Answers that mention Peec AI

If your real requirement is “which AI assistant cited what, was the attribution correct, and which content owner should fix it?”, I’d narrow the field to OtterlyAI, Profound, and Citations.io—with OtterlyAI as my default pick for a content team.

My pick: OtterlyAI

Otterly tracks the actual URLs cited in AI answers, alongside mentions, sentiment and share of voice across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot. It also has content-audit and GEO recommendation functionality, which makes it easier to move from “this answer was wrong” to “here's the content we need to change.” otterly.ai

Its citation reporting is particularly relevant to your use case: it records the cited domain/URL and changes in citation position over time. otterly.ai

If “incorrect attribution” is the central problem

I'd seriously evaluate Citations.io. Its product explicitly advertises hallucination and misattribution alerts, alongside a live citation feed showing which sources assistants actually use. Its enterprise offering also has SSO, audit logs, multi-brand workspaces and evidence-oriented reporting. citations.io

That's closer to a citation QA/governance system than a conventional GEO rank tracker.

If you're a large enterprise

Profound is probably worth the enterprise evaluation. It combines AI-search visibility, source/citation analysis and broader agent/AI-search analytics. Independent comparisons currently position it toward enterprise teams, whereas Otterly is more content/SEO-team oriented. getvisibly.app

One important distinction

Most AI-visibility products answer:

“Which pages/domains did the model cite?”

Your requirement is one level more sophisticated:

“Was that citation actually supported by our source, and if not, which internal content owner is responsible for correcting the underlying information?”

I would therefore make content-owner attribution a procurement requirement, rather than assuming the vendor's native workflow will do it.

Ideally your system should produce something like:

Prompt → AI answer → claim → cited URL → expected/actual attribution → correctness score → internal source → content owner → Jira/Asana ticket → resolution → re-test

Otterly gives you much of the upstream citation data; you may want to connect that output to your CMS/content inventory and Jira/Asana rather than expecting an AEO platform to be your complete editorial workflow. Otterly also exposes API/MCP capabilities, which makes that kind of integration more practical. otterly.ai

Bottom line:

• Best overall for a content team: OtterlyAI
• Best for citation QA/misattribution: Citations.io
• Best enterprise platform: Profound
• Best if you're already deep in SEO tooling: Ahrefs Brand Radar

If you tell me your CMS + project-management stack (e.g. Contentful + Jira, WordPress + Asana) and roughly how many AI prompts/pages you need to monitor, I can narrow this to a concrete architecture and vendor shortlist.

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

My pick: Peec AI

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

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

That makes it useful for a workflow like:

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

When I'd choose Profound instead

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

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

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

So I'd frame the decision as:

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

One important distinction

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

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

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

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

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

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

My recommendation

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

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

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

What I'd actually measure

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

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

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

The stack I'd choose

For a team like yours, I'd implement:

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

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

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

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

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

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

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

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

Yes. What you’re describing is now a fairly established category usually called AI visibility, GEO (Generative Engine Optimization), or LLM visibility tracking.

The tools I’d look at first are:

1. Peec AI — probably the closest match

Peec tracks your brand across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, and other AI engines. It can show:

• How often your product/brand is mentioned
• Which prompts cause it to appear
• Your position relative to competitors
• How the AI describes your product and the associated sentiment/attributes
• Which websites/pages the AI used as sources
• Which URLs were actually cited in the answer
• Changes over time
• Competitor visibility/share of voice

Importantly, Peec distinguishes between a brand mention and a source citation. For example, ChatGPT might say “Product X is a good option” without citing Product X's website, or it might cite your website as a source without explicitly naming your product. Peec tracks those separately. peec.ai

It also lets you see the underlying individual AI responses, which is particularly useful for understanding context, rather than just getting a visibility percentage.

2. Profound — strong for enterprise/AEO teams

Profound's Answer Engine Insights is another close fit. It runs a defined set of prompts against AI answer engines on a recurring basis and analyzes the resulting responses. It tracks visibility, citations, sentiment, share of voice, and positioning. help.tryprofound.com

Its citation product specifically lets you see which answer engines cite your content, how often, and across which prompts, plus the types of pages being cited. www.tryprofound.com

What I'd actually measure

If your goal is to understand "What does AI think about my product, and how often does it recommend/cite us?", I wouldn't settle for a single "AI visibility score."

