Answers that mention Peec AI

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

1. Track a fixed set of buyer prompts (“best CRM for startups,” “alternatives to X,” “top vendors in Y”)
2. Measure brand mentions vs competitors
3. Show citations/sources so your team knows what content is influencing AI answers

The market is still evolving, but these are the strongest fits. Slate Semrush

Prism News Semrush## My shortlist by team size/budget

Best overall for a small marketing team

Peec AI or Otterly.AI

Good fit if you have:

• 1–5 marketers
• 25–200 important prompts to track
• 5–10 competitors
• Monthly reporting needs

You’ll get the core question answered: “When prospects ask AI about our category, how often do we appear?” Slate Spawned

Best if you already pay for SEO software

Semrush

Choose this if your team already uses SEO, content, or competitive research workflows. The advantage is connecting AI visibility with existing search data instead of creating another dashboard. Semrush

Best if leadership wants enterprise-grade reporting

Profound

Better when you need:

• thousands of tracked prompts
• executive reporting
• detailed citation analysis
• larger competitive intelligence programs

It is likely overkill for most small teams. Built In

What I would measure first

Before buying, define a “prompt universe” of maybe 50–100 prompts:

• Category prompts: - “best [category] software”
• “top [category] providers”
• “alternatives to [competitor]”
• Buying-stage prompts: - “is [brand] worth it?”
• “[brand] vs [competitor]”
• “best solution for [use case]”

Track:

• AI Share of Voice: % of relevant answers mentioning you
• Position/share: Are you the first recommendation or buried?
• Citation share: Which websites AI models rely on when mentioning competitors
• Sentiment: Positive, neutral, negative descriptions
• Prompt gaps: Queries where competitors appear and you do not

My recommendation for most small teams

I’d start with:

1. Otterly.AI if you mainly need monitoring/reporting
2. Peec AI if you want a focused AI visibility dashboard
3. Semrush if your marketing stack already revolves around SEO
4. Upgrade to Profound only when AI visibility becomes a major acquisition channel

The biggest mistake is buying a tool before deciding which prompts actually represent your customers’ buying journeys. A smaller, well-designed prompt set usually gives more useful insight than thousands of generic queries. Spawned

For a small marketing team, I’d avoid the enterprise AEO/GEO platforms unless you have a large SEO budget. The best fit is usually a lightweight AI visibility tracker that can answer:

• “How often does our brand appear in ChatGPT, Claude, Gemini, Perplexity, etc.?”
• “Which competitors are winning?”
• “Which prompts are we missing?”
• “Why are they being cited instead of us?” Spawned Built In

Here are the strongest options:

My shortlist for a small marketing team

Best overall: Otterly.ai

Good if your goal is: “Give me a weekly dashboard showing whether we’re gaining or losing AI visibility.”

Best dedicated AI share-of-voice tool: Peec AI

Good if AI search is becoming a major acquisition channel and you want prompt-level tracking. Semrush

Best if you already pay for SEO software: Semrush

Good if you want one system covering:

• traditional SEO
• competitor research
• AI visibility
• content opportunities Semrush

Best budget approach

For many small teams, I’d start with:

1. Pick 50–200 buyer-intent prompts.
2. Track them weekly across ChatGPT, Claude, Gemini, and Perplexity.
3. Measure: - brand mention rate
4. competitor mention rate
5. average position in answers
6. citations/source domains
7. sentiment/accuracy

A tool that only counts mentions is less valuable than one that shows which prompts you lose and what sources competitors are getting cited from. Spawned visibility.so

If you tell me your industry (B2B SaaS, ecommerce, local business, etc.), monthly budget, and whether you already use Semrush/Ahrefs/HubSpot, I can narrow this to 2–3 choices.

Yes. What you’re looking for is generally called AI visibility / answer-engine monitoring, but there’s an important distinction: you want a tool that monitors the actual claims AI assistants make about specific products, not just whether your brand is mentioned.

Best fit: tryprofound.com

Profound is probably the closest match. It can:

• Run your own prompts against ChatGPT, Claude, Gemini, Perplexity, Copilot and other AI answer engines on a daily basis.
• Track the actual responses and citations.
• Let you define prompts around your products and product attributes.
• Detect inaccurate claims about your brand through its FactCheck functionality, including the sources behind those claims.
• Trigger alerts when monitored metrics cross thresholds. Profound Profound Knowledge Base

So you could monitor prompts like:

“What are the differences between Product A and Product B?”
“What does [Product A] cost?”
“Does [Product A] support X?”
“What are the best alternatives to [Product A]?”

