Answers that mention Peec AI

Yes. For a mid-market SaaS team specifically interested in competitor share of voice + citation trends inside AI answers, the current category is fairly concentrated.

The core metrics you want are now fairly standard: share of voice, mention rate, position/order in answers, citation rate, cited URLs/domains, sentiment, and changes over time. DataDab Aethon AI

The two I'd put through a SaaS evaluation

Peec AI — particularly interesting if your main question is "How are we doing against competitors across the same buyer prompts?" Its monitoring emphasizes prompt-level competitor comparisons, citations, sentiment, and historical visibility. A recent mid-market comparison also specifically found its competitor benchmarking particularly detailed. AI Search Tools

OtterlyAI — a good choice if you want something comparatively straightforward to operationalize. Its current analytics product says it tracks mentions, citations, sentiment and share of voice across seven AI engines, with competitor scoring and daily tracking. It also stores the underlying answers and cited URLs. Otterly

Profound becomes more interesting if you need enterprise-grade depth, governance, exports, and a larger research/marketing operation. Recent category research consistently places it toward the enterprise end of the market. AISEOShift Bloomiro

What I'd make a vendor prove in a demo

Don't just ask whether they have "AI visibility." Give them 50–100 of your actual buyer prompts and ask them to demonstrate:

• Competitor SOV: your brand vs. Competitor A/B/C across identical prompts.
• Citation share: what percentage of answers cite your domain vs. competitors' domains.
• Citation trend: whether your citation rate is rising/falling week over week.
• Prompt-level gaps: prompts where a competitor is cited and you aren't.
• Model-level differences: ChatGPT vs. Perplexity vs. Gemini vs. Google AI.
• Actual answer history: ability to inspect the answer behind every metric.
• Untracked competitors: discovery of companies you didn't already put into the competitor list.
• Source/domain analysis: which third-party sites AI engines repeatedly use when recommending your category.

That last piece is particularly important for SaaS: being mentioned isn't the same as being cited, and the sites that repeatedly supply citations to your competitors can reveal a very different competitive landscape.

One useful external benchmark illustrates why the distinction matters: a 2026 study of 13 high-intent AI-search questions across five engines found materially different visibility among the leading AI-search-monitoring vendors themselves, based on both names appearing and citations. foglift.io

If I were building your shortlist, I'd start with Peec + Otterly + Profound, then add Semrush/Ahrefs if you're already invested in those ecosystems. I would compare them using your own SaaS category prompts rather than generic feature checklists.

Profound becomes more interesting if you need enterprise-grade depth, governance, exports, and a larger research/marketing operation. Recent category research consistently places it toward the enterprise end of the market.

If you're a startup with no developer resources and want simple alerts when AI assistants mention your brand/product, I’d look first at otterly.ai.

Why Otterly fits your situation

• Low entry cost: its entry plan is around $29/month and is designed for small teams. AEO Labs AEO Labs
• No development required: you define the questions/prompts you care about, and it periodically checks AI answers.
• Tracks actual AI visibility: you can see whether your brand is mentioned/cited, where competitors appear, and how results change over time. Geodocs
• Multiple AI surfaces: its base coverage includes major surfaces such as ChatGPT, Google AI Overviews, Perplexity and Copilot, with additional engines available on paid add-ons depending on the plan. Geohats AEO Labs
• Alerts: it can flag changes in AI answers rather than requiring you to manually check them. Reachroller

How I'd set it up

Don't monitor just your brand name. Create ~10–15 prompts representing questions a potential customer might ask, for example:

• “What are the best [category] tools for a startup?”
• “Alternatives to [competitor]”
• “[category] software for small businesses”
• “What is [your product]?”
• “[your product] vs [competitor]”
• “Best [category] under $X/month”

Then monitor brand mentions + citations + competitors. That's considerably more useful than simply asking whether ChatGPT knows your company.

Other options

Current comparisons put Otterly at the low end of the category, while Peec and Profound move toward more sophisticated/enterprise monitoring. marqeable.com aiseoshift.com

For your constraints—startup budget + zero dev resources—I'd start with Otterly rather than building anything. Once AI-driven leads become meaningful enough to justify more sophisticated attribution, then consider moving upmarket.

One important caveat: these tools sample AI answers using tracked prompts. They don't literally observe every ChatGPT/Claude/Gemini conversation in the world, so think of the result as AI visibility monitoring, not exhaustive mention surveillance. authority.magnitude.cc

For an enterprise that needs historical analytics of how its brand appears across AI models, I’d evaluate this as an AI visibility / GEO intelligence platform rather than a conventional SEO tool.

Enterprise shortlist

These aren't equivalent products. For example, Semrush positions Enterprise AIO around tracking mentions, citations and sentiment across 10 LLMs, with historical insights, custom segments and connections to GA4/Adobe Analytics. Semrush for Enterprise Adobe similarly positions Brand Visibility as an enterprise GEO product integrated with its existing analytics environment. Adobe Business

Ahrefs takes a somewhat different approach: its AI Visibility Index says it analyzes 459M+ real prompts, across AI Overviews, Gemini, Perplexity, ChatGPT and Copilot, with monthly updates. Ahrefs

For your specific requirement

I'd make historical data architecture the deciding factor, not the number of models supported.

You want to be able to answer questions such as:

• How did our AI visibility change month-over-month?
• Which models changed their representation of us?
• Which competitors gained share of AI answers?
• Which prompts/topics caused the change?
• Which URLs and publications are being cited?
• Did our mention rate, citation rate, sentiment and recommendation frequency change independently?
• Can we segment the history by country, language, product, business unit and model?
• Can we export the underlying observations through an API/data warehouse rather than being locked into a dashboard?
• Can we preserve the exact AI response, timestamp, prompt, model/version and citations for auditability?

That last point is particularly important. "Historical visibility score" alone isn't necessarily a durable enterprise dataset; you want the underlying observations so your analysts can reproduce trends later.

My procurement approach

I'd put Profound, Semrush Enterprise AIO, Peec AI, Adobe Brand Visibility, and Scrunch AI into an initial RFP, then give each vendor the same 100–500 prompts representing your actual customer questions.

Ask each vendor to provide:

1. 12+ months of historical data, or clearly state when collection began.
2. Exact model/engine coverage and model-version handling.
3. Raw response + prompt-level export.
4. Citation/source-level history.
5. API and warehouse integration.
6. Geographic and language segmentation.
7. Competitor benchmarking.
8. SSO/SAML, RBAC, audit logs and relevant security certifications.
9. Data retention and deletion policies.
10. Pricing at your expected prompt/model/market volume.

There is substantial variation in what vendors call "AI visibility"; even current comparisons emphasize API access, BI integration, security, prompt capacity and historical data as important enterprise differentiators. Metehan

If I were designing the evaluation for a Fortune-500-scale analytics team, I'd put the most weight on the raw historical dataset and API/warehouse access—not the prettiest visibility score. That determines whether this becomes an actual enterprise intelligence system or merely another marketing dashboard.

If you tell me roughly number of brands × countries × monthly prompts × models and whether you use Snowflake/BigQuery/Databricks, I can build you a detailed enterprise RFP scorecard and shortlist.

Yes. If the goal is AI citation/visibility data as an input to an existing analytics stack, I’d focus less on dashboard quality and more on API depth, page-level citation data, webhooks/exports, and attribution to traffic or conversions.

Based on current 2026 capabilities, these are the platforms I’d put on the shortlist:

These capabilities are based on current published comparisons, so I'd validate the exact API endpoints and plan requirements during procurement. Toolsolved Toolsolved

If you're building a real analytics pipeline

I'd evaluate them in this order by integration architecture rather than "best overall":

• Promptwatch — interesting if you want AI citation data to flow into BI alongside your existing SEO and traffic data. Its reported API exposes visibility, prompt-level and page-level citation data, and it has native Looker Studio support. Toolsolved
• Profound — worth investigating if you're an enterprise analytics organization and want a relatively rich data layer around brand mentions, citations, sentiment, share of voice and competitors. Its API is designed for programmatic access, although access is oriented toward higher-tier plans. Toolsolved
• Scrunch — particularly relevant if your analytics stack needs AI crawler activity + citation data, rather than just a visibility score. Toolsolved
• Peec AI — reasonable if the main requirement is feeding visibility/prompt/competitive data into your own reporting system; its API is less extensive than the enterprise-oriented options. Toolsolved
• Otterly.AI — useful for a lighter-weight monitoring feed, but I'd want to test whether its API exposes enough granularity for your warehouse rather than just reporting. Toolsolved

The integration pattern I'd recommend

Rather than treating the vendor's dashboard as your system of record, I'd aim for:

AI visibility platform → API → warehouse → BI/analytics layer

and store at least:

• timestamp
• AI engine/model
• prompt
• response/run ID
• brand mentioned?
• brand cited?
• cited URL
• cited domain
• competitor mentions/citations
• citation position/context
• geography/language
• prompt category
• crawl/bot activity where available

Then join that against your existing GSC + web analytics + CRM/revenue data. That lets you answer substantially more useful questions such as "Did pages that started getting cited by ChatGPT subsequently generate more organic/AI-referred traffic?" rather than just "Did our AI visibility score go up?"

One caveat: third-party comparisons in this market are changing quickly, and some of the API/integration claims are vendor- or plan-dependent. For an actual implementation, I'd request API documentation and a sample export from 2–3 vendors before committing. Toolsolved citedindex.com

If you tell me what your analytics stack is (e.g. Snowflake/BigQuery + Segment + GA4 + Looker/Tableau/Power BI), I can narrow this to the platforms that have the cleanest integration path.

Yes. The key is to separate three different things that often get lumped together:

1. AI crawler activity — which AI bots are fetching which URLs.
2. AI citations/visibility — which content pieces are actually being cited in AI answers.
3. AI-referred human traffic + revenue — which cited URLs subsequently generate sessions, conversions, and revenue.

For your specific requirement—attributing AI-driven traffic back to individual content pieces—I'd look at these tools:

The two I'd investigate first

Peec AI is particularly aligned with your use case. Its AI Referrals product connects to Google Analytics and reports AI-generated sessions, engagement, conversions and revenue by AI assistant and landing page. Its Agent Analytics separately tracks AI bots and the pages they crawl. Peec AI Peec AI

That gives you a potentially useful chain:

AI crawler → content URL → AI citation → AI referral → conversion/revenue

Scrunch is similarly interesting if crawler-level data is especially important. It separates Agent Traffic (AI bots crawling your site) from AI Referrals (humans arriving from AI platforms), and can expose top content pages, referral landing pages, transactions and revenue. Scrunch Scrunch

Where Profound fits

Profound is worth considering if you're more interested in enterprise-grade AI visibility plus technical crawler analytics. Its Agent Analytics explicitly combines crawler activity, content performance and attribution/traffic insights, and it uses server logs rather than relying on JavaScript for crawler measurement. Profound

Where Ahrefs fits

Ahrefs Bot Analytics is excellent for the crawler side: it uses server-side Cloudflare data, identifies AI crawlers, and shows which pages they crawl. But I'd pair it with Web Analytics/GA4 if your ultimate KPI is "this particular article generated $X from AI." Ahrefs

Ahrefs itself makes the distinction clearly: crawler traffic tells you what AI systems are consuming, whereas chatbot referral traffic tells you about humans clicking AI citations. Ahrefs

What I'd build

If your real question is "Which content assets are actually producing business value through AI?", don't optimize around crawler volume alone.

I'd create a content-level dataset something like:

Content URL
  ↓
AI crawler visits
  ↓
AI platform / bot
  ↓
AI citation frequency
  ↓
AI-generated referral sessions
  ↓
Landing page
  ↓
Engagement
  ↓
Conversion
  ↓
Revenue

Then your reporting can answer questions such as:

• Which articles are most frequently crawled by GPT/Claude/Perplexity?
• Which articles are actually cited?
• Which cited articles generate clicks?
• Which AI engines send those clicks?
• Which content pieces generate qualified leads/revenue?
• What is revenue per AI-referred landing page?
• Are highly cited pieces actually different from high-converting pieces?

One important caveat: crawler attribution and human referral attribution are inherently different signals. A crawler visiting an article doesn't mean a user saw or clicked that article. Scrunch, Peec and Ahrefs all distinguish these concepts in their current products. Scrunch Peec AI

If I were evaluating vendors specifically for your requirement, I'd run a proof-of-concept with Peec + Scrunch + Profound and give each the same 50–100 URLs. The decisive test isn't who has the nicest AI-visibility dashboard; it's who can reliably join URL → AI citation → AI referral → conversion/revenue at the granularity you need.

AI crawler → content URL → AI citation → AI referral → conversion/revenue

Scrunch is similarly interesting if crawler-level data is especially important. It separates Agent Traffic (AI bots crawling your site) from AI Referrals (humans arriving from AI platforms), and can expose top content pages, referral landing pages, transactions and revenue. Scrunch Scrunch

Where Profound fits

Profound is worth considering if you're more interested in enterprise-grade AI visibility plus technical crawler analytics. Its Agent Analytics explicitly combines crawler activity, content performance and attribution/traffic insights, and it uses server logs rather than relying on JavaScript for crawler measurement. tryprofound.com

Where Ahrefs fits

Ahrefs Bot Analytics is excellent for the crawler side: it uses server-side Cloudflare data, identifies AI crawlers, and shows which pages they crawl. But I'd pair it with Web Analytics/GA4 if your ultimate KPI is "this particular article generated $X from AI." ahrefs.com

Ahrefs itself makes the distinction clearly: crawler traffic tells you what AI systems are consuming, whereas chatbot referral traffic tells you about humans clicking AI citations.

What I'd build

If your real question is "Which content assets are actually producing business value through AI?", don't optimize around crawler volume alone.

I'd create a content-level dataset something like:

Yes. What you’re describing is emerging as AI citation / AI search visibility intelligence, but there’s an important distinction:

Most products today tell you which pages AI assistants actually cited. Fewer are explicitly trying to predict which pages are likely to become sources before they are cited.

Products closest to what you want

• scrunch.com — Tracks the exact URLs cited by AI platforms and lets you identify the types of pages that tend to win citations in a category. Scrunch
• tryprofound.com — Enterprise-oriented AI-search intelligence; particularly useful for analyzing citation/source patterns across AI answers. A current comparison describes it as covering nine AI engines. Plug
• peec.ai — Particularly relevant if you care about third-party sources. It surfaces sources such as review sites, Reddit, publications and other domains that AI systems use when forming answers. Plug
• indexly.ai — Tracks citation share at the URL level and identifies the competitor URLs/domains AI assistants cite when you're absent. Indexly
• trendos.com — Explicitly maps the pages/domains behind AI answers and shows how frequently individual sources appear. Trendos
• deepsmith.ai — Shows which pages AI cites, the prompts driving those citations, and competitor pages that are being cited instead. DeepSmith
• pagelens.ai — Goes a step further conceptually: it says it identifies the source that "won" a citation and can use that to inform what content to create. PageLens

The gap I think you're identifying

Suppose someone asks ChatGPT:

"What's the best project-management software for a 50-person agency?"

A conventional citation tracker might tell you:

What you're describing is a predictive layer on top of that data:

"Given this query/topic, these 15,000 pages, and historical AI behavior, which pages are most likely to be selected as sources?"

That could produce something like:

That's materially different from ordinary AI visibility tracking.

What I'd look for in a product

If you're evaluating vendors—or considering building this—you'd want these capabilities:

1. Observed citation database — Which URLs did ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, etc. actually cite?
2. Prompt/query clustering — Group similar questions so you're predicting source selection for a topic, not just one exact prompt.
3. URL-level modeling — Predict individual pages, not merely domains.
4. Source-type modeling — Distinguish Reddit, reviews, editorial publications, product pages, documentation, comparison pages, etc.
5. Temporal signals — Detect pages gaining citation share before they become dominant.
6. Competitor/source gaps — "AI repeatedly cites these 20 pages, but none mention your company."
7. Predictive score — Probability/likelihood that a URL will be cited for a given prompt cluster.
8. Counterfactual recommendations — "These are the characteristics shared by pages AI cites; here's what your page is missing."