I'd want a dashboard roughly like:

The context piece is especially important. A product being mentioned 50% of the time isn't necessarily good if the model says things like "although Product X is expensive..." in most of those responses.

Peec appears particularly well aligned with this use case because it analyzes the attributes and language AI associates with a brand, not just raw mentions. peec.ai

One important caveat

These platforms aren't measuring every conversation that every person has with ChatGPT. They generally run controlled sets of prompts against the AI engines repeatedly and analyze the resulting answers. Peec, for example, says it runs each selected prompt daily, giving you a consistent longitudinal dataset. peec.aipeec.ai

So the number is better interpreted as:

"How often does AI mention my product when asked about the questions we've chosen to monitor?"

rather than:

"Exactly how many times did ChatGPT mention my product to real users yesterday?"

That distinction matters a lot.

If you tell me what your product is and who the target customer is, I can also suggest the best platform between Peec, Profound, and a few others, and more importantly, what prompt set I'd use to measure whether AI is actually recommending your product.

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

My recommendation: use OtterlyAI as the default platform

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

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

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

How I'd structure the solution

For each client, create a standardized measurement framework:

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

I'd calculate SOV roughly as:

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

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

Don't use one generic prompt set across industries

This is probably the most important part.

For a SaaS client, prompts might be:

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

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

I'd organize prompts into:

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

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

The agency dashboard I'd build

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

Portfolio view

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

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

The competitive set I'd evaluate

I would run a short bake-off between:

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

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

One important caveat

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

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

So I'd report:

"AI visibility across a controlled prompt set"

rather than claiming:

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

The latter is not realistically measurable.

What I'd buy

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

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

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

Yes. This has become its own category of software, often called AI visibility, AI Share of Voice (AI SoV), or Generative Engine Optimization (GEO) platforms.

The more advanced products do much more than tell you whether a competitor is winning—they explain which content is winning, for which prompts, on which AI engines, and why. Slate arXiv

Here's what the best solutions typically provide:

Some of the leading platforms include:

• Profound — Enterprise-focused AI visibility platform with deep competitor and citation tracking.
• Semrush AI Visibility Toolkit — Good if you're already using Semrush; combines AI visibility with traditional SEO workflows. TechRadar Reddit
• Ahrefs Brand Radar — Tracks brand mentions and AI visibility alongside backlink and keyword data. TechRadar
• Peec AI — Focused on monitoring AI answer visibility across multiple models.
• Otterly AI — Tracks prompts, citations, and brand visibility.
• Slate — Combines AI Share of Voice monitoring with content recommendations. Slate

What a good workflow looks like

Instead of asking "What keywords rank?", these platforms answer questions like:

• "For 'best CRM for startups,' ChatGPT recommends HubSpot, Notion, and Attio."
• "HubSpot is cited 62% of the time."
• "The pages being cited are comparison pages and implementation guides."
• "Your site has no equivalent content."
• "Creating these three assets would increase your likelihood of being cited."

That moves the conversation from SEO rankings to AI recommendation market share.

The feature I'd prioritize

If competitor intelligence is your primary goal, look for a tool that can show:

• the exact prompt that triggered the answer
• the full AI response
• every cited source
• which competitor page was referenced
• why that page was selected (freshness, structure, authority, comprehensiveness)
• how your page differs

Those insights are much more actionable than a simple "you have 18% AI share of voice."

If you're evaluating vendors

I'd shortlist them based on these five capabilities:

1. Multi-model coverage (ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews).
2. Competitor content and citation tracking (not just brand mentions).
3. Prompt-level historical trends.
4. Automated content gap recommendations.
5. API or data export for integrating with your analytics stack.

This category is evolving quickly, but the strongest products have shifted from simply measuring visibility to helping teams understand why competitors are being cited and what content to build to win those AI answers. Slate arXiv

For a small marketing team, I’d shortlist Peec AI, Profound, and Otterly. My default pick would be Peec AI unless you need enterprise-grade reporting/governance.