…and flag responses where the assistant says something that conflicts with your canonical product data.

Other options

• otterly.ai — good for scheduled monitoring of brand/product mentions, citations, and visibility across major AI search engines; it also offers an API if you want to build your own alerting layer. Otterly
• Peec AI — another option for monitoring AI-generated brand/product visibility and citations, particularly for marketing teams. Conbersa
• A custom monitoring pipeline — best if “outdated” has a precise definition for you. You can maintain a canonical product database, periodically query assistants with a fixed prompt set, compare their answers against that database, and alert Slack/email when an attribute changes or becomes incorrect.

For your exact use case, I'd lean toward Profound because it explicitly positions its product around finding inaccurate claims and their sources, rather than merely counting mentions. Profound

If you tell me what kind of product information tends to go stale (pricing, specs, availability, compatibility, features, etc.), I can also suggest the best architecture for automatically catching it.

If your goal is specifically “Are AI assistants mentioning us, and does that translate into incremental website traffic?”, I’d narrow the field to Profound, Peec AI, and Otterly.AI.

1. Profound — best if traffic uplift is the priority

tryprofound.com

This is the one I'd evaluate first if you're trying to connect AI visibility → actual site visits → business impact.

Profound's Answer Engine Insights tracks visibility, share of voice, positioning, sentiment, and citations across AI answer engines. More importantly for your use case, its Agent Analytics tracks AI-driven traffic to your site and attribution/conversions from that traffic. Profound Profound

It also has prompt-volume data based on real user conversations, which is useful for distinguishing “we're mentioned more” from “we're appearing for questions people actually ask.” Profound

I'd choose it if: you're going to report AI search as a real acquisition channel to marketing leadership.

2. Peec AI — best pure visibility/trend analytics

peec.ai

Peec is particularly good for answering:

• How often are we mentioned in ChatGPT?
• Which prompts produce those mentions?
• Where do we rank relative to competitors?
• What sentiment does the AI have toward us?
• Which websites are influencing/cited in the answers?
• Is our share of voice increasing over time?

It tracks visibility, position, sentiment and share of voice across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode and other engines. Peec AI Peec AI

I'd choose it if: your primary KPI is AI answer visibility / competitive share of voice, and you already have GA4 or another analytics stack for downstream traffic.

3. Otterly.AI — best lower-cost starting point

otterly.ai

Otterly is worth considering if you mainly need prompt monitoring, mentions, rankings, citations and competitor tracking without buying an enterprise platform. Current 2026 comparisons put its entry pricing substantially below Profound. Marqeable

I'd choose it if: you're testing whether AI visibility monitoring is worth institutionalizing before investing heavily.

The important distinction

I'd separate your measurement stack into two layers:

Layer 1 — “Did the assistant feature us?”

Track:

Prompt → AI engine → mention → position → citation → competitor mentions → sentiment

Profound and Peec are both strong here.

Layer 2 — “Did being featured actually drive incremental business?”

Track:

AI citation/mention → click → landing page → session → conversion → revenue

This is where Profound currently has the clearest native positioning, because it explicitly provides AI traffic attribution rather than stopping at visibility metrics. Profound

And I wouldn't use an AI visibility score alone as evidence of traffic uplift. A brand can go from 20% → 40% AI visibility without generating meaningful incremental visits if the answers don't expose/cite a clickable page, or if users don't click through.

My recommendation

If I were building this measurement program today:

1. Profound — primary platform if you have the budget and care about attribution.
2. Peec AI — strongest alternative if your emphasis is AI-answer trends, competitive visibility and source/citation intelligence.
3. GA4 + Search Console + your CRM — use these as the ground truth for actual traffic, leads and revenue.
4. Define a cohort of high-value commercial prompts and measure AI visibility before/after content or PR changes.
5. Separately track AI-referred sessions and conversion rate so you can calculate actual incremental value rather than reporting “AI mentions increased 32%.”

One particularly useful KPI would be AI-assisted revenue per 1,000 tracked prompts. That combines demand × visibility × click-through × conversion, and is much harder to game than a generic “AI visibility score.”

If you tell me your company type (B2B SaaS, ecommerce, media, etc.) and approximate monthly traffic, I can narrow this to the best 2–3 tools and give you a feature/pricing/measurement comparison.