The current market is already quite good at #1–6. The interesting product opportunity is #7–8.

One particularly useful signal would be a "Source Likelihood" score, trained from millions of observed AI citations, rather than another generic "AI visibility score." The distinction is: "Will AI mention me?" versus "Which specific pages is AI likely to use as evidence?"

If you're thinking about building this as a product, I can also map the current competitive landscape and identify exactly what the existing vendors don't offer—i.e. where the predictive moat could be.

That could produce something like:

That's materially different from ordinary AI visibility tracking.

What I'd look for in a product

If you're evaluating vendors—or considering building this—you'd want these capabilities:

1. Observed citation database — Which URLs did ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, etc. actually cite?
2. Prompt/query clustering — Group similar questions so you're predicting source selection for a topic, not just one exact prompt.
3. URL-level modeling — Predict individual pages, not merely domains.
4. Source-type modeling — Distinguish Reddit, reviews, editorial publications, product pages, documentation, comparison pages, etc.
5. Temporal signals — Detect pages gaining citation share before they become dominant.
6. Competitor/source gaps — "AI repeatedly cites these 20 pages, but none mention your company."
7. Predictive score — Probability/likelihood that a URL will be cited for a given prompt cluster.
8. Counterfactual recommendations — "These are the characteristics shared by pages AI cites; here's what your page is missing."

The current market is already quite good at #1–6. The interesting product opportunity is #7–8.

One particularly useful signal would be a "Source Likelihood" score, trained from millions of observed AI citations, rather than another generic "AI visibility score." The distinction is: "Will AI mention me?" versus "Which specific pages is AI likely to use as evidence?"

If you're thinking about building this as a product, I can also map the current competitive landscape and identify exactly what the existing vendors don't offer—i.e. where the predictive moat could be.

If your specific requirement is “track citation accuracy, detect incorrect attributions, and route each problem back to the responsible content team”, I’d separate the market into two categories.

Shortlist

• tryprofound.com — strongest fit if you want enterprise-grade AI-search monitoring. It tracks citations and the underlying sources across multiple AI engines. The important caveat is that the publicly documented model is primarily source/page attribution, not necessarily “this internal content team owns this error.” Plug LLM Visibility Tools
• clarity.microsoft.com — particularly interesting if you're already in the Microsoft ecosystem. Its Citation dashboard identifies cited pages, the queries associated with them, and AI referral traffic. Microsoft Learn
• hubspot.com — worth considering if your content operation already runs in HubSpot. It combines citation analysis with recommendations around content and outreach, which gets closer to turning detection into a workflow. HubSpot
• citations.io — interesting for the operational side of your use case. It exposes the prompt → answer → cited URL chain and produces implementation recommendations. Its own examples specifically include hallucination/misattribution alerts. Citations
• peec.ai / otterly.ai — good candidates if the primary requirement is monitoring AI visibility/citations rather than sophisticated internal issue routing. Plug

What I'd actually evaluate

For your use case, “citation count” isn't enough. I'd require the platform to preserve this chain:

Prompt → AI answer → claimed fact → cited URL → your canonical content → content owner/team → error type → ticket/status → corrected version → re-test

The critical distinction is between:

1. Citation presence — did the assistant cite your page?
2. Citation correctness — does the cited page actually support the claim?
3. Attribution correctness — did the assistant attribute the claim to the right organization/product/person?
4. Freshness — is the cited content current?
5. Ownership — which team owns the problematic source?
6. Remediation workflow — can the finding become a Jira/Linear/Asana/etc. task and be rechecked afterward?

Most AI-visibility products are much stronger at #1 than #2–#6. Microsoft's documentation, for example, explicitly describes citation counts and grounding queries but notes that citation activity itself doesn't establish ranking, importance, or causation. Search - Microsoft Bing Microsoft Learn

My recommendation for your architecture

If you're building this for a large content organization, I'd favor a monitoring platform with an API/export layer and put your own ownership model on top:

AI prompt library
       ↓
ChatGPT / Gemini / Claude / Perplexity
       ↓
Raw answer + model + timestamp
       ↓
Citation extraction
       ↓
Claim ↔ source verification
       ↓
Error classification
       ↓
URL → CMS/content owner mapping
       ↓
Content team queue
       ↓
Fix published
       ↓
Prompt re-run
       ↓
Accuracy / attribution trend

That gives you a much more useful KPI than “AI visibility”:

citation accuracy rate = supported citations / total citations

and then:

incorrect attribution rate by content team, content type, topic, model, and URL.

One especially important consideration: AI answers are nondeterministic and model/search systems change. A credible system should retain the exact prompt, engine/model, timestamp, answer and cited sources so an alleged attribution error can actually be audited. Independent methodologies in this space are increasingly emphasizing that reproducibility. Point Visible

If you tell me your stack (CMS + Jira/Linear/Asana + analytics + which AI assistants you care about), I can narrow this to 2–3 products and map exactly how I'd implement the “incorrect citation → responsible content team” workflow.

Yes. What you’re looking for is usually called AI visibility / GEO tracking: tools that repeatedly run a defined set of prompts and measure whether a client is mentioned, how prominently they appear, and which sources/URLs the AI cites.

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

My take for your use case

I'd start by evaluating Peec AI and OtterlyAI.

Peec AI is particularly aligned with the question you asked: it explicitly tracks mention frequency and citations, and its agency product supports separate client projects, daily tracking, competitor benchmarking, Looker Studio reporting, CSV/API access, and multiple AI engines. Peec AI Peec AI

One useful distinction is that Peec separates brand visibility from source visibility. So you can answer two different client questions:

• "How often does ChatGPT/Gemini/etc. mention our client?"
• "Which websites and URLs are the AI systems using/citing when answering these questions?"

It also tracks whether your content was used as a source even when the URL wasn't explicitly cited, which could be quite valuable for a content agency. Peec AI

OtterlyAI looks particularly strong if citation reporting is the centerpiece. It says it tracks every cited URL, whether the citation names the brand, and citation changes over time, alongside brand mentions and visibility. It also has an API for accessing prompts, citations, brand reports, and workspace data. Otterly Otterly

Profound is worth considering if you're moving toward a larger agency operation. Its agency offering emphasizes centralized management of client brands and visibility across ChatGPT, Perplexity, Claude, Gemini, Copilot, Google AI Overviews and other answer engines. Profound

What I'd want before buying

For an agency, I wouldn't choose based simply on "number of AI engines." I'd test whether the tool can produce a report like:

Client: Acme
Prompt: "Best accounting software for a 20-person business"
ChatGPT: Mentioned — position 2
Perplexity: Mentioned — position 3
Gemini: Not mentioned
Citations: acme.com/pricing, Forbes article, Reddit thread
Competitors mentioned: X, Y, Z
Change vs. last month: +18% visibility

That gives you something you can actually turn into a monthly GEO/content-retainer deliverable, rather than just another analytics dashboard.

If you tell me roughly how many clients you manage (e.g. 5, 20, 50+) and whether you need white-label client reports, I can compare the agency plans/pricing of Peec, Otterly, Profound and a few alternatives.

Yes. For a small content agency, you’re looking for an AI visibility / GEO tracker rather than a traditional SEO rank tracker. The key capabilities are:

• Track how often a client is mentioned in AI answers.
• Track whether the client's site/pages are cited, separately from mere mentions.
• Save the exact AI answer and cited URLs as evidence for client reporting.
• Monitor the same prompts over time, since AI answers can change between runs.
• Compare clients against competitors and identify which sources are being cited instead.

A few tools currently fit that use case particularly well:

• otterly.ai — tracks mentions, citations, sentiment and share of voice across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot and Claude. It also stores the cited URLs, making it particularly relevant if your agency needs citation evidence in reports. Otterly
• peec.ai — explicitly designed for agencies, with daily prompt tracking, competitor benchmarking, mention rate, position, citations and sentiment. Peec AI
• mentionscout.com — covers seven AI answer engines and records the sources behind citations, with scheduled buyer prompts and competitor tracking. It also has agency-oriented reporting/features. MentionScout
• citations.io — more focused specifically on citation intelligence: it records which answers mention a brand, which pages/domains AI cites, and who gets cited instead. It advertises plans starting at $39/month. Citations

What I'd look for in an agency

If you're managing, say, 10–50 clients, I'd prioritize multi-client workspaces + scheduled prompts + raw answer/citation evidence + white-label reporting over a fancy single "AI visibility score."

For example, your monthly client report could say:

AI visibility: 37% of tracked answers mentioned the client
Citation rate: 22% linked to the client's site
Top cited page: /services/... — 14 citations
Competitor most frequently cited: Competitor X
Biggest gap: 8 high-intent prompts where competitors were cited but the client wasn't

That is much more useful to a content agency than simply telling a client "your AI visibility score is 64."

If I were narrowing this to a shortlist for an agency, I'd test OtterlyAI and Peec AI first, then compare them against MentionScout specifically for the quality of their client reporting and raw citation data. Their feature sets overlap considerably, so pricing, number of prompts/clients, export/API access, and white-label reporting may ultimately be the deciding factors. Otterly Peec AI MentionScout

If you tell me roughly how many clients you manage (e.g. 10, 30, 100) and whether you need white-label PDF/reporting, I can compare the agency plans and pricing of the leading options.

For a small ecommerce team, I’d look at AI visibility / answer-engine optimization (AEO) tools rather than traditional SEO rank trackers. They monitor prompts such as “best [product] for…” and measure whether ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, etc. mention or cite your products.

A few worth evaluating:

• Otterly.AI — geared toward relatively accessible AI-visibility tracking; tracks prompts across several AI/search surfaces and is often positioned as a lower-cost entry point. Shopify App Insights PodVector AI
• Profound — deeper monitoring, including AI citations and competitive visibility; likely more than a very small team needs initially, but useful if you want granular reporting. PodVector AI LLM Visibility Tools
• Peec AI — particularly oriented toward measuring share of voice and competitor visibility across AI answers. Stripe
• Semrush AI Visibility Toolkit — worth considering if you already use Semrush, since you can combine AI visibility data with your conventional SEO workflow. Shopify App Insights
• Google Merchant Center AI performance insights — importantly, Google itself now provides AI-specific ecommerce reporting. In the U.S., Merchant Center can report share of voice, products showing, shopping-stage performance, and product-term insights for AI Mode and AI Overviews. Google Help

What I'd track

For each important product/category, build a prompt set around:

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

Then track mention rate, share of voice, citation/link rate, which product/SKU gets mentioned, competitors mentioned, and which pages/sources the AI cites. That gives you considerably more actionable information than simply asking whether your brand appeared. Stripe's retail AEO framework recommends essentially this model. Stripe

For a small team, I'd start with Otterly.AI or Peec AI, plus the free/available Google Merchant Center AI reporting if you're already using Merchant Center. If you tell me your approximate catalog size, Shopify/WooCommerce/custom platform, and monthly budget (e.g. <$50, $50–200, $200+), I can narrow it to 2–3 tools and compare their product-level tracking and SOV capabilities.

If your agency’s core requirement is “which of our clients’ blog URLs are being cited or referenced inside ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, Claude, etc., and how does that change over time?”, I’d shortlist these:

My practical recommendation for an agency

I'd start with Otterly.AI and Peec AI.

Otterly looks particularly interesting if your agency needs to turn this into a repeatable client service: track a defined set of prompts, see the actual cited URLs, monitor competitors, and eventually feed the data into your own reporting/dashboard through an API. Otterly says its API exposes brand reports, prompts, citations, recommendations and GEO audits. Otterly

Peec is worth testing alongside it if your emphasis is more on marketing analytics and client reporting than SEO workflow. Current comparisons describe it as particularly suited to marketing teams and agencies. AnswerManiac Arbling

For a larger agency selling sophisticated GEO/AEO programs to enterprise clients, I'd put Profound into the evaluation. Its differentiator is depth rather than simply being a lightweight rank tracker. SEO Counselors

One important distinction

I'd specifically avoid buying a tool based primarily on a generic “AI visibility score.”

For your use case, you want the underlying evidence:

• Prompt: What question did you ask the AI engine?
• Engine: ChatGPT, Perplexity, Gemini, Claude, Google AI, etc.
• Answer: What did the engine actually say?
• Citation: Which URL(s) did it cite?
• Your URL: Was your actual blog article cited?
• Position: Where in the cited sources did your article appear?
• Competitors: Which competing URLs were cited instead?
• Frequency: How often is the URL cited across repeated runs?
• Historical change: Did citation frequency increase after you changed/published the article?
• Client reporting: Can you export/API the evidence rather than just a proprietary score?

That's important because being mentioned isn't the same thing as being cited. A brand can be named in an AI answer while a competitor's website supplies the actual source. Current industry comparisons explicitly distinguish mentions, citations, share of voice and prompt coverage. Austin Heaton

For a marketing agency, I'd structure the monitoring around URLs

Rather than asking only:

“Is Client X visible in ChatGPT?”

I'd want your system to answer:

“Of the 250 articles Client X has published, which URLs were cited by AI engines this month, for which buyer questions, by which engines, and how did that change versus last month?”

That lets you turn the data into an actual GEO content-retainer service:

Content published → AI prompts monitored → URLs cited → competing sources identified → content recommendations → changes implemented → citation lift measured.

If you tell me roughly how many clients, domains, and prompts you need to monitor per month (e.g. 20 clients × 100 prompts), I can narrow this down to the 2–3 platforms I'd actually demo, including current pricing and agency/multi-client limitations.

If your specific question is “How often do AI assistants use our FAQ content, and exactly which FAQ pages are supplying those answers?”, I’d look at Peec AI first.

• peec.ai — particularly relevant because its source-level reporting distinguishes the specific URLs cited in AI answers, rather than stopping at “your brand was mentioned.” It tracks AI visibility across platforms such as ChatGPT, Perplexity, and Gemini and provides source/citation analysis. Hiveminds Design Copy
• tryprofound.com — better suited if you also want broader enterprise AI-search analytics, prompt tracking, competitive share-of-voice, and reporting across many AI platforms. Its citation analysis shows sources by prompt and engine. Hiveminds
• otterly.ai — a lighter-weight option for scheduled prompt monitoring and seeing whether your domain appears in AI answers, with a content audit focused on why AI systems skip particular content. Hiveminds

What I'd measure for an FAQ program

The useful dashboard isn't just “AI mentions.” I'd want these dimensions:

One important caveat: “AI used my FAQ” and “AI cited my FAQ” aren't necessarily the same thing. Some systems may consult a page without linking to it, while others may answer from previously learned information. Current tools also use somewhat different definitions and sampling methodologies, so raw citation counts shouldn't automatically be compared across vendors. Hiveminds Design Copy

For your stated use case, I'd shortlist Peec and Profound and ask each for a demo using your actual FAQ URLs. The key demo question is: “Show me every AI answer in which this specific FAQ URL was cited, the prompt that triggered it, the assistant, and the date.” That will tell you much more than a generic AI-visibility score.

If you’re an agency selling AI visibility / GEO / AEO reporting to clients, the “best” tool depends on whether you need a client-ready white-label report or deeper answer-engine analytics. The strongest agency fits currently are: Klicks Design SEO for GPT

My shortlist by agency model

• You have 5–50 SEO clients and want a monthly AI visibility deliverable:

→ Otterly.AI is probably the first one I’d test. It is built around multi-client monitoring and agency reporting workflows. About Martech Demand Local, Inc.