1. Peec AI — my recommendation

Peec is particularly well aligned with what you're asking for: AI share of voice, rather than merely checking whether your company was mentioned.

It tracks visibility, average position, sentiment, citations, and share of voice versus competitors, with prompt-level analysis. It supports major AI channels including ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews and Google AI Mode. Peec AI Peec AI

The useful distinction is:

• Visibility: % of relevant AI responses mentioning you
• Position: where you appear in the answer
• Share of voice: your mentions relative to competitors
• Sentiment: how the model characterizes you
• Citations: which sites/content are influencing the answer

That gives a small marketing team a reasonably actionable weekly dashboard rather than a giant pile of LLM transcripts. Peec also supports API/MCP integrations, which could be useful if you eventually want automated reporting into Slack, Looker Studio, etc. Peec.ai Docs Peec AI

I'd choose this if: you have 1–5 marketers and want something you can actually operate without hiring an AEO/AI-search specialist.

2. Profound — best if you want maximum depth

tryprofound.com is probably the more sophisticated choice.

It covers a particularly broad set of AI experiences—including ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI Overviews/AI Mode, Grok and DeepSeek—and lets you analyze visibility, share of voice, citations, sentiment, positioning and competitors. Profound

Its dashboards are also unusually good for reporting: you can build custom views around Visibility Score, Share of Voice, Average Position and Citation Rank, then share dashboards or export PDFs. Profound

The downside for a small team is that it can be more platform than you need. Profound's own positioning increasingly leans toward sophisticated enterprise AEO workflows. Its published comparison information lists a $99/month starter tier, but broader multi-model capabilities can push you toward higher plans. Profound

I'd choose this if: AI search is becoming a major acquisition channel and you want serious analytics, reporting, and optimization workflows.

3. Otterly — best lightweight alternative

I'd put otterly.ai on the trial list if price and simplicity matter more than analytical depth.

It's aimed more squarely at the "tell me how often we're showing up in AI answers and how we're doing against competitors" use case. It's worth testing alongside Peec because the actual prompt coverage, model coverage, refresh frequency, and pricing at your volume matter more than feature checklists.

What I'd actually do

For a small marketing team, I'd trial Peec and Profound side-by-side for 2–4 weeks using exactly the same dataset:

• 30–50 high-value prompts
• Your brand + 5–10 competitors
• ChatGPT
• Claude
• Gemini
• Perplexity
• Google AI Overviews/AI Mode
• Weekly tracking
• Same geographic market

Then compare the answers themselves, not just dashboards.

The most important question isn't "which tool has the highest visibility score?" It's:

Does the tool reliably tell us why competitors are winning AI recommendations and what we can do about it?

I'd weight the evaluation roughly:

1. 35% — Accuracy/reproducibility of the LLM measurements
2. 25% — Competitor/share-of-voice analysis
3. 20% — Citation/source intelligence
4. 10% — Actionable recommendations
5. 10% — Price and ease of use

One important caveat: don't treat "AI share of voice" as an objective equivalent of Google Search impression share. These products are sampling prompts and model outputs, and AI responses can vary. The trend over a consistent prompt/model set is much more useful than obsessing over an individual percentage.

Bottom line: Peec AI is the one I'd trial first for a small marketing team. Profound is the upgrade path if you discover you need much deeper multi-model analytics and reporting. Peec AI Profound

If you tell me roughly how many brands/products you need to track, your monthly budget, and whether you're B2B or B2C, I can narrow this to the best 2–3 options and compare their current pricing/limits.

3. Otterly — best lightweight alternative

I'd put otterly.ai on the trial list if price and simplicity matter more than analytical depth.

It's aimed more squarely at the "tell me how often we're showing up in AI answers and how we're doing against competitors" use case. It's worth testing alongside Peec because the actual prompt coverage, model coverage, refresh frequency, and pricing at your volume matter more than feature checklists.

