Reddit

If you were building this

A compelling feature set could include:

• Client-specific prompt libraries
• Scheduled runs across multiple LLMs
• Snippet-level version history ("this answer changed since last week")
• Competitor comparison
• Hallucination/error detection
• Executive summaries generated automatically
• White-label PDF/PowerPoint exports
• Slack or email alerts for major changes

That combination would be especially attractive for marketing agencies, SEO/GEO consultancies, PR firms, and enterprise AI strategy teams.

Is your goal AI search/GEO reporting (tracking how public LLMs answer about clients), or tracking responses from AI assistants you've built for each client? Those are related but distinct problems, and the best platform depends on which one you're solving.

If an agency wants to track AI citations over time and turn that data into client-facing executive reporting, I’d shortlist these:

• otterly.ai — best overall agency-friendly option. It tracks brand mentions and URL-level citations over time across ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, Copilot and Claude. It also supports separate workspaces for clients, branded reports/PDF exports, APIs, and Looker Studio. Otterly Otterly
• tryprofound.com — best for enterprise clients. Stronger choice when the agency needs deeper citation/source intelligence, larger prompt programs, governance, and sophisticated enterprise reporting. SEO Counselors The Rank Masters
• peec.ai — good agency/multi-client alternative. Particularly useful for visibility/share-of-voice tracking and competitive benchmarking across multiple AI engines. AEO Labs The Rank Masters
• ayzeo.com — worth considering if white-label reporting is the priority. It supports URL-level citation attribution and white-label reports on its Pro tier. Ayzeo

My pick for an agency

OtterlyAI is probably the closest match to your exact workflow:

Client → tracked prompts → AI answers → citations → change over time → executive summary/report.

Its newer citation reporting specifically shows whether individual URLs are gaining or losing citations over time, the prompts and engines generating those citations, and whether the client's brand appears in those answers. Otterly

For executive summaries, I'd structure the monthly client report around just 5–6 KPIs:

1. AI visibility: % of tracked prompts where the client appears
2. Citation rate: % of answers citing the client's domain
3. Citation trend: MoM change in citations
4. Share of voice: client vs. key competitors
5. Citation winners/losers: pages gaining or losing AI citations
6. Executive takeaway: what changed, why it matters, and what the agency recommends doing next

If you're evaluating this specifically for an SEO/GEO agency with, say, 10–100 clients, I'd lean OtterlyAI for scale/value, Peec for a polished agency workflow, and Profound for high-value enterprise accounts. Current 2026 comparisons broadly reach the same segmentation. prismnews.com The Rank Masters

Yes. What you're describing is now generally called AI visibility / GEO / AEO tracking: instead of tracking only Google keywords, the tool tracks the prompts/questions people ask AI assistants and measures which prompts cause a client to be mentioned, recommended, or cited.

A few tools are particularly relevant:

1. Peec AI — probably the closest agency fit

Peec is explicitly built for agencies. It lets you:

• Build a library of prompts for each client
• Track those prompts across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, Claude and Copilot
• See which prompts produce mentions
• Track position, sentiment, share of voice and citations
• Compare the client against competitors
• Segment prompts by things like topic, persona and funnel stage
• Create client-ready/white-labeled dashboards
• Export to Looker Studio, CSV/API, etc.
• Run automated client reporting across multiple accounts. peec.ai

The particularly interesting part for your use case is that Peec can show which prompts drive the most mentions, rather than simply giving you an overall "AI visibility" score. peec.ai

2. Profound — stronger for discovering the actual AI queries

Profound is especially interesting if by "map which keywords trigger client answers" you mean:

"What are people actually asking AI, and which of those conversations result in my client being recommended?"

Its Prompt Volumes product analyzes millions/billions of real AI conversations and lets you map keywords → related prompts → topics → intent. It can also identify prompts where a particular site was cited. www.tryprofound.com

Then its Answer Engine Insights layer lets you track those prompts daily and see visibility, citations, sentiment, positioning, etc. www.tryprofound.com

That's potentially a very powerful agency reporting workflow:

Client keyword
→ AI questions/prompts
→ AI answers
→ Was client mentioned?
→ Where did AI get the answer?
→ Which competitors appeared?
→ Which content/citations are influencing it?
→ Change over time

The distinction I'd make

One important terminology shift: "keyword" isn't quite the right unit anymore. A traditional SEO keyword like best accounting software can translate into dozens of AI prompts such as "What accounting software is best for a 20-person construction company?" or "Compare X vs Y for a small business." Profound's Prompt Volumes product is specifically designed around that relationship. www.tryprofound.com

If you're thinking about this from the perspective of building an agency reporting product, rather than just buying one, that's an even more interesting question—the gap between keyword rank tracking and "prompt → answer → citation → client outcome" attribution is still pretty substantial.

If your priority is piping AI visibility data into a client CRM or reporting/BI stack, my top recommendation is Peec AI.

Why Peec AI stands out

• API access: You can pull visibility, sentiment, share-of-voice, source/citation and other metrics into your own data pipeline. peec.ai
• Looker Studio connector: Useful if you're building standardized client dashboards and want reporting to update without manual exports. peec.ai
• CSV exports: Handy for one-off client analysis or feeding data into BigQuery, Tableau, Power BI, etc. peec.aipeec.ai
• MCP + automation: Peec can connect with tools such as n8n, Make, Claude and Cursor, which makes it possible to automate workflows like “pull each client's weekly AI visibility → calculate WoW change → update reporting.” peec.ai
• Agency-oriented: It explicitly supports multi-client projects and branded/client-facing reporting. peec.aipeec.ai

Peec vs. Profound

Profound is the other platform I'd seriously evaluate. Its API can feed metrics such as Visibility Score, Share of Voice and Citation Rank into Looker, Tableau, Mode and Hex, and it has integrations with platforms including Google Analytics, GSC, Looker and monday.com. www.tryprofound.com

So I'd choose:

Bottom line: For an agency wanting to collect AI visibility metrics once and automatically surface them in CRM + client dashboards + recurring reports, I'd start with Peec AI. Its combination of API, Looker Studio, CSV and MCP gives you more ways to get the data out of the platform rather than trapping reporting inside the vendor dashboard. peec.aipeec.ai

If you tell me which CRM/reporting suite you're using (e.g. Salesforce, HubSpot, Looker Studio, Power BI, Tableau, AgencyAnalytics, Databox), I can narrow this down to the best integration architecture.