I'd choose it if: you're testing whether AI visibility monitoring is worth institutionalizing before investing heavily.

The important distinction

I'd separate your measurement stack into two layers:

Layer 1 — “Did the assistant feature us?”

Track:

Prompt → AI engine → mention → position → citation → competitor mentions → sentiment

Profound and Peec are both strong here.

Layer 2 — “Did being featured actually drive incremental business?”

Track:

AI citation/mention → click → landing page → session → conversion → revenue

This is where Profound currently has the clearest native positioning, because it explicitly provides AI traffic attribution rather than stopping at visibility metrics.

And I wouldn't use an AI visibility score alone as evidence of traffic uplift. A brand can go from 20% → 40% AI visibility without generating meaningful incremental visits if the answers don't expose/cite a clickable page, or if users don't click through.

My recommendation

If I were building this measurement program today:

If you mean tracking when your brand/product appears in ChatGPT, Gemini, Perplexity, Claude, Copilot, etc., and tying that visibility to actual traffic or revenue, you’re looking for AI visibility / AEO / GEO analytics tools rather than traditional SEO rank trackers.

The strongest options right now:

For measuring “AI answer → traffic uplift,” look for these features

The best platforms should let you measure:

1. Prompt visibility

• Example: “best CRM for startups”
• How often your brand appears
• Position/order of mention
• Sentiment/context
• Citation tracking

• Which URLs AI assistants cite
• Which third-party sites influence answers
• Where competitors are winning citations
• Share of voice

• “Of 1,000 category questions, how often are we included vs competitors?”
• Traffic attribution

• AI referral traffic in analytics
• Assisted conversions
• Landing pages influenced by AI discovery
• Before/after experiments

• Publish/update content
• Measure whether AI mentions increase
• Compare against traffic and conversions

My shortlist by use case

• Enterprise marketing team: Profound or Quattr
• B2B company already on HubSpot: HubSpot AEO
• SEO team adding AI search: Semrush or Ahrefs
• Startup wanting affordable monitoring: Elmo, Peec, or Otterly
• Need to prove revenue impact: Profound + GA4/CRM attribution setup

One caveat: AI referral attribution is still immature. Many users see an AI answer, remember the brand, and visit later through direct traffic or Google, so simple “AI referral clicks” undercount the effect. Research from Profound suggests AI mentions can correlate with significant downstream browsing lift, but the measurement challenge is connecting exposure to later behavior. Profound

A practical stack many teams use:

• AI visibility tracker (Profound / HubSpot AEO / Peec / Elmo)
• GA4 + Search Console
• CRM attribution (HubSpot, Salesforce, etc.)
• Looker Studio dashboard combining AI mentions → visits → pipeline

If you’re a B2B SaaS, ecommerce brand, publisher, or local business, the best choice changes quite a bit.

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

Here's how the landscape breaks down:

What these tools actually do

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

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

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

What actually increases citations

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

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

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

If you're on a budget

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

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

Enterprise vs. SMB

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

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

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

Here's how the landscape breaks down:

What these tools actually do

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

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

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

What actually increases citations

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

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

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

If you're on a budget

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

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

Enterprise vs. SMB

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

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

If you're a freelance SEO consultant managing multiple clients, I'd start with otterly.ai.

It is specifically built to monitor AI-search visibility: whether a client is mentioned, recommended, or cited in answers across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, and Copilot. You can define prompts around the questions your clients care about and track visibility and citations over time. Otterly

My shortlist

• OtterlyAI — best fit for a freelance consultant. Relatively accessible pricing, prompt tracking, competitor comparisons, citation monitoring, and an API. Otterly KITLabs
• Peec AI — good for straightforward AI-visibility analytics. A strong option if you primarily want reporting and competitive visibility data rather than a broader GEO workflow. Conbersa Arbling
• Profound — powerful but more enterprise-oriented. Better if your clients have substantial GEO budgets and need deeper source/citation intelligence. SEO Counselors
• Semrush AI Visibility Toolkit — worth considering if you already use Semrush. It combines AI visibility monitoring with your existing SEO/content workflow rather than requiring a separate platform. Semrush

For a freelance SEO business, my pick would be OtterlyAI because the core job is exactly what you're describing: "Show me how Client A appears when potential customers ask AI about their category, and how that changes month to month."