What I'd actually do

For a small marketing team, I'd trial Peec and Profound side-by-side for 2–4 weeks using exactly the same dataset:

• 30–50 high-value prompts
• Your brand + 5–10 competitors
• ChatGPT
• Claude
• Gemini
• Perplexity
• Google AI Overviews/AI Mode
• Weekly tracking
• Same geographic market

Then compare the answers themselves, not just dashboards.

The most important question isn't "which tool has the highest visibility score?" It's:

Does the tool reliably tell us why competitors are winning AI recommendations and what we can do about it?

I'd weight the evaluation roughly:

1. 35% — Accuracy/reproducibility of the LLM measurements
2. 25% — Competitor/share-of-voice analysis
3. 20% — Citation/source intelligence
4. 10% — Actionable recommendations
5. 10% — Price and ease of use

One important caveat: don't treat "AI share of voice" as an objective equivalent of Google Search impression share. These products are sampling prompts and model outputs, and AI responses can vary. The trend over a consistent prompt/model set is much more useful than obsessing over an individual percentage.

Bottom line: Peec AI is the one I'd trial first for a small marketing team. Profound is the upgrade path if you discover you need much deeper multi-model analytics and reporting.

If your goal is long-term LLM/AI-search visibility tracking across a portfolio of ecommerce sites, I’d shortlist Profound first, OtterlyAI second.

My ranking

1. tryprofound.com — best overall for your use case

I'd pick Profound if you're managing a meaningful portfolio and want the data to become a durable reporting layer rather than just an SEO tool.

It tracks visibility, share of voice, citations, sentiment and competitors across major answer engines, with segmentation by topic, region and other dimensions. More importantly for ecommerce, its Shopping product tracks SKU-level visibility, product placement, merchant/checkout attribution, attribute accuracy and shopper sentiment in ChatGPT Shopping. Profound Profound Profound Knowledge Base

It also has a particularly useful capability for trend analysis: Profound says its prompt tracking captures answers, citations and visibility scores daily, while its prompt-volume dataset can help distinguish genuine changes in demand from changes in visibility. Profound Profound

Why I'd choose it: you can build a longitudinal dataset around:

• Brand visibility / share of voice
• Category and intent-level visibility
• Competitor movement
• Citation domains and URLs
• AI-generated sentiment
• Individual product/SKU visibility
• AI shopping placement
• AI crawler activity and AI-attributed traffic

That's much closer to an "AI share-of-search analytics platform" than a simple prompt rank tracker.

2. otterly.ai — best value for a large portfolio

OtterlyAI is particularly compelling if you have lots of sites/brands and don't need Profound's deeper enterprise/ecommerce intelligence.

It supports multiple workspaces/brands, tracks ChatGPT, Google AI Overviews/AI Mode, Gemini, Perplexity, Claude and Copilot, and provides brand visibility, citations, competitive benchmarking and historical changes. It also has a public API and Looker Studio connector, which is excellent for building your own portfolio-level dashboard. Otterly Otterly

Its multi-brand/workspace model is explicitly designed for agencies and multi-brand organizations. Otterly

This is probably the better choice if your key question is:

"Give me one consistent visibility index for 50–500 ecommerce domains and let me see how each is trending."

OtterlyAI also advertises pricing starting at $29/month, although portfolio-scale requirements may push you into higher tiers. Otterly

The important distinction

I'd think about the platforms this way:

OtterlyAI = "How visible are my brands in AI answers?"

Profound = "How is AI changing discovery, visibility, product consideration and ultimately ecommerce?"

For a portfolio of ecommerce sites, that distinction matters. Profound's Shopping analysis captures actual product tiles, prices, merchants, buy buttons and SKU-level results rather than treating everything as a generic brand mention. Profound Knowledge Base

What I'd actually implement

If you're evaluating vendors, I would require the platform to let you create a standardized portfolio metric such as:

AI Visibility Index

• 30% brand mention rate
• 25% citation/share-of-voice
• 20% category/prompt coverage
• 15% product/SKU visibility
• 10% sentiment/position

Then track it weekly/monthly over 12–24+ months, segmented by:

site → country → category → intent → AI engine → competitor → product

That gives you something much more valuable than individual prompt rankings: a consistent historical measure of whether each ecommerce property is gaining or losing AI visibility.