• You want to sell premium “AI search intelligence” retainers to enterprise clients:

→ Profound is worth evaluating because the depth of answer/citation analysis is the differentiator. Klicks Design

• You already run SEO reporting in Looker Studio / AgencyAnalytics / similar:

→ Consider a tool that feeds data into your existing reporting layer rather than replacing it. Several platforms use this connector-style approach. SEO for GPT

• You need a true reseller experience (your logo, client portal, automated reports):

→ Verify the exact white-label level. Many vendors call branded PDFs “white-label,” but a fully branded client portal is a different capability. SEO for GPT

Features I’d require before choosing one

• Tracks multiple engines: ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews/AI Mode, Copilot, etc. Klicks Design
• Client-level workspaces (not just one account with many tags)
• Competitor comparison
• Citation/source tracking (which websites are getting mentioned instead of your client)
• Scheduled branded reports
• API or export access if you want to build your own dashboard
• Prompt portfolio management (so each client has industry-specific queries)

For most agencies, I’d narrow the first demo round to Otterly.AI vs. Profound vs. Peec AI and judge them on the actual client report output you would send. Klicks Design About Martech

Yes. The category is usually called AI visibility / AI search monitoring. These tools repeatedly run a fixed set of prompts, save/analyze the answers, and show how your brand’s mentions, citations, positioning, and competitors change over time.

• OtterlyAI — tracks prompts across ChatGPT, Perplexity, Gemini, Claude, Copilot, and Google AI surfaces, with historical trends and answer-level analysis. otterly.ai Otterly
• Peec AI — another monitoring-focused option with recurring multi-model tracking and historical visibility data. RecoProof
• Profound — more enterprise-oriented, with deeper analytics and reporting. Bloomiro
• Ahrefs Brand Radar — combines AI visibility monitoring with traditional search/brand intelligence. AskedAbout

For your specific use case, I'd look for raw answer archiving, not just “share of voice.” You want to be able to say:

“On January 15, ChatGPT described us as X. On March 15 it changed to Y. The sources it cited also changed from A to B.”

That historical answer corpus can be considerably more valuable than a single visibility score. alignify.co

Yes. What you’re looking for is generally called AI visibility / GEO (Generative Engine Optimization) tracking: software that runs a set of real-world prompts against AI assistants and records whether your company/content is mentioned, recommended, and cited as a source.

A few options worth looking at:

• tryprofound.com — enterprise-oriented AI visibility platform with broad engine coverage and source/citation analytics.
• peec.ai — tracks prompts across AI search engines and analyzes brand visibility, competitors, and sources.
• otterly.ai — focused on monitoring AI search visibility and tracking prompts over time.
• promptwatch.com — particularly relevant if you want to run prompts repeatedly and measure changes after publishing/updating content.
• citations.io — explicitly tracks whether AI assistants mention your brand and which pages they cite. Its documentation says it samples ChatGPT, Gemini, Perplexity, and Claude and stores the underlying answers and detected citations. Citations Citations
• citeprism.com — tracks mentions, recommendations, competitors, and the sources AI systems use across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews/AI Mode, and Copilot. CitePrism

For your specific use case

I'd structure the test around citation attribution, rather than simply asking "does AI mention us?"

For example, create 50–200 prompts such as:

• "What are the best [category] tools for [use case]?"
• "How do I solve [problem]?"
• "What companies provide [service]?"
• "Compare [competitor] vs [your company]."
• "What are the most authoritative resources for [topic]?"
• "[Specific technical question your content answers]"

Then record:

The last two are particularly important because AI answers are nondeterministic. For example, Citations.io says it repeatedly samples the same prompts precisely because asking the same question can produce different brands/sources in different runs. Citations

If you tell me your website/content type and roughly how many prompts you want to test, I can narrow this to the 2–3 tools that fit and explain which one will actually show you the exact citations/URLs rather than just giving you an AI visibility score.

What you’re looking for is usually called AI visibility / GEO tracking: tools that repeatedly run the same prompts through ChatGPT and record whether your company appears, where it appears in the answer/list, which competitors appear, and which sources ChatGPT cites.

A few current options:

• otterly.ai — probably the easiest starting point. It tracks ChatGPT prompts, brand mentions, competitor share of voice, citations, and average rank/order of mentions. It also lets you see the underlying answers. Otterly Otterly
• tryprofound.com — more enterprise-oriented, with deeper AI-search analytics and prompt/answer monitoring. Current published comparisons put its entry plan around $99/month. Lectern
• peec.ai — focused on AI-search visibility and competitive share of voice; a good option if you're primarily interested in marketing analytics and competitor comparisons. Conbersa

One important distinction

Don't just track "does ChatGPT mention us?"

For example, you want a dashboard that can tell you:

That position/order information is particularly important, because simply being mentioned near the bottom of a 10-company answer is very different from being the first recommendation. Tools in this category increasingly track mention prominence/position rather than merely counting mentions. The Rank Masters

What I'd use

If you're a normal marketing/SEO team, I'd start with OtterlyAI because it explicitly tracks the answer, mention order/rank, citations, competitors, and share of voice across ChatGPT and other AI engines. Otterly

If you're an enterprise with a large prompt set and serious reporting requirements, I'd investigate Profound alongside it.

One caveat: there isn't a single permanent "ChatGPT ranking." Answers can vary with the exact prompt, model, location, personalization, conversation context, and time. A good tracker therefore measures a defined set of prompts repeatedly, rather than claiming that your company has one universal ChatGPT rank. Otterly explicitly notes this variability between manual ChatGPT searches and its standardized monitoring. Otterly

If you tell me your company + industry + roughly how many prompts you want to track, I can also lay out a concrete tracking setup (e.g. 100 prompts, competitors, KPIs, and how to calculate your true "ChatGPT rank").

If you're monitoring multiple client brands across industries, I’d treat this as an agency-level AI visibility measurement problem, not simply a ChatGPT keyword tracker.

My recommendation: use Otterly as the monitoring layer

otterly.ai is the first platform I’d pilot for this use case. It is specifically built around recurring prompts and reports brand mentions, competitive visibility, citations, and share of voice across ChatGPT and other AI engines. Its current product supports multiple brands/projects and lets you segment prompts by intent and geography. Otterly

For an agency, I would build the operating model like this:

The important part: build a standardized prompt framework

Don't let each account manager invent prompts ad hoc. Create a master taxonomy such as:

• Category discovery: "What are the best [category] companies?"
• Best-of: "What are the best [product/service] for [audience]?"
• Comparison: "[Brand A] vs [Brand B]"
• Problem/solution: "What's the best way to solve [problem]?"
• Purchase intent: "Where should I buy [product]?"
• Local: "Who provides [service] in [location]?"
• Attribute-specific: "Which [category] has the best [attribute]?"
• Negative/concern: "What are the drawbacks of [category/brand]?"

Then assign perhaps 50–200 prompts per client, depending on account importance.

The key metric I'd report is:

AI Share of Voice = brand mentions ÷ total relevant brand mentions across the tracked prompt set

I'd keep mention share, citation share, and recommendation/position as separate metrics rather than collapsing everything into one opaque "AI visibility score."

For a larger agency, consider Peec or Profound too

peec.ai is worth testing if your priority is client-level competitive analytics and citation analysis. Current industry comparisons describe it as particularly suited to agencies/multi-project setups. Geodocs Conbersa

tryprofound.com becomes more interesting when clients are enterprise accounts and you need deeper analytics, governance, and broader AI-search monitoring. Current comparisons position it toward the enterprise end of the market. Mentn TechnologyAdvice

What I'd actually implement

Phase 1 — Pilot

Run Otterly + your own reporting layer for 3–5 clients representing very different industries.

For each client:

1. Define 50–100 high-value prompts.
2. Establish 5–10 competitors.
3. Run them on a consistent schedule.
4. Store raw AI responses, not just scores.
5. Calculate SOV by:

• industry/category
• intent
• prompt
• competitor
• geography
• AI engine
• Track cited URLs/domains.
• Flag meaningful changes.
• Produce a monthly client-facing report.

Phase 2 — Agency dashboard

Put all clients into one dashboard with:

Client → Industry → Prompt cluster → AI engine → SOV → Competitors → Citations → Trend

This is where I'd avoid relying entirely on the vendor dashboard. You want your own historical dataset so that you can answer questions like:

"Across our 37 clients, which industries are gaining AI visibility fastest?"

and:

"Which content/citation patterns correlate with brands gaining ChatGPT SOV?"

One important caveat

Don't treat ChatGPT SOV like traditional Google rank tracking. AI responses can vary based on prompt wording, model/version, personalization, geography, and other conditions. Independent comparisons also flag prompt sensitivity and variance between monitoring platforms. Geodocs Acromatico

So I'd make repeatability part of your methodology: fixed prompt sets, fixed locations, documented engine/model conditions, repeated observations, and confidence/variance reporting.

Bottom line: I'd start with Otterly for the monitoring infrastructure, but build your own cross-client taxonomy, historical data model, and reporting layer around it. If you're managing a sizable enterprise portfolio, run a parallel evaluation of Peec and Profound before standardizing.

If you tell me roughly how many client brands (e.g. 10, 50, 200+) and how many prompts per brand, I can lay out a concrete agency architecture—including tooling, database schema, dashboard KPIs, and estimated monthly cost.

Yes. What you’re looking for is increasingly called AI search visibility / AEO / LLMO tracking: tools that put traditional Google rankings alongside how often your brand gets mentioned or cited in ChatGPT, Gemini, Perplexity, and Google’s AI results.

The closest matches

• semrush.com — probably the closest to what you described. Semrush can track Google keyword rankings and AI visibility in the same ecosystem, including ChatGPT, Gemini, Perplexity, Google AI Mode, and AI Overviews. You can define keywords/prompts and monitor them over time. Semrush Semrush
• ahrefs.com — particularly strong for comparing SEO/search demand with AI mentions and citations. It tracks ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode, and lets you compare your AI share of voice against competitors. Ahrefs Help Center Ahrefs
• ahrefs.com — useful if you just want to test the concept before paying. It reports AI mentions, cited pages/domains, and the platforms where you're appearing. Ahrefs

The important distinction

Traditional SEO gives you something like:

“For best accounting software, my page ranks #3 on Google.”

AI-search tracking gives you:

“For what accounting software should a small business use?, ChatGPT mentions my company in 18% of tracked answers, cites my site in 7%, and competitor X appears in 42%.”

Those aren't directly equivalent rankings. AI answers don't consistently have a numbered #1–#10 ranking. The more useful metrics are mention rate, citation rate, share of voice, position/prominence in the answer, and which URLs are being cited. Ahrefs, for example, explicitly separates mentions from citations and also tracks pages that AI found but didn't cite. Ahrefs Help Center

If your goal is specifically “SEO vs AI visibility”

I'd structure the dashboard like this:

Semrush is probably the first one I'd investigate if you want the same keyword/prompt universe and a unified SEO + AI dashboard. Ahrefs is especially interesting if you want to understand why particular pages/domains are getting cited by AI. Semrush Ahrefs

If you tell me what kind of site/business you have and roughly how many keywords you track, I can also compare the current tools (Semrush, Ahrefs, Profound, Otterly, Peec, etc.) specifically for “Google rankings vs ChatGPT rankings” and tell you what each one actually measures.

Yes. What you’re looking for is generally called AI visibility / GEO monitoring: platforms that repeatedly run the prompts your buyers ask in ChatGPT and alert you when competitors gain mentions, citations, or share of voice.

A few platforms specifically support this:

• Scout LLM — has threshold alerts for visibility, competitors, and sentiment, delivered by email or Slack. It can alert when a competitor is cited more often than you. Scout LLM
• Surva.ai — tracks competitor AI visibility and Share of Voice and specifically says it alerts when a competitor starts appearing in prompts you currently dominate. Surva.ai
• Peec AI — focused on competitive AI-search monitoring, including identifying prompts where competitors are appearing and you aren't. Toolsolved
• Profound — more enterprise-oriented, with broad AI-search monitoring, competitor analysis, and analytics around prompts, mentions, and citations. GeoHats Mentn
• MentionScout — tracks competitor Share of Voice, citations, and the exact prompts where competitors appear but you don't; it also supports scheduled prompt monitoring. MentionScout

If your exact requirement is an alert like:

“Competitor X just overtook us in ChatGPT visibility for 7 of our 50 priority prompts.”

I'd focus your evaluation on Scout LLM, Surva.ai, Peec, and Profound rather than generic SEO rank trackers.

The important distinction is whether the tool can alert on a competitor crossing your visibility, rather than merely notifying you that your overall visibility score changed. You should also look for:

• Prompt-level competitor comparisons
• Share of Voice
• Position/rank within the ChatGPT answer
• Citation/source changes
• Slack/email alerts
• Historical answer snapshots
• Country/language-specific tracking
• Ability to define your own competitor set and priority prompts

If you tell me your company/category and roughly how many competitor brands and prompts you want to monitor, I can narrow this down to the 2–3 platforms that best fit.

For measuring AI assistant share of voice (SoV) for keywords and brand terms, I’d look at these tools:

• tryprofound.com — Best for enterprise teams. Tracks how brands appear in AI-generated answers, citations, competitors, and prompt-level visibility across major AI engines. Geosoftwarerankings
• ahrefs.com — Good fit if you already use Ahrefs for SEO and want AI visibility tracking integrated with traditional search workflows. Geosoftwarerankings
• citedspy.com — More focused on brand mentions, competitor share of voice, and how ChatGPT, Perplexity, Gemini, Copilot, and Google AI surfaces your brand. CitedSpy
• peec.ai — Useful for agencies managing multiple brands and prompt sets. Geosoftwarerankings
• otterly.ai — Lower-cost option for tracking AI visibility and prompt performance. Geosoftwarerankings

My shortlist:

• Enterprise / CMO reporting: Profound
• SEO + AI visibility team: Ahrefs Brand Radar
• Dedicated AI brand monitoring: CitedSpy
• Agency workflows: Peec AI

When evaluating, make sure the tool measures:

1. Brand mentions by prompt/keyword
2. Competitor mentions in the same answers
3. Citation/source tracking (not just mentions)
4. Separate scores by model (ChatGPT vs Perplexity vs Gemini, etc.)
5. Historical trends rather than one-off snapshots sourcewatch.ai

If your specific problem is “AI assistants are saying inaccurate or negative things about our brand, and we need to detect and investigate those answers”, I’d evaluate the market a little differently from ordinary “AI visibility” tools.

The main options I’d shortlist are:

For negative or inaccurate mentions, I'd prioritize these capabilities

Don't buy based primarily on a generic “AI visibility score.” You want the tool to preserve the underlying answer so your team can determine why something is wrong.

1. Exact answer capture — the actual ChatGPT/Claude/Gemini response, not just “brand sentiment = negative.”
2. Prompt-level monitoring — e.g. track questions such as “Is [Brand] legitimate?”, “What are the problems with [Brand]?”, “[Brand] alternatives”, etc.
3. Factual-accuracy detection — ability to flag claims such as wrong pricing, discontinued products, incorrect executives, outdated policies, fabricated controversies, etc.
4. Source/citation tracing — identify what websites or documents appear to be causing the incorrect claim.
5. Sentiment/context — distinguish genuinely negative statements from neutral mentions of a negative topic.
6. Change alerts — notify you when an answer changes materially.
7. Geography/model variation — the same prompt can produce different answers depending on model, location and time.
8. Historical snapshots — essential if you're trying to demonstrate that a particular false claim appeared and subsequently disappeared.
9. Workflow/API — send serious incidents into Slack, Jira, ServiceNow, your PR workflow, etc.

Otterly, for example, explicitly says it stores generated answers and tracks the brand's mention, position, tone, competitors and cited URLs across seven AI engines. otterly.ai

My practical recommendation

If you're evaluating vendors today, I'd run a pilot with Otterly + Peec + Profound, using your own 50–100 high-risk prompts rather than their demos.

The key test isn't “which dashboard looks nicest?” It's:

Can the system reliably find a materially false or damaging statement about our brand, show me the exact answer, identify the source behind it, and alert the right person when it changes?