One important caveat: AI answers are volatile and can be personalized, so don't treat these as equivalent to traditional Google rankings. The monitoring tools are essentially running controlled prompts and measuring mentions/citations, which gives you a useful trend and competitive signal, not a universal "AI rank." Otterly Arbling

If you tell me how many clients you manage (e.g. 5, 20, 50+) and your monthly tool budget, I can narrow this down to the best tool and plan for your freelance setup.

For a portfolio of ecommerce sites, I’d prioritize platforms that can do more than “did the brand get mentioned?” You want longitudinal tracking of:

• Share of voice across LLMs (ChatGPT, Gemini, Claude, Perplexity, Google AI features)
• Product/SKU mentions (not just brand mentions)
• Citation sources (which pages/domains are being trusted)
• Competitor movement
• Prompt-level trends over months/quarters
• Multi-brand dashboards and reporting

The market is still evolving, but these are the strongest fits today. ecommerceguide GrowByData

Best overall for an ecommerce portfolio: Profound

Best for: enterprise ecommerce groups, agencies, multi-brand operators

Why it stands out:

• Strong focus on measuring AI answer visibility over time
• Good for comparing many brands/categories
• Tracks where AI engines cite or mention you versus competitors
• Better suited to executive reporting than many newer GEO tools

Tradeoff:

• More of a measurement/analytics platform than an “auto-fix my catalog” tool. Shopify App Insights GrowByData

Best if you already run SEO: Semrush AI visibility features

Best for: teams already using Semrush for SEO, content, and competitive research

Advantages:

• Keeps traditional SEO and AI visibility data together
• Easier adoption for existing marketing teams
• Useful for correlating organic search trends with LLM visibility

Tradeoff:

• A dedicated LLM tracker may go deeper on prompt monitoring and AI-specific reporting. Sixthshop Red-engage

Best lightweight monitoring: Peec AI or Otterly.AI

Best for: smaller portfolios or getting started

Good for:

• Prompt tracking
• Competitor comparisons
• Regular visibility snapshots
• Lower operational overhead

Tradeoff:

• Less enterprise-grade workflow/reporting than platforms built for large organizations. Shopify App Insights Red-engage

Best ecommerce-specific optimization angle

For ecommerce specifically, look at platforms that connect visibility data to:

• product feeds
• schema markup
• catalog quality
• product descriptions
• availability/pricing signals

Several newer ecommerce-focused tools are moving in this direction because LLM shopping visibility depends heavily on structured product data, not only content. AI Visibility Software Ranketta

My shortlist by portfolio size

Metrics I’d actually put on a quarterly dashboard

1. AI Share of Voice

• % of tracked shopping prompts where your brands appear
• Citation Share

• % of cited URLs belonging to your domains
• Product Recommendation Rate

• % of category prompts where your SKUs appear
• Competitor displacement

• Which competitors replaced you in AI answers
• LLM referral revenue

• Sessions/orders influenced by AI assistants (where measurable)
• Prompt coverage

• Number of important buying-intent queries being monitored

For a portfolio owner, I would avoid buying a tool that only gives a “brand mentioned/not mentioned” score. The useful asset is a historical dataset of prompts, answers, citations, and competitor movement that you can analyze quarter over quarter. github.com

If your goal is long-term LLM visibility tracking across a portfolio of ecommerce sites, I’d shortlist Profound and Peec AI, with Profound as my first choice for a serious multi-site portfolio.

My ranking

1. Profound — best overall

For your use case, this is the one I'd evaluate first.

Profound tracks visibility, share of voice, citations, sentiment, positioning, competitors, regions and topics over time. More importantly for ecommerce, it has a dedicated Shopping product that goes down to the SKU level, tracking product placement, merchant attribution, product attributes and competitive positioning in ChatGPT Shopping. Profound Profound

It also runs tracked prompts daily and says it queries the actual consumer-facing experiences rather than relying solely on model APIs, which is valuable when you're trying to establish a consistent longitudinal dataset. Profound Profound Knowledge Base

Why I'd pick it for a portfolio:

• Multiple brands/sites and competitive sets
• Historical visibility/share-of-voice trends
• Engine-by-engine analysis
• Citation/source tracking
• Product/SKU-level ecommerce visibility
• AI crawler and AI traffic analytics
• Real prompt-volume data
• Stronger enterprise reporting/analysis

The big advantage is that you can eventually connect "Are we being seen?" → "Which products are being recommended?" → "Which sites are influencing the answers?" → "Is AI actually sending us traffic?"

tryprofound.com

2. Peec AI — probably the best alternative

I'd look very closely at Peec if your primary requirement is clean longitudinal visibility analytics rather than a broader enterprise AEO platform.