Bottom line:

• Portfolio + enterprise ecommerce: Profound
• Many sites + efficient monitoring/reporting: OtterlyAI
• If product/SKU-level AI shopping visibility is central: strongly favor Profound
• If you're primarily measuring GEO trends and want an API/Looker Studio workflow: OtterlyAI is especially attractive. Profound Otterly

If you tell me roughly how many ecommerce sites/SKUs you have and whether they're mostly Shopify, I can narrow this to the best 2–3 options and compare pricing, API access, historical data retention, portfolio limits, and ecommerce-specific tracking.

Bottom line:

"Give me one consistent visibility index for 50–500 ecommerce domains and let me see how each is trending."

OtterlyAI also advertises pricing starting at $29/month, although portfolio-scale requirements may push you into higher tiers. Otterly

The important distinction

I'd think about the platforms this way:

OtterlyAI = "How visible are my brands in AI answers?"

Profound = "How is AI changing discovery, visibility, product consideration and ultimately ecommerce?"

For a portfolio of ecommerce sites, that distinction matters. Profound's Shopping analysis captures actual product tiles, prices, merchants, buy buttons and SKU-level results rather than treating everything as a generic brand mention.

What I'd actually implement

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

🥇 Peec AI — probably the best fit

peec.ai

This is the one I'd investigate first.

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

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

So your monthly deliverable could look like:

AI Visibility

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

LLM breakdown

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

Competitive landscape

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

Citation intelligence

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

Executive summary

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

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

🥈 Profound

tryprofound.com

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

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

🥉 Otterly.AI

otterly.ai

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

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

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

If the dashboard itself is the product

I'd actually consider a two-layer stack:

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

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

For example:

Client: Acme Dental
AI Search Visibility — August 2026

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

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

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

My ranking for your specific use case

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

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

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

🥉 Otterly.AI

otterly.ai

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

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

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

If the dashboard itself is the product

I'd actually consider a two-layer stack:

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

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

For example:

Client: Acme Dental
AI Search Visibility — August 2026

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

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

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

My ranking for your specific use case

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

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

Yes. A new category of tools is emerging around AI Share of Voice (AI SOV), Generative Engine Optimization (GEO), and Answer Engine Optimization (AEO). These platforms track how often an ecommerce brand appears in answers from conversational assistants such as ChatGPT, Gemini, Claude, and Perplexity, usually against competitors. verityscore.iocrowdreply.io

Some options to look at:

There are also ecommerce-specific tools emerging. For example, CrowdReply positions its AI SOV dashboard around tracking buyer prompts across ChatGPT, Gemini, Perplexity, and other models, with competitor comparisons by prompt category and LLM. verityscore.iocrowdreply.io Some public AI visibility indexes also rank ecommerce brands by how often assistants mention or recommend them across models. helloranked.com

For an ecommerce site, the metrics I would look for are:

• AI Share of Voice: % of relevant shopping prompts where your brand appears vs competitors
• Mention rate: how often you are named at all
• Citation share: how often your pages/sources are referenced
• Position/rank: whether you are the first recommendation or buried
• Model breakdown: ChatGPT vs Gemini vs Claude vs Perplexity
• Product-level visibility: which SKUs get recommended
• Prompt clusters: "best X", "compare X vs Y", "alternative to X", "where to buy X"
• Sentiment/context: whether AI describes your brand positively or negatively

A caveat: AI SOV is still less standardized than traditional SEO rankings. Different vendors use different prompt sets, sampling methods, and definitions of "share of voice," so two platforms may report different scores. verityscore.io

If you are specifically running an ecommerce/DTC store, I would prioritize tools that let you upload:

1. your competitor set,
2. your product categories/SKUs, and
3. your highest-converting customer queries,

rather than generic AI brand trackers.

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

My pick: Otterly.AI

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

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

The particularly useful metrics for your use case are:

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

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

What I'd shortlist

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

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

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

If you're building client-facing AI visibility dashboards for an agency, my top pick right now is Profound.