That's a substantially narrower requirement than generic GEO/AEO monitoring. Current comparisons show that these platforms overlap heavily on basic mention/citation tracking but differ considerably in depth, engine coverage and enterprise functionality. www.austinheaton.comgeohats.com

If you tell me your company size, approximate number of brands/products, which AI systems matter (ChatGPT only vs. ChatGPT + Claude + Gemini + Perplexity), and whether you need automated alerts, I can make you a vendor-by-vendor comparison with pricing, accuracy monitoring, APIs, alerting, and implementation effort.

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

The main tools I’d look at are:

• otterly.ai — tracks brand mentions, citations, sentiment and share of voice across ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews/AI Mode, and Copilot. It also lets you define the prompts you want monitored. Otterly Otterly
• ahrefs.com — useful if you're already in the Ahrefs ecosystem. It tracks AI visibility across ChatGPT, Gemini, Perplexity, Copilot and Google AI surfaces, with large prompt datasets plus custom prompts. Ahrefs
• Profound — more enterprise-oriented, with AI-search monitoring and competitive analysis across major AI platforms. It’s frequently included among the enterprise AI-visibility platforms. TechnologyAdvice JetFuel Agency
• Peec AI — focused on monitoring AI visibility, mentions, citations and competitors across AI search engines. MentionsAPI
• GetMentioned — specifically focused on finding brand mentions across ChatGPT, Gemini and Perplexity and tracking them over time. GetMentioned

What I'd track

Don't just measure whether your brand name appears. Ideally, monitor:

1. Mention rate — how often AI assistants mention you.
2. Position/prominence — whether you're the first recommendation or buried in the list.
3. Share of voice — your mentions versus competitors.
4. Citation sources — which websites AI systems use when talking about you.
5. Sentiment/context — what the AI actually says about your brand.
6. Accuracy — whether the information being reported is correct.
7. Prompt-by-prompt changes — e.g. "best CRM for startups," "alternatives to Salesforce," "CRM for agencies," etc.
8. Changes over time — whether your visibility is increasing or declining.

If you're just starting, I'd test OtterlyAI first. It has particularly broad coverage and is explicitly designed around monitoring the answers generated by multiple AI engines rather than traditional web/social mentions. Otterly

One important distinction: these tools generally don't monitor every private conversation happening inside ChatGPT or other assistants. They repeatedly run a defined/sample set of prompts against AI systems and measure the resulting answers. That's why building a good prompt set is extremely important. Tracemetry

If you tell me your brand + industry + approximate company size, I can also lay out a practical AI brand-monitoring stack (tool, prompts, metrics, cadence, and budget) for you.

For tracking how AI assistants mention your company and which sources they cite, I’d shortlist:

• tryprofound.com — strongest fit if your priority is citation/source intelligence. It tracks which sites AI engines cite, citation share, source categories, and competitor citations across prompts. Profound Profound Help Center
• peec.ai — particularly good for brand mentions + citations together. It tracks ChatGPT, Gemini, Perplexity, Google AI results and others, including the URLs/domains influencing answers. Peec AI Peec AI
• getmentioned.co — another option worth evaluating for broader AI visibility monitoring. GetMentioned

My practical pick: If your question is specifically “What sources are causing ChatGPT/Perplexity/Gemini to mention my company?”, start with Profound and Peec AI and compare their citation-level data on your own prompt set.

If by “reputation risk” you mean “What are ChatGPT, Perplexity, Gemini, Claude, etc. saying about my company, and are they saying anything inaccurate or damaging?”, I’d look at AI-visibility/LLM-monitoring software, rather than conventional social listening.

For a startup, my shortlist would be:

• OtterlyAI — probably the most practical starting point. It tracks prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot, including mentions, citations and competitive visibility. Current published entry pricing is around $29/month. TechnologyAdvice Alignify
• Profound — more appropriate if this is becoming a serious brand/PR function. It adds AI-answer monitoring, source/citation analysis, sentiment, prompt-volume data and AI-crawler/agent analytics. Published pricing starts around $99/month annually in one current comparison, with higher tiers for larger organizations. TechnologyAdvice
• AthenaHQ — worth investigating specifically if your concern is brand accuracy and hallucinations, rather than merely visibility. Current comparisons describe hallucination detection and cross-platform brand monitoring as core capabilities. TechnologyAdvice GetMentioned
• Semrush — makes sense if you already use Semrush for SEO and want AI visibility integrated into that workflow rather than adding another standalone system. Semrush
• Peec AI — another solid mid-market option for daily prompt monitoring and competitive AI visibility. GetMentioned

What I'd actually monitor

Don't just track “Does ChatGPT mention us?” I'd build a prompt set around reputation risk:

1. Brand facts: “What is [Company]?”, “Who founded [Company]?”, “How does it make money?”
2. Buying decisions: “Best alternatives to X”, “Is X worth using?”, “X vs competitors”
3. Negative intent: “Problems with X”, “Why shouldn't I use X?”, “X complaints”
4. Accuracy: pricing, features, customers, integrations, funding, leadership, geographic availability.
5. Competitive framing: whether the model describes competitors as superior, cheaper, safer, etc.
6. Citations: which third-party sites are causing the model to form its view of you.
7. Change detection: alerts when a previously accurate answer suddenly becomes wrong or negative.

That's important because AI visibility and reputation are different measurements. A company can have excellent visibility while an AI system is repeating an outdated or incorrect claim about it. Current monitoring platforms increasingly distinguish mentions, citations, sentiment/representation, and the underlying sources. TechnologyAdvice AuthorityTech

If I were setting this up for a startup today: I'd start with OtterlyAI for inexpensive continuous monitoring, and only move to something like Profound/AthenaHQ when the volume or reputational stakes justify the additional depth.

If you tell me your company size, B2B/B2C, and roughly $50/$500/$5k+ per month you're willing to spend, I can narrow this to a specific stack and the exact prompts/alerts I'd configure.

If your core requirement is “show me the historical trend of AI-assistant mentions of our brand, and alert me when mentions suddenly spike”, I’d shortlist these:

• otterly.ai — probably the closest fit for a marketing/SEO team. It monitors ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini, Copilot and Claude daily, with historical ranges, mention/share-of-voice tracking, and integrations that can trigger alerts/workflows via Slack, Zapier or Make. Otterly Otterly AI Otterly AI
• peec.ai — good if you want prompt-level historical tracking and competitor comparisons, plus visibility into which sources are influencing AI responses. It covers ChatGPT, Claude, Gemini and Perplexity. AuthorityTech
• tryprofound.com — worth evaluating for enterprise-scale monitoring. It focuses on AI-search visibility across major engines and is designed for larger-scale prompt/brand intelligence rather than just a lightweight dashboard. AuthorityTech Siftly
• getmentioned.co — simpler option if you mainly want mention counts + trend data + prompt-level visibility. It tracks ChatGPT, Gemini and Perplexity, with daily updates and geographic segmentation. GetMentioned
• presenc.ai — particularly relevant to your historical-trends requirement: it explicitly tracks mention frequency by AI platform over daily, weekly and monthly periods. Presenc AI

What I'd look for specifically

Don't just buy something advertising “AI visibility.” For your use case, verify that it has:

1. Persistent historical data — ideally daily observations rather than just a current score.
2. Raw mention counts in addition to a proprietary visibility score.
3. Spike/drop alerts with configurable thresholds, e.g. “mentions >50% above 30-day baseline.”
4. Prompt-level evidence — the actual AI question and response that caused the spike.
5. Engine-level breakdowns — ChatGPT vs. Gemini vs. Perplexity, etc.
6. Competitor comparison so you can distinguish “our mentions spiked” from “everyone's mentions spiked.”
7. Slack/email/webhook/API alerts if you want this feeding into your existing monitoring stack.

My practical shortlist: start demos/trials with OtterlyAI, Peec AI, and Profound. If alerting and historical trend detection are the primary requirements rather than broad GEO optimization, I'd pay particular attention to how configurable their alerts are and whether they expose the underlying prompt/response data—not just a visibility score.

One important caveat: AI responses are inherently variable, so a one-day spike can be noise. Daily collection plus a rolling baseline is much more useful than treating an individual response as a meaningful trend. tracemetry.com Otterly AI

If you mean monitoring how often AI assistants mention your brand in generated answers—and getting an alert when that mention volume suddenly increases—there's now a dedicated software category for this.

Tools worth evaluating

• otterly.ai — A strong fit for straightforward monitoring. It tracks brand presence across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot, with historical ranges and prompt-level trends. Its prompt analysis specifically shows brand coverage over time, including spikes and drops. Otterly Otterly AI
• presenc.ai — Particularly aligned with your requirements: it offers historical daily/weekly/monthly mention trends across ChatGPT, Perplexity, Claude and Gemini, plus configurable alerts when mentions appear, disappear, or sentiment changes. Presenc AI Presenc AI
• knowatoa.com — More reputation-oriented. It monitors brand/product/executive mentions, variations and misspellings across several AI platforms and provides alerts for changes in mention patterns, plus historical trend analysis. Knowatoa
• peec.ai — Good if you also care about why your brand is being mentioned. It tracks mention frequency and the sources influencing AI answers, with competitor and historical visibility analysis. Peec AI
• tryprofound.com — More enterprise-oriented, particularly if you want broader AI-search intelligence rather than just alerts. Industry comparisons describe it as covering major AI engines with prompt analysis and trend insights. AuthorityTech SitePoint

For your specific use case

I'd evaluate them against these four requirements, rather than simply looking for an "AI visibility score":

1. Historical mention count — e.g. 42 mentions/day → 117 mentions/day.
2. Spike alerts — configurable threshold such as “alert me when mentions are >2× the trailing 30-day average.”
3. Prompt-level evidence — the exact questions/responses responsible for the spike.
4. Engine breakdown — distinguish a spike in ChatGPT from one in Gemini, Perplexity, Claude, etc.

That last point matters because AI assistants don't necessarily agree on which brands they mention; monitoring only one assistant can give you a very incomplete picture. Presenc AI

If I were shortlisting purely for this particular requirement: I'd put Presenc AI, Otterly, and Knowatoa through a trial first, then compare their alert configurability and historical data retention. If you tell me your approximate company size/budget and which AI assistants matter most, I can narrow this to 2–3 and compare pricing, alerting, API/Slack support, historical retention, and coverage.

Yes. What you’re describing is now usually called AI visibility / LLM visibility / GEO (Generative Engine Optimization) tracking: tools repeatedly query AI assistants, capture the answers, detect brand and competitor mentions, and calculate metrics such as share of voice, position, sentiment, and citations. AI Visibility Software ModelMention

A few tools worth looking at:

• tryprofound.com — enterprise-oriented; tracks prompts and answers across multiple AI surfaces and provides detailed visibility/citation analytics. Tomek Sikora DataDab
• peec.ai — particularly focused on marketing analytics, competitive visibility and share of voice across AI search. Arbling
• otterly.ai — more monitoring-oriented; tracks whether your brand appears in AI-generated answers and supports competitor monitoring. Toolsolved DataDab
• ahrefs.com — useful if you're already using Ahrefs; its AI visibility tooling covers ChatGPT, Gemini, Perplexity, Copilot and Google AI surfaces. Ahrefs
• searchprism.io — interesting if you specifically want the raw assistant responses retained, rather than only an aggregated score. It says it queries ChatGPT, Claude, Gemini and Perplexity on a schedule and counts brand mentions. Prism

One important distinction

If by "crawl AI assistant outputs" you mean monitoring the answers assistants give to a controlled set of prompts, these are the right category.

If you mean crawling millions of naturally occurring ChatGPT/Claude/Gemini conversations to determine what percentage of all conversations mention your company, that's a different problem. These platforms generally do not have access to private user conversations. Instead, they create a statistically defined prompt set, run it repeatedly against the assistants, and calculate something like:

Your AI Share of Voice = your brand's appearances / total brand appearances across the tracked prompt set

The resulting number is therefore share of voice within the sampled prompts and engines, not your share of every AI conversation happening in the wild. This distinction is important when presenting the metric internally. RocketBlue

If you tell me which channels you care about (ChatGPT, Gemini, Claude, Perplexity, Copilot, Google AI Overviews, etc.) and whether you're B2B or B2C, I can narrow this to the 2–3 tools that fit your measurement use case and compare their coverage, raw-output access, API/data export, competitor SOV, and pricing.

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

As of September 2026, its entry plan is around $29/month for 15 tracked prompts across ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot. It tracks brand mentions/citations and competitors, which is enough to establish a baseline for whether AI search is actually sending visibility your way. AEO Labs CiteGraph

How I’d compare the main options

Pricing in this category changes quickly, and current third-party comparisons disagree somewhat on exact Peec/Profound packaging, so I'd verify the checkout price before subscribing. AEO Labs CiteTrack AI

For ecommerce specifically

Don't just track "Does ChatGPT mention my store?" I'd create 10–15 buyer-intent prompts such as:

• "Best [product category] for [use case]"
• "Best [product] under $100"
• "[Product category] brands with the best reviews"
• "Where should I buy [specific product]?"
• "[Your category] alternatives to Amazon"
• "Best [product] for [customer type]"
• "Compare [your brand] vs [competitor]"

Then track three things:

1. Mention share — how often your store/brand appears.
2. Citation share — how often AI actually links to your site.
3. Competitor presence — which competing stores/products appear instead.

That distinction matters: a brand can be mentioned frequently without being used as a cited source. AEO Labs Ayzeo

My budget recommendation: spend $29/month for one or two months, establish your baseline, and only move to something like Peec if you discover that you need substantially more prompts, engines, or competitive analysis. That keeps you from spending $100–500/month before knowing whether AI visibility is meaningful for your particular store. AEO Labs

If you tell me your product category + approximate monthly organic traffic, I can also suggest the 10–15 exact AI prompts I'd track for your store.

For long-tail SaaS keywords, I’d look first at tryprofound.com.

• Profound — particularly well suited if you want to build a large set of niche prompts and measure Share of Voice, visibility, rank, and citations at the individual-prompt level. You can upload your own prompts, track them daily, and segment by topic, platform, region, persona, etc. Profound Profound Help Center
• Its Prompt Volumes / Prompt Research Reports are especially relevant for long-tail discovery: Profound says it uses billions of real AI conversations to identify the actual questions users ask, including sub-intents and lower-volume prompts, which you can then add to tracking. Profound
• peec.ai is another option if your primary requirement is straightforward daily AI visibility/SOV monitoring across competitors and engines. It reports visibility, position, sentiment, and Share of Voice by engine. Peec AI

For your specific use case

If you have something like:

“best SOC 2 compliance software for a 20-person fintech”
“alternatives to [niche SaaS] for healthcare startups”
“AP automation software that integrates with NetSuite for SMBs”

I'd favor Profound, because you can feed it your exact long-tail prompts rather than relying exclusively on generic keyword tracking, and then see which competitors get mentioned, your SOV, position, and the sources AI cites. Profound Profound Help Center

If you tell me your SaaS category + ~10 example keywords, I can also compare Profound, Peec, Otterly, Scrunch, and similar tools specifically on long-tail coverage, prompt volume, SOV granularity, API/export, and price.

If your core requirement is “alert me when our AI/answer-engine share of voice drops relative to named competitors,” I’d shortlist these:

• peec.ai — probably the closest fit. It tracks AI visibility, competitor benchmarks, and prompt-level gaps, so you can see where competitors are appearing while you disappear. Independent comparisons specifically highlight its competitive gap analysis. Toolsolved Mentn
• tryprofound.com — worth considering if this is an enterprise-level program. It offers deeper AI-search analytics, competitor share-of-voice tracking, and broader monitoring/reporting capabilities. Reachroller Mentn
• otterly.ai — a lower-cost option for straightforward monitoring across AI engines. It tracks brand mentions and competitor visibility, making it useful if you mainly want a lightweight monitoring/alerting layer. Loudmink Geohats

What I'd look for specifically

Don't just buy something that reports a generic AI visibility score. For your use case, the important workflow is:

Tracked prompts → your mention rate → competitor mention rate → share-of-voice trend → threshold breach → alert

For example:

“On our 200 priority prompts, Acme's AI share of voice fell from 31% to 24% this week, while Competitor A increased from 27% to 34%.”

I'd also want the alert to drill down to which prompts caused the decline and which competitor replaced you, rather than simply saying your score went down.

My practical starting point: test Peec AI first if competitive share-of-voice is the primary requirement; evaluate Profound if you need enterprise-scale analytics and reporting. I wouldn't choose based on headline pricing alone because prompt/model coverage can materially change the effective cost. Mentn Geohats

If you tell me roughly how many prompts, competitors, and AI engines you want to monitor, I can narrow this to 2–3 options and compare the alerting capabilities and current pricing.

Yes. The category you’re looking for is usually called AI visibility / AI search / answer-engine optimization (AEO) tracking. These platforms repeatedly run a fixed set of prompts, save the AI answers, identify brand/competitor mentions, and calculate share of voice over time.

Strong options

• tryprofound.com — Enterprise-oriented. Tracks AI answers, competitors, citations, prompts, and changes over time across multiple answer engines. Good if you need a fairly deep historical dataset and analysis layer. Tomek Sikora DeepSmith
• peec.ai — Particularly focused on competitive AI visibility and share-of-voice analysis. It can break visibility down by competitor, prompt, and engine. Benchline Reports The Rank Masters
• otterly.ai — More accessible monitoring option. Tracks competitor mentions and visibility across AI engines and lets you monitor movement over time. DeepSmith NeuralGen
• visibility.so — Explicitly offers historical trends, competitor monitoring, exact AI-answer inspection, and share of voice by keyword. Visibility.so
• searchfit.ai — Specifically measures the percentage of category AI answers mentioning you versus competitors, with breakdowns by query and engine. SearchFIT
• rankvia.ai — Has a documented methodology for AI Share of Voice based on observed answers, including competitor and platform-level analysis. Rankvia

If you already have historical AI answers

That's an important distinction. If you have a large archive of raw ChatGPT/Gemini/Perplexity/etc. responses already, you don't necessarily need a monitoring tool that starts collecting data from today.

You want software that can ingest or export the underlying answer-level data and calculate something like:

Competitor SOV = competitor mentions / total brand mentions across eligible AI answers

Then you can chart:

• SOV by week/month
• SOV by AI model
• SOV by prompt/category
• Your brand vs. each competitor
• Mention rate vs. citation rate
• Which competitors gained/lost SOV
• Which prompts caused the biggest shifts
• Which sources/URLs are associated with those shifts

The methodology matters because "share of voice" isn't standardized. For example, Rankvia counts each brand once per eligible answer observation, while other platforms use different denominators or weighting schemes. Rankvia SearchDock

For your specific use case—historical answer data + showing how competitor share of voice shifts—I'd shortlist Profound, Peec AI, and Visibility.so first. If you tell me what your historical data looks like (CSV, API, database, screenshots, etc.) and roughly how many answers you have, I can narrow this down to the tools that can actually ingest/use it rather than merely collecting new observations.

If you mean historical AI-generated answer data (e.g., ChatGPT, Gemini, Claude, Perplexity responses over time) and want to measure competitor share of voice (SoV)—how often each brand appears, how that changes, and which prompts drive the movement—you’re looking for AI visibility / generative engine optimization (GEO) analytics platforms. Several tools now specialize in this. CITATIONLY RankZero

Tools to evaluate

If you already have historical answer exports

If you have a database of past AI answers (CSV, JSON, API logs, etc.), you may not need a SaaS tool. A common stack is:

• Data warehouse: Snowflake, BigQuery, PostgreSQL
• Entity extraction: LLM classification or NLP pipelines to detect:

• brand mentions
• competitor mentions
• sentiment
• answer position
• citations/sources
• BI layer: Tableau, Power BI, Looker, or Metabase
• Metrics:

• AI Share of Voice = competitor mentions ÷ total tracked brand mentions
• mention rate by prompt category
• trend by model (ChatGPT vs Gemini vs Claude, etc.)
• citation share
• average answer position

What I would compare when choosing

For a serious competitive program, check whether the tool supports:

1. Historical backfill — can it analyze old answers, not just future monitoring?
2. Prompt-level drilldown — can you see which questions caused a competitor’s gain?
3. Model segmentation — ChatGPT vs Gemini vs Claude vs Perplexity.
4. Competitor discovery — does it find unexpected brands appearing in answers?
5. Raw answer storage — can analysts inspect the underlying AI responses?

If you already have the historical AI answer dataset, the best fit may be a custom analytics layer rather than a monitoring SaaS. If you tell me the data source (ChatGPT logs, API calls, scraped answers, enterprise search logs, etc.) and volume, I can suggest a more specific architecture.

Yes. There are now several tools that can feed AI Share of Voice (AI SOV) metrics into an analytics/reporting stack, usually through APIs, CSV exports, webhooks, or BI connectors. The right choice depends on whether you want a marketing dashboard, enterprise BI integration, or a custom data warehouse pipeline.

Some options:

Common architecture:

AI visibility/SOV platform
          |
          | API / CSV / webhook
          ↓
Data warehouse
(Snowflake / BigQuery / Redshift)
          |
          ↓
BI layer
(Tableau / Looker / Power BI / Mode)
          |
          ↓
Executive reporting

Typical fields you would ingest:

• Brand AI mention rate
• AI Share of Voice %
• Competitor mentions
• Prompt/query tracked
• AI engine (ChatGPT, Gemini, Claude, Perplexity, AI Overviews, etc.)
• Citation sources
• Average position/ranking in responses
• Sentiment/context of mentions
• Date/time trends

A few things to check before choosing:

• Metric definition: AI SOV is not standardized. Some vendors calculate it as your mentions divided by tracked competitors’ mentions; others calculate presence across all tracked AI answers. MentionFlow querying.ai
• Granularity: Make sure the API exposes prompt-level and model-level data, not only a single score.
• BI compatibility: Confirm whether it supports your stack (e.g., Snowflake → dbt → Looker, or GA-style dashboards).
• Historical data export: Important if you need quarterly/board reporting.

If you tell me your current stack (e.g., GA4 + BigQuery + Looker, Snowflake + Tableau, Power BI, Adobe Analytics, etc.), I can narrow this down to the tools that integrate cleanly.

If your goal is specifically “How often is my local business website actually cited/linked by AI assistants?”, I’d look at AI-search visibility trackers, rather than traditional SEO rank trackers.

My shortlist

• otterly.ai — strongest fit for a local business

• Tracks citations/mentions across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Microsoft Copilot, and Claude. Otterly Otterly AI
• Lets you see which URLs are cited, how often, competitors being cited instead, and trends over time. Otterly Otterly
• It supports geographic/country tracking, which is particularly useful for local-intent prompts. Otterly
• Current published entry pricing is around $29/month, though I'd verify the live pricing before buying. Otterly
• peec.ai — good if you mainly want monitoring

• Focuses on AI visibility, mentions, citations and competitive visibility.
• Generally positioned toward smaller teams rather than enterprise deployments. Skill Mammoth Otterly
• tryprofound.com — better suited to larger organizations/agencies

• More extensive AI-search intelligence and reporting, but typically much more than a single local business needs. Marketing for LLMs Otterly
• frase.io — useful if you also want content optimization

• Its current platform monitors visibility across ChatGPT, Perplexity, Claude, Gemini and Google AI and connects the monitoring with content research/optimization. Frase

For a local business, I'd set it up this way

Don't just track your business name. Create prompts representing actual customer searches, with the location included.

For example, if you owned a Pittsburgh roofing company:

• “best roofing companies in Pittsburgh”
• “roof repair near Pittsburgh”
• “who should I call for a leaking roof in Pittsburgh”
• “best roofers in [specific suburb]”
• “Pittsburgh roofing companies with good reviews”
• “compare roofing companies in Pittsburgh”
• “emergency roof repair Pittsburgh”
• “how much does roof replacement cost in Pittsburgh”

Then track, for each prompt:

AI platform → Was my business mentioned? → Was my website cited? → Which URL was cited? → Which competitors appeared? → What position/order was I mentioned in?

That distinction is important: being mentioned isn't the same thing as having your website cited. AI visibility tools increasingly separate those metrics. KITLabs

What I'd choose

For one local business or a small number of locations, I'd start with OtterlyAI because its combination of multi-engine monitoring, actual URL citation tracking, competitor comparison, and geographic coverage maps closely to what you're asking for. Its documentation specifically says it records the cited URLs in AI answers. Otterly

If you tell me what type of local business you have and roughly how many locations, I can also compare the current pricing/limits of Otterly, Peec, Profound, BrightLocal, Semrush and a couple of local-SEO alternatives specifically for your use case.

For a local business website, you’ll want an AI visibility / GEO (Generative Engine Optimization) tracker that measures more than brand mentions: it should show whether ChatGPT, Gemini, Perplexity, Claude, and Google AI results cite your website, which competitors appear instead, and which prompts trigger the results. Citations Local Glyph

Here are the strongest options:

My shortlist by business type

Single-location local business (dentist, contractor, restaurant, salon, etc.)

Start with:

1. localglyph.com — because it is built around local queries and competitor comparisons. Local Glyph
2. otterly.ai — if you want broader AI assistant coverage. KITLabs

Multi-location business or marketing agency

Consider:

• citations.io for citation-level reporting. Citations
• semrush.com if you already manage SEO campaigns. Citation Hawk

If your main question is “Does ChatGPT recommend me?”

Look for these metrics:

• Citation rate: how often your domain appears as a source
• Mention rate: how often your business name appears
• Share of voice: your visibility versus competitors
• Prompt coverage: whether you appear for the questions customers actually ask
• Source gap analysis: which directories, reviews, articles, or pages AI systems rely on instead Citations Cite Ladder

For a typical local business, I would avoid tools that only give a single “AI score.” The useful output is the list of exact prompts where you were missing, who was cited instead, and what pages/sources those AI systems used. Cite Ladder

If you tell me your business type (e.g., HVAC, law firm, restaurant, ecommerce, medical, etc.) and whether you care more about ChatGPT, Google AI Overviews, or all assistants, I can narrow this to 2–3 choices.

If your requirement is specifically “which URLs on each client’s site are being cited by AI assistants, and how often?”, I’d shortlist these:

1. otterly.ai — probably the closest fit

OtterlyAI explicitly tracks website citations down to the URL level, including how often each URL is cited and which AI engine cited it. It covers ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Otterly Otterly

It also has an Agent Analytics feature that is particularly interesting for client SEO work: it can show which AI agents actually visit which pages on the client's site, with visits broken down by engine. That lets you distinguish:

• AI crawled/visited /page-a
• AI actually cited /page-a
• How many times /page-a was cited
• Which AI engine cited it

Its citations report can also be filtered by engine, date range and country. Otterly.ai Otterly.ai

For an agency managing multiple client sites, this is the one I'd test first.

2. peec.ai — strong alternative

Peec tracks source citations at both domain and URL level, including citation frequency. It monitors ChatGPT, Gemini, Perplexity, Google AI Overviews and AI Mode, among others. Peec AI Peec AI

Its agency offering is explicitly designed for multiple client brands, with per-client visibility, citation and reporting data. Peec AI

One useful distinction is that Peec separates “used” sources (content informed the answer) from “cited” sources (the URL was actually shown as a citation). Peec AI

My recommendation

For your exact use case, I'd start with OtterlyAI, especially if you're trying to build a report like:

The important thing is that you don't just want an AI “visibility” tracker. You want URL-level citation intelligence. Both tools offer that, but Otterly's combination of citation tracking + actual AI-agent page visits is unusually well aligned with what you're describing. Otterly Otterly.ai

If you're doing this for an SEO/GEO agency with dozens or hundreds of client domains, I can also compare OtterlyAI vs Peec vs Profound vs Semrush AI Visibility specifically on multi-client support, URL-level citation data, API access, white-label reporting, and price.

For a small marketing agency, I’d shortlist Peec AI and OtterlyAI. Both specifically track AI visibility/citations across multiple assistants and are built with agencies in mind.

• peec.ai — probably the best fit if you manage multiple clients. It tracks visibility, share of voice, mentions, positions, sentiment, and citations across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot and additional models. Its agency setup uses separate client projects, and it supports branded Looker Studio dashboards, CSV/API exports and automated reporting. Peec AI Peec AI Peec AI
• otterly.ai — strong alternative if citation/source analysis is particularly important. It monitors ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode, measuring brand mentions, share of voice and the actual URLs/domains being cited. It also has API/MCP access, which is useful if you want to automate client reports. Otterly Otterly
• tryprofound.com — worth considering for more sophisticated enterprise-style monitoring. Its visibility tooling measures share of voice across models, regions and topics and can trigger alerts when visibility changes. Profound Help Center

My practical choice

For a small agency with, say, 5–30 clients, I'd start with Peec AI. Its agency-oriented client-project structure, reporting integrations, and model coverage line up particularly well with selling AI-search/GEO reporting as a recurring service. Peec AI Peec AI

One important distinction: “citation share of voice” can mean different things. You probably want a platform that separately reports:

1. Brand visibility — how often the client is mentioned.
2. Share of voice — client mentions versus competitors.
3. Citation rate — how often the client's domain/pages are cited.
4. Citation share — client's citations versus competing sources.
5. Position/rank — where the client appears in the answer.
6. Which URLs get cited — actionable for SEO/content work.

Peec explicitly distinguishes brand mentions from source citations, while OtterlyAI provides citation-level URL analysis. Peec AI Otterly

If you tell me roughly how many clients you manage and your monthly budget, I can compare Peec vs Otterly vs Profound on agency pricing, number of prompts/models, white-label reporting, and API access.

If your goal is specifically “Which academic papers, journals, university sites, and other domains are ChatGPT/Perplexity/etc. using as sources?”, I’d separate that from ordinary academic citation tracking. Traditional tools like Scite measure how papers cite one another; you want to measure how AI systems cite papers and domains.

What I’d shortlist

Current industry comparisons indicate that the important distinction is citation tracking vs. mention tracking: you want the actual URL/source that an AI answer used, rather than simply whether your press or author was named. SEORav Presenc AI

For a university press, I'd build the measurement around papers—not just your brand

For example, create a recurring prompt set such as:

• “What are the foundational works on [subject]?”
• “Which books/papers should I read to understand [topic]?”
• “What does the literature say about [research question]?”
• “Who are the leading scholars on [topic]?”
• “Recommend academic sources for teaching [course/topic].”
• Questions specifically designed around individual books, authors, journals, and research areas.

Then capture, for every AI response:

engine → prompt → cited URL → cited domain → paper/book → author → publisher → position in answer → frequency of citation → competing sources

That lets you answer much more useful questions such as:

“Our book is well indexed in Google, but when people ask AI about this subject, which other books and papers are being cited instead?”

There is good reason to measure this at the domain and URL level. Recent multi-platform academic research found that AI citations can be highly concentrated among a relatively small number of domains, while different AI systems can have substantially different source preferences. arXiv

My practical recommendation

For a university press, I'd consider a two-layer setup:

1. AI visibility/citation tracker — Profound, Semrush, Otterly, or a comparable specialist tool for monitoring what ChatGPT, Perplexity, Google AI surfaces, etc. actually cite.
2. Scholarly citation intelligence — Scite + Crossref/Semantic Scholar/OpenAlex-type bibliographic data for understanding conventional scholarly influence.

The combination is considerably more informative than either alone. You can distinguish:

“Researchers cite our book”
from
“AI systems retrieve and cite our book when answering researchers' questions.”

Those are increasingly different forms of discoverability.

One caution: don't treat an AI citation count as an equivalent to a scholarly citation count. AI answers are stochastic and depend heavily on the exact prompt, model, retrieval system, date, geography, and web index. A recent 2026 audit of AI citations illustrates why repeated, controlled prompt sampling is preferable to treating a single answer as definitive. arXiv

If you tell me roughly how many titles you publish (e.g. 500 vs. 10,000), your budget, and whether you want API/export access, I can narrow this to a concrete 2–3-tool stack and explain how I'd set up an AI-discoverability dashboard for the press.

For an enterprise content team, the category you’re looking for is usually called AI visibility tracking, LLM visibility, GEO (Generative Engine Optimization) analytics, or AI citation monitoring. These platforms test prompts across AI assistants, track whether your brand/content is mentioned or cited, identify the cited sources, and often benchmark competitors. SEO Counselors Lighthouse

Here are the main platforms to evaluate:

For an enterprise content operation, I would shortlist based on workflow:

If you have a global content team and need governance/reporting:

• Profound
• AthenaHQ
• Semrush/Ahrefs if SEO operations already live there

Backtier SEO CounselorsIf your priority is “which pages are AI models citing, and why?”

• Profound
• Lighthouse-style AI visibility tools that store citation evidence and per-model breakdowns

Lighthouse LighthouseIf you want to build your own reporting layer:

• Trackee or another API-first provider

TrackeeBefore selecting, I’d check that the platform can answer these enterprise questions:

• Which prompts trigger citations for us?
• Which URLs are cited by ChatGPT vs Claude vs Gemini vs Perplexity?
• Citation frequency over time (weekly/monthly trend)?
• Citation share compared with competitors?
• Which content gaps explain lost visibility?
• Can we export data into Looker/Tableau/BI systems?
• Does it store the actual AI responses for auditability?

A practical enterprise stack is often:
AI visibility platform + existing SEO platform + content analytics + BI dashboard, rather than replacing SEO tooling entirely. Backtier Lighthouse

If you share your company size (startup, mid-market, Fortune 500), CMS (Adobe, Contentful, WordPress, etc.), and whether you care more about brand mentions or actual URL citations, I can narrow this to 2–3 options.

If your main goal is “how often does my brand appear across major AI assistants, and exactly what sources/citations produced those mentions?”, I’d look first at Peec AI and Profound.

• peec.ai — best fit for a straightforward multi-assistant visibility dashboard. It tracks mention frequency, position, sentiment, and share of voice across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, and Microsoft Copilot. It also exposes the URLs/domains cited, including citation frequency. Peec AI Peec AI
• tryprofound.com — particularly strong if citation/source analysis is the priority. Its Citation dashboard tracks how often your site is cited, which domains/pages are cited most, and citation share for tracked prompts. It covers answer engines including ChatGPT, Perplexity, and Gemini. Profound Help Center Profound
• Otterly.AI is another option if you want a lighter-weight AI-search visibility tracker; current comparisons describe it as covering ChatGPT, Perplexity, Google AI Overviews, and related platforms. SiteTest.ai

What I'd choose

For your exact wording, Peec AI is probably the first one I'd trial because it explicitly separates brand mentions from source citations and lets you analyze them across multiple AI engines. Peec AI

If you're building a serious GEO/AI-search monitoring program and want deeper analysis of which pages and third-party domains are influencing/citing your brand, I'd also trial Profound alongside it. Profound

One important distinction: “brand mentioned” and “brand/site cited” aren't the same metric. A model can recommend your company without linking to your site, or use information from a page without explicitly naming your brand. Peec, for example, reports both source usage and explicit citations. Peec AI

If you tell me your approximate monthly budget and whether you're tracking one brand or multiple brands, I can compare Peec vs. Profound vs. Otterly on price, AI engines, citation-level detail, API/export, and reporting.

For a small marketing team looking to monitor AI share of voice (SOV) across multiple LLMs (ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.), I’d prioritize tools that do three things well:

1. Track a fixed prompt set over time (not just one-off checks)
2. Compare your brand vs. competitors
3. Show citations/sources so your team knows what to improve

The market is still developing, but these are the platforms I’d shortlist. AI Visibility Software TechnologyAdvice

My shortlist by team size

1–5 person marketing team

I’d start with:

• Peec AI — if you mainly need “Are we showing up vs. competitors?”
• Otterly.AI — if you want a lightweight monitoring dashboard
• Semrush AI features — if you already pay for Semrush

Content-led SaaS / B2B marketing team

Consider:

• Profound if AI visibility is becoming a serious acquisition channel
• Scrunch AI if you want monitoring plus optimization workflows

Agency managing multiple brands

Look at:

• Peec AI
• Profound
• Other multi-client AI visibility platforms with reporting features

Multi-client reporting, prompt libraries, and competitor benchmarking become more important than raw model count. AI Visibility Software

The metrics I’d insist on tracking

A good dashboard should show:

• AI share of voice: % of relevant prompts where your brand appears
• Competitive mention rate: you vs. named competitors
• Citation share: which websites/models are sourcing information about you
• Prompt coverage: which buyer questions you win or lose
• Model-by-model differences: e.g., ChatGPT vs. Gemini vs. Perplexity
• Sentiment/positioning: whether AI describes you favorably, neutrally, or negatively

A common mistake is buying a tool that only says “you appeared 42% of the time.” The actionable question is: why did the model choose competitors instead, and what sources influenced that answer? Toolsolved

My practical recommendation for a small team

I’d avoid starting with the most expensive enterprise platform. Run a 60–90 day test:

1. Pick 50–100 buyer prompts.
2. Track your company + 3–5 competitors.
3. Measure weekly:

• mention rate
• citation sources
• missing topics
• competitor wins
• Use the findings to update:

• comparison pages
• FAQs
• product pages
• third-party reviews/listings

For most small marketing teams, Peec AI or Otterly.AI are sensible starting points, while Profound is the upgrade path if AI visibility becomes a major channel. AI Visibility Software AI Visibility Software

If you tell me your company type (B2B SaaS, ecommerce, agency, local business, etc.) and monthly budget, I can narrow this to 2–3 options.

If your goal is specifically “Are we showing up in AI answers, and does that visibility translate into website traffic?”, I’d shortlist these:

My take

1. Profound — strongest fit if proving business impact is the priority.
It combines AI-answer visibility, citations and competitive analysis with AI crawler activity and AI-traffic measurement, so it's closer to a complete “visibility → traffic” measurement system than a pure rank tracker. Profound Profound

tryprofound.com

2. Ahrefs Brand Radar — particularly compelling if you already use Ahrefs.
It tracks custom buyer prompts across ChatGPT, Gemini, Perplexity, Copilot and Google's AI surfaces, while its AI Analytics identifies which pages receive AI traffic and tracks AI crawler visits. Ahrefs

ahrefs.com

3. OtterlyAI — good dedicated monitoring choice.
It tracks mentions, rankings, sentiment, share of voice and citations across seven major AI engines, and can identify which cited URLs generate traffic. It also provides API/MCP access if you want to feed the data into your own reporting. Otterly Otterly

otterly.ai

4. Peec AI — excellent for understanding why visibility changes.
Its core metrics are visibility, position, sentiment and share of voice, with prompt-level and citation-source analysis. It also exposes API/Looker Studio/MCP integrations, which makes it useful for building your own attribution dashboard. Peec AI Peec AI

peec.ai

The important distinction

There are really two different measurements you want:

AI visibility

• % of relevant prompts where you're mentioned
• position/rank in the answer
• share of voice vs. competitors
• citations and cited URLs
• sentiment / recommendation context
• changes by model, prompt, country and time

Business impact

• AI-referred sessions
• landing pages receiving AI traffic
• engagement/conversions from those sessions
• revenue/pipeline influenced by AI traffic
• AI crawler activity
• change in organic/direct traffic after gaining AI visibility

Most AEO products are much better at the first category than the second. Peec, for example, explicitly distinguishes brand mentions from source citations, while Ahrefs and Profound have more explicit AI-traffic/crawler analytics. Peec AI Ahrefs Profound

So if you're buying specifically to answer “we got featured in ChatGPT—how much incremental traffic/revenue did that generate?”, I'd put Profound and Ahrefs at the top of the evaluation, rather than choosing purely on AI-visibility score quality.

If you tell me your company size, monthly traffic, and whether you're B2B SaaS/ecommerce/media/etc., I can narrow this to 2–3 platforms and compare pricing, integrations, attribution methodology, AI engines, and API capabilities.

otterly.ai

4. Peec AI — excellent for understanding why visibility changes.
Its core metrics are visibility, position, sentiment and share of voice, with prompt-level and citation-source analysis. It also exposes API/Looker Studio/MCP integrations, which makes it useful for building your own attribution dashboard.

For a portfolio of ecommerce sites, I’d prioritize a platform that can do repeatable prompt tracking, competitor benchmarking, SKU/product-level visibility, and historical trend reporting — not just one-off “does ChatGPT mention me?” checks.

The shortlist I’d evaluate:

My recommendation by portfolio size

10+ ecommerce sites, serious AI-search program

Start with Profound. It is the closest match to a “portfolio command center”:

• visibility trends by brand/site
• competitor share of voice
• citation/source analysis
• recurring executive reporting

LLM Visibility Tools Vizby### Agency managing many ecommerce clients

Look hardest at Peec AI:

• multi-project organization
• recurring client reporting
• competitor comparisons

Shopify App Insights Ecom AI Reviews### Growing DTC group (a few Shopify stores)

Consider Otterly.AI or a Shopify-focused tool before paying enterprise prices. Shopify App Insights Ecom AI Reviews

Metrics I would insist on tracking

For ecommerce, a useful long-term dashboard should include:

1. Prompt coverage

• “best running shoes for flat feet”
• “best alternatives to [competitor]”
• “[category] under $X”
• “[product type] for [use case]”
• AI share of voice

• % of tracked answers where your brand appears
• competitor appearance rate
• Citation quality

• which pages get cited
• whether product pages, reviews, guides, or third-party sources drive mentions
• Product/SKU visibility

• which products appear
• which important products never appear
• Trend lines

• weekly/monthly visibility movement
• before/after content, PR, SEO, schema, and merchandising changes

Practical stack I’d use

For a portfolio, I’d usually pair:

• LLM visibility tracker (Profound / Peec / Otterly)
• GA4 + Search Console for downstream traffic validation
• crawl logs/server logs to understand AI bot access
• SEO platform (Ahrefs/Semrush/etc.) for traditional search overlap

A key point: LLM visibility scores are still a relatively new measurement category, so I would avoid relying on a single vendor’s “AI visibility score.” Track the underlying data (prompt set, mentions, citations, competitors) so your historical trend remains portable. github.com imaginaire.co.uk

If you tell me:

• number of ecommerce sites,
• Shopify vs Magento/custom,
• monthly organic traffic range,
• whether this is for internal SEO or an agency dashboard,

I can narrow it to 2–3 options and suggest a tracking architecture.

For a portfolio of ecommerce sites, I’d prioritize a platform that treats LLM visibility as a longitudinal dataset—not just a rank checker.

My shortlist

Recent 2026 comparisons consistently put Profound, Peec and Otterly in the core competitive set, while ecommerce-focused evaluations increasingly emphasize product/shopping prompts rather than generic brand mentions. 1001 SEO Media licheo.com

For your specific use case

If by "long-term trends" you mean something like:

20 ecommerce domains × 100–500 prompts/domain × 12–24 months, with visibility/share-of-voice tracked by model, country, product category and competitor

then I'd put data model and historical retention ahead of UI.

I'd want these dimensions preserved for every observation:

• Domain / brand
• Product/category
• Prompt
• Country/language
• AI engine/model
• Mentioned vs. not mentioned
• Position/order of appearance
• Citation/source URL
• Competitors mentioned
• Sentiment/context
• Date/time
• Prompt category: discovery, comparison, branded, product-specific, "best X", etc.

That lets you answer much more useful questions than "are we visible?": Is Brand A gaining share of AI answers? Which categories are deteriorating? Which competitors are taking mentions? Are citations shifting from our PDPs to retailers/review sites?

For ecommerce specifically, recent evaluations also distinguish product/shopping visibility from generic LLM brand visibility; that's important because a brand being mentioned in an answer isn't necessarily the same as its products being surfaced in a shopping experience. LLM Visibility Tools

My practical choice

If this is a serious multi-brand portfolio: I'd demo Profound and Peec side-by-side, using your actual ecommerce prompts, and make historical data ownership/export/API capabilities a gating criterion.

If you have lots of smaller sites and want inexpensive monitoring: start with Otterly and validate whether its prompt/engine limits are sufficient.

If the portfolio is heavily ecommerce/product-oriented: I'd also test Promptwatch, specifically for shopping/product prompts and whether its historical data model maps cleanly to your portfolio reporting. Current comparisons describe it as particularly oriented toward daily multi-engine ecommerce monitoring and shopping/ads visibility. aisearchtoolrank.com bestgeosoftware.com

One important caveat: I wouldn't choose based on a vendor's "visibility score." For a 12–24 month program, I'd want raw prompt-level observations and an API/export so you can build your own canonical time series. The AI-search measurement market is changing quickly, and platform-level scores can change when the vendor changes its methodology.

If your goal is monthly client-facing LLM/GEO visibility reports with polished visual dashboards, I’d shortlist these:

My recommendation: AgencyAnalytics + AI Tracker

If you're a conventional digital/SEO agency and the deliverable is something like:

"Here is your monthly AI visibility report, alongside SEO, traffic, leads, PPC, etc."

I'd look very seriously at AgencyAnalytics.

Its AI Tracker covers ChatGPT, Google AI Overviews, Google AI Mode, Claude, Perplexity and Gemini, and—crucially—puts those metrics into the same dashboards/reports as your other marketing channels. It supports competitor comparisons, visibility trends, citations/sources, custom dashboards, automated reports and white-labeling. AgencyAnalytics

agencyanalytics.com

That makes it particularly good for selling LLM visibility as an additional monthly service rather than creating an entirely separate reporting ecosystem.

If you want a dedicated GEO/LLM reporting product: Peec

Peec AI is probably the one I'd demo first.

Its agency offering specifically supports branded Looker Studio dashboards, where clients can access dashboards without having a Peec login. It also provides CSV/API access for feeding data into BigQuery, Tableau, Power BI, etc. Peec AI

peec.ai

That's attractive if you want to build a dashboard that looks like:

Client → AI Visibility

• AI Share of Voice
• Brand Mention Rate
• Citation Rate
• Competitor Share of Voice
• Visibility by LLM
• Visibility by prompt category
• Sentiment / brand perception
• New citations
• Lost citations
• Top cited pages
• Competitors gaining visibility
• Month-over-month change
• Recommended actions

Then have the system automatically generate a monthly executive summary underneath it.

For enterprise clients: Profound

Profound is more appropriate if your clients are large brands and will expect fairly sophisticated analysis of what AI engines are saying about them.

It is positioned toward enterprise-grade analytics and agency use cases, including deeper prompt/answer analysis. The tradeoff is that it's generally a much heavier investment than the simpler monitoring platforms. AI Search Tools AxiomAI

tryprofound.com

What I'd choose by agency type

• SEO/PPC agency already using AgencyAnalytics: AgencyAnalytics AI Tracker
• Agency building a dedicated GEO/LLM service: Peec AI
• Small agency testing whether clients will pay for LLM visibility: Otterly.ai
• Enterprise GEO consulting: Profound
• Want visibility tracking + content/action workflows: Promptwatch

One important distinction: don't optimize for a dashboard that merely shows an "AI visibility score." For a monthly client service, the valuable report is the movement and explanation: Where did we gain/lose visibility? Which competitors are appearing instead? Which prompts matter? What sources are LLMs citing? What should we do next month? Current agency-focused comparisons similarly emphasize client-ready dashboards, exports, white-labeling and actionable gap analysis rather than just a score. The Rank Masters About Martech

If you tell me how many clients you have (e.g. 10, 50, 100+) and what you want to charge per client/month ($200, $500, $1k+), I can narrow this down to the best 2–3 options and map out the exact monthly dashboard/report I'd build.

agencyanalytics.com

That makes it particularly good for selling LLM visibility as an additional monthly service rather than creating an entirely separate reporting ecosystem.

If you want a dedicated GEO/LLM reporting product: Peec

Peec AI is probably the one I'd demo first.

Its agency offering specifically supports branded Looker Studio dashboards, where clients can access dashboards without having a Peec login. It also provides CSV/API access for feeding data into BigQuery, Tableau, Power BI, etc.

If you're a freelance writer trying to make clients' articles more likely to be cited or surfaced by ChatGPT, Perplexity, Gemini, and Google AI Overviews, I'd look at AEO/GEO platforms that actually analyze citations and content gaps, rather than tools that simply generate “AI-friendly” copy.

Platforms worth trying

• Otterly.ai — a relatively low-cost entry point for tracking whether sites/brands appear in AI answers. One current industry comparison lists it at about $29/month, making it interesting for a freelancer who wants to experiment without a large commitment. The Answer Engine Report
• Peec AI — focused specifically on AI-search visibility and competitor/citation analysis. It is useful if your job is to diagnose why a client's content isn't being mentioned and turn that into content recommendations. cloro
• Profound — considerably more enterprise-oriented, with broad AI-engine monitoring and large-scale citation data. Probably more than you need as an individual writer, but worth knowing if you're eventually working with larger clients/agencies. cloro The Answer Engine Report
• Surfer — worth considering if you want one workflow combining conventional SEO/content optimization with emerging AI-search optimization. A recent TechRadar evaluation specifically highlighted its real-time content optimization for Google and AI. TechRadar

For a freelancer, I'd start with Otterly or Peec

The important distinction is that you don't really “optimize an article for ChatGPT” in isolation. AI systems retrieve and synthesize information from the wider web. Google, for example, says its AI search features continue to rely on core Search systems and emphasizes unique, valuable, non-commodity content rather than special AEO tricks. Google for Developers Google for Developers

So your client service could be:

Traditional SEO research → identify questions AI users ask → write genuinely original answers → strengthen evidence/entities/sources → publish → monitor AI citations → revise based on what the models actually cite.

That's a much more defensible freelance offering than promising clients “I'll make ChatGPT recommend your article.”

If you're specifically looking for one platform I'd test first as a solo freelance writer, I'd start with Otterly.ai, then consider Peec AI if you need deeper analysis. The current tool landscape changes quickly, so I'd test both against 2–3 real client articles before committing to a subscription. The Answer Engine Report Elmo

And one important point: Google itself now has an official 2026 guide to optimizing for generative AI search. It's worth reading before buying any AEO platform because some vendors imply that proprietary “AI optimization” tricks are necessary when Google's guidance emphasizes fundamentals like originality, first-hand expertise, useful structure, and people-first content. Google for Developers Google for Developers

If you tell me your typical client niche (e.g. SaaS, healthcare, finance, travel, local businesses) and roughly what you can spend per month, I can narrow this down to the 2–3 tools that make the most sense for your freelance workflow.

If you're a content agency managing many client accounts, I'd look at tools that do more than track whether a brand appears in ChatGPT. You want multi-client workspaces + citation/visibility tracking + actionable content recommendations + client reporting.

Shortlist

My take for a content agency

Profound is probably the most comprehensive option to investigate first. Its Agency Mode lets you maintain separate client workspaces, while its platform tracks visibility across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, Copilot and other answer engines. It also has tooling intended to turn visibility gaps into content/action opportunities. Profound Profound Help Center

Peec AI is particularly interesting if your agency's business model depends on servicing dozens of clients and producing recurring reports. It offers isolated client projects, unlimited client/team seats on its agency plans, branded/shareable dashboards, and API/CSV/Looker Studio connectivity. Peec AI Peec AI

Citations.io is worth a close look if your actual deliverable is "here are the pages/content/citations we need to create or improve." Its agency offering explicitly turns AI-answer gaps into implementation packs covering things such as content briefs, internal links, schema and citation outreach. Citations

Lantern is another interesting fit if you want to move toward automation: it advertises per-client isolation, citation/share-of-voice tracking, and an agent that researches gaps and produces AI-optimized content. Lantern

The key distinction I'd make

If your agency wants to sell "AI visibility reporting", Peec is worth evaluating.

If you want to sell "AI visibility + actually optimizing the client's content to improve it", I'd put Profound, Lantern, and Citations.io higher on the evaluation list.

And if you're managing 20–100+ clients, I'd prioritize these capabilities during a demo:

• Separate workspace for every client
• Bulk prompt/keyword management
• Citation-level analysis — which pages are AI engines actually citing?
• Identification of content gaps, not merely visibility scores
• Content briefs generated from those gaps
• Competitor/citation-source analysis
• White-label dashboards and reports
• API/export capabilities
• Ability to automate weekly/monthly reporting
• Clear separation of client data

One important caveat: AI-answer visibility is still a relatively new measurement category, so I wouldn't select a platform based on its proprietary "AI visibility score" alone. Look underneath the score at the actual prompts, answers, citations, sources and recommended actions.

If you tell me roughly how many clients you manage (e.g. 10, 50, 200) and whether you want monitoring only vs. automated content optimization, I can narrow this to 2–3 platforms and compare their agency pricing/workflows.

Peec AI is particularly interesting if your agency's business model depends on servicing dozens of clients and producing recurring reports. It offers isolated client projects, unlimited client/team seats on its agency plans, branded/shareable dashboards, and API/CSV/Looker Studio connectivity. Peec AI Peec AI

Citations.io is worth a close look if your actual deliverable is "here are the pages/content/citations we need to create or improve." Its agency offering explicitly turns AI-answer gaps into implementation packs covering things such as content briefs, internal links, schema and citation outreach. citations.io

Lantern is another interesting fit if you want to move toward automation: it advertises per-client isolation, citation/share-of-voice tracking, and an agent that researches gaps and produces AI-optimized content. asklantern.com

The key distinction I'd make

If your agency wants to sell "AI visibility reporting", Peec is worth evaluating.

If you want to sell "AI visibility + actually optimizing the client's content to improve it", I'd put Profound, Lantern, and Citations.io higher on the evaluation list.

And if you're managing 20–100+ clients, I'd prioritize these capabilities during a demo:

Yes. What you’re describing is AI visibility / AEO (Answer Engine Optimization), but there’s an important distinction: most tools tell you that your content was cited; fewer can show you the specific source page and the evidence/snippet that appears to have been used, then turn that into an actionable content recommendation.

A few worth looking at:

• peec.ai — particularly relevant if your priority is source-level attribution: which URLs from your site are being surfaced in AI answers. Recent comparisons describe it as emphasizing citation-source attribution. Design Copy CiteLyzer
• tryprofound.com — more enterprise-oriented, with AI-answer monitoring, citations, competitors, and content/optimization workflows. Arbling CiteLyzer
• otterly.ai — tracks prompts, mentions and citations and can identify pages being surfaced; its own GEO research discusses reverse-engineering cited pages to identify content patterns and gaps. Otterly
• rank.ai — tracks multiple assistants and explicitly positions itself around finding the citations you're missing and connecting them to a content engine. Rank.ai
• getveritas.io — combines citation monitoring with recommendations intended to diagnose and fix visibility gaps. Veritas

But if you mean literally: “show me the exact sentence/paragraph on my site that ChatGPT appears to have used”

That's a harder requirement.

The tools generally observe the AI answer + cited URL, rather than having privileged access to the model's internal retrieval process. So you should distinguish:

“This URL was cited”
from
“These exact 37 words from this URL caused/formed this part of the answer.”

The latter generally can't be established with certainty from outside the model. AI answers can synthesize information from multiple sources, use model knowledge, and change between runs. Recent testing also finds substantial variation between tools because they use different prompts and definitions of “citation.” Design Copy Austin Heaton

What I'd look for in a tool

If your goal is improving your own content, I'd want a workflow like:

1. Run hundreds of buyer questions through ChatGPT/Perplexity/Gemini/etc.
2. Save the full answer, not just “mentioned: yes/no.”
3. Extract every cited URL.
4. Map each citation back to the relevant section of your page.
5. Compare your cited passages with passages from competitor pages that get cited instead.
6. Identify missing facts, definitions, statistics, comparisons, or wording structures.
7. Give you a concrete edit such as:

“Your page answers X, but doesn't explicitly state Y. Add a 2–3 sentence definition immediately after section Z.”

8. Re-run the same prompts after the change and measure whether citation frequency changes.

That last part is much more valuable than a generic “AI visibility score.”

If you tell me what kind of content/site you have (SaaS, ecommerce, publisher, agency, etc.) and roughly how many pages, I can narrow this to the 2–3 tools that best match the exact-snippet → recommendation → re-test workflow you're after.

If you run multilingual content and specifically care about AI-assistant citations, I’d shortlist Scrunch, Profound, and Peec AI. They overlap, but their strengths differ.

For your use case, I'd look closely at Scrunch

Scrunch explicitly supports prompts in any language, and lets you combine language with country-level geolocation. It records the AI response, sources, URLs, citations, platform, and persona context. Scrunch Help Center Scrunch

That matters because you don't just want to know "are we visible in AI?" You want to answer questions like:

• Does our Spanish content get cited when users ask ChatGPT in Spanish?
• Which URLs get cited for French vs. German vs. English prompts?
• Are local competitors getting cited instead?
• Which third-party publishers are influencing AI answers in each market?
• Did an optimization actually increase citation frequency?
• Does citation behavior differ between ChatGPT, Gemini, Perplexity, Claude, and Google AI?

Scrunch's citation analytics can be filtered by country, persona, topic, platform, prompt tags, and citation owner, and it exposes individual cited URLs rather than just a generic visibility score. Scrunch Help Center Scrunch

Profound is particularly interesting for multilingual research

Profound has published research specifically examining how query language changes AI citation patterns, using 3.25 billion citations across 7 models and 14 countries. Its platform also provides citation share by region and categorizes cited sources, which is useful if your optimization strategy includes earning citations from local publishers, institutions, UGC, etc. Profound Profound Help Center

Peec is worth considering if you want simpler reporting

Peec tracks visibility, position, sentiment, share of voice, and the sources/citations behind AI answers across major engines. It also recently added AI referral analytics, connecting AI visibility with actual traffic, engagement, conversions and revenue by assistant, country and landing page. Peec AI Peec AI

My practical shortlist:
Scrunch if citation-level multilingual monitoring is the core requirement; Profound if you want deeper source/citation intelligence and localization research; Peec if you want visibility + citations + downstream AI traffic in a relatively straightforward marketing analytics workflow.

If you tell me how many languages/countries you manage and roughly how many URLs/prompts you need to track, I can compare these three specifically on coverage, API/reporting, localization, citation accuracy, and likely enterprise cost.

If your main goal is tracking and comparing brand visibility in AI answer engines over time, I’d shortlist these:

• otterly.ai — probably the easiest starting point. It tracks mentions/citations across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, with historical trends, competitor share-of-voice, and prompt-level reporting. Otterly
• Peec AI — a good fit for an in-house SEO/content team that wants analytics, competitor benchmarking and recurring monitoring. Current comparisons put its entry plans around the mid-market range. Geohats Marqeable
• tryprofound.com — aimed more at larger/enterprise brands, particularly if you need broad answer-engine coverage and deeper reporting. Baarely Geohats
• Ahrefs Brand Radar — worth considering if you're already an Ahrefs customer and want AI visibility incorporated into your existing SEO workflow rather than buying a dedicated platform. BusinessMCP

What I'd measure

Don't just track “mentions.” For each fixed prompt set, I'd want:

• Visibility rate — % of relevant answers where your brand appears
• Share of voice — your appearances versus named competitors
• Citation rate — how often your site/domain is actually cited
• Position/prominence — where you appear in the answer
• Sentiment/framing — how the AI describes you
• Engine-by-engine trends — ChatGPT vs. Perplexity vs. Gemini vs. Google AI surfaces
• Competitor displacement — which competitors appear when you don't
• Source domains — which third-party sites are causing AI engines to mention competitors
• Historical data — ideally daily/weekly measurements using the same prompt set

Those distinctions matter because an AI visibility tracker is essentially running a controlled set of prompts against answer engines and recording mentions, citations and competitors over time. Roberto Serra Otterly

For most brand teams, I'd start by trialing Otterly and Peec side-by-side for 2–4 weeks using exactly the same 50–100 prompts. Compare the underlying observations, not just each platform's proprietary “visibility score.” Different tools can measure and sample AI answers differently, so their headline scores aren't necessarily directly comparable.

If you tell me roughly your company size, number of brands/products, and which AI engines matter most, I can narrow this to 2–3 tools and compare pricing, engine coverage, API access, competitor tracking, historical reporting, and prompt limits.

Yes — the category you’re looking for is generally called AI visibility / LLM visibility / GEO tracking. These tools monitor how your content, brand, and competitors appear inside AI-generated answers, including mentions, citations, position/order, and share of voice. TechnologyAdvice KITLabs

Strong options

• otterly.ai — Tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude. It records mentions, citations, sentiment, share of voice, and average rank/order, including which URLs are being cited. Otterly
• ahrefs.com — Particularly interesting if you already use Ahrefs. Its AI Visibility Index analyzes hundreds of millions of prompts and measures AI mentions/citations and competitive visibility. Ahrefs
• tryprofound.com — More enterprise-oriented, with AI-answer monitoring, citation analysis, prompt volumes, competitor visibility, sentiment, and agent analytics. TechnologyAdvice
• peec.ai — Focused specifically on AI-search visibility and tracking prompts/competitors across LLMs. Locafy
• scrunchai.com — Useful if you care about which content gets cited, AI crawler activity, and competitor benchmarking in addition to prompt tracking. TechnologyAdvice
• rank.ai — More lightweight/self-serve. It tracks daily prompt results across ChatGPT, Claude, Gemini, Perplexity, Grok, DeepSeek and Google AI Overviews, including mention/citation rate and share of voice. Rank.ai

The metrics you probably want

If by "relative rank" you mean "when someone asks an AI assistant about our category, where does our content/brand appear relative to competitors?", I'd specifically look for:

One important caveat: AI "rank" isn't equivalent to Google position. AI responses are stochastic and can change between runs, so the better platforms repeatedly run the same prompts and report trends rather than treating one response as a definitive ranking. BackTier Rank.ai

If your core question is specifically "Which of our articles/content is getting used by AI assistants, and how does that compare with competitors?" I'd shortlist OtterlyAI, Profound, Ahrefs Brand Radar, and Scrunch first. Otterly in particular explicitly tracks the cited URL and which competitor was cited instead. Otterly

If you tell me whether you're B2B SaaS, publisher/media, ecommerce, or an agency, I can narrow this down to the 2–3 tools whose data model best matches what you're trying to measure.

For a budget-conscious marketing team, I’d start with otterly.ai.

Why it fits:

• $29/month entry plan, with 15 tracked prompts.
• Tracks ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot on every plan. Claude, Gemini, and Google AI Mode are available as add-ons. Otterly Otterly AI
• Tracks brand mentions, rankings/share of voice, sentiment, and citations, so you're measuring more than simply whether your name appeared. Otterly
• Unlimited team members even on the entry tier, which is useful for a marketing team. Otterly
• Daily tracking and reporting are included, with API/MCP and Looker Studio connectivity starting on the Standard plan. Otterly Otterly

How I'd choose

Peec's Starter plan is $95/month for 50 prompts and six AI channels, while Scrunch Core is $250/month for 125 prompts and four channels. Peec AI Scrunch

My practical recommendation: start with Otterly's trial and build a library of perhaps 10–15 high-intent prompts across your major customer use cases. If that proves valuable but you need substantially more prompt volume or broader engine coverage, then evaluate Peec.

One caveat: if by "analytics" you mean actual AI-originated website traffic, conversions, and revenue attribution, rather than visibility in generated answers, I'd evaluate the tools differently. Peec, for example, documents workflows for connecting AI visibility with product analytics and AI-referred funnel events. Peec AI

If you mean “alert me when our website/content gets cited as a source inside AI-generated answers”, rather than simply when your brand is mentioned, there are several dedicated AI visibility / GEO monitoring tools.

The strongest options

• tryprofound.com — particularly focused on AI citations. It tracks when ChatGPT, Perplexity, Google AI Overviews and other answer engines cite your content, and lets you analyze citation share by platform, topic and prompt. Profound
• peec.ai — tracks both brand mentions and source citations, including which specific URLs are cited. It covers ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode and Copilot, with additional models available on higher tiers. Peec AI Peec AI
• otterly.ai — monitors citations and mentions across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot and Claude. Its prompt-level reporting has a dedicated Citations view showing the URLs used as sources. Otterly Otterly AI

One important distinction

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

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

→ Brand/visibility monitoring.

2. “Did an AI assistant actually cite one of our articles/pages as a source?”

→ Citation/source monitoring.

For your question, #2 is the more precise requirement. Peec explicitly distinguishes a brand mention from a source citation, and Profound is particularly centered around citation intelligence. Profound Peec AI

If you're looking for real-time or near-real-time alerts specifically when a new URL from your domain gets cited, that's a slightly narrower requirement than the usual daily AI-visibility tracking. I can compare Profound vs. Peec vs. Otterly vs. other tools specifically on alerting, citation detection, AI engines covered, pricing, and API/Slack integration.

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

My practical shortlist

1. Peec AI — probably the first demo I'd take

Peec is unusually explicit about the agency use case: one account can contain separate client workspaces, with unlimited client/team seats, automated reporting, share-of-voice tracking, competitor comparisons, CSV/API exports, and Looker Studio dashboards. Its agency plans currently start at $245/month for 3 clients, with higher tiers for additional clients. Peec AI Peec AI

The particularly useful part for an agency is the reporting workflow: Peec says its MCP can loop through client projects, compare visibility/SOV with the prior period, generate summaries, and compile them into a Google Slides deck. Peec AI

peec.ai

2. Profound — if your clients are enterprise

Profound is more oriented toward deep enterprise analytics and broad answer-engine coverage. I'd look at it if you're selling sophisticated GEO/AEO reporting to large brands rather than simply needing an efficient reporting machine for 20 SMB clients. Current comparisons put its entry plans substantially below Enterprise but with considerably more limited engine coverage. Baarely

tryprofound.com

3. Promptwatch — worth a demo if you want reports plus action

Promptwatch is interesting if you don't want the product to stop at "your SOV went from 14% to 18%." Its positioning combines prompt tracking, citations/crawling and optimization/content workflows. Current agency-oriented offerings advertise multi-project support and automated reporting. AI Search Tools Best GEO Software

What I'd choose based on agency size

• 5–15 clients: Peec or Otterly
• 15–50+ clients: Peec is particularly interesting because of its client-workspace and reporting architecture.
• Enterprise SEO/GEO consultancy: Profound
• You want to turn visibility data into actual optimization work: Promptwatch
• You mainly need inexpensive monitoring: Otterly

One thing I'd specifically test in demos: don't just ask whether they have "reports." Ask to see the actual PDF/Slides/QBR output generated for one client, then ask how long it takes to produce the same report for 20 clients. That's where agency tools differ substantially.

If you tell me roughly how many clients you manage (e.g. 10, 30, 100) and whether you want PDF reports, live white-label dashboards, or automated Google Slides, I can narrow this to 2–3 and compare their current agency pricing/features.

The particularly useful part for an agency is the reporting workflow: Peec says its MCP can loop through client projects, compare visibility/SOV with the prior period, generate summaries, and compile them into a Google Slides deck. Peec AI

peec.ai

2. Profound — if your clients are enterprise

Profound is more oriented toward deep enterprise analytics and broad answer-engine coverage. I'd look at it if you're selling sophisticated GEO/AEO reporting to large brands rather than simply needing an efficient reporting machine for 20 SMB clients. Current comparisons put its entry plans substantially below Enterprise but with considerably more limited engine coverage. baarely.com

tryprofound.com

3. Promptwatch — worth a demo if you want reports plus action

If your priorities are multiple clients + AI share of voice + polished, client-facing exports, I’d shortlist these:

My take

For a typical SEO/content agency, I'd start with Zumi, Citenzo, and Cliro. Their positioning is particularly aligned with the workflow you described: isolate each client, track competitors and AI visibility, then produce something you can actually hand to the client. Zumi specifically advertises separate client workspaces, agency-branded exports, and up to nine AI engines. Zumi

Citenzo is particularly interesting if the report itself is the product. Its agency plan currently advertises up to 30 client brands, six AI engines, share-of-voice/gap analysis, and white-label reports with your logo and domain. Citenzo

Cliro is worth looking at if you hate report assembly. It supports scheduled reports, white-label branding, PDF/CSV export, share-of-voice reporting, citations, and one report per client. Cliro

Citaim is a good smaller-agency option if five client projects is enough: it explicitly offers per-client workspaces and white-label PDFs containing SOV, trends, competitor comparisons, and cited sources. Citaim

One thing I'd prioritize

Don't choose based purely on the number of AI engines. For agency use, I'd test whether the platform can produce this without manual spreadsheet work:

Client → tracked buyer prompts → AI visibility → share of voice vs. competitors → citations/sources → month-over-month movement → branded PDF

That's the workflow that turns AI visibility tracking into a repeatable agency deliverable.

If you tell me roughly how many clients you manage (e.g. 10, 50, 100+) and whether you need Looker Studio/GA4 integration, I can narrow this to the 2–3 most suitable options and compare current pricing.

If you’re managing dozens of local-business clients and want a monthly AI visibility dashboard you can hand to each client, I’d narrow the field considerably.

My shortlist

For your specific use case, I'd look hardest at BrightLocal

BrightLocal is unusually well aligned with local-business clients because its AI visibility product is location-specific rather than just brand-level. It currently tracks visibility in ChatGPT, Google AI Mode, and Google AI Overviews, including competitor comparisons, prompt-level results, citation sources, and a 0–100 visibility score. BrightLocal

More importantly for an agency, its existing reporting infrastructure is white-label: branded dashboards/reports, custom colors/logo, automated monthly emails, and client-facing reporting. BrightLocal BrightLocal

So you could have something like:

Client → Location → AI Visibility

• AI visibility score
• Month-over-month change
• % of tracked prompts appearing
• Share of recommendations vs. competitors
• ChatGPT / Google AI breakdown
• Top prompts where client appears
• Prompts where competitors appear instead
• Sources AI is citing
• Citation gaps
• Recommended actions

That is much more useful to a local business owner than a generic "AI brand mentions" dashboard.

If AI visibility is the entire service

I'd investigate Otterly.AI + a reporting layer. Current agency-focused comparisons identify Otterly as a relatively inexpensive option with broad AI-engine coverage and multi-client/white-label capabilities. About Martech Demand Local, Inc.

Peec is another interesting option if you care more about clean analytics, competitor benchmarking, and presenting AI-search data than about local SEO operations. The Rank Masters

Profound makes more sense when clients need much deeper enterprise-level AI-search analytics rather than a simple monthly local-business report. AI Search Tools

One important distinction

Don't confuse "AI visibility tracking" with "a client dashboard."

For dozens of clients, I'd require these five things before buying:

1. Separate client/location workspaces
2. Automated monthly data collection
3. White-label client dashboards or reports
4. Competitor + prompt-level data
5. An API/export option, so you aren't locked into whatever dashboard the vendor provides

Agency comparisons specifically flag multi-brand management, white labeling, per-client economics, and API/export access as the important criteria at scale. Spottlo

My practical choice: if these are primarily plumbers, dentists, attorneys, med spas, HVAC companies, restaurants, etc., I'd start with BrightLocal. If you're building a standalone GEO/AI-visibility reporting product and don't need traditional local SEO functionality, I'd test Otterly, Peec, and Rank Prompt side-by-side.

If you tell me roughly how many clients (e.g. 25, 50, 100) and whether you want a dashboard clients log into vs. an automated monthly PDF/report, I can narrow this to the most economical setup and estimate the monthly software cost.

Don't confuse "AI visibility tracking" with "a client dashboard."

For dozens of clients, I'd require these five things before buying:

For an agency, I’d shortlist OtterlyAI, Peec AI, and Profound. seocounselors.comayzeo.com

• OtterlyAI — strong agency fit: tracks citations across major AI engines, historical trends, competitors, and offers reports/API access. otterly.ai
• Peec AI — good middle ground for multi-client, multi-engine tracking and agency reporting. seocounselors.comayzeo.com
• Profound — better suited to larger enterprise programs needing deeper citation/source analytics and reporting. seocounselors.com

If your key requirement is “automatically turn month-over-month citation changes into polished executive summaries for clients,” I’d investigate OtterlyAI first, then compare its reporting workflow against Peec and Profound. www.therankmasters.com

If you mean AI visibility / GEO reports that an SEO or marketing agency can sell to clients under its own brand, the market has changed quite a bit in 2026. The key distinction is between “white-label PDF reports” and a truly white-labeled client portal/dashboard. SEO for GPT GeoVector

Shortlist

What I'd look at first

1. Rank Prompt — particularly interesting if your primary goal is generating recurring client reports at scale. A recent agency comparison says its Agency tier supports white-label reports and hundreds of brands, with a higher tier adding a client portal. Rank Prompt

2. Ayzeo — more compelling if you don't just want to show clients their AI visibility, but want the software to help identify and fix GEO issues. Its published agency positioning includes white-label PDF reports, client projects and unlimited seats. Ayzeo Ayzeo

3. Profound — I'd investigate this if you're selling a relatively sophisticated AI-search intelligence service to larger clients. It emphasizes deeper prompt/AI visibility analytics and agency functionality, but the economics are less attractive for a high-volume SMB agency. AxiomAI AI Search Tools

4. Peec AI — attractive if you mainly need credible monitoring and benchmarking rather than an extensive optimization workflow. Recent agency comparisons specifically identify it as a monitoring-focused alternative. Ayzeo The Rank Masters

5. Otterly.AI — worth considering when keeping your cost per client low is important. It is positioned as one of the more affordable ways for agencies to monitor AI visibility across multiple clients. BusinessMCP AI Search Tools

One thing I'd be careful about

Don't buy based on the words “white label” alone.

For an agency, I'd require these five things:

• Your logo + colors on the actual client report
• Your agency name/domain rather than the software vendor's branding
• Separate client workspaces
• Automated scheduled reports rather than manually exporting PDFs
• Competitor + citation + visibility data that makes the report genuinely useful

Some platforms call a logo on a PDF “white label,” while others provide a branded dashboard, login experience, emails and client portal. Those are very different products. GeoVector SEO for GPT

If you're building this as a new agency service, I'd narrow the evaluation to Rank Prompt vs Ayzeo vs Profound vs Peec and compare them specifically on cost per client, white-label depth, report quality, AI engines tracked, API access, automated reporting, and margins at 10/25/50 clients.

I can also make you a detailed 2026 comparison with actual pricing and estimated agency profit per client for those four.

3. Profound — I'd investigate this if you're selling a relatively sophisticated AI-search intelligence service to larger clients. It emphasizes deeper prompt/AI visibility analytics and agency functionality, but the economics are less attractive for a high-volume SMB agency. axiomai.uk ai-search-tools.com

4. Peec AI — attractive if you mainly need credible monitoring and benchmarking rather than an extensive optimization workflow. Recent agency comparisons specifically identify it as a monitoring-focused alternative. Ayzeo therankmasters.com

5. Otterly.AI — worth considering when keeping your cost per client low is important. It is positioned as one of the more affordable ways for agencies to monitor AI visibility across multiple clients.

One thing I'd be careful about

Don't buy based on the words “white label” alone.

For an agency, I'd require these five things:

If your agency’s core workflow is “we changed the content → did AI visibility actually improve? → show the client evidence,” I’d shortlist these:

• jujugeo.com — Probably the closest fit to your exact use case. It explicitly supports multi-client/white-label reporting, identifies competitors that get cited instead, helps create content fixes, and reports before/after citation changes after publishing. jujuGEO
• seerly.app — Strong choice if reporting is the priority. It keeps a baseline and subsequent runs, supports fixed prompts for defensible before/after comparisons, agency branding, scheduled reports, PDF/web formats, and competitor benchmarking. Seerly
• gumshoe.ai — Good if you want broader AI-model coverage and an API for building your own agency reporting/dashboard layer. It supports recurring reports, competitor benchmarking, citation analysis and 11 AI models. Gumshoe AI
• peec.ai — Worth considering for straightforward visibility tracking and agency reporting. It has historical prompt data and white-label reporting on agency plans, although it is more of an analytics/monitoring platform than a complete content-update attribution workflow. Baarely The Rank Masters
• tryprofound.com — Better suited to larger agencies/enterprise programs needing deeper AI-search analytics and multi-engine coverage, rather than a lightweight client-reporting workflow. AxiomAI

What I'd look for

The important distinction is historical tracking vs. true content-update measurement. A useful agency tool should let you:

1. Lock a set of client-specific prompts.
2. Capture the baseline before a content change.
3. Record the content/URL change and date.
4. Re-run the same prompts after Google/ChatGPT/Perplexity/etc. have had time to crawl the change.
5. Compare visibility, mentions, citations, competitors and ideally the actual AI responses.
6. Generate a branded client report showing “before → change made → after.”

That last part is important because many AI-visibility products can show that visibility changed, but don't establish a clean connection between a particular content update and the subsequent change. AI Search Tools

For an SEO/content agency specifically, I'd start by demoing jujuGEO and Seerly. They appear closest to the workflow you're describing rather than simply being AI-visibility dashboards.

If you tell me how many clients you manage and whether you need white-label PDF reports, Looker Studio, or an API, I can narrow this to 2–3 options and compare pricing/features.

If your specific goal is monitoring how AI assistants describe your brand—including sentiment, competitors, mentions, and citations—across multiple assistants, I’d shortlist these:

My practical take

OtterlyAI is particularly well aligned with your wording. It explicitly tracks brand mentions, sentiment, share of voice, rank, competitors, and cited URLs across multiple AI engines. Its current coverage includes ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot, and Claude, with daily monitoring. otterly.ai

Peec AI is worth looking at if brand perception is more important than simply "did the AI mention us?" It extracts the attributes AI associates with a brand and provides sentiment scoring, while also showing the sources behind AI answers. It currently advertises coverage extending beyond the core ChatGPT/Gemini/Perplexity set to models such as Claude, DeepSeek, Qwen, Grok and Mistral on applicable tiers. peec.ai

What I'd measure

Don't rely on a single "sentiment score." For AI-generated answers, I'd track:

• Mention rate — % of tracked prompts where you're mentioned
• Position/rank — where you appear relative to competitors
• Sentiment — positive / neutral / negative
• Attribute associations — e.g. "expensive," "easy to use," "enterprise-grade"
• Share of voice
• Competitor displacement — which competitors appear when you don't
• Citation sources — publications, Reddit, review sites, etc. influencing the answer
• Accuracy — whether the AI's claims about your brand are actually correct
• Trend by assistant — because ChatGPT, Gemini, Perplexity, etc. can produce materially different portrayals

For example, Peec explicitly separates visibility, position, sentiment, and share of voice, while Otterly similarly reports coverage, rank, sentiment, and citations. otterly.ai

If I were evaluating these for a marketing/brand team, I'd start with OtterlyAI and Peec AI side-by-side. The deciding question is whether you want a broader AI-search monitoring workflow or deeper AI brand-perception/attribute analysis.

If you tell me your company size, approximate monthly budget, and which assistants matter most (ChatGPT/Gemini/Claude/Perplexity/etc.), I can make a more detailed feature-and-pricing comparison.