It explicitly tracks visibility, position, sentiment and share of voice, with daily trends and competitor comparisons. Its dashboard can be segmented by country, topic and model, which is particularly useful when you're managing multiple ecommerce categories/markets. Peec AI Peec.ai Docs

A particularly useful distinction is that Peec separates:

• Brand visibility — AI mentions your brand
• Source visibility — AI actually cites your domain/content

That distinction is extremely useful for ecommerce SEO because being mentioned by an LLM isn't necessarily the same thing as having your product/category pages become trusted sources. Peec.ai Docs

Peec has also added ecommerce-oriented product visibility, including individual-product appearance, win rate, position, share of voice and competitive products in AI shopping results. Peec AI

peec.ai

What I'd actually track

For a portfolio, I wouldn't make "LLM visibility" a single KPI. I'd build a portfolio-level AI visibility scorecard around:

1. Brand visibility %
2. Share of voice vs. competitors
3. Average answer position
4. Citation rate
5. Share of citations to your own domains
6. Product/SKU visibility
7. Category visibility
8. AI Shopping placement
9. Sentiment / attribute accuracy
10. AI-referred sessions and revenue
11. Visibility by engine
12. Visibility by country
13. Visibility by prompt/topic cluster

The important one for an ecommerce portfolio is SKU/category visibility over time. A site can have stable brand visibility while losing its most commercially important product queries to competitors.

Bottom line

If you have 5–50+ ecommerce sites/brands: I'd start with Profound.

If you want a more analytics-focused platform and potentially a simpler deployment: I'd trial Peec AI alongside it.

I would not choose purely based on how many LLMs a vendor claims to monitor. For a multi-year program, the more important question is whether the platform gives you stable prompt cohorts, historical data, competitor baselines, source/citation data, and product-level tracking. Those are what make the dataset useful 12–24 months from now. Peec.ai Docs Profound

If you're choosing this for an agency/portfolio company environment, I can also compare Profound vs Peec vs Otterly vs Scrunch specifically on multi-domain limits, historical data, API/export, pricing, and white-label reporting.

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

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

Tools worth looking at

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

The important distinction

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

1. “How AI-ready is this page?”

URL → score 0–100 → recommendations

Things like:

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

Tools like AI Search Visibility are closest to this.

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

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

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

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

If you're thinking about building this

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

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

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

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

Why you score well

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

Why you lose citations

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

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

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

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

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

If your goal is specifically “create content outlines that increase the odds of being quoted/cited by ChatGPT, Perplexity, Gemini, Claude, etc.”, I’d use a GEO/AEO-oriented workflow rather than a conventional SEO outline generator.

My picks

I'd start with Surfer

Surfer's current Content Editor explicitly combines outline generation, competitor analysis, entities/facts, and AI-search optimization. Its newer workflow includes an AI Search Score, and its “Coverage Booster” is designed to add missing facts that can improve LLM citations. docs.surferseo.comgenerative.qadocs.surferseo.com

That makes it substantially more relevant to your objective than something that simply says “here are the headings ranking pages use.”

Surfer Content Editor documentation

But there's an important distinction

Being rank-optimized isn't the same as being quote-optimized.

For conversational-model citations, I'd want an outline to force the writer to produce:

• A direct answer near the top — don't make the model extract your conclusion from 1,500 words.
• Self-contained claims — each important assertion should make sense when quoted out of context.
• Definitions of entities/concepts — particularly in the first ~200 words.
• Specific numbers, dates, comparisons, and criteria rather than vague marketing language.
• Original data or analysis where possible.
• Tables/comparison frameworks for questions involving alternatives.
• FAQ-style questions corresponding to actual conversational queries.
• Clear source attribution for factual claims.
• Author/expert credentials where expertise matters.
• Freshness signals for topics that change rapidly.

Recent GEO research suggests that structural factors like clear entity definitions, comparison tables, original data, FAQ structure, and strong authorship can correlate with higher citation rates—but these effects vary substantially by model and query type. generative.qa

And the landscape is moving quickly: for example, Axios recently reported a substantial change in Reddit's share of ChatGPT citations, illustrating why you shouldn't build your strategy around a single supposed “GEO hack.” www.axios.com

The workflow I'd actually use

1. Surfer → generate the initial SEO/GEO outline

2. Manually convert the outline into “citation units”

For every major section, ask:

If an LLM wanted to quote exactly 2–3 sentences from this section as its answer, have we written those sentences?

For example, instead of:

Benefits of X

I'd structure the brief more like:

What are the benefits of X?

Answer in 40–60 words: Give the direct answer, including the 2–3 most important benefits.
Evidence: Include quantitative evidence where available.
Comparison: Explain when X is preferable to Y.
Caveat: Identify the most important limitation.

That creates extractable, self-contained passages, rather than merely an SEO-friendly article.

3. Run the finished content through a GEO visibility tracker

That's the step most “AI outline generators” don't solve. You need to actually ask the target models representative questions and track:

• Are you mentioned?
• Are you cited?
• Which page gets cited?
• Which competitor gets cited instead?
• Which passage appears to have been used?
• What questions produce no citation?

Surfer now has AI visibility tracking alongside its content workflow, while dedicated GEO platforms specialize more heavily in this measurement layer. docs.surferseo.com

Bottom line:
If you want one piece of software, I'd pick Surfer. If you're building a serious content operation, I'd use Surfer/Clearscope for the outline + a dedicated GEO visibility tracker for feedback.

If you tell me what kind of content you're producing (B2B SaaS, ecommerce, affiliate, publisher, professional services, etc.), I can also tell you exactly what I would put into the outline template to maximize LLM quotability.

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

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

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

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

HubSpot AEO / AI Visibility documentation

If you want deeper competitive intelligence

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

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

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

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

For an agency delivering monthly LLM visibility reports to clients, I’d prioritize tools with:

• Multi-client workspaces
• White-label dashboards or PDFs
• Automated monthly reporting
• Share-of-voice tracking across ChatGPT/Gemini/Perplexity/Claude/etc.
• Citation/source tracking (“why did the model recommend them?”)
• Client-friendly visuals (trend charts, competitor comparisons, scorecards)

The strongest options right now:

AI Visibility Software Demand Local, Inc.## My shortlist by agency type

1. Boutique agency (5–30 clients)

Best picks:

• Peec AI
• Otterly.AI
• Rank Prompt

Why:

• Easier client segmentation
• Faster monthly report creation
• Lower operational overhead
• Enough visuals for recurring retainers

Rank Prompt Demand Local, Inc.### 2. Premium SEO/AEO agency selling $2k–$10k/month retainers

Best pick:

• Profound

Why:

• More defensible reporting
• Better executive-level insights
• Stronger “why competitors win” analysis
• Better fit for enterprise stakeholders

CrowdReply### 3. Agency that wants a branded client portal

Look specifically at:

• Rank Prompt
• LLM Pulse

These focus more on reselling the reporting experience rather than just internal analytics. SEO for GPT Rank Prompt

Dashboard metrics I’d include in a monthly client report

A good LLM visibility dashboard should show:

1. AI Visibility Score

• Overall presence across tracked prompts
• Share of Voice

• Client mentions vs competitors
• Prompt Coverage

• % of target buyer questions where the brand appears
• Citation Performance

• Which websites/content sources AI models trust
• Competitor Movement

• “Competitor X gained 18% visibility this month”
• Recommended Actions

• Content gaps
• PR/link opportunities
• Entity/profile improvements

If I were building an agency reporting stack

A practical setup:

• Tracking: Peec AI or Profound
• Client-facing dashboard: Looker Studio / AgencyAnalytics-style portal
• Monthly PDF: Automated branded report
• Strategy layer: Human-written insights and recommendations

Avoid sending clients a raw “AI score.” The value is explaining:

“Your competitors are being cited for these topics, these sources influence AI answers, and here are the actions that can increase your probability of being recommended.”

For most agencies, I’d start by testing Peec AI vs Profound vs Rank Prompt because they map closest to the recurring client-reporting workflow. AI Visibility Software Rank Prompt

If your goal is an agency-grade monthly deliverable that clients can log into and visually understand, I’d narrow the field considerably.

My shortlist

Recent agency comparisons consistently put Peec, Otterly, Profound, and Rank Prompt near the top, but there's an important distinction: a branded PDF/report is not the same thing as a white-label client portal. SEOforGPT Semrush

1. Peec AI — my pick for most agencies

peec.ai

I'd start here if you're building a recurring "AI visibility / GEO" service for SMB and mid-market clients.

It's particularly suited to tracking things like:

• AI visibility/share of voice
• Brand mentions
• Competitor visibility
• Which prompts trigger your client's brand
• Which sources/citations AI engines use
• Changes over time

Recent comparisons specifically identify Peec as a strong agency/multi-client option, with pricing reported around the ~$95/month starting range depending on plan/usage. AEO Labs Caraxes

Why I like it: the data lends itself naturally to a monthly client narrative: visibility → competitors → citations → opportunities → recommendations.

2. Otterly.AI — best inexpensive option

otterly.ai

If you're trying to launch the service without adding a huge software expense, I'd seriously test Otterly.

It has particularly broad AI-search coverage and is reported to start around $29/month, with agency/white-label capabilities on higher tiers. Sightivo Demand Local, Inc.

I'd choose it over Peec if your priority is lots of clients + low tooling cost rather than the most sophisticated analytics.

3. Profound — best for enterprise

tryprofound.com

This is the one I'd investigate if your agency serves large brands, enterprise SEO teams, or clients that want serious AI-search intelligence rather than a pretty monthly report.

Profound emphasizes source-level citation intelligence, prompt libraries, governance/security, and larger-scale AI visibility programs. Recent comparisons put it firmly in the enterprise category. SEO Counselors AxiomAI

The downside is economics: it's difficult to justify for a small agency portfolio.

4. Rank Prompt — worth a serious look for white-label agencies

rankprompt.com

This is interesting if the reporting itself is something you want to productize and resell.

Recent agency-focused comparisons highlight its white-label reporting and client-portal functionality, including higher-tier agency plans designed around large numbers of brands. Rank Prompt SEOforGPT

So I'd put this ahead of Peec if your actual business model is:

"Every client gets an AI Visibility Dashboard branded as [My Agency]."

rather than simply:

"We use AI visibility data in our monthly SEO report."

What I'd actually build

I wouldn't make the client's monthly report a giant dump of AI metrics.

I'd structure the dashboard around 5 screens:

1. AI Visibility

• Overall visibility score
• Share of voice
• Change vs. previous month
• Visibility by AI engine
• Competitive Landscape

• Client vs. 5–10 competitors
• Who is gaining/losing visibility
• Competitive share of voice
• Prompts

• Top commercial prompts
• % of prompts where client appears
• Average position/ranking
• Biggest wins/losses
• Citations

• Domains AI engines cite most frequently
• Client's pages being cited
• Competitor sources being cited instead
• Citation opportunities
• Executive Summary

• What changed
• Why it changed
• What we're doing about it next month

That last screen is crucial. Clients generally don't care that "ChatGPT visibility increased 7.3%" unless you translate it into what that means for their business.

My recommendation by agency type

Small agency / launching an AI visibility retainer:
→ Otterly

Established SEO/content agency with 10–50 clients:
→ Peec AI

Agency wants a genuinely white-label client portal:
→ Rank Prompt

Enterprise SEO/GEO consultancy:
→ Profound

Already heavily invested in Semrush:
→ Semrush AI Visibility Toolkit

There is also a useful hybrid approach: use a dedicated visibility engine such as Peec/Otterly/Profound for the underlying data, then push the metrics into Looker Studio or your existing agency reporting platform. Some agency reporting workflows explicitly recommend this approach because it gives you much more control over the client-facing presentation. therankmasters.com

If you tell me roughly how many clients you want to report on (e.g. 10, 50, 200) and whether you need a true white-label portal vs. just automated monthly PDFs/dashboards, I can narrow this to the 2–3 best choices and compare their current pricing/features.

4. Rank Prompt — worth a serious look for white-label agencies

rankprompt.com

This is interesting if the reporting itself is something you want to productize and resell.

Recent agency-focused comparisons highlight its white-label reporting and client-portal functionality, including higher-tier agency plans designed around large numbers of brands.

So I'd put this ahead of Peec if your actual business model is:

"Every client gets an AI Visibility Dashboard branded as [My Agency]."

rather than simply:

"We use AI visibility data in our monthly SEO report."

What I'd actually build

I wouldn't make the client's monthly report a giant dump of AI metrics.