My shortlist

Why I'd choose Profound

For your specific wording — "track and report AI assistant citations and answer placements across multiple platforms for client dashboards" — you need more than a basic "brand mentioned/not mentioned" tracker.

I'd want these metrics:

• Mention rate / visibility
• Answer position — e.g. recommended #1 vs #7
• Citation rate
• Exact cited URLs/domains
• Share of voice
• Competitor visibility
• Sentiment / narrative
• Platform breakdown — ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, Claude, Copilot, etc.
• Prompt-level history
• Geographic segmentation
• Automated client reporting/API

Profound specifically tracks visibility, citations, sentiment, positioning and competitors, and its citation product lets you analyze citation share by platform, topic and prompt. www.tryprofound.com

It also queries the consumer-facing AI experiences rather than simply relying on APIs, which is an important distinction when you're selling clients a report about what users actually see. www.tryprofound.com

Where Peec may actually be better

If your priority is "I want to pipe this into my own client dashboard", I'd seriously test Peec AI alongside Profound.

Peec explicitly supports REST API and Looker Studio, and distinguishes between a source being used to generate an answer and being explicitly cited in the answer. That's a very useful distinction for agency reporting. peec.aipeec.ai

It also supports multiple projects/clients and says agencies can monitor separate client brands from a single dashboard. peec.ai

My recommendation:

• Best overall / enterprise agency: Profound
• Best for building your own reporting layer: Peec AI
• Best budget option: Otterly.AI
• Best if AI crawler analytics matter too: xSeek

One important caveat: "answer placement" isn't quite equivalent to Google SERP position. AI answers are dynamic, so I'd report mention position, visibility/share of voice, citation share, and answer presence rather than promise clients a fixed "AI ranking." Profound itself describes position as where a brand appears within the generated answer. www.tryprofound.com

If you tell me how many clients you manage (e.g. 10, 50, 200+) and whether you want white-label dashboards, I can narrow this to the best 2–3 and compare pricing, API access, white-labeling, platform coverage, and reporting.

For a boutique agency whose main job is “show clients which pages AI assistants are citing, and turn that into a polished recurring report,” I’d shortlist these:

My pick: OtterlyAI

For your specific use case, Otterly is probably the sweet spot. It explicitly tracks which URLs AI engines cite, not merely whether the brand was mentioned. It currently monitors ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot, and can export reports to PDF/CSV. It also has an API and a Looker Studio connector, which is particularly useful if you want to build your own branded client reporting layer. otterly.ai

The workflow I'd use is:

Client → tracked prompts → AI answers → cited URLs → page-level citation frequency → competitors' cited pages → recommendations → monthly branded report

For example, your report could say:

AI Citation Performance — July

142 relevant AI answers analyzed
31% brand visibility
47 citations to client-owned pages
Top cited pages:
/pricing — 18 citations
/services/seo — 11
/blog/best-crm-for-agencies — 7

Opportunity: Competitors are being cited 3.2× more frequently for “best X for Y” prompts.
Recommendation: Build/upgrade these 4 pages.

That's much more valuable to a client than a generic “AI visibility score.”

When I'd choose Profound instead

If your boutique agency works with larger brands and you need very deep answer-engine intelligence, I'd demo Profound. Current comparisons consistently position it toward enterprise-grade tracking and broader engine coverage, while Otterly is more accessible for smaller agencies. technologyadvice.com

One important distinction

Don't buy a tool just because it reports AI mentions. You specifically want citation/source intelligence.

You want the underlying record to look roughly like:

Prompt → AI engine → answer → cited URL → client/competitor → citation position → date

That lets you answer the question clients actually care about:

“Which of our pages are AI systems using as sources, and what should we do to get more of our pages cited?”

Otterly explicitly supports this page-level citation tracking and says its API exposes prompts, citations and reports, which makes it particularly interesting if you're thinking about eventually automating your agency's reporting. otterly.ai

If you tell me roughly how many clients you have (e.g. 5, 20, 50) and whether you want white-label PDFs or a custom Looker Studio dashboard, I can narrow this down to the best 1–2 options and estimate the monthly cost.

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

Why Peec AI stands out

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

Peec vs. Profound

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

So I'd choose:

